diff --git a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free The ultimate guide to the most popular game in the series.md b/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free The ultimate guide to the most popular game in the series.md deleted file mode 100644 index 71076f16473e62cd510a137e4edcf39b0756ef7f..0000000000000000000000000000000000000000 --- a/spaces/1acneusushi/gradio-2dmoleculeeditor/data/Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free The ultimate guide to the most popular game in the series.md +++ /dev/null @@ -1,168 +0,0 @@ -
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Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free

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Introduction

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If you are a fan of role-playing games, you have probably heard of Final Fantasy VII, one of the most popular and influential titles in the genre. But did you know that you can download and play it for free on your PC, thanks to a lossless repack by RG ILITA? In this article, we will explain what Final Fantasy VII is, what a lossless repack is, who RG ILITA is, how to download and install the game, and why you should play it.

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What is Final Fantasy VII?

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Final Fantasy VII is a 1997 role-playing video game developed by Square for the PlayStation console. It is the seventh main installment in the Final Fantasy series, which is known for its epic stories, memorable characters, and innovative gameplay. The game's story follows Cloud Strife, a mercenary who joins an eco-terrorist organization called AVALANCHE to stop a world-controlling megacorporation called Shinra from using the planet's life essence as an energy source. Along the way, he and his allies face Sephiroth, a superhuman who seeks to wound the planet and harness its healing power in order to be reborn as a demigod. The game features 3D graphics, full motion video, and a turn-based combat system with real-time elements. The game has been widely praised for its story, characters, music, and gameplay, and has sold over 13 million copies worldwide. It has also spawned several spin-offs, sequels, and a remake.

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What is a lossless repack?

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A lossless repack is a compressed version of a game that retains all the original data and quality without any loss or degradation. A lossless repack reduces the size of the game file by removing unnecessary or redundant data, such as languages, videos, or sounds that are not used by the player. A lossless repack also includes a crack that bypasses the copy protection or digital rights management (DRM) of the game, allowing it to be played without a license or activation. A lossless repack is different from a lossy repack, which reduces the size of the game file by lowering the quality or resolution of some data, such as graphics or audio.

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Who is RG ILITA?

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RG ILITA is a group of hackers and programmers who specialize in creating lossless repacks of games for PC. They are known for their high-quality repacks that are fast to download and easy to install. They also provide detailed instructions and troubleshooting tips for their repacks. RG ILITA stands for Russian Guys In Lossless IT Adventures.

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How to download and install Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free

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Requirements

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Before you download and install Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free, you need to make sure that your PC meets the minimum requirements for the game. Here are the minimum requirements:

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OSProcessorMemoryGraphicsDirectXHard Drive
Microsoft Windows XP/Vista/7 (32/64bits)2GHz1 GB RAMDirectX 9.0c-compatible graphic card9.0c3 GB HD space
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You also need to have a Square Enix account to play the game.

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Steps

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To download and install Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free, follow these steps:

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  1. Go to https://rgilita.com/final-fantasy-vii-eng-lossless-repack-by-rg-ilita-crack-free/, which is the official website of RG ILITA.
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  2. Click on the "Download" button and choose a mirror site from where you want to download the game file.
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  3. The game file is about 1.5 GB in size and will be downloaded as a .rar archive.
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  4. Extract the .rar archive using WinRAR or any other software that can handle .rar files.
    5. -
  5. You will get a folder named "Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA". Open it and run "setup.exe" as administrator.
    6. -
  6. Follow the instructions on the screen and choose where you want to install the game.
    7. -
  7. The installation process will take some time depending on your PC's performance.
    8. -
  8. After the installation is complete, run "FF7_Launcher.exe" from the game folder as administrator.
    9. -
  9. Login with your Square Enix account or create one if you don't have one.
    10. -
  10. You can now play Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free on your PC!
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Troubleshooting

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If you encounter any problems while downloading or installing Final Fantasy VII - (ENG) [Lossless Repack] by RG ILITA crack free, here are some possible solutions:

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    • Courses Courses
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    • Subscribe to the Blog RSS feed var blogAnalyticsRefiners = []; var blogAnalyticsTotal = 730; var blogAnalyticsQuery = ""; 8 Essential Leadership Communication Skills
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    On 25 August 2000, a vehicle patrol consisting of 11 soldiers of The Royal Irish Regiment and one Sierra Leonean soldier entered the territory of a vicious armed gang known as the West Side Boys. The patrol was surrounded and forced to surrender.

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    Examples of G2G Initiatives. One example of a G2G e-government initiative is the Northeast Gang Information System (NEGIS). NEGIS issponsored by the Department of Justice and serves as a shared resource for street gang informationfor states in the northeast, including Connecticut, Rhode Island, Vermont, Massachusetts, and NewYork. It includes information such as gang-related activities, gang intelligence, and a referencelibrary. NEGIS connects the state police departments of the participant-states, which, in turn,transmit the information to the states' other law enforcement agencies.

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    Motivating Forces Behind the G2B Sector. There are two primary forces driving the G2B sector. The first is the business community. In manyindustries, the use of electronic means to carry out various activities, such as procurement, sales, andhiring is commonplace. For example, the auto industry created an electronic business exchangecalled Covisint in fall 2000. Covisint provides an online environment for automakers and partssuppliers to buy and sell goods, share information, and collaborate on new products. (16) In addition,the software industry is producing an ever-growing number of products focused on moving routinebusiness activities online. Many companies would like to extend the cost savings realized in theirbusiness-to-business (B2B) transactions to their business with federal, state, and local governments(B2G).

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    Examples of G2C Initiatives. Although many examples of G2C initiatives can be found at the local and state level, there are also examples at thefederal level. One of these examples is the establishment of the FirstGov Web site. (28) FirstGov, apublic-private partnership, is administered by GSA. Established in September 2000, FirstGov isdesigned to serve as the online portal (29) for 51million pages of government information, services, andonline transactions. According to the Web site, FirstGov "has the most comprehensive search ofgovernment anywhere on the Internet." Its creators also hope that FirstGov will serve as "the catalystfor a growing electronic government."

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    Several lines of evidence support the idea that, in macrophages, IFN-γ induces an early delay in phagosome maturation. It has been observed that, in IFN-γ-activated macrophages, there is a delay in the progression of phagosomes to phagolysosomes (Tsang et al., 2000). The acidification rate, proteolytic activity (Yates et al., 2007) and the acquisition of LAMP-1, lysozyme-C and β-hexosaminidase (Jutras et al., 2008; Trost et al., 2009) by early phagosomes is reduced by IFN-γ. Here, we provide functional evidence indicating that the reported delay in maturation and proteolytic activity is, in part, due to increased levels of Rab20 in phagosomes. Rab20, in turn, transiently recruits Rabex-5, thereby increasing the levels of active Rab5a on phagosomal membranes. Compelling evidence also indicates that the early phagosomal delay has an important physiological function. In macrophages, relative slow phagosomal acidification and antigen degradation is crucial for efficient peptide antigen cross-presentation (Jutras et al., 2008; Trost et al., 2009). Even very subtle differences in acidification have a profound impact on proteolysis events occurring in phagosomes (Jiang et al., 2012). Our observations support the idea that small temporal changes mediated by Rab20 during phagosome maturation result in crucial changes in its function. However, the relevance of Rab20 function to antigen presentation or other phagosomal activities remains to be demonstrated. We postulate that IFN-γ has at least two effects on phagosomal functions; one is the relatively early delay in phagosome maturation and the second, operating later, is the enhancement of phagosomal maturation. In this context, Rab20 contributes to the early phagosomal functions of IFN-γ (supplementary material Fig. S3).

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    \ No newline at end of file diff --git a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/qu2cu/qu2cu.py b/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/qu2cu/qu2cu.py deleted file mode 100644 index 97a665f63adf88681328a69c5c0a3c6814bf3719..0000000000000000000000000000000000000000 --- a/spaces/cloudtheboi/Lofi4All/.pythonlibs/lib/python3.10/site-packages/fontTools/qu2cu/qu2cu.py +++ /dev/null @@ -1,408 +0,0 @@ -# cython: language_level=3 -# distutils: define_macros=CYTHON_TRACE_NOGIL=1 - -# Copyright 2023 Google Inc. All Rights Reserved. -# Copyright 2023 Behdad Esfahbod. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -try: - import cython - - COMPILED = cython.compiled -except (AttributeError, ImportError): - # if cython not installed, use mock module with no-op decorators and types - from fontTools.misc import cython - - COMPILED = False - -from fontTools.misc.bezierTools import splitCubicAtTC -from collections import namedtuple -import math -from typing import ( - List, - Tuple, - Union, -) - - -__all__ = ["quadratic_to_curves"] - - -# Copied from cu2qu -@cython.cfunc -@cython.returns(cython.int) -@cython.locals( - tolerance=cython.double, - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p3=cython.complex, -) -@cython.locals(mid=cython.complex, deriv3=cython.complex) -def cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance): - """Check if a cubic Bezier lies within a given distance of the origin. - - "Origin" means *the* origin (0,0), not the start of the curve. Note that no - checks are made on the start and end positions of the curve; this function - only checks the inside of the curve. - - Args: - p0 (complex): Start point of curve. - p1 (complex): First handle of curve. - p2 (complex): Second handle of curve. - p3 (complex): End point of curve. - tolerance (double): Distance from origin. - - Returns: - bool: True if the cubic Bezier ``p`` entirely lies within a distance - ``tolerance`` of the origin, False otherwise. - """ - # First check p2 then p1, as p2 has higher error early on. - if abs(p2) <= tolerance and abs(p1) <= tolerance: - return True - - # Split. - mid = (p0 + 3 * (p1 + p2) + p3) * 0.125 - if abs(mid) > tolerance: - return False - deriv3 = (p3 + p2 - p1 - p0) * 0.125 - return cubic_farthest_fit_inside( - p0, (p0 + p1) * 0.5, mid - deriv3, mid, tolerance - ) and cubic_farthest_fit_inside(mid, mid + deriv3, (p2 + p3) * 0.5, p3, tolerance) - - -@cython.locals( - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p1_2_3=cython.complex, -) -def elevate_quadratic(p0, p1, p2): - """Given a quadratic bezier curve, return its degree-elevated cubic.""" - - # https://pomax.github.io/bezierinfo/#reordering - p1_2_3 = p1 * (2 / 3) - return ( - p0, - (p0 * (1 / 3) + p1_2_3), - (p2 * (1 / 3) + p1_2_3), - p2, - ) - - -@cython.cfunc -@cython.locals( - start=cython.int, - n=cython.int, - k=cython.int, - prod_ratio=cython.double, - sum_ratio=cython.double, - ratio=cython.double, - t=cython.double, - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p3=cython.complex, -) -def merge_curves(curves, start, n): - """Give a cubic-Bezier spline, reconstruct one cubic-Bezier - that has the same endpoints and tangents and approxmates - the spline.""" - - # Reconstruct the t values of the cut segments - prod_ratio = 1.0 - sum_ratio = 1.0 - ts = [1] - for k in range(1, n): - ck = curves[start + k] - c_before = curves[start + k - 1] - - # |t_(k+1) - t_k| / |t_k - t_(k - 1)| = ratio - assert ck[0] == c_before[3] - ratio = abs(ck[1] - ck[0]) / abs(c_before[3] - c_before[2]) - - prod_ratio *= ratio - sum_ratio += prod_ratio - ts.append(sum_ratio) - - # (t(n) - t(n - 1)) / (t_(1) - t(0)) = prod_ratio - - ts = [t / sum_ratio for t in ts[:-1]] - - p0 = curves[start][0] - p1 = curves[start][1] - p2 = curves[start + n - 1][2] - p3 = curves[start + n - 1][3] - - # Build the curve by scaling the control-points. - p1 = p0 + (p1 - p0) / (ts[0] if ts else 1) - p2 = p3 + (p2 - p3) / ((1 - ts[-1]) if ts else 1) - - curve = (p0, p1, p2, p3) - - return curve, ts - - -@cython.locals( - count=cython.int, - num_offcurves=cython.int, - i=cython.int, - off1=cython.complex, - off2=cython.complex, - on=cython.complex, -) -def add_implicit_on_curves(p): - q = list(p) - count = 0 - num_offcurves = len(p) - 2 - for i in range(1, num_offcurves): - off1 = p[i] - off2 = p[i + 1] - on = off1 + (off2 - off1) * 0.5 - q.insert(i + 1 + count, on) - count += 1 - return q - - -Point = Union[Tuple[float, float], complex] - - -@cython.locals( - cost=cython.int, - is_complex=cython.int, -) -def quadratic_to_curves( - quads: List[List[Point]], - max_err: float = 0.5, - all_cubic: bool = False, -) -> List[Tuple[Point, ...]]: - """Converts a connecting list of quadratic splines to a list of quadratic - and cubic curves. - - A quadratic spline is specified as a list of points. Either each point is - a 2-tuple of X,Y coordinates, or each point is a complex number with - real/imaginary components representing X,Y coordinates. - - The first and last points are on-curve points and the rest are off-curve - points, with an implied on-curve point in the middle between every two - consequtive off-curve points. - - Returns: - The output is a list of tuples of points. Points are represented - in the same format as the input, either as 2-tuples or complex numbers. - - Each tuple is either of length three, for a quadratic curve, or four, - for a cubic curve. Each curve's last point is the same as the next - curve's first point. - - Args: - quads: quadratic splines - - max_err: absolute error tolerance; defaults to 0.5 - - all_cubic: if True, only cubic curves are generated; defaults to False - """ - is_complex = type(quads[0][0]) is complex - if not is_complex: - quads = [[complex(x, y) for (x, y) in p] for p in quads] - - q = [quads[0][0]] - costs = [1] - cost = 1 - for p in quads: - assert q[-1] == p[0] - for i in range(len(p) - 2): - cost += 1 - costs.append(cost) - costs.append(cost) - qq = add_implicit_on_curves(p)[1:] - costs.pop() - q.extend(qq) - cost += 1 - costs.append(cost) - - curves = spline_to_curves(q, costs, max_err, all_cubic) - - if not is_complex: - curves = [tuple((c.real, c.imag) for c in curve) for curve in curves] - return curves - - -Solution = namedtuple("Solution", ["num_points", "error", "start_index", "is_cubic"]) - - -@cython.locals( - i=cython.int, - j=cython.int, - k=cython.int, - start=cython.int, - i_sol_count=cython.int, - j_sol_count=cython.int, - this_sol_count=cython.int, - tolerance=cython.double, - err=cython.double, - error=cython.double, - i_sol_error=cython.double, - j_sol_error=cython.double, - all_cubic=cython.int, - is_cubic=cython.int, - count=cython.int, - p0=cython.complex, - p1=cython.complex, - p2=cython.complex, - p3=cython.complex, - v=cython.complex, - u=cython.complex, -) -def spline_to_curves(q, costs, tolerance=0.5, all_cubic=False): - """ - q: quadratic spline with alternating on-curve / off-curve points. - - costs: cumulative list of encoding cost of q in terms of number of - points that need to be encoded. Implied on-curve points do not - contribute to the cost. If all points need to be encoded, then - costs will be range(1, len(q)+1). - """ - - assert len(q) >= 3, "quadratic spline requires at least 3 points" - - # Elevate quadratic segments to cubic - elevated_quadratics = [ - elevate_quadratic(*q[i : i + 3]) for i in range(0, len(q) - 2, 2) - ] - - # Find sharp corners; they have to be oncurves for sure. - forced = set() - for i in range(1, len(elevated_quadratics)): - p0 = elevated_quadratics[i - 1][2] - p1 = elevated_quadratics[i][0] - p2 = elevated_quadratics[i][1] - if abs(p1 - p0) + abs(p2 - p1) > tolerance + abs(p2 - p0): - forced.add(i) - - # Dynamic-Programming to find the solution with fewest number of - # cubic curves, and within those the one with smallest error. - sols = [Solution(0, 0, 0, False)] - impossible = Solution(len(elevated_quadratics) * 3 + 1, 0, 1, False) - start = 0 - for i in range(1, len(elevated_quadratics) + 1): - best_sol = impossible - for j in range(start, i): - j_sol_count, j_sol_error = sols[j].num_points, sols[j].error - - if not all_cubic: - # Solution with quadratics between j:i - this_count = costs[2 * i - 1] - costs[2 * j] + 1 - i_sol_count = j_sol_count + this_count - i_sol_error = j_sol_error - i_sol = Solution(i_sol_count, i_sol_error, i - j, False) - if i_sol < best_sol: - best_sol = i_sol - - if this_count <= 3: - # Can't get any better than this in the path below - continue - - # Fit elevated_quadratics[j:i] into one cubic - try: - curve, ts = merge_curves(elevated_quadratics, j, i - j) - except ZeroDivisionError: - continue - - # Now reconstruct the segments from the fitted curve - reconstructed_iter = splitCubicAtTC(*curve, *ts) - reconstructed = [] - - # Knot errors - error = 0 - for k, reconst in enumerate(reconstructed_iter): - orig = elevated_quadratics[j + k] - err = abs(reconst[3] - orig[3]) - error = max(error, err) - if error > tolerance: - break - reconstructed.append(reconst) - if error > tolerance: - # Not feasible - continue - - # Interior errors - for k, reconst in enumerate(reconstructed): - orig = elevated_quadratics[j + k] - p0, p1, p2, p3 = tuple(v - u for v, u in zip(reconst, orig)) - - if not cubic_farthest_fit_inside(p0, p1, p2, p3, tolerance): - error = tolerance + 1 - break - if error > tolerance: - # Not feasible - continue - - # Save best solution - i_sol_count = j_sol_count + 3 - i_sol_error = max(j_sol_error, error) - i_sol = Solution(i_sol_count, i_sol_error, i - j, True) - if i_sol < best_sol: - best_sol = i_sol - - if i_sol_count == 3: - # Can't get any better than this - break - - sols.append(best_sol) - if i in forced: - start = i - - # Reconstruct solution - splits = [] - cubic = [] - i = len(sols) - 1 - while i: - count, is_cubic = sols[i].start_index, sols[i].is_cubic - splits.append(i) - cubic.append(is_cubic) - i -= count - curves = [] - j = 0 - for i, is_cubic in reversed(list(zip(splits, cubic))): - if is_cubic: - curves.append(merge_curves(elevated_quadratics, j, i - j)[0]) - else: - for k in range(j, i): - curves.append(q[k * 2 : k * 2 + 3]) - j = i - - return curves - - -def main(): - from fontTools.cu2qu.benchmark import generate_curve - from fontTools.cu2qu import curve_to_quadratic - - tolerance = 0.05 - reconstruct_tolerance = tolerance * 1 - curve = generate_curve() - quadratics = curve_to_quadratic(curve, tolerance) - print( - "cu2qu tolerance %g. qu2cu tolerance %g." % (tolerance, reconstruct_tolerance) - ) - print("One random cubic turned into %d quadratics." % len(quadratics)) - curves = quadratic_to_curves([quadratics], reconstruct_tolerance) - print("Those quadratics turned back into %d cubics. " % len(curves)) - print("Original curve:", curve) - print("Reconstructed curve(s):", curves) - - -if __name__ == "__main__": - main() diff --git a/spaces/cmotions/new_beatles_songs/app.py b/spaces/cmotions/new_beatles_songs/app.py deleted file mode 100644 index 1a25a3a4711b1bf9686226c3f7403ba9510862cd..0000000000000000000000000000000000000000 --- a/spaces/cmotions/new_beatles_songs/app.py +++ /dev/null @@ -1,116 +0,0 @@ -# Import libraries -from transformers import pipeline -from numpy import random -import gradio as gr -import re -import torch -from torch import autocast -import os - -# Array with song cover art styles -image_input_styles = ["Random", "Pencil sketch", "Oil painting", "Pop art", "Piet Mondriaan"] - -# Get image type for image input -""" -The default setting for the art style dropdown is "Random". The below function determines which style is chosen -If set to "Random", copy the art style array and remove "Random" to prevent "Random" from being a chosen art style -""" -def get_image_input(title, given_input_style): - if given_input_style == 'Random': - image_input_styles_new = image_input_styles.copy() - image_input_styles_new.pop(0) - random_choice = random.randint(len(image_input_styles_new)-1) - final_style = image_input_styles_new[random_choice] - else: - final_style = given_input_style - image_input = 'Cover for ' + title.lower() + ' in style of ' + final_style - return image_input, final_style - -# Available models for generate lyrics pipeline -# checkpoint = 'wvangils/GPT-Medium-Beatles-Lyrics-finetuned-newlyrics' -# checkpoint = 'wvangils/GPT-Neo-125m-Beatles-Lyrics-finetuned-newlyrics' -checkpoint = 'wvangils/BLOOM-560m-Beatles-Lyrics-finetuned' - -# Setup all the pipelines we need -title_generator = pipeline('summarization', model='czearing/story-to-title') -lyrics_generator = pipeline("text-generation", model=checkpoint) - -# For the image generator we use stable diffusion from an existing HuggingFace space, Gradio accelerated backend -stable_diffusion = gr.Blocks.load(name="spaces/stabilityai/stable-diffusion-1") - -# Create 4 images for the given prompt and receive the first one -# This function uses an existing HuggingFace space where the number of created images cannot be modified -def get_image(prompt): - gallery_dir = stable_diffusion(prompt, fn_index=2) - images = [os.path.join(gallery_dir, img) for img in os.listdir(gallery_dir)] - return [images[0]] - -# Lyrics generation -def generate_beatles(input_prompt, temperature, top_p, given_input_style): - # Create generator for different models - generated_lyrics = lyrics_generator(input_prompt - , max_length = 100 - , num_return_sequences = 1 - , return_full_text = True - , temperature = temperature - , top_p = top_p # Default 1.0 - , no_repeat_ngram_size = 3 # Default = 0 - , repetition_penalty = 1.0 # Default = 1.0 - )[0]["generated_text"] - - # Put lyrics in the right form - lyrics_sentences = re.sub('\n', '. ', generated_lyrics) - - # Create a title based on the generated lyrics - title = title_generator(lyrics_sentences, min_length=1, max_length=10, repetition_penalty=2.5)[0]['summary_text'] - - # Create an image based on the generated title - image_input, image_style = get_image_input(title, given_input_style) - - # Generate the image - image = get_image(image_input) - return (title, generated_lyrics, image, image_style) - -# Create textboxes for input and output -input_box = gr.Textbox(label="Write the start of a song here", placeholder="Write the start of a new song here", value="Looking out of my window", lines=2, max_lines=5) -gen_lyrics = gr.Textbox(label="Song lyrics", lines=15) -gen_title = gr.Textbox(label="Proposed songtitle", lines=1) -gen_image = gr.Gallery(label="Proposed song cover").style(grid=1, height="auto") -gen_image_style = gr.Textbox(label="Image style", lines=1) - -# Layout and text around the app -title='Beatles lyrics generator' -description="

    We've fine-tuned multiple language models on lyrics from The Beatles to generate Beatles-like text. Below are the results we obtained fine-tuning a GPT Neo model. After generation a title is generated using this model. On top we use the generated title to suggest an album cover using Stable Diffusion 1.4. Give it a try!

    " -article="""

    These text generation models that output Beatles-like text were created by data scientists working for Cmotions. - We tried several text generation models that we were able to load in Colab: a general GPT2-medium model, the Eleuther AI small-sized GPT model GPT-Neo and the new kid on the block build by the Bigscience initiative BLOOM 560m. - Further we've put together a Huggingface dataset containing all known lyrics created by The Beatles. Currently we are fine-tuning models and are evaluating the results. Once finished we will publish a blog at this location with all the steps we took including a Python notebook using Huggingface. - The default output contains 100 tokens and has a repetition penalty of 1.0. -

    """ -css = """ - .gr-button-primary { - text-indent: -9999px; - line-height: 0; - } - .gr-button-primary:after { - content: "Beatlify!"; - text-indent: 0; - display: block; - line-height: initial; - } -""" - -# Let users select their own temperature and top-p -temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Change the temperature \r\n (higher temperature = more creative in lyrics generation, but posibbly less Beatly)", value=0.7, show_label=True) #high = sensitive for low probability tokens -top_p = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, label="Change top probability of the next word \n (higher top probability = more words to choose from for the next word, but possibly less Beatly)", value=0.5, show_label=True) -given_input_style = gr.Dropdown(choices=image_input_styles, value="Random", label="Choose the art style for the lyrics cover", show_label=True) - -# Use generate Beatles function in demo-app Gradio -gr.Interface(fn=generate_beatles - , inputs=[input_box, temperature, top_p, given_input_style] - , outputs=[gen_title, gen_lyrics, gen_image, gen_image_style] - , title=title - , css=css - , description=description - , article=article - , allow_flagging='never' - ).launch() \ No newline at end of file diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/eatqi.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/eatqi.c deleted file mode 100644 index e4f12b3db2ba5544eb207586c72082362a3c4cc5..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/eatqi.c +++ /dev/null @@ -1,188 +0,0 @@ -/* - * Electronic Arts TQI Video Decoder - * Copyright (c) 2007-2009 Peter Ross - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * Electronic Arts TQI Video Decoder - * @author Peter Ross - * @see http://wiki.multimedia.cx/index.php?title=Electronic_Arts_TQI - */ - -#include "libavutil/mem_internal.h" - -#include "avcodec.h" -#include "blockdsp.h" -#include "bswapdsp.h" -#include "codec_internal.h" -#include "decode.h" -#include "get_bits.h" -#include "aandcttab.h" -#include "eaidct.h" -#include "mpeg12data.h" -#include "mpeg12dec.h" - -typedef struct TqiContext { - AVCodecContext *avctx; - GetBitContext gb; - BlockDSPContext bdsp; - BswapDSPContext bsdsp; - - void *bitstream_buf; - unsigned int bitstream_buf_size; - - int mb_x, mb_y; - uint16_t intra_matrix[64]; - int last_dc[3]; - - DECLARE_ALIGNED(32, int16_t, block)[6][64]; -} TqiContext; - -static av_cold int tqi_decode_init(AVCodecContext *avctx) -{ - TqiContext *t = avctx->priv_data; - - ff_blockdsp_init(&t->bdsp); - ff_bswapdsp_init(&t->bsdsp); - - avctx->framerate = (AVRational){ 15, 1 }; - avctx->pix_fmt = AV_PIX_FMT_YUV420P; - ff_mpeg12_init_vlcs(); - return 0; -} - -static int tqi_decode_mb(TqiContext *t, int16_t (*block)[64]) -{ - int n; - - t->bdsp.clear_blocks(block[0]); - for (n = 0; n < 6; n++) { - int ret = ff_mpeg1_decode_block_intra(&t->gb, - t->intra_matrix, - ff_zigzag_direct, - t->last_dc, block[n], n, 1); - if (ret < 0) { - av_log(t->avctx, AV_LOG_ERROR, "ac-tex damaged at %d %d\n", - t->mb_x, t->mb_y); - return ret; - } - } - - return 0; -} - -static inline void tqi_idct_put(AVCodecContext *avctx, AVFrame *frame, - int16_t (*block)[64]) -{ - TqiContext *t = avctx->priv_data; - ptrdiff_t linesize = frame->linesize[0]; - uint8_t *dest_y = frame->data[0] + t->mb_y * 16 * linesize + t->mb_x * 16; - uint8_t *dest_cb = frame->data[1] + t->mb_y * 8 * frame->linesize[1] + t->mb_x * 8; - uint8_t *dest_cr = frame->data[2] + t->mb_y * 8 * frame->linesize[2] + t->mb_x * 8; - - ff_ea_idct_put_c(dest_y , linesize, block[0]); - ff_ea_idct_put_c(dest_y + 8, linesize, block[1]); - ff_ea_idct_put_c(dest_y + 8*linesize , linesize, block[2]); - ff_ea_idct_put_c(dest_y + 8*linesize + 8, linesize, block[3]); - - if (!(avctx->flags & AV_CODEC_FLAG_GRAY)) { - ff_ea_idct_put_c(dest_cb, frame->linesize[1], block[4]); - ff_ea_idct_put_c(dest_cr, frame->linesize[2], block[5]); - } -} - -static void tqi_calculate_qtable(TqiContext *t, int quant) -{ - const int64_t qscale = (215 - 2*quant)*5; - int i; - - t->intra_matrix[0] = (ff_inv_aanscales[0] * ff_mpeg1_default_intra_matrix[0]) >> 11; - for(i=1; i<64; i++) - t->intra_matrix[i] = (ff_inv_aanscales[i] * ff_mpeg1_default_intra_matrix[i] * qscale + 32) >> 14; -} - -static int tqi_decode_frame(AVCodecContext *avctx, AVFrame *frame, - int *got_frame, AVPacket *avpkt) -{ - const uint8_t *buf = avpkt->data; - int buf_size = avpkt->size; - const uint8_t *buf_end = buf+buf_size; - TqiContext *t = avctx->priv_data; - int ret, w, h; - - if (buf_size < 12) - return AVERROR_INVALIDDATA; - - t->avctx = avctx; - - w = AV_RL16(&buf[0]); - h = AV_RL16(&buf[2]); - tqi_calculate_qtable(t, buf[4]); - buf += 8; - - ret = ff_set_dimensions(avctx, w, h); - if (ret < 0) - return ret; - - if ((ret = ff_get_buffer(avctx, frame, 0)) < 0) - return ret; - - av_fast_padded_malloc(&t->bitstream_buf, &t->bitstream_buf_size, - buf_end - buf); - if (!t->bitstream_buf) - return AVERROR(ENOMEM); - t->bsdsp.bswap_buf(t->bitstream_buf, (const uint32_t *) buf, - (buf_end - buf) / 4); - init_get_bits(&t->gb, t->bitstream_buf, 8 * (buf_end - buf)); - - t->last_dc[0] = - t->last_dc[1] = - t->last_dc[2] = 0; - for (t->mb_y = 0; t->mb_y < (h + 15) / 16; t->mb_y++) { - for (t->mb_x = 0; t->mb_x < (w + 15) / 16; t->mb_x++) { - if (tqi_decode_mb(t, t->block) < 0) - goto end; - tqi_idct_put(avctx, frame, t->block); - } - } - end: - - *got_frame = 1; - return buf_size; -} - -static av_cold int tqi_decode_end(AVCodecContext *avctx) -{ - TqiContext *t = avctx->priv_data; - av_freep(&t->bitstream_buf); - return 0; -} - -const FFCodec ff_eatqi_decoder = { - .p.name = "eatqi", - CODEC_LONG_NAME("Electronic Arts TQI Video"), - .p.type = AVMEDIA_TYPE_VIDEO, - .p.id = AV_CODEC_ID_TQI, - .priv_data_size = sizeof(TqiContext), - .init = tqi_decode_init, - .close = tqi_decode_end, - FF_CODEC_DECODE_CB(tqi_decode_frame), - .p.capabilities = AV_CODEC_CAP_DR1, -}; diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ffjni.h b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ffjni.h deleted file mode 100644 index 6027bac0ab12dcd2af1e85f842d968a6f6100a08..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/ffjni.h +++ /dev/null @@ -1,145 +0,0 @@ -/* - * JNI utility functions - * - * Copyright (c) 2015-2016 Matthieu Bouron - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -#ifndef AVCODEC_FFJNI_H -#define AVCODEC_FFJNI_H - -#include - -/* - * Attach permanently a JNI environment to the current thread and retrieve it. - * - * If successfully attached, the JNI environment will automatically be detached - * at thread destruction. - * - * @param attached pointer to an integer that will be set to 1 if the - * environment has been attached to the current thread or 0 if it is - * already attached. - * @param log_ctx context used for logging, can be NULL - * @return the JNI environment on success, NULL otherwise - */ -JNIEnv *ff_jni_get_env(void *log_ctx); - -/* - * Convert a jstring to its utf characters equivalent. - * - * @param env JNI environment - * @param string Java string to convert - * @param log_ctx context used for logging, can be NULL - * @return a pointer to an array of unicode characters on success, NULL - * otherwise - */ -char *ff_jni_jstring_to_utf_chars(JNIEnv *env, jstring string, void *log_ctx); - -/* - * Convert utf chars to its jstring equivalent. - * - * @param env JNI environment - * @param utf_chars a pointer to an array of unicode characters - * @param log_ctx context used for logging, can be NULL - * @return a Java string object on success, NULL otherwise - */ -jstring ff_jni_utf_chars_to_jstring(JNIEnv *env, const char *utf_chars, void *log_ctx); - -/* - * Extract the error summary from a jthrowable in the form of "className: errorMessage" - * - * @param env JNI environment - * @param exception exception to get the summary from - * @param error address pointing to the error, the value is updated if a - * summary can be extracted - * @param log_ctx context used for logging, can be NULL - * @return 0 on success, < 0 otherwise - */ -int ff_jni_exception_get_summary(JNIEnv *env, jthrowable exception, char **error, void *log_ctx); - -/* - * Check if an exception has occurred,log it using av_log and clear it. - * - * @param env JNI environment - * @param log value used to enable logging if an exception has occurred, - * 0 disables logging, != 0 enables logging - * @param log_ctx context used for logging, can be NULL - */ -int ff_jni_exception_check(JNIEnv *env, int log, void *log_ctx); - -/* - * Jni field type. - */ -enum FFJniFieldType { - - FF_JNI_CLASS, - FF_JNI_FIELD, - FF_JNI_STATIC_FIELD, - FF_JNI_METHOD, - FF_JNI_STATIC_METHOD - -}; - -/* - * Jni field describing a class, a field or a method to be retrieved using - * the ff_jni_init_jfields method. - */ -struct FFJniField { - - const char *name; - const char *method; - const char *signature; - enum FFJniFieldType type; - int offset; - int mandatory; - -}; - -/* - * Retrieve class references, field ids and method ids to an arbitrary structure. - * - * @param env JNI environment - * @param jfields a pointer to an arbitrary structure where the different - * fields are declared and where the FFJNIField mapping table offsets are - * pointing to - * @param jfields_mapping null terminated array of FFJNIFields describing - * the class/field/method to be retrieved - * @param global make the classes references global. It is the caller - * responsibility to properly release global references. - * @param log_ctx context used for logging, can be NULL - * @return 0 on success, < 0 otherwise - */ -int ff_jni_init_jfields(JNIEnv *env, void *jfields, const struct FFJniField *jfields_mapping, int global, void *log_ctx); - -/* - * Delete class references, field ids and method ids of an arbitrary structure. - * - * @param env JNI environment - * @param jfields a pointer to an arbitrary structure where the different - * fields are declared and where the FFJNIField mapping table offsets are - * pointing to - * @param jfields_mapping null terminated array of FFJNIFields describing - * the class/field/method to be deleted - * @param global threat the classes references as global and delete them - * accordingly - * @param log_ctx context used for logging, can be NULL - * @return 0 on success, < 0 otherwise - */ -int ff_jni_reset_jfields(JNIEnv *env, void *jfields, const struct FFJniField *jfields_mapping, int global, void *log_ctx); - -#endif /* AVCODEC_FFJNI_H */ diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/amrwbdec_mips.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/amrwbdec_mips.c deleted file mode 100644 index 5dc054361b1a93e3d2ddbaafb821545a09a992fb..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/mips/amrwbdec_mips.c +++ /dev/null @@ -1,189 +0,0 @@ -/* - * Copyright (c) 2012 - * MIPS Technologies, Inc., California. - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions - * are met: - * 1. Redistributions of source code must retain the above copyright - * notice, this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright - * notice, this list of conditions and the following disclaimer in the - * documentation and/or other materials provided with the distribution. - * 3. Neither the name of the MIPS Technologies, Inc., nor the names of its - * contributors may be used to endorse or promote products derived from - * this software without specific prior written permission. - * - * THIS SOFTWARE IS PROVIDED BY THE MIPS TECHNOLOGIES, INC. ``AS IS'' AND - * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE - * ARE DISCLAIMED. IN NO EVENT SHALL THE MIPS TECHNOLOGIES, INC. BE LIABLE - * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS - * OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) - * HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT - * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY - * OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF - * SUCH DAMAGE. - * - * Author: Nedeljko Babic (nbabic@mips.com) - * - * This file is part of FFmpeg. - * - * FFmpeg is free software; you can redistribute it and/or - * modify it under the terms of the GNU Lesser General Public - * License as published by the Free Software Foundation; either - * version 2.1 of the License, or (at your option) any later version. - * - * FFmpeg is distributed in the hope that it will be useful, - * but WITHOUT ANY WARRANTY; without even the implied warranty of - * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU - * Lesser General Public License for more details. - * - * You should have received a copy of the GNU Lesser General Public - * License along with FFmpeg; if not, write to the Free Software - * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA - */ - -/** - * @file - * Reference: libavcodec/amrwbdec.c - */ -#include "libavutil/avutil.h" -#include "libavcodec/amrwbdata.h" -#include "amrwbdec_mips.h" - -#if HAVE_INLINE_ASM -#if !HAVE_MIPS32R6 && !HAVE_MIPS64R6 -void ff_hb_fir_filter_mips(float *out, const float fir_coef[HB_FIR_SIZE + 1], - float mem[HB_FIR_SIZE], const float *in) -{ - int i; - float data[AMRWB_SFR_SIZE_16k + HB_FIR_SIZE]; // past and current samples - - memcpy(data, mem, HB_FIR_SIZE * sizeof(float)); - memcpy(data + HB_FIR_SIZE, in, AMRWB_SFR_SIZE_16k * sizeof(float)); - - for (i = 0; i < AMRWB_SFR_SIZE_16k; i++) { - float output; - float * p_data = (data+i); - - /** - * inner loop is entirely unrolled and instructions are scheduled - * to minimize pipeline stall - */ - __asm__ volatile( - "mtc1 $zero, %[output] \n\t" - "lwc1 $f0, 0(%[p_data]) \n\t" - "lwc1 $f1, 0(%[fir_coef]) \n\t" - "lwc1 $f2, 4(%[p_data]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f3, 4(%[fir_coef]) \n\t" - "lwc1 $f4, 8(%[p_data]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - "lwc1 $f5, 8(%[fir_coef]) \n\t" - - "lwc1 $f0, 12(%[p_data]) \n\t" - "lwc1 $f1, 12(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f2, 16(%[p_data]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f3, 16(%[fir_coef]) \n\t" - "lwc1 $f4, 20(%[p_data]) \n\t" - "lwc1 $f5, 20(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - - "lwc1 $f0, 24(%[p_data]) \n\t" - "lwc1 $f1, 24(%[fir_coef]) \n\t" - "lwc1 $f2, 28(%[p_data]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f3, 28(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f4, 32(%[p_data]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - "lwc1 $f5, 32(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - - "lwc1 $f0, 36(%[p_data]) \n\t" - "lwc1 $f1, 36(%[fir_coef]) \n\t" - "lwc1 $f2, 40(%[p_data]) \n\t" - "lwc1 $f3, 40(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f4, 44(%[p_data]) \n\t" - "lwc1 $f5, 44(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - - "lwc1 $f0, 48(%[p_data]) \n\t" - "lwc1 $f1, 48(%[fir_coef]) \n\t" - "lwc1 $f2, 52(%[p_data]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f3, 52(%[fir_coef]) \n\t" - "lwc1 $f4, 56(%[p_data]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f5, 56(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - - "lwc1 $f0, 60(%[p_data]) \n\t" - "lwc1 $f1, 60(%[fir_coef]) \n\t" - "lwc1 $f2, 64(%[p_data]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f3, 64(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f4, 68(%[p_data]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - "lwc1 $f5, 68(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - - "lwc1 $f0, 72(%[p_data]) \n\t" - "lwc1 $f1, 72(%[fir_coef]) \n\t" - "lwc1 $f2, 76(%[p_data]) \n\t" - "lwc1 $f3, 76(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f4, 80(%[p_data]) \n\t" - "lwc1 $f5, 80(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - - "lwc1 $f0, 84(%[p_data]) \n\t" - "lwc1 $f1, 84(%[fir_coef]) \n\t" - "lwc1 $f2, 88(%[p_data]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f3, 88(%[fir_coef]) \n\t" - "lwc1 $f4, 92(%[p_data]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f5, 92(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - - "lwc1 $f0, 96(%[p_data]) \n\t" - "lwc1 $f1, 96(%[fir_coef]) \n\t" - "lwc1 $f2, 100(%[p_data]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f3, 100(%[fir_coef]) \n\t" - "lwc1 $f4, 104(%[p_data]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f5, 104(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - - "lwc1 $f0, 108(%[p_data]) \n\t" - "lwc1 $f1, 108(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "lwc1 $f2, 112(%[p_data]) \n\t" - "lwc1 $f3, 112(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - "lwc1 $f4, 116(%[p_data]) \n\t" - "lwc1 $f5, 116(%[fir_coef]) \n\t" - "lwc1 $f0, 120(%[p_data]) \n\t" - "madd.s %[output], %[output], $f2, $f3 \n\t" - "lwc1 $f1, 120(%[fir_coef]) \n\t" - "madd.s %[output], %[output], $f4, $f5 \n\t" - "madd.s %[output], %[output], $f0, $f1 \n\t" - - : [output]"=&f"(output) - : [fir_coef]"r"(fir_coef), [p_data]"r"(p_data) - : "$f0", "$f1", "$f2", "$f3", "$f4", "$f5", "memory" - ); - out[i] = output; - } - memcpy(mem, data + AMRWB_SFR_SIZE_16k, HB_FIR_SIZE * sizeof(float)); -} -#endif /* !HAVE_MIPS32R6 && !HAVE_MIPS64R6 */ -#endif /* HAVE_INLINE_ASM */ diff --git a/spaces/congsaPfin/Manga-OCR/logs/ Seni Severdim - Yildiz Usmonova feat Yasar Gunacgun 2020 .md b/spaces/congsaPfin/Manga-OCR/logs/ Seni Severdim - Yildiz Usmonova feat Yasar Gunacgun 2020 .md deleted file mode 100644 index d7b3ce1e3df44a6d243750c8e0f35c0b3aef7896..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/ Seni Severdim - Yildiz Usmonova feat Yasar Gunacgun 2020 .md +++ /dev/null @@ -1,151 +0,0 @@ - -

    How to Download Turkish Songs Online

    -

    Turkish pop music is a popular genre that combines elements of traditional Turkish folk music, Western pop, rock, jazz, and other styles. It has a rich history and a diverse range of artists and songs. If you are a fan of Turkish pop music or want to explore this fascinating musical culture, you might want to download some Turkish songs online. But how can you do that? What are the best sites to download Turkish songs? In this article, we will answer these questions and provide you with some tips and tricks to enjoy Turkish pop music on your devices.

    -

    Introduction

    -

    What is Turkish pop music?

    -

    Turkish pop music is a genre that emerged in the late 1950s as Turkish artists started to cover and adapt Western popular styles, such as rock and roll, tango, and jazz. Since then, Turkish pop music has evolved and diversified, incorporating influences from various regional and global musical traditions, such as Arabesque, Anatolian rock, Balkan folk, hip hop, electronic, and more. Some of the most famous Turkish pop artists include Tarkan, Sezen Aksu, Sertab Erener, Ajda Pekkan, Kenan Doğulu, Yıldız Tilbe, and many others. Turkish pop music is characterized by its catchy melodies, rhythmic diversity, emotional lyrics, and fusion of modern and traditional sounds.

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    Why download Turkish songs?

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    There are many reasons why you might want to download Turkish songs online. Here are some of them:

    -
      -
    • You love Turkish pop music and want to listen to it offline or on different devices.
      • -
    • You want to discover new Turkish artists and songs that are not available on mainstream streaming platforms.
      • -
    • You want to learn more about Turkish culture and language through music.
      • -
    • You want to support Turkish musicians and the local music industry.
      • -
    • You want to create your own playlists and mixtapes of Turkish pop music.
      • -
    -

    Whatever your reason is, downloading Turkish songs online can be a fun and rewarding experience. But where can you find the best sites to download Turkish songs? Let's find out in the next section.

    -

    Best sites to download Turkish songs

    -

    Free Music Archive

    -

    One of the best sites to download Turkish songs online is Free Music Archive. This is a website that offers free and legal access to thousands of songs from various genres and countries. You can find a lot of Turkish pop music on this site, as well as other types of Turkish music, such as folk, rock, rap, instrumental, and more. You can browse by genre, artist, album, or playlist, or use the search function to find what you are looking for.

    -

    Pros and cons

    -

    Some of the pros of using Free Music Archive are:

    -
      -
    • It is free and legal.
      • -
    • It has a large and diverse collection of Turkish music.
      • -
    • It allows you to stream or download songs in high quality.
      • -
    • It has a user-friendly interface and easy navigation.
      • -
    -

    Some of the cons of using Free Music Archive are:

    -
      -
    • It does not have the latest or most popular Turkish songs.
      • -
    • It does not have lyrics or translations for the songs.
      • -
    • It does not have an app or offline mode.
      • -
    -

    How to download

    -

    To download Turkish songs from Free Music Archive, you need to follow these steps:

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      -
    1. Go to [Free Music Archive](^1^) and select "Turkish" from the "Genres" menu.
      2. -
    2. Choose a song that you like and click on the "Download" button next to it.
      3. -
    3. Save the file to your device and enjoy your Turkish music.
      4. -
    -

    SoundCloud

    -

    Another great site to download Turkish songs online is SoundCloud. This is a platform that allows artists and users to upload, share, and stream music. You can find a lot of Turkish pop music on SoundCloud, as well as other genres and styles. You can follow your favorite artists, discover new ones, create playlists, and interact with other music lovers. You can also download some songs for free or with a subscription.

    -

    Pros and cons

    -

    Some of the pros of using SoundCloud are:

    -
      -
    • It has a huge and updated collection of Turkish music.
      • -
    • It has a social and interactive aspect.
      • -
    • It has an app and an offline mode.
      • -
    • It has lyrics and translations for some songs.
      • -
    -

    Some of the cons of using SoundCloud are:

    -
      -
    • It is not free and legal for all songs.
      • -
    • It has ads and limitations for free users.
      • -
    • It does not have a clear genre or country filter.
      • -
    -

    How to download

    -

    To download Turkish songs from SoundCloud, you need to follow these steps:

    -
      -
    1. Go to [SoundCloud] and sign up or log in.
      2. -
    2. Use the search function to find Turkish songs or artists that you like.
      3. -
    3. If the song has a "Download" button under it, click on it and save the file to your device.
      4. -
    4. If the song does not have a "Download" button, you can use a third-party tool, such as [SoundCloud Downloader], to download it. Just copy the URL of the song and paste it into the tool, then click on "Download" and save the file to your device.
      5. -
    -

    Torrent sites

    -

    A third option to download Turkish songs online is to use torrent sites. These are websites that allow users to share files through peer-to-peer networks. You can find a lot of Turkish pop music on torrent sites, as well as other types of media, such as movies, games, books, and more. You can download entire albums or discographies of Turkish artists, or individual songs that you like.

    -

    Pros and cons

    -

    Some of the pros of using torrent sites are:

    -
      -
    • They have a vast and varied collection of Turkish music.
      • -
    • They have high-quality and fast downloads.
      • -
    • They have ratings and comments for the files.
      • -
    -

    Some of the cons of using torrent sites are:

    -
      -
    • They are not free and legal.
      • -
    • They are risky and unsafe for your device and privacy.
      • -
    • They require additional software, such as a torrent client and a VPN.
      • -
    -

    How to download

    -

    To download Turkish songs from torrent sites, you need to follow these steps:

    -
      -
    1. Download and install a torrent client, such as [BitTorrent] or [uTorrent].
      2. -
    2. Download and install a VPN, such as [NordVPN] or [ExpressVPN], to protect your identity and bypass geo-restrictions.
      3. -
    3. Go to a torrent site, such as [The Pirate Bay] or [1337x], and search for Turkish songs or artists that you like.
      4. -
    4. Select a file that has a high number of seeders and leechers, and a positive rating and comment.
      5. -
    5. Click on the "Download" or "Magnet" link and open it with your torrent client.
      6. -
    6. Wait for the download to finish and save the file to your device.
      7. -
    -

    Conclusion

    -

    Summary of the main points

    -

    In this article, we have discussed how to download Turkish songs online. We have explained what Turkish pop music is and why you might want to download it. We have also reviewed three of the best sites to download Turkish songs: Free Music Archive, SoundCloud, and torrent sites. We have compared their pros and cons, and provided you with step-by-step instructions on how to download from each site.

    -

    Call to action

    -

    Now that you know how to download Turkish songs online, you can enjoy listening to them anytime and anywhere. You can discover new Turkish artists and songs, learn more about Turkish culture and language, support Turkish musicians, and create your own playlists and mixtapes. You can also share your favorite Turkish songs with your friends and family, or on social media. Turkish pop music is a vibrant and diverse genre that deserves your attention and appreciation. So what are you waiting for? Start downloading Turkish songs online today!

    -

    Frequently Asked Questions

    -

    What is the best format to download Turkish songs?

    -

    The best format to download Turkish songs depends on your preference and device. Generally, MP3 is the most common and compatible format for audio files. However, if you want higher quality and smaller size, you can opt for other formats, such as FLAC, AAC, or OGG.

    -

    How can I convert Turkish songs to different formats?

    -

    If you want to convert Turkish songs to different formats, you can use online tools, such as [Online Audio Converter] or [Zamzar]. Just upload your file, choose your desired format, and download the converted file.

    -

    How can I edit Turkish songs?

    -

    If you want to edit Turkish songs, such as cutting, merging, adding effects, or changing the volume, you can use online tools, such as [Audio Trimmer] or [Audacity]. Just upload your file, make your changes, and download the edited file.

    -

    How can I find the lyrics and translations of Turkish songs?

    -

    If you want to find the lyrics and translations of Turkish songs, you can use online tools, such as [Lyrics Translate] or [Musixmatch]. Just enter the name of the song or artist, and you will see the lyrics in Turkish and other languages.

    -

    How can I learn Turkish through songs?

    -

    If you want to learn Turkish through songs, you can use online tools, such as [Lirica] or [Lyricstraining]. These are apps that help you learn languages through music. They have interactive exercises, quizzes, games, and videos based on popular songs in different languages.

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    • All cars and tracks unlocked: You can access all the cars and tracks in the modded version without having to complete any levels or achievements. This way, you can try out different cars and tracks and find your favorite ones.
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    Car Game 3D: Car Racing Mod APK also has some disadvantages that you should be aware of before downloading and installing it. Some of these disadvantages are:

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    Comparison Table of Car Game 3D: Car Racing Mod APK and Original Version

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    To help you decide whether to download and install Car Game 3D: Car Racing Mod APK or not, here is a comparison table of the features and differences between the modded version and the original version:

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    FeatureMod APKOriginal Version
    MoneyUnlimitedLimited
    GemsUnlimitedLimited
    CarsAll unlockedSome locked
    TracksAll unlockedSome locked
    AdsNo adsAds present
    RootNo root requiredNo root required
    -

    Conclusion

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    In conclusion, Car Game 3D: Car Racing Mod APK is a fun and exciting racing game for Android devices that offers many features and benefits over the original version. You can get unlimited money and gems, access all cars and tracks, enjoy the game without ads, and install the game without rooting your device. However, you should also be aware of the possible compatibility issues, risk of malware or viruses, and potential ban from the official game server that come with the modded version. Therefore, you should download and install Car Game 3D: Car Racing Mod APK at your own risk and discretion.

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    Here are some frequently asked questions about Car Game 3D: Car Racing Mod APK:

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    Q1: Is Car Game 3D: Car Racing Mod APK safe to use?

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    A1: Car Game 3D: Car Racing Mod APK is safe to use if you download it from a reliable source that has positive reviews and ratings. However, if you download it from an untrusted source that has negative reviews and ratings, you may encounter malware or viruses that can harm your device or steal your data. Therefore, you should always be careful and cautious when downloading any modded apk file from the internet.

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    Q2: How can I update Car Game 3D: Car Racing Mod APK?

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    A2: You can update Car Game 3D: Car Racing Mod APK by downloading and installing the latest version of the modded apk file from our website or any other trusted source. You should always check for updates regularly to enjoy the new features and improvements of the game.

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    Q3: Can I play Car Game 3D: Car Racing Mod APK online with other players?

    - are also using the modded version. However, you may not be able to play online with other players who are using the original version, as they may have a different game version or server. You may also face a ban from the official game server if you are detected using the modded version online. Therefore, you should be careful and discreet when playing online with the modded version.

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    A4: Here are some tips and tricks for playing Car Game 3D: Car Racing Mod APK:

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    • Select a track that matches your skill level and interest. You can choose from different tracks, such as city, desert, snow, night, and more, each with its own challenges and obstacles. You can also adjust the difficulty level of the game, from easy to hard, to suit your preference.
      • -
    • Use the controls and the boosters wisely to win the race. You can use the controls on the screen to steer, accelerate, brake, and drift your car. You can also use the boosters on the track to speed up your car or slow down your opponents. To use a booster, tap on it when you are near it.
      • -
    • Practice and improve your skills in Career or Free Mode. You can play against the AI in Career or Free Mode to practice and improve your skills. You can also earn money and gems by completing levels and achievements in Career Mode.
      • -
    • Compete and have fun in Multiplayer or Time Trial Mode. You can play against other players online in Multiplayer Mode or against yourself in Time Trial Mode. You can also chat with other players and make friends or rivals in Multiplayer Mode.
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    Q5: Where can I find more information about Car Game 3D: Car Racing Mod APK?

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    A5: You can find more information about Car Game 3D: Car Racing Mod APK on our website or on the official website of the original game. You can also watch videos or read reviews of the game on YouTube or other platforms. You can also contact us if you have any questions or feedback about the game.

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    If you are looking for a reliable and convenient delivery service in Indonesia or the Philippines, you might want to try J&T Express. J&T Express is a mobile app that allows you to book, track, and manage your shipments with ease. In this article, we will show you what J&T Express is, what features and benefits it offers, and how to download and use the app on your Android device.

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    The J&T Express app has several features that make it easy and convenient to use. Some of these features are:

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    By using the J&T Express app, you can enjoy several benefits that can enhance your delivery experience. Some of these benefits are:

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    How to Download J&T Express APK for Android Devices

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    If you want to use the J&T Express app on your Android device, you will need to download and install the APK file from a trusted source. The APK file is a package file that contains all the necessary files and data for installing an app on an Android device. Here are the steps to download and install the J&T Express APK for Android devices:

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    Step 1: Enable Unknown Sources

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    Before you can install an APK file on your Android device , you will need to enable the unknown sources option on your device settings. This will allow you to install apps from sources other than the Google Play Store. To enable unknown sources, follow these steps:

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    Step 2: Find and Download the APK File

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    Next, you will need to find and download the J&T Express APK file from a trusted source. You can use your browser or a file manager app to search for the APK file online. Make sure that the source is reputable and safe, and that the APK file is compatible with your device and Android version. To download the APK file, follow these steps:

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    Finally, you will need to install and launch the J&T Express app on your device. To install and launch the app, follow these steps:

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    Now that you have installed and launched the J&T Express app on your device, you can start using it to book, track, and manage your shipments. Here are some tips on how to use the J&T Express app:

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    To track your shipment status and location with J&T Express, you will need to enter your booking number or scan your QR code. To track your shipment, follow these steps:

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    1. On the app's home screen, tap on Track Shipment.
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    Conclusion

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    J&T Express is a mobile app that provides delivery services for online businesses and customers in Indonesia and the Philippines. It offers fast, safe, affordable, and convenient delivery solutions for various types of goods. You can easily book, track, and manage your shipments with the app on your Android device. To download and install the J&T Express APK for Android devices, you will need to enable unknown sources, find and download the APK file from a trusted source, and install and launch the app. You can then register and login with your phone number or email address, and start using the app to book, track, and manage your shipments.

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    Minecraft Trial Mod APK Unlimited Items is a modification that adds various features and enhancements to the trial version of Minecraft. It allows you to have unlimited items, blocks, and resources in your inventory and creative menu. You can also access all the items from different mods that are compatible with Forge mod installer.

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      The gameplay tips and tricks for using the mod features

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      Playing Minecraft Trial Mod APK Unlimited Items is very easy and fun. You can use the mod features to enhance your trial gameplay and experience new possibilities. Here are some gameplay tips and tricks for using the mod features:

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      • To access unlimited items, blocks, and resources, you can open your inventory or creative menu and scroll through the tabs. You can also search for any item or block by typing its name in the search bar.
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      • To craft any item or block without needing any resources or recipes, you can open your crafting table or inventory and select any item or block from the output slot. You can also use commands like /give or /setblock to get any item or block.
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      • To place or break any block without any restrictions or limitations, you can use any tool or weapon in your hand and tap or hold on any block. You can also use commands like /fill or /clone to place or remove multiple blocks at once.
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      • To use any tool or weapon without durability or cooldown, you can equip any tool or weapon in your hand and use it as normal. You can also enchant any tool or weapon with commands like /enchant or /effect.
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      • To fly in survival mode with double-tap jump, you can double-tap the jump button on your screen and start flying in any direction. You can also adjust your flying speed with commands like /flyspeed or /gamerule.
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      • To spawn any mob or entity with eggs or commands, you can use any spawn egg in your hand and tap on any surface. You can also use commands like /summon or /spawnpoint to spawn any mob or entity at a specific location.
        • -
      • To change the time, weather, difficulty, game mode, or cheats with commands, you can use commands like /time, /weather, /difficulty, /gamemode, or /gamerule in the chat window. You can also use command blocks or buttons to execute commands automatically.
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      The comparison of the mod gameplay with the original trial gameplay

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      Minecraft Trial Mod APK Unlimited Items is a great way to enjoy Minecraft Trial with more fun and freedom. It gives you access to unlimited items, blocks, and resources that you can use to create anything you want. It also lets you fly in survival mode, spawn any mob or entity, change the game settings, and more.

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      However, playing with Minecraft Trial Mod APK Unlimited Items is not the same as playing with the original trial gameplay. The original trial gameplay is more challenging and rewarding, as you have to gather resources, craft items, build structures, and survive in a limited world. It also gives you a taste of what Minecraft is like before you buy it.

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      Conclusion

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      A summary of the main points and benefits of the mod

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      Minecraft Trial Mod APK Unlimited Items is a modification that adds various features and enhancements to the trial version of Minecraft. It allows you to have unlimited items, blocks, and resources in your inventory and creative menu. You can also access all the items from different mods that are compatible with Forge mod installer.

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      With this mod, you can enjoy Minecraft Trial with more fun and freedom. You can create anything you want, fly in survival mode, spawn any mob or entity, change the game settings, and more. You can also play with your friends on LAN multiplayer and share your creations.

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      A call to action for the readers to try the mod

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      \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Project Makeover Premium APK A Fun and Fashionable Game for Everyone.md b/spaces/congsaPfin/Manga-OCR/logs/Project Makeover Premium APK A Fun and Fashionable Game for Everyone.md deleted file mode 100644 index 91cd81374be82795637fd96da1c90cccdbea4e43..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Project Makeover Premium APK A Fun and Fashionable Game for Everyone.md +++ /dev/null @@ -1,85 +0,0 @@ -
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      Do you love fashion and makeover games? Do you enjoy helping people look their best and feel confident? If you answered yes to these questions, then you will love Project Makeover, a game that lets you transform people's lives with your fashion skills. And if you want to make the game even more fun and exciting, you should try Project Makeover Premium APK, a modified version of the original game that gives you unlimited money and gems, unlocks all the items in the game, and removes ads and other distractions. In this article, we will tell you everything you need to know about Project Makeover Premium APK, including what it is, how to download and install it, and why you should play it.

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      What is Project Makeover?

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      A game that lets you transform people's lives with your fashion skills

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      Project Makeover is a game that combines fashion, makeover, and puzzle elements. In this game, you will meet different clients who need your help to improve their appearance and personality. You will have to choose their outfits, hairstyles, makeup, accessories, and even their home decor. You will also have to solve match-3 puzzles to earn money and gems that you can use to buy more items for your clients. Your goal is to make your clients happy and satisfied with their new look.

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      Project Makeover is not just a game about fashion and makeover. It is also a game that features a captivating storyline and characters that will make you feel emotionally invested in the game. You will get to know each client's backstory, personality, dreams, and goals. You will also get to interact with other characters in the game, such as your assistant Amber, your rival stylist Rachel, your mentor Coco Chanelle, etc. You will witness how your makeover changes not only your client's appearance but also their life. You will see how they become more confident, happy, successful, and loved after your makeover.

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      What is Project Makeover Premium APK?

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      A modified version of the original game that gives you unlimited money and gems

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      Project Makeover Premium APK is a modified version of the original game that gives you unlimited money and gems. This means that you can buy any item in the game without worrying about running out of resources. You can also

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      A version that unlocks all the outfits, accessories, and furniture in the game

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      Project Makeover Premium APK is also a version that unlocks all the outfits, accessories, and furniture in the game. This means that you can access any item in the game without having to unlock them by playing the puzzles or spending money and gems. You can choose from a wide range of items that suit different styles, themes, and occasions. You can mix and match different items to create your own unique look for your clients. You can also customize your own studio and home with various furniture and decorations. With Project Makeover Premium APK, you can unleash your creativity and style without any restrictions.

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      A version that removes ads and other distractions from the game

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      Project Makeover Premium APK is also a version that removes ads and other distractions from the game. This means that you can play the game without having to watch annoying ads that interrupt your gameplay or waste your time. You can also avoid other distractions, such as pop-ups, notifications, or requests that may affect your concentration or enjoyment of the game. With Project Makeover Premium APK, you can focus on the game and have a smooth and pleasant gaming experience.

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      How to download and install Project Makeover Premium APK?

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      The first step to download and install Project Makeover Premium APK is to find a trusted source that provides the APK file. You can search online for websites that offer Project Makeover Premium APK for free download. However, you should be careful and avoid downloading from unverified or suspicious sources that may contain viruses or malware that can harm your device or steal your personal information. You should also check the reviews and ratings of the website before downloading to ensure its reliability and quality.

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      Step 2: Enable unknown sources on your device settings

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      The second step to download and install Project Makeover Premium APK is to enable unknown sources on your device settings. This is because Project Makeover Premium APK is not available on the official Google Play Store or App Store, so you need to allow your device to install apps from sources other than these platforms. To do this, you need to go to your device settings, then security or privacy, then enable unknown sources or allow installation from unknown sources. This will allow you to install Project Makeover Premium APK on your device.

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      Step 3: Install the APK file and launch the game

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      The third step to download and install Project Makeover Premium APK is to install the APK file and launch the game. To do this, you need to locate the downloaded APK file on your device storage, then tap on it to start the installation process. Follow the instructions on the screen to complete the installation. Once the installation is done, you can launch the game by tapping on its icon on your home screen or app drawer. You can now enjoy playing Project Makeover Premium APK with unlimited money and gems, unlocked items, and no ads.

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      Why should you play Project Makeover Premium APK?

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      One of the reasons why you should play Project Makeover Premium APK is to enjoy a fun and relaxing game that lets you express your creativity and style. Project Makeover is a game that allows you to transform people's lives with your fashion skills. You can choose from a variety of outfits, hairstyles, makeup, accessories, and furniture that suit different clients' needs and preferences. You can also create your own unique look by mixing and matching different items. You can have fun playing with different colors, patterns, textures, and shapes. You can also relax by solving match-3 puzzles that are easy and enjoyable.

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      Another reason why you should play Project Makeover Premium APK is to experience a satisfying makeover process that improves people's lives and happiness. Project Makeover is a game that features a captivating storyline and characters that will make you feel emotionally invested in the game. You will get to know each client's backstory, personality, dreams, and goals. You will also get to see how your makeover changes not only their appearance but also their life. You will see how they become more confident, happy, successful, and loved after your makeover. You will feel proud of yourself for making a positive difference in someone's life.

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      To explore a variety of themes, genres, and scenarios in the game's story mode

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      Conclusion

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      Project Makeover Premium APK is a modified version of the original game that gives you unlimited money and gems, unlocks all the items in the game, and removes ads and other distractions. It is a fun and relaxing game that lets you transform people's lives with your fashion skills. It is also a game that features a captivating storyline and characters that will make you feel emotionally invested in the game. It is also a game that offers various challenges and rewards that will keep you engaged and motivated. It is also a game that allows you to explore a variety of themes, genres, and scenarios in the game's story mode. If you love fashion and makeover games, you should definitely try Project Makeover Premium APK and enjoy the ultimate makeover experience.

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      FAQs

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      A: Yes, Project Makeover Premium APK is safe to download and install as long as you download it from a trusted source that provides the original and virus-free APK file. You should also check the reviews and ratings of the website before downloading to ensure its reliability and quality.

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      A: No, you do not need to root or jailbreak your device to play Project Makeover Premium APK. You just need to enable unknown sources on your device settings and install the APK file as instructed.

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      A: Yes, you can play Project Makeover Premium APK offline without an internet connection. However, some features of the game may require an internet connection, such as updating the game or accessing social media.

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      Q: Can I sync my progress in Project Makeover Premium APK with the original game?

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      A: No, you cannot sync your progress in Project Makeover Premium APK with the original game. They are separate versions of the game that have different data and settings. You will have to start from scratch if you switch between them.

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      A: Yes, you can play Project Makeover Premium APK with your friends by connecting your Facebook account to the game. You can then invite your friends to play with you or compete with them on the leaderboard.

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      \ No newline at end of file diff --git a/spaces/congsaPfin/Manga-OCR/logs/Temple Run 2 The Most Exciting Running Game Ever for iPhone and iPad.md b/spaces/congsaPfin/Manga-OCR/logs/Temple Run 2 The Most Exciting Running Game Ever for iPhone and iPad.md deleted file mode 100644 index acddffe936f5b53fde00b60cdff873a09fd2086e..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Temple Run 2 The Most Exciting Running Game Ever for iPhone and iPad.md +++ /dev/null @@ -1,132 +0,0 @@ -
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      Temple Run 2 is one of the most popular running games for mobile devices, with over a billion downloads worldwide. It is the sequel to Temple Run, which redefined mobile gaming with its addictive gameplay, simple controls, and endless fun. In Temple Run 2, you have to run, jump, slide, and turn as you escape from a giant monkey that chases you through a perilous temple. Along the way, you have to collect coins, gems, power-ups, and achievements, while avoiding obstacles like fire, waterfalls, zip lines, and mine carts. If you are looking for a thrilling and challenging game that will keep you entertained for hours, Temple Run 2 is the perfect choice for you.

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      In this article, we will show you how to download Temple Run 2 for iOS devices, such as iPhone and iPad. We will also tell you about the features, tips and tricks, and reviews of the game, so you can enjoy it to the fullest. Let's get started!

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      Temple Run 2 Features

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      Temple Run 2 has many features that make it an exciting and engaging game. Here are some of them:

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      New graphics and environments

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      Temple Run 2 has improved graphics that give a more realistic and immersive feel to the game. The game also has different environments that change as you run, such as cliffs, forests, mines, and waterfalls. Each environment has its own challenges and surprises that will test your skills and reflexes.

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      New obstacles and power-ups

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      Temple Run 2 has new obstacles that add more variety and difficulty to the game. You have to dodge fire-breathing statues, jump over gaps, slide under rotating grinders, and more. You also have to use zip lines and mine carts to navigate through some sections of the game. The game also has new power-ups that give you special abilities and advantages. For example, you can use a shield to protect yourself from obstacles, a boost to run faster, a coin magnet to attract more coins, and a score multiplier to increase your points.

      -

      New characters and abilities

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      Temple Run 2 has four playable characters that you can unlock and customize. Each character has its own special ability that can help you in different situations. For example, Guy Dangerous can use a shield for longer periods of time, Scarlett Fox can activate a speed boost more often, Barry Bones can collect more coins with his coin bonus power-up, and Karma Lee can resurrect herself with fewer gems. You can also upgrade your characters' abilities by spending coins or gems.

      -

      Temple Run 2 Tips and Tricks

      -

      Temple Run 2 is a fun but challenging game that requires quick thinking and fast reactions. Here are some tips and tricks that can help you improve your performance and score higher:

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      -

      How to stay alive longer

      -
        -
      • The most important thing is to focus on staying alive rather than collecting coins or power-ups. Sometimes it is better to avoid risky moves that can cost you your life.
        • -
      • Keep an eye on what is coming next. The game gets faster and harder as you run, so you have to anticipate the obstacles and plan your moves accordingly.
        • -
      • Learn how to turn while jumping or sliding. This can help you avoid dead ends or switch lanes quickly when needed.
        • -
      • Use green gems wisely. Green gems can

        use green gems to revive yourself after you die. However, the cost of revival increases each time you use it, so save them for when you really need them.

        • -
      • Take advantage of the daily and weekly challenges. These are tasks that reward you with extra coins, gems, or power-ups if you complete them. They can also help you practice your skills and learn new strategies.
        • -
      -

      How to collect more coins and gems

      -
        -
      • Coins and gems are the currency of the game. You can use them to unlock and upgrade characters, abilities, power-ups, and artifacts. You can also use them to buy head starts, save me's, and score multipliers.
        • -
      • The easiest way to collect more coins and gems is to run as far as you can. The longer you run, the more coins and gems you will encounter.
        • -
      • Another way to collect more coins and gems is to use power-ups. The coin magnet attracts all the coins in your vicinity, the gem bonus gives you a chance to find gems in coin piles, and the coin bonus increases the value of the coins you collect.
        • -
      • You can also collect more coins and gems by completing objectives. Objectives are milestones that reward you with coins or gems when you achieve them. They can be things like running a certain distance, collecting a certain number of coins or power-ups, or using a certain character or ability.
        • -
      • Finally, you can collect more coins and gems by watching videos or completing offers. These are optional ways to earn some extra currency by watching ads or downloading other apps. However, be careful not to spend too much time or data on these methods.
        • -
      -

      How to use power-ups effectively

      -
        -
      • Power-ups are items that give you special abilities or advantages for a limited time. They can be found randomly on the path or activated by using power meters.
        • -
      • The most common power-ups are shield, boost, coin magnet, and score multiplier. Shield protects you from obstacles, boost makes you run faster, coin magnet attracts all the coins in your vicinity, and score multiplier increases your points.
        • -
      • The best way to use power-ups effectively is to know when and how to use them. For example, shield is useful when you encounter a lot of obstacles or fire-breathing statues, boost is useful when you want to escape from the monkey or reach a checkpoint faster, coin magnet is useful when there are a lot of coins around you, and score multiplier is useful when you want to increase your score quickly.
        • -
      • You can also upgrade your power-ups by spending coins or gems. Upgrading your power-ups will make them last longer or have stronger effects.
        • -
      -

      Temple Run 2 Reviews

      -

      Temple Run 2 is a highly rated game that has received positive feedback from both users and critics. Here are some of the reviews of the game:

      -

      What users say about the game

      -

      According to the App Store, Temple Run 2 has an average rating of 4.7 out of 5 stars from over 1.5 million ratings. Here are some of the user reviews:

      -
      "This game is awesome! It's so addictive and fun! I love the graphics and the different environments. It's challenging but not impossible. I recommend it to anyone who likes running games."
      -
      "I've been playing this game for years and I still love it. It's one of the best games ever made. It has everything: action, adventure, suspense, thrill, and humor. It never gets boring or repetitive."
      -
      "This game is amazing! It's so much better than the first one. It has more features, more characters, more power-ups, more obstacles, and more fun! It's also very smooth and easy to play."
      -

      What critics say about the game

      -

      According to Metacritic, Temple Run 2 has an average score of 79 out of 100 from 14 critics. Here are some of the critic reviews:

      -
      "Temple Run 2 is a great example of how to make a sequel that improves on the original in every way. It's faster, smoother, prettier, and more varied than its predecessor. It's also incredibly addictive and fun to play."
      -
      "Temple Run 2 is a solid runner that delivers what fans expect from the genre: endless running, jumping, sliding, and turning with simple controls and addictive gameplay. It also adds some new elements that make it stand out from other runners."
      -
      "Temple Run 2 is a worthy successor to Temple Run that offers more of the same but better. It has stunning graphics, diverse environments, new obstacles, [user](# new obstacles, and more challenges. It's a must-have for fans of the original and newcomers alike."
      -

      Temple Run 2 Download Link

      -

      Temple Run 2 is available for free on the App Store for iOS devices. You can download it by following these steps:

      -

      How to download the game from the App Store

      -
        -
      1. Open the App Store on your iOS device.
        2. -
      2. Search for "Temple Run 2" or use this link: [Temple Run 2].
        3. -
      3. Tap on the "Get" button to start downloading the game.
        4. -
      4. Wait for the download to finish and then tap on the "Open" button to launch the game.
        5. -
      -

      How to install and play the game on your iOS device

      -
        -
      1. After launching the game, you will see a loading screen with the Temple Run 2 logo.
        2. -
      2. When the loading is done, you will see a menu screen with four options: Play, Objectives, Store, and Options.
        3. -
      3. Tap on the "Play" button to start running. You can choose your character and power-ups before you run.
        4. -
      4. To control your character, swipe left or right to turn, swipe up to jump, swipe down to slide, and tilt your device to move sideways.
        5. -
      5. To pause the game, tap on the pause icon at the top left corner of the screen. You can resume, restart, or quit the game from there.
        6. -
      -

      Conclusion

      -

      Temple Run 2 is an amazing running game that will keep you hooked for hours. It has stunning graphics, diverse environments, new obstacles, and more challenges than the original Temple Run. It also has four playable characters, each with their own special ability, and various power-ups that can help you in your run. You can download Temple Run 2 for free from the App Store and enjoy it on your iOS device. If you are looking for a fun and exciting game that will test your skills and reflexes, Temple Run 2 is the perfect choice for you.

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      So what are you waiting for? Download Temple Run 2 now and start running!

      -

      Frequently Asked Questions

      -

      Here are some of the frequently asked questions about Temple Run 2:

      -

      Q: How do I unlock new characters?

      -

      A: You can unlock new characters by spending coins or gems in the store. You can also unlock some characters by completing certain objectives or achievements.

      -

      Q: How do I upgrade my abilities?

      -

      A: You can upgrade your abilities by spending coins or gems in the store. You can also upgrade some abilities by completing certain objectives or achievements.

      -

      Q: How do I get more coins and gems?

      -

      A: You can get more coins and gems by running as far as you can, using power-ups, completing objectives, watching videos, or completing offers.

      -

      Q: How do I change the settings of the game?

      -

      A: You can change the settings of the game by tapping on the options button at the menu screen. You can adjust the sound, music, tilt sensitivity, tutorial, notifications, and credits from there.

      -

      Q: How do I contact the developers of the game?

      -

      A: You can contact the developers of the game by tapping on the credits button at the options screen. You can find their email address, website, social media accounts, and other information from there.

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      -
      -
      \ No newline at end of file diff --git a/spaces/cooelf/Multimodal-CoT/timm/utils/model.py b/spaces/cooelf/Multimodal-CoT/timm/utils/model.py deleted file mode 100644 index bd46e2f49c6d5ee2de304bfda1456bd1716c6886..0000000000000000000000000000000000000000 --- a/spaces/cooelf/Multimodal-CoT/timm/utils/model.py +++ /dev/null @@ -1,92 +0,0 @@ -""" Model / state_dict utils - -Hacked together by / Copyright 2020 Ross Wightman -""" -from .model_ema import ModelEma -import torch -import fnmatch - -def unwrap_model(model): - if isinstance(model, ModelEma): - return unwrap_model(model.ema) - else: - return model.module if hasattr(model, 'module') else model - - -def get_state_dict(model, unwrap_fn=unwrap_model): - return unwrap_fn(model).state_dict() - - -def avg_sq_ch_mean(model, input, output): - "calculate average channel square mean of output activations" - return torch.mean(output.mean(axis=[0,2,3])**2).item() - - -def avg_ch_var(model, input, output): - "calculate average channel variance of output activations" - return torch.mean(output.var(axis=[0,2,3])).item()\ - - -def avg_ch_var_residual(model, input, output): - "calculate average channel variance of output activations" - return torch.mean(output.var(axis=[0,2,3])).item() - - -class ActivationStatsHook: - """Iterates through each of `model`'s modules and matches modules using unix pattern - matching based on `hook_fn_locs` and registers `hook_fn` to the module if there is - a match. - - Arguments: - model (nn.Module): model from which we will extract the activation stats - hook_fn_locs (List[str]): List of `hook_fn` locations based on Unix type string - matching with the name of model's modules. - hook_fns (List[Callable]): List of hook functions to be registered at every - module in `layer_names`. - - Inspiration from https://docs.fast.ai/callback.hook.html. - - Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example - on how to plot Signal Propogation Plots using `ActivationStatsHook`. - """ - - def __init__(self, model, hook_fn_locs, hook_fns): - self.model = model - self.hook_fn_locs = hook_fn_locs - self.hook_fns = hook_fns - if len(hook_fn_locs) != len(hook_fns): - raise ValueError("Please provide `hook_fns` for each `hook_fn_locs`, \ - their lengths are different.") - self.stats = dict((hook_fn.__name__, []) for hook_fn in hook_fns) - for hook_fn_loc, hook_fn in zip(hook_fn_locs, hook_fns): - self.register_hook(hook_fn_loc, hook_fn) - - def _create_hook(self, hook_fn): - def append_activation_stats(module, input, output): - out = hook_fn(module, input, output) - self.stats[hook_fn.__name__].append(out) - return append_activation_stats - - def register_hook(self, hook_fn_loc, hook_fn): - for name, module in self.model.named_modules(): - if not fnmatch.fnmatch(name, hook_fn_loc): - continue - module.register_forward_hook(self._create_hook(hook_fn)) - - -def extract_spp_stats(model, - hook_fn_locs, - hook_fns, - input_shape=[8, 3, 224, 224]): - """Extract average square channel mean and variance of activations during - forward pass to plot Signal Propogation Plots (SPP). - - Paper: https://arxiv.org/abs/2101.08692 - - Example Usage: https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 - """ - x = torch.normal(0., 1., input_shape) - hook = ActivationStatsHook(model, hook_fn_locs=hook_fn_locs, hook_fns=hook_fns) - _ = model(x) - return hook.stats - \ No newline at end of file diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/utils/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/utils/__init__.py deleted file mode 100644 index a263e31c1e3977712827ca229bbc04910b4e928e..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/cnn/utils/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .flops_counter import get_model_complexity_info -from .fuse_conv_bn import fuse_conv_bn -from .sync_bn import revert_sync_batchnorm -from .weight_init import (INITIALIZERS, Caffe2XavierInit, ConstantInit, - KaimingInit, NormalInit, PretrainedInit, - TruncNormalInit, UniformInit, XavierInit, - bias_init_with_prob, caffe2_xavier_init, - constant_init, initialize, kaiming_init, normal_init, - trunc_normal_init, uniform_init, xavier_init) - -__all__ = [ - 'get_model_complexity_info', 'bias_init_with_prob', 'caffe2_xavier_init', - 'constant_init', 'kaiming_init', 'normal_init', 'trunc_normal_init', - 'uniform_init', 'xavier_init', 'fuse_conv_bn', 'initialize', - 'INITIALIZERS', 'ConstantInit', 'XavierInit', 'NormalInit', - 'TruncNormalInit', 'UniformInit', 'KaimingInit', 'PretrainedInit', - 'Caffe2XavierInit', 'revert_sync_batchnorm' -] diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/multi_scale_deform_attn.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/multi_scale_deform_attn.py deleted file mode 100644 index fe755eaa931565aab77ecc387990328c01447343..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/ops/multi_scale_deform_attn.py +++ /dev/null @@ -1,358 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import math -import warnings - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd.function import Function, once_differentiable - -from annotator.mmpkg.mmcv import deprecated_api_warning -from annotator.mmpkg.mmcv.cnn import constant_init, xavier_init -from annotator.mmpkg.mmcv.cnn.bricks.registry import ATTENTION -from annotator.mmpkg.mmcv.runner import BaseModule -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['ms_deform_attn_backward', 'ms_deform_attn_forward']) - - -class MultiScaleDeformableAttnFunction(Function): - - @staticmethod - def forward(ctx, value, value_spatial_shapes, value_level_start_index, - sampling_locations, attention_weights, im2col_step): - """GPU version of multi-scale deformable attention. - - Args: - value (Tensor): The value has shape - (bs, num_keys, mum_heads, embed_dims//num_heads) - value_spatial_shapes (Tensor): Spatial shape of - each feature map, has shape (num_levels, 2), - last dimension 2 represent (h, w) - sampling_locations (Tensor): The location of sampling points, - has shape - (bs ,num_queries, num_heads, num_levels, num_points, 2), - the last dimension 2 represent (x, y). - attention_weights (Tensor): The weight of sampling points used - when calculate the attention, has shape - (bs ,num_queries, num_heads, num_levels, num_points), - im2col_step (Tensor): The step used in image to column. - - Returns: - Tensor: has shape (bs, num_queries, embed_dims) - """ - - ctx.im2col_step = im2col_step - output = ext_module.ms_deform_attn_forward( - value, - value_spatial_shapes, - value_level_start_index, - sampling_locations, - attention_weights, - im2col_step=ctx.im2col_step) - ctx.save_for_backward(value, value_spatial_shapes, - value_level_start_index, sampling_locations, - attention_weights) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - """GPU version of backward function. - - Args: - grad_output (Tensor): Gradient - of output tensor of forward. - - Returns: - Tuple[Tensor]: Gradient - of input tensors in forward. - """ - value, value_spatial_shapes, value_level_start_index,\ - sampling_locations, attention_weights = ctx.saved_tensors - grad_value = torch.zeros_like(value) - grad_sampling_loc = torch.zeros_like(sampling_locations) - grad_attn_weight = torch.zeros_like(attention_weights) - - ext_module.ms_deform_attn_backward( - value, - value_spatial_shapes, - value_level_start_index, - sampling_locations, - attention_weights, - grad_output.contiguous(), - grad_value, - grad_sampling_loc, - grad_attn_weight, - im2col_step=ctx.im2col_step) - - return grad_value, None, None, \ - grad_sampling_loc, grad_attn_weight, None - - -def multi_scale_deformable_attn_pytorch(value, value_spatial_shapes, - sampling_locations, attention_weights): - """CPU version of multi-scale deformable attention. - - Args: - value (Tensor): The value has shape - (bs, num_keys, mum_heads, embed_dims//num_heads) - value_spatial_shapes (Tensor): Spatial shape of - each feature map, has shape (num_levels, 2), - last dimension 2 represent (h, w) - sampling_locations (Tensor): The location of sampling points, - has shape - (bs ,num_queries, num_heads, num_levels, num_points, 2), - the last dimension 2 represent (x, y). - attention_weights (Tensor): The weight of sampling points used - when calculate the attention, has shape - (bs ,num_queries, num_heads, num_levels, num_points), - - Returns: - Tensor: has shape (bs, num_queries, embed_dims) - """ - - bs, _, num_heads, embed_dims = value.shape - _, num_queries, num_heads, num_levels, num_points, _ =\ - sampling_locations.shape - value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], - dim=1) - sampling_grids = 2 * sampling_locations - 1 - sampling_value_list = [] - for level, (H_, W_) in enumerate(value_spatial_shapes): - # bs, H_*W_, num_heads, embed_dims -> - # bs, H_*W_, num_heads*embed_dims -> - # bs, num_heads*embed_dims, H_*W_ -> - # bs*num_heads, embed_dims, H_, W_ - value_l_ = value_list[level].flatten(2).transpose(1, 2).reshape( - bs * num_heads, embed_dims, H_, W_) - # bs, num_queries, num_heads, num_points, 2 -> - # bs, num_heads, num_queries, num_points, 2 -> - # bs*num_heads, num_queries, num_points, 2 - sampling_grid_l_ = sampling_grids[:, :, :, - level].transpose(1, 2).flatten(0, 1) - # bs*num_heads, embed_dims, num_queries, num_points - sampling_value_l_ = F.grid_sample( - value_l_, - sampling_grid_l_, - mode='bilinear', - padding_mode='zeros', - align_corners=False) - sampling_value_list.append(sampling_value_l_) - # (bs, num_queries, num_heads, num_levels, num_points) -> - # (bs, num_heads, num_queries, num_levels, num_points) -> - # (bs, num_heads, 1, num_queries, num_levels*num_points) - attention_weights = attention_weights.transpose(1, 2).reshape( - bs * num_heads, 1, num_queries, num_levels * num_points) - output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * - attention_weights).sum(-1).view(bs, num_heads * embed_dims, - num_queries) - return output.transpose(1, 2).contiguous() - - -@ATTENTION.register_module() -class MultiScaleDeformableAttention(BaseModule): - """An attention module used in Deformable-Detr. - - `Deformable DETR: Deformable Transformers for End-to-End Object Detection. - `_. - - Args: - embed_dims (int): The embedding dimension of Attention. - Default: 256. - num_heads (int): Parallel attention heads. Default: 64. - num_levels (int): The number of feature map used in - Attention. Default: 4. - num_points (int): The number of sampling points for - each query in each head. Default: 4. - im2col_step (int): The step used in image_to_column. - Default: 64. - dropout (float): A Dropout layer on `inp_identity`. - Default: 0.1. - batch_first (bool): Key, Query and Value are shape of - (batch, n, embed_dim) - or (n, batch, embed_dim). Default to False. - norm_cfg (dict): Config dict for normalization layer. - Default: None. - init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. - Default: None. - """ - - def __init__(self, - embed_dims=256, - num_heads=8, - num_levels=4, - num_points=4, - im2col_step=64, - dropout=0.1, - batch_first=False, - norm_cfg=None, - init_cfg=None): - super().__init__(init_cfg) - if embed_dims % num_heads != 0: - raise ValueError(f'embed_dims must be divisible by num_heads, ' - f'but got {embed_dims} and {num_heads}') - dim_per_head = embed_dims // num_heads - self.norm_cfg = norm_cfg - self.dropout = nn.Dropout(dropout) - self.batch_first = batch_first - - # you'd better set dim_per_head to a power of 2 - # which is more efficient in the CUDA implementation - def _is_power_of_2(n): - if (not isinstance(n, int)) or (n < 0): - raise ValueError( - 'invalid input for _is_power_of_2: {} (type: {})'.format( - n, type(n))) - return (n & (n - 1) == 0) and n != 0 - - if not _is_power_of_2(dim_per_head): - warnings.warn( - "You'd better set embed_dims in " - 'MultiScaleDeformAttention to make ' - 'the dimension of each attention head a power of 2 ' - 'which is more efficient in our CUDA implementation.') - - self.im2col_step = im2col_step - self.embed_dims = embed_dims - self.num_levels = num_levels - self.num_heads = num_heads - self.num_points = num_points - self.sampling_offsets = nn.Linear( - embed_dims, num_heads * num_levels * num_points * 2) - self.attention_weights = nn.Linear(embed_dims, - num_heads * num_levels * num_points) - self.value_proj = nn.Linear(embed_dims, embed_dims) - self.output_proj = nn.Linear(embed_dims, embed_dims) - self.init_weights() - - def init_weights(self): - """Default initialization for Parameters of Module.""" - constant_init(self.sampling_offsets, 0.) - thetas = torch.arange( - self.num_heads, - dtype=torch.float32) * (2.0 * math.pi / self.num_heads) - grid_init = torch.stack([thetas.cos(), thetas.sin()], -1) - grid_init = (grid_init / - grid_init.abs().max(-1, keepdim=True)[0]).view( - self.num_heads, 1, 1, - 2).repeat(1, self.num_levels, self.num_points, 1) - for i in range(self.num_points): - grid_init[:, :, i, :] *= i + 1 - - self.sampling_offsets.bias.data = grid_init.view(-1) - constant_init(self.attention_weights, val=0., bias=0.) - xavier_init(self.value_proj, distribution='uniform', bias=0.) - xavier_init(self.output_proj, distribution='uniform', bias=0.) - self._is_init = True - - @deprecated_api_warning({'residual': 'identity'}, - cls_name='MultiScaleDeformableAttention') - def forward(self, - query, - key=None, - value=None, - identity=None, - query_pos=None, - key_padding_mask=None, - reference_points=None, - spatial_shapes=None, - level_start_index=None, - **kwargs): - """Forward Function of MultiScaleDeformAttention. - - Args: - query (Tensor): Query of Transformer with shape - (num_query, bs, embed_dims). - key (Tensor): The key tensor with shape - `(num_key, bs, embed_dims)`. - value (Tensor): The value tensor with shape - `(num_key, bs, embed_dims)`. - identity (Tensor): The tensor used for addition, with the - same shape as `query`. Default None. If None, - `query` will be used. - query_pos (Tensor): The positional encoding for `query`. - Default: None. - key_pos (Tensor): The positional encoding for `key`. Default - None. - reference_points (Tensor): The normalized reference - points with shape (bs, num_query, num_levels, 2), - all elements is range in [0, 1], top-left (0,0), - bottom-right (1, 1), including padding area. - or (N, Length_{query}, num_levels, 4), add - additional two dimensions is (w, h) to - form reference boxes. - key_padding_mask (Tensor): ByteTensor for `query`, with - shape [bs, num_key]. - spatial_shapes (Tensor): Spatial shape of features in - different levels. With shape (num_levels, 2), - last dimension represents (h, w). - level_start_index (Tensor): The start index of each level. - A tensor has shape ``(num_levels, )`` and can be represented - as [0, h_0*w_0, h_0*w_0+h_1*w_1, ...]. - - Returns: - Tensor: forwarded results with shape [num_query, bs, embed_dims]. - """ - - if value is None: - value = query - - if identity is None: - identity = query - if query_pos is not None: - query = query + query_pos - if not self.batch_first: - # change to (bs, num_query ,embed_dims) - query = query.permute(1, 0, 2) - value = value.permute(1, 0, 2) - - bs, num_query, _ = query.shape - bs, num_value, _ = value.shape - assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value - - value = self.value_proj(value) - if key_padding_mask is not None: - value = value.masked_fill(key_padding_mask[..., None], 0.0) - value = value.view(bs, num_value, self.num_heads, -1) - sampling_offsets = self.sampling_offsets(query).view( - bs, num_query, self.num_heads, self.num_levels, self.num_points, 2) - attention_weights = self.attention_weights(query).view( - bs, num_query, self.num_heads, self.num_levels * self.num_points) - attention_weights = attention_weights.softmax(-1) - - attention_weights = attention_weights.view(bs, num_query, - self.num_heads, - self.num_levels, - self.num_points) - if reference_points.shape[-1] == 2: - offset_normalizer = torch.stack( - [spatial_shapes[..., 1], spatial_shapes[..., 0]], -1) - sampling_locations = reference_points[:, :, None, :, None, :] \ - + sampling_offsets \ - / offset_normalizer[None, None, None, :, None, :] - elif reference_points.shape[-1] == 4: - sampling_locations = reference_points[:, :, None, :, None, :2] \ - + sampling_offsets / self.num_points \ - * reference_points[:, :, None, :, None, 2:] \ - * 0.5 - else: - raise ValueError( - f'Last dim of reference_points must be' - f' 2 or 4, but get {reference_points.shape[-1]} instead.') - if torch.cuda.is_available() and value.is_cuda: - output = MultiScaleDeformableAttnFunction.apply( - value, spatial_shapes, level_start_index, sampling_locations, - attention_weights, self.im2col_step) - else: - output = multi_scale_deformable_attn_pytorch( - value, spatial_shapes, sampling_locations, attention_weights) - - output = self.output_proj(output) - - if not self.batch_first: - # (num_query, bs ,embed_dims) - output = output.permute(1, 0, 2) - - return self.dropout(output) + identity diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/checkpoint.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/checkpoint.py deleted file mode 100644 index d690be1dfe70b1b82eaac8fe4db7022b35d5426c..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/runner/checkpoint.py +++ /dev/null @@ -1,707 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import io -import os -import os.path as osp -import pkgutil -import re -import time -import warnings -from collections import OrderedDict -from importlib import import_module -from tempfile import TemporaryDirectory - -import torch -import torchvision -from torch.optim import Optimizer -from torch.utils import model_zoo - -import annotator.mmpkg.mmcv as mmcv -from ..fileio import FileClient -from ..fileio import load as load_file -from ..parallel import is_module_wrapper -from ..utils import mkdir_or_exist -from .dist_utils import get_dist_info - -ENV_MMCV_HOME = 'MMCV_HOME' -ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME' -DEFAULT_CACHE_DIR = '~/.cache' - - -def _get_mmcv_home(): - mmcv_home = os.path.expanduser( - os.getenv( - ENV_MMCV_HOME, - os.path.join( - os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'mmcv'))) - - mkdir_or_exist(mmcv_home) - return mmcv_home - - -def load_state_dict(module, state_dict, strict=False, logger=None): - """Load state_dict to a module. - - This method is modified from :meth:`torch.nn.Module.load_state_dict`. - Default value for ``strict`` is set to ``False`` and the message for - param mismatch will be shown even if strict is False. - - Args: - module (Module): Module that receives the state_dict. - state_dict (OrderedDict): Weights. - strict (bool): whether to strictly enforce that the keys - in :attr:`state_dict` match the keys returned by this module's - :meth:`~torch.nn.Module.state_dict` function. Default: ``False``. - logger (:obj:`logging.Logger`, optional): Logger to log the error - message. If not specified, print function will be used. - """ - unexpected_keys = [] - all_missing_keys = [] - err_msg = [] - - metadata = getattr(state_dict, '_metadata', None) - state_dict = state_dict.copy() - if metadata is not None: - state_dict._metadata = metadata - - # use _load_from_state_dict to enable checkpoint version control - def load(module, prefix=''): - # recursively check parallel module in case that the model has a - # complicated structure, e.g., nn.Module(nn.Module(DDP)) - if is_module_wrapper(module): - module = module.module - local_metadata = {} if metadata is None else metadata.get( - prefix[:-1], {}) - module._load_from_state_dict(state_dict, prefix, local_metadata, True, - all_missing_keys, unexpected_keys, - err_msg) - for name, child in module._modules.items(): - if child is not None: - load(child, prefix + name + '.') - - load(module) - load = None # break load->load reference cycle - - # ignore "num_batches_tracked" of BN layers - missing_keys = [ - key for key in all_missing_keys if 'num_batches_tracked' not in key - ] - - if unexpected_keys: - err_msg.append('unexpected key in source ' - f'state_dict: {", ".join(unexpected_keys)}\n') - if missing_keys: - err_msg.append( - f'missing keys in source state_dict: {", ".join(missing_keys)}\n') - - rank, _ = get_dist_info() - if len(err_msg) > 0 and rank == 0: - err_msg.insert( - 0, 'The model and loaded state dict do not match exactly\n') - err_msg = '\n'.join(err_msg) - if strict: - raise RuntimeError(err_msg) - elif logger is not None: - logger.warning(err_msg) - else: - print(err_msg) - - -def get_torchvision_models(): - model_urls = dict() - for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__): - if ispkg: - continue - _zoo = import_module(f'torchvision.models.{name}') - if hasattr(_zoo, 'model_urls'): - _urls = getattr(_zoo, 'model_urls') - model_urls.update(_urls) - return model_urls - - -def get_external_models(): - mmcv_home = _get_mmcv_home() - default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json') - default_urls = load_file(default_json_path) - assert isinstance(default_urls, dict) - external_json_path = osp.join(mmcv_home, 'open_mmlab.json') - if osp.exists(external_json_path): - external_urls = load_file(external_json_path) - assert isinstance(external_urls, dict) - default_urls.update(external_urls) - - return default_urls - - -def get_mmcls_models(): - mmcls_json_path = osp.join(mmcv.__path__[0], 'model_zoo/mmcls.json') - mmcls_urls = load_file(mmcls_json_path) - - return mmcls_urls - - -def get_deprecated_model_names(): - deprecate_json_path = osp.join(mmcv.__path__[0], - 'model_zoo/deprecated.json') - deprecate_urls = load_file(deprecate_json_path) - assert isinstance(deprecate_urls, dict) - - return deprecate_urls - - -def _process_mmcls_checkpoint(checkpoint): - state_dict = checkpoint['state_dict'] - new_state_dict = OrderedDict() - for k, v in state_dict.items(): - if k.startswith('backbone.'): - new_state_dict[k[9:]] = v - new_checkpoint = dict(state_dict=new_state_dict) - - return new_checkpoint - - -class CheckpointLoader: - """A general checkpoint loader to manage all schemes.""" - - _schemes = {} - - @classmethod - def _register_scheme(cls, prefixes, loader, force=False): - if isinstance(prefixes, str): - prefixes = [prefixes] - else: - assert isinstance(prefixes, (list, tuple)) - for prefix in prefixes: - if (prefix not in cls._schemes) or force: - cls._schemes[prefix] = loader - else: - raise KeyError( - f'{prefix} is already registered as a loader backend, ' - 'add "force=True" if you want to override it') - # sort, longer prefixes take priority - cls._schemes = OrderedDict( - sorted(cls._schemes.items(), key=lambda t: t[0], reverse=True)) - - @classmethod - def register_scheme(cls, prefixes, loader=None, force=False): - """Register a loader to CheckpointLoader. - - This method can be used as a normal class method or a decorator. - - Args: - prefixes (str or list[str] or tuple[str]): - The prefix of the registered loader. - loader (function, optional): The loader function to be registered. - When this method is used as a decorator, loader is None. - Defaults to None. - force (bool, optional): Whether to override the loader - if the prefix has already been registered. Defaults to False. - """ - - if loader is not None: - cls._register_scheme(prefixes, loader, force=force) - return - - def _register(loader_cls): - cls._register_scheme(prefixes, loader_cls, force=force) - return loader_cls - - return _register - - @classmethod - def _get_checkpoint_loader(cls, path): - """Finds a loader that supports the given path. Falls back to the local - loader if no other loader is found. - - Args: - path (str): checkpoint path - - Returns: - loader (function): checkpoint loader - """ - - for p in cls._schemes: - if path.startswith(p): - return cls._schemes[p] - - @classmethod - def load_checkpoint(cls, filename, map_location=None, logger=None): - """load checkpoint through URL scheme path. - - Args: - filename (str): checkpoint file name with given prefix - map_location (str, optional): Same as :func:`torch.load`. - Default: None - logger (:mod:`logging.Logger`, optional): The logger for message. - Default: None - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - - checkpoint_loader = cls._get_checkpoint_loader(filename) - class_name = checkpoint_loader.__name__ - mmcv.print_log( - f'load checkpoint from {class_name[10:]} path: {filename}', logger) - return checkpoint_loader(filename, map_location) - - -@CheckpointLoader.register_scheme(prefixes='') -def load_from_local(filename, map_location): - """load checkpoint by local file path. - - Args: - filename (str): local checkpoint file path - map_location (str, optional): Same as :func:`torch.load`. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - - if not osp.isfile(filename): - raise IOError(f'{filename} is not a checkpoint file') - checkpoint = torch.load(filename, map_location=map_location) - return checkpoint - - -@CheckpointLoader.register_scheme(prefixes=('http://', 'https://')) -def load_from_http(filename, map_location=None, model_dir=None): - """load checkpoint through HTTP or HTTPS scheme path. In distributed - setting, this function only download checkpoint at local rank 0. - - Args: - filename (str): checkpoint file path with modelzoo or - torchvision prefix - map_location (str, optional): Same as :func:`torch.load`. - model_dir (string, optional): directory in which to save the object, - Default: None - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - rank, world_size = get_dist_info() - rank = int(os.environ.get('LOCAL_RANK', rank)) - if rank == 0: - checkpoint = model_zoo.load_url( - filename, model_dir=model_dir, map_location=map_location) - if world_size > 1: - torch.distributed.barrier() - if rank > 0: - checkpoint = model_zoo.load_url( - filename, model_dir=model_dir, map_location=map_location) - return checkpoint - - -@CheckpointLoader.register_scheme(prefixes='pavi://') -def load_from_pavi(filename, map_location=None): - """load checkpoint through the file path prefixed with pavi. In distributed - setting, this function download ckpt at all ranks to different temporary - directories. - - Args: - filename (str): checkpoint file path with pavi prefix - map_location (str, optional): Same as :func:`torch.load`. - Default: None - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - assert filename.startswith('pavi://'), \ - f'Expected filename startswith `pavi://`, but get {filename}' - model_path = filename[7:] - - try: - from pavi import modelcloud - except ImportError: - raise ImportError( - 'Please install pavi to load checkpoint from modelcloud.') - - model = modelcloud.get(model_path) - with TemporaryDirectory() as tmp_dir: - downloaded_file = osp.join(tmp_dir, model.name) - model.download(downloaded_file) - checkpoint = torch.load(downloaded_file, map_location=map_location) - return checkpoint - - -@CheckpointLoader.register_scheme(prefixes='s3://') -def load_from_ceph(filename, map_location=None, backend='petrel'): - """load checkpoint through the file path prefixed with s3. In distributed - setting, this function download ckpt at all ranks to different temporary - directories. - - Args: - filename (str): checkpoint file path with s3 prefix - map_location (str, optional): Same as :func:`torch.load`. - backend (str, optional): The storage backend type. Options are 'ceph', - 'petrel'. Default: 'petrel'. - - .. warning:: - :class:`mmcv.fileio.file_client.CephBackend` will be deprecated, - please use :class:`mmcv.fileio.file_client.PetrelBackend` instead. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - allowed_backends = ['ceph', 'petrel'] - if backend not in allowed_backends: - raise ValueError(f'Load from Backend {backend} is not supported.') - - if backend == 'ceph': - warnings.warn( - 'CephBackend will be deprecated, please use PetrelBackend instead') - - # CephClient and PetrelBackend have the same prefix 's3://' and the latter - # will be chosen as default. If PetrelBackend can not be instantiated - # successfully, the CephClient will be chosen. - try: - file_client = FileClient(backend=backend) - except ImportError: - allowed_backends.remove(backend) - file_client = FileClient(backend=allowed_backends[0]) - - with io.BytesIO(file_client.get(filename)) as buffer: - checkpoint = torch.load(buffer, map_location=map_location) - return checkpoint - - -@CheckpointLoader.register_scheme(prefixes=('modelzoo://', 'torchvision://')) -def load_from_torchvision(filename, map_location=None): - """load checkpoint through the file path prefixed with modelzoo or - torchvision. - - Args: - filename (str): checkpoint file path with modelzoo or - torchvision prefix - map_location (str, optional): Same as :func:`torch.load`. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - model_urls = get_torchvision_models() - if filename.startswith('modelzoo://'): - warnings.warn('The URL scheme of "modelzoo://" is deprecated, please ' - 'use "torchvision://" instead') - model_name = filename[11:] - else: - model_name = filename[14:] - return load_from_http(model_urls[model_name], map_location=map_location) - - -@CheckpointLoader.register_scheme(prefixes=('open-mmlab://', 'openmmlab://')) -def load_from_openmmlab(filename, map_location=None): - """load checkpoint through the file path prefixed with open-mmlab or - openmmlab. - - Args: - filename (str): checkpoint file path with open-mmlab or - openmmlab prefix - map_location (str, optional): Same as :func:`torch.load`. - Default: None - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - - model_urls = get_external_models() - prefix_str = 'open-mmlab://' - if filename.startswith(prefix_str): - model_name = filename[13:] - else: - model_name = filename[12:] - prefix_str = 'openmmlab://' - - deprecated_urls = get_deprecated_model_names() - if model_name in deprecated_urls: - warnings.warn(f'{prefix_str}{model_name} is deprecated in favor ' - f'of {prefix_str}{deprecated_urls[model_name]}') - model_name = deprecated_urls[model_name] - model_url = model_urls[model_name] - # check if is url - if model_url.startswith(('http://', 'https://')): - checkpoint = load_from_http(model_url, map_location=map_location) - else: - filename = osp.join(_get_mmcv_home(), model_url) - if not osp.isfile(filename): - raise IOError(f'{filename} is not a checkpoint file') - checkpoint = torch.load(filename, map_location=map_location) - return checkpoint - - -@CheckpointLoader.register_scheme(prefixes='mmcls://') -def load_from_mmcls(filename, map_location=None): - """load checkpoint through the file path prefixed with mmcls. - - Args: - filename (str): checkpoint file path with mmcls prefix - map_location (str, optional): Same as :func:`torch.load`. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - - model_urls = get_mmcls_models() - model_name = filename[8:] - checkpoint = load_from_http( - model_urls[model_name], map_location=map_location) - checkpoint = _process_mmcls_checkpoint(checkpoint) - return checkpoint - - -def _load_checkpoint(filename, map_location=None, logger=None): - """Load checkpoint from somewhere (modelzoo, file, url). - - Args: - filename (str): Accept local filepath, URL, ``torchvision://xxx``, - ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for - details. - map_location (str, optional): Same as :func:`torch.load`. - Default: None. - logger (:mod:`logging.Logger`, optional): The logger for error message. - Default: None - - Returns: - dict or OrderedDict: The loaded checkpoint. It can be either an - OrderedDict storing model weights or a dict containing other - information, which depends on the checkpoint. - """ - return CheckpointLoader.load_checkpoint(filename, map_location, logger) - - -def _load_checkpoint_with_prefix(prefix, filename, map_location=None): - """Load partial pretrained model with specific prefix. - - Args: - prefix (str): The prefix of sub-module. - filename (str): Accept local filepath, URL, ``torchvision://xxx``, - ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for - details. - map_location (str | None): Same as :func:`torch.load`. Default: None. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - - checkpoint = _load_checkpoint(filename, map_location=map_location) - - if 'state_dict' in checkpoint: - state_dict = checkpoint['state_dict'] - else: - state_dict = checkpoint - if not prefix.endswith('.'): - prefix += '.' - prefix_len = len(prefix) - - state_dict = { - k[prefix_len:]: v - for k, v in state_dict.items() if k.startswith(prefix) - } - - assert state_dict, f'{prefix} is not in the pretrained model' - return state_dict - - -def load_checkpoint(model, - filename, - map_location=None, - strict=False, - logger=None, - revise_keys=[(r'^module\.', '')]): - """Load checkpoint from a file or URI. - - Args: - model (Module): Module to load checkpoint. - filename (str): Accept local filepath, URL, ``torchvision://xxx``, - ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for - details. - map_location (str): Same as :func:`torch.load`. - strict (bool): Whether to allow different params for the model and - checkpoint. - logger (:mod:`logging.Logger` or None): The logger for error message. - revise_keys (list): A list of customized keywords to modify the - state_dict in checkpoint. Each item is a (pattern, replacement) - pair of the regular expression operations. Default: strip - the prefix 'module.' by [(r'^module\\.', '')]. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - checkpoint = _load_checkpoint(filename, map_location, logger) - # OrderedDict is a subclass of dict - if not isinstance(checkpoint, dict): - raise RuntimeError( - f'No state_dict found in checkpoint file {filename}') - # get state_dict from checkpoint - if 'state_dict' in checkpoint: - state_dict = checkpoint['state_dict'] - else: - state_dict = checkpoint - - # strip prefix of state_dict - metadata = getattr(state_dict, '_metadata', OrderedDict()) - for p, r in revise_keys: - state_dict = OrderedDict( - {re.sub(p, r, k): v - for k, v in state_dict.items()}) - # Keep metadata in state_dict - state_dict._metadata = metadata - - # load state_dict - load_state_dict(model, state_dict, strict, logger) - return checkpoint - - -def weights_to_cpu(state_dict): - """Copy a model state_dict to cpu. - - Args: - state_dict (OrderedDict): Model weights on GPU. - - Returns: - OrderedDict: Model weights on GPU. - """ - state_dict_cpu = OrderedDict() - for key, val in state_dict.items(): - state_dict_cpu[key] = val.cpu() - # Keep metadata in state_dict - state_dict_cpu._metadata = getattr(state_dict, '_metadata', OrderedDict()) - return state_dict_cpu - - -def _save_to_state_dict(module, destination, prefix, keep_vars): - """Saves module state to `destination` dictionary. - - This method is modified from :meth:`torch.nn.Module._save_to_state_dict`. - - Args: - module (nn.Module): The module to generate state_dict. - destination (dict): A dict where state will be stored. - prefix (str): The prefix for parameters and buffers used in this - module. - """ - for name, param in module._parameters.items(): - if param is not None: - destination[prefix + name] = param if keep_vars else param.detach() - for name, buf in module._buffers.items(): - # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d - if buf is not None: - destination[prefix + name] = buf if keep_vars else buf.detach() - - -def get_state_dict(module, destination=None, prefix='', keep_vars=False): - """Returns a dictionary containing a whole state of the module. - - Both parameters and persistent buffers (e.g. running averages) are - included. Keys are corresponding parameter and buffer names. - - This method is modified from :meth:`torch.nn.Module.state_dict` to - recursively check parallel module in case that the model has a complicated - structure, e.g., nn.Module(nn.Module(DDP)). - - Args: - module (nn.Module): The module to generate state_dict. - destination (OrderedDict): Returned dict for the state of the - module. - prefix (str): Prefix of the key. - keep_vars (bool): Whether to keep the variable property of the - parameters. Default: False. - - Returns: - dict: A dictionary containing a whole state of the module. - """ - # recursively check parallel module in case that the model has a - # complicated structure, e.g., nn.Module(nn.Module(DDP)) - if is_module_wrapper(module): - module = module.module - - # below is the same as torch.nn.Module.state_dict() - if destination is None: - destination = OrderedDict() - destination._metadata = OrderedDict() - destination._metadata[prefix[:-1]] = local_metadata = dict( - version=module._version) - _save_to_state_dict(module, destination, prefix, keep_vars) - for name, child in module._modules.items(): - if child is not None: - get_state_dict( - child, destination, prefix + name + '.', keep_vars=keep_vars) - for hook in module._state_dict_hooks.values(): - hook_result = hook(module, destination, prefix, local_metadata) - if hook_result is not None: - destination = hook_result - return destination - - -def save_checkpoint(model, - filename, - optimizer=None, - meta=None, - file_client_args=None): - """Save checkpoint to file. - - The checkpoint will have 3 fields: ``meta``, ``state_dict`` and - ``optimizer``. By default ``meta`` will contain version and time info. - - Args: - model (Module): Module whose params are to be saved. - filename (str): Checkpoint filename. - optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. - meta (dict, optional): Metadata to be saved in checkpoint. - file_client_args (dict, optional): Arguments to instantiate a - FileClient. See :class:`mmcv.fileio.FileClient` for details. - Default: None. - `New in version 1.3.16.` - """ - if meta is None: - meta = {} - elif not isinstance(meta, dict): - raise TypeError(f'meta must be a dict or None, but got {type(meta)}') - meta.update(mmcv_version=mmcv.__version__, time=time.asctime()) - - if is_module_wrapper(model): - model = model.module - - if hasattr(model, 'CLASSES') and model.CLASSES is not None: - # save class name to the meta - meta.update(CLASSES=model.CLASSES) - - checkpoint = { - 'meta': meta, - 'state_dict': weights_to_cpu(get_state_dict(model)) - } - # save optimizer state dict in the checkpoint - if isinstance(optimizer, Optimizer): - checkpoint['optimizer'] = optimizer.state_dict() - elif isinstance(optimizer, dict): - checkpoint['optimizer'] = {} - for name, optim in optimizer.items(): - checkpoint['optimizer'][name] = optim.state_dict() - - if filename.startswith('pavi://'): - if file_client_args is not None: - raise ValueError( - 'file_client_args should be "None" if filename starts with' - f'"pavi://", but got {file_client_args}') - try: - from pavi import modelcloud - from pavi import exception - except ImportError: - raise ImportError( - 'Please install pavi to load checkpoint from modelcloud.') - model_path = filename[7:] - root = modelcloud.Folder() - model_dir, model_name = osp.split(model_path) - try: - model = modelcloud.get(model_dir) - except exception.NodeNotFoundError: - model = root.create_training_model(model_dir) - with TemporaryDirectory() as tmp_dir: - checkpoint_file = osp.join(tmp_dir, model_name) - with open(checkpoint_file, 'wb') as f: - torch.save(checkpoint, f) - f.flush() - model.create_file(checkpoint_file, name=model_name) - else: - file_client = FileClient.infer_client(file_client_args, filename) - with io.BytesIO() as f: - torch.save(checkpoint, f) - file_client.put(f.getvalue(), filename) diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/visualization/image.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/visualization/image.py deleted file mode 100644 index 378de2104f6554389fcb2e6a3904283345fd74b0..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmcv/visualization/image.py +++ /dev/null @@ -1,152 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import cv2 -import numpy as np - -from annotator.mmpkg.mmcv.image import imread, imwrite -from .color import color_val - - -def imshow(img, win_name='', wait_time=0): - """Show an image. - - Args: - img (str or ndarray): The image to be displayed. - win_name (str): The window name. - wait_time (int): Value of waitKey param. - """ - cv2.imshow(win_name, imread(img)) - if wait_time == 0: # prevent from hanging if windows was closed - while True: - ret = cv2.waitKey(1) - - closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1 - # if user closed window or if some key pressed - if closed or ret != -1: - break - else: - ret = cv2.waitKey(wait_time) - - -def imshow_bboxes(img, - bboxes, - colors='green', - top_k=-1, - thickness=1, - show=True, - win_name='', - wait_time=0, - out_file=None): - """Draw bboxes on an image. - - Args: - img (str or ndarray): The image to be displayed. - bboxes (list or ndarray): A list of ndarray of shape (k, 4). - colors (list[str or tuple or Color]): A list of colors. - top_k (int): Plot the first k bboxes only if set positive. - thickness (int): Thickness of lines. - show (bool): Whether to show the image. - win_name (str): The window name. - wait_time (int): Value of waitKey param. - out_file (str, optional): The filename to write the image. - - Returns: - ndarray: The image with bboxes drawn on it. - """ - img = imread(img) - img = np.ascontiguousarray(img) - - if isinstance(bboxes, np.ndarray): - bboxes = [bboxes] - if not isinstance(colors, list): - colors = [colors for _ in range(len(bboxes))] - colors = [color_val(c) for c in colors] - assert len(bboxes) == len(colors) - - for i, _bboxes in enumerate(bboxes): - _bboxes = _bboxes.astype(np.int32) - if top_k <= 0: - _top_k = _bboxes.shape[0] - else: - _top_k = min(top_k, _bboxes.shape[0]) - for j in range(_top_k): - left_top = (_bboxes[j, 0], _bboxes[j, 1]) - right_bottom = (_bboxes[j, 2], _bboxes[j, 3]) - cv2.rectangle( - img, left_top, right_bottom, colors[i], thickness=thickness) - - if show: - imshow(img, win_name, wait_time) - if out_file is not None: - imwrite(img, out_file) - return img - - -def imshow_det_bboxes(img, - bboxes, - labels, - class_names=None, - score_thr=0, - bbox_color='green', - text_color='green', - thickness=1, - font_scale=0.5, - show=True, - win_name='', - wait_time=0, - out_file=None): - """Draw bboxes and class labels (with scores) on an image. - - Args: - img (str or ndarray): The image to be displayed. - bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or - (n, 5). - labels (ndarray): Labels of bboxes. - class_names (list[str]): Names of each classes. - score_thr (float): Minimum score of bboxes to be shown. - bbox_color (str or tuple or :obj:`Color`): Color of bbox lines. - text_color (str or tuple or :obj:`Color`): Color of texts. - thickness (int): Thickness of lines. - font_scale (float): Font scales of texts. - show (bool): Whether to show the image. - win_name (str): The window name. - wait_time (int): Value of waitKey param. - out_file (str or None): The filename to write the image. - - Returns: - ndarray: The image with bboxes drawn on it. - """ - assert bboxes.ndim == 2 - assert labels.ndim == 1 - assert bboxes.shape[0] == labels.shape[0] - assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5 - img = imread(img) - img = np.ascontiguousarray(img) - - if score_thr > 0: - assert bboxes.shape[1] == 5 - scores = bboxes[:, -1] - inds = scores > score_thr - bboxes = bboxes[inds, :] - labels = labels[inds] - - bbox_color = color_val(bbox_color) - text_color = color_val(text_color) - - for bbox, label in zip(bboxes, labels): - bbox_int = bbox.astype(np.int32) - left_top = (bbox_int[0], bbox_int[1]) - right_bottom = (bbox_int[2], bbox_int[3]) - cv2.rectangle( - img, left_top, right_bottom, bbox_color, thickness=thickness) - label_text = class_names[ - label] if class_names is not None else f'cls {label}' - if len(bbox) > 4: - label_text += f'|{bbox[-1]:.02f}' - cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2), - cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color) - - if show: - imshow(img, win_name, wait_time) - if out_file is not None: - imwrite(img, out_file) - return img diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/utils/logger.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/utils/logger.py deleted file mode 100644 index 0c37733358e3e21479b41f54220bfe34b482009c..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/mmpkg/mmseg/utils/logger.py +++ /dev/null @@ -1,27 +0,0 @@ -import logging - -from annotator.mmpkg.mmcv.utils import get_logger - - -def get_root_logger(log_file=None, log_level=logging.INFO): - """Get the root logger. - - The logger will be initialized if it has not been initialized. By default a - StreamHandler will be added. If `log_file` is specified, a FileHandler will - also be added. The name of the root logger is the top-level package name, - e.g., "mmseg". - - Args: - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the root logger. - log_level (int): The root logger level. Note that only the process of - rank 0 is affected, while other processes will set the level to - "Error" and be silent most of the time. - - Returns: - logging.Logger: The root logger. - """ - - logger = get_logger(name='mmseg', log_file=log_file, log_level=log_level) - - return logger diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py deleted file mode 100644 index 88bb10d44026ba9f21756eaea9e550841cd59b9f..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/core/seg/sampler/ohem_pixel_sampler.py +++ /dev/null @@ -1,76 +0,0 @@ -import torch -import torch.nn.functional as F - -from ..builder import PIXEL_SAMPLERS -from .base_pixel_sampler import BasePixelSampler - - -@PIXEL_SAMPLERS.register_module() -class OHEMPixelSampler(BasePixelSampler): - """Online Hard Example Mining Sampler for segmentation. - - Args: - context (nn.Module): The context of sampler, subclass of - :obj:`BaseDecodeHead`. - thresh (float, optional): The threshold for hard example selection. - Below which, are prediction with low confidence. If not - specified, the hard examples will be pixels of top ``min_kept`` - loss. Default: None. - min_kept (int, optional): The minimum number of predictions to keep. - Default: 100000. - """ - - def __init__(self, context, thresh=None, min_kept=100000): - super(OHEMPixelSampler, self).__init__() - self.context = context - assert min_kept > 1 - self.thresh = thresh - self.min_kept = min_kept - - def sample(self, seg_logit, seg_label): - """Sample pixels that have high loss or with low prediction confidence. - - Args: - seg_logit (torch.Tensor): segmentation logits, shape (N, C, H, W) - seg_label (torch.Tensor): segmentation label, shape (N, 1, H, W) - - Returns: - torch.Tensor: segmentation weight, shape (N, H, W) - """ - with torch.no_grad(): - assert seg_logit.shape[2:] == seg_label.shape[2:] - assert seg_label.shape[1] == 1 - seg_label = seg_label.squeeze(1).long() - batch_kept = self.min_kept * seg_label.size(0) - valid_mask = seg_label != self.context.ignore_index - seg_weight = seg_logit.new_zeros(size=seg_label.size()) - valid_seg_weight = seg_weight[valid_mask] - if self.thresh is not None: - seg_prob = F.softmax(seg_logit, dim=1) - - tmp_seg_label = seg_label.clone().unsqueeze(1) - tmp_seg_label[tmp_seg_label == self.context.ignore_index] = 0 - seg_prob = seg_prob.gather(1, tmp_seg_label).squeeze(1) - sort_prob, sort_indices = seg_prob[valid_mask].sort() - - if sort_prob.numel() > 0: - min_threshold = sort_prob[min(batch_kept, - sort_prob.numel() - 1)] - else: - min_threshold = 0.0 - threshold = max(min_threshold, self.thresh) - valid_seg_weight[seg_prob[valid_mask] < threshold] = 1. - else: - losses = self.context.loss_decode( - seg_logit, - seg_label, - weight=None, - ignore_index=self.context.ignore_index, - reduction_override='none') - # faster than topk according to https://github.com/pytorch/pytorch/issues/22812 # noqa - _, sort_indices = losses[valid_mask].sort(descending=True) - valid_seg_weight[sort_indices[:batch_kept]] = 1. - - seg_weight[valid_mask] = valid_seg_weight - - return seg_weight diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/utils/weight_init.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/utils/weight_init.py deleted file mode 100644 index 38141ba3d61f64ddfc0a31574b4648cbad96d7dd..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmseg/models/utils/weight_init.py +++ /dev/null @@ -1,62 +0,0 @@ -"""Modified from https://github.com/rwightman/pytorch-image- -models/blob/master/timm/models/layers/drop.py.""" - -import math -import warnings - -import torch - - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - """Reference: https://people.sc.fsu.edu/~jburkardt/presentations - /truncated_normal.pdf""" - - def norm_cdf(x): - # Computes standard normal cumulative distribution function - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn( - 'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. ' - 'The distribution of values may be incorrect.', - stacklevel=2) - - with torch.no_grad(): - # Values are generated by using a truncated uniform distribution and - # then using the inverse CDF for the normal distribution. - # Get upper and lower cdf values - lower_bound = norm_cdf((a - mean) / std) - upper_bound = norm_cdf((b - mean) / std) - - # Uniformly fill tensor with values from [l, u], then translate to - # [2l-1, 2u-1]. - tensor.uniform_(2 * lower_bound - 1, 2 * upper_bound - 1) - - # Use inverse cdf transform for normal distribution to get truncated - # standard normal - tensor.erfinv_() - - # Transform to proper mean, std - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - - # Clamp to ensure it's in the proper range - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - r"""Fills the input Tensor with values drawn from a truncated - normal distribution. The values are effectively drawn from the - normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` - with values outside :math:`[a, b]` redrawn until they are within - the bounds. The method used for generating the random values works - best when :math:`a \leq \text{mean} \leq b`. - Args: - tensor (``torch.Tensor``): an n-dimensional `torch.Tensor` - mean (float): the mean of the normal distribution - std (float): the standard deviation of the normal distribution - a (float): the minimum cutoff value - b (float): the maximum cutoff value - """ - return _no_grad_trunc_normal_(tensor, mean, std, a, b) diff --git a/spaces/course-demos/audio-reverse/app.py b/spaces/course-demos/audio-reverse/app.py deleted file mode 100644 index c5269366b3ec2dccf9a335d2cfc6acb10ab60c29..0000000000000000000000000000000000000000 --- a/spaces/course-demos/audio-reverse/app.py +++ /dev/null @@ -1,9 +0,0 @@ -import numpy as np -import gradio as gr - -def reverse_audio(audio): - sr, data = audio - return (sr, np.flipud(data)) - -mic = gr.Audio(source="microphone", type="numpy", label="Speak here...") -gr.Interface(reverse_audio, mic, "audio", css=".footer{display:none !important}").launch() \ No newline at end of file diff --git a/spaces/cpwan/RLOR-TSP/envs/vrp_data.py b/spaces/cpwan/RLOR-TSP/envs/vrp_data.py deleted file mode 100644 index 2e3d9ebc25c860e7a56d2dd7ba6ad44b35f8c81c..0000000000000000000000000000000000000000 --- a/spaces/cpwan/RLOR-TSP/envs/vrp_data.py +++ /dev/null @@ -1,57 +0,0 @@ -import logging -import pickle - -import numpy as np - -root_dir = "/home/cwan5/OR/attention-learn-to-route/data/vrp/" - - -def load(filename, root_dir=root_dir): - return pickle.load(open(root_dir + filename, "rb")) - - -file_catalog = { - "test": { - 20: "vrp20_test_seed1234.pkl", - 50: "vrp50_test_seed1234.pkl", - 100: "vrp100_test_seed1234.pkl", - }, - "eval": { - 20: "vrp20_validation_seed4321.pkl", - 50: "vrp50_validation_seed4321.pkl", - 100: "vrp100_validation_seed4321.pkl", - }, -} - - -def make_instance(args): - depot, loc, demand, capacity, *args = args - grid_size = 1 - if len(args) > 0: - depot_types, customer_types, grid_size = args - return { - "loc": np.array(loc) / grid_size, - "demand": np.array(demand) / capacity, - "depot": np.array(depot) / grid_size, - } - - -class lazyClass: - data = { - "test": {}, - "eval": {}, - } - - def __getitem__(self, index): - partition, nodes, idx = index - if not (partition in self.data) or not (nodes in self.data[partition]): - logging.warning( - f"Data sepecified by ({partition}, {nodes}) was not initialized. Attepmting to load it for the first time." - ) - data = load(file_catalog[partition][nodes]) - self.data[partition][nodes] = [make_instance(instance) for instance in data] - - return self.data[partition][nodes][idx] - - -VRPDataset = lazyClass() diff --git a/spaces/cscan/vocal_remover/lib/spec_utils.py b/spaces/cscan/vocal_remover/lib/spec_utils.py deleted file mode 100644 index af34f70d8cf4f47a3c4d0e8dc9506be567018bb5..0000000000000000000000000000000000000000 --- a/spaces/cscan/vocal_remover/lib/spec_utils.py +++ /dev/null @@ -1,227 +0,0 @@ -import os - -import librosa -import numpy as np -import soundfile as sf - - -def crop_center(h1, h2): - h1_shape = h1.size() - h2_shape = h2.size() - - if h1_shape[3] == h2_shape[3]: - return h1 - elif h1_shape[3] < h2_shape[3]: - raise ValueError('h1_shape[3] must be greater than h2_shape[3]') - - # s_freq = (h2_shape[2] - h1_shape[2]) // 2 - # e_freq = s_freq + h1_shape[2] - s_time = (h1_shape[3] - h2_shape[3]) // 2 - e_time = s_time + h2_shape[3] - h1 = h1[:, :, :, s_time:e_time] - - return h1 - - -def wave_to_spectrogram(wave, hop_length, n_fft): - wave_left = np.asfortranarray(wave[0]) - wave_right = np.asfortranarray(wave[1]) - - spec_left = librosa.stft(wave_left, n_fft, hop_length=hop_length) - spec_right = librosa.stft(wave_right, n_fft, hop_length=hop_length) - spec = np.asfortranarray([spec_left, spec_right]) - - return spec - - -def spectrogram_to_image(spec, mode='magnitude'): - if mode == 'magnitude': - if np.iscomplexobj(spec): - y = np.abs(spec) - else: - y = spec - y = np.log10(y ** 2 + 1e-8) - elif mode == 'phase': - if np.iscomplexobj(spec): - y = np.angle(spec) - else: - y = spec - - y -= y.min() - y *= 255 / y.max() - img = np.uint8(y) - - if y.ndim == 3: - img = img.transpose(1, 2, 0) - img = np.concatenate([ - np.max(img, axis=2, keepdims=True), img - ], axis=2) - - return img - - -def aggressively_remove_vocal(X, y, weight): - X_mag = np.abs(X) - y_mag = np.abs(y) - # v_mag = np.abs(X_mag - y_mag) - v_mag = X_mag - y_mag - v_mag *= v_mag > y_mag - - y_mag = np.clip(y_mag - v_mag * weight, 0, np.inf) - - return y_mag * np.exp(1.j * np.angle(y)) - - -def merge_artifacts(y_mask, thres=0.05, min_range=64, fade_size=32): - if min_range < fade_size * 2: - raise ValueError('min_range must be >= fade_size * 2') - - idx = np.where(y_mask.min(axis=(0, 1)) > thres)[0] - start_idx = np.insert(idx[np.where(np.diff(idx) != 1)[0] + 1], 0, idx[0]) - end_idx = np.append(idx[np.where(np.diff(idx) != 1)[0]], idx[-1]) - artifact_idx = np.where(end_idx - start_idx > min_range)[0] - weight = np.zeros_like(y_mask) - if len(artifact_idx) > 0: - start_idx = start_idx[artifact_idx] - end_idx = end_idx[artifact_idx] - old_e = None - for s, e in zip(start_idx, end_idx): - if old_e is not None and s - old_e < fade_size: - s = old_e - fade_size * 2 - - if s != 0: - weight[:, :, s:s + fade_size] = np.linspace(0, 1, fade_size) - else: - s -= fade_size - - if e != y_mask.shape[2]: - weight[:, :, e - fade_size:e] = np.linspace(1, 0, fade_size) - else: - e += fade_size - - weight[:, :, s + fade_size:e - fade_size] = 1 - old_e = e - - v_mask = 1 - y_mask - y_mask += weight * v_mask - - return y_mask - - -def align_wave_head_and_tail(a, b, sr): - a, _ = librosa.effects.trim(a) - b, _ = librosa.effects.trim(b) - - a_mono = a[:, :sr * 4].sum(axis=0) - b_mono = b[:, :sr * 4].sum(axis=0) - - a_mono -= a_mono.mean() - b_mono -= b_mono.mean() - - offset = len(a_mono) - 1 - delay = np.argmax(np.correlate(a_mono, b_mono, 'full')) - offset - - if delay > 0: - a = a[:, delay:] - else: - b = b[:, np.abs(delay):] - - if a.shape[1] < b.shape[1]: - b = b[:, :a.shape[1]] - else: - a = a[:, :b.shape[1]] - - return a, b - - -def cache_or_load(mix_path, inst_path, sr, hop_length, n_fft): - mix_basename = os.path.splitext(os.path.basename(mix_path))[0] - inst_basename = os.path.splitext(os.path.basename(inst_path))[0] - - cache_dir = 'sr{}_hl{}_nf{}'.format(sr, hop_length, n_fft) - mix_cache_dir = os.path.join(os.path.dirname(mix_path), cache_dir) - inst_cache_dir = os.path.join(os.path.dirname(inst_path), cache_dir) - os.makedirs(mix_cache_dir, exist_ok=True) - os.makedirs(inst_cache_dir, exist_ok=True) - - mix_cache_path = os.path.join(mix_cache_dir, mix_basename + '.npy') - inst_cache_path = os.path.join(inst_cache_dir, inst_basename + '.npy') - - if os.path.exists(mix_cache_path) and os.path.exists(inst_cache_path): - X = np.load(mix_cache_path) - y = np.load(inst_cache_path) - else: - X, _ = librosa.load( - mix_path, sr, False, dtype=np.float32, res_type='kaiser_fast') - y, _ = librosa.load( - inst_path, sr, False, dtype=np.float32, res_type='kaiser_fast') - - X, y = align_wave_head_and_tail(X, y, sr) - - X = wave_to_spectrogram(X, hop_length, n_fft) - y = wave_to_spectrogram(y, hop_length, n_fft) - - np.save(mix_cache_path, X) - np.save(inst_cache_path, y) - - return X, y, mix_cache_path, inst_cache_path - - -def spectrogram_to_wave(spec, hop_length=1024): - if spec.ndim == 2: - wave = librosa.istft(spec, hop_length=hop_length) - elif spec.ndim == 3: - spec_left = np.asfortranarray(spec[0]) - spec_right = np.asfortranarray(spec[1]) - - wave_left = librosa.istft(spec_left, hop_length=hop_length) - wave_right = librosa.istft(spec_right, hop_length=hop_length) - wave = np.asfortranarray([wave_left, wave_right]) - - return wave - - -if __name__ == "__main__": - import cv2 - import sys - - bins = 2048 // 2 + 1 - freq_to_bin = 2 * bins / 44100 - unstable_bins = int(200 * freq_to_bin) - stable_bins = int(22050 * freq_to_bin) - reduction_weight = np.concatenate([ - np.linspace(0, 1, unstable_bins, dtype=np.float32)[:, None], - np.linspace(1, 0, stable_bins - unstable_bins, dtype=np.float32)[:, None], - np.zeros((bins - stable_bins, 1)) - ], axis=0) * 0.2 - - X, _ = librosa.load( - sys.argv[1], 44100, False, dtype=np.float32, res_type='kaiser_fast') - y, _ = librosa.load( - sys.argv[2], 44100, False, dtype=np.float32, res_type='kaiser_fast') - - X, y = align_wave_head_and_tail(X, y, 44100) - X_spec = wave_to_spectrogram(X, 1024, 2048) - y_spec = wave_to_spectrogram(y, 1024, 2048) - - X_mag = np.abs(X_spec) - y_mag = np.abs(y_spec) - # v_mag = np.abs(X_mag - y_mag) - v_mag = X_mag - y_mag - v_mag *= v_mag > y_mag - - # y_mag = np.clip(y_mag - v_mag * reduction_weight, 0, np.inf) - y_spec = y_mag * np.exp(1j * np.angle(y_spec)) - v_spec = v_mag * np.exp(1j * np.angle(X_spec)) - - X_image = spectrogram_to_image(X_mag) - y_image = spectrogram_to_image(y_mag) - v_image = spectrogram_to_image(v_mag) - - cv2.imwrite('test_X.jpg', X_image) - cv2.imwrite('test_y.jpg', y_image) - cv2.imwrite('test_v.jpg', v_image) - - sf.write('test_X.wav', spectrogram_to_wave(X_spec).T, 44100) - sf.write('test_y.wav', spectrogram_to_wave(y_spec).T, 44100) - sf.write('test_v.wav', spectrogram_to_wave(v_spec).T, 44100) diff --git a/spaces/dawood/PDFChatGpt-test/README.md b/spaces/dawood/PDFChatGpt-test/README.md deleted file mode 100644 index dfbf023be456d0b34ae2bb623313cebec0a4e826..0000000000000000000000000000000000000000 --- a/spaces/dawood/PDFChatGpt-test/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: ChatGpt with large pdf -emoji: 🏢 -colorFrom: indigo -colorTo: green -sdk: gradio -sdk_version: 3.20.1 -app_file: app.py -pinned: false -license: afl-3.0 -duplicated_from: Sortoite/PDFChatGpt ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference \ No newline at end of file diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/vegalite/v5/api.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/vegalite/v5/api.py deleted file mode 100644 index ed449bcab3fe7b2679f1ffaadc97402f43381869..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/altair/vegalite/v5/api.py +++ /dev/null @@ -1,3434 +0,0 @@ -import warnings - -import hashlib -import io -import json -import jsonschema -import pandas as pd -from toolz.curried import pipe as _pipe -import itertools -import sys -from typing import cast - -# Have to rename it here as else it overlaps with schema.core.Type -from typing import Type as TypingType - -from .schema import core, channels, mixins, Undefined, SCHEMA_URL - -from .data import data_transformers -from ... import utils, expr -from .display import renderers, VEGALITE_VERSION, VEGAEMBED_VERSION, VEGA_VERSION -from .theme import themes - -if sys.version_info >= (3, 11): - from typing import Self -else: - from typing_extensions import Self - - -# ------------------------------------------------------------------------ -# Data Utilities -def _dataset_name(values): - """Generate a unique hash of the data - - Parameters - ---------- - values : list or dict - A list/dict representation of data values. - - Returns - ------- - name : string - A unique name generated from the hash of the values. - """ - if isinstance(values, core.InlineDataset): - values = values.to_dict() - if values == [{}]: - return "empty" - values_json = json.dumps(values, sort_keys=True) - hsh = hashlib.md5(values_json.encode()).hexdigest() - return "data-" + hsh - - -def _consolidate_data(data, context): - """If data is specified inline, then move it to context['datasets'] - - This function will modify context in-place, and return a new version of data - """ - values = Undefined - kwds = {} - - if isinstance(data, core.InlineData): - if data.name is Undefined and data.values is not Undefined: - if isinstance(data.values, core.InlineDataset): - values = data.to_dict()["values"] - else: - values = data.values - kwds = {"format": data.format} - - elif isinstance(data, dict): - if "name" not in data and "values" in data: - values = data["values"] - kwds = {k: v for k, v in data.items() if k != "values"} - - if values is not Undefined: - name = _dataset_name(values) - data = core.NamedData(name=name, **kwds) - context.setdefault("datasets", {})[name] = values - - return data - - -def _prepare_data(data, context=None): - """Convert input data to data for use within schema - - Parameters - ---------- - data : - The input dataset in the form of a DataFrame, dictionary, altair data - object, or other type that is recognized by the data transformers. - context : dict (optional) - The to_dict context in which the data is being prepared. This is used - to keep track of information that needs to be passed up and down the - recursive serialization routine, such as global named datasets. - """ - if data is Undefined: - return data - - # convert dataframes or objects with __geo_interface__ to dict - elif isinstance(data, pd.DataFrame) or hasattr(data, "__geo_interface__"): - data = _pipe(data, data_transformers.get()) - - # convert string input to a URLData - elif isinstance(data, str): - data = core.UrlData(data) - - elif hasattr(data, "__dataframe__"): - data = _pipe(data, data_transformers.get()) - - # consolidate inline data to top-level datasets - if context is not None and data_transformers.consolidate_datasets: - data = _consolidate_data(data, context) - - # if data is still not a recognized type, then return - if not isinstance(data, (dict, core.Data)): - warnings.warn("data of type {} not recognized".format(type(data)), stacklevel=1) - - return data - - -# ------------------------------------------------------------------------ -# Aliases & specializations -Bin = core.BinParams -Impute = core.ImputeParams -Title = core.TitleParams - - -class LookupData(core.LookupData): - @utils.use_signature(core.LookupData) - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def to_dict(self, *args, **kwargs): - """Convert the chart to a dictionary suitable for JSON export.""" - copy = self.copy(deep=False) - copy.data = _prepare_data(copy.data, kwargs.get("context")) - return super(LookupData, copy).to_dict(*args, **kwargs) - - -class FacetMapping(core.FacetMapping): - _class_is_valid_at_instantiation = False - - @utils.use_signature(core.FacetMapping) - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - - def to_dict(self, *args, **kwargs): - copy = self.copy(deep=False) - context = kwargs.get("context", {}) - data = context.get("data", None) - if isinstance(self.row, str): - copy.row = core.FacetFieldDef(**utils.parse_shorthand(self.row, data)) - if isinstance(self.column, str): - copy.column = core.FacetFieldDef(**utils.parse_shorthand(self.column, data)) - return super(FacetMapping, copy).to_dict(*args, **kwargs) - - -# ------------------------------------------------------------------------ -# Encoding will contain channel objects that aren't valid at instantiation -core.FacetedEncoding._class_is_valid_at_instantiation = False - -# ------------------------------------------------------------------------ -# These are parameters that are valid at the top level, but are not valid -# for specs that are within a composite chart -# (layer, hconcat, vconcat, facet, repeat) -TOPLEVEL_ONLY_KEYS = {"background", "config", "autosize", "padding", "$schema"} - - -def _get_channels_mapping(): - mapping = {} - for attr in dir(channels): - cls = getattr(channels, attr) - if isinstance(cls, type) and issubclass(cls, core.SchemaBase): - mapping[cls] = attr.replace("Value", "").lower() - return mapping - - -# ------------------------------------------------------------------------- -# Tools for working with parameters -class Parameter(expr.core.OperatorMixin, object): - """A Parameter object""" - - _counter = 0 - - @classmethod - def _get_name(cls): - cls._counter += 1 - return f"param_{cls._counter}" - - def __init__(self, name): - if name is None: - name = self._get_name() - self.name = name - - @utils.deprecation.deprecated( - message="'ref' is deprecated. No need to call '.ref()' anymore." - ) - def ref(self): - "'ref' is deprecated. No need to call '.ref()' anymore." - return self.to_dict() - - def to_dict(self): - if self.param_type == "variable": - return {"expr": self.name} - elif self.param_type == "selection": - return { - "param": self.name.to_dict() - if hasattr(self.name, "to_dict") - else self.name - } - - def __invert__(self): - if self.param_type == "selection": - return SelectionPredicateComposition({"not": {"param": self.name}}) - else: - return expr.core.OperatorMixin.__invert__(self) - - def __and__(self, other): - if self.param_type == "selection": - if isinstance(other, Parameter): - other = {"param": other.name} - return SelectionPredicateComposition({"and": [{"param": self.name}, other]}) - else: - return expr.core.OperatorMixin.__and__(self, other) - - def __or__(self, other): - if self.param_type == "selection": - if isinstance(other, Parameter): - other = {"param": other.name} - return SelectionPredicateComposition({"or": [{"param": self.name}, other]}) - else: - return expr.core.OperatorMixin.__or__(self, other) - - def __repr__(self): - return "Parameter({0!r}, {1})".format(self.name, self.param) - - def _to_expr(self): - return self.name - - def _from_expr(self, expr): - return ParameterExpression(expr=expr) - - def __getattr__(self, field_name): - if field_name.startswith("__") and field_name.endswith("__"): - raise AttributeError(field_name) - _attrexpr = expr.core.GetAttrExpression(self.name, field_name) - # If self is a SelectionParameter and field_name is in its - # fields or encodings list, then we want to return an expression. - if check_fields_and_encodings(self, field_name): - return SelectionExpression(_attrexpr) - return expr.core.GetAttrExpression(self.name, field_name) - - # TODO: Are there any special cases to consider for __getitem__? - # This was copied from v4. - def __getitem__(self, field_name): - return expr.core.GetItemExpression(self.name, field_name) - - -# Enables use of ~, &, | with compositions of selection objects. -class SelectionPredicateComposition(core.PredicateComposition): - def __invert__(self): - return SelectionPredicateComposition({"not": self.to_dict()}) - - def __and__(self, other): - return SelectionPredicateComposition({"and": [self.to_dict(), other.to_dict()]}) - - def __or__(self, other): - return SelectionPredicateComposition({"or": [self.to_dict(), other.to_dict()]}) - - -class ParameterExpression(expr.core.OperatorMixin, object): - def __init__(self, expr): - self.expr = expr - - def to_dict(self): - return {"expr": repr(self.expr)} - - def _to_expr(self): - return repr(self.expr) - - def _from_expr(self, expr): - return ParameterExpression(expr=expr) - - -class SelectionExpression(expr.core.OperatorMixin, object): - def __init__(self, expr): - self.expr = expr - - def to_dict(self): - return {"expr": repr(self.expr)} - - def _to_expr(self): - return repr(self.expr) - - def _from_expr(self, expr): - return SelectionExpression(expr=expr) - - -def check_fields_and_encodings(parameter, field_name): - for prop in ["fields", "encodings"]: - try: - if field_name in getattr(parameter.param.select, prop): - return True - except (AttributeError, TypeError): - pass - - return False - - -# ------------------------------------------------------------------------ -# Top-Level Functions - - -def value(value, **kwargs): - """Specify a value for use in an encoding""" - return dict(value=value, **kwargs) - - -def param( - name=None, - value=Undefined, - bind=Undefined, - empty=Undefined, - expr=Undefined, - **kwds, -): - """Create a named parameter. See https://altair-viz.github.io/user_guide/interactions.html for examples. Although both variable parameters and selection parameters can be created using this 'param' function, to create a selection parameter, it is recommended to use either 'selection_point' or 'selection_interval' instead. - - Parameters - ---------- - name : string (optional) - The name of the parameter. If not specified, a unique name will be - created. - value : any (optional) - The default value of the parameter. If not specified, the parameter - will be created without a default value. - bind : :class:`Binding` (optional) - Binds the parameter to an external input element such as a slider, - selection list or radio button group. - empty : boolean (optional) - For selection parameters, the predicate of empty selections returns - True by default. Override this behavior, by setting this property - 'empty=False'. - expr : :class:`Expr` (optional) - An expression for the value of the parameter. This expression may - include other parameters, in which case the parameter will - automatically update in response to upstream parameter changes. - **kwds : - additional keywords will be used to construct a parameter. If 'select' - is among the keywords, then a selection parameter will be created. - Otherwise, a variable parameter will be created. - - Returns - ------- - parameter: Parameter - The parameter object that can be used in chart creation. - """ - parameter = Parameter(name) - - if empty is not Undefined: - parameter.empty = empty - if parameter.empty == "none": - warnings.warn( - """The value of 'empty' should be True or False.""", - utils.AltairDeprecationWarning, - stacklevel=1, - ) - parameter.empty = False - elif parameter.empty == "all": - warnings.warn( - """The value of 'empty' should be True or False.""", - utils.AltairDeprecationWarning, - stacklevel=1, - ) - parameter.empty = True - elif (parameter.empty is False) or (parameter.empty is True): - pass - else: - raise ValueError("The value of 'empty' should be True or False.") - - if "init" in kwds: - warnings.warn( - """Use 'value' instead of 'init'.""", - utils.AltairDeprecationWarning, - stacklevel=1, - ) - if value is Undefined: - kwds["value"] = kwds.pop("init") - else: - # If both 'value' and 'init' are set, we ignore 'init'. - kwds.pop("init") - - if "select" not in kwds: - parameter.param = core.VariableParameter( - name=parameter.name, bind=bind, value=value, expr=expr, **kwds - ) - parameter.param_type = "variable" - elif "views" in kwds: - parameter.param = core.TopLevelSelectionParameter( - name=parameter.name, bind=bind, value=value, expr=expr, **kwds - ) - parameter.param_type = "selection" - else: - parameter.param = core.SelectionParameter( - name=parameter.name, bind=bind, value=value, expr=expr, **kwds - ) - parameter.param_type = "selection" - - return parameter - - -def _selection(type=Undefined, **kwds): - # We separate out the parameter keywords from the selection keywords - param_kwds = {} - - for kwd in {"name", "bind", "value", "empty", "init", "views"}: - if kwd in kwds: - param_kwds[kwd] = kwds.pop(kwd) - - if type == "interval": - select = core.IntervalSelectionConfig(type=type, **kwds) - elif type == "point": - select = core.PointSelectionConfig(type=type, **kwds) - elif type in ["single", "multi"]: - select = core.PointSelectionConfig(type="point", **kwds) - warnings.warn( - """The types 'single' and 'multi' are now - combined and should be specified using "selection_point()".""", - utils.AltairDeprecationWarning, - stacklevel=1, - ) - else: - raise ValueError("""'type' must be 'point' or 'interval'""") - - return param(select=select, **param_kwds) - - -@utils.deprecation.deprecated( - message="""'selection' is deprecated. - Use 'selection_point()' or 'selection_interval()' instead; these functions also include more helpful docstrings.""" -) -def selection(type=Undefined, **kwds): - """ - Users are recommended to use either 'selection_point' or 'selection_interval' instead, depending on the type of parameter they want to create. - - Create a selection parameter. - - Parameters - ---------- - type : enum('point', 'interval') (required) - Determines the default event processing and data query for the - selection. Vega-Lite currently supports two selection types: - * "point" - to select multiple discrete data values; the first - value is selected on click and additional values toggled on - shift-click. - * "interval" - to select a continuous range of data values on - drag. - **kwds : - additional keywords to control the selection. - """ - - return _selection(type=type, **kwds) - - -def selection_interval( - name=None, - value=Undefined, - bind=Undefined, - empty=Undefined, - expr=Undefined, - encodings=Undefined, - on=Undefined, - clear=Undefined, - resolve=Undefined, - mark=Undefined, - translate=Undefined, - zoom=Undefined, - **kwds, -): - """Create an interval selection parameter. Selection parameters define data queries that are driven by direct manipulation from user input (e.g., mouse clicks or drags). Interval selection parameters are used to select a continuous range of data values on drag, whereas point selection parameters (`selection_point`) are used to select multiple discrete data values.) - - Parameters - ---------- - name : string (optional) - The name of the parameter. If not specified, a unique name will be - created. - value : any (optional) - The default value of the parameter. If not specified, the parameter - will be created without a default value. - bind : :class:`Binding` (optional) - Binds the parameter to an external input element such as a slider, - selection list or radio button group. - empty : boolean (optional) - For selection parameters, the predicate of empty selections returns - True by default. Override this behavior, by setting this property - 'empty=False'. - expr : :class:`Expr` (optional) - An expression for the value of the parameter. This expression may - include other parameters, in which case the parameter will - automatically update in response to upstream parameter changes. - encodings : List[str] (optional) - A list of encoding channels. The corresponding data field values - must match for a data tuple to fall within the selection. - on : string (optional) - A Vega event stream (object or selector) that triggers the selection. - For interval selections, the event stream must specify a start and end. - clear : string or boolean (optional) - Clears the selection, emptying it of all values. This property can - be an Event Stream or False to disable clear. Default is 'dblclick'. - resolve : enum('global', 'union', 'intersect') (optional) - With layered and multi-view displays, a strategy that determines - how selections' data queries are resolved when applied in a filter - transform, conditional encoding rule, or scale domain. - One of: - - * 'global': only one brush exists for the entire SPLOM. When the - user begins to drag, any previous brushes are cleared, and a - new one is constructed. - * 'union': each cell contains its own brush, and points are - highlighted if they lie within any of these individual brushes. - * 'intersect': each cell contains its own brush, and points are - highlighted only if they fall within all of these individual - brushes. - - The default is 'global'. - mark : :class:`Mark` (optional) - An interval selection also adds a rectangle mark to depict the - extents of the interval. The mark property can be used to - customize the appearance of the mark. - translate : string or boolean (optional) - When truthy, allows a user to interactively move an interval - selection back-and-forth. Can be True, False (to disable panning), - or a Vega event stream definition which must include a start and - end event to trigger continuous panning. Discrete panning (e.g., - pressing the left/right arrow keys) will be supported in future - versions. - The default value is True, which corresponds to - [mousedown, window:mouseup] > window:mousemove! - This default allows users to click and drag within an interval - selection to reposition it. - zoom : string or boolean (optional) - When truthy, allows a user to interactively resize an interval - selection. Can be True, False (to disable zooming), or a Vega - event stream definition. Currently, only wheel events are supported, - but custom event streams can still be used to specify filters, - debouncing, and throttling. Future versions will expand the set of - events that can trigger this transformation. - The default value is True, which corresponds to wheel!. This - default allows users to use the mouse wheel to resize an interval - selection. - **kwds : - Additional keywords to control the selection. - - Returns - ------- - parameter: Parameter - The parameter object that can be used in chart creation. - """ - return _selection( - type="interval", - name=name, - value=value, - bind=bind, - empty=empty, - expr=expr, - encodings=encodings, - on=on, - clear=clear, - resolve=resolve, - mark=mark, - translate=translate, - zoom=zoom, - **kwds, - ) - - -def selection_point( - name=None, - value=Undefined, - bind=Undefined, - empty=Undefined, - expr=Undefined, - encodings=Undefined, - fields=Undefined, - on=Undefined, - clear=Undefined, - resolve=Undefined, - toggle=Undefined, - nearest=Undefined, - **kwds, -): - """Create a point selection parameter. Selection parameters define data queries that are driven by direct manipulation from user input (e.g., mouse clicks or drags). Point selection parameters are used to select multiple discrete data values; the first value is selected on click and additional values toggled on shift-click. To select a continuous range of data values on drag interval selection parameters (`selection_interval`) can be used instead. - - Parameters - ---------- - name : string (optional) - The name of the parameter. If not specified, a unique name will be - created. - value : any (optional) - The default value of the parameter. If not specified, the parameter - will be created without a default value. - bind : :class:`Binding` (optional) - Binds the parameter to an external input element such as a slider, - selection list or radio button group. - empty : boolean (optional) - For selection parameters, the predicate of empty selections returns - True by default. Override this behavior, by setting this property - 'empty=False'. - expr : :class:`Expr` (optional) - An expression for the value of the parameter. This expression may - include other parameters, in which case the parameter will - automatically update in response to upstream parameter changes. - encodings : List[str] (optional) - A list of encoding channels. The corresponding data field values - must match for a data tuple to fall within the selection. - fields : List[str] (optional) - A list of field names whose values must match for a data tuple to - fall within the selection. - on : string (optional) - A Vega event stream (object or selector) that triggers the selection. - For interval selections, the event stream must specify a start and end. - clear : string or boolean (optional) - Clears the selection, emptying it of all values. This property can - be an Event Stream or False to disable clear. Default is 'dblclick'. - resolve : enum('global', 'union', 'intersect') (optional) - With layered and multi-view displays, a strategy that determines - how selections' data queries are resolved when applied in a filter - transform, conditional encoding rule, or scale domain. - One of: - - * 'global': only one brush exists for the entire SPLOM. When the - user begins to drag, any previous brushes are cleared, and a - new one is constructed. - * 'union': each cell contains its own brush, and points are - highlighted if they lie within any of these individual brushes. - * 'intersect': each cell contains its own brush, and points are - highlighted only if they fall within all of these individual - brushes. - - The default is 'global'. - toggle : string or boolean (optional) - Controls whether data values should be toggled (inserted or - removed from a point selection) or only ever inserted into - point selections. - One of: - - * True (default): the toggle behavior, which corresponds to - "event.shiftKey". As a result, data values are toggled - when the user interacts with the shift-key pressed. - * False: disables toggling behaviour; the selection will - only ever contain a single data value corresponding - to the most recent interaction. - * A Vega expression which is re-evaluated as the user interacts. - If the expression evaluates to True, the data value is - toggled into or out of the point selection. If the expression - evaluates to False, the point selection is first cleared, and - the data value is then inserted. For example, setting the - value to the Vega expression True will toggle data values - without the user pressing the shift-key. - - nearest : boolean (optional) - When true, an invisible voronoi diagram is computed to accelerate - discrete selection. The data value nearest the mouse cursor is - added to the selection. The default is False, which means that - data values must be interacted with directly (e.g., clicked on) - to be added to the selection. - **kwds : - Additional keywords to control the selection. - - Returns - ------- - parameter: Parameter - The parameter object that can be used in chart creation. - """ - return _selection( - type="point", - name=name, - value=value, - bind=bind, - empty=empty, - expr=expr, - encodings=encodings, - fields=fields, - on=on, - clear=clear, - resolve=resolve, - toggle=toggle, - nearest=nearest, - **kwds, - ) - - -@utils.deprecation.deprecated( - message="'selection_multi' is deprecated. Use 'selection_point'" -) -@utils.use_signature(core.PointSelectionConfig) -def selection_multi(**kwargs): - """'selection_multi' is deprecated. Use 'selection_point'""" - return _selection(type="point", **kwargs) - - -@utils.deprecation.deprecated( - message="'selection_single' is deprecated. Use 'selection_point'" -) -@utils.use_signature(core.PointSelectionConfig) -def selection_single(**kwargs): - """'selection_single' is deprecated. Use 'selection_point'""" - return _selection(type="point", **kwargs) - - -@utils.use_signature(core.Binding) -def binding(input, **kwargs): - """A generic binding""" - return core.Binding(input=input, **kwargs) - - -@utils.use_signature(core.BindCheckbox) -def binding_checkbox(**kwargs): - """A checkbox binding""" - return core.BindCheckbox(input="checkbox", **kwargs) - - -@utils.use_signature(core.BindRadioSelect) -def binding_radio(**kwargs): - """A radio button binding""" - return core.BindRadioSelect(input="radio", **kwargs) - - -@utils.use_signature(core.BindRadioSelect) -def binding_select(**kwargs): - """A select binding""" - return core.BindRadioSelect(input="select", **kwargs) - - -@utils.use_signature(core.BindRange) -def binding_range(**kwargs): - """A range binding""" - return core.BindRange(input="range", **kwargs) - - -# TODO: update the docstring -def condition(predicate, if_true, if_false, **kwargs): - """A conditional attribute or encoding - - Parameters - ---------- - predicate: Selection, PredicateComposition, expr.Expression, dict, or string - the selection predicate or test predicate for the condition. - if a string is passed, it will be treated as a test operand. - if_true: - the spec or object to use if the selection predicate is true - if_false: - the spec or object to use if the selection predicate is false - **kwargs: - additional keyword args are added to the resulting dict - - Returns - ------- - spec: dict or VegaLiteSchema - the spec that describes the condition - """ - test_predicates = (str, expr.Expression, core.PredicateComposition) - - if isinstance(predicate, Parameter): - if predicate.param_type == "selection" or predicate.param.expr is Undefined: - condition = {"param": predicate.name} - if "empty" in kwargs: - condition["empty"] = kwargs.pop("empty") - elif isinstance(predicate.empty, bool): - condition["empty"] = predicate.empty - else: - condition = {"test": predicate.param.expr} - elif isinstance(predicate, test_predicates): - condition = {"test": predicate} - elif isinstance(predicate, dict): - condition = predicate - else: - raise NotImplementedError( - "condition predicate of type {}" "".format(type(predicate)) - ) - - if isinstance(if_true, core.SchemaBase): - # convert to dict for now; the from_dict call below will wrap this - # dict in the appropriate schema - if_true = if_true.to_dict() - elif isinstance(if_true, str): - if isinstance(if_false, str): - raise ValueError( - "A field cannot be used for both the `if_true` and `if_false` values of a condition. One of them has to specify a `value` or `datum` definition." - ) - else: - if_true = utils.parse_shorthand(if_true) - if_true.update(kwargs) - condition.update(if_true) - - if isinstance(if_false, core.SchemaBase): - # For the selection, the channel definitions all allow selections - # already. So use this SchemaBase wrapper if possible. - selection = if_false.copy() - selection.condition = condition - elif isinstance(if_false, str): - selection = {"condition": condition, "shorthand": if_false} - selection.update(kwargs) - else: - selection = dict(condition=condition, **if_false) - - return selection - - -# -------------------------------------------------------------------- -# Top-level objects - - -class TopLevelMixin(mixins.ConfigMethodMixin): - """Mixin for top-level chart objects such as Chart, LayeredChart, etc.""" - - _class_is_valid_at_instantiation = False - - def to_dict(self, *args, **kwargs) -> dict: - """Convert the chart to a dictionary suitable for JSON export""" - # We make use of three context markers: - # - 'data' points to the data that should be referenced for column type - # inference. - # - 'top_level' is a boolean flag that is assumed to be true; if it's - # true then a "$schema" arg is added to the dict. - # - 'datasets' is a dict of named datasets that should be inserted - # in the top-level object - - # note: not a deep copy because we want datasets and data arguments to - # be passed by reference - context = kwargs.get("context", {}).copy() - context.setdefault("datasets", {}) - is_top_level = context.get("top_level", True) - - # TopLevelMixin instance does not necessarily have copy defined but due to how - # Altair is set up this should hold. Too complex to type hint right now - copy = self.copy(deep=False) # type: ignore[attr-defined] - original_data = getattr(copy, "data", Undefined) - copy.data = _prepare_data(original_data, context) - - if original_data is not Undefined: - context["data"] = original_data - - # remaining to_dict calls are not at top level - context["top_level"] = False - kwargs["context"] = context - - # TopLevelMixin instance does not necessarily have to_dict defined - # but due to how Altair is set up this should hold. - # Too complex to type hint right now - dct = super(TopLevelMixin, copy).to_dict(*args, **kwargs) # type: ignore[misc] - - # TODO: following entries are added after validation. Should they be validated? - if is_top_level: - # since this is top-level we add $schema if it's missing - if "$schema" not in dct: - dct["$schema"] = SCHEMA_URL - - # apply theme from theme registry - the_theme = themes.get() - # Use assert to tell type checkers that it is not None. Holds true - # as there is always a default theme set when importing Altair - assert the_theme is not None - dct = utils.update_nested(the_theme(), dct, copy=True) - - # update datasets - if context["datasets"]: - dct.setdefault("datasets", {}).update(context["datasets"]) - - return dct - - def to_html( - self, - base_url="https://cdn.jsdelivr.net/npm", - output_div="vis", - embed_options=None, - json_kwds=None, - fullhtml=True, - requirejs=False, - ) -> str: - return utils.spec_to_html( - self.to_dict(), - mode="vega-lite", - vegalite_version=VEGALITE_VERSION, - vegaembed_version=VEGAEMBED_VERSION, - vega_version=VEGA_VERSION, - base_url=base_url, - output_div=output_div, - embed_options=embed_options, - json_kwds=json_kwds, - fullhtml=fullhtml, - requirejs=requirejs, - ) - - def save( - self, - fp, - format=None, - override_data_transformer=True, - scale_factor=1.0, - vegalite_version=VEGALITE_VERSION, - vega_version=VEGA_VERSION, - vegaembed_version=VEGAEMBED_VERSION, - **kwargs, - ): - """Save a chart to file in a variety of formats - - Supported formats are json, html, png, svg, pdf; the last three require - the altair_saver package to be installed. - - Parameters - ---------- - fp : string filename or file-like object - file in which to write the chart. - format : string (optional) - the format to write: one of ['json', 'html', 'png', 'svg', 'pdf']. - If not specified, the format will be determined from the filename. - override_data_transformer : `boolean` (optional) - If True (default), then the save action will be done with - the MaxRowsError disabled. If False, then do not change the data - transformer. - scale_factor : float - For svg or png formats, scale the image by this factor when saving. - This can be used to control the size or resolution of the output. - Default is 1.0 - **kwargs : - Additional keyword arguments are passed to the output method - associated with the specified format. - - """ - from ...utils.save import save - - kwds = dict( - chart=self, - fp=fp, - format=format, - scale_factor=scale_factor, - vegalite_version=vegalite_version, - vega_version=vega_version, - vegaembed_version=vegaembed_version, - **kwargs, - ) - - # By default we override the data transformer. This makes it so - # that save() will succeed even for large datasets that would - # normally trigger a MaxRowsError - if override_data_transformer: - with data_transformers.disable_max_rows(): - result = save(**kwds) - else: - result = save(**kwds) - return result - - # Fallback for when rendering fails; the full repr is too long to be - # useful in nearly all cases. - def __repr__(self): - return "alt.{}(...)".format(self.__class__.__name__) - - # Layering and stacking - def __add__(self, other): - if not isinstance(other, TopLevelMixin): - raise ValueError("Only Chart objects can be layered.") - return layer(self, other) - - def __and__(self, other): - if not isinstance(other, TopLevelMixin): - raise ValueError("Only Chart objects can be concatenated.") - return vconcat(self, other) - - def __or__(self, other): - if not isinstance(other, TopLevelMixin): - raise ValueError("Only Chart objects can be concatenated.") - return hconcat(self, other) - - def repeat( - self, - repeat=Undefined, - row=Undefined, - column=Undefined, - layer=Undefined, - columns=Undefined, - **kwargs, - ) -> "RepeatChart": - """Return a RepeatChart built from the chart - - Fields within the chart can be set to correspond to the row or - column using `alt.repeat('row')` and `alt.repeat('column')`. - - Parameters - ---------- - repeat : list - a list of data column names to be repeated. This cannot be - used along with the ``row``, ``column`` or ``layer`` argument. - row : list - a list of data column names to be mapped to the row facet - column : list - a list of data column names to be mapped to the column facet - layer : list - a list of data column names to be layered. This cannot be - used along with the ``row``, ``column`` or ``repeat`` argument. - columns : int - the maximum number of columns before wrapping. Only referenced - if ``repeat`` is specified. - **kwargs : - additional keywords passed to RepeatChart. - - Returns - ------- - chart : RepeatChart - a repeated chart. - """ - repeat_specified = repeat is not Undefined - rowcol_specified = row is not Undefined or column is not Undefined - layer_specified = layer is not Undefined - - if repeat_specified and rowcol_specified: - raise ValueError( - "repeat argument cannot be combined with row/column argument." - ) - elif repeat_specified and layer_specified: - raise ValueError("repeat argument cannot be combined with layer argument.") - - if repeat_specified: - repeat = repeat - elif layer_specified: - repeat = core.LayerRepeatMapping(layer=layer, row=row, column=column) - else: - repeat = core.RepeatMapping(row=row, column=column) - - return RepeatChart(spec=self, repeat=repeat, columns=columns, **kwargs) - - def properties(self, **kwargs) -> Self: - """Set top-level properties of the Chart. - - Argument names and types are the same as class initialization. - """ - # ignore type as copy comes from another class for subclasses of TopLevelMixin - copy = self.copy(deep=False) # type: ignore[attr-defined] - for key, val in kwargs.items(): - if key == "selection" and isinstance(val, Parameter): - # TODO: Can this be removed - # For backward compatibility with old selection interface. - setattr(copy, key, {val.name: val.selection}) - else: - # Don't validate data, because it hasn't been processed. - if key != "data": - # ignore type as validate_property comes from SchemaBase, - # not from TopLevelMixin - self.validate_property(key, val) # type: ignore[attr-defined] - setattr(copy, key, val) - return copy - - def project( - self, - type=Undefined, - center=Undefined, - clipAngle=Undefined, - clipExtent=Undefined, - coefficient=Undefined, - distance=Undefined, - fraction=Undefined, - lobes=Undefined, - parallel=Undefined, - precision=Undefined, - radius=Undefined, - ratio=Undefined, - reflectX=Undefined, - reflectY=Undefined, - rotate=Undefined, - scale=Undefined, - spacing=Undefined, - tilt=Undefined, - translate=Undefined, - **kwds, - ) -> Self: - """Add a geographic projection to the chart. - - This is generally used either with ``mark_geoshape`` or with the - ``latitude``/``longitude`` encodings. - - Available projection types are - ['albers', 'albersUsa', 'azimuthalEqualArea', 'azimuthalEquidistant', - 'conicConformal', 'conicEqualArea', 'conicEquidistant', 'equalEarth', 'equirectangular', - 'gnomonic', 'identity', 'mercator', 'orthographic', 'stereographic', 'transverseMercator'] - - Parameters - ---------- - type : ProjectionType - The cartographic projection to use. This value is case-insensitive, for example - `"albers"` and `"Albers"` indicate the same projection type. You can find all valid - projection types [in the - documentation](https://vega.github.io/vega-lite/docs/projection.html#projection-types). - - **Default value:** `equalEarth` - center : List(float) - Sets the projection’s center to the specified center, a two-element array of - longitude and latitude in degrees. - - **Default value:** `[0, 0]` - clipAngle : float - Sets the projection’s clipping circle radius to the specified angle in degrees. If - `null`, switches to [antimeridian](http://bl.ocks.org/mbostock/3788999) cutting - rather than small-circle clipping. - clipExtent : List(List(float)) - Sets the projection’s viewport clip extent to the specified bounds in pixels. The - extent bounds are specified as an array `[[x0, y0], [x1, y1]]`, where `x0` is the - left-side of the viewport, `y0` is the top, `x1` is the right and `y1` is the - bottom. If `null`, no viewport clipping is performed. - coefficient : float - - distance : float - - fraction : float - - lobes : float - - parallel : float - - precision : Mapping(required=[length]) - Sets the threshold for the projection’s [adaptive - resampling](http://bl.ocks.org/mbostock/3795544) to the specified value in pixels. - This value corresponds to the [Douglas–Peucker - distance](http://en.wikipedia.org/wiki/Ramer%E2%80%93Douglas%E2%80%93Peucker_algorithm). - If precision is not specified, returns the projection’s current resampling - precision which defaults to `√0.5 ≅ 0.70710…`. - radius : float - - ratio : float - - reflectX : boolean - - reflectY : boolean - - rotate : List(float) - Sets the projection’s three-axis rotation to the specified angles, which must be a - two- or three-element array of numbers [`lambda`, `phi`, `gamma`] specifying the - rotation angles in degrees about each spherical axis. (These correspond to yaw, - pitch and roll.) - - **Default value:** `[0, 0, 0]` - scale : float - Sets the projection's scale (zoom) value, overriding automatic fitting. - - spacing : float - - tilt : float - - translate : List(float) - Sets the projection's translation (pan) value, overriding automatic fitting. - - """ - projection = core.Projection( - center=center, - clipAngle=clipAngle, - clipExtent=clipExtent, - coefficient=coefficient, - distance=distance, - fraction=fraction, - lobes=lobes, - parallel=parallel, - precision=precision, - radius=radius, - ratio=ratio, - reflectX=reflectX, - reflectY=reflectY, - rotate=rotate, - scale=scale, - spacing=spacing, - tilt=tilt, - translate=translate, - type=type, - **kwds, - ) - return self.properties(projection=projection) - - def _add_transform(self, *transforms): - """Copy the chart and add specified transforms to chart.transform""" - copy = self.copy(deep=["transform"]) - if copy.transform is Undefined: - copy.transform = [] - copy.transform.extend(transforms) - return copy - - def transform_aggregate( - self, aggregate=Undefined, groupby=Undefined, **kwds - ) -> Self: - """ - Add an :class:`AggregateTransform` to the schema. - - Parameters - ---------- - aggregate : List(:class:`AggregatedFieldDef`) - Array of objects that define fields to aggregate. - groupby : List(string) - The data fields to group by. If not specified, a single group containing all data - objects will be used. - **kwds : - additional keywords are converted to aggregates using standard - shorthand parsing. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - Examples - -------- - The aggregate transform allows you to specify transforms directly using - the same shorthand syntax as used in encodings: - - >>> import altair as alt - >>> chart1 = alt.Chart().transform_aggregate( - ... mean_acc='mean(Acceleration)', - ... groupby=['Origin'] - ... ) - >>> print(chart1.transform[0].to_json()) # doctest: +NORMALIZE_WHITESPACE - { - "aggregate": [ - { - "as": "mean_acc", - "field": "Acceleration", - "op": "mean" - } - ], - "groupby": [ - "Origin" - ] - } - - It also supports including AggregatedFieldDef instances or dicts directly, - so you can create the above transform like this: - - >>> chart2 = alt.Chart().transform_aggregate( - ... [alt.AggregatedFieldDef(field='Acceleration', op='mean', - ... **{'as': 'mean_acc'})], - ... groupby=['Origin'] - ... ) - >>> chart2.transform == chart1.transform - True - - See Also - -------- - alt.AggregateTransform : underlying transform object - - """ - if aggregate is Undefined: - aggregate = [] - for key, val in kwds.items(): - parsed = utils.parse_shorthand(val) - dct = { - "as": key, - "field": parsed.get("field", Undefined), - "op": parsed.get("aggregate", Undefined), - } - aggregate.append(core.AggregatedFieldDef(**dct)) - return self._add_transform( - core.AggregateTransform(aggregate=aggregate, groupby=groupby) - ) - - def transform_bin(self, as_=Undefined, field=Undefined, bin=True, **kwargs) -> Self: - """ - Add a :class:`BinTransform` to the schema. - - Parameters - ---------- - as_ : anyOf(string, List(string)) - The output fields at which to write the start and end bin values. - bin : anyOf(boolean, :class:`BinParams`) - An object indicating bin properties, or simply ``true`` for using default bin - parameters. - field : string - The data field to bin. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - Examples - -------- - >>> import altair as alt - >>> chart = alt.Chart().transform_bin("x_binned", "x") - >>> chart.transform[0] - BinTransform({ - as: 'x_binned', - bin: True, - field: 'x' - }) - - >>> chart = alt.Chart().transform_bin("x_binned", "x", - ... bin=alt.Bin(maxbins=10)) - >>> chart.transform[0] - BinTransform({ - as: 'x_binned', - bin: BinParams({ - maxbins: 10 - }), - field: 'x' - }) - - See Also - -------- - alt.BinTransform : underlying transform object - - """ - if as_ is not Undefined: - if "as" in kwargs: - raise ValueError( - "transform_bin: both 'as_' and 'as' passed as arguments." - ) - kwargs["as"] = as_ - kwargs["bin"] = bin - kwargs["field"] = field - return self._add_transform(core.BinTransform(**kwargs)) - - def transform_calculate(self, as_=Undefined, calculate=Undefined, **kwargs) -> Self: - """ - Add a :class:`CalculateTransform` to the schema. - - Parameters - ---------- - as_ : string - The field for storing the computed formula value. - calculate : string or alt.expr expression - A `expression `__ - string. Use the variable ``datum`` to refer to the current data object. - **kwargs - transforms can also be passed by keyword argument; see Examples - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - Examples - -------- - >>> import altair as alt - >>> from altair import datum, expr - - >>> chart = alt.Chart().transform_calculate(y = 2 * expr.sin(datum.x)) - >>> chart.transform[0] - CalculateTransform({ - as: 'y', - calculate: (2 * sin(datum.x)) - }) - - It's also possible to pass the ``CalculateTransform`` arguments directly: - - >>> kwds = {'as': 'y', 'calculate': '2 * sin(datum.x)'} - >>> chart = alt.Chart().transform_calculate(**kwds) - >>> chart.transform[0] - CalculateTransform({ - as: 'y', - calculate: '2 * sin(datum.x)' - }) - - As the first form is easier to write and understand, that is the - recommended method. - - See Also - -------- - alt.CalculateTransform : underlying transform object - """ - if as_ is Undefined: - as_ = kwargs.pop("as", Undefined) - elif "as" in kwargs: - raise ValueError( - "transform_calculate: both 'as_' and 'as' passed as arguments." - ) - if as_ is not Undefined or calculate is not Undefined: - dct = {"as": as_, "calculate": calculate} - self = self._add_transform(core.CalculateTransform(**dct)) - for as_, calculate in kwargs.items(): - dct = {"as": as_, "calculate": calculate} - self = self._add_transform(core.CalculateTransform(**dct)) - return self - - def transform_density( - self, - density, - as_=Undefined, - bandwidth=Undefined, - counts=Undefined, - cumulative=Undefined, - extent=Undefined, - groupby=Undefined, - maxsteps=Undefined, - minsteps=Undefined, - steps=Undefined, - ) -> Self: - """Add a :class:`DensityTransform` to the spec. - - Parameters - ---------- - density : str - The data field for which to perform density estimation. - as_ : [str, str] - The output fields for the sample value and corresponding density estimate. - **Default value:** ``["value", "density"]`` - bandwidth : float - The bandwidth (standard deviation) of the Gaussian kernel. If unspecified or set to - zero, the bandwidth value is automatically estimated from the input data using - Scott’s rule. - counts : boolean - A boolean flag indicating if the output values should be probability estimates - (false) or smoothed counts (true). - **Default value:** ``false`` - cumulative : boolean - A boolean flag indicating whether to produce density estimates (false) or cumulative - density estimates (true). - **Default value:** ``false`` - extent : List([float, float]) - A [min, max] domain from which to sample the distribution. If unspecified, the - extent will be determined by the observed minimum and maximum values of the density - value field. - groupby : List(str) - The data fields to group by. If not specified, a single group containing all data - objects will be used. - maxsteps : float - The maximum number of samples to take along the extent domain for plotting the - density. **Default value:** ``200`` - minsteps : float - The minimum number of samples to take along the extent domain for plotting the - density. **Default value:** ``25`` - steps : float - The exact number of samples to take along the extent domain for plotting the - density. If specified, overrides both minsteps and maxsteps to set an exact number - of uniform samples. Potentially useful in conjunction with a fixed extent to ensure - consistent sample points for stacked densities. - """ - return self._add_transform( - core.DensityTransform( - density=density, - bandwidth=bandwidth, - counts=counts, - cumulative=cumulative, - extent=extent, - groupby=groupby, - maxsteps=maxsteps, - minsteps=minsteps, - steps=steps, - **{"as": as_}, - ) - ) - - def transform_impute( - self, - impute, - key, - frame=Undefined, - groupby=Undefined, - keyvals=Undefined, - method=Undefined, - value=Undefined, - ) -> Self: - """ - Add an :class:`ImputeTransform` to the schema. - - Parameters - ---------- - impute : string - The data field for which the missing values should be imputed. - key : string - A key field that uniquely identifies data objects within a group. - Missing key values (those occurring in the data but not in the current group) will - be imputed. - frame : List(anyOf(None, float)) - A frame specification as a two-element array used to control the window over which - the specified method is applied. The array entries should either be a number - indicating the offset from the current data object, or null to indicate unbounded - rows preceding or following the current data object. For example, the value ``[-5, - 5]`` indicates that the window should include five objects preceding and five - objects following the current object. - **Default value:** : ``[null, null]`` indicating that the window includes all - objects. - groupby : List(string) - An optional array of fields by which to group the values. - Imputation will then be performed on a per-group basis. - keyvals : anyOf(List(Mapping(required=[])), :class:`ImputeSequence`) - Defines the key values that should be considered for imputation. - An array of key values or an object defining a `number sequence - `__. - If provided, this will be used in addition to the key values observed within the - input data. If not provided, the values will be derived from all unique values of - the ``key`` field. For ``impute`` in ``encoding``, the key field is the x-field if - the y-field is imputed, or vice versa. - If there is no impute grouping, this property *must* be specified. - method : :class:`ImputeMethod` - The imputation method to use for the field value of imputed data objects. - One of ``value``, ``mean``, ``median``, ``max`` or ``min``. - **Default value:** ``"value"`` - value : Mapping(required=[]) - The field value to use when the imputation ``method`` is ``"value"``. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.ImputeTransform : underlying transform object - """ - return self._add_transform( - core.ImputeTransform( - impute=impute, - key=key, - frame=frame, - groupby=groupby, - keyvals=keyvals, - method=method, - value=value, - ) - ) - - def transform_joinaggregate( - self, joinaggregate=Undefined, groupby=Undefined, **kwargs - ) -> Self: - """ - Add a :class:`JoinAggregateTransform` to the schema. - - Parameters - ---------- - joinaggregate : List(:class:`JoinAggregateFieldDef`) - The definition of the fields in the join aggregate, and what calculations to use. - groupby : List(string) - The data fields for partitioning the data objects into separate groups. If - unspecified, all data points will be in a single group. - **kwargs - joinaggregates can also be passed by keyword argument; see Examples. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - Examples - -------- - >>> import altair as alt - >>> chart = alt.Chart().transform_joinaggregate(x='sum(y)') - >>> chart.transform[0] - JoinAggregateTransform({ - joinaggregate: [JoinAggregateFieldDef({ - as: 'x', - field: 'y', - op: 'sum' - })] - }) - - See Also - -------- - alt.JoinAggregateTransform : underlying transform object - """ - if joinaggregate is Undefined: - joinaggregate = [] - for key, val in kwargs.items(): - parsed = utils.parse_shorthand(val) - dct = { - "as": key, - "field": parsed.get("field", Undefined), - "op": parsed.get("aggregate", Undefined), - } - joinaggregate.append(core.JoinAggregateFieldDef(**dct)) - return self._add_transform( - core.JoinAggregateTransform(joinaggregate=joinaggregate, groupby=groupby) - ) - - # TODO: Update docstring - def transform_filter(self, filter, **kwargs) -> Self: - """ - Add a :class:`FilterTransform` to the schema. - - Parameters - ---------- - filter : a filter expression or :class:`PredicateComposition` - The `filter` property must be one of the predicate definitions: - (1) a string or alt.expr expression - (2) a range predicate - (3) a selection predicate - (4) a logical operand combining (1)-(3) - (5) a Selection object - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.FilterTransform : underlying transform object - - """ - if isinstance(filter, Parameter): - new_filter = {"param": filter.name} - if "empty" in kwargs: - new_filter["empty"] = kwargs.pop("empty") - elif isinstance(filter.empty, bool): - new_filter["empty"] = filter.empty - filter = new_filter - return self._add_transform(core.FilterTransform(filter=filter, **kwargs)) - - def transform_flatten(self, flatten, as_=Undefined) -> Self: - """Add a :class:`FlattenTransform` to the schema. - - Parameters - ---------- - flatten : List(string) - An array of one or more data fields containing arrays to flatten. - If multiple fields are specified, their array values should have a parallel - structure, ideally with the same length. - If the lengths of parallel arrays do not match, - the longest array will be used with ``null`` values added for missing entries. - as : List(string) - The output field names for extracted array values. - **Default value:** The field name of the corresponding array field - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.FlattenTransform : underlying transform object - """ - return self._add_transform( - core.FlattenTransform(flatten=flatten, **{"as": as_}) - ) - - def transform_fold(self, fold, as_=Undefined) -> Self: - """Add a :class:`FoldTransform` to the spec. - - Parameters - ---------- - fold : List(string) - An array of data fields indicating the properties to fold. - as : [string, string] - The output field names for the key and value properties produced by the fold - transform. Default: ``["key", "value"]`` - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - Chart.transform_pivot : pivot transform - opposite of fold. - alt.FoldTransform : underlying transform object - """ - return self._add_transform(core.FoldTransform(fold=fold, **{"as": as_})) - - def transform_loess( - self, - on, - loess, - as_=Undefined, - bandwidth=Undefined, - groupby=Undefined, - ) -> Self: - """Add a :class:`LoessTransform` to the spec. - - Parameters - ---------- - on : str - The data field of the independent variable to use a predictor. - loess : str - The data field of the dependent variable to smooth. - as_ : [str, str] - The output field names for the smoothed points generated by the loess transform. - **Default value:** The field names of the input x and y values. - bandwidth : float - A bandwidth parameter in the range ``[0, 1]`` that determines the amount of - smoothing. **Default value:** ``0.3`` - groupby : List(str) - The data fields to group by. If not specified, a single group containing all data - objects will be used. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - Chart.transform_regression: regression transform - alt.LoessTransform : underlying transform object - """ - return self._add_transform( - core.LoessTransform( - loess=loess, on=on, bandwidth=bandwidth, groupby=groupby, **{"as": as_} - ) - ) - - def transform_lookup( - self, - lookup=Undefined, - from_=Undefined, - as_=Undefined, - default=Undefined, - **kwargs, - ) -> Self: - """Add a :class:`DataLookupTransform` or :class:`SelectionLookupTransform` to the chart - - Parameters - ---------- - lookup : string - Key in primary data source. - from_ : anyOf(:class:`LookupData`, :class:`LookupSelection`) - Secondary data reference. - as_ : anyOf(string, List(string)) - The output fields on which to store the looked up data values. - - For data lookups, this property may be left blank if ``from_.fields`` - has been specified (those field names will be used); if ``from_.fields`` - has not been specified, ``as_`` must be a string. - - For selection lookups, this property is optional: if unspecified, - looked up values will be stored under a property named for the selection; - and if specified, it must correspond to ``from_.fields``. - default : string - The default value to use if lookup fails. **Default value:** ``null`` - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.DataLookupTransform : underlying transform object - alt.SelectionLookupTransform : underlying transform object - """ - if as_ is not Undefined: - if "as" in kwargs: - raise ValueError( - "transform_lookup: both 'as_' and 'as' passed as arguments." - ) - kwargs["as"] = as_ - if from_ is not Undefined: - if "from" in kwargs: - raise ValueError( - "transform_lookup: both 'from_' and 'from' passed as arguments." - ) - kwargs["from"] = from_ - kwargs["lookup"] = lookup - kwargs["default"] = default - return self._add_transform(core.LookupTransform(**kwargs)) - - def transform_pivot( - self, - pivot, - value, - groupby=Undefined, - limit=Undefined, - op=Undefined, - ) -> Self: - """Add a :class:`PivotTransform` to the chart. - - Parameters - ---------- - pivot : str - The data field to pivot on. The unique values of this field become new field names - in the output stream. - value : str - The data field to populate pivoted fields. The aggregate values of this field become - the values of the new pivoted fields. - groupby : List(str) - The optional data fields to group by. If not specified, a single group containing - all data objects will be used. - limit : float - An optional parameter indicating the maximum number of pivoted fields to generate. - The default ( ``0`` ) applies no limit. The pivoted ``pivot`` names are sorted in - ascending order prior to enforcing the limit. - **Default value:** ``0`` - op : string - The aggregation operation to apply to grouped ``value`` field values. - **Default value:** ``sum`` - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - Chart.transform_fold : fold transform - opposite of pivot. - alt.PivotTransform : underlying transform object - """ - return self._add_transform( - core.PivotTransform( - pivot=pivot, value=value, groupby=groupby, limit=limit, op=op - ) - ) - - def transform_quantile( - self, - quantile, - as_=Undefined, - groupby=Undefined, - probs=Undefined, - step=Undefined, - ) -> Self: - """Add a :class:`QuantileTransform` to the chart - - Parameters - ---------- - quantile : str - The data field for which to perform quantile estimation. - as : [str, str] - The output field names for the probability and quantile values. - groupby : List(str) - The data fields to group by. If not specified, a single group containing all data - objects will be used. - probs : List(float) - An array of probabilities in the range (0, 1) for which to compute quantile values. - If not specified, the *step* parameter will be used. - step : float - A probability step size (default 0.01) for sampling quantile values. All values from - one-half the step size up to 1 (exclusive) will be sampled. This parameter is only - used if the *probs* parameter is not provided. **Default value:** ``["prob", "value"]`` - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.QuantileTransform : underlying transform object - """ - return self._add_transform( - core.QuantileTransform( - quantile=quantile, - groupby=groupby, - probs=probs, - step=step, - **{"as": as_}, - ) - ) - - def transform_regression( - self, - on, - regression, - as_=Undefined, - extent=Undefined, - groupby=Undefined, - method=Undefined, - order=Undefined, - params=Undefined, - ) -> Self: - """Add a :class:`RegressionTransform` to the chart. - - Parameters - ---------- - on : str - The data field of the independent variable to use a predictor. - regression : str - The data field of the dependent variable to predict. - as_ : [str, str] - The output field names for the smoothed points generated by the regression - transform. **Default value:** The field names of the input x and y values. - extent : [float, float] - A [min, max] domain over the independent (x) field for the starting and ending - points of the generated trend line. - groupby : List(str) - The data fields to group by. If not specified, a single group containing all data - objects will be used. - method : enum('linear', 'log', 'exp', 'pow', 'quad', 'poly') - The functional form of the regression model. One of ``"linear"``, ``"log"``, - ``"exp"``, ``"pow"``, ``"quad"``, or ``"poly"``. **Default value:** ``"linear"`` - order : float - The polynomial order (number of coefficients) for the 'poly' method. - **Default value:** ``3`` - params : boolean - A boolean flag indicating if the transform should return the regression model - parameters (one object per group), rather than trend line points. - The resulting objects include a ``coef`` array of fitted coefficient values - (starting with the intercept term and then including terms of increasing order) - and an ``rSquared`` value (indicating the total variance explained by the model). - **Default value:** ``false`` - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - Chart.transform_loess : LOESS transform - alt.RegressionTransform : underlying transform object - """ - return self._add_transform( - core.RegressionTransform( - regression=regression, - on=on, - extent=extent, - groupby=groupby, - method=method, - order=order, - params=params, - **{"as": as_}, - ) - ) - - def transform_sample(self, sample=1000) -> Self: - """ - Add a :class:`SampleTransform` to the schema. - - Parameters - ---------- - sample : float - The maximum number of data objects to include in the sample. Default: 1000. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.SampleTransform : underlying transform object - """ - return self._add_transform(core.SampleTransform(sample)) - - def transform_stack( - self, as_, stack, groupby, offset=Undefined, sort=Undefined - ) -> Self: - """ - Add a :class:`StackTransform` to the schema. - - Parameters - ---------- - as_ : anyOf(string, List(string)) - Output field names. This can be either a string or an array of strings with - two elements denoting the name for the fields for stack start and stack end - respectively. - If a single string(eg."val") is provided, the end field will be "val_end". - stack : string - The field which is stacked. - groupby : List(string) - The data fields to group by. - offset : enum('zero', 'center', 'normalize') - Mode for stacking marks. Default: 'zero'. - sort : List(:class:`SortField`) - Field that determines the order of leaves in the stacked charts. - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - See Also - -------- - alt.StackTransform : underlying transform object - """ - return self._add_transform( - core.StackTransform( - stack=stack, groupby=groupby, offset=offset, sort=sort, **{"as": as_} - ) - ) - - def transform_timeunit( - self, - as_=Undefined, - field=Undefined, - timeUnit=Undefined, - **kwargs, - ) -> Self: - """ - Add a :class:`TimeUnitTransform` to the schema. - - Parameters - ---------- - as_ : string - The output field to write the timeUnit value. - field : string - The data field to apply time unit. - timeUnit : :class:`TimeUnit` - The timeUnit. - **kwargs - transforms can also be passed by keyword argument; see Examples - - Returns - ------- - self : Chart object - returns chart to allow for chaining - - Examples - -------- - >>> import altair as alt - >>> from altair import datum, expr - - >>> chart = alt.Chart().transform_timeunit(month='month(date)') - >>> chart.transform[0] - TimeUnitTransform({ - as: 'month', - field: 'date', - timeUnit: 'month' - }) - - It's also possible to pass the ``TimeUnitTransform`` arguments directly; - this is most useful in cases where the desired field name is not a - valid python identifier: - - >>> kwds = {'as': 'month', 'timeUnit': 'month', 'field': 'The Month'} - >>> chart = alt.Chart().transform_timeunit(**kwds) - >>> chart.transform[0] - TimeUnitTransform({ - as: 'month', - field: 'The Month', - timeUnit: 'month' - }) - - As the first form is easier to write and understand, that is the - recommended method. - - See Also - -------- - alt.TimeUnitTransform : underlying transform object - - """ - if as_ is Undefined: - as_ = kwargs.pop("as", Undefined) - else: - if "as" in kwargs: - raise ValueError( - "transform_timeunit: both 'as_' and 'as' passed as arguments." - ) - if as_ is not Undefined: - dct = {"as": as_, "timeUnit": timeUnit, "field": field} - self = self._add_transform(core.TimeUnitTransform(**dct)) - for as_, shorthand in kwargs.items(): - dct = utils.parse_shorthand( - shorthand, - parse_timeunits=True, - parse_aggregates=False, - parse_types=False, - ) - dct.pop("type", None) - dct["as"] = as_ - if "timeUnit" not in dct: - raise ValueError("'{}' must include a valid timeUnit".format(shorthand)) - self = self._add_transform(core.TimeUnitTransform(**dct)) - return self - - def transform_window( - self, - window=Undefined, - frame=Undefined, - groupby=Undefined, - ignorePeers=Undefined, - sort=Undefined, - **kwargs, - ) -> Self: - """Add a :class:`WindowTransform` to the schema - - Parameters - ---------- - window : List(:class:`WindowFieldDef`) - The definition of the fields in the window, and what calculations to use. - frame : List(anyOf(None, float)) - A frame specification as a two-element array indicating how the sliding window - should proceed. The array entries should either be a number indicating the offset - from the current data object, or null to indicate unbounded rows preceding or - following the current data object. The default value is ``[null, 0]``, indicating - that the sliding window includes the current object and all preceding objects. The - value ``[-5, 5]`` indicates that the window should include five objects preceding - and five objects following the current object. Finally, ``[null, null]`` indicates - that the window frame should always include all data objects. The only operators - affected are the aggregation operations and the ``first_value``, ``last_value``, and - ``nth_value`` window operations. The other window operations are not affected by - this. - - **Default value:** : ``[null, 0]`` (includes the current object and all preceding - objects) - groupby : List(string) - The data fields for partitioning the data objects into separate windows. If - unspecified, all data points will be in a single group. - ignorePeers : boolean - Indicates if the sliding window frame should ignore peer values. (Peer values are - those considered identical by the sort criteria). The default is false, causing the - window frame to expand to include all peer values. If set to true, the window frame - will be defined by offset values only. This setting only affects those operations - that depend on the window frame, namely aggregation operations and the first_value, - last_value, and nth_value window operations. - - **Default value:** ``false`` - sort : List(:class:`SortField`) - A sort field definition for sorting data objects within a window. If two data - objects are considered equal by the comparator, they are considered “peer” values of - equal rank. If sort is not specified, the order is undefined: data objects are - processed in the order they are observed and none are considered peers (the - ignorePeers parameter is ignored and treated as if set to ``true`` ). - **kwargs - transforms can also be passed by keyword argument; see Examples - - Examples - -------- - A cumulative line chart - - >>> import altair as alt - >>> import numpy as np - >>> import pandas as pd - >>> data = pd.DataFrame({'x': np.arange(100), - ... 'y': np.random.randn(100)}) - >>> chart = alt.Chart(data).mark_line().encode( - ... x='x:Q', - ... y='ycuml:Q' - ... ).transform_window( - ... ycuml='sum(y)' - ... ) - >>> chart.transform[0] - WindowTransform({ - window: [WindowFieldDef({ - as: 'ycuml', - field: 'y', - op: 'sum' - })] - }) - - """ - if kwargs: - if window is Undefined: - window = [] - for as_, shorthand in kwargs.items(): - kwds = {"as": as_} - kwds.update( - utils.parse_shorthand( - shorthand, - parse_aggregates=False, - parse_window_ops=True, - parse_timeunits=False, - parse_types=False, - ) - ) - window.append(core.WindowFieldDef(**kwds)) - - return self._add_transform( - core.WindowTransform( - window=window, - frame=frame, - groupby=groupby, - ignorePeers=ignorePeers, - sort=sort, - ) - ) - - # Display-related methods - - def _repr_mimebundle_(self, include=None, exclude=None): - """Return a MIME bundle for display in Jupyter frontends.""" - # Catch errors explicitly to get around issues in Jupyter frontend - # see https://github.com/ipython/ipython/issues/11038 - try: - dct = self.to_dict() - except Exception: - utils.display_traceback(in_ipython=True) - return {} - else: - return renderers.get()(dct) - - def display(self, renderer=Undefined, theme=Undefined, actions=Undefined, **kwargs): - """Display chart in Jupyter notebook or JupyterLab - - Parameters are passed as options to vega-embed within supported frontends. - See https://github.com/vega/vega-embed#options for details. - - Parameters - ---------- - renderer : string ('canvas' or 'svg') - The renderer to use - theme : string - The Vega theme name to use; see https://github.com/vega/vega-themes - actions : bool or dict - Specify whether action links ("Open In Vega Editor", etc.) are - included in the view. - **kwargs : - Additional parameters are also passed to vega-embed as options. - - """ - from IPython.display import display - - if renderer is not Undefined: - kwargs["renderer"] = renderer - if theme is not Undefined: - kwargs["theme"] = theme - if actions is not Undefined: - kwargs["actions"] = actions - - if kwargs: - options = renderers.options.copy() - options["embed_options"] = options.get("embed_options", {}).copy() - options["embed_options"].update(kwargs) - with renderers.enable(**options): - display(self) - else: - display(self) - - @utils.deprecation.deprecated(message="'serve' is deprecated. Use 'show' instead.") - def serve( - self, - ip="127.0.0.1", - port=8888, - n_retries=50, - files=None, - jupyter_warning=True, - open_browser=True, - http_server=None, - **kwargs, - ): - """ - 'serve' is deprecated. Use 'show' instead. - - Open a browser window and display a rendering of the chart - - Parameters - ---------- - html : string - HTML to serve - ip : string (default = '127.0.0.1') - ip address at which the HTML will be served. - port : int (default = 8888) - the port at which to serve the HTML - n_retries : int (default = 50) - the number of nearby ports to search if the specified port - is already in use. - files : dictionary (optional) - dictionary of extra content to serve - jupyter_warning : bool (optional) - if True (default), then print a warning if this is used - within the Jupyter notebook - open_browser : bool (optional) - if True (default), then open a web browser to the given HTML - http_server : class (optional) - optionally specify an HTTPServer class to use for showing the - figure. The default is Python's basic HTTPServer. - **kwargs : - additional keyword arguments passed to the save() method - - """ - from ...utils.server import serve - - html = io.StringIO() - self.save(html, format="html", **kwargs) - html.seek(0) - - serve( - html.read(), - ip=ip, - port=port, - n_retries=n_retries, - files=files, - jupyter_warning=jupyter_warning, - open_browser=open_browser, - http_server=http_server, - ) - - def show(self, embed_opt=None, open_browser=None): - """Show the chart in an external browser window. - - This requires a recent version of the altair_viewer package. - - Parameters - ---------- - embed_opt : dict (optional) - The Vega embed options that control the dispay of the chart. - open_browser : bool (optional) - Specify whether a browser window should be opened. If not specified, - a browser window will be opened only if the server is not already - connected to a browser. - """ - try: - import altair_viewer # type: ignore - except ImportError as err: - raise ValueError( - "'show' method requires the altair_viewer package. " - "See http://github.com/altair-viz/altair_viewer" - ) from err - altair_viewer.show(self, embed_opt=embed_opt, open_browser=open_browser) - - @utils.use_signature(core.Resolve) - def _set_resolve(self, **kwargs): - """Copy the chart and update the resolve property with kwargs""" - if not hasattr(self, "resolve"): - raise ValueError( - "{} object has no attribute " "'resolve'".format(self.__class__) - ) - copy = self.copy(deep=["resolve"]) - if copy.resolve is Undefined: - copy.resolve = core.Resolve() - for key, val in kwargs.items(): - copy.resolve[key] = val - return copy - - @utils.use_signature(core.AxisResolveMap) - def resolve_axis(self, *args, **kwargs) -> Self: - return self._set_resolve(axis=core.AxisResolveMap(*args, **kwargs)) - - @utils.use_signature(core.LegendResolveMap) - def resolve_legend(self, *args, **kwargs) -> Self: - return self._set_resolve(legend=core.LegendResolveMap(*args, **kwargs)) - - @utils.use_signature(core.ScaleResolveMap) - def resolve_scale(self, *args, **kwargs) -> Self: - return self._set_resolve(scale=core.ScaleResolveMap(*args, **kwargs)) - - -class _EncodingMixin: - @utils.use_signature(core.FacetedEncoding) - def encode(self, *args, **kwargs) -> Self: - # Convert args to kwargs based on their types. - kwargs = utils.infer_encoding_types(args, kwargs, channels) - - # get a copy of the dict representation of the previous encoding - # ignore type as copy method comes from SchemaBase - copy = self.copy(deep=["encoding"]) # type: ignore[attr-defined] - encoding = copy._get("encoding", {}) - if isinstance(encoding, core.VegaLiteSchema): - encoding = {k: v for k, v in encoding._kwds.items() if v is not Undefined} - - # update with the new encodings, and apply them to the copy - encoding.update(kwargs) - copy.encoding = core.FacetedEncoding(**encoding) - return copy - - def facet( - self, - facet=Undefined, - row=Undefined, - column=Undefined, - data=Undefined, - columns=Undefined, - **kwargs, - ) -> "FacetChart": - """Create a facet chart from the current chart. - - Faceted charts require data to be specified at the top level; if data - is not specified, the data from the current chart will be used at the - top level. - - Parameters - ---------- - facet : string or alt.Facet (optional) - The data column to use as an encoding for a wrapped facet. - If specified, then neither row nor column may be specified. - column : string or alt.Column (optional) - The data column to use as an encoding for a column facet. - May be combined with row argument, but not with facet argument. - row : string or alt.Column (optional) - The data column to use as an encoding for a row facet. - May be combined with column argument, but not with facet argument. - data : string or dataframe (optional) - The dataset to use for faceting. If not supplied, then data must - be specified in the top-level chart that calls this method. - columns : integer - the maximum number of columns for a wrapped facet. - - Returns - ------- - self : - for chaining - """ - facet_specified = facet is not Undefined - rowcol_specified = row is not Undefined or column is not Undefined - - if facet_specified and rowcol_specified: - raise ValueError( - "facet argument cannot be combined with row/column argument." - ) - - # Remove "ignore" statement once Undefined is no longer typed as Any - if data is Undefined: # type: ignore - # Remove "ignore" statement once Undefined is no longer typed as Any - if self.data is Undefined: # type: ignore - raise ValueError( - "Facet charts require data to be specified at the top level." - ) - # ignore type as copy comes from another class - self = self.copy(deep=False) # type: ignore[attr-defined] - # Remove "ignore" statement once Undefined is no longer typed as Any - data, self.data = self.data, Undefined # type: ignore - - if facet_specified: - if isinstance(facet, str): - facet = channels.Facet(facet) - else: - facet = FacetMapping(row=row, column=column) - - return FacetChart(spec=self, facet=facet, data=data, columns=columns, **kwargs) - - -class Chart( - TopLevelMixin, _EncodingMixin, mixins.MarkMethodMixin, core.TopLevelUnitSpec -): - """Create a basic Altair/Vega-Lite chart. - - Although it is possible to set all Chart properties as constructor attributes, - it is more idiomatic to use methods such as ``mark_point()``, ``encode()``, - ``transform_filter()``, ``properties()``, etc. See Altair's documentation - for details and examples: http://altair-viz.github.io/. - - Parameters - ---------- - data : Data - An object describing the data source - mark : AnyMark - A string describing the mark type (one of `"bar"`, `"circle"`, `"square"`, `"tick"`, - `"line"`, * `"area"`, `"point"`, `"rule"`, `"geoshape"`, and `"text"`) or a - MarkDef object. - encoding : FacetedEncoding - A key-value mapping between encoding channels and definition of fields. - autosize : anyOf(AutosizeType, AutoSizeParams) - Sets how the visualization size should be determined. If a string, should be one of - `"pad"`, `"fit"` or `"none"`. Object values can additionally specify parameters for - content sizing and automatic resizing. `"fit"` is only supported for single and - layered views that don't use `rangeStep`. Default value: `pad` - background : string - CSS color property to use as the background of visualization. - - **Default value:** none (transparent) - config : Config - Vega-Lite configuration object. This property can only be defined at the top-level - of a specification. - description : string - Description of this mark for commenting purpose. - height : float - The height of a visualization. - name : string - Name of the visualization for later reference. - padding : Padding - The default visualization padding, in pixels, from the edge of the visualization - canvas to the data rectangle. If a number, specifies padding for all sides. If an - object, the value should have the format `{"left": 5, "top": 5, "right": 5, - "bottom": 5}` to specify padding for each side of the visualization. Default - value: `5` - projection : Projection - An object defining properties of geographic projection. Works with `"geoshape"` - marks and `"point"` or `"line"` marks that have a channel (one or more of `"X"`, - `"X2"`, `"Y"`, `"Y2"`) with type `"latitude"`, or `"longitude"`. - selection : Mapping(required=[]) - A key-value mapping between selection names and definitions. - title : anyOf(string, TitleParams) - Title for the plot. - transform : List(Transform) - An array of data transformations such as filter and new field calculation. - width : float - The width of a visualization. - """ - - def __init__( - self, - data=Undefined, - encoding=Undefined, - mark=Undefined, - width=Undefined, - height=Undefined, - **kwargs, - ): - super(Chart, self).__init__( - data=data, - encoding=encoding, - mark=mark, - width=width, - height=height, - **kwargs, - ) - - _counter = 0 - - @classmethod - def _get_name(cls): - cls._counter += 1 - return f"view_{cls._counter}" - - @classmethod - def from_dict(cls, dct, validate=True) -> "Chart": # type: ignore[override] # Not the same signature as SchemaBase.from_dict. Would ideally be aligned in the future - """Construct class from a dictionary representation - - Parameters - ---------- - dct : dictionary - The dict from which to construct the class - validate : boolean - If True (default), then validate the input against the schema. - - Returns - ------- - obj : Chart object - The wrapped schema - - Raises - ------ - jsonschema.ValidationError : - if validate=True and dct does not conform to the schema - """ - for class_ in TopLevelMixin.__subclasses__(): - if class_ is Chart: - class_ = cast(TypingType[TopLevelMixin], super(Chart, cls)) - try: - # TopLevelMixin classes don't necessarily have from_dict defined - # but all classes which are used here have due to how Altair is - # designed. Too complex to type check right now. - return class_.from_dict(dct, validate=validate) # type: ignore[attr-defined] - except jsonschema.ValidationError: - pass - - # As a last resort, try using the Root vegalite object - return core.Root.from_dict(dct, validate) - - def to_dict(self, *args, **kwargs) -> dict: - """Convert the chart to a dictionary suitable for JSON export.""" - context = kwargs.get("context", {}) - if self.data is Undefined and "data" not in context: - # No data specified here or in parent: inject empty data - # for easier specification of datum encodings. - copy = self.copy(deep=False) - copy.data = core.InlineData(values=[{}]) - return super(Chart, copy).to_dict(*args, **kwargs) - return super().to_dict(*args, **kwargs) - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params: - return self - copy = self.copy(deep=["params"]) - if copy.params is Undefined: - copy.params = [] - - for s in params: - copy.params.append(s.param) - return copy - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *params) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*params) - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - encodings = [] - if bind_x: - encodings.append("x") - if bind_y: - encodings.append("y") - return self.add_params(selection_interval(bind="scales", encodings=encodings)) - - -def _check_if_valid_subspec(spec, classname): - """Check if the spec is a valid sub-spec. - - If it is not, then raise a ValueError - """ - err = ( - 'Objects with "{0}" attribute cannot be used within {1}. ' - "Consider defining the {0} attribute in the {1} object instead." - ) - - if not isinstance(spec, (core.SchemaBase, dict)): - raise ValueError("Only chart objects can be used in {0}.".format(classname)) - for attr in TOPLEVEL_ONLY_KEYS: - if isinstance(spec, core.SchemaBase): - val = getattr(spec, attr, Undefined) - else: - val = spec.get(attr, Undefined) - if val is not Undefined: - raise ValueError(err.format(attr, classname)) - - -def _check_if_can_be_layered(spec): - """Check if the spec can be layered.""" - - def _get(spec, attr): - if isinstance(spec, core.SchemaBase): - return spec._get(attr) - else: - return spec.get(attr, Undefined) - - encoding = _get(spec, "encoding") - if encoding is not Undefined: - for channel in ["row", "column", "facet"]: - if _get(encoding, channel) is not Undefined: - raise ValueError( - "Faceted charts cannot be layered. Instead, layer the charts before faceting." - ) - if isinstance(spec, (Chart, LayerChart)): - return - - if not isinstance(spec, (core.SchemaBase, dict)): - raise ValueError("Only chart objects can be layered.") - if _get(spec, "facet") is not Undefined: - raise ValueError( - "Faceted charts cannot be layered. Instead, layer the charts before faceting." - ) - if isinstance(spec, FacetChart) or _get(spec, "facet") is not Undefined: - raise ValueError( - "Faceted charts cannot be layered. Instead, layer the charts before faceting." - ) - if isinstance(spec, RepeatChart) or _get(spec, "repeat") is not Undefined: - raise ValueError( - "Repeat charts cannot be layered. Instead, layer the charts before repeating." - ) - if isinstance(spec, ConcatChart) or _get(spec, "concat") is not Undefined: - raise ValueError( - "Concatenated charts cannot be layered. Instead, layer the charts before concatenating." - ) - if isinstance(spec, HConcatChart) or _get(spec, "hconcat") is not Undefined: - raise ValueError( - "Concatenated charts cannot be layered. Instead, layer the charts before concatenating." - ) - if isinstance(spec, VConcatChart) or _get(spec, "vconcat") is not Undefined: - raise ValueError( - "Concatenated charts cannot be layered. Instead, layer the charts before concatenating." - ) - - -class RepeatChart(TopLevelMixin, core.TopLevelRepeatSpec): - """A chart repeated across rows and columns with small changes""" - - # Because TopLevelRepeatSpec is defined as a union as of Vega-Lite schema 4.9, - # we set the arguments explicitly here. - # TODO: Should we instead use tools/schemapi/codegen._get_args? - @utils.use_signature(core.TopLevelRepeatSpec) - def __init__( - self, - repeat=Undefined, - spec=Undefined, - align=Undefined, - autosize=Undefined, - background=Undefined, - bounds=Undefined, - center=Undefined, - columns=Undefined, - config=Undefined, - data=Undefined, - datasets=Undefined, - description=Undefined, - name=Undefined, - padding=Undefined, - params=Undefined, - resolve=Undefined, - spacing=Undefined, - title=Undefined, - transform=Undefined, - usermeta=Undefined, - **kwds, - ): - _check_if_valid_subspec(spec, "RepeatChart") - _spec_as_list = [spec] - params, _spec_as_list = _combine_subchart_params(params, _spec_as_list) - spec = _spec_as_list[0] - if isinstance(spec, (Chart, LayerChart)): - params = _repeat_names(params, repeat, spec) - super(RepeatChart, self).__init__( - repeat=repeat, - spec=spec, - align=align, - autosize=autosize, - background=background, - bounds=bounds, - center=center, - columns=columns, - config=config, - data=data, - datasets=datasets, - description=description, - name=name, - padding=padding, - params=params, - resolve=resolve, - spacing=spacing, - title=title, - transform=transform, - usermeta=usermeta, - **kwds, - ) - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - copy = self.copy(deep=False) - copy.spec = copy.spec.interactive(name=name, bind_x=bind_x, bind_y=bind_y) - return copy - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params or self.spec is Undefined: - return self - copy = self.copy() - copy.spec = copy.spec.add_params(*params) - return copy.copy() - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *selections) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*selections) - - -def repeat(repeater="repeat"): - """Tie a channel to the row or column within a repeated chart - - The output of this should be passed to the ``field`` attribute of - a channel. - - Parameters - ---------- - repeater : {'row'|'column'|'repeat'|'layer'} - The repeater to tie the field to. Default is 'repeat'. - - Returns - ------- - repeat : RepeatRef object - """ - if repeater not in ["row", "column", "repeat", "layer"]: - raise ValueError("repeater must be one of ['row', 'column', 'repeat', 'layer']") - return core.RepeatRef(repeat=repeater) - - -class ConcatChart(TopLevelMixin, core.TopLevelConcatSpec): - """A chart with horizontally-concatenated facets""" - - @utils.use_signature(core.TopLevelConcatSpec) - def __init__(self, data=Undefined, concat=(), columns=Undefined, **kwargs): - # TODO: move common data to top level? - for spec in concat: - _check_if_valid_subspec(spec, "ConcatChart") - super(ConcatChart, self).__init__( - data=data, concat=list(concat), columns=columns, **kwargs - ) - self.data, self.concat = _combine_subchart_data(self.data, self.concat) - self.params, self.concat = _combine_subchart_params(self.params, self.concat) - - def __ior__(self, other): - _check_if_valid_subspec(other, "ConcatChart") - self.concat.append(other) - self.data, self.concat = _combine_subchart_data(self.data, self.concat) - self.params, self.concat = _combine_subchart_params(self.params, self.concat) - return self - - def __or__(self, other): - copy = self.copy(deep=["concat"]) - copy |= other - return copy - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - encodings = [] - if bind_x: - encodings.append("x") - if bind_y: - encodings.append("y") - return self.add_params(selection_interval(bind="scales", encodings=encodings)) - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params or not self.concat: - return self - copy = self.copy() - copy.concat = [chart.add_params(*params) for chart in copy.concat] - return copy - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *selections) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*selections) - - -def concat(*charts, **kwargs): - """Concatenate charts horizontally""" - return ConcatChart(concat=charts, **kwargs) - - -class HConcatChart(TopLevelMixin, core.TopLevelHConcatSpec): - """A chart with horizontally-concatenated facets""" - - @utils.use_signature(core.TopLevelHConcatSpec) - def __init__(self, data=Undefined, hconcat=(), **kwargs): - # TODO: move common data to top level? - for spec in hconcat: - _check_if_valid_subspec(spec, "HConcatChart") - super(HConcatChart, self).__init__(data=data, hconcat=list(hconcat), **kwargs) - self.data, self.hconcat = _combine_subchart_data(self.data, self.hconcat) - self.params, self.hconcat = _combine_subchart_params(self.params, self.hconcat) - - def __ior__(self, other): - _check_if_valid_subspec(other, "HConcatChart") - self.hconcat.append(other) - self.data, self.hconcat = _combine_subchart_data(self.data, self.hconcat) - self.params, self.hconcat = _combine_subchart_params(self.params, self.hconcat) - return self - - def __or__(self, other): - copy = self.copy(deep=["hconcat"]) - copy |= other - return copy - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - encodings = [] - if bind_x: - encodings.append("x") - if bind_y: - encodings.append("y") - return self.add_params(selection_interval(bind="scales", encodings=encodings)) - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params or not self.hconcat: - return self - copy = self.copy() - copy.hconcat = [chart.add_params(*params) for chart in copy.hconcat] - return copy - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *selections) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*selections) - - -def hconcat(*charts, **kwargs): - """Concatenate charts horizontally""" - return HConcatChart(hconcat=charts, **kwargs) - - -class VConcatChart(TopLevelMixin, core.TopLevelVConcatSpec): - """A chart with vertically-concatenated facets""" - - @utils.use_signature(core.TopLevelVConcatSpec) - def __init__(self, data=Undefined, vconcat=(), **kwargs): - # TODO: move common data to top level? - for spec in vconcat: - _check_if_valid_subspec(spec, "VConcatChart") - super(VConcatChart, self).__init__(data=data, vconcat=list(vconcat), **kwargs) - self.data, self.vconcat = _combine_subchart_data(self.data, self.vconcat) - self.params, self.vconcat = _combine_subchart_params(self.params, self.vconcat) - - def __iand__(self, other): - _check_if_valid_subspec(other, "VConcatChart") - self.vconcat.append(other) - self.data, self.vconcat = _combine_subchart_data(self.data, self.vconcat) - self.params, self.vconcat = _combine_subchart_params(self.params, self.vconcat) - return self - - def __and__(self, other): - copy = self.copy(deep=["vconcat"]) - copy &= other - return copy - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - encodings = [] - if bind_x: - encodings.append("x") - if bind_y: - encodings.append("y") - return self.add_params(selection_interval(bind="scales", encodings=encodings)) - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params or not self.vconcat: - return self - copy = self.copy() - copy.vconcat = [chart.add_params(*params) for chart in copy.vconcat] - return copy - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *selections) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*selections) - - -def vconcat(*charts, **kwargs): - """Concatenate charts vertically""" - return VConcatChart(vconcat=charts, **kwargs) - - -class LayerChart(TopLevelMixin, _EncodingMixin, core.TopLevelLayerSpec): - """A Chart with layers within a single panel""" - - @utils.use_signature(core.TopLevelLayerSpec) - def __init__(self, data=Undefined, layer=(), **kwargs): - # TODO: move common data to top level? - # TODO: check for conflicting interaction - for spec in layer: - _check_if_valid_subspec(spec, "LayerChart") - _check_if_can_be_layered(spec) - super(LayerChart, self).__init__(data=data, layer=list(layer), **kwargs) - self.data, self.layer = _combine_subchart_data(self.data, self.layer) - # Currently (Vega-Lite 5.5) the same param can't occur on two layers - self.layer = _remove_duplicate_params(self.layer) - self.params, self.layer = _combine_subchart_params(self.params, self.layer) - - # Some properties are not allowed within layer; we'll move to parent. - layer_props = ("height", "width", "view") - combined_dict, self.layer = _remove_layer_props(self, self.layer, layer_props) - - for prop in combined_dict: - self[prop] = combined_dict[prop] - - def __iadd__(self, other): - _check_if_valid_subspec(other, "LayerChart") - _check_if_can_be_layered(other) - self.layer.append(other) - self.data, self.layer = _combine_subchart_data(self.data, self.layer) - self.params, self.layer = _combine_subchart_params(self.params, self.layer) - return self - - def __add__(self, other): - copy = self.copy(deep=["layer"]) - copy += other - return copy - - def add_layers(self, *layers) -> Self: - copy = self.copy(deep=["layer"]) - for layer in layers: - copy += layer - return copy - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - if not self.layer: - raise ValueError( - "LayerChart: cannot call interactive() until a " "layer is defined" - ) - copy = self.copy(deep=["layer"]) - copy.layer[0] = copy.layer[0].interactive( - name=name, bind_x=bind_x, bind_y=bind_y - ) - return copy - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params or not self.layer: - return self - copy = self.copy() - copy.layer[0] = copy.layer[0].add_params(*params) - return copy.copy() - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *selections) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*selections) - - -def layer(*charts, **kwargs): - """layer multiple charts""" - return LayerChart(layer=charts, **kwargs) - - -class FacetChart(TopLevelMixin, core.TopLevelFacetSpec): - """A Chart with layers within a single panel""" - - @utils.use_signature(core.TopLevelFacetSpec) - def __init__( - self, - data=Undefined, - spec=Undefined, - facet=Undefined, - params=Undefined, - **kwargs, - ): - _check_if_valid_subspec(spec, "FacetChart") - _spec_as_list = [spec] - params, _spec_as_list = _combine_subchart_params(params, _spec_as_list) - spec = _spec_as_list[0] - super(FacetChart, self).__init__( - data=data, spec=spec, facet=facet, params=params, **kwargs - ) - - def interactive(self, name=None, bind_x=True, bind_y=True) -> Self: - """Make chart axes scales interactive - - Parameters - ---------- - name : string - The parameter name to use for the axes scales. This name should be - unique among all parameters within the chart. - bind_x : boolean, default True - If true, then bind the interactive scales to the x-axis - bind_y : boolean, default True - If true, then bind the interactive scales to the y-axis - - Returns - ------- - chart : - copy of self, with interactive axes added - - """ - copy = self.copy(deep=False) - copy.spec = copy.spec.interactive(name=name, bind_x=bind_x, bind_y=bind_y) - return copy - - def add_params(self, *params) -> Self: - """Add one or more parameters to the chart.""" - if not params or self.spec is Undefined: - return self - copy = self.copy() - copy.spec = copy.spec.add_params(*params) - return copy.copy() - - @utils.deprecation.deprecated( - message="'add_selection' is deprecated. Use 'add_params' instead." - ) - def add_selection(self, *selections) -> Self: - """'add_selection' is deprecated. Use 'add_params' instead.""" - return self.add_params(*selections) - - -def topo_feature(url, feature, **kwargs): - """A convenience function for extracting features from a topojson url - - Parameters - ---------- - url : string - An URL from which to load the data set. - - feature : string - The name of the TopoJSON object set to convert to a GeoJSON feature collection. For - example, in a map of the world, there may be an object set named `"countries"`. - Using the feature property, we can extract this set and generate a GeoJSON feature - object for each country. - - **kwargs : - additional keywords passed to TopoDataFormat - """ - return core.UrlData( - url=url, format=core.TopoDataFormat(type="topojson", feature=feature, **kwargs) - ) - - -def _combine_subchart_data(data, subcharts): - def remove_data(subchart): - if subchart.data is not Undefined: - subchart = subchart.copy() - subchart.data = Undefined - return subchart - - if not subcharts: - # No subcharts = nothing to do. - pass - elif data is Undefined: - # Top level has no data; all subchart data must - # be identical to proceed. - subdata = subcharts[0].data - if subdata is not Undefined and all(c.data is subdata for c in subcharts): - data = subdata - subcharts = [remove_data(c) for c in subcharts] - else: - # Top level has data; subchart data must be either - # undefined or identical to proceed. - if all(c.data is Undefined or c.data is data for c in subcharts): - subcharts = [remove_data(c) for c in subcharts] - - return data, subcharts - - -def _viewless_dict(param): - d = param.to_dict() - d.pop("views", None) - return d - - -def _needs_name(subchart): - # Only `Chart` objects need a name - if (subchart.name is not Undefined) or (not isinstance(subchart, Chart)): - return False - - # Variable parameters won't receive a views property. - if all(isinstance(p, core.VariableParameter) for p in subchart.params): - return False - - return True - - -# Convert SelectionParameters to TopLevelSelectionParameters with a views property. -def _prepare_to_lift(param): - param = param.copy() - - if isinstance(param, core.VariableParameter): - return param - - if isinstance(param, core.SelectionParameter): - return core.TopLevelSelectionParameter(**param.to_dict(), views=[]) - - if param.views is Undefined: - param.views = [] - - return param - - -def _remove_duplicate_params(layer): - subcharts = [subchart.copy() for subchart in layer] - found_params = [] - - for subchart in subcharts: - if (not hasattr(subchart, "params")) or (subchart.params is Undefined): - continue - - params = [] - - # Ensure the same selection parameter doesn't appear twice - for param in subchart.params: - if isinstance(param, core.VariableParameter): - params.append(param) - continue - - p = param.copy() - pd = _viewless_dict(p) - - if pd not in found_params: - params.append(p) - found_params.append(pd) - - if len(params) == 0: - subchart.params = Undefined - else: - subchart.params = params - - return subcharts - - -def _combine_subchart_params(params, subcharts): - if params is Undefined: - params = [] - - # List of triples related to params, (param, dictionary minus views, views) - param_info = [] - - # Put parameters already found into `param_info` list. - for param in params: - p = _prepare_to_lift(param) - param_info.append( - ( - p, - _viewless_dict(p), - [] if isinstance(p, core.VariableParameter) else p.views, - ) - ) - - subcharts = [subchart.copy() for subchart in subcharts] - - for subchart in subcharts: - if (not hasattr(subchart, "params")) or (subchart.params is Undefined): - continue - - if _needs_name(subchart): - subchart.name = subchart._get_name() - - for param in subchart.params: - p = _prepare_to_lift(param) - pd = _viewless_dict(p) - - dlist = [d for _, d, _ in param_info] - found = pd in dlist - - if isinstance(p, core.VariableParameter) and found: - continue - - if isinstance(p, core.VariableParameter) and not found: - param_info.append((p, pd, [])) - continue - - # At this stage in the loop, p must be a TopLevelSelectionParameter. - - if isinstance(subchart, Chart) and (subchart.name not in p.views): - p.views.append(subchart.name) - - if found: - i = dlist.index(pd) - _, _, old_views = param_info[i] - new_views = [v for v in p.views if v not in old_views] - old_views += new_views - else: - param_info.append((p, pd, p.views)) - - subchart.params = Undefined - - for p, _, v in param_info: - if len(v) > 0: - p.views = v - - subparams = [p for p, _, _ in param_info] - - if len(subparams) == 0: - subparams = Undefined - - return subparams, subcharts - - -def _get_repeat_strings(repeat): - if isinstance(repeat, list): - return repeat - elif isinstance(repeat, core.LayerRepeatMapping): - klist = ["row", "column", "layer"] - elif isinstance(repeat, core.RepeatMapping): - klist = ["row", "column"] - rclist = [k for k in klist if repeat[k] is not Undefined] - rcstrings = [[f"{k}_{v}" for v in repeat[k]] for k in rclist] - return ["".join(s) for s in itertools.product(*rcstrings)] - - -def _extend_view_name(v, r, spec): - # prevent the same extension from happening more than once - if isinstance(spec, Chart): - if v.endswith("child__" + r): - return v - else: - return f"{v}_child__{r}" - elif isinstance(spec, LayerChart): - if v.startswith("child__" + r): - return v - else: - return f"child__{r}_{v}" - - -def _repeat_names(params, repeat, spec): - if params is Undefined: - return params - - repeat = _get_repeat_strings(repeat) - params_named = [] - - for param in params: - if not isinstance(param, core.TopLevelSelectionParameter): - params_named.append(param) - continue - p = param.copy() - views = [] - repeat_strings = _get_repeat_strings(repeat) - for v in param.views: - if isinstance(spec, Chart): - if any(v.endswith(f"child__{r}") for r in repeat_strings): - views.append(v) - else: - views += [_extend_view_name(v, r, spec) for r in repeat_strings] - elif isinstance(spec, LayerChart): - if any(v.startswith(f"child__{r}") for r in repeat_strings): - views.append(v) - else: - views += [_extend_view_name(v, r, spec) for r in repeat_strings] - - p.views = views - params_named.append(p) - - return params_named - - -def _remove_layer_props(chart, subcharts, layer_props): - def remove_prop(subchart, prop): - # If subchart is a UnitSpec, then subchart["height"] raises a KeyError - try: - if subchart[prop] is not Undefined: - subchart = subchart.copy() - subchart[prop] = Undefined - except KeyError: - pass - return subchart - - output_dict = {} - - if not subcharts: - # No subcharts = nothing to do. - return output_dict, subcharts - - for prop in layer_props: - if chart[prop] is Undefined: - # Top level does not have this prop. - # Check for consistent props within the subcharts. - values = [] - for c in subcharts: - # If c is a UnitSpec, then c["height"] raises a KeyError. - try: - val = c[prop] - if val is not Undefined: - values.append(val) - except KeyError: - pass - if len(values) == 0: - pass - elif all(v == values[0] for v in values[1:]): - output_dict[prop] = values[0] - else: - raise ValueError(f"There are inconsistent values {values} for {prop}") - else: - # Top level has this prop; subchart must either not have the prop - # or it must be Undefined or identical to proceed. - if all( - getattr(c, prop, Undefined) is Undefined or c[prop] == chart[prop] - for c in subcharts - ): - output_dict[prop] = chart[prop] - else: - raise ValueError(f"There are inconsistent values {values} for {prop}") - subcharts = [remove_prop(c, prop) for c in subcharts] - - return output_dict, subcharts - - -@utils.use_signature(core.SequenceParams) -def sequence(start, stop=None, step=Undefined, as_=Undefined, **kwds): - """Sequence generator.""" - if stop is None: - start, stop = 0, start - params = core.SequenceParams(start=start, stop=stop, step=step, **{"as": as_}) - return core.SequenceGenerator(sequence=params, **kwds) - - -@utils.use_signature(core.GraticuleParams) -def graticule(**kwds): - """Graticule generator.""" - if not kwds: - # graticule: True indicates default parameters - graticule = True - else: - graticule = core.GraticuleParams(**kwds) - return core.GraticuleGenerator(graticule=graticule) - - -def sphere(): - """Sphere generator.""" - return core.SphereGenerator(sphere=True) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_f_p_g_m.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_f_p_g_m.py deleted file mode 100644 index df23041d65617af9c1f6feb00db970b7870c2268..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/tables/_f_p_g_m.py +++ /dev/null @@ -1,49 +0,0 @@ -from . import DefaultTable -from . import ttProgram - - -class table__f_p_g_m(DefaultTable.DefaultTable): - def decompile(self, data, ttFont): - program = ttProgram.Program() - program.fromBytecode(data) - self.program = program - - def compile(self, ttFont): - return self.program.getBytecode() - - def toXML(self, writer, ttFont): - self.program.toXML(writer, ttFont) - - def fromXML(self, name, attrs, content, ttFont): - program = ttProgram.Program() - program.fromXML(name, attrs, content, ttFont) - self.program = program - - def __bool__(self): - """ - >>> fpgm = table__f_p_g_m() - >>> bool(fpgm) - False - >>> p = ttProgram.Program() - >>> fpgm.program = p - >>> bool(fpgm) - False - >>> bc = bytearray([0]) - >>> p.fromBytecode(bc) - >>> bool(fpgm) - True - >>> p.bytecode.pop() - 0 - >>> bool(fpgm) - False - """ - return hasattr(self, "program") and bool(self.program) - - __nonzero__ = __bool__ - - -if __name__ == "__main__": - import sys - import doctest - - sys.exit(doctest.testmod().failed) diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/ttCollection.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/ttCollection.py deleted file mode 100644 index 3ab579ee001ebb099c1cc310b9898f9c8119a567..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/fontTools/ttLib/ttCollection.py +++ /dev/null @@ -1,127 +0,0 @@ -from fontTools.ttLib.ttFont import TTFont -from fontTools.ttLib.sfnt import readTTCHeader, writeTTCHeader -from io import BytesIO -import struct -import logging - -log = logging.getLogger(__name__) - - -class TTCollection(object): - - """Object representing a TrueType Collection / OpenType Collection. - The main API is self.fonts being a list of TTFont instances. - - If shareTables is True, then different fonts in the collection - might point to the same table object if the data for the table was - the same in the font file. Note, however, that this might result - in suprises and incorrect behavior if the different fonts involved - have different GlyphOrder. Use only if you know what you are doing. - """ - - def __init__(self, file=None, shareTables=False, **kwargs): - fonts = self.fonts = [] - if file is None: - return - - assert "fontNumber" not in kwargs, kwargs - - closeStream = False - if not hasattr(file, "read"): - file = open(file, "rb") - closeStream = True - - tableCache = {} if shareTables else None - - header = readTTCHeader(file) - for i in range(header.numFonts): - font = TTFont(file, fontNumber=i, _tableCache=tableCache, **kwargs) - fonts.append(font) - - # don't close file if lazy=True, as the TTFont hold a reference to the original - # file; the file will be closed once the TTFonts are closed in the - # TTCollection.close(). We still want to close the file if lazy is None or - # False, because in that case the TTFont no longer need the original file - # and we want to avoid 'ResourceWarning: unclosed file'. - if not kwargs.get("lazy") and closeStream: - file.close() - - def __enter__(self): - return self - - def __exit__(self, type, value, traceback): - self.close() - - def close(self): - for font in self.fonts: - font.close() - - def save(self, file, shareTables=True): - """Save the font to disk. Similarly to the constructor, - the 'file' argument can be either a pathname or a writable - file object. - """ - if not hasattr(file, "write"): - final = None - file = open(file, "wb") - else: - # assume "file" is a writable file object - # write to a temporary stream to allow saving to unseekable streams - final = file - file = BytesIO() - - tableCache = {} if shareTables else None - - offsets_offset = writeTTCHeader(file, len(self.fonts)) - offsets = [] - for font in self.fonts: - offsets.append(file.tell()) - font._save(file, tableCache=tableCache) - file.seek(0, 2) - - file.seek(offsets_offset) - file.write(struct.pack(">%dL" % len(self.fonts), *offsets)) - - if final: - final.write(file.getvalue()) - file.close() - - def saveXML(self, fileOrPath, newlinestr="\n", writeVersion=True, **kwargs): - - from fontTools.misc import xmlWriter - - writer = xmlWriter.XMLWriter(fileOrPath, newlinestr=newlinestr) - - if writeVersion: - from fontTools import version - - version = 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S=a.doc.lineAt(t),o={line:S.number-1,col:t-S.from,pos:t};for(let P of O.verify(a.sliceDoc(t,r),Q))$.push(VO(P,a.doc,o))}return $}}function V(O,Q,i,a){return i.line(O+a.line).from+Q+(O==1?a.col-1:-1)}function VO(O,Q,i){let a=V(O.line,O.column,Q,i),$={from:a,to:O.endLine!=null&&O.endColumn!=1?V(O.endLine,O.endColumn,Q,i):a,message:O.message,source:O.ruleId?"eslint:"+O.ruleId:"eslint",severity:O.severity==1?"warning":"error"};if(O.fix){let{range:t,text:r}=O.fix,S=t[0]+i.pos-a,o=t[1]+i.pos-a;$.actions=[{name:"fix",apply(P,Z){P.dispatch({changes:{from:Z+S,to:Z+o,insert:r},scrollIntoView:!0})}}]}return $}export{wO as autoCloseTags,UO as completionPath,AO as esLint,EO as javascript,X as javascriptLanguage,yO as jsxLanguage,WO as localCompletionSource,IO as scopeCompletionSource,bO as snippets,jO as tsxLanguage,uO as typescriptLanguage}; -//# sourceMappingURL=index-0940a57e.js.map diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_api.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_api.py deleted file mode 100644 index 571289cf2b31c1d864d72d71b216456a9cf5b216..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/httpx/_api.py +++ /dev/null @@ -1,445 +0,0 @@ -import typing -from contextlib import contextmanager - -from ._client import Client -from ._config import DEFAULT_TIMEOUT_CONFIG -from ._models import Response -from ._types import ( - AuthTypes, - CertTypes, - CookieTypes, - HeaderTypes, - ProxiesTypes, - QueryParamTypes, - RequestContent, - RequestData, - RequestFiles, - TimeoutTypes, - URLTypes, - VerifyTypes, -) - - -def request( - method: str, - url: URLTypes, - *, - params: typing.Optional[QueryParamTypes] = None, - content: typing.Optional[RequestContent] = None, - data: typing.Optional[RequestData] = None, - files: typing.Optional[RequestFiles] = None, - json: typing.Optional[typing.Any] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - follow_redirects: bool = False, - verify: VerifyTypes = True, - cert: typing.Optional[CertTypes] = None, - trust_env: bool = True, -) -> Response: - """ - Sends an HTTP request. - - **Parameters:** - - * **method** - HTTP method for the new `Request` object: `GET`, `OPTIONS`, - `HEAD`, `POST`, `PUT`, `PATCH`, or `DELETE`. - * **url** - URL for the new `Request` object. - * **params** - *(optional)* Query parameters to include in the URL, as a - string, dictionary, or sequence of two-tuples. - * **content** - *(optional)* Binary content to include in the body of the - request, as bytes or a byte iterator. - * **data** - *(optional)* Form data to include in the body of the request, - as a dictionary. - * **files** - *(optional)* A dictionary of upload files to include in the - body of the request. - * **json** - *(optional)* A JSON serializable object to include in the body - of the request. - * **headers** - *(optional)* Dictionary of HTTP headers to include in the - request. - * **cookies** - *(optional)* Dictionary of Cookie items to include in the - request. - * **auth** - *(optional)* An authentication class to use when sending the - request. - * **proxies** - *(optional)* A dictionary mapping proxy keys to proxy URLs. - * **timeout** - *(optional)* The timeout configuration to use when sending - the request. - * **follow_redirects** - *(optional)* Enables or disables HTTP redirects. - * **verify** - *(optional)* SSL certificates (a.k.a CA bundle) used to - verify the identity of requested hosts. Either `True` (default CA bundle), - a path to an SSL certificate file, an `ssl.SSLContext`, or `False` - (which will disable verification). - * **cert** - *(optional)* An SSL certificate used by the requested host - to authenticate the client. Either a path to an SSL certificate file, or - two-tuple of (certificate file, key file), or a three-tuple of (certificate - file, key file, password). - * **trust_env** - *(optional)* Enables or disables usage of environment - variables for configuration. - - **Returns:** `Response` - - Usage: - - ``` - >>> import httpx - >>> response = httpx.request('GET', 'https://httpbin.org/get') - >>> response - - ``` - """ - with Client( - cookies=cookies, - proxies=proxies, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) as client: - return client.request( - method=method, - url=url, - content=content, - data=data, - files=files, - json=json, - params=params, - headers=headers, - auth=auth, - follow_redirects=follow_redirects, - ) - - -@contextmanager -def stream( - method: str, - url: URLTypes, - *, - params: typing.Optional[QueryParamTypes] = None, - content: typing.Optional[RequestContent] = None, - data: typing.Optional[RequestData] = None, - files: typing.Optional[RequestFiles] = None, - json: typing.Optional[typing.Any] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - follow_redirects: bool = False, - verify: VerifyTypes = True, - cert: typing.Optional[CertTypes] = None, - trust_env: bool = True, -) -> typing.Iterator[Response]: - """ - Alternative to `httpx.request()` that streams the response body - instead of loading it into memory at once. - - **Parameters**: See `httpx.request`. - - See also: [Streaming Responses][0] - - [0]: /quickstart#streaming-responses - """ - with Client( - cookies=cookies, - proxies=proxies, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) as client: - with client.stream( - method=method, - url=url, - content=content, - data=data, - files=files, - json=json, - params=params, - headers=headers, - auth=auth, - follow_redirects=follow_redirects, - ) as response: - yield response - - -def get( - url: URLTypes, - *, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends a `GET` request. - - **Parameters**: See `httpx.request`. - - Note that the `data`, `files`, `json` and `content` parameters are not available - on this function, as `GET` requests should not include a request body. - """ - return request( - "GET", - url, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) - - -def options( - url: URLTypes, - *, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends an `OPTIONS` request. - - **Parameters**: See `httpx.request`. - - Note that the `data`, `files`, `json` and `content` parameters are not available - on this function, as `OPTIONS` requests should not include a request body. - """ - return request( - "OPTIONS", - url, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) - - -def head( - url: URLTypes, - *, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends a `HEAD` request. - - **Parameters**: See `httpx.request`. - - Note that the `data`, `files`, `json` and `content` parameters are not available - on this function, as `HEAD` requests should not include a request body. - """ - return request( - "HEAD", - url, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) - - -def post( - url: URLTypes, - *, - content: typing.Optional[RequestContent] = None, - data: typing.Optional[RequestData] = None, - files: typing.Optional[RequestFiles] = None, - json: typing.Optional[typing.Any] = None, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends a `POST` request. - - **Parameters**: See `httpx.request`. - """ - return request( - "POST", - url, - content=content, - data=data, - files=files, - json=json, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) - - -def put( - url: URLTypes, - *, - content: typing.Optional[RequestContent] = None, - data: typing.Optional[RequestData] = None, - files: typing.Optional[RequestFiles] = None, - json: typing.Optional[typing.Any] = None, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends a `PUT` request. - - **Parameters**: See `httpx.request`. - """ - return request( - "PUT", - url, - content=content, - data=data, - files=files, - json=json, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) - - -def patch( - url: URLTypes, - *, - content: typing.Optional[RequestContent] = None, - data: typing.Optional[RequestData] = None, - files: typing.Optional[RequestFiles] = None, - json: typing.Optional[typing.Any] = None, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends a `PATCH` request. - - **Parameters**: See `httpx.request`. - """ - return request( - "PATCH", - url, - content=content, - data=data, - files=files, - json=json, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) - - -def delete( - url: URLTypes, - *, - params: typing.Optional[QueryParamTypes] = None, - headers: typing.Optional[HeaderTypes] = None, - cookies: typing.Optional[CookieTypes] = None, - auth: typing.Optional[AuthTypes] = None, - proxies: typing.Optional[ProxiesTypes] = None, - follow_redirects: bool = False, - cert: typing.Optional[CertTypes] = None, - verify: VerifyTypes = True, - timeout: TimeoutTypes = DEFAULT_TIMEOUT_CONFIG, - trust_env: bool = True, -) -> Response: - """ - Sends a `DELETE` request. - - **Parameters**: See `httpx.request`. - - Note that the `data`, `files`, `json` and `content` parameters are not available - on this function, as `DELETE` requests should not include a request body. - """ - return request( - "DELETE", - url, - params=params, - headers=headers, - cookies=cookies, - auth=auth, - proxies=proxies, - follow_redirects=follow_redirects, - cert=cert, - verify=verify, - timeout=timeout, - trust_env=trust_env, - ) diff --git a/spaces/deepaksarika01/youtube-video-qa-lamini/app.py b/spaces/deepaksarika01/youtube-video-qa-lamini/app.py deleted file mode 100644 index 04fba5a62d271aab1e993d360a61f7757c3afd35..0000000000000000000000000000000000000000 --- a/spaces/deepaksarika01/youtube-video-qa-lamini/app.py +++ /dev/null @@ -1,12 +0,0 @@ -from model import lamini, qa_template -from utils import getSubsText - -VID_ID="dWqNgzZwVJQ" - -llm_loader = lamini() -llm = llm_loader.load_model() -subs = getSubsText(VID_ID) -qa = qa_template(llm) -_ = qa.load(subs) - -qa.start_gradio(title="TEST") diff --git a/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_config.py b/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_config.py deleted file mode 100644 index 510892c2ff8a4cf6ed70cddd3dcbb40fe33ce613..0000000000000000000000000000000000000000 --- a/spaces/deepwisdom/MetaGPT/tests/metagpt/utils/test_config.py +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" -@Time : 2023/5/1 11:19 -@Author : alexanderwu -@File : test_config.py -@Modified By: mashenquan, 2013/8/20, Add `test_options`; remove global configuration `CONFIG`, enable configuration support for business isolation. -""" -from pathlib import Path - -import pytest - -from metagpt.config import Config - - -def test_config_class_get_key_exception(): - with pytest.raises(Exception) as exc_info: - config = Config() - config.get('wtf') - assert str(exc_info.value) == "Key 'wtf' not found in environment variables or in the YAML file" - - -def test_config_yaml_file_not_exists(): - with pytest.raises(Exception) as exc_info: - Config(Path('wtf.yaml')) - assert str(exc_info.value) == "Set OPENAI_API_KEY or Anthropic_API_KEY first" - - -def test_options(): - filename = Path(__file__).resolve().parent.parent.parent.parent / "config/config.yaml" - config = Config(filename) - opts = config.runtime_options - assert opts - - -if __name__ == '__main__': - test_options() diff --git a/spaces/deprem-ml/deprem-ocr-test/app.py b/spaces/deprem-ml/deprem-ocr-test/app.py deleted file mode 100644 index bed888e0ffe2f75200e303531ecc4dd7b85b9b82..0000000000000000000000000000000000000000 --- a/spaces/deprem-ml/deprem-ocr-test/app.py +++ /dev/null @@ -1,143 +0,0 @@ -from deprem_ocr.ocr import DepremOCR -import numpy as np -import gradio as gr -import openai -import ast -import os - -from openai_api import OpenAI_API -import utils - - -openai.api_key = os.getenv("API_KEY") -depremOCR = DepremOCR() - - -def get_text(input_img): - result = depremOCR.apply_ocr(np.array(input_img)) - print(result) - return result - - -# Submit button -def get_parsed_address(input_img): - - address_full_text = get_text(input_img) - return openai_response(address_full_text) - - -# Open API on change -def text_dict(input): - eval_result = ast.literal_eval(input) - utils.write_db(eval_result) - - return ( - str(eval_result["city"]), - str(eval_result["distinct"]), - str(eval_result["neighbourhood"]), - str(eval_result["street"]), - str(eval_result["address"]), - str(eval_result["tel"]), - str(eval_result["name_surname"]), - str(eval_result["no"]), - ) - - -def openai_response(ocr_input): - prompt = f"""Tabular Data Extraction You are a highly intelligent and accurate tabular data extractor from - plain text input and especially from emergency text that carries address information, your inputs can be text - of arbitrary size, but the output should be in [{{'tabular': {{'entity_type': 'entity'}} }}] JSON format Force it - to only extract keys that are shared as an example in the examples section, if a key value is not found in the - text input, then it should be ignored. Have only city, distinct, neighbourhood, - street, no, tel, name_surname, address Examples: Input: Deprem sırasında evimizde yer alan adresimiz: İstanbul, - Beşiktaş, Yıldız Mahallesi, Cumhuriyet Caddesi No: 35, cep telefonu numaram 5551231256, adim Ahmet Yilmaz - Output: {{'city': 'İstanbul', 'distinct': 'Beşiktaş', 'neighbourhood': 'Yıldız Mahallesi', 'street': 'Cumhuriyet Caddesi', 'no': '35', 'tel': '5551231256', 'name_surname': 'Ahmet Yılmaz', 'address': 'İstanbul, Beşiktaş, Yıldız Mahallesi, Cumhuriyet Caddesi No: 35'}} - Input: {ocr_input} - Output: - """ - - openai_client = OpenAI_API() - response = openai_client.single_request(prompt) - resp = response["choices"][0]["text"] - print(resp) - resp = eval(resp.replace("'{", "{").replace("}'", "}")) - resp["input"] = ocr_input - dict_keys = [ - "city", - "distinct", - "neighbourhood", - "street", - "no", - "tel", - "name_surname", - "address", - "input", - ] - for key in dict_keys: - if key not in resp.keys(): - resp[key] = "" - return resp - -def ner_response(ocr_input): - API_URL = "https://api-inference.huggingface.co/models/deprem-ml/deprem-ner" - headers = {"Authorization": "Bearer xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"} - - def query(payload): - response = requests.post(API_URL, headers=headers, json=payload) - return response.json() - - output = query({ - "inputs": ocr_input, - }) - return output - -# User Interface -with gr.Blocks() as demo: - gr.Markdown( - """ - # Enkaz Bildirme Uygulaması - """ - ) - gr.Markdown( - "Bu uygulamada ekran görüntüsü sürükleyip bırakarak AFAD'a enkaz bildirimi yapabilirsiniz. Mesajı metin olarak da girebilirsiniz, tam adresi ayrıştırıp döndürür. API olarak kullanmak isterseniz sayfanın en altında use via api'ya tıklayın." - ) - with gr.Row(): - img_area = gr.Image(label="Ekran Görüntüsü yükleyin 👇") - ocr_result = gr.Textbox(label="Metin yükleyin 👇 ") - open_api_text = gr.Textbox(label="Tam Adres") - submit_button = gr.Button(label="Yükle") - with gr.Column(): - with gr.Row(): - city = gr.Textbox(label="İl") - distinct = gr.Textbox(label="İlçe") - with gr.Row(): - neighbourhood = gr.Textbox(label="Mahalle") - street = gr.Textbox(label="Sokak/Cadde/Bulvar") - with gr.Row(): - tel = gr.Textbox(label="Telefon") - with gr.Row(): - name_surname = gr.Textbox(label="İsim Soyisim") - address = gr.Textbox(label="Adres") - with gr.Row(): - no = gr.Textbox(label="Kapı No") - - submit_button.click( - get_parsed_address, - inputs=img_area, - outputs=open_api_text, - api_name="upload_image", - ) - - ocr_result.change( - openai_response, ocr_result, open_api_text, api_name="upload-text" - ) - - open_api_text.change( - text_dict, - open_api_text, - [city, distinct, neighbourhood, street, address, tel, name_surname, no], - ) - - -if __name__ == "__main__": - demo.launch() diff --git a/spaces/devashish07/food_vision_mini/README.md b/spaces/devashish07/food_vision_mini/README.md deleted file mode 100644 index c15083f11373dcbc8b60d22476e49288609c3baa..0000000000000000000000000000000000000000 --- a/spaces/devashish07/food_vision_mini/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Food Vision Mini -emoji: 😻 -colorFrom: blue -colorTo: yellow -sdk: gradio -sdk_version: 3.17.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/diacanFperku/AutoGPT/Geostudio 2007 Crack [PORTABLE] Serial Numbers.md b/spaces/diacanFperku/AutoGPT/Geostudio 2007 Crack [PORTABLE] Serial Numbers.md deleted file mode 100644 index 71ea100e6ae8cacac9b0db4559d8149bfbae58b3..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Geostudio 2007 Crack [PORTABLE] Serial Numbers.md +++ /dev/null @@ -1,18 +0,0 @@ -

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      diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/search/strided_tensor.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/search/strided_tensor.py deleted file mode 100644 index 2bdc13d13eaea795b697f586e2e57f9cda649492..0000000000000000000000000000000000000000 --- a/spaces/diagaiwei/ir_chinese_medqa/colbert/search/strided_tensor.py +++ /dev/null @@ -1,223 +0,0 @@ -from struct import pack -import torch -from torch._C import device - -from colbert.utils.utils import flatten, print_message - -from .strided_tensor_core import StridedTensorCore, _create_mask, _create_view - -import os -import pathlib -from torch.utils.cpp_extension import load - - -class StridedTensor(StridedTensorCore): - def __init__(self, packed_tensor, lengths, dim=None, use_gpu=True): - super().__init__(packed_tensor, lengths, dim=dim, use_gpu=use_gpu) - - StridedTensor.try_load_torch_extensions(use_gpu) - - @classmethod - def try_load_torch_extensions(cls, use_gpu): - if hasattr(cls, "loaded_extensions") or use_gpu: - return - - print_message(f"Loading segmented_lookup_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...") - segmented_lookup_cpp = load( - name="segmented_lookup_cpp", - sources=[ - os.path.join( - pathlib.Path(__file__).parent.resolve(), "segmented_lookup.cpp" - ), - ], - extra_cflags=["-O3"], - verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True", - ) - cls.segmented_lookup = segmented_lookup_cpp.segmented_lookup_cpp - - cls.loaded_extensions = True - - @classmethod - def pad_packed(cls, packed_tensor, lengths): - assert False, "This seems to be incorrect but I can't see why. Is it the inner_dims in the views?" - - packed_tensor, lengths = packed_tensor.cuda().contiguous(), lengths.cuda() - - inner_dims = packed_tensor.size()[1:] - stride = lengths.max().item() - offsets = torch.cumsum(lengths, dim=0) - lengths[0] - - padding = torch.zeros(stride, *inner_dims, device=packed_tensor.device, dtype=packed_tensor.dtype) - packed_tensor = torch.cat((packed_tensor, padding)) - - view = _create_view(packed_tensor, stride, inner_dims)[offsets] - mask = _create_mask(lengths, stride, like=view) - - return view, mask - - def _prepare_lookup(self, pids): - if isinstance(pids, list): - pids = torch.tensor(pids) - - assert pids.dim() == 1 - - if self.use_gpu: - pids = pids.cuda() - pids = pids.long() - lengths = self.lengths[pids] - if self.use_gpu: - lengths = lengths.cuda() - offsets = self.offsets[pids] - - return pids, lengths, offsets - - def lookup(self, pids, output='packed'): - pids, lengths, offsets = self._prepare_lookup(pids) - - if self.use_gpu: - stride = lengths.max().item() - stride = next(s for s in self.strides if stride <= s) - - tensor = self.views[stride][offsets] - if self.use_gpu: - tensor = tensor.cuda() - - mask = _create_mask(lengths, stride, use_gpu=self.use_gpu) - - if output == 'padded': - return tensor, mask - - assert output == 'packed' - - tensor = tensor[mask] - else: - tensor = StridedTensor.segmented_lookup(self.tensor, pids, lengths, offsets) - - return tensor, lengths - - def lookup_staggered(self, pids, output='packed'): - permute_idxs, unordered_tensors, unordered_lengths, unordered_masks = self.lookup_packed_unordered(pids) - - output_tensor = torch.empty(permute_idxs.size(0), self.max_stride, *self.inner_dims, - dtype=unordered_tensors[0].dtype, device=unordered_tensors[0].device) - - output_mask = torch.zeros(permute_idxs.size(0), self.max_stride, - dtype=unordered_masks[0].dtype, device=unordered_masks[0].device) - - offset = 0 - for tensor, mask in zip(unordered_tensors, unordered_masks): - endpos = offset + tensor.size(0) - output_tensor[offset:endpos, :tensor.size(1)] = tensor - output_mask[offset:endpos, :mask.size(1)] = mask - offset = endpos - - output_mask = output_mask[permute_idxs] - output_tensor = output_tensor[permute_idxs] - - if output == 'padded': - return output_tensor, output_mask - - assert output == 'packed' - - output_tensor = output_tensor[output_mask] - - return output_tensor, unordered_lengths[permute_idxs] - - def lookup_packed_unordered(self, pids): - pids, lengths, offsets = self._prepare_lookup(pids) - - lengths2 = lengths.clone() - sentinel = self.strides[-1] + 1 - order = torch.arange(pids.size(0), device='cuda' if self.use_gpu else 'cpu') - - all_orders = [] - all_tensors = [] - all_lengths = [] - all_masks = [] - - for stride in self.strides: - is_shorter = lengths2 <= stride - - if is_shorter.sum() == 0: - continue - - order_ = order[is_shorter] - tensor_, lengths_, mask_ = self._lookup_with_stride(stride, lengths[is_shorter], offsets[is_shorter]) - - all_orders.append(order_) - all_tensors.append(tensor_) - all_lengths.append(lengths_) - all_masks.append(mask_) - - lengths2[is_shorter] = sentinel - - assert lengths2.allclose(torch.tensor([sentinel], device='cuda' if self.use_gpu else 'cpu')) - - all_orders = torch.cat(all_orders) - permute_idxs = torch.sort(all_orders).indices - - return permute_idxs, all_tensors, torch.cat(all_lengths), all_masks - - def _lookup_with_stride(self, stride, lengths, offsets): - tensor = self.views[stride][offsets] - if self.use_gpu: - tensor = tensor.cuda() - - mask = _create_mask(lengths, stride, use_gpu=self.use_gpu) - # tensor = tensor[mask] - - return tensor, lengths, mask - - -if __name__ == '__main__': - # lst = [] - # for _ in range(10): - # lst.append(list(range(random.randint(0, 10)))) - - # print(lst) - - # t = StridedTensor.from_nested_list(lst) - # print(t.lookup([9])) - - import os - import pickle - - index_path = '/future/u/okhattab/root/unit/indexes/2021/08/residual.NQ-micro' - with open(os.path.join(index_path, "centroid_idx_to_embedding_ids.pickle"), "rb") as f: - ivf_list = pickle.load(f) - - assert len(ivf_list) == max(ivf_list.keys()) + 1 - ivf_list = [ivf_list[i] for i in range(len(ivf_list))] - - for x in ivf_list: - assert type(x) is list - assert type(x[0]) is int - - ncentroids = len(ivf_list) - - ivf = StridedTensor.from_nested_list(ivf_list) - - import time - - torch.cuda.synchronize() - t = time.time() - - N = 100 - for _ in range(N): - probed_centroids = torch.randint(0, ncentroids, size=(32, 8)).flatten() - emb_ids, emb_ids_lengths = ivf.lookup(probed_centroids).as_packed_tensor() - - torch.cuda.synchronize() - print((time.time() - t) * 1000 / N, 'ms') - - print(emb_ids_lengths) - - slow_result = flatten([ivf_list[idx] for idx in probed_centroids.flatten().tolist()]) - print(emb_ids.size(), len(slow_result)) - - for a, b in zip(slow_result, emb_ids.flatten().tolist()): - assert a == b, (a, b) - - print("#> Done!") - - print(ivf.lookup(probed_centroids).as_padded_tensor()[0].size()) diff --git a/spaces/diagaiwei/ir_chinese_medqa/colbert/training/lazy_batcher.py b/spaces/diagaiwei/ir_chinese_medqa/colbert/training/lazy_batcher.py deleted file mode 100644 index a41c3bff99e4a72f41d734e0ba01e663eb0c56e8..0000000000000000000000000000000000000000 --- a/spaces/diagaiwei/ir_chinese_medqa/colbert/training/lazy_batcher.py +++ /dev/null @@ -1,75 +0,0 @@ -import os -import ujson - -from functools import partial -from colbert.infra.config.config import ColBERTConfig -from colbert.utils.utils import print_message, zipstar -from colbert.modeling.tokenization import QueryTokenizer, DocTokenizer, tensorize_triples -from colbert.evaluation.loaders import load_collection - -from colbert.data.collection import Collection -from colbert.data.queries import Queries -from colbert.data.examples import Examples - -# from colbert.utils.runs import Run - - -class LazyBatcher(): - def __init__(self, config: ColBERTConfig, triples, queries, collection, rank=0, nranks=1): - self.bsize, self.accumsteps = config.bsize, config.accumsteps - self.nway = config.nway - - self.query_tokenizer = QueryTokenizer(config) - self.doc_tokenizer = DocTokenizer(config) - self.tensorize_triples = partial(tensorize_triples, self.query_tokenizer, self.doc_tokenizer) - self.position = 0 - - self.triples = Examples.cast(triples, nway=self.nway).tolist(rank, nranks) - self.queries = Queries.cast(queries) - self.collection = Collection.cast(collection) - - def __iter__(self): - return self - - def __len__(self): - return len(self.triples) - - def __next__(self): - offset, endpos = self.position, min(self.position + self.bsize, len(self.triples)) - self.position = endpos - - if offset + self.bsize > len(self.triples): - raise StopIteration - - all_queries, all_passages, all_scores = [], [], [] - - for position in range(offset, endpos): - query, *pids = self.triples[position] - pids = pids[:self.nway] - - query = self.queries[query] - - try: - pids, scores = zipstar(pids) - except: - scores = [] - - passages = [self.collection[pid] for pid in pids] - - all_queries.append(query) - all_passages.extend(passages) - all_scores.extend(scores) - - assert len(all_scores) in [0, len(all_passages)], len(all_scores) - - return self.collate(all_queries, all_passages, all_scores) - - def collate(self, queries, passages, scores): - assert len(queries) == self.bsize - assert len(passages) == self.nway * self.bsize - - return self.tensorize_triples(queries, passages, scores, self.bsize // self.accumsteps, self.nway) - - # def skip_to_batch(self, batch_idx, intended_batch_size): - # Run.warn(f'Skipping to batch #{batch_idx} (with intended_batch_size = {intended_batch_size}) for training.') - # self.position = intended_batch_size * batch_idx diff --git a/spaces/diffusers/controlnet-canny/app.py b/spaces/diffusers/controlnet-canny/app.py deleted file mode 100644 index 796a6dfea63922a74178e4132dc374e28180a5ca..0000000000000000000000000000000000000000 --- a/spaces/diffusers/controlnet-canny/app.py +++ /dev/null @@ -1,73 +0,0 @@ -from diffusers import StableDiffusionControlNetPipeline, ControlNetModel -from diffusers import UniPCMultistepScheduler -import cv2 -import gradio as gr -import numpy as np -import torch -from PIL import Image - -# Constants -low_threshold = 100 -high_threshold = 200 - -# Models -controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16) -pipe = StableDiffusionControlNetPipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 -) -pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) - -# This command loads the individual model components on GPU on-demand. So, we don't -# need to explicitly call pipe.to("cuda"). -pipe.enable_model_cpu_offload() - -pipe.enable_xformers_memory_efficient_attention() - -# Generator seed, -generator = torch.manual_seed(0) - -def get_canny_filter(image): - if not isinstance(image, np.ndarray): - image = np.array(image) - - image = cv2.Canny(image, low_threshold, high_threshold) - image = image[:, :, None] - image = np.concatenate([image, image, image], axis=2) - canny_image = Image.fromarray(image) - return canny_image - - -def generate_images(image, prompt): - canny_image = get_canny_filter(image) - output = pipe( - prompt, - canny_image, - generator=generator, - num_images_per_prompt=3, - num_inference_steps=20, - ) - all_outputs = [] - all_outputs.append(canny_image) - for image in output.images: - all_outputs.append(image) - return all_outputs - - -gr.Interface( - generate_images, - inputs=[ - gr.Image(type="pil"), - gr.Textbox( - label="Enter your prompt", - max_lines=1, - placeholder="Sandra Oh, best quality, extremely detailed", - ), - ], - outputs=gr.Gallery().style(grid=[2], height="auto"), - title="Generate controlled outputs with ControlNet and Stable Diffusion. ", - description="This Space uses Canny edge maps as the additional conditioning.", - examples=[["input_image_vermeer.png", "Sandra Oh, best quality, extremely detailed"]], - allow_flagging=False, -).launch(enable_queue=True) - - diff --git a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/models.py b/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/models.py deleted file mode 100644 index d4afe44d883691610c5903e602a3ca245fcb3a5c..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-2dall-Bert-VITS2/models.py +++ /dev/null @@ -1,707 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -import attentions -import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm - -from commons import init_weights, get_padding -from text import symbols, num_tones, num_languages -class DurationDiscriminator(nn.Module): #vits2 - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.dur_proj = nn.Conv1d(1, filter_channels, 1) - - self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.pre_out_norm_1 = modules.LayerNorm(filter_channels) - self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.pre_out_norm_2 = modules.LayerNorm(filter_channels) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - self.output_layer = nn.Sequential( - nn.Linear(filter_channels, 1), - nn.Sigmoid() - ) - - def forward_probability(self, x, x_mask, dur, g=None): - dur = self.dur_proj(dur) - x = torch.cat([x, dur], dim=1) - x = self.pre_out_conv_1(x * x_mask) - x = torch.relu(x) - x = self.pre_out_norm_1(x) - x = self.drop(x) - x = self.pre_out_conv_2(x * x_mask) - x = torch.relu(x) - x = self.pre_out_norm_2(x) - x = self.drop(x) - x = x * x_mask - x = x.transpose(1, 2) - output_prob = self.output_layer(x) - return output_prob - - def forward(self, x, x_mask, dur_r, dur_hat, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - - output_probs = [] - for dur in [dur_r, dur_hat]: - output_prob = self.forward_probability(x, x_mask, dur, g) - output_probs.append(output_prob) - - return output_probs - -class TransformerCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - n_flows=4, - gin_channels=0, - share_parameter=False - ): - - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - - self.wn = attentions.FFT(hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, isflow = True, gin_channels = self.gin_channels) if share_parameter else None - - for i in range(n_flows): - self.flows.append( - modules.TransformerCouplingLayer(channels, hidden_channels, kernel_size, n_layers, n_heads, p_dropout, filter_channels, mean_only=True, wn_sharing_parameter=self.wn, gin_channels = self.gin_channels)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1, 2]) - logq = torch.sum(-0.5 * (math.log(2 * math.pi) + (e_q ** 2)) * x_mask, [1, 2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2 * math.pi) + (z ** 2)) * x_mask, [1, 2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size // 2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=0): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - self.emb = nn.Embedding(len(symbols), hidden_channels) - nn.init.normal_(self.emb.weight, 0.0, hidden_channels ** -0.5) - self.tone_emb = nn.Embedding(num_tones, hidden_channels) - nn.init.normal_(self.tone_emb.weight, 0.0, hidden_channels ** -0.5) - self.language_emb = nn.Embedding(num_languages, hidden_channels) - nn.init.normal_(self.language_emb.weight, 0.0, hidden_channels ** -0.5) - self.bert_proj = nn.Conv1d(1024, hidden_channels, 1) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=self.gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, tone, language, bert, g=None): - x = (self.emb(x)+ self.tone_emb(tone)+ self.language_emb(language)+self.bert_proj(bert).transpose(1,2)) * math.sqrt(self.hidden_channels) # [b, t, h] - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - x = self.encoder(x * x_mask, x_mask, g=g) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, - gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, - upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel // (2 ** i), upsample_initial_channel // (2 ** (i + 1)), - k, u, padding=(k - u) // 2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - -class ReferenceEncoder(nn.Module): - ''' - inputs --- [N, Ty/r, n_mels*r] mels - outputs --- [N, ref_enc_gru_size] - ''' - - def __init__(self, spec_channels, gin_channels=0): - - super().__init__() - self.spec_channels = spec_channels - ref_enc_filters = [32, 32, 64, 64, 128, 128] - K = len(ref_enc_filters) - filters = [1] + ref_enc_filters - convs = [weight_norm(nn.Conv2d(in_channels=filters[i], - out_channels=filters[i + 1], - kernel_size=(3, 3), - stride=(2, 2), - padding=(1, 1))) for i in range(K)] - self.convs = nn.ModuleList(convs) - # self.wns = nn.ModuleList([weight_norm(num_features=ref_enc_filters[i]) for i in range(K)]) - - out_channels = self.calculate_channels(spec_channels, 3, 2, 1, K) - self.gru = nn.GRU(input_size=ref_enc_filters[-1] * out_channels, - hidden_size=256 // 2, - batch_first=True) - self.proj = nn.Linear(128, gin_channels) - - def forward(self, inputs, mask=None): - N = inputs.size(0) - out = inputs.view(N, 1, -1, self.spec_channels) # [N, 1, Ty, n_freqs] - for conv in self.convs: - out = conv(out) - # out = wn(out) - out = F.relu(out) # [N, 128, Ty//2^K, n_mels//2^K] - - out = out.transpose(1, 2) # [N, Ty//2^K, 128, n_mels//2^K] - T = out.size(1) - N = out.size(0) - out = out.contiguous().view(N, T, -1) # [N, Ty//2^K, 128*n_mels//2^K] - - self.gru.flatten_parameters() - memory, out = self.gru(out) # out --- [1, N, 128] - - return self.proj(out.squeeze(0)) - - def calculate_channels(self, L, kernel_size, stride, pad, n_convs): - for i in range(n_convs): - L = (L - kernel_size + 2 * pad) // stride + 1 - return L - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=256, - gin_channels=256, - use_sdp=True, - n_flow_layer = 4, - n_layers_trans_flow = 3, - flow_share_parameter = False, - use_transformer_flow = True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - self.n_layers_trans_flow = n_layers_trans_flow - self.use_spk_conditioned_encoder = kwargs.get("use_spk_conditioned_encoder", True) - self.use_sdp = use_sdp - self.use_noise_scaled_mas = kwargs.get("use_noise_scaled_mas", False) - self.mas_noise_scale_initial = kwargs.get("mas_noise_scale_initial", 0.01) - self.noise_scale_delta = kwargs.get("noise_scale_delta", 2e-6) - self.current_mas_noise_scale = self.mas_noise_scale_initial - if self.use_spk_conditioned_encoder and gin_channels > 0: - self.enc_gin_channels = gin_channels - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - gin_channels=self.enc_gin_channels) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, - upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, - gin_channels=gin_channels) - if use_transformer_flow: - self.flow = TransformerCouplingBlock(inter_channels, hidden_channels, filter_channels, n_heads, n_layers_trans_flow, 5, p_dropout, n_flow_layer, gin_channels=gin_channels,share_parameter= flow_share_parameter) - else: - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, n_flow_layer, gin_channels=gin_channels) - self.sdp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers >= 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - else: - self.ref_enc = ReferenceEncoder(spec_channels, gin_channels) - - def forward(self, x, x_lengths, y, y_lengths, sid, tone, language, bert): - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1) - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - - with torch.no_grad(): - # negative cross-entropy - s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), - s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] - neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 - if self.use_noise_scaled_mas: - epsilon = torch.std(neg_cent) * torch.randn_like(neg_cent) * self.current_mas_noise_scale - neg_cent = neg_cent + epsilon - - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() - - w = attn.sum(2) - - l_length_sdp = self.sdp(x, x_mask, w, g=g) - l_length_sdp = l_length_sdp / torch.sum(x_mask) - - logw_ = torch.log(w + 1e-6) * x_mask - logw = self.dp(x, x_mask, g=g) - l_length_dp = torch.sum((logw - logw_) ** 2, [1, 2]) / torch.sum(x_mask) # for averaging - - l_length = l_length_dp + l_length_sdp - - # expand prior - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) - o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q), (x, logw, logw_) - - def infer(self, x, x_lengths, sid, tone, language, bert, noise_scale=.667, length_scale=1, noise_scale_w=0.8, max_len=None, sdp_ratio=0,y=None): - #x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert) - # g = self.gst(y) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = self.ref_enc(y.transpose(1,2)).unsqueeze(-1) - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, tone, language, bert,g=g) - logw = self.sdp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) * (sdp_ratio) + self.dp(x, x_mask, g=g) * (1 - sdp_ratio) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, - 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:, :, :max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) diff --git a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.c b/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.c deleted file mode 100644 index 5f8af54d32474f821e9d1f4d2679d78128722596..0000000000000000000000000000000000000000 --- a/spaces/digitalxingtong/Xingtong-Longread-Bert-VITS2/monotonic_align/core.c +++ /dev/null @@ -1,26530 +0,0 @@ -/* Generated by Cython 3.0.0 */ - -/* BEGIN: Cython Metadata -{ - "distutils": { - "name": "monotonic_align.core", - "sources": [ - "core.pyx" - ] - }, - "module_name": "monotonic_align.core" -} -END: Cython Metadata */ - -#ifndef PY_SSIZE_T_CLEAN -#define PY_SSIZE_T_CLEAN -#endif /* PY_SSIZE_T_CLEAN */ -#if defined(CYTHON_LIMITED_API) && 0 - #ifndef Py_LIMITED_API - #if CYTHON_LIMITED_API+0 > 0x03030000 - #define Py_LIMITED_API CYTHON_LIMITED_API - #else - #define Py_LIMITED_API 0x03030000 - #endif - #endif -#endif - -#include "Python.h" -#ifndef Py_PYTHON_H - #error Python headers needed to compile C extensions, please install development version of Python. -#elif PY_VERSION_HEX < 0x02070000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000) - #error Cython requires Python 2.7+ or Python 3.3+. -#else -#define CYTHON_ABI "3_0_0" -#define __PYX_ABI_MODULE_NAME "_cython_" CYTHON_ABI -#define __PYX_TYPE_MODULE_PREFIX __PYX_ABI_MODULE_NAME "." -#define CYTHON_HEX_VERSION 0x030000F0 -#define CYTHON_FUTURE_DIVISION 1 -#include -#ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(_WIN32) && !defined(WIN32) && !defined(MS_WINDOWS) - #ifndef __stdcall - #define __stdcall - #endif - #ifndef __cdecl - #define __cdecl - #endif - #ifndef __fastcall - #define __fastcall - #endif -#endif -#ifndef DL_IMPORT - #define DL_IMPORT(t) t -#endif -#ifndef DL_EXPORT - #define DL_EXPORT(t) t -#endif -#define __PYX_COMMA , -#ifndef HAVE_LONG_LONG - #define HAVE_LONG_LONG -#endif -#ifndef PY_LONG_LONG - #define PY_LONG_LONG LONG_LONG -#endif -#ifndef Py_HUGE_VAL - #define Py_HUGE_VAL HUGE_VAL -#endif -#if defined(GRAALVM_PYTHON) - /* For very preliminary testing purposes. Most variables are set the same as PyPy. - The existence of this section does not imply that anything works or is even tested */ - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 1 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) - #endif - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 0 - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(PYPY_VERSION) - #define CYTHON_COMPILING_IN_PYPY 1 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 0 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 0 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #if PY_VERSION_HEX < 0x03050000 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS (PY_MAJOR_VERSION >= 3) - #endif - #if PY_VERSION_HEX < 0x03090000 - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #elif !defined(CYTHON_PEP489_MULTI_PHASE_INIT) - #define CYTHON_PEP489_MULTI_PHASE_INIT 1 - #endif - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 0 - #undef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1 && PYPY_VERSION_NUM >= 0x07030C00) - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - #endif -#elif defined(CYTHON_LIMITED_API) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #define CYTHON_COMPILING_IN_LIMITED_API 1 - #define CYTHON_COMPILING_IN_GRAAL 0 - #define CYTHON_COMPILING_IN_NOGIL 0 - #undef CYTHON_CLINE_IN_TRACEBACK - #define CYTHON_CLINE_IN_TRACEBACK 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_TYPE_SPECS - #define CYTHON_USE_TYPE_SPECS 1 - #undef CYTHON_USE_PYTYPE_LOOKUP - #define CYTHON_USE_PYTYPE_LOOKUP 0 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #endif - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_GIL - #define CYTHON_FAST_GIL 0 - #undef CYTHON_METH_FASTCALL - #define CYTHON_METH_FASTCALL 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 - #ifndef CYTHON_PEP487_INIT_SUBCLASS - #define CYTHON_PEP487_INIT_SUBCLASS 1 - #endif - #undef CYTHON_PEP489_MULTI_PHASE_INIT - #define CYTHON_PEP489_MULTI_PHASE_INIT 0 - #undef CYTHON_USE_MODULE_STATE - #define CYTHON_USE_MODULE_STATE 1 - #ifndef CYTHON_USE_TP_FINALIZE - #define CYTHON_USE_TP_FINALIZE 1 - #endif - #undef CYTHON_USE_DICT_VERSIONS - #define CYTHON_USE_DICT_VERSIONS 0 - #undef CYTHON_USE_EXC_INFO_STACK - #define CYTHON_USE_EXC_INFO_STACK 0 - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 0 - 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#define CYTHON_USE_EXC_INFO_STACK 0 - #elif !defined(CYTHON_USE_EXC_INFO_STACK) - #define CYTHON_USE_EXC_INFO_STACK 1 - #endif - #ifndef CYTHON_UPDATE_DESCRIPTOR_DOC - #define CYTHON_UPDATE_DESCRIPTOR_DOC 1 - #endif -#endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if !defined(CYTHON_VECTORCALL) -#define CYTHON_VECTORCALL (CYTHON_FAST_PYCCALL && PY_VERSION_HEX >= 0x030800B1) -#endif -#define CYTHON_BACKPORT_VECTORCALL (CYTHON_METH_FASTCALL && PY_VERSION_HEX < 0x030800B1) -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_MAJOR_VERSION < 3 - #include "longintrepr.h" - #endif - #undef SHIFT - #undef BASE - #undef MASK - #ifdef SIZEOF_VOID_P - enum { __pyx_check_sizeof_voidp = 1 / (int)(SIZEOF_VOID_P == sizeof(void*)) }; - #endif -#endif -#ifndef __has_attribute - #define __has_attribute(x) 0 -#endif -#ifndef __has_cpp_attribute - #define __has_cpp_attribute(x) 0 -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#ifndef CYTHON_UNUSED - #if defined(__cplusplus) - /* for clang __has_cpp_attribute(maybe_unused) is true even before C++17 - * but leads to warnings with -pedantic, since it is a C++17 feature */ - #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) - #if __has_cpp_attribute(maybe_unused) - #define CYTHON_UNUSED [[maybe_unused]] - #endif - #endif - #endif -#endif -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -#ifndef CYTHON_UNUSED_VAR -# if defined(__cplusplus) - template void CYTHON_UNUSED_VAR( const T& ) { } -# else -# define CYTHON_UNUSED_VAR(x) (void)(x) -# endif -#endif -#ifndef CYTHON_MAYBE_UNUSED_VAR - #define CYTHON_MAYBE_UNUSED_VAR(x) CYTHON_UNUSED_VAR(x) -#endif -#ifndef CYTHON_NCP_UNUSED -# if CYTHON_COMPILING_IN_CPYTHON -# define CYTHON_NCP_UNUSED -# else -# define CYTHON_NCP_UNUSED CYTHON_UNUSED -# endif -#endif -#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) -#ifdef _MSC_VER - #ifndef _MSC_STDINT_H_ - #if _MSC_VER < 1300 - typedef unsigned char uint8_t; - typedef unsigned short uint16_t; - typedef unsigned int uint32_t; - #else - typedef unsigned __int8 uint8_t; - typedef unsigned __int16 uint16_t; - typedef unsigned __int32 uint32_t; - #endif - #endif - #if _MSC_VER < 1300 - #ifdef _WIN64 - typedef unsigned long long __pyx_uintptr_t; - #else - typedef unsigned int __pyx_uintptr_t; - #endif - #else - #ifdef _WIN64 - typedef unsigned __int64 __pyx_uintptr_t; - #else - typedef unsigned __int32 __pyx_uintptr_t; - #endif - #endif -#else - #include - typedef uintptr_t __pyx_uintptr_t; -#endif -#ifndef CYTHON_FALLTHROUGH - #if defined(__cplusplus) - /* for clang __has_cpp_attribute(fallthrough) is true even before C++17 - * but leads to warnings with -pedantic, since it is a C++17 feature */ - #if ((defined(_MSVC_LANG) && _MSVC_LANG >= 201703L) || __cplusplus >= 201703L) - #if __has_cpp_attribute(fallthrough) - #define CYTHON_FALLTHROUGH [[fallthrough]] - #endif - #endif - #ifndef CYTHON_FALLTHROUGH - #if __has_cpp_attribute(clang::fallthrough) - #define CYTHON_FALLTHROUGH [[clang::fallthrough]] - #elif __has_cpp_attribute(gnu::fallthrough) - #define CYTHON_FALLTHROUGH [[gnu::fallthrough]] - #endif - #endif - #endif - #ifndef CYTHON_FALLTHROUGH - #if __has_attribute(fallthrough) - #define CYTHON_FALLTHROUGH __attribute__((fallthrough)) - #else - #define CYTHON_FALLTHROUGH - #endif - #endif - #if defined(__clang__) && defined(__apple_build_version__) - #if __apple_build_version__ < 7000000 - #undef CYTHON_FALLTHROUGH - #define CYTHON_FALLTHROUGH - #endif - #endif -#endif -#ifdef __cplusplus - template - struct __PYX_IS_UNSIGNED_IMPL {static const bool value = T(0) < T(-1);}; - #define __PYX_IS_UNSIGNED(type) (__PYX_IS_UNSIGNED_IMPL::value) -#else - #define __PYX_IS_UNSIGNED(type) (((type)-1) > 0) -#endif -#if CYTHON_COMPILING_IN_PYPY == 1 - #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x030A0000) -#else - #define __PYX_NEED_TP_PRINT_SLOT (PY_VERSION_HEX >= 0x030800b4 && PY_VERSION_HEX < 0x03090000) -#endif -#define __PYX_REINTERPRET_FUNCION(func_pointer, other_pointer) ((func_pointer)(void(*)(void))(other_pointer)) - -#ifndef CYTHON_INLINE - #if defined(__clang__) - #define CYTHON_INLINE __inline__ __attribute__ ((__unused__)) - #elif defined(__GNUC__) - #define CYTHON_INLINE __inline__ - #elif defined(_MSC_VER) - #define CYTHON_INLINE __inline - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_INLINE inline - #else - #define CYTHON_INLINE - #endif -#endif - -#define __PYX_BUILD_PY_SSIZE_T "n" -#define CYTHON_FORMAT_SSIZE_T "z" -#if PY_MAJOR_VERSION < 3 - #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" - #define __Pyx_DefaultClassType PyClass_Type - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#else - #define __Pyx_BUILTIN_MODULE_NAME "builtins" - #define __Pyx_DefaultClassType PyType_Type -#if PY_VERSION_HEX >= 0x030B00A1 - static CYTHON_INLINE PyCodeObject* __Pyx_PyCode_New(int a, int p, int k, int l, int s, int f, - PyObject *code, PyObject *c, PyObject* n, PyObject *v, - PyObject *fv, PyObject *cell, PyObject* fn, - PyObject *name, int fline, PyObject *lnos) { - PyObject *kwds=NULL, *argcount=NULL, *posonlyargcount=NULL, *kwonlyargcount=NULL; - PyObject *nlocals=NULL, *stacksize=NULL, *flags=NULL, *replace=NULL, *empty=NULL; - const char *fn_cstr=NULL; - const char *name_cstr=NULL; - PyCodeObject *co=NULL, *result=NULL; - PyObject *type, *value, *traceback; - PyErr_Fetch(&type, &value, &traceback); - if (!(kwds=PyDict_New())) goto end; - if (!(argcount=PyLong_FromLong(a))) goto end; - if (PyDict_SetItemString(kwds, "co_argcount", argcount) != 0) goto end; - if (!(posonlyargcount=PyLong_FromLong(p))) goto end; - if (PyDict_SetItemString(kwds, "co_posonlyargcount", posonlyargcount) != 0) goto end; - if (!(kwonlyargcount=PyLong_FromLong(k))) goto end; - if (PyDict_SetItemString(kwds, "co_kwonlyargcount", kwonlyargcount) != 0) goto end; - if (!(nlocals=PyLong_FromLong(l))) goto end; - if (PyDict_SetItemString(kwds, "co_nlocals", nlocals) != 0) goto end; - if (!(stacksize=PyLong_FromLong(s))) goto end; - if (PyDict_SetItemString(kwds, "co_stacksize", stacksize) != 0) goto end; - if (!(flags=PyLong_FromLong(f))) goto end; - if (PyDict_SetItemString(kwds, "co_flags", flags) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_code", code) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_consts", c) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_names", n) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_varnames", v) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_freevars", fv) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_cellvars", cell) != 0) goto end; - if (PyDict_SetItemString(kwds, "co_linetable", lnos) != 0) goto end; - if (!(fn_cstr=PyUnicode_AsUTF8AndSize(fn, NULL))) goto end; - if (!(name_cstr=PyUnicode_AsUTF8AndSize(name, NULL))) goto end; - if (!(co = PyCode_NewEmpty(fn_cstr, name_cstr, fline))) goto end; - if (!(replace = PyObject_GetAttrString((PyObject*)co, "replace"))) goto end; - if (!(empty = PyTuple_New(0))) goto end; - result = (PyCodeObject*) PyObject_Call(replace, empty, kwds); - end: - Py_XDECREF((PyObject*) co); - Py_XDECREF(kwds); - Py_XDECREF(argcount); - Py_XDECREF(posonlyargcount); - Py_XDECREF(kwonlyargcount); - Py_XDECREF(nlocals); - Py_XDECREF(stacksize); - Py_XDECREF(replace); - Py_XDECREF(empty); - if (type) { - PyErr_Restore(type, value, traceback); - } - return result; - } -#elif PY_VERSION_HEX >= 0x030800B2 && !CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_NewWithPosOnlyArgs(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#else - #define __Pyx_PyCode_New(a, p, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ - PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) -#endif -#endif -#if PY_VERSION_HEX >= 0x030900A4 || defined(Py_IS_TYPE) - #define __Pyx_IS_TYPE(ob, type) Py_IS_TYPE(ob, type) -#else - #define __Pyx_IS_TYPE(ob, type) (((const PyObject*)ob)->ob_type == (type)) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_Is) - #define __Pyx_Py_Is(x, y) Py_Is(x, y) -#else - #define __Pyx_Py_Is(x, y) ((x) == (y)) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsNone) - #define __Pyx_Py_IsNone(ob) Py_IsNone(ob) -#else - #define __Pyx_Py_IsNone(ob) __Pyx_Py_Is((ob), Py_None) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsTrue) - #define __Pyx_Py_IsTrue(ob) Py_IsTrue(ob) -#else - #define __Pyx_Py_IsTrue(ob) __Pyx_Py_Is((ob), Py_True) -#endif -#if PY_VERSION_HEX >= 0x030A00B1 || defined(Py_IsFalse) - #define __Pyx_Py_IsFalse(ob) Py_IsFalse(ob) -#else - #define __Pyx_Py_IsFalse(ob) __Pyx_Py_Is((ob), Py_False) -#endif -#define __Pyx_NoneAsNull(obj) (__Pyx_Py_IsNone(obj) ? NULL : (obj)) -#if PY_VERSION_HEX >= 0x030900F0 && !CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyObject_GC_IsFinalized(o) PyObject_GC_IsFinalized(o) -#else - #define __Pyx_PyObject_GC_IsFinalized(o) _PyGC_FINALIZED(o) -#endif -#ifndef CO_COROUTINE - #define CO_COROUTINE 0x80 -#endif -#ifndef CO_ASYNC_GENERATOR - #define CO_ASYNC_GENERATOR 0x200 -#endif -#ifndef Py_TPFLAGS_CHECKTYPES - #define Py_TPFLAGS_CHECKTYPES 0 -#endif -#ifndef Py_TPFLAGS_HAVE_INDEX - #define Py_TPFLAGS_HAVE_INDEX 0 -#endif -#ifndef Py_TPFLAGS_HAVE_NEWBUFFER - #define Py_TPFLAGS_HAVE_NEWBUFFER 0 -#endif -#ifndef Py_TPFLAGS_HAVE_FINALIZE - #define Py_TPFLAGS_HAVE_FINALIZE 0 -#endif -#ifndef Py_TPFLAGS_SEQUENCE - #define Py_TPFLAGS_SEQUENCE 0 -#endif -#ifndef Py_TPFLAGS_MAPPING - #define Py_TPFLAGS_MAPPING 0 -#endif -#ifndef METH_STACKLESS - #define METH_STACKLESS 0 -#endif -#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL) - #ifndef METH_FASTCALL - #define METH_FASTCALL 0x80 - #endif - typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs); - typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args, - Py_ssize_t nargs, PyObject *kwnames); -#else - #define __Pyx_PyCFunctionFast _PyCFunctionFast - #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords -#endif -#if CYTHON_METH_FASTCALL - #define __Pyx_METH_FASTCALL METH_FASTCALL - #define __Pyx_PyCFunction_FastCall __Pyx_PyCFunctionFast - #define __Pyx_PyCFunction_FastCallWithKeywords __Pyx_PyCFunctionFastWithKeywords -#else - #define __Pyx_METH_FASTCALL METH_VARARGS - #define __Pyx_PyCFunction_FastCall PyCFunction - #define __Pyx_PyCFunction_FastCallWithKeywords PyCFunctionWithKeywords -#endif -#if CYTHON_VECTORCALL - #define __pyx_vectorcallfunc vectorcallfunc - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET PY_VECTORCALL_ARGUMENTS_OFFSET - #define __Pyx_PyVectorcall_NARGS(n) PyVectorcall_NARGS((size_t)(n)) -#elif CYTHON_BACKPORT_VECTORCALL - typedef PyObject *(*__pyx_vectorcallfunc)(PyObject *callable, PyObject *const *args, - size_t nargsf, PyObject *kwnames); - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET ((size_t)1 << (8 * sizeof(size_t) - 1)) - #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(((size_t)(n)) & ~__Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET)) -#else - #define __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET 0 - #define __Pyx_PyVectorcall_NARGS(n) ((Py_ssize_t)(n)) -#endif -#if PY_VERSION_HEX < 0x030900B1 - #define __Pyx_PyType_FromModuleAndSpec(m, s, b) ((void)m, PyType_FromSpecWithBases(s, b)) - typedef PyObject *(*__Pyx_PyCMethod)(PyObject *, PyTypeObject *, PyObject *const *, size_t, PyObject *); -#else - #define __Pyx_PyType_FromModuleAndSpec(m, s, b) PyType_FromModuleAndSpec(m, s, b) - #define __Pyx_PyCMethod PyCMethod -#endif -#ifndef METH_METHOD - #define METH_METHOD 0x200 -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) - #define PyObject_Malloc(s) PyMem_Malloc(s) - #define PyObject_Free(p) PyMem_Free(p) - #define PyObject_Realloc(p) PyMem_Realloc(p) -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) -#else - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyThreadState_Current PyThreadState_Get() -#elif !CYTHON_FAST_THREAD_STATE - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#elif PY_VERSION_HEX >= 0x03060000 - #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet() -#elif PY_VERSION_HEX >= 0x03000000 - #define __Pyx_PyThreadState_Current PyThreadState_GET() -#else - #define __Pyx_PyThreadState_Current _PyThreadState_Current -#endif -#if CYTHON_COMPILING_IN_LIMITED_API -static CYTHON_INLINE void *__Pyx_PyModule_GetState(PyObject *op) -{ - void *result; - result = PyModule_GetState(op); - if (!result) - Py_FatalError("Couldn't find the module state"); - return result; -} -#endif -#define __Pyx_PyObject_GetSlot(obj, name, func_ctype) __Pyx_PyType_GetSlot(Py_TYPE(obj), name, func_ctype) -#if CYTHON_COMPILING_IN_LIMITED_API - #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((func_ctype) PyType_GetSlot((type), Py_##name)) -#else - #define __Pyx_PyType_GetSlot(type, name, func_ctype) ((type)->name) -#endif -#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT) -#include "pythread.h" -#define Py_tss_NEEDS_INIT 0 -typedef int Py_tss_t; -static CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) { - *key = PyThread_create_key(); - return 0; -} -static CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) { - Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t)); - *key = Py_tss_NEEDS_INIT; - return key; -} -static CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) { - PyObject_Free(key); -} -static CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) { - return *key != Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) { - PyThread_delete_key(*key); - *key = Py_tss_NEEDS_INIT; -} -static CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) { - return PyThread_set_key_value(*key, value); -} -static CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) { - return PyThread_get_key_value(*key); -} -#endif -#if PY_MAJOR_VERSION < 3 - #if CYTHON_COMPILING_IN_PYPY - #if PYPY_VERSION_NUM < 0x07030600 - #if defined(__cplusplus) && __cplusplus >= 201402L - [[deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")]] - #elif defined(__GNUC__) || defined(__clang__) - __attribute__ ((__deprecated__("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6"))) - #elif defined(_MSC_VER) - __declspec(deprecated("`with nogil:` inside a nogil function will not release the GIL in PyPy2 < 7.3.6")) - #endif - static CYTHON_INLINE int PyGILState_Check(void) { - return 0; - } - #else // PYPY_VERSION_NUM < 0x07030600 - #endif // PYPY_VERSION_NUM < 0x07030600 - #else - static CYTHON_INLINE int PyGILState_Check(void) { - PyThreadState * tstate = _PyThreadState_Current; - return tstate && (tstate == PyGILState_GetThisThreadState()); - } - #endif -#endif -#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized) -#define __Pyx_PyDict_NewPresized(n) ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n)) -#else -#define __Pyx_PyDict_NewPresized(n) PyDict_New() -#endif -#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX > 0x030600B4 && CYTHON_USE_UNICODE_INTERNALS -#define __Pyx_PyDict_GetItemStrWithError(dict, name) _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash) -static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStr(PyObject *dict, PyObject *name) { - PyObject *res = __Pyx_PyDict_GetItemStrWithError(dict, name); - if (res == NULL) PyErr_Clear(); - return res; -} -#elif PY_MAJOR_VERSION >= 3 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07020000) -#define __Pyx_PyDict_GetItemStrWithError PyDict_GetItemWithError -#define __Pyx_PyDict_GetItemStr PyDict_GetItem -#else -static CYTHON_INLINE PyObject * __Pyx_PyDict_GetItemStrWithError(PyObject *dict, PyObject *name) { -#if CYTHON_COMPILING_IN_PYPY - return PyDict_GetItem(dict, name); -#else - PyDictEntry *ep; - PyDictObject *mp = (PyDictObject*) dict; - long hash = ((PyStringObject *) name)->ob_shash; - assert(hash != -1); - ep = (mp->ma_lookup)(mp, name, hash); - if (ep == NULL) { - return NULL; - } - return ep->me_value; -#endif -} -#define __Pyx_PyDict_GetItemStr PyDict_GetItem -#endif -#if CYTHON_USE_TYPE_SLOTS - #define __Pyx_PyType_GetFlags(tp) (((PyTypeObject *)tp)->tp_flags) - #define __Pyx_PyType_HasFeature(type, feature) ((__Pyx_PyType_GetFlags(type) & (feature)) != 0) - #define __Pyx_PyObject_GetIterNextFunc(obj) (Py_TYPE(obj)->tp_iternext) -#else - #define __Pyx_PyType_GetFlags(tp) (PyType_GetFlags((PyTypeObject *)tp)) - #define __Pyx_PyType_HasFeature(type, feature) PyType_HasFeature(type, feature) - #define __Pyx_PyObject_GetIterNextFunc(obj) PyIter_Next -#endif -#if CYTHON_USE_TYPE_SPECS && PY_VERSION_HEX >= 0x03080000 -#define __Pyx_PyHeapTypeObject_GC_Del(obj) {\ - PyTypeObject *type = Py_TYPE(obj);\ - assert(__Pyx_PyType_HasFeature(type, Py_TPFLAGS_HEAPTYPE));\ - PyObject_GC_Del(obj);\ - Py_DECREF(type);\ -} -#else -#define __Pyx_PyHeapTypeObject_GC_Del(obj) PyObject_GC_Del(obj) -#endif -#if CYTHON_COMPILING_IN_LIMITED_API - #define CYTHON_PEP393_ENABLED 1 - #define __Pyx_PyUnicode_READY(op) (0) - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GetLength(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_ReadChar(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((void)u, 1114111U) - #define __Pyx_PyUnicode_KIND(u) ((void)u, (0)) - #define __Pyx_PyUnicode_DATA(u) ((void*)u) - #define __Pyx_PyUnicode_READ(k, d, i) ((void)k, PyUnicode_ReadChar((PyObject*)(d), i)) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GetLength(u)) -#elif PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) - #define CYTHON_PEP393_ENABLED 1 - #if PY_VERSION_HEX >= 0x030C0000 - #define __Pyx_PyUnicode_READY(op) (0) - #else - #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ - 0 : _PyUnicode_Ready((PyObject *)(op))) - #endif - #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) - #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) - #define __Pyx_PyUnicode_KIND(u) ((int)PyUnicode_KIND(u)) - #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) - #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, (Py_UCS4) ch) - #if PY_VERSION_HEX >= 0x030C0000 - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_LENGTH(u)) - #else - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x03090000 - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : ((PyCompactUnicodeObject *)(u))->wstr_length)) - #else - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? 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__Pyx_NewRef(obj) : PySequence_Tuple(obj)) -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject*); -#if CYTHON_ASSUME_SAFE_MACROS -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #if PY_VERSION_HEX >= 0x030C00A7 - #ifndef _PyLong_SIGN_MASK - #define _PyLong_SIGN_MASK 3 - #endif - #ifndef _PyLong_NON_SIZE_BITS - #define _PyLong_NON_SIZE_BITS 3 - #endif - #define __Pyx_PyLong_Sign(x) (((PyLongObject*)x)->long_value.lv_tag & _PyLong_SIGN_MASK) - #define __Pyx_PyLong_IsNeg(x) ((__Pyx_PyLong_Sign(x) & 2) != 0) - #define __Pyx_PyLong_IsNonNeg(x) (!__Pyx_PyLong_IsNeg(x)) - #define __Pyx_PyLong_IsZero(x) (__Pyx_PyLong_Sign(x) & 1) - #define __Pyx_PyLong_IsPos(x) (__Pyx_PyLong_Sign(x) == 0) - #define __Pyx_PyLong_CompactValueUnsigned(x) (__Pyx_PyLong_Digits(x)[0]) - #define __Pyx_PyLong_DigitCount(x) ((Py_ssize_t) (((PyLongObject*)x)->long_value.lv_tag >> _PyLong_NON_SIZE_BITS)) - #define __Pyx_PyLong_SignedDigitCount(x)\ - ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * __Pyx_PyLong_DigitCount(x)) - #if defined(PyUnstable_Long_IsCompact) && defined(PyUnstable_Long_CompactValue) - #define __Pyx_PyLong_IsCompact(x) PyUnstable_Long_IsCompact((PyLongObject*) x) - #define __Pyx_PyLong_CompactValue(x) PyUnstable_Long_CompactValue((PyLongObject*) x) - #else - #define __Pyx_PyLong_IsCompact(x) (((PyLongObject*)x)->long_value.lv_tag < (2 << _PyLong_NON_SIZE_BITS)) - #define __Pyx_PyLong_CompactValue(x) ((1 - (Py_ssize_t) __Pyx_PyLong_Sign(x)) * (Py_ssize_t) __Pyx_PyLong_Digits(x)[0]) - #endif - typedef Py_ssize_t __Pyx_compact_pylong; - typedef size_t __Pyx_compact_upylong; - #else // Py < 3.12 - #define __Pyx_PyLong_IsNeg(x) (Py_SIZE(x) < 0) - #define __Pyx_PyLong_IsNonNeg(x) (Py_SIZE(x) >= 0) - #define __Pyx_PyLong_IsZero(x) (Py_SIZE(x) == 0) - #define __Pyx_PyLong_IsPos(x) (Py_SIZE(x) > 0) - #define __Pyx_PyLong_CompactValueUnsigned(x) ((Py_SIZE(x) == 0) ? 0 : __Pyx_PyLong_Digits(x)[0]) - #define __Pyx_PyLong_DigitCount(x) __Pyx_sst_abs(Py_SIZE(x)) - #define __Pyx_PyLong_SignedDigitCount(x) Py_SIZE(x) - #define __Pyx_PyLong_IsCompact(x) (Py_SIZE(x) == 0 || Py_SIZE(x) == 1 || Py_SIZE(x) == -1) - #define __Pyx_PyLong_CompactValue(x)\ - ((Py_SIZE(x) == 0) ? (sdigit) 0 : ((Py_SIZE(x) < 0) ? -(sdigit)__Pyx_PyLong_Digits(x)[0] : (sdigit)__Pyx_PyLong_Digits(x)[0])) - typedef sdigit __Pyx_compact_pylong; - typedef digit __Pyx_compact_upylong; - #endif - #if PY_VERSION_HEX >= 0x030C00A5 - #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->long_value.ob_digit) - #else - #define __Pyx_PyLong_Digits(x) (((PyLongObject*)x)->ob_digit) - #endif -#endif -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = (char) c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; 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-static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A6 -#define __Pyx_PyErr_SetNone(exc) (Py_INCREF(exc), __Pyx_ErrRestore((exc), NULL, NULL)) -#else -#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) -#endif -#else -#define __Pyx_PyErr_Clear() PyErr_Clear() -#define __Pyx_PyErr_SetNone(exc) PyErr_SetNone(exc) -#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) -#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) -#define __Pyx_ErrRestoreInState(tstate, type, value, tb) PyErr_Restore(type, value, tb) -#define __Pyx_ErrFetchInState(tstate, type, value, tb) PyErr_Fetch(type, value, tb) -#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) -#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) -#endif - -/* PyObjectGetAttrStr.proto */ -#if CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name); -#else -#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) -#endif - -/* PyObjectGetAttrStrNoError.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStrNoError(PyObject* obj, PyObject* attr_name); - -/* GetBuiltinName.proto */ -static PyObject *__Pyx_GetBuiltinName(PyObject *name); - -/* TupleAndListFromArray.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyList_FromArray(PyObject *const *src, Py_ssize_t n); -static CYTHON_INLINE PyObject* __Pyx_PyTuple_FromArray(PyObject *const *src, Py_ssize_t n); -#endif - -/* IncludeStringH.proto */ -#include - -/* BytesEquals.proto */ -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); - -/* UnicodeEquals.proto */ -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); - -/* fastcall.proto */ -#define __Pyx_Arg_VARARGS(args, i) PyTuple_GET_ITEM(args, i) -#define __Pyx_NumKwargs_VARARGS(kwds) PyDict_Size(kwds) -#define __Pyx_KwValues_VARARGS(args, nargs) NULL -#define __Pyx_GetKwValue_VARARGS(kw, kwvalues, s) __Pyx_PyDict_GetItemStrWithError(kw, s) -#define __Pyx_KwargsAsDict_VARARGS(kw, kwvalues) PyDict_Copy(kw) -#if CYTHON_METH_FASTCALL - #define __Pyx_Arg_FASTCALL(args, i) args[i] - #define __Pyx_NumKwargs_FASTCALL(kwds) PyTuple_GET_SIZE(kwds) - #define __Pyx_KwValues_FASTCALL(args, nargs) ((args) + (nargs)) - static CYTHON_INLINE PyObject * __Pyx_GetKwValue_FASTCALL(PyObject *kwnames, PyObject *const *kwvalues, PyObject *s); - #define __Pyx_KwargsAsDict_FASTCALL(kw, kwvalues) _PyStack_AsDict(kwvalues, kw) -#else - #define __Pyx_Arg_FASTCALL __Pyx_Arg_VARARGS - #define __Pyx_NumKwargs_FASTCALL __Pyx_NumKwargs_VARARGS - #define __Pyx_KwValues_FASTCALL __Pyx_KwValues_VARARGS - #define __Pyx_GetKwValue_FASTCALL __Pyx_GetKwValue_VARARGS - #define __Pyx_KwargsAsDict_FASTCALL __Pyx_KwargsAsDict_VARARGS -#endif -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_ArgsSlice_VARARGS(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_VARARGS(args, start), stop - start) -#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) __Pyx_PyTuple_FromArray(&__Pyx_Arg_FASTCALL(args, start), stop - start) -#else -#define __Pyx_ArgsSlice_VARARGS(args, start, stop) PyTuple_GetSlice(args, start, stop) -#define __Pyx_ArgsSlice_FASTCALL(args, start, stop) PyTuple_GetSlice(args, start, stop) -#endif - -/* RaiseArgTupleInvalid.proto */ -static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, - Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); - -/* RaiseDoubleKeywords.proto */ -static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); - -/* ParseKeywords.proto */ -static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject *const *kwvalues, - PyObject **argnames[], - PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, - const char* function_name); - -/* ArgTypeTest.proto */ -#define __Pyx_ArgTypeTest(obj, type, none_allowed, name, exact)\ - ((likely(__Pyx_IS_TYPE(obj, type) | (none_allowed && (obj == Py_None)))) ? 1 :\ - __Pyx__ArgTypeTest(obj, type, name, exact)) -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact); - -/* RaiseException.proto */ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); - -/* PyFunctionFastCall.proto */ -#if CYTHON_FAST_PYCALL -#if !CYTHON_VECTORCALL -#define __Pyx_PyFunction_FastCall(func, args, nargs)\ - __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs); -#endif -#define __Pyx_BUILD_ASSERT_EXPR(cond)\ - (sizeof(char [1 - 2*!(cond)]) - 1) -#ifndef Py_MEMBER_SIZE -#define Py_MEMBER_SIZE(type, member) sizeof(((type *)0)->member) -#endif -#if !CYTHON_VECTORCALL -#if PY_VERSION_HEX >= 0x03080000 - #include "frameobject.h" -#if PY_VERSION_HEX >= 0x030b00a6 - #ifndef Py_BUILD_CORE - #define Py_BUILD_CORE 1 - #endif - #include "internal/pycore_frame.h" -#endif - #define __Pxy_PyFrame_Initialize_Offsets() - #define __Pyx_PyFrame_GetLocalsplus(frame) ((frame)->f_localsplus) -#else - static size_t __pyx_pyframe_localsplus_offset = 0; - #include "frameobject.h" - #define __Pxy_PyFrame_Initialize_Offsets()\ - ((void)__Pyx_BUILD_ASSERT_EXPR(sizeof(PyFrameObject) == offsetof(PyFrameObject, f_localsplus) + Py_MEMBER_SIZE(PyFrameObject, f_localsplus)),\ - (void)(__pyx_pyframe_localsplus_offset = ((size_t)PyFrame_Type.tp_basicsize) - Py_MEMBER_SIZE(PyFrameObject, f_localsplus))) - #define __Pyx_PyFrame_GetLocalsplus(frame)\ - (assert(__pyx_pyframe_localsplus_offset), (PyObject **)(((char *)(frame)) + __pyx_pyframe_localsplus_offset)) -#endif -#endif -#endif - -/* PyObjectCall.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); -#else -#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) -#endif - -/* PyObjectCallMethO.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); -#endif - -/* PyObjectFastCall.proto */ -#define __Pyx_PyObject_FastCall(func, args, nargs) __Pyx_PyObject_FastCallDict(func, args, (size_t)(nargs), NULL) -static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs); - -/* RaiseUnexpectedTypeError.proto */ -static int __Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj); - -/* GCCDiagnostics.proto */ -#if !defined(__INTEL_COMPILER) && defined(__GNUC__) && (__GNUC__ > 4 || (__GNUC__ == 4 && __GNUC_MINOR__ >= 6)) -#define __Pyx_HAS_GCC_DIAGNOSTIC -#endif - -/* BuildPyUnicode.proto */ -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char); - -/* CIntToPyUnicode.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char); - -/* CIntToPyUnicode.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char); - -/* JoinPyUnicode.proto */ -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - Py_UCS4 max_char); - -/* StrEquals.proto */ -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals -#else -#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals -#endif - -/* PyObjectFormatSimple.proto */ -#if CYTHON_COMPILING_IN_PYPY - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - PyObject_Format(s, f)) -#elif PY_MAJOR_VERSION < 3 - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - likely(PyString_CheckExact(s)) ? PyUnicode_FromEncodedObject(s, NULL, "strict") :\ - PyObject_Format(s, f)) -#elif CYTHON_USE_TYPE_SLOTS - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - likely(PyLong_CheckExact(s)) ? PyLong_Type.tp_repr(s) :\ - likely(PyFloat_CheckExact(s)) ? PyFloat_Type.tp_repr(s) :\ - PyObject_Format(s, f)) -#else - #define __Pyx_PyObject_FormatSimple(s, f) (\ - likely(PyUnicode_CheckExact(s)) ? (Py_INCREF(s), s) :\ - PyObject_Format(s, f)) -#endif - -CYTHON_UNUSED static int __pyx_array_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *); /*proto*/ -/* GetAttr.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *, PyObject *); - -/* GetItemInt.proto */ -#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) :\ - (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) :\ - __Pyx_GetItemInt_Generic(o, to_py_func(i)))) -#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) :\ - (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL)) -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - int wraparound, int boundscheck); -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j); -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, - int is_list, int wraparound, int boundscheck); - -/* PyObjectCallOneArg.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); - -/* ObjectGetItem.proto */ -#if CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key); -#else -#define __Pyx_PyObject_GetItem(obj, key) PyObject_GetItem(obj, key) -#endif - -/* KeywordStringCheck.proto */ -static int __Pyx_CheckKeywordStrings(PyObject *kw, const char* function_name, int kw_allowed); - -/* DivInt[Py_ssize_t].proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t, Py_ssize_t); - -/* UnaryNegOverflows.proto */ -#define __Pyx_UNARY_NEG_WOULD_OVERFLOW(x)\ - (((x) < 0) & ((unsigned long)(x) == 0-(unsigned long)(x))) - -/* GetAttr3.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *, PyObject *, PyObject *); - -/* PyDictVersioning.proto */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -#define __PYX_DICT_VERSION_INIT ((PY_UINT64_T) -1) -#define __PYX_GET_DICT_VERSION(dict) (((PyDictObject*)(dict))->ma_version_tag) -#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var)\ - (version_var) = __PYX_GET_DICT_VERSION(dict);\ - (cache_var) = (value); -#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) {\ - static PY_UINT64_T __pyx_dict_version = 0;\ - static PyObject *__pyx_dict_cached_value = NULL;\ - if (likely(__PYX_GET_DICT_VERSION(DICT) == __pyx_dict_version)) {\ - (VAR) = __pyx_dict_cached_value;\ - } else {\ - (VAR) = __pyx_dict_cached_value = (LOOKUP);\ - __pyx_dict_version = __PYX_GET_DICT_VERSION(DICT);\ - }\ -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj); -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj); -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version); -#else -#define __PYX_GET_DICT_VERSION(dict) (0) -#define __PYX_UPDATE_DICT_CACHE(dict, value, cache_var, version_var) -#define __PYX_PY_DICT_LOOKUP_IF_MODIFIED(VAR, DICT, LOOKUP) (VAR) = (LOOKUP); -#endif - -/* GetModuleGlobalName.proto */ -#if CYTHON_USE_DICT_VERSIONS -#define __Pyx_GetModuleGlobalName(var, name) do {\ - static PY_UINT64_T __pyx_dict_version = 0;\ - static PyObject *__pyx_dict_cached_value = NULL;\ - (var) = (likely(__pyx_dict_version == __PYX_GET_DICT_VERSION(__pyx_d))) ?\ - (likely(__pyx_dict_cached_value) ? __Pyx_NewRef(__pyx_dict_cached_value) : __Pyx_GetBuiltinName(name)) :\ - __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} while(0) -#define __Pyx_GetModuleGlobalNameUncached(var, name) do {\ - PY_UINT64_T __pyx_dict_version;\ - PyObject *__pyx_dict_cached_value;\ - (var) = __Pyx__GetModuleGlobalName(name, &__pyx_dict_version, &__pyx_dict_cached_value);\ -} while(0) -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value); -#else -#define __Pyx_GetModuleGlobalName(var, name) (var) = __Pyx__GetModuleGlobalName(name) -#define __Pyx_GetModuleGlobalNameUncached(var, name) (var) = __Pyx__GetModuleGlobalName(name) -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name); -#endif - -/* AssertionsEnabled.proto */ -#define __Pyx_init_assertions_enabled() -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define __pyx_assertions_enabled() (1) -#elif PY_VERSION_HEX < 0x03080000 || CYTHON_COMPILING_IN_PYPY || defined(Py_LIMITED_API) - #define __pyx_assertions_enabled() (!Py_OptimizeFlag) -#elif CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030900A6 - static int __pyx_assertions_enabled_flag; - #define __pyx_assertions_enabled() (__pyx_assertions_enabled_flag) - #undef __Pyx_init_assertions_enabled - static void __Pyx_init_assertions_enabled(void) { - __pyx_assertions_enabled_flag = ! _PyInterpreterState_GetConfig(__Pyx_PyThreadState_Current->interp)->optimization_level; - } -#else - #define __pyx_assertions_enabled() (!Py_OptimizeFlag) -#endif - -/* RaiseTooManyValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); - -/* RaiseNeedMoreValuesToUnpack.proto */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); - -/* RaiseNoneIterError.proto */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); - -/* ExtTypeTest.proto */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); - -/* GetTopmostException.proto */ -#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE -static _PyErr_StackItem * __Pyx_PyErr_GetTopmostException(PyThreadState *tstate); -#endif - -/* SaveResetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); -#else -#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) -#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) -#endif - -/* GetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* SwapException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSwap(type, value, tb) __Pyx__ExceptionSwap(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* Import.proto */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); - -/* ImportDottedModule.proto */ -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple); -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple); -#endif - -/* ssize_strlen.proto */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s); - -/* FastTypeChecks.proto */ -#if CYTHON_COMPILING_IN_CPYTHON -#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) __Pyx_IsAnySubtype2(Py_TYPE(obj), (PyTypeObject *)type1, (PyTypeObject *)type2) -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type); -static CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2); -#else -#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_TypeCheck2(obj, type1, type2) (PyObject_TypeCheck(obj, (PyTypeObject *)type1) || PyObject_TypeCheck(obj, (PyTypeObject *)type2)) -#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type) -#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2)) -#endif -#define __Pyx_PyErr_ExceptionMatches2(err1, err2) __Pyx_PyErr_GivenExceptionMatches2(__Pyx_PyErr_CurrentExceptionType(), err1, err2) -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) - -CYTHON_UNUSED static int __pyx_memoryview_getbuffer(PyObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /*proto*/ -/* ListCompAppend.proto */ -#if CYTHON_USE_PYLIST_INTERNALS && CYTHON_ASSUME_SAFE_MACROS -static CYTHON_INLINE int __Pyx_ListComp_Append(PyObject* list, PyObject* x) { - PyListObject* L = (PyListObject*) list; - Py_ssize_t len = Py_SIZE(list); - if (likely(L->allocated > len)) { - Py_INCREF(x); - PyList_SET_ITEM(list, len, x); - __Pyx_SET_SIZE(list, len + 1); - return 0; - } - return PyList_Append(list, x); -} -#else -#define __Pyx_ListComp_Append(L,x) PyList_Append(L,x) -#endif - -/* PySequenceMultiply.proto */ -#define __Pyx_PySequence_Multiply_Left(mul, seq) __Pyx_PySequence_Multiply(seq, mul) -static CYTHON_INLINE PyObject* __Pyx_PySequence_Multiply(PyObject *seq, Py_ssize_t mul); - -/* SetItemInt.proto */ -#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck)\ - (__Pyx_fits_Py_ssize_t(i, type, is_signed) ?\ - __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) :\ - (is_list ? 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- -/* OverlappingSlices.proto */ -static int __pyx_slices_overlap(__Pyx_memviewslice *slice1, - __Pyx_memviewslice *slice2, - int ndim, size_t itemsize); - -/* IsLittleEndian.proto */ -static CYTHON_INLINE int __Pyx_Is_Little_Endian(void); - -/* BufferFormatCheck.proto */ -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type); - -/* TypeInfoCompare.proto */ -static int __pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b); - -/* MemviewSliceValidateAndInit.proto */ -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *, int writable_flag); - -/* ObjectToMemviewSlice.proto */ -static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *, int writable_flag); - -/* MemviewSliceCopyTemplate.proto */ -static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object); - -/* MemviewSliceInit.proto */ -#define __Pyx_BUF_MAX_NDIMS %(BUF_MAX_NDIMS)d -#define __Pyx_MEMVIEW_DIRECT 1 -#define __Pyx_MEMVIEW_PTR 2 -#define __Pyx_MEMVIEW_FULL 4 -#define __Pyx_MEMVIEW_CONTIG 8 -#define __Pyx_MEMVIEW_STRIDED 16 -#define __Pyx_MEMVIEW_FOLLOW 32 -#define __Pyx_IS_C_CONTIG 1 -#define __Pyx_IS_F_CONTIG 2 -static int __Pyx_init_memviewslice( - struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference); -static CYTHON_INLINE int __pyx_add_acquisition_count_locked( - __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); -static CYTHON_INLINE int __pyx_sub_acquisition_count_locked( - __pyx_atomic_int_type *acquisition_count, PyThread_type_lock lock); -#define __pyx_get_slice_count_pointer(memview) (&memview->acquisition_count) -#define __PYX_INC_MEMVIEW(slice, have_gil) __Pyx_INC_MEMVIEW(slice, have_gil, __LINE__) -#define __PYX_XCLEAR_MEMVIEW(slice, have_gil) __Pyx_XCLEAR_MEMVIEW(slice, have_gil, __LINE__) -static CYTHON_INLINE void __Pyx_INC_MEMVIEW(__Pyx_memviewslice *, int, int); -static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *, int, int); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntToPy.proto */ -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); - -/* CIntFromPy.proto */ -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); - -/* CIntFromPy.proto */ -static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *); - -/* FormatTypeName.proto */ -#if CYTHON_COMPILING_IN_LIMITED_API -typedef PyObject *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%U" -static __Pyx_TypeName __Pyx_PyType_GetName(PyTypeObject* tp); -#define __Pyx_DECREF_TypeName(obj) Py_XDECREF(obj) -#else -typedef const char *__Pyx_TypeName; -#define __Pyx_FMT_TYPENAME "%.200s" -#define __Pyx_PyType_GetName(tp) ((tp)->tp_name) -#define __Pyx_DECREF_TypeName(obj) -#endif - -/* CheckBinaryVersion.proto */ -static int __Pyx_check_binary_version(void); - -/* InitStrings.proto */ -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); - -/* #### Code section: module_declarations ### */ -static PyObject *__pyx_array_get_memview(struct __pyx_array_obj *__pyx_v_self); /* proto*/ -static char *__pyx_memoryview_get_item_pointer(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto*/ -static PyObject *__pyx_memoryview_is_slice(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assignment(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_dst, PyObject *__pyx_v_src); /* proto*/ -static PyObject *__pyx_memoryview_setitem_slice_assign_scalar(struct __pyx_memoryview_obj *__pyx_v_self, struct __pyx_memoryview_obj *__pyx_v_dst, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_setitem_indexed(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview_convert_item_to_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryview_assign_item_from_object(struct __pyx_memoryview_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryview__get_base(struct __pyx_memoryview_obj *__pyx_v_self); /* proto*/ -static PyObject *__pyx_memoryviewslice_convert_item_to_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp); /* proto*/ -static PyObject *__pyx_memoryviewslice_assign_item_from_object(struct __pyx_memoryviewslice_obj *__pyx_v_self, char *__pyx_v_itemp, PyObject *__pyx_v_value); /* proto*/ -static PyObject *__pyx_memoryviewslice__get_base(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto*/ - -/* Module declarations from "cython.view" */ - -/* Module declarations from "cython.dataclasses" */ - -/* Module declarations from "cython" */ - -/* Module declarations from "monotonic_align.core" */ -static PyObject *__pyx_collections_abc_Sequence = 0; -static PyObject *generic = 0; -static PyObject *strided = 0; -static PyObject *indirect = 0; -static PyObject *contiguous = 0; -static PyObject *indirect_contiguous = 0; -static int __pyx_memoryview_thread_locks_used; -static PyThread_type_lock __pyx_memoryview_thread_locks[8]; -static void __pyx_f_15monotonic_align_4core_maximum_path_each(__Pyx_memviewslice, __Pyx_memviewslice, int, int, struct __pyx_opt_args_15monotonic_align_4core_maximum_path_each *__pyx_optional_args); /*proto*/ -static void __pyx_f_15monotonic_align_4core_maximum_path_c(__Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, __Pyx_memviewslice, int __pyx_skip_dispatch); /*proto*/ -static int __pyx_array_allocate_buffer(struct __pyx_array_obj *); /*proto*/ -static struct __pyx_array_obj *__pyx_array_new(PyObject *, Py_ssize_t, char *, char *, char *); /*proto*/ -static PyObject *__pyx_memoryview_new(PyObject *, int, int, __Pyx_TypeInfo *); /*proto*/ -static CYTHON_INLINE int __pyx_memoryview_check(PyObject *); /*proto*/ -static PyObject *_unellipsify(PyObject *, int); /*proto*/ -static int assert_direct_dimensions(Py_ssize_t *, int); /*proto*/ -static struct __pyx_memoryview_obj *__pyx_memview_slice(struct __pyx_memoryview_obj *, PyObject *); /*proto*/ -static int __pyx_memoryview_slice_memviewslice(__Pyx_memviewslice *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int *, Py_ssize_t, Py_ssize_t, Py_ssize_t, int, int, int, int); /*proto*/ -static char *__pyx_pybuffer_index(Py_buffer *, char *, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memslice_transpose(__Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_fromslice(__Pyx_memviewslice, int, PyObject *(*)(char *), int (*)(char *, PyObject *), int); /*proto*/ -static __Pyx_memviewslice *__pyx_memoryview_get_slice_from_memoryview(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static void __pyx_memoryview_slice_copy(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object(struct __pyx_memoryview_obj *); /*proto*/ -static PyObject *__pyx_memoryview_copy_object_from_slice(struct __pyx_memoryview_obj *, __Pyx_memviewslice *); /*proto*/ -static Py_ssize_t abs_py_ssize_t(Py_ssize_t); /*proto*/ -static char __pyx_get_best_slice_order(__Pyx_memviewslice *, int); /*proto*/ -static void _copy_strided_to_strided(char *, Py_ssize_t *, char *, Py_ssize_t *, Py_ssize_t *, Py_ssize_t *, int, size_t); /*proto*/ -static void copy_strided_to_strided(__Pyx_memviewslice *, __Pyx_memviewslice *, int, size_t); /*proto*/ -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *, int); /*proto*/ -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *, Py_ssize_t *, Py_ssize_t, int, char); /*proto*/ -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *, __Pyx_memviewslice *, char, int); /*proto*/ -static int __pyx_memoryview_err_extents(int, Py_ssize_t, Py_ssize_t); /*proto*/ -static int __pyx_memoryview_err_dim(PyObject *, PyObject *, int); /*proto*/ -static int __pyx_memoryview_err(PyObject *, PyObject *); /*proto*/ -static int __pyx_memoryview_err_no_memory(void); /*proto*/ -static int __pyx_memoryview_copy_contents(__Pyx_memviewslice, __Pyx_memviewslice, int, int, int); /*proto*/ -static void __pyx_memoryview_broadcast_leading(__Pyx_memviewslice *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_copying(__Pyx_memviewslice *, int, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice_with_gil(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_refcount_objects_in_slice(char *, Py_ssize_t *, Py_ssize_t *, int, int); /*proto*/ -static void __pyx_memoryview_slice_assign_scalar(__Pyx_memviewslice *, int, size_t, void *, int); /*proto*/ -static void __pyx_memoryview__slice_assign_scalar(char *, Py_ssize_t *, Py_ssize_t *, int, size_t, void *); /*proto*/ -static PyObject *__pyx_unpickle_Enum__set_state(struct __pyx_MemviewEnum_obj *, PyObject *); /*proto*/ -/* #### Code section: typeinfo ### */ -static __Pyx_TypeInfo __Pyx_TypeInfo_int = { "int", NULL, sizeof(int), { 0 }, 0, __PYX_IS_UNSIGNED(int) ? 'U' : 'I', __PYX_IS_UNSIGNED(int), 0 }; -static __Pyx_TypeInfo __Pyx_TypeInfo_float = { "float", NULL, sizeof(float), { 0 }, 0, 'R', 0, 0 }; -/* #### Code section: before_global_var ### */ -#define __Pyx_MODULE_NAME "monotonic_align.core" -extern int __pyx_module_is_main_monotonic_align__core; -int __pyx_module_is_main_monotonic_align__core = 0; - -/* Implementation of "monotonic_align.core" */ -/* #### Code section: global_var ### */ -static PyObject *__pyx_builtin_range; -static PyObject *__pyx_builtin___import__; -static PyObject *__pyx_builtin_ValueError; -static PyObject *__pyx_builtin_MemoryError; -static PyObject *__pyx_builtin_enumerate; -static PyObject *__pyx_builtin_TypeError; -static PyObject *__pyx_builtin_AssertionError; -static PyObject *__pyx_builtin_Ellipsis; -static PyObject *__pyx_builtin_id; -static PyObject *__pyx_builtin_IndexError; -/* #### Code section: string_decls ### */ -static const char __pyx_k_[] = ": "; -static const char __pyx_k_O[] = "O"; -static const char __pyx_k_c[] = "c"; -static const char __pyx_k__2[] = "."; -static const char __pyx_k__3[] = "*"; -static const char __pyx_k__6[] = "'"; -static const char __pyx_k__7[] = ")"; -static const char __pyx_k_gc[] = "gc"; -static const char __pyx_k_id[] = "id"; -static const char __pyx_k__23[] = "?"; -static const char __pyx_k_abc[] = "abc"; -static const char __pyx_k_and[] = " and "; -static const char __pyx_k_got[] = " (got "; -static const char __pyx_k_new[] = "__new__"; -static const char __pyx_k_obj[] = "obj"; -static const char __pyx_k_sys[] = "sys"; -static const char __pyx_k_base[] = "base"; -static const char __pyx_k_dict[] = "__dict__"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_mode[] = "mode"; -static const char __pyx_k_name[] = "name"; -static const char __pyx_k_ndim[] = "ndim"; -static const char __pyx_k_pack[] = "pack"; -static const char __pyx_k_size[] = "size"; -static const char __pyx_k_spec[] = "__spec__"; -static const char __pyx_k_step[] = "step"; -static const char __pyx_k_stop[] = "stop"; -static const char __pyx_k_t_xs[] = "t_xs"; -static const char __pyx_k_t_ys[] = "t_ys"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_ASCII[] = "ASCII"; -static const char __pyx_k_class[] = "__class__"; -static const char __pyx_k_count[] = "count"; -static const char __pyx_k_error[] = "error"; -static const char __pyx_k_flags[] = "flags"; -static const char __pyx_k_index[] = "index"; -static const char __pyx_k_paths[] = "paths"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_shape[] = "shape"; -static const char __pyx_k_start[] = "start"; -static const char __pyx_k_enable[] = "enable"; -static const char __pyx_k_encode[] = "encode"; -static const char __pyx_k_format[] = "format"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_name_2[] = "__name__"; -static const char __pyx_k_pickle[] = "pickle"; -static const char __pyx_k_reduce[] = "__reduce__"; -static const char __pyx_k_struct[] = "struct"; -static const char __pyx_k_unpack[] = "unpack"; -static const char __pyx_k_update[] = "update"; -static const char __pyx_k_values[] = "values"; -static const char __pyx_k_disable[] = "disable"; -static const char __pyx_k_fortran[] = "fortran"; -static const char __pyx_k_memview[] = "memview"; -static const char __pyx_k_Ellipsis[] = "Ellipsis"; -static const char __pyx_k_Sequence[] = "Sequence"; -static const char __pyx_k_core_pyx[] = "core.pyx"; -static const char __pyx_k_getstate[] = "__getstate__"; -static const char __pyx_k_itemsize[] = "itemsize"; -static const char __pyx_k_pyx_type[] = "__pyx_type"; -static const char __pyx_k_register[] = "register"; -static const char __pyx_k_setstate[] = "__setstate__"; -static const char __pyx_k_TypeError[] = "TypeError"; -static const char __pyx_k_enumerate[] = "enumerate"; -static const char __pyx_k_isenabled[] = "isenabled"; -static const char __pyx_k_pyx_state[] = "__pyx_state"; -static const char __pyx_k_reduce_ex[] = "__reduce_ex__"; -static const char __pyx_k_IndexError[] = "IndexError"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_pyx_result[] = "__pyx_result"; -static const char __pyx_k_pyx_vtable[] = "__pyx_vtable__"; -static const char __pyx_k_MemoryError[] = "MemoryError"; -static const char __pyx_k_PickleError[] = "PickleError"; -static const char __pyx_k_collections[] = "collections"; -static const char __pyx_k_initializing[] = "_initializing"; -static const char __pyx_k_is_coroutine[] = "_is_coroutine"; -static const char __pyx_k_pyx_checksum[] = "__pyx_checksum"; -static const char __pyx_k_stringsource[] = ""; -static const char __pyx_k_version_info[] = "version_info"; -static const char __pyx_k_class_getitem[] = "__class_getitem__"; -static const char __pyx_k_reduce_cython[] = "__reduce_cython__"; -static const char __pyx_k_AssertionError[] = "AssertionError"; -static const char __pyx_k_maximum_path_c[] = "maximum_path_c"; -static const char __pyx_k_View_MemoryView[] = "View.MemoryView"; -static const char __pyx_k_allocate_buffer[] = "allocate_buffer"; -static const char __pyx_k_collections_abc[] = "collections.abc"; -static const char __pyx_k_dtype_is_object[] = "dtype_is_object"; -static const char __pyx_k_pyx_PickleError[] = "__pyx_PickleError"; -static const char __pyx_k_setstate_cython[] = "__setstate_cython__"; -static const char __pyx_k_pyx_unpickle_Enum[] = "__pyx_unpickle_Enum"; -static const char __pyx_k_asyncio_coroutines[] = "asyncio.coroutines"; -static const char __pyx_k_cline_in_traceback[] = "cline_in_traceback"; -static const char __pyx_k_strided_and_direct[] = ""; -static const char __pyx_k_monotonic_align_core[] = "monotonic_align.core"; -static const char __pyx_k_strided_and_indirect[] = ""; -static const char __pyx_k_Invalid_shape_in_axis[] = "Invalid shape in axis "; -static const char __pyx_k_contiguous_and_direct[] = ""; -static const char __pyx_k_Cannot_index_with_type[] = "Cannot index with type '"; -static const char __pyx_k_MemoryView_of_r_object[] = ""; -static const char __pyx_k_MemoryView_of_r_at_0x_x[] = ""; -static const char __pyx_k_contiguous_and_indirect[] = ""; -static const char __pyx_k_Dimension_d_is_not_direct[] = "Dimension %d is not direct"; -static const char __pyx_k_Index_out_of_bounds_axis_d[] = "Index out of bounds (axis %d)"; -static const char __pyx_k_Step_may_not_be_zero_axis_d[] = "Step may not be zero (axis %d)"; -static const char __pyx_k_itemsize_0_for_cython_array[] = "itemsize <= 0 for cython.array"; -static const char __pyx_k_unable_to_allocate_array_data[] = "unable to allocate array data."; -static const char __pyx_k_strided_and_direct_or_indirect[] = ""; -static const char __pyx_k_All_dimensions_preceding_dimensi[] = "All dimensions preceding dimension %d must be indexed and not sliced"; -static const char __pyx_k_Buffer_view_does_not_expose_stri[] = "Buffer view does not expose strides"; -static const char __pyx_k_Can_only_create_a_buffer_that_is[] = "Can only create a buffer that is contiguous in memory."; -static const char __pyx_k_Cannot_assign_to_read_only_memor[] = "Cannot assign to read-only memoryview"; -static const char __pyx_k_Cannot_create_writable_memory_vi[] = "Cannot create writable memory view from read-only memoryview"; -static const char __pyx_k_Cannot_transpose_memoryview_with[] = "Cannot transpose memoryview with indirect dimensions"; -static const char __pyx_k_Empty_shape_tuple_for_cython_arr[] = "Empty shape tuple for cython.array"; -static const char __pyx_k_Incompatible_checksums_0x_x_vs_0[] = "Incompatible checksums (0x%x vs (0x82a3537, 0x6ae9995, 0xb068931) = (name))"; -static const char __pyx_k_Indirect_dimensions_not_supporte[] = "Indirect dimensions not supported"; -static const char __pyx_k_Invalid_mode_expected_c_or_fortr[] = "Invalid mode, expected 'c' or 'fortran', got "; -static const char __pyx_k_Out_of_bounds_on_buffer_access_a[] = "Out of bounds on buffer access (axis "; -static const char __pyx_k_Unable_to_convert_item_to_object[] = "Unable to convert item to object"; -static const char __pyx_k_got_differing_extents_in_dimensi[] = "got differing extents in dimension "; -static const char __pyx_k_no_default___reduce___due_to_non[] = "no default __reduce__ due to non-trivial __cinit__"; -static const char __pyx_k_unable_to_allocate_shape_and_str[] = "unable to allocate shape and strides."; -/* #### Code section: decls ### */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array___cinit__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_shape, Py_ssize_t __pyx_v_itemsize, PyObject *__pyx_v_format, PyObject *__pyx_v_mode, int __pyx_v_allocate_buffer); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_2__getbuffer__(struct __pyx_array_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static void __pyx_array___pyx_pf_15View_dot_MemoryView_5array_4__dealloc__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_5array_7memview___get__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static Py_ssize_t __pyx_array___pyx_pf_15View_dot_MemoryView_5array_6__len__(struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_8__getattr__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_attr); /* proto */ -static PyObject *__pyx_array___pyx_pf_15View_dot_MemoryView_5array_10__getitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item); /* proto */ -static int __pyx_array___pyx_pf_15View_dot_MemoryView_5array_12__setitem__(struct __pyx_array_obj *__pyx_v_self, PyObject *__pyx_v_item, PyObject *__pyx_v_value); /* proto */ -static PyObject *__pyx_pf___pyx_array___reduce_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_array_2__setstate_cython__(CYTHON_UNUSED struct __pyx_array_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum___init__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v_name); /* proto */ -static PyObject *__pyx_MemviewEnum___pyx_pf_15View_dot_MemoryView_4Enum_2__repr__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum___reduce_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_MemviewEnum_2__setstate_cython__(struct __pyx_MemviewEnum_obj *__pyx_v_self, PyObject *__pyx_v___pyx_state); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview___cinit__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_obj, int __pyx_v_flags, int __pyx_v_dtype_is_object); /* proto */ -static void __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_2__dealloc__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_4__getitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_6__setitem__(struct __pyx_memoryview_obj *__pyx_v_self, PyObject *__pyx_v_index, PyObject *__pyx_v_value); /* proto */ -static int __pyx_memoryview___pyx_pf_15View_dot_MemoryView_10memoryview_8__getbuffer__(struct __pyx_memoryview_obj *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_1T___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4base___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_5shape___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_7strides___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_10suboffsets___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4ndim___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_8itemsize___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_6nbytes___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView_10memoryview_4size___get__(struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static Py_ssize_t 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proto */ -static PyObject *__pyx_pf___pyx_memoryview___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryview_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryview_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static void __pyx_memoryviewslice___pyx_pf_15View_dot_MemoryView_16_memoryviewslice___dealloc__(struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryviewslice___reduce_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self); /* proto */ -static PyObject *__pyx_pf___pyx_memoryviewslice_2__setstate_cython__(CYTHON_UNUSED struct __pyx_memoryviewslice_obj *__pyx_v_self, CYTHON_UNUSED PyObject *__pyx_v___pyx_state); /* proto */ -static PyObject *__pyx_pf_15View_dot_MemoryView___pyx_unpickle_Enum(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_v___pyx_type, long __pyx_v___pyx_checksum, PyObject 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__pyx_mstate_global->__pyx_kp_s_Step_may_not_be_zero_axis_d -#define __pyx_n_s_TypeError __pyx_mstate_global->__pyx_n_s_TypeError -#define __pyx_kp_s_Unable_to_convert_item_to_object __pyx_mstate_global->__pyx_kp_s_Unable_to_convert_item_to_object -#define __pyx_n_s_ValueError __pyx_mstate_global->__pyx_n_s_ValueError -#define __pyx_n_s_View_MemoryView __pyx_mstate_global->__pyx_n_s_View_MemoryView -#define __pyx_kp_u__2 __pyx_mstate_global->__pyx_kp_u__2 -#define __pyx_n_s__23 __pyx_mstate_global->__pyx_n_s__23 -#define __pyx_n_s__3 __pyx_mstate_global->__pyx_n_s__3 -#define __pyx_kp_u__6 __pyx_mstate_global->__pyx_kp_u__6 -#define __pyx_kp_u__7 __pyx_mstate_global->__pyx_kp_u__7 -#define __pyx_n_s_abc __pyx_mstate_global->__pyx_n_s_abc -#define __pyx_n_s_allocate_buffer __pyx_mstate_global->__pyx_n_s_allocate_buffer -#define __pyx_kp_u_and __pyx_mstate_global->__pyx_kp_u_and -#define __pyx_n_s_asyncio_coroutines __pyx_mstate_global->__pyx_n_s_asyncio_coroutines -#define __pyx_n_s_base __pyx_mstate_global->__pyx_n_s_base -#define __pyx_n_s_c __pyx_mstate_global->__pyx_n_s_c -#define __pyx_n_u_c __pyx_mstate_global->__pyx_n_u_c -#define __pyx_n_s_class __pyx_mstate_global->__pyx_n_s_class -#define __pyx_n_s_class_getitem __pyx_mstate_global->__pyx_n_s_class_getitem -#define __pyx_n_s_cline_in_traceback __pyx_mstate_global->__pyx_n_s_cline_in_traceback -#define __pyx_n_s_collections __pyx_mstate_global->__pyx_n_s_collections -#define __pyx_kp_s_collections_abc __pyx_mstate_global->__pyx_kp_s_collections_abc -#define __pyx_kp_s_contiguous_and_direct __pyx_mstate_global->__pyx_kp_s_contiguous_and_direct -#define __pyx_kp_s_contiguous_and_indirect __pyx_mstate_global->__pyx_kp_s_contiguous_and_indirect -#define __pyx_kp_s_core_pyx __pyx_mstate_global->__pyx_kp_s_core_pyx -#define __pyx_n_s_count __pyx_mstate_global->__pyx_n_s_count -#define __pyx_n_s_dict __pyx_mstate_global->__pyx_n_s_dict -#define __pyx_kp_u_disable __pyx_mstate_global->__pyx_kp_u_disable 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__pyx_mstate_global->__pyx_n_s_import -#define __pyx_n_s_index __pyx_mstate_global->__pyx_n_s_index -#define __pyx_n_s_initializing __pyx_mstate_global->__pyx_n_s_initializing -#define __pyx_n_s_is_coroutine __pyx_mstate_global->__pyx_n_s_is_coroutine -#define __pyx_kp_u_isenabled __pyx_mstate_global->__pyx_kp_u_isenabled -#define __pyx_n_s_itemsize __pyx_mstate_global->__pyx_n_s_itemsize -#define __pyx_kp_s_itemsize_0_for_cython_array __pyx_mstate_global->__pyx_kp_s_itemsize_0_for_cython_array -#define __pyx_n_s_main __pyx_mstate_global->__pyx_n_s_main -#define __pyx_n_s_maximum_path_c __pyx_mstate_global->__pyx_n_s_maximum_path_c -#define __pyx_n_s_memview __pyx_mstate_global->__pyx_n_s_memview -#define __pyx_n_s_mode __pyx_mstate_global->__pyx_n_s_mode -#define __pyx_n_s_monotonic_align_core __pyx_mstate_global->__pyx_n_s_monotonic_align_core -#define __pyx_n_s_name __pyx_mstate_global->__pyx_n_s_name -#define __pyx_n_s_name_2 __pyx_mstate_global->__pyx_n_s_name_2 -#define 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Py_ssize_t dst_extent = dst_shape[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t src_stride = src_strides[0] - * cdef Py_ssize_t dst_stride = dst_strides[0] - */ - __pyx_v_dst_extent = (__pyx_v_dst_shape[0]); - - /* "View.MemoryView":1146 - * cdef Py_ssize_t src_extent = src_shape[0] - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] # <<<<<<<<<<<<<< - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - */ - __pyx_v_src_stride = (__pyx_v_src_strides[0]); - - /* "View.MemoryView":1147 - * cdef Py_ssize_t dst_extent = dst_shape[0] - * cdef Py_ssize_t src_stride = src_strides[0] - * cdef Py_ssize_t dst_stride = dst_strides[0] # <<<<<<<<<<<<<< - * - * if ndim == 1: - */ - __pyx_v_dst_stride = (__pyx_v_dst_strides[0]); - - /* "View.MemoryView":1149 - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - * if ndim == 1: # <<<<<<<<<<<<<< - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - */ - __pyx_t_1 = (__pyx_v_ndim == 1); - if (__pyx_t_1) { - - /* "View.MemoryView":1150 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - __pyx_t_2 = (__pyx_v_src_stride > 0); - if (__pyx_t_2) { - } else { - __pyx_t_1 = __pyx_t_2; - goto __pyx_L5_bool_binop_done; - } - __pyx_t_2 = (__pyx_v_dst_stride > 0); - if (__pyx_t_2) { - } else { - __pyx_t_1 = __pyx_t_2; - goto __pyx_L5_bool_binop_done; - } - - /* "View.MemoryView":1151 - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - */ - __pyx_t_2 = (((size_t)__pyx_v_src_stride) == __pyx_v_itemsize); - if (__pyx_t_2) { - __pyx_t_2 = (__pyx_v_itemsize == ((size_t)__pyx_v_dst_stride)); - } - __pyx_t_1 = __pyx_t_2; - __pyx_L5_bool_binop_done:; - - /* "View.MemoryView":1150 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - if (__pyx_t_1) { - - /* "View.MemoryView":1152 - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, (__pyx_v_itemsize * __pyx_v_dst_extent))); - - /* "View.MemoryView":1150 - * - * if ndim == 1: - * if (src_stride > 0 and dst_stride > 0 and # <<<<<<<<<<<<<< - * src_stride == itemsize == dst_stride): - * memcpy(dst_data, src_data, itemsize * dst_extent) - */ - goto __pyx_L4; - } - - /* "View.MemoryView":1154 - * memcpy(dst_data, src_data, itemsize * dst_extent) - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - */ - /*else*/ { - __pyx_t_3 = __pyx_v_dst_extent; - __pyx_t_4 = __pyx_t_3; - for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { - __pyx_v_i = __pyx_t_5; - - /* "View.MemoryView":1155 - * else: - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) # <<<<<<<<<<<<<< - * src_data += src_stride - * dst_data += dst_stride - */ - (void)(memcpy(__pyx_v_dst_data, __pyx_v_src_data, __pyx_v_itemsize)); - - /* "View.MemoryView":1156 - * for i in range(dst_extent): - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * else: - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1157 - * memcpy(dst_data, src_data, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * else: - * for i in range(dst_extent): - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L4:; - - /* "View.MemoryView":1149 - * cdef Py_ssize_t dst_stride = dst_strides[0] - * - * if ndim == 1: # <<<<<<<<<<<<<< - * if (src_stride > 0 and dst_stride > 0 and - * src_stride == itemsize == dst_stride): - */ - goto __pyx_L3; - } - - /* "View.MemoryView":1159 - * dst_data += dst_stride - * else: - * for i in range(dst_extent): # <<<<<<<<<<<<<< - * _copy_strided_to_strided(src_data, src_strides + 1, - * dst_data, dst_strides + 1, - */ - /*else*/ { - __pyx_t_3 = __pyx_v_dst_extent; - __pyx_t_4 = __pyx_t_3; - for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { - __pyx_v_i = __pyx_t_5; - - /* "View.MemoryView":1160 - * else: - * for i in range(dst_extent): - * _copy_strided_to_strided(src_data, src_strides + 1, # <<<<<<<<<<<<<< - * dst_data, dst_strides + 1, - * src_shape + 1, dst_shape + 1, - */ - _copy_strided_to_strided(__pyx_v_src_data, (__pyx_v_src_strides + 1), __pyx_v_dst_data, (__pyx_v_dst_strides + 1), (__pyx_v_src_shape + 1), (__pyx_v_dst_shape + 1), (__pyx_v_ndim - 1), __pyx_v_itemsize); - - /* "View.MemoryView":1164 - * src_shape + 1, dst_shape + 1, - * ndim - 1, itemsize) - * src_data += src_stride # <<<<<<<<<<<<<< - * dst_data += dst_stride - * - */ - __pyx_v_src_data = (__pyx_v_src_data + __pyx_v_src_stride); - - /* "View.MemoryView":1165 - * ndim - 1, itemsize) - * src_data += src_stride - * dst_data += dst_stride # <<<<<<<<<<<<<< - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, - */ - __pyx_v_dst_data = (__pyx_v_dst_data + __pyx_v_dst_stride); - } - } - __pyx_L3:; - - /* "View.MemoryView":1137 - * - * @cython.cdivision(True) - * cdef void _copy_strided_to_strided(char *src_data, Py_ssize_t *src_strides, # <<<<<<<<<<<<<< - * char *dst_data, Py_ssize_t *dst_strides, - * Py_ssize_t *src_shape, Py_ssize_t *dst_shape, - */ - - /* function exit code */ -} - -/* "View.MemoryView":1167 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) noexcept nogil: - */ - -static void copy_strided_to_strided(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_dst, int __pyx_v_ndim, size_t __pyx_v_itemsize) { - - /* "View.MemoryView":1170 - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) noexcept nogil: - * _copy_strided_to_strided(src.data, src.strides, dst.data, dst.strides, # <<<<<<<<<<<<<< - * src.shape, dst.shape, ndim, itemsize) - * - */ - _copy_strided_to_strided(__pyx_v_src->data, __pyx_v_src->strides, __pyx_v_dst->data, __pyx_v_dst->strides, __pyx_v_src->shape, __pyx_v_dst->shape, __pyx_v_ndim, __pyx_v_itemsize); - - /* "View.MemoryView":1167 - * dst_data += dst_stride - * - * cdef void copy_strided_to_strided(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *dst, - * int ndim, size_t itemsize) noexcept nogil: - */ - - /* function exit code */ -} - -/* "View.MemoryView":1174 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef Py_ssize_t shape, size = src.memview.view.itemsize - */ - -static Py_ssize_t __pyx_memoryview_slice_get_size(__Pyx_memviewslice *__pyx_v_src, int __pyx_v_ndim) { - Py_ssize_t __pyx_v_shape; - Py_ssize_t __pyx_v_size; - Py_ssize_t __pyx_r; - Py_ssize_t __pyx_t_1; - Py_ssize_t *__pyx_t_2; - Py_ssize_t *__pyx_t_3; - Py_ssize_t *__pyx_t_4; - - /* "View.MemoryView":1176 - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef Py_ssize_t shape, size = src.memview.view.itemsize # <<<<<<<<<<<<<< - * - * for shape in src.shape[:ndim]: - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_size = __pyx_t_1; - - /* "View.MemoryView":1178 - * cdef Py_ssize_t shape, size = src.memview.view.itemsize - * - * for shape in src.shape[:ndim]: # <<<<<<<<<<<<<< - * size *= shape - * - */ - __pyx_t_3 = (__pyx_v_src->shape + __pyx_v_ndim); - for (__pyx_t_4 = __pyx_v_src->shape; __pyx_t_4 < __pyx_t_3; __pyx_t_4++) { - __pyx_t_2 = __pyx_t_4; - __pyx_v_shape = (__pyx_t_2[0]); - - /* "View.MemoryView":1179 - * - * for shape in src.shape[:ndim]: - * size *= shape # <<<<<<<<<<<<<< - * - * return size - */ - __pyx_v_size = (__pyx_v_size * __pyx_v_shape); - } - - /* "View.MemoryView":1181 - * size *= shape - * - * return size # <<<<<<<<<<<<<< - * - * @cname('__pyx_fill_contig_strides_array') - */ - __pyx_r = __pyx_v_size; - goto __pyx_L0; - - /* "View.MemoryView":1174 - * - * @cname('__pyx_memoryview_slice_get_size') - * cdef Py_ssize_t slice_get_size(__Pyx_memviewslice *src, int ndim) noexcept nogil: # <<<<<<<<<<<<<< - * "Return the size of the memory occupied by the slice in number of bytes" - * cdef Py_ssize_t shape, size = src.memview.view.itemsize - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1184 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) noexcept nogil: - */ - -static Py_ssize_t __pyx_fill_contig_strides_array(Py_ssize_t *__pyx_v_shape, Py_ssize_t *__pyx_v_strides, Py_ssize_t __pyx_v_stride, int __pyx_v_ndim, char __pyx_v_order) { - int __pyx_v_idx; - Py_ssize_t __pyx_r; - int __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - int __pyx_t_4; - - /* "View.MemoryView":1193 - * cdef int idx - * - * if order == 'F': # <<<<<<<<<<<<<< - * for idx in range(ndim): - * strides[idx] = stride - */ - __pyx_t_1 = (__pyx_v_order == 'F'); - if (__pyx_t_1) { - - /* "View.MemoryView":1194 - * - * if order == 'F': - * for idx in range(ndim): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride *= shape[idx] - */ - __pyx_t_2 = __pyx_v_ndim; - __pyx_t_3 = __pyx_t_2; - for (__pyx_t_4 = 0; __pyx_t_4 < __pyx_t_3; __pyx_t_4+=1) { - __pyx_v_idx = __pyx_t_4; - - /* "View.MemoryView":1195 - * if order == 'F': - * for idx in range(ndim): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride *= shape[idx] - * else: - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1196 - * for idx in range(ndim): - * strides[idx] = stride - * stride *= shape[idx] # <<<<<<<<<<<<<< - * else: - * for idx in range(ndim - 1, -1, -1): - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - - /* "View.MemoryView":1193 - * cdef int idx - * - * if order == 'F': # <<<<<<<<<<<<<< - * for idx in range(ndim): - * strides[idx] = stride - */ - goto __pyx_L3; - } - - /* "View.MemoryView":1198 - * stride *= shape[idx] - * else: - * for idx in range(ndim - 1, -1, -1): # <<<<<<<<<<<<<< - * strides[idx] = stride - * stride *= shape[idx] - */ - /*else*/ { - for (__pyx_t_2 = (__pyx_v_ndim - 1); __pyx_t_2 > -1; __pyx_t_2-=1) { - __pyx_v_idx = __pyx_t_2; - - /* "View.MemoryView":1199 - * else: - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride # <<<<<<<<<<<<<< - * stride *= shape[idx] - * - */ - (__pyx_v_strides[__pyx_v_idx]) = __pyx_v_stride; - - /* "View.MemoryView":1200 - * for idx in range(ndim - 1, -1, -1): - * strides[idx] = stride - * stride *= shape[idx] # <<<<<<<<<<<<<< - * - * return stride - */ - __pyx_v_stride = (__pyx_v_stride * (__pyx_v_shape[__pyx_v_idx])); - } - } - __pyx_L3:; - - /* "View.MemoryView":1202 - * stride *= shape[idx] - * - * return stride # <<<<<<<<<<<<<< - * - * @cname('__pyx_memoryview_copy_data_to_temp') - */ - __pyx_r = __pyx_v_stride; - goto __pyx_L0; - - /* "View.MemoryView":1184 - * - * @cname('__pyx_fill_contig_strides_array') - * cdef Py_ssize_t fill_contig_strides_array( # <<<<<<<<<<<<<< - * Py_ssize_t *shape, Py_ssize_t *strides, Py_ssize_t stride, - * int ndim, char order) noexcept nogil: - */ - - /* function exit code */ - __pyx_L0:; - return __pyx_r; -} - -/* "View.MemoryView":1205 - * - * @cname('__pyx_memoryview_copy_data_to_temp') - * cdef void *copy_data_to_temp(__Pyx_memviewslice *src, # <<<<<<<<<<<<<< - * __Pyx_memviewslice *tmpslice, - * char order, - */ - -static void *__pyx_memoryview_copy_data_to_temp(__Pyx_memviewslice *__pyx_v_src, __Pyx_memviewslice *__pyx_v_tmpslice, char __pyx_v_order, int __pyx_v_ndim) { - int __pyx_v_i; - void *__pyx_v_result; - size_t __pyx_v_itemsize; - size_t __pyx_v_size; - void *__pyx_r; - Py_ssize_t __pyx_t_1; - int __pyx_t_2; - int __pyx_t_3; - struct __pyx_memoryview_obj *__pyx_t_4; - int __pyx_t_5; - int __pyx_t_6; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; - #ifdef WITH_THREAD - PyGILState_STATE __pyx_gilstate_save; - #endif - - /* "View.MemoryView":1216 - * cdef void *result - * - * cdef size_t itemsize = src.memview.view.itemsize # <<<<<<<<<<<<<< - * cdef size_t size = slice_get_size(src, ndim) - * - */ - __pyx_t_1 = __pyx_v_src->memview->view.itemsize; - __pyx_v_itemsize = __pyx_t_1; - - /* "View.MemoryView":1217 - * - * cdef size_t itemsize = src.memview.view.itemsize - * cdef size_t size = slice_get_size(src, ndim) # <<<<<<<<<<<<<< - * - * result = malloc(size) - */ - __pyx_v_size = __pyx_memoryview_slice_get_size(__pyx_v_src, __pyx_v_ndim); - - /* "View.MemoryView":1219 - * cdef size_t size = slice_get_size(src, ndim) - * - * result = malloc(size) # <<<<<<<<<<<<<< - * if not result: - * _err_no_memory() - */ - __pyx_v_result = malloc(__pyx_v_size); - - /* "View.MemoryView":1220 - * - * result = malloc(size) - * if not result: # <<<<<<<<<<<<<< - * _err_no_memory() - * - */ - __pyx_t_2 = (!(__pyx_v_result != 0)); - if (__pyx_t_2) { - - /* "View.MemoryView":1221 - * result = malloc(size) - * if not result: - * _err_no_memory() # <<<<<<<<<<<<<< - * - * - */ - __pyx_t_3 = __pyx_memoryview_err_no_memory(); if (unlikely(__pyx_t_3 == ((int)-1))) __PYX_ERR(1, 1221, __pyx_L1_error) - - /* "View.MemoryView":1220 - * - * result = malloc(size) - * if not result: # <<<<<<<<<<<<<< - * _err_no_memory() - * - */ - } - - /* "View.MemoryView":1224 - * - * - * tmpslice.data = result # <<<<<<<<<<<<<< - * tmpslice.memview = src.memview - * for i in range(ndim): - */ - __pyx_v_tmpslice->data = ((char *)__pyx_v_result); - - /* "View.MemoryView":1225 - * - * tmpslice.data = result - * tmpslice.memview = src.memview # <<<<<<<<<<<<<< - * for i in range(ndim): - * tmpslice.shape[i] = src.shape[i] - */ - __pyx_t_4 = __pyx_v_src->memview; - __pyx_v_tmpslice->memview = __pyx_t_4; - - /* "View.MemoryView":1226 - * tmpslice.data = result - * tmpslice.memview = src.memview - * for i in range(ndim): # <<<<<<<<<<<<<< - * tmpslice.shape[i] = src.shape[i] - * tmpslice.suboffsets[i] = -1 - */ - __pyx_t_3 = __pyx_v_ndim; - __pyx_t_5 = __pyx_t_3; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = __pyx_t_6; - - /* "View.MemoryView":1227 - * tmpslice.memview = src.memview - * for i in range(ndim): - * tmpslice.shape[i] = src.shape[i] # <<<<<<<<<<<<<< - * tmpslice.suboffsets[i] = -1 - * - */ - (__pyx_v_tmpslice->shape[__pyx_v_i]) = (__pyx_v_src->shape[__pyx_v_i]); - - /* "View.MemoryView":1228 - * for i in range(ndim): - * tmpslice.shape[i] = src.shape[i] - * tmpslice.suboffsets[i] = -1 # <<<<<<<<<<<<<< - * - * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order) - */ - (__pyx_v_tmpslice->suboffsets[__pyx_v_i]) = -1L; - } - - /* "View.MemoryView":1230 - * tmpslice.suboffsets[i] = -1 - * - * fill_contig_strides_array(&tmpslice.shape[0], &tmpslice.strides[0], itemsize, ndim, order) # <<<<<<<<<<<<<< - * - * - */ - (void)(__pyx_fill_contig_strides_array((&(__pyx_v_tmpslice->shape[0])), (&(__pyx_v_tmpslice->strides[0])), __pyx_v_itemsize, __pyx_v_ndim, __pyx_v_order)); - - /* "View.MemoryView":1233 - * - * - * for i in range(ndim): # <<<<<<<<<<<<<< - * if tmpslice.shape[i] == 1: - * tmpslice.strides[i] = 0 - */ - __pyx_t_3 = __pyx_v_ndim; - __pyx_t_5 = __pyx_t_3; - for (__pyx_t_6 = 0; __pyx_t_6 < __pyx_t_5; __pyx_t_6+=1) { - __pyx_v_i = 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0, /*tp_subclasses*/ - 0, /*tp_weaklist*/ - 0, /*tp_del*/ - 0, /*tp_version_tag*/ - #if PY_VERSION_HEX >= 0x030400a1 - #if CYTHON_USE_TP_FINALIZE - 0, /*tp_finalize*/ - #else - NULL, /*tp_finalize*/ - #endif - #endif - #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) - 0, /*tp_vectorcall*/ - #endif - #if __PYX_NEED_TP_PRINT_SLOT == 1 - 0, /*tp_print*/ - #endif - #if PY_VERSION_HEX >= 0x030C0000 - 0, /*tp_watched*/ - #endif - #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 - 0, /*tp_pypy_flags*/ - #endif -}; -#endif -static struct __pyx_vtabstruct_memoryview __pyx_vtable_memoryview; - -static PyObject *__pyx_tp_new_memoryview(PyTypeObject *t, PyObject *a, PyObject *k) { - struct __pyx_memoryview_obj *p; - PyObject *o; - #if CYTHON_COMPILING_IN_LIMITED_API - allocfunc alloc_func = (allocfunc)PyType_GetSlot(t, Py_tp_alloc); - o = alloc_func(t, 0); - #else - if (likely(!__Pyx_PyType_HasFeature(t, Py_TPFLAGS_IS_ABSTRACT))) { - o = (*t->tp_alloc)(t, 0); - } else { - o = (PyObject *) PyBaseObject_Type.tp_new(t, __pyx_empty_tuple, 0); - } - if (unlikely(!o)) return 0; - #endif - p = ((struct __pyx_memoryview_obj *)o); - p->__pyx_vtab = __pyx_vtabptr_memoryview; - p->obj = Py_None; Py_INCREF(Py_None); - p->_size = Py_None; Py_INCREF(Py_None); - p->_array_interface = Py_None; Py_INCREF(Py_None); - p->view.obj = NULL; - if (unlikely(__pyx_memoryview___cinit__(o, a, k) < 0)) goto bad; - return o; - bad: - Py_DECREF(o); o = 0; - return NULL; -} - -static void __pyx_tp_dealloc_memoryview(PyObject *o) { - struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; - #if CYTHON_USE_TP_FINALIZE - if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { - if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc_memoryview) { - if (PyObject_CallFinalizerFromDealloc(o)) return; - } - } - #endif - PyObject_GC_UnTrack(o); - { - PyObject *etype, *eval, *etb; - PyErr_Fetch(&etype, &eval, &etb); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); - __pyx_memoryview___dealloc__(o); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); - PyErr_Restore(etype, eval, etb); - } - Py_CLEAR(p->obj); - Py_CLEAR(p->_size); - Py_CLEAR(p->_array_interface); - (*Py_TYPE(o)->tp_free)(o); -} - -static int __pyx_tp_traverse_memoryview(PyObject *o, visitproc v, void *a) { - int e; - struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; - if (p->obj) { - e = (*v)(p->obj, a); if (e) return e; - } - if (p->_size) { - e = (*v)(p->_size, a); if (e) return e; - } - if (p->_array_interface) { - e = (*v)(p->_array_interface, a); if (e) return e; - } - if (p->view.obj) { - e = (*v)(p->view.obj, a); if (e) return e; - } - return 0; -} - -static int __pyx_tp_clear_memoryview(PyObject *o) { - PyObject* tmp; - struct __pyx_memoryview_obj *p = (struct __pyx_memoryview_obj *)o; - tmp = ((PyObject*)p->obj); - p->obj = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - tmp = ((PyObject*)p->_size); - p->_size = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - tmp = ((PyObject*)p->_array_interface); - p->_array_interface = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - Py_CLEAR(p->view.obj); - return 0; -} -static PyObject *__pyx_sq_item_memoryview(PyObject *o, Py_ssize_t i) { - PyObject *r; - PyObject *x = PyInt_FromSsize_t(i); if(!x) return 0; - r = Py_TYPE(o)->tp_as_mapping->mp_subscript(o, x); - Py_DECREF(x); - return r; -} - -static int __pyx_mp_ass_subscript_memoryview(PyObject *o, PyObject *i, PyObject *v) { - if (v) { - return __pyx_memoryview___setitem__(o, i, v); - } - else { - __Pyx_TypeName o_type_name; - o_type_name = __Pyx_PyType_GetName(Py_TYPE(o)); - PyErr_Format(PyExc_NotImplementedError, - "Subscript deletion not supported by " __Pyx_FMT_TYPENAME, o_type_name); - __Pyx_DECREF_TypeName(o_type_name); - return -1; - } -} - -static PyObject *__pyx_getprop___pyx_memoryview_T(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_1T_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_base(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_4base_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_shape(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_5shape_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_strides(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_7strides_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_suboffsets(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_10suboffsets_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_ndim(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_4ndim_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_itemsize(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_8itemsize_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_nbytes(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_6nbytes_1__get__(o); -} - -static PyObject *__pyx_getprop___pyx_memoryview_size(PyObject *o, CYTHON_UNUSED void *x) { - return __pyx_pw_15View_dot_MemoryView_10memoryview_4size_1__get__(o); -} - -static PyObject *__pyx_specialmethod___pyx_memoryview___repr__(PyObject *self, CYTHON_UNUSED PyObject *arg) { - return __pyx_memoryview___repr__(self); -} - -static PyMethodDef __pyx_methods_memoryview[] = { - {"__repr__", (PyCFunction)__pyx_specialmethod___pyx_memoryview___repr__, METH_NOARGS|METH_COEXIST, 0}, - {"is_c_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_c_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"is_f_contig", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_is_f_contig, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"copy", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"copy_fortran", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_memoryview_copy_fortran, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryview_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {0, 0, 0, 0} -}; - -static struct PyGetSetDef __pyx_getsets_memoryview[] = { - {(char *)"T", __pyx_getprop___pyx_memoryview_T, 0, (char *)0, 0}, - {(char *)"base", __pyx_getprop___pyx_memoryview_base, 0, (char *)0, 0}, - {(char *)"shape", __pyx_getprop___pyx_memoryview_shape, 0, (char *)0, 0}, - {(char *)"strides", __pyx_getprop___pyx_memoryview_strides, 0, (char *)0, 0}, - {(char *)"suboffsets", __pyx_getprop___pyx_memoryview_suboffsets, 0, (char *)0, 0}, - {(char *)"ndim", __pyx_getprop___pyx_memoryview_ndim, 0, (char *)0, 0}, - {(char *)"itemsize", __pyx_getprop___pyx_memoryview_itemsize, 0, (char *)0, 0}, - {(char *)"nbytes", __pyx_getprop___pyx_memoryview_nbytes, 0, (char *)0, 0}, - {(char *)"size", __pyx_getprop___pyx_memoryview_size, 0, (char *)0, 0}, - {0, 0, 0, 0, 0} -}; -#if CYTHON_USE_TYPE_SPECS -#if !CYTHON_COMPILING_IN_LIMITED_API - -static PyBufferProcs __pyx_tp_as_buffer_memoryview = { - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getreadbuffer*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getwritebuffer*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getsegcount*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getcharbuffer*/ - #endif - __pyx_memoryview_getbuffer, /*bf_getbuffer*/ - 0, /*bf_releasebuffer*/ -}; -#endif -static PyType_Slot __pyx_type___pyx_memoryview_slots[] = { - {Py_tp_dealloc, (void *)__pyx_tp_dealloc_memoryview}, - {Py_tp_repr, (void *)__pyx_memoryview___repr__}, - {Py_sq_length, (void *)__pyx_memoryview___len__}, - {Py_sq_item, (void *)__pyx_sq_item_memoryview}, - {Py_mp_length, (void *)__pyx_memoryview___len__}, - {Py_mp_subscript, (void *)__pyx_memoryview___getitem__}, - {Py_mp_ass_subscript, (void *)__pyx_mp_ass_subscript_memoryview}, - {Py_tp_str, (void *)__pyx_memoryview___str__}, - #if defined(Py_bf_getbuffer) - {Py_bf_getbuffer, (void *)__pyx_memoryview_getbuffer}, - #endif - {Py_tp_traverse, (void *)__pyx_tp_traverse_memoryview}, - {Py_tp_clear, (void *)__pyx_tp_clear_memoryview}, - {Py_tp_methods, (void *)__pyx_methods_memoryview}, - {Py_tp_getset, (void *)__pyx_getsets_memoryview}, - {Py_tp_new, (void *)__pyx_tp_new_memoryview}, - {0, 0}, -}; -static PyType_Spec __pyx_type___pyx_memoryview_spec = { - "monotonic_align.core.memoryview", - sizeof(struct __pyx_memoryview_obj), - 0, - Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, - __pyx_type___pyx_memoryview_slots, -}; -#else - -static PySequenceMethods __pyx_tp_as_sequence_memoryview = { - __pyx_memoryview___len__, /*sq_length*/ - 0, /*sq_concat*/ - 0, /*sq_repeat*/ - __pyx_sq_item_memoryview, /*sq_item*/ - 0, /*sq_slice*/ - 0, /*sq_ass_item*/ - 0, /*sq_ass_slice*/ - 0, /*sq_contains*/ - 0, /*sq_inplace_concat*/ - 0, /*sq_inplace_repeat*/ -}; - -static PyMappingMethods __pyx_tp_as_mapping_memoryview = { - __pyx_memoryview___len__, /*mp_length*/ - __pyx_memoryview___getitem__, /*mp_subscript*/ - __pyx_mp_ass_subscript_memoryview, /*mp_ass_subscript*/ -}; - -static PyBufferProcs __pyx_tp_as_buffer_memoryview = { - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getreadbuffer*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getwritebuffer*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getsegcount*/ - #endif - #if PY_MAJOR_VERSION < 3 - 0, /*bf_getcharbuffer*/ - #endif - __pyx_memoryview_getbuffer, /*bf_getbuffer*/ - 0, /*bf_releasebuffer*/ -}; - -static PyTypeObject __pyx_type___pyx_memoryview = { - PyVarObject_HEAD_INIT(0, 0) - "monotonic_align.core.""memoryview", /*tp_name*/ - sizeof(struct __pyx_memoryview_obj), /*tp_basicsize*/ - 0, /*tp_itemsize*/ - __pyx_tp_dealloc_memoryview, /*tp_dealloc*/ - #if PY_VERSION_HEX < 0x030800b4 - 0, /*tp_print*/ - #endif - #if PY_VERSION_HEX >= 0x030800b4 - 0, /*tp_vectorcall_offset*/ - #endif - 0, /*tp_getattr*/ - 0, /*tp_setattr*/ - #if PY_MAJOR_VERSION < 3 - 0, /*tp_compare*/ - #endif - #if PY_MAJOR_VERSION >= 3 - 0, /*tp_as_async*/ - #endif - __pyx_memoryview___repr__, /*tp_repr*/ - 0, /*tp_as_number*/ - &__pyx_tp_as_sequence_memoryview, /*tp_as_sequence*/ - &__pyx_tp_as_mapping_memoryview, /*tp_as_mapping*/ - 0, /*tp_hash*/ - 0, /*tp_call*/ - __pyx_memoryview___str__, /*tp_str*/ - 0, /*tp_getattro*/ - 0, /*tp_setattro*/ - &__pyx_tp_as_buffer_memoryview, /*tp_as_buffer*/ - Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_HAVE_NEWBUFFER|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/ - 0, /*tp_doc*/ - __pyx_tp_traverse_memoryview, /*tp_traverse*/ - __pyx_tp_clear_memoryview, /*tp_clear*/ - 0, /*tp_richcompare*/ - 0, /*tp_weaklistoffset*/ - 0, /*tp_iter*/ - 0, /*tp_iternext*/ - __pyx_methods_memoryview, /*tp_methods*/ - 0, /*tp_members*/ - __pyx_getsets_memoryview, /*tp_getset*/ - 0, /*tp_base*/ - 0, /*tp_dict*/ - 0, /*tp_descr_get*/ - 0, /*tp_descr_set*/ - #if !CYTHON_USE_TYPE_SPECS - 0, /*tp_dictoffset*/ - #endif - 0, /*tp_init*/ - 0, /*tp_alloc*/ - __pyx_tp_new_memoryview, /*tp_new*/ - 0, /*tp_free*/ - 0, /*tp_is_gc*/ - 0, /*tp_bases*/ - 0, /*tp_mro*/ - 0, /*tp_cache*/ - 0, /*tp_subclasses*/ - 0, /*tp_weaklist*/ - 0, /*tp_del*/ - 0, /*tp_version_tag*/ - #if PY_VERSION_HEX >= 0x030400a1 - #if CYTHON_USE_TP_FINALIZE - 0, /*tp_finalize*/ - #else - NULL, /*tp_finalize*/ - #endif - #endif - #if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) - 0, /*tp_vectorcall*/ - #endif - #if __PYX_NEED_TP_PRINT_SLOT == 1 - 0, /*tp_print*/ - #endif - #if PY_VERSION_HEX >= 0x030C0000 - 0, /*tp_watched*/ - #endif - #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 - 0, /*tp_pypy_flags*/ - #endif -}; -#endif -static struct __pyx_vtabstruct__memoryviewslice __pyx_vtable__memoryviewslice; - -static PyObject *__pyx_tp_new__memoryviewslice(PyTypeObject *t, PyObject *a, PyObject *k) { - struct __pyx_memoryviewslice_obj *p; - PyObject *o = __pyx_tp_new_memoryview(t, a, k); - if (unlikely(!o)) return 0; - p = ((struct __pyx_memoryviewslice_obj *)o); - p->__pyx_base.__pyx_vtab = (struct __pyx_vtabstruct_memoryview*)__pyx_vtabptr__memoryviewslice; - p->from_object = Py_None; Py_INCREF(Py_None); - p->from_slice.memview = NULL; - return o; -} - -static void __pyx_tp_dealloc__memoryviewslice(PyObject *o) { - struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; - #if CYTHON_USE_TP_FINALIZE - if (unlikely((PY_VERSION_HEX >= 0x03080000 || __Pyx_PyType_HasFeature(Py_TYPE(o), Py_TPFLAGS_HAVE_FINALIZE)) && __Pyx_PyObject_GetSlot(o, tp_finalize, destructor)) && !__Pyx_PyObject_GC_IsFinalized(o)) { - if (__Pyx_PyObject_GetSlot(o, tp_dealloc, destructor) == __pyx_tp_dealloc__memoryviewslice) { - if (PyObject_CallFinalizerFromDealloc(o)) return; - } - } - #endif - PyObject_GC_UnTrack(o); - { - PyObject *etype, *eval, *etb; - PyErr_Fetch(&etype, &eval, &etb); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) + 1); - __pyx_memoryviewslice___dealloc__(o); - __Pyx_SET_REFCNT(o, Py_REFCNT(o) - 1); - PyErr_Restore(etype, eval, etb); - } - Py_CLEAR(p->from_object); - PyObject_GC_Track(o); - __pyx_tp_dealloc_memoryview(o); -} - -static int __pyx_tp_traverse__memoryviewslice(PyObject *o, visitproc v, void *a) { - int e; - struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; - e = __pyx_tp_traverse_memoryview(o, v, a); if (e) return e; - if (p->from_object) { - e = (*v)(p->from_object, a); if (e) return e; - } - return 0; -} - -static int __pyx_tp_clear__memoryviewslice(PyObject *o) { - PyObject* tmp; - struct __pyx_memoryviewslice_obj *p = (struct __pyx_memoryviewslice_obj *)o; - __pyx_tp_clear_memoryview(o); - tmp = ((PyObject*)p->from_object); - p->from_object = Py_None; Py_INCREF(Py_None); - Py_XDECREF(tmp); - __PYX_XCLEAR_MEMVIEW(&p->from_slice, 1); - return 0; -} - -static PyMethodDef __pyx_methods__memoryviewslice[] = { - {"__reduce_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_1__reduce_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {"__setstate_cython__", (PyCFunction)(void*)(__Pyx_PyCFunction_FastCallWithKeywords)__pyx_pw___pyx_memoryviewslice_3__setstate_cython__, __Pyx_METH_FASTCALL|METH_KEYWORDS, 0}, - {0, 0, 0, 0} -}; -#if CYTHON_USE_TYPE_SPECS -static PyType_Slot __pyx_type___pyx_memoryviewslice_slots[] = { - {Py_tp_dealloc, (void *)__pyx_tp_dealloc__memoryviewslice}, - {Py_tp_doc, (void *)PyDoc_STR("Internal class for passing memoryview slices to Python")}, - {Py_tp_traverse, (void *)__pyx_tp_traverse__memoryviewslice}, - {Py_tp_clear, (void *)__pyx_tp_clear__memoryviewslice}, - {Py_tp_methods, (void *)__pyx_methods__memoryviewslice}, - 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- #else - PyErr_Format(PyExc_TypeError, - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif -bad: - return -1; -} - -/* ArgTypeTest */ -static int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact) -{ - __Pyx_TypeName type_name; - __Pyx_TypeName obj_type_name; - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - else if (exact) { - #if PY_MAJOR_VERSION == 2 - if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; - #endif - } - else { - if (likely(__Pyx_TypeCheck(obj, type))) return 1; - } - type_name = __Pyx_PyType_GetName(type); - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "Argument '%.200s' has incorrect type (expected " __Pyx_FMT_TYPENAME - ", got " __Pyx_FMT_TYPENAME ")", name, type_name, obj_type_name); - __Pyx_DECREF_TypeName(type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return 0; -} - -/* RaiseException */ -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - __Pyx_PyThreadState_declare - CYTHON_UNUSED_VAR(cause); - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_PyThreadState_assign - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } - if (cause) { - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { - #if PY_VERSION_HEX >= 0x030C00A6 - PyException_SetTraceback(value, tb); - #elif CYTHON_FAST_THREAD_STATE - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#else - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -/* PyFunctionFastCall */ -#if CYTHON_FAST_PYCALL && !CYTHON_VECTORCALL -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = __Pyx_PyThreadState_Current; - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = __Pyx_PyFrame_GetLocalsplus(f); - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, Py_ssize_t nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, (int)nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif - -/* PyObjectCall */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = Py_TYPE(func)->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectCallMethO */ -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -/* PyObjectFastCall */ -static PyObject* __Pyx_PyObject_FastCall_fallback(PyObject *func, PyObject **args, size_t nargs, PyObject *kwargs) { - PyObject *argstuple; - PyObject *result; - size_t i; - argstuple = PyTuple_New((Py_ssize_t)nargs); - if (unlikely(!argstuple)) return NULL; - for (i = 0; i < nargs; i++) { - Py_INCREF(args[i]); - PyTuple_SET_ITEM(argstuple, (Py_ssize_t)i, args[i]); - } - result = __Pyx_PyObject_Call(func, argstuple, kwargs); - Py_DECREF(argstuple); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_FastCallDict(PyObject *func, PyObject **args, size_t _nargs, PyObject *kwargs) { - Py_ssize_t nargs = __Pyx_PyVectorcall_NARGS(_nargs); -#if CYTHON_COMPILING_IN_CPYTHON - if (nargs == 0 && kwargs == NULL) { -#if defined(__Pyx_CyFunction_USED) && defined(NDEBUG) - if (__Pyx_IsCyOrPyCFunction(func)) -#else - if (PyCFunction_Check(func)) -#endif - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) { - return __Pyx_PyObject_CallMethO(func, NULL); - } - } - } - else if (nargs == 1 && kwargs == NULL) { - if (PyCFunction_Check(func)) - { - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, args[0]); - } - } - } -#endif - #if PY_VERSION_HEX < 0x030800B1 - #if CYTHON_FAST_PYCCALL - if (PyCFunction_Check(func)) { - if (kwargs) { - return _PyCFunction_FastCallDict(func, args, nargs, kwargs); - } else { - return _PyCFunction_FastCallKeywords(func, args, nargs, NULL); - } - } - #if PY_VERSION_HEX >= 0x030700A1 - if (!kwargs && __Pyx_IS_TYPE(func, &PyMethodDescr_Type)) { - return _PyMethodDescr_FastCallKeywords(func, args, nargs, NULL); - } - #endif - #endif - #if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs); - } - #endif - #endif - #if CYTHON_VECTORCALL - vectorcallfunc f = _PyVectorcall_Function(func); - if (f) { - return f(func, args, (size_t)nargs, kwargs); - } - #elif defined(__Pyx_CyFunction_USED) && CYTHON_BACKPORT_VECTORCALL - if (__Pyx_CyFunction_CheckExact(func)) { - __pyx_vectorcallfunc f = __Pyx_CyFunction_func_vectorcall(func); - if (f) return f(func, args, (size_t)nargs, kwargs); - } - #endif - if (nargs == 0) { - return __Pyx_PyObject_Call(func, __pyx_empty_tuple, kwargs); - } - return __Pyx_PyObject_FastCall_fallback(func, args, (size_t)nargs, kwargs); -} - -/* RaiseUnexpectedTypeError */ -static int -__Pyx_RaiseUnexpectedTypeError(const char *expected, PyObject *obj) -{ - __Pyx_TypeName obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, "Expected %s, got " __Pyx_FMT_TYPENAME, - expected, obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return 0; -} - -/* CIntToDigits */ -static const char DIGIT_PAIRS_10[2*10*10+1] = { - "00010203040506070809" - "10111213141516171819" - "20212223242526272829" - "30313233343536373839" - "40414243444546474849" - "50515253545556575859" - "60616263646566676869" - "70717273747576777879" - "80818283848586878889" - "90919293949596979899" -}; -static const char DIGIT_PAIRS_8[2*8*8+1] = { - "0001020304050607" - "1011121314151617" - "2021222324252627" - "3031323334353637" - "4041424344454647" - "5051525354555657" - "6061626364656667" - "7071727374757677" -}; -static const char DIGITS_HEX[2*16+1] = { - "0123456789abcdef" - "0123456789ABCDEF" -}; - -/* BuildPyUnicode */ -static PyObject* __Pyx_PyUnicode_BuildFromAscii(Py_ssize_t ulength, char* chars, int clength, - int prepend_sign, char padding_char) { - PyObject *uval; - Py_ssize_t uoffset = ulength - clength; -#if CYTHON_USE_UNICODE_INTERNALS - Py_ssize_t i; -#if CYTHON_PEP393_ENABLED - void *udata; - uval = PyUnicode_New(ulength, 127); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_DATA(uval); -#else - Py_UNICODE *udata; - uval = PyUnicode_FromUnicode(NULL, ulength); - if (unlikely(!uval)) return NULL; - udata = PyUnicode_AS_UNICODE(uval); -#endif - if (uoffset > 0) { - i = 0; - if (prepend_sign) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, 0, '-'); - i++; - } - for (; i < uoffset; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, i, padding_char); - } - } - for (i=0; i < clength; i++) { - __Pyx_PyUnicode_WRITE(PyUnicode_1BYTE_KIND, udata, uoffset+i, chars[i]); - } -#else - { - PyObject *sign = NULL, *padding = NULL; - uval = NULL; - if (uoffset > 0) { - prepend_sign = !!prepend_sign; - if (uoffset > prepend_sign) { - padding = PyUnicode_FromOrdinal(padding_char); - if (likely(padding) && uoffset > prepend_sign + 1) { - PyObject *tmp; - PyObject *repeat = PyInt_FromSsize_t(uoffset - prepend_sign); - if (unlikely(!repeat)) goto done_or_error; - tmp = PyNumber_Multiply(padding, repeat); - Py_DECREF(repeat); - Py_DECREF(padding); - padding = tmp; - } - if (unlikely(!padding)) goto done_or_error; - } - if (prepend_sign) { - sign = PyUnicode_FromOrdinal('-'); - if (unlikely(!sign)) goto done_or_error; - } - } - uval = PyUnicode_DecodeASCII(chars, clength, NULL); - if (likely(uval) && padding) { - PyObject *tmp = PyNumber_Add(padding, uval); - Py_DECREF(uval); - uval = tmp; - } - if (likely(uval) && sign) { - PyObject *tmp = PyNumber_Add(sign, uval); - Py_DECREF(uval); - uval = tmp; - } -done_or_error: - Py_XDECREF(padding); - Py_XDECREF(sign); - } -#endif - return uval; -} - -/* CIntToPyUnicode */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_int(int value, Py_ssize_t width, char padding_char, char format_char) { - char digits[sizeof(int)*3+2]; - char *dpos, *end = digits + sizeof(int)*3+2; - const char *hex_digits = DIGITS_HEX; - Py_ssize_t length, ulength; - int prepend_sign, last_one_off; - int remaining; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (format_char == 'X') { - hex_digits += 16; - format_char = 'x'; - } - remaining = value; - last_one_off = 0; - dpos = end; - do { - int digit_pos; - switch (format_char) { - case 'o': - digit_pos = abs((int)(remaining % (8*8))); - remaining = (int) (remaining / (8*8)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); - last_one_off = (digit_pos < 8); - break; - case 'd': - digit_pos = abs((int)(remaining % (10*10))); - remaining = (int) (remaining / (10*10)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); - last_one_off = (digit_pos < 10); - break; - case 'x': - *(--dpos) = hex_digits[abs((int)(remaining % 16))]; - remaining = (int) (remaining / 16); - break; - default: - assert(0); - break; - } - } while (unlikely(remaining != 0)); - assert(!last_one_off || *dpos == '0'); - dpos += last_one_off; - length = end - dpos; - ulength = length; - prepend_sign = 0; - if (!is_unsigned && value <= neg_one) { - if (padding_char == ' ' || width <= length + 1) { - *(--dpos) = '-'; - ++length; - } else { - prepend_sign = 1; - } - ++ulength; - } - if (width > ulength) { - ulength = width; - } - if (ulength == 1) { - return PyUnicode_FromOrdinal(*dpos); - } - return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); -} - -/* CIntToPyUnicode */ -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_From_Py_ssize_t(Py_ssize_t value, Py_ssize_t width, char padding_char, char format_char) { - char digits[sizeof(Py_ssize_t)*3+2]; - char *dpos, *end = digits + sizeof(Py_ssize_t)*3+2; - const char *hex_digits = DIGITS_HEX; - Py_ssize_t length, ulength; - int prepend_sign, last_one_off; - Py_ssize_t remaining; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const Py_ssize_t neg_one = (Py_ssize_t) -1, const_zero = (Py_ssize_t) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (format_char == 'X') { - hex_digits += 16; - format_char = 'x'; - } - remaining = value; - last_one_off = 0; - dpos = end; - do { - int digit_pos; - switch (format_char) { - case 'o': - digit_pos = abs((int)(remaining % (8*8))); - remaining = (Py_ssize_t) (remaining / (8*8)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_8 + digit_pos * 2, 2); - last_one_off = (digit_pos < 8); - break; - case 'd': - digit_pos = abs((int)(remaining % (10*10))); - remaining = (Py_ssize_t) (remaining / (10*10)); - dpos -= 2; - memcpy(dpos, DIGIT_PAIRS_10 + digit_pos * 2, 2); - last_one_off = (digit_pos < 10); - break; - case 'x': - *(--dpos) = hex_digits[abs((int)(remaining % 16))]; - remaining = (Py_ssize_t) (remaining / 16); - break; - default: - assert(0); - break; - } - } while (unlikely(remaining != 0)); - assert(!last_one_off || *dpos == '0'); - dpos += last_one_off; - length = end - dpos; - ulength = length; - prepend_sign = 0; - if (!is_unsigned && value <= neg_one) { - if (padding_char == ' ' || width <= length + 1) { - *(--dpos) = '-'; - ++length; - } else { - prepend_sign = 1; - } - ++ulength; - } - if (width > ulength) { - ulength = width; - } - if (ulength == 1) { - return PyUnicode_FromOrdinal(*dpos); - } - return __Pyx_PyUnicode_BuildFromAscii(ulength, dpos, (int) length, prepend_sign, padding_char); -} - -/* JoinPyUnicode */ -static PyObject* __Pyx_PyUnicode_Join(PyObject* value_tuple, Py_ssize_t value_count, Py_ssize_t result_ulength, - Py_UCS4 max_char) { -#if CYTHON_USE_UNICODE_INTERNALS && CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - PyObject *result_uval; - int result_ukind, kind_shift; - Py_ssize_t i, char_pos; - void *result_udata; - CYTHON_MAYBE_UNUSED_VAR(max_char); -#if CYTHON_PEP393_ENABLED - result_uval = PyUnicode_New(result_ulength, max_char); - if (unlikely(!result_uval)) return NULL; - result_ukind = (max_char <= 255) ? PyUnicode_1BYTE_KIND : (max_char <= 65535) ? PyUnicode_2BYTE_KIND : PyUnicode_4BYTE_KIND; - kind_shift = (result_ukind == PyUnicode_4BYTE_KIND) ? 2 : result_ukind - 1; - result_udata = PyUnicode_DATA(result_uval); -#else - result_uval = PyUnicode_FromUnicode(NULL, result_ulength); - if (unlikely(!result_uval)) return NULL; - result_ukind = sizeof(Py_UNICODE); - kind_shift = (result_ukind == 4) ? 2 : result_ukind - 1; - result_udata = PyUnicode_AS_UNICODE(result_uval); -#endif - assert(kind_shift == 2 || kind_shift == 1 || kind_shift == 0); - char_pos = 0; - for (i=0; i < value_count; i++) { - int ukind; - Py_ssize_t ulength; - void *udata; - PyObject *uval = PyTuple_GET_ITEM(value_tuple, i); - if (unlikely(__Pyx_PyUnicode_READY(uval))) - goto bad; - ulength = __Pyx_PyUnicode_GET_LENGTH(uval); - if (unlikely(!ulength)) - continue; - if (unlikely((PY_SSIZE_T_MAX >> kind_shift) - ulength < char_pos)) - goto overflow; - ukind = __Pyx_PyUnicode_KIND(uval); - udata = __Pyx_PyUnicode_DATA(uval); - if (!CYTHON_PEP393_ENABLED || ukind == result_ukind) { - memcpy((char *)result_udata + (char_pos << kind_shift), udata, (size_t) (ulength << kind_shift)); - } else { - #if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030300F0 || defined(_PyUnicode_FastCopyCharacters) - _PyUnicode_FastCopyCharacters(result_uval, char_pos, uval, 0, ulength); - #else - Py_ssize_t j; - for (j=0; j < ulength; j++) { - Py_UCS4 uchar = __Pyx_PyUnicode_READ(ukind, udata, j); - __Pyx_PyUnicode_WRITE(result_ukind, result_udata, char_pos+j, uchar); - } - #endif - } - char_pos += ulength; - } - return result_uval; -overflow: - PyErr_SetString(PyExc_OverflowError, "join() result is too long for a Python string"); -bad: - Py_DECREF(result_uval); - return NULL; -#else - CYTHON_UNUSED_VAR(max_char); - CYTHON_UNUSED_VAR(result_ulength); - CYTHON_UNUSED_VAR(value_count); - return PyUnicode_Join(__pyx_empty_unicode, value_tuple); -#endif -} - -/* GetAttr */ -static CYTHON_INLINE PyObject *__Pyx_GetAttr(PyObject *o, PyObject *n) { -#if CYTHON_USE_TYPE_SLOTS -#if PY_MAJOR_VERSION >= 3 - if (likely(PyUnicode_Check(n))) -#else - if (likely(PyString_Check(n))) -#endif - return __Pyx_PyObject_GetAttrStr(o, n); -#endif - return PyObject_GetAttr(o, n); -} - -/* GetItemInt */ -static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) { - PyObject *r; - if (unlikely(!j)) return NULL; - r = PyObject_GetItem(o, j); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyList_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyList_GET_SIZE(o)))) { - PyObject *r = PyList_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - Py_ssize_t wrapped_i = i; - if (wraparound & unlikely(i < 0)) { - wrapped_i += PyTuple_GET_SIZE(o); - } - if ((!boundscheck) || likely(__Pyx_is_valid_index(wrapped_i, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, wrapped_i); - Py_INCREF(r); - return r; - } - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -#else - return PySequence_GetItem(o, i); -#endif -} -static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list, - CYTHON_NCP_UNUSED int wraparound, - CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o); - if ((!boundscheck) || (likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o))))) { - PyObject *r = PyList_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } - else if (PyTuple_CheckExact(o)) { - Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyTuple_GET_SIZE(o)))) { - PyObject *r = PyTuple_GET_ITEM(o, n); - Py_INCREF(r); - return r; - } - } else { - PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; - PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; - if (mm && mm->mp_subscript) { - PyObject *r, *key = PyInt_FromSsize_t(i); - if (unlikely(!key)) return NULL; - r = mm->mp_subscript(o, key); - Py_DECREF(key); - return r; - } - if (likely(sm && sm->sq_item)) { - if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { - Py_ssize_t l = sm->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return NULL; - PyErr_Clear(); - } - } - return sm->sq_item(o, i); - } - } -#else - if (is_list || PySequence_Check(o)) { - return PySequence_GetItem(o, i); - } -#endif - return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); -} - -/* PyObjectCallOneArg */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *args[2] = {NULL, arg}; - return __Pyx_PyObject_FastCall(func, args+1, 1 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); -} - -/* ObjectGetItem */ -#if CYTHON_USE_TYPE_SLOTS -static PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject *index) { - PyObject *runerr = NULL; - Py_ssize_t key_value; - key_value = __Pyx_PyIndex_AsSsize_t(index); - if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) { - return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1); - } - if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) { - __Pyx_TypeName index_type_name = __Pyx_PyType_GetName(Py_TYPE(index)); - PyErr_Clear(); - PyErr_Format(PyExc_IndexError, - "cannot fit '" __Pyx_FMT_TYPENAME "' into an index-sized integer", index_type_name); - __Pyx_DECREF_TypeName(index_type_name); - } - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem_Slow(PyObject *obj, PyObject *key) { - __Pyx_TypeName obj_type_name; - if (likely(PyType_Check(obj))) { - PyObject *meth = __Pyx_PyObject_GetAttrStrNoError(obj, __pyx_n_s_class_getitem); - if (meth) { - PyObject *result = __Pyx_PyObject_CallOneArg(meth, key); - Py_DECREF(meth); - return result; - } - } - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - PyErr_Format(PyExc_TypeError, - "'" __Pyx_FMT_TYPENAME "' object is not subscriptable", obj_type_name); - __Pyx_DECREF_TypeName(obj_type_name); - return NULL; -} -static PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject *key) { - PyTypeObject *tp = Py_TYPE(obj); - PyMappingMethods *mm = tp->tp_as_mapping; - PySequenceMethods *sm = tp->tp_as_sequence; - if (likely(mm && mm->mp_subscript)) { - return mm->mp_subscript(obj, key); - } - if (likely(sm && sm->sq_item)) { - return __Pyx_PyObject_GetIndex(obj, key); - } - return __Pyx_PyObject_GetItem_Slow(obj, key); -} -#endif - -/* KeywordStringCheck */ -static int __Pyx_CheckKeywordStrings( - PyObject *kw, - const char* function_name, - int kw_allowed) -{ - PyObject* key = 0; - Py_ssize_t pos = 0; -#if CYTHON_COMPILING_IN_PYPY - if (!kw_allowed && PyDict_Next(kw, &pos, &key, 0)) - goto invalid_keyword; - return 1; -#else - if (CYTHON_METH_FASTCALL && likely(PyTuple_Check(kw))) { - if (unlikely(PyTuple_GET_SIZE(kw) == 0)) - return 1; - if (!kw_allowed) { - key = PyTuple_GET_ITEM(kw, 0); - goto invalid_keyword; - } -#if PY_VERSION_HEX < 0x03090000 - for (pos = 0; pos < PyTuple_GET_SIZE(kw); pos++) { - key = PyTuple_GET_ITEM(kw, pos); - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } -#endif - return 1; - } - while (PyDict_Next(kw, &pos, &key, 0)) { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_Check(key))) - #endif - if (unlikely(!PyUnicode_Check(key))) - goto invalid_keyword_type; - } - if (!kw_allowed && unlikely(key)) - goto invalid_keyword; - return 1; -invalid_keyword_type: - PyErr_Format(PyExc_TypeError, - "%.200s() keywords must be strings", function_name); - return 0; -#endif -invalid_keyword: - #if PY_MAJOR_VERSION < 3 - PyErr_Format(PyExc_TypeError, - "%.200s() got an unexpected keyword argument '%.200s'", - function_name, PyString_AsString(key)); - #else - PyErr_Format(PyExc_TypeError, - "%s() got an unexpected keyword argument '%U'", - function_name, key); - #endif - return 0; -} - -/* DivInt[Py_ssize_t] */ -static CYTHON_INLINE Py_ssize_t __Pyx_div_Py_ssize_t(Py_ssize_t a, Py_ssize_t b) { - Py_ssize_t q = a / b; - Py_ssize_t r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* GetAttr3 */ -static PyObject *__Pyx_GetAttr3Default(PyObject *d) { - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - if (unlikely(!__Pyx_PyErr_ExceptionMatches(PyExc_AttributeError))) - return NULL; - __Pyx_PyErr_Clear(); - Py_INCREF(d); - return d; -} -static CYTHON_INLINE PyObject *__Pyx_GetAttr3(PyObject *o, PyObject *n, PyObject *d) { - PyObject *r; -#if CYTHON_USE_TYPE_SLOTS - if (likely(PyString_Check(n))) { - r = __Pyx_PyObject_GetAttrStrNoError(o, n); - if (unlikely(!r) && likely(!PyErr_Occurred())) { - r = __Pyx_NewRef(d); - } - return r; - } -#endif - r = PyObject_GetAttr(o, n); - return (likely(r)) ? r : __Pyx_GetAttr3Default(d); -} - -/* PyDictVersioning */ -#if CYTHON_USE_DICT_VERSIONS && CYTHON_USE_TYPE_SLOTS -static CYTHON_INLINE PY_UINT64_T __Pyx_get_tp_dict_version(PyObject *obj) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - return likely(dict) ? __PYX_GET_DICT_VERSION(dict) : 0; -} -static CYTHON_INLINE PY_UINT64_T __Pyx_get_object_dict_version(PyObject *obj) { - PyObject **dictptr = NULL; - Py_ssize_t offset = Py_TYPE(obj)->tp_dictoffset; - if (offset) { -#if CYTHON_COMPILING_IN_CPYTHON - dictptr = (likely(offset > 0)) ? (PyObject **) ((char *)obj + offset) : _PyObject_GetDictPtr(obj); -#else - dictptr = _PyObject_GetDictPtr(obj); -#endif - } - return (dictptr && *dictptr) ? __PYX_GET_DICT_VERSION(*dictptr) : 0; -} -static CYTHON_INLINE int __Pyx_object_dict_version_matches(PyObject* obj, PY_UINT64_T tp_dict_version, PY_UINT64_T obj_dict_version) { - PyObject *dict = Py_TYPE(obj)->tp_dict; - if (unlikely(!dict) || unlikely(tp_dict_version != __PYX_GET_DICT_VERSION(dict))) - return 0; - return obj_dict_version == __Pyx_get_object_dict_version(obj); -} -#endif - -/* GetModuleGlobalName */ -#if CYTHON_USE_DICT_VERSIONS -static PyObject *__Pyx__GetModuleGlobalName(PyObject *name, PY_UINT64_T *dict_version, PyObject **dict_cached_value) -#else -static CYTHON_INLINE PyObject *__Pyx__GetModuleGlobalName(PyObject *name) -#endif -{ - PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 - result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } else if (unlikely(PyErr_Occurred())) { - return NULL; - } -#elif CYTHON_COMPILING_IN_LIMITED_API - if (unlikely(!__pyx_m)) { - return NULL; - } - result = PyObject_GetAttr(__pyx_m, name); - if (likely(result)) { - return result; - } -#else - result = PyDict_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } -#endif -#else - result = PyObject_GetItem(__pyx_d, name); - __PYX_UPDATE_DICT_CACHE(__pyx_d, result, *dict_cached_value, *dict_version) - if (likely(result)) { - return __Pyx_NewRef(result); - } - PyErr_Clear(); -#endif - return __Pyx_GetBuiltinName(name); -} - -/* RaiseTooManyValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -/* RaiseNeedMoreValuesToUnpack */ -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -/* RaiseNoneIterError */ -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -/* ExtTypeTest */ -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - __Pyx_TypeName obj_type_name; - __Pyx_TypeName type_name; - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(__Pyx_TypeCheck(obj, type))) - return 1; - obj_type_name = __Pyx_PyType_GetName(Py_TYPE(obj)); - type_name = __Pyx_PyType_GetName(type); - PyErr_Format(PyExc_TypeError, - "Cannot convert " __Pyx_FMT_TYPENAME " to " __Pyx_FMT_TYPENAME, - obj_type_name, type_name); - __Pyx_DECREF_TypeName(obj_type_name); - __Pyx_DECREF_TypeName(type_name); - return 0; -} - -/* GetTopmostException */ -#if CYTHON_USE_EXC_INFO_STACK && CYTHON_FAST_THREAD_STATE -static _PyErr_StackItem * -__Pyx_PyErr_GetTopmostException(PyThreadState *tstate) -{ - _PyErr_StackItem *exc_info = tstate->exc_info; - while ((exc_info->exc_value == NULL || exc_info->exc_value == Py_None) && - exc_info->previous_item != NULL) - { - exc_info = exc_info->previous_item; - } - return exc_info; -} -#endif - -/* SaveResetException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - PyObject *exc_value = exc_info->exc_value; - if (exc_value == NULL || exc_value == Py_None) { - *value = NULL; - *type = NULL; - *tb = NULL; - } else { - *value = exc_value; - Py_INCREF(*value); - *type = (PyObject*) Py_TYPE(exc_value); - Py_INCREF(*type); - *tb = PyException_GetTraceback(exc_value); - } - #elif CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = __Pyx_PyErr_GetTopmostException(tstate); - *type = exc_info->exc_type; - *value = exc_info->exc_value; - *tb = exc_info->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); - #else - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); - #endif -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 - _PyErr_StackItem *exc_info = tstate->exc_info; - PyObject *tmp_value = exc_info->exc_value; - exc_info->exc_value = value; - Py_XDECREF(tmp_value); - Py_XDECREF(type); - Py_XDECREF(tb); - #else - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = type; - exc_info->exc_value = value; - exc_info->exc_traceback = tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); - #endif -} -#endif - -/* GetException */ -#if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) -#endif -{ - PyObject *local_type = NULL, *local_value, *local_tb = NULL; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if PY_VERSION_HEX >= 0x030C00A6 - local_value = tstate->current_exception; - tstate->current_exception = 0; - if (likely(local_value)) { - local_type = (PyObject*) Py_TYPE(local_value); - Py_INCREF(local_type); - local_tb = PyException_GetTraceback(local_value); - } - #else - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; - #endif -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE && PY_VERSION_HEX >= 0x030C00A6 - if (unlikely(tstate->current_exception)) -#elif CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - #if CYTHON_USE_EXC_INFO_STACK - { - _PyErr_StackItem *exc_info = tstate->exc_info; - #if PY_VERSION_HEX >= 0x030B00a4 - tmp_value = exc_info->exc_value; - exc_info->exc_value = local_value; - tmp_type = NULL; - tmp_tb = NULL; - Py_XDECREF(local_type); - Py_XDECREF(local_tb); - #else - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = local_type; - exc_info->exc_value = local_value; - exc_info->exc_traceback = local_tb; - #endif - } - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - #endif - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* SwapException */ -#if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSwap(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - #if CYTHON_USE_EXC_INFO_STACK && PY_VERSION_HEX >= 0x030B00a4 - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_value = exc_info->exc_value; - exc_info->exc_value = *value; - if (tmp_value == NULL || tmp_value == Py_None) { - Py_XDECREF(tmp_value); - tmp_value = NULL; - tmp_type = NULL; - tmp_tb = NULL; - } else { - tmp_type = (PyObject*) Py_TYPE(tmp_value); - Py_INCREF(tmp_type); - #if CYTHON_COMPILING_IN_CPYTHON - tmp_tb = ((PyBaseExceptionObject*) tmp_value)->traceback; - Py_XINCREF(tmp_tb); - #else - tmp_tb = PyException_GetTraceback(tmp_value); - #endif - } - #elif CYTHON_USE_EXC_INFO_STACK - _PyErr_StackItem *exc_info = tstate->exc_info; - tmp_type = exc_info->exc_type; - tmp_value = exc_info->exc_value; - tmp_tb = exc_info->exc_traceback; - exc_info->exc_type = *type; - exc_info->exc_value = *value; - exc_info->exc_traceback = *tb; - #else - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = *type; - tstate->exc_value = *value; - tstate->exc_traceback = *tb; - #endif - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#else -static CYTHON_INLINE void __Pyx_ExceptionSwap(PyObject **type, PyObject **value, PyObject **tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_GetExcInfo(&tmp_type, &tmp_value, &tmp_tb); - PyErr_SetExcInfo(*type, *value, *tb); - *type = tmp_type; - *value = tmp_value; - *tb = tmp_tb; -} -#endif - -/* Import */ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *module = 0; - PyObject *empty_dict = 0; - PyObject *empty_list = 0; - #if PY_MAJOR_VERSION < 3 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (unlikely(!py_import)) - goto bad; - if (!from_list) { - empty_list = PyList_New(0); - if (unlikely(!empty_list)) - goto bad; - from_list = empty_list; - } - #endif - empty_dict = PyDict_New(); - if (unlikely(!empty_dict)) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if ((1) && (strchr(__Pyx_MODULE_NAME, '.'))) { - #if CYTHON_COMPILING_IN_LIMITED_API - module = PyImport_ImportModuleLevelObject( - name, empty_dict, empty_dict, from_list, 1); - #else - module = PyImport_ImportModuleLevelObject( - name, __pyx_d, empty_dict, from_list, 1); - #endif - if (unlikely(!module)) { - if (unlikely(!PyErr_ExceptionMatches(PyExc_ImportError))) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_MAJOR_VERSION < 3 - PyObject *py_level = PyInt_FromLong(level); - if (unlikely(!py_level)) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, __pyx_d, empty_dict, from_list, py_level, (PyObject *)NULL); - Py_DECREF(py_level); - #else - #if CYTHON_COMPILING_IN_LIMITED_API - module = PyImport_ImportModuleLevelObject( - name, empty_dict, empty_dict, from_list, level); - #else - module = PyImport_ImportModuleLevelObject( - name, __pyx_d, empty_dict, from_list, level); - #endif - #endif - } - } -bad: - Py_XDECREF(empty_dict); - Py_XDECREF(empty_list); - #if PY_MAJOR_VERSION < 3 - Py_XDECREF(py_import); - #endif - return module; -} - -/* ImportDottedModule */ -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx__ImportDottedModule_Error(PyObject *name, PyObject *parts_tuple, Py_ssize_t count) { - PyObject *partial_name = NULL, *slice = NULL, *sep = NULL; - if (unlikely(PyErr_Occurred())) { - PyErr_Clear(); - } - if (likely(PyTuple_GET_SIZE(parts_tuple) == count)) { - partial_name = name; - } else { - slice = PySequence_GetSlice(parts_tuple, 0, count); - if (unlikely(!slice)) - goto bad; - sep = PyUnicode_FromStringAndSize(".", 1); - if (unlikely(!sep)) - goto bad; - partial_name = PyUnicode_Join(sep, slice); - } - PyErr_Format( -#if PY_MAJOR_VERSION < 3 - PyExc_ImportError, - "No module named '%s'", PyString_AS_STRING(partial_name)); -#else -#if PY_VERSION_HEX >= 0x030600B1 - PyExc_ModuleNotFoundError, -#else - PyExc_ImportError, -#endif - "No module named '%U'", partial_name); -#endif -bad: - Py_XDECREF(sep); - Py_XDECREF(slice); - Py_XDECREF(partial_name); - return NULL; -} -#endif -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx__ImportDottedModule_Lookup(PyObject *name) { - PyObject *imported_module; -#if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) - PyObject *modules = PyImport_GetModuleDict(); - if (unlikely(!modules)) - return NULL; - imported_module = __Pyx_PyDict_GetItemStr(modules, name); - Py_XINCREF(imported_module); -#else - imported_module = PyImport_GetModule(name); -#endif - return imported_module; -} -#endif -#if PY_MAJOR_VERSION >= 3 -static PyObject *__Pyx_ImportDottedModule_WalkParts(PyObject *module, PyObject *name, PyObject *parts_tuple) { - Py_ssize_t i, nparts; - nparts = PyTuple_GET_SIZE(parts_tuple); - for (i=1; i < nparts && module; i++) { - PyObject *part, *submodule; -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS - part = PyTuple_GET_ITEM(parts_tuple, i); -#else - part = PySequence_ITEM(parts_tuple, i); -#endif - submodule = __Pyx_PyObject_GetAttrStrNoError(module, part); -#if !(CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS) - Py_DECREF(part); -#endif - Py_DECREF(module); - module = submodule; - } - if (unlikely(!module)) { - return __Pyx__ImportDottedModule_Error(name, parts_tuple, i); - } - return module; -} -#endif -static PyObject *__Pyx__ImportDottedModule(PyObject *name, PyObject *parts_tuple) { -#if PY_MAJOR_VERSION < 3 - PyObject *module, *from_list, *star = __pyx_n_s__3; - CYTHON_UNUSED_VAR(parts_tuple); - from_list = PyList_New(1); - if (unlikely(!from_list)) - return NULL; - Py_INCREF(star); - PyList_SET_ITEM(from_list, 0, star); - module = __Pyx_Import(name, from_list, 0); - Py_DECREF(from_list); - return module; -#else - PyObject *imported_module; - PyObject *module = __Pyx_Import(name, NULL, 0); - if (!parts_tuple || unlikely(!module)) - return module; - imported_module = __Pyx__ImportDottedModule_Lookup(name); - if (likely(imported_module)) { - Py_DECREF(module); - return imported_module; - } - PyErr_Clear(); - return __Pyx_ImportDottedModule_WalkParts(module, name, parts_tuple); -#endif -} -static PyObject *__Pyx_ImportDottedModule(PyObject *name, PyObject *parts_tuple) { -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030400B1 - PyObject *module = __Pyx__ImportDottedModule_Lookup(name); - if (likely(module)) { - PyObject *spec = __Pyx_PyObject_GetAttrStrNoError(module, __pyx_n_s_spec); - if (likely(spec)) { - PyObject *unsafe = __Pyx_PyObject_GetAttrStrNoError(spec, __pyx_n_s_initializing); - if (likely(!unsafe || !__Pyx_PyObject_IsTrue(unsafe))) { - Py_DECREF(spec); - spec = NULL; - } - Py_XDECREF(unsafe); - } - if (likely(!spec)) { - PyErr_Clear(); - return module; - } - Py_DECREF(spec); - Py_DECREF(module); - } else if (PyErr_Occurred()) { - PyErr_Clear(); - } -#endif - return __Pyx__ImportDottedModule(name, parts_tuple); -} - -/* ssize_strlen */ -static CYTHON_INLINE Py_ssize_t __Pyx_ssize_strlen(const char *s) { - size_t len = strlen(s); - if (unlikely(len > PY_SSIZE_T_MAX)) { - PyErr_SetString(PyExc_OverflowError, "byte string is too long"); - return -1; - } - return (Py_ssize_t) len; -} - -/* FastTypeChecks */ -#if CYTHON_COMPILING_IN_CPYTHON -static int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) { - while (a) { - a = __Pyx_PyType_GetSlot(a, tp_base, PyTypeObject*); - if (a == b) - return 1; - } - return b == &PyBaseObject_Type; -} -static CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (a == b) return 1; - mro = a->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(a, b); -} -static CYTHON_INLINE int __Pyx_IsAnySubtype2(PyTypeObject *cls, PyTypeObject *a, PyTypeObject *b) { - PyObject *mro; - if (cls == a || cls == b) return 1; - mro = cls->tp_mro; - if (likely(mro)) { - Py_ssize_t i, n; - n = PyTuple_GET_SIZE(mro); - for (i = 0; i < n; i++) { - PyObject *base = PyTuple_GET_ITEM(mro, i); - if (base == (PyObject *)a || base == (PyObject *)b) - return 1; - } - return 0; - } - return __Pyx_InBases(cls, a) || __Pyx_InBases(cls, b); -} -#if PY_MAJOR_VERSION == 2 -static int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) { - PyObject *exception, *value, *tb; - int res; - __Pyx_PyThreadState_declare - __Pyx_PyThreadState_assign - __Pyx_ErrFetch(&exception, &value, &tb); - res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0; - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - if (!res) { - res = PyObject_IsSubclass(err, exc_type2); - if (unlikely(res == -1)) { - PyErr_WriteUnraisable(err); - res = 0; - } - } - __Pyx_ErrRestore(exception, value, tb); - return res; -} -#else -static CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) { - if (exc_type1) { - return __Pyx_IsAnySubtype2((PyTypeObject*)err, (PyTypeObject*)exc_type1, (PyTypeObject*)exc_type2); - } else { - return __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2); - } -} -#endif -static int __Pyx_PyErr_GivenExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) { - Py_ssize_t i, n; - assert(PyExceptionClass_Check(exc_type)); - n = PyTuple_GET_SIZE(tuple); -#if PY_MAJOR_VERSION >= 3 - for (i=0; itp_as_sequence && type->tp_as_sequence->sq_repeat)) { - return type->tp_as_sequence->sq_repeat(seq, mul); - } else -#endif - { - return __Pyx_PySequence_Multiply_Generic(seq, mul); - } -} - -/* SetItemInt */ -static int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) { - int r; - if (unlikely(!j)) return -1; - r = PyObject_SetItem(o, j, v); - Py_DECREF(j); - return r; -} -static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v, int is_list, - CYTHON_NCP_UNUSED int wraparound, CYTHON_NCP_UNUSED int boundscheck) { -#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS - if (is_list || PyList_CheckExact(o)) { - Py_ssize_t n = (!wraparound) ? i : ((likely(i >= 0)) ? i : i + PyList_GET_SIZE(o)); - if ((!boundscheck) || likely(__Pyx_is_valid_index(n, PyList_GET_SIZE(o)))) { - PyObject* old = PyList_GET_ITEM(o, n); - Py_INCREF(v); - PyList_SET_ITEM(o, n, v); - Py_DECREF(old); - return 1; - } - } else { - PyMappingMethods *mm = Py_TYPE(o)->tp_as_mapping; - PySequenceMethods *sm = Py_TYPE(o)->tp_as_sequence; - if (mm && mm->mp_ass_subscript) { - int r; - PyObject *key = PyInt_FromSsize_t(i); - if (unlikely(!key)) return -1; - r = mm->mp_ass_subscript(o, key, v); - Py_DECREF(key); - return r; - } - if (likely(sm && sm->sq_ass_item)) { - if (wraparound && unlikely(i < 0) && likely(sm->sq_length)) { - Py_ssize_t l = sm->sq_length(o); - if (likely(l >= 0)) { - i += l; - } else { - if (!PyErr_ExceptionMatches(PyExc_OverflowError)) - return -1; - PyErr_Clear(); - } - } - return sm->sq_ass_item(o, i, v); - } - } -#else -#if CYTHON_COMPILING_IN_PYPY - if (is_list || (PySequence_Check(o) && !PyDict_Check(o))) -#else - if (is_list || PySequence_Check(o)) -#endif - { - return PySequence_SetItem(o, i, v); - } -#endif - return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); -} - -/* RaiseUnboundLocalError */ -static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) { - PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname); -} - -/* DivInt[long] */ -static CYTHON_INLINE long __Pyx_div_long(long a, long b) { - long q = a / b; - long r = a - q*b; - q -= ((r != 0) & ((r ^ b) < 0)); - return q; -} - -/* ImportFrom */ -static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name) { - PyObject* value = __Pyx_PyObject_GetAttrStr(module, name); - if (unlikely(!value) && PyErr_ExceptionMatches(PyExc_AttributeError)) { - const char* module_name_str = 0; - PyObject* module_name = 0; - PyObject* module_dot = 0; - PyObject* full_name = 0; - PyErr_Clear(); - module_name_str = PyModule_GetName(module); - if (unlikely(!module_name_str)) { goto modbad; } - module_name = PyUnicode_FromString(module_name_str); - if (unlikely(!module_name)) { goto modbad; } - module_dot = PyUnicode_Concat(module_name, __pyx_kp_u__2); - if (unlikely(!module_dot)) { goto modbad; } - full_name = PyUnicode_Concat(module_dot, name); - if (unlikely(!full_name)) { goto modbad; } - #if PY_VERSION_HEX < 0x030700A1 || (CYTHON_COMPILING_IN_PYPY && PYPY_VERSION_NUM < 0x07030400) - { - PyObject *modules = PyImport_GetModuleDict(); - if (unlikely(!modules)) - goto modbad; - value = PyObject_GetItem(modules, full_name); - } - #else - value = PyImport_GetModule(full_name); - #endif - modbad: - Py_XDECREF(full_name); - Py_XDECREF(module_dot); - Py_XDECREF(module_name); - } - if (unlikely(!value)) { - PyErr_Format(PyExc_ImportError, - #if PY_MAJOR_VERSION < 3 - "cannot import name %.230s", PyString_AS_STRING(name)); - #else - "cannot import name %S", name); - #endif - } - return value; -} - -/* HasAttr */ -static CYTHON_INLINE int __Pyx_HasAttr(PyObject *o, PyObject *n) { - PyObject *r; - if (unlikely(!__Pyx_PyBaseString_Check(n))) { - PyErr_SetString(PyExc_TypeError, - "hasattr(): attribute name must be string"); - return -1; - } - r = __Pyx_GetAttr(o, n); - if (!r) { - PyErr_Clear(); - return 0; - } else { - Py_DECREF(r); - return 1; - } -} - -/* ErrOccurredWithGIL */ -static CYTHON_INLINE int __Pyx_ErrOccurredWithGIL(void) { - int err; - #ifdef WITH_THREAD - PyGILState_STATE _save = PyGILState_Ensure(); - #endif - err = !!PyErr_Occurred(); - #ifdef WITH_THREAD - PyGILState_Release(_save); - #endif - return err; -} - -/* PyObject_GenericGetAttrNoDict */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject *__Pyx_RaiseGenericGetAttributeError(PyTypeObject *tp, PyObject *attr_name) { - __Pyx_TypeName type_name = __Pyx_PyType_GetName(tp); - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", - type_name, attr_name); -#else - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", - type_name, PyString_AS_STRING(attr_name)); -#endif - __Pyx_DECREF_TypeName(type_name); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_GenericGetAttrNoDict(PyObject* obj, PyObject* attr_name) { - PyObject *descr; - PyTypeObject *tp = Py_TYPE(obj); - if (unlikely(!PyString_Check(attr_name))) { - return PyObject_GenericGetAttr(obj, attr_name); - } - assert(!tp->tp_dictoffset); - descr = _PyType_Lookup(tp, attr_name); - if (unlikely(!descr)) { - return __Pyx_RaiseGenericGetAttributeError(tp, attr_name); - } - Py_INCREF(descr); - #if PY_MAJOR_VERSION < 3 - if (likely(PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_HAVE_CLASS))) - #endif - { - descrgetfunc f = Py_TYPE(descr)->tp_descr_get; - if (unlikely(f)) { - PyObject *res = f(descr, obj, (PyObject *)tp); - Py_DECREF(descr); - return res; - } - } - return descr; -} -#endif - -/* PyObject_GenericGetAttr */ -#if CYTHON_USE_TYPE_SLOTS && CYTHON_USE_PYTYPE_LOOKUP && PY_VERSION_HEX < 0x03070000 -static PyObject* __Pyx_PyObject_GenericGetAttr(PyObject* obj, PyObject* attr_name) { - if (unlikely(Py_TYPE(obj)->tp_dictoffset)) { - return PyObject_GenericGetAttr(obj, attr_name); - } - return __Pyx_PyObject_GenericGetAttrNoDict(obj, attr_name); -} -#endif - -/* FixUpExtensionType */ -#if CYTHON_USE_TYPE_SPECS -static int __Pyx_fix_up_extension_type_from_spec(PyType_Spec *spec, PyTypeObject *type) { -#if PY_VERSION_HEX > 0x030900B1 || CYTHON_COMPILING_IN_LIMITED_API - CYTHON_UNUSED_VAR(spec); - CYTHON_UNUSED_VAR(type); -#else - const PyType_Slot *slot = spec->slots; - while (slot && slot->slot && slot->slot != Py_tp_members) - slot++; - if (slot && slot->slot == Py_tp_members) { - int changed = 0; -#if !(PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON) - const -#endif - PyMemberDef *memb = (PyMemberDef*) slot->pfunc; - while (memb && memb->name) { - if (memb->name[0] == '_' && memb->name[1] == '_') { -#if PY_VERSION_HEX < 0x030900b1 - if (strcmp(memb->name, "__weaklistoffset__") == 0) { - assert(memb->type == T_PYSSIZET); - assert(memb->flags == READONLY); - type->tp_weaklistoffset = memb->offset; - changed = 1; - } - else if (strcmp(memb->name, "__dictoffset__") == 0) { - assert(memb->type == T_PYSSIZET); - assert(memb->flags == READONLY); - type->tp_dictoffset = memb->offset; - changed = 1; - } -#if CYTHON_METH_FASTCALL - else if (strcmp(memb->name, "__vectorcalloffset__") == 0) { - assert(memb->type == T_PYSSIZET); - assert(memb->flags == READONLY); -#if PY_VERSION_HEX >= 0x030800b4 - type->tp_vectorcall_offset = memb->offset; -#else - type->tp_print = (printfunc) memb->offset; -#endif - changed = 1; - } -#endif -#else - if ((0)); -#endif -#if PY_VERSION_HEX <= 0x030900b1 && CYTHON_COMPILING_IN_CPYTHON - else if (strcmp(memb->name, "__module__") == 0) { - PyObject *descr; - assert(memb->type == T_OBJECT); - assert(memb->flags == 0 || memb->flags == READONLY); - descr = PyDescr_NewMember(type, memb); - if (unlikely(!descr)) - return -1; - if (unlikely(PyDict_SetItem(type->tp_dict, PyDescr_NAME(descr), descr) < 0)) { - Py_DECREF(descr); - return -1; - } - Py_DECREF(descr); - changed = 1; - } -#endif - } - memb++; - } - if (changed) - PyType_Modified(type); - } -#endif - return 0; -} -#endif - -/* PyObjectCallNoArg */ -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) { - PyObject *arg = NULL; - return __Pyx_PyObject_FastCall(func, (&arg)+1, 0 | __Pyx_PY_VECTORCALL_ARGUMENTS_OFFSET); -} - -/* PyObjectGetMethod */ -static int __Pyx_PyObject_GetMethod(PyObject *obj, PyObject *name, PyObject **method) { - PyObject *attr; -#if CYTHON_UNPACK_METHODS && CYTHON_COMPILING_IN_CPYTHON && CYTHON_USE_PYTYPE_LOOKUP - __Pyx_TypeName type_name; - PyTypeObject *tp = Py_TYPE(obj); - PyObject *descr; - descrgetfunc f = NULL; - PyObject **dictptr, *dict; - int meth_found = 0; - assert (*method == NULL); - if (unlikely(tp->tp_getattro != PyObject_GenericGetAttr)) { - attr = __Pyx_PyObject_GetAttrStr(obj, name); - goto try_unpack; - } - if (unlikely(tp->tp_dict == NULL) && unlikely(PyType_Ready(tp) < 0)) { - return 0; - } - descr = _PyType_Lookup(tp, name); - if (likely(descr != NULL)) { - Py_INCREF(descr); -#if defined(Py_TPFLAGS_METHOD_DESCRIPTOR) && Py_TPFLAGS_METHOD_DESCRIPTOR - if (__Pyx_PyType_HasFeature(Py_TYPE(descr), Py_TPFLAGS_METHOD_DESCRIPTOR)) -#elif PY_MAJOR_VERSION >= 3 - #ifdef __Pyx_CyFunction_USED - if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type) || __Pyx_CyFunction_Check(descr))) - #else - if (likely(PyFunction_Check(descr) || __Pyx_IS_TYPE(descr, &PyMethodDescr_Type))) - #endif -#else - #ifdef __Pyx_CyFunction_USED - if (likely(PyFunction_Check(descr) || __Pyx_CyFunction_Check(descr))) - #else - if (likely(PyFunction_Check(descr))) - #endif -#endif - { - meth_found = 1; - } else { - f = Py_TYPE(descr)->tp_descr_get; - if (f != NULL && PyDescr_IsData(descr)) { - attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); - Py_DECREF(descr); - goto try_unpack; - } - } - } - dictptr = _PyObject_GetDictPtr(obj); - if (dictptr != NULL && (dict = *dictptr) != NULL) { - Py_INCREF(dict); - attr = __Pyx_PyDict_GetItemStr(dict, name); - if (attr != NULL) { - Py_INCREF(attr); - Py_DECREF(dict); - Py_XDECREF(descr); - goto try_unpack; - } - Py_DECREF(dict); - } - if (meth_found) { - *method = descr; - return 1; - } - if (f != NULL) { - attr = f(descr, obj, (PyObject *)Py_TYPE(obj)); - Py_DECREF(descr); - goto try_unpack; - } - if (likely(descr != NULL)) { - *method = descr; - return 0; - } - type_name = __Pyx_PyType_GetName(tp); - PyErr_Format(PyExc_AttributeError, -#if PY_MAJOR_VERSION >= 3 - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%U'", - type_name, name); -#else - "'" __Pyx_FMT_TYPENAME "' object has no attribute '%.400s'", - type_name, PyString_AS_STRING(name)); -#endif - __Pyx_DECREF_TypeName(type_name); - return 0; -#else - attr = __Pyx_PyObject_GetAttrStr(obj, name); - goto try_unpack; -#endif -try_unpack: -#if CYTHON_UNPACK_METHODS - if (likely(attr) && PyMethod_Check(attr) && likely(PyMethod_GET_SELF(attr) == obj)) { - PyObject *function = PyMethod_GET_FUNCTION(attr); - Py_INCREF(function); - Py_DECREF(attr); - *method = function; - return 1; - } -#endif - *method = attr; - return 0; -} - -/* PyObjectCallMethod0 */ -static PyObject* __Pyx_PyObject_CallMethod0(PyObject* obj, PyObject* method_name) { - PyObject *method = NULL, *result = NULL; - int is_method = __Pyx_PyObject_GetMethod(obj, method_name, &method); - if (likely(is_method)) { - result = __Pyx_PyObject_CallOneArg(method, obj); - Py_DECREF(method); - return result; - } - if (unlikely(!method)) goto bad; - result = __Pyx_PyObject_CallNoArg(method); - Py_DECREF(method); -bad: - return result; -} - -/* ValidateBasesTuple */ -#if CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API || CYTHON_USE_TYPE_SPECS -static int __Pyx_validate_bases_tuple(const char *type_name, Py_ssize_t dictoffset, PyObject *bases) { - Py_ssize_t i, n = PyTuple_GET_SIZE(bases); - for (i = 1; i < n; i++) - { - PyObject *b0 = PyTuple_GET_ITEM(bases, i); - PyTypeObject *b; -#if PY_MAJOR_VERSION < 3 - if (PyClass_Check(b0)) - { - PyErr_Format(PyExc_TypeError, "base class '%.200s' is an old-style class", - PyString_AS_STRING(((PyClassObject*)b0)->cl_name)); - return -1; - } -#endif - b = (PyTypeObject*) b0; - if (!__Pyx_PyType_HasFeature(b, Py_TPFLAGS_HEAPTYPE)) - { - __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); - PyErr_Format(PyExc_TypeError, - "base class '" __Pyx_FMT_TYPENAME "' is not a heap type", b_name); - __Pyx_DECREF_TypeName(b_name); - return -1; - } - if (dictoffset == 0 && b->tp_dictoffset) - { - __Pyx_TypeName b_name = __Pyx_PyType_GetName(b); - PyErr_Format(PyExc_TypeError, - "extension type '%.200s' has no __dict__ slot, " - "but base type '" __Pyx_FMT_TYPENAME "' has: " - "either add 'cdef dict __dict__' to the extension type " - "or add '__slots__ = [...]' to the base type", - type_name, b_name); - __Pyx_DECREF_TypeName(b_name); - return -1; - } - } - return 0; -} -#endif - -/* PyType_Ready */ -static int __Pyx_PyType_Ready(PyTypeObject *t) { -#if CYTHON_USE_TYPE_SPECS || !(CYTHON_COMPILING_IN_CPYTHON || CYTHON_COMPILING_IN_LIMITED_API) || defined(PYSTON_MAJOR_VERSION) - (void)__Pyx_PyObject_CallMethod0; -#if CYTHON_USE_TYPE_SPECS - (void)__Pyx_validate_bases_tuple; -#endif - return PyType_Ready(t); -#else - int r; - PyObject *bases = __Pyx_PyType_GetSlot(t, tp_bases, PyObject*); - if (bases && unlikely(__Pyx_validate_bases_tuple(t->tp_name, t->tp_dictoffset, bases) == -1)) - return -1; -#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) - { - int gc_was_enabled; - #if PY_VERSION_HEX >= 0x030A00b1 - gc_was_enabled = PyGC_Disable(); - (void)__Pyx_PyObject_CallMethod0; - #else - PyObject *ret, *py_status; - PyObject *gc = NULL; - #if PY_VERSION_HEX >= 0x030700a1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM+0 >= 0x07030400) - gc = PyImport_GetModule(__pyx_kp_u_gc); - #endif - if (unlikely(!gc)) gc = PyImport_Import(__pyx_kp_u_gc); - if (unlikely(!gc)) return -1; - py_status = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_isenabled); - if (unlikely(!py_status)) { - Py_DECREF(gc); - return -1; - } - gc_was_enabled = __Pyx_PyObject_IsTrue(py_status); - Py_DECREF(py_status); - if (gc_was_enabled > 0) { - ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_disable); - if (unlikely(!ret)) { - Py_DECREF(gc); - return -1; - } - Py_DECREF(ret); - } else if (unlikely(gc_was_enabled == -1)) { - Py_DECREF(gc); - return -1; - } - #endif - t->tp_flags |= Py_TPFLAGS_HEAPTYPE; -#if PY_VERSION_HEX >= 0x030A0000 - t->tp_flags |= Py_TPFLAGS_IMMUTABLETYPE; -#endif -#else - (void)__Pyx_PyObject_CallMethod0; -#endif - r = PyType_Ready(t); -#if PY_VERSION_HEX >= 0x03050000 && !defined(PYSTON_MAJOR_VERSION) - t->tp_flags &= ~Py_TPFLAGS_HEAPTYPE; - #if PY_VERSION_HEX >= 0x030A00b1 - if (gc_was_enabled) - PyGC_Enable(); - #else - if (gc_was_enabled) { - PyObject *tp, *v, *tb; - PyErr_Fetch(&tp, &v, &tb); - ret = __Pyx_PyObject_CallMethod0(gc, __pyx_kp_u_enable); - if (likely(ret || r == -1)) { - Py_XDECREF(ret); - PyErr_Restore(tp, v, tb); - } else { - Py_XDECREF(tp); - Py_XDECREF(v); - Py_XDECREF(tb); - r = -1; - } - } - Py_DECREF(gc); - #endif - } -#endif - return r; -#endif -} - -/* SetVTable */ -static int __Pyx_SetVtable(PyTypeObject *type, void *vtable) { - PyObject *ob = PyCapsule_New(vtable, 0, 0); - if (unlikely(!ob)) - goto bad; -#if CYTHON_COMPILING_IN_LIMITED_API - if (unlikely(PyObject_SetAttr((PyObject *) type, __pyx_n_s_pyx_vtable, ob) < 0)) -#else - if (unlikely(PyDict_SetItem(type->tp_dict, __pyx_n_s_pyx_vtable, ob) < 0)) -#endif - goto bad; - Py_DECREF(ob); - return 0; -bad: - Py_XDECREF(ob); - return -1; -} - -/* GetVTable */ -static void* __Pyx_GetVtable(PyTypeObject *type) { - void* ptr; -#if CYTHON_COMPILING_IN_LIMITED_API - PyObject *ob = PyObject_GetAttr((PyObject *)type, __pyx_n_s_pyx_vtable); -#else - PyObject *ob = PyObject_GetItem(type->tp_dict, __pyx_n_s_pyx_vtable); -#endif - if (!ob) - goto bad; - ptr = PyCapsule_GetPointer(ob, 0); - if (!ptr && !PyErr_Occurred()) - PyErr_SetString(PyExc_RuntimeError, "invalid vtable found for imported type"); - Py_DECREF(ob); - return ptr; -bad: - Py_XDECREF(ob); - return NULL; -} - -/* MergeVTables */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_MergeVtables(PyTypeObject *type) { - int i; - void** base_vtables; - __Pyx_TypeName tp_base_name; - __Pyx_TypeName base_name; - void* unknown = (void*)-1; - PyObject* bases = type->tp_bases; - int base_depth = 0; - { - PyTypeObject* base = type->tp_base; - while (base) { - base_depth += 1; - base = base->tp_base; - } - } - base_vtables = (void**) malloc(sizeof(void*) * (size_t)(base_depth + 1)); - base_vtables[0] = unknown; - for (i = 1; i < PyTuple_GET_SIZE(bases); i++) { - void* base_vtable = __Pyx_GetVtable(((PyTypeObject*)PyTuple_GET_ITEM(bases, i))); - if (base_vtable != NULL) { - int j; - PyTypeObject* base = type->tp_base; - for (j = 0; j < base_depth; j++) { - if (base_vtables[j] == unknown) { - base_vtables[j] = __Pyx_GetVtable(base); - base_vtables[j + 1] = unknown; - } - if (base_vtables[j] == base_vtable) { - break; - } else if (base_vtables[j] == NULL) { - goto bad; - } - base = base->tp_base; - } - } - } - PyErr_Clear(); - free(base_vtables); - return 0; -bad: - tp_base_name = __Pyx_PyType_GetName(type->tp_base); - base_name = __Pyx_PyType_GetName((PyTypeObject*)PyTuple_GET_ITEM(bases, i)); - PyErr_Format(PyExc_TypeError, - "multiple bases have vtable conflict: '" __Pyx_FMT_TYPENAME "' and '" __Pyx_FMT_TYPENAME "'", tp_base_name, base_name); - __Pyx_DECREF_TypeName(tp_base_name); - __Pyx_DECREF_TypeName(base_name); - free(base_vtables); - return -1; -} -#endif - -/* SetupReduce */ -#if !CYTHON_COMPILING_IN_LIMITED_API -static int __Pyx_setup_reduce_is_named(PyObject* meth, PyObject* name) { - int ret; - PyObject *name_attr; - name_attr = __Pyx_PyObject_GetAttrStrNoError(meth, __pyx_n_s_name_2); - if (likely(name_attr)) { - ret = PyObject_RichCompareBool(name_attr, name, Py_EQ); - } else { - ret = -1; - } - if (unlikely(ret < 0)) { - PyErr_Clear(); - ret = 0; - } - Py_XDECREF(name_attr); - return ret; -} -static int __Pyx_setup_reduce(PyObject* type_obj) { - int ret = 0; - PyObject *object_reduce = NULL; - PyObject *object_getstate = NULL; - PyObject *object_reduce_ex = NULL; - PyObject *reduce = NULL; - PyObject *reduce_ex = NULL; - PyObject *reduce_cython = NULL; - PyObject *setstate = NULL; - PyObject *setstate_cython = NULL; - PyObject *getstate = NULL; -#if CYTHON_USE_PYTYPE_LOOKUP - getstate = _PyType_Lookup((PyTypeObject*)type_obj, __pyx_n_s_getstate); -#else - getstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_getstate); - if (!getstate && PyErr_Occurred()) { - goto __PYX_BAD; - } -#endif - if (getstate) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_getstate = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_getstate); -#else - object_getstate = __Pyx_PyObject_GetAttrStrNoError((PyObject*)&PyBaseObject_Type, __pyx_n_s_getstate); - if (!object_getstate && PyErr_Occurred()) { - goto __PYX_BAD; - } -#endif - if (object_getstate != getstate) { - goto __PYX_GOOD; - } - } -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce_ex = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#else - object_reduce_ex = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce_ex); if (!object_reduce_ex) goto __PYX_BAD; -#endif - reduce_ex = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce_ex); if (unlikely(!reduce_ex)) goto __PYX_BAD; - if (reduce_ex == object_reduce_ex) { -#if CYTHON_USE_PYTYPE_LOOKUP - object_reduce = _PyType_Lookup(&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#else - object_reduce = __Pyx_PyObject_GetAttrStr((PyObject*)&PyBaseObject_Type, __pyx_n_s_reduce); if (!object_reduce) goto __PYX_BAD; -#endif - reduce = __Pyx_PyObject_GetAttrStr(type_obj, __pyx_n_s_reduce); if (unlikely(!reduce)) goto __PYX_BAD; - if (reduce == object_reduce || __Pyx_setup_reduce_is_named(reduce, __pyx_n_s_reduce_cython)) { - reduce_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_reduce_cython); - if (likely(reduce_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce, reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_reduce_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (reduce == object_reduce || PyErr_Occurred()) { - goto __PYX_BAD; - } - setstate = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate); - if (!setstate) PyErr_Clear(); - if (!setstate || __Pyx_setup_reduce_is_named(setstate, __pyx_n_s_setstate_cython)) { - setstate_cython = __Pyx_PyObject_GetAttrStrNoError(type_obj, __pyx_n_s_setstate_cython); - if (likely(setstate_cython)) { - ret = PyDict_SetItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate, setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - ret = PyDict_DelItem(((PyTypeObject*)type_obj)->tp_dict, __pyx_n_s_setstate_cython); if (unlikely(ret < 0)) goto __PYX_BAD; - } else if (!setstate || PyErr_Occurred()) { - goto __PYX_BAD; - } - } - PyType_Modified((PyTypeObject*)type_obj); - } - } - goto __PYX_GOOD; -__PYX_BAD: - if (!PyErr_Occurred()) { - __Pyx_TypeName type_obj_name = - __Pyx_PyType_GetName((PyTypeObject*)type_obj); - PyErr_Format(PyExc_RuntimeError, - "Unable to initialize pickling for " __Pyx_FMT_TYPENAME, type_obj_name); - __Pyx_DECREF_TypeName(type_obj_name); - } - ret = -1; -__PYX_GOOD: -#if !CYTHON_USE_PYTYPE_LOOKUP - Py_XDECREF(object_reduce); - Py_XDECREF(object_reduce_ex); - Py_XDECREF(object_getstate); - Py_XDECREF(getstate); -#endif - Py_XDECREF(reduce); - Py_XDECREF(reduce_ex); - Py_XDECREF(reduce_cython); - Py_XDECREF(setstate); - Py_XDECREF(setstate_cython); - return ret; -} -#endif - -/* FetchSharedCythonModule */ -static PyObject *__Pyx_FetchSharedCythonABIModule(void) { - PyObject *abi_module = PyImport_AddModule((char*) __PYX_ABI_MODULE_NAME); - if (unlikely(!abi_module)) return NULL; - Py_INCREF(abi_module); - return abi_module; -} - -/* FetchCommonType */ -static int __Pyx_VerifyCachedType(PyObject *cached_type, - const char *name, - Py_ssize_t basicsize, - Py_ssize_t expected_basicsize) { - if (!PyType_Check(cached_type)) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s is not a type object", name); - return -1; - } - if (basicsize != expected_basicsize) { - PyErr_Format(PyExc_TypeError, - "Shared Cython type %.200s has the wrong size, try recompiling", - name); - return -1; - } - return 0; -} -#if !CYTHON_USE_TYPE_SPECS -static PyTypeObject* __Pyx_FetchCommonType(PyTypeObject* type) { - PyObject* abi_module; - const char* object_name; - PyTypeObject *cached_type = NULL; - abi_module = __Pyx_FetchSharedCythonABIModule(); - if (!abi_module) return NULL; - object_name = strrchr(type->tp_name, '.'); - object_name = object_name ? object_name+1 : type->tp_name; - cached_type = (PyTypeObject*) PyObject_GetAttrString(abi_module, object_name); - if (cached_type) { - if (__Pyx_VerifyCachedType( - (PyObject *)cached_type, - object_name, - cached_type->tp_basicsize, - type->tp_basicsize) < 0) { - goto bad; - } - goto done; - } - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - if (PyType_Ready(type) < 0) goto bad; - if (PyObject_SetAttrString(abi_module, object_name, (PyObject *)type) < 0) - goto bad; - Py_INCREF(type); - cached_type = type; -done: - Py_DECREF(abi_module); - return cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} -#else -static PyTypeObject *__Pyx_FetchCommonTypeFromSpec(PyObject *module, PyType_Spec *spec, PyObject *bases) { - PyObject *abi_module, *cached_type = NULL; - const char* object_name = strrchr(spec->name, '.'); - object_name = object_name ? object_name+1 : spec->name; - abi_module = __Pyx_FetchSharedCythonABIModule(); - if (!abi_module) return NULL; - cached_type = PyObject_GetAttrString(abi_module, object_name); - if (cached_type) { - Py_ssize_t basicsize; -#if CYTHON_COMPILING_IN_LIMITED_API - PyObject *py_basicsize; - py_basicsize = PyObject_GetAttrString(cached_type, "__basicsize__"); - if (unlikely(!py_basicsize)) goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (unlikely(basicsize == (Py_ssize_t)-1) && PyErr_Occurred()) goto bad; -#else - basicsize = likely(PyType_Check(cached_type)) ? ((PyTypeObject*) cached_type)->tp_basicsize : -1; -#endif - if (__Pyx_VerifyCachedType( - cached_type, - object_name, - basicsize, - spec->basicsize) < 0) { - goto bad; - } - goto done; - } - if (!PyErr_ExceptionMatches(PyExc_AttributeError)) goto bad; - PyErr_Clear(); - CYTHON_UNUSED_VAR(module); - cached_type = __Pyx_PyType_FromModuleAndSpec(abi_module, spec, bases); - if (unlikely(!cached_type)) goto bad; - if (unlikely(__Pyx_fix_up_extension_type_from_spec(spec, (PyTypeObject *) cached_type) < 0)) goto bad; - if (PyObject_SetAttrString(abi_module, object_name, cached_type) < 0) goto bad; -done: - Py_DECREF(abi_module); - assert(cached_type == NULL || PyType_Check(cached_type)); - return (PyTypeObject *) cached_type; -bad: - Py_XDECREF(cached_type); - cached_type = NULL; - goto done; -} -#endif - -/* PyVectorcallFastCallDict */ -#if CYTHON_METH_FASTCALL -static PyObject *__Pyx_PyVectorcall_FastCallDict_kw(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) -{ - PyObject *res = NULL; - PyObject *kwnames; - PyObject **newargs; - PyObject **kwvalues; - Py_ssize_t i, pos; - size_t j; - PyObject *key, *value; - unsigned long keys_are_strings; - Py_ssize_t nkw = PyDict_GET_SIZE(kw); - newargs = (PyObject **)PyMem_Malloc((nargs + (size_t)nkw) * sizeof(args[0])); - if (unlikely(newargs == NULL)) { - PyErr_NoMemory(); - return NULL; - } - for (j = 0; j < nargs; j++) newargs[j] = args[j]; - kwnames = PyTuple_New(nkw); - if (unlikely(kwnames == NULL)) { - PyMem_Free(newargs); - return NULL; - } - kwvalues = newargs + nargs; - pos = i = 0; - keys_are_strings = Py_TPFLAGS_UNICODE_SUBCLASS; - while (PyDict_Next(kw, &pos, &key, &value)) { - keys_are_strings &= Py_TYPE(key)->tp_flags; - Py_INCREF(key); - Py_INCREF(value); - PyTuple_SET_ITEM(kwnames, i, key); - kwvalues[i] = value; - i++; - } - if (unlikely(!keys_are_strings)) { - PyErr_SetString(PyExc_TypeError, "keywords must be strings"); - goto cleanup; - } - res = vc(func, newargs, nargs, kwnames); -cleanup: - Py_DECREF(kwnames); - for (i = 0; i < nkw; i++) - Py_DECREF(kwvalues[i]); - PyMem_Free(newargs); - return res; -} -static CYTHON_INLINE PyObject *__Pyx_PyVectorcall_FastCallDict(PyObject *func, __pyx_vectorcallfunc vc, PyObject *const *args, size_t nargs, PyObject *kw) -{ - if (likely(kw == NULL) || PyDict_GET_SIZE(kw) == 0) { - return vc(func, args, nargs, NULL); - } - return __Pyx_PyVectorcall_FastCallDict_kw(func, vc, args, nargs, kw); -} -#endif - -/* CythonFunctionShared */ -static CYTHON_INLINE void __Pyx__CyFunction_SetClassObj(__pyx_CyFunctionObject* f, PyObject* classobj) { -#if PY_VERSION_HEX < 0x030900B1 - __Pyx_Py_XDECREF_SET( - __Pyx_CyFunction_GetClassObj(f), - ((classobj) ? __Pyx_NewRef(classobj) : NULL)); -#else - __Pyx_Py_XDECREF_SET( - ((PyCMethodObject *) (f))->mm_class, - (PyTypeObject*)((classobj) ? __Pyx_NewRef(classobj) : NULL)); -#endif -} -static PyObject * -__Pyx_CyFunction_get_doc(__pyx_CyFunctionObject *op, void *closure) -{ - CYTHON_UNUSED_VAR(closure); - if (unlikely(op->func_doc == NULL)) { - if (((PyCFunctionObject*)op)->m_ml->ml_doc) { -#if PY_MAJOR_VERSION >= 3 - op->func_doc = PyUnicode_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); -#else - op->func_doc = PyString_FromString(((PyCFunctionObject*)op)->m_ml->ml_doc); -#endif - if (unlikely(op->func_doc == NULL)) - return NULL; - } else { - Py_INCREF(Py_None); - return Py_None; - } - } - Py_INCREF(op->func_doc); - return op->func_doc; -} -static int -__Pyx_CyFunction_set_doc(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (value == NULL) { - value = Py_None; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_doc, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_name(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (unlikely(op->func_name == NULL)) { -#if PY_MAJOR_VERSION >= 3 - op->func_name = PyUnicode_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); -#else - op->func_name = PyString_InternFromString(((PyCFunctionObject*)op)->m_ml->ml_name); -#endif - if (unlikely(op->func_name == NULL)) - return NULL; - } - Py_INCREF(op->func_name); - return op->func_name; -} -static int -__Pyx_CyFunction_set_name(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__name__ must be set to a string object"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_name, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_qualname(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - Py_INCREF(op->func_qualname); - return op->func_qualname; -} -static int -__Pyx_CyFunction_set_qualname(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); -#if PY_MAJOR_VERSION >= 3 - if (unlikely(value == NULL || !PyUnicode_Check(value))) -#else - if (unlikely(value == NULL || !PyString_Check(value))) -#endif - { - PyErr_SetString(PyExc_TypeError, - "__qualname__ must be set to a string object"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_qualname, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_dict(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (unlikely(op->func_dict == NULL)) { - op->func_dict = PyDict_New(); - if (unlikely(op->func_dict == NULL)) - return NULL; - } - Py_INCREF(op->func_dict); - return op->func_dict; -} -static int -__Pyx_CyFunction_set_dict(__pyx_CyFunctionObject *op, PyObject *value, void *context) -{ - CYTHON_UNUSED_VAR(context); - if (unlikely(value == NULL)) { - PyErr_SetString(PyExc_TypeError, - "function's dictionary may not be deleted"); - return -1; - } - if (unlikely(!PyDict_Check(value))) { - PyErr_SetString(PyExc_TypeError, - "setting function's dictionary to a non-dict"); - return -1; - } - Py_INCREF(value); - __Pyx_Py_XDECREF_SET(op->func_dict, value); - return 0; -} -static PyObject * -__Pyx_CyFunction_get_globals(__pyx_CyFunctionObject *op, void *context) -{ - CYTHON_UNUSED_VAR(context); 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- Py_VISIT(m->defaults_tuple); - Py_VISIT(m->defaults_kwdict); - Py_VISIT(m->func_is_coroutine); - if (m->defaults) { - PyObject **pydefaults = __Pyx_CyFunction_Defaults(PyObject *, m); - int i; - for (i = 0; i < m->defaults_pyobjects; i++) - Py_VISIT(pydefaults[i]); - } - return 0; -} -static PyObject* -__Pyx_CyFunction_repr(__pyx_CyFunctionObject *op) -{ -#if PY_MAJOR_VERSION >= 3 - return PyUnicode_FromFormat("", - op->func_qualname, (void *)op); -#else - return PyString_FromFormat("", - PyString_AsString(op->func_qualname), (void *)op); -#endif -} -static PyObject * __Pyx_CyFunction_CallMethod(PyObject *func, PyObject *self, PyObject *arg, PyObject *kw) { - PyCFunctionObject* f = (PyCFunctionObject*)func; - PyCFunction meth = f->m_ml->ml_meth; - Py_ssize_t size; - switch (f->m_ml->ml_flags & (METH_VARARGS | METH_KEYWORDS | METH_NOARGS | METH_O)) { - case METH_VARARGS: - if (likely(kw == NULL || PyDict_Size(kw) == 0)) - return (*meth)(self, arg); - break; - case METH_VARARGS | METH_KEYWORDS: - return (*(PyCFunctionWithKeywords)(void*)meth)(self, arg, kw); 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-#else -static PyTypeObject __pyx_CyFunctionType_type = { - PyVarObject_HEAD_INIT(0, 0) - __PYX_TYPE_MODULE_PREFIX "cython_function_or_method", - sizeof(__pyx_CyFunctionObject), - 0, - (destructor) __Pyx_CyFunction_dealloc, -#if !CYTHON_METH_FASTCALL - 0, -#elif CYTHON_BACKPORT_VECTORCALL - (printfunc)offsetof(__pyx_CyFunctionObject, func_vectorcall), -#else - offsetof(PyCFunctionObject, vectorcall), -#endif - 0, - 0, -#if PY_MAJOR_VERSION < 3 - 0, -#else - 0, -#endif - (reprfunc) __Pyx_CyFunction_repr, - 0, - 0, - 0, - 0, - __Pyx_CyFunction_CallAsMethod, - 0, - 0, - 0, - 0, -#ifdef Py_TPFLAGS_METHOD_DESCRIPTOR - Py_TPFLAGS_METHOD_DESCRIPTOR | -#endif -#ifdef _Py_TPFLAGS_HAVE_VECTORCALL - _Py_TPFLAGS_HAVE_VECTORCALL | -#endif - Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC | Py_TPFLAGS_BASETYPE, - 0, - (traverseproc) __Pyx_CyFunction_traverse, - (inquiry) __Pyx_CyFunction_clear, - 0, -#if PY_VERSION_HEX < 0x030500A0 - offsetof(__pyx_CyFunctionObject, func_weakreflist), -#else - offsetof(PyCFunctionObject, m_weakreflist), -#endif - 0, - 0, - __pyx_CyFunction_methods, - __pyx_CyFunction_members, - __pyx_CyFunction_getsets, - 0, - 0, - __Pyx_PyMethod_New, - 0, - offsetof(__pyx_CyFunctionObject, func_dict), - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, -#if PY_VERSION_HEX >= 0x030400a1 - 0, -#endif -#if PY_VERSION_HEX >= 0x030800b1 && (!CYTHON_COMPILING_IN_PYPY || PYPY_VERSION_NUM >= 0x07030800) - 0, -#endif -#if __PYX_NEED_TP_PRINT_SLOT - 0, -#endif -#if PY_VERSION_HEX >= 0x030C0000 - 0, -#endif -#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX >= 0x03090000 && PY_VERSION_HEX < 0x030a0000 - 0, -#endif -}; -#endif -static int __pyx_CyFunction_init(PyObject *module) { -#if CYTHON_USE_TYPE_SPECS - __pyx_CyFunctionType = __Pyx_FetchCommonTypeFromSpec(module, &__pyx_CyFunctionType_spec, NULL); -#else - CYTHON_UNUSED_VAR(module); - __pyx_CyFunctionType = __Pyx_FetchCommonType(&__pyx_CyFunctionType_type); -#endif - if (unlikely(__pyx_CyFunctionType == NULL)) { - return -1; - } - return 0; -} -static CYTHON_INLINE void *__Pyx_CyFunction_InitDefaults(PyObject *func, size_t size, int pyobjects) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults = PyObject_Malloc(size); - if (unlikely(!m->defaults)) - return PyErr_NoMemory(); - memset(m->defaults, 0, size); - m->defaults_pyobjects = pyobjects; - m->defaults_size = size; - return m->defaults; -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsTuple(PyObject *func, PyObject *tuple) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_tuple = tuple; - Py_INCREF(tuple); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetDefaultsKwDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->defaults_kwdict = dict; - Py_INCREF(dict); -} -static CYTHON_INLINE void __Pyx_CyFunction_SetAnnotationsDict(PyObject *func, PyObject *dict) { - __pyx_CyFunctionObject *m = (__pyx_CyFunctionObject *) func; - m->func_annotations = dict; - Py_INCREF(dict); -} - -/* CythonFunction */ -static PyObject *__Pyx_CyFunction_New(PyMethodDef *ml, int flags, PyObject* qualname, - PyObject *closure, PyObject *module, PyObject* globals, PyObject* code) { - PyObject *op = __Pyx_CyFunction_Init( - PyObject_GC_New(__pyx_CyFunctionObject, __pyx_CyFunctionType), - ml, flags, qualname, closure, module, globals, code - ); - if (likely(op)) { - PyObject_GC_Track(op); - } - return op; -} - -/* CLineInTraceback */ -#ifndef CYTHON_CLINE_IN_TRACEBACK -static int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line) { - PyObject *use_cline; - PyObject *ptype, *pvalue, *ptraceback; -#if CYTHON_COMPILING_IN_CPYTHON - PyObject **cython_runtime_dict; -#endif - CYTHON_MAYBE_UNUSED_VAR(tstate); - if (unlikely(!__pyx_cython_runtime)) { - return c_line; - } - __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback); -#if CYTHON_COMPILING_IN_CPYTHON - cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime); - if (likely(cython_runtime_dict)) { - __PYX_PY_DICT_LOOKUP_IF_MODIFIED( - use_cline, *cython_runtime_dict, - __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback)) - } else -#endif - { - PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStrNoError(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback); - if (use_cline_obj) { - use_cline = PyObject_Not(use_cline_obj) ? 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- } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, ((size_t)new_max) * sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} -#endif - -/* AddTraceback */ -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -#if PY_VERSION_HEX >= 0x030b00a6 - #ifndef Py_BUILD_CORE - #define Py_BUILD_CORE 1 - #endif - #include "internal/pycore_frame.h" -#endif -#if CYTHON_COMPILING_IN_LIMITED_API -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - if (c_line) { - (void) __pyx_cfilenm; - (void) __Pyx_CLineForTraceback(__Pyx_PyThreadState_Current, c_line); - } - _PyTraceback_Add(funcname, filename, py_line); -} -#else -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = NULL; - PyObject *py_funcname = NULL; - #if PY_MAJOR_VERSION < 3 - PyObject *py_srcfile = NULL; - py_srcfile = PyString_FromString(filename); - if (!py_srcfile) goto bad; - #endif - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - if (!py_funcname) goto bad; - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - if (!py_funcname) goto bad; - funcname = PyUnicode_AsUTF8(py_funcname); - if (!funcname) goto bad; - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - if (!py_funcname) goto bad; - #endif - } - #if PY_MAJOR_VERSION < 3 - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); 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- return number; -} -static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { - PyErr_Format(PyExc_ValueError, - "Unexpected format string character: '%c'", ch); -} -static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { - switch (ch) { - case '?': return "'bool'"; - case 'c': return "'char'"; - case 'b': return "'signed char'"; - case 'B': return "'unsigned char'"; - case 'h': return "'short'"; - case 'H': return "'unsigned short'"; - case 'i': return "'int'"; - case 'I': return "'unsigned int'"; - case 'l': return "'long'"; - case 'L': return "'unsigned long'"; - case 'q': return "'long long'"; - case 'Q': return "'unsigned long long'"; - case 'f': return (is_complex ? "'complex float'" : "'float'"); - case 'd': return (is_complex ? "'complex double'" : "'double'"); - case 'g': return (is_complex ? "'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparsable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, int is_complex) { - CYTHON_UNUSED_VAR(is_complex); - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, int is_complex) { - CYTHON_UNUSED_VAR(is_complex); - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case '?': case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number, ndim; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ndim = ctx->head->field->type->ndim; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_Is_Little_Endian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - CYTHON_FALLTHROUGH; - case '?': case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if ((ctx->enc_type == *ts) && (got_Z == ctx->is_complex) && - (ctx->enc_packmode == ctx->new_packmode) && (!ctx->is_valid_array)) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - CYTHON_FALLTHROUGH; - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} - -/* TypeInfoCompare */ - static int -__pyx_typeinfo_cmp(__Pyx_TypeInfo *a, __Pyx_TypeInfo *b) -{ - int i; - if (!a || !b) - return 0; - if (a == b) - return 1; - if (a->size != b->size || a->typegroup != b->typegroup || - a->is_unsigned != b->is_unsigned || a->ndim != b->ndim) { - if (a->typegroup == 'H' || b->typegroup == 'H') { - return a->size == b->size; - } else { - return 0; - } - } - if (a->ndim) { - for (i = 0; i < a->ndim; i++) - if (a->arraysize[i] != b->arraysize[i]) - return 0; - } - if (a->typegroup == 'S') { - if (a->flags != b->flags) - return 0; - if (a->fields || b->fields) { - if (!(a->fields && b->fields)) - return 0; - for (i = 0; a->fields[i].type && b->fields[i].type; i++) { - __Pyx_StructField *field_a = a->fields + i; - __Pyx_StructField *field_b = b->fields + i; - if (field_a->offset != field_b->offset || - !__pyx_typeinfo_cmp(field_a->type, field_b->type)) - return 0; - } - return !a->fields[i].type && !b->fields[i].type; - } - } - return 1; -} - -/* MemviewSliceValidateAndInit */ - static int -__pyx_check_strides(Py_buffer *buf, int dim, int ndim, int spec) -{ - if (buf->shape[dim] <= 1) - return 1; - if (buf->strides) { - if (spec & __Pyx_MEMVIEW_CONTIG) { - if (spec & (__Pyx_MEMVIEW_PTR|__Pyx_MEMVIEW_FULL)) { - if (unlikely(buf->strides[dim] != sizeof(void *))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly contiguous " - "in dimension %d.", dim); - goto fail; - } - } else if (unlikely(buf->strides[dim] != buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_FOLLOW) { - Py_ssize_t stride = buf->strides[dim]; - if (stride < 0) - stride = -stride; - if (unlikely(stride < buf->itemsize)) { - PyErr_SetString(PyExc_ValueError, - "Buffer and memoryview are not contiguous " - "in the same dimension."); - goto fail; - } - } - } else { - if (unlikely(spec & __Pyx_MEMVIEW_CONTIG && dim != ndim - 1)) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not contiguous in " - "dimension %d", dim); - goto fail; - } else if (unlikely(spec & (__Pyx_MEMVIEW_PTR))) { - PyErr_Format(PyExc_ValueError, - "C-contiguous buffer is not indirect in " - "dimension %d", dim); - goto fail; - } else if (unlikely(buf->suboffsets)) { - PyErr_SetString(PyExc_ValueError, - "Buffer exposes suboffsets but no strides"); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_check_suboffsets(Py_buffer *buf, int dim, int ndim, int spec) -{ - CYTHON_UNUSED_VAR(ndim); - if (spec & __Pyx_MEMVIEW_DIRECT) { - if (unlikely(buf->suboffsets && buf->suboffsets[dim] >= 0)) { - PyErr_Format(PyExc_ValueError, - "Buffer not compatible with direct access " - "in dimension %d.", dim); - goto fail; - } - } - if (spec & __Pyx_MEMVIEW_PTR) { - if (unlikely(!buf->suboffsets || (buf->suboffsets[dim] < 0))) { - PyErr_Format(PyExc_ValueError, - "Buffer is not indirectly accessible " - "in dimension %d.", dim); - goto fail; - } - } - return 1; -fail: - return 0; -} -static int -__pyx_verify_contig(Py_buffer *buf, int ndim, int c_or_f_flag) -{ - int i; - if (c_or_f_flag & __Pyx_IS_F_CONTIG) { - Py_ssize_t stride = 1; - for (i = 0; i < ndim; i++) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not fortran contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } else if (c_or_f_flag & __Pyx_IS_C_CONTIG) { - Py_ssize_t stride = 1; - for (i = ndim - 1; i >- 1; i--) { - if (unlikely(stride * buf->itemsize != buf->strides[i] && buf->shape[i] > 1)) { - PyErr_SetString(PyExc_ValueError, - "Buffer not C contiguous."); - goto fail; - } - stride = stride * buf->shape[i]; - } - } - return 1; -fail: - return 0; -} -static int __Pyx_ValidateAndInit_memviewslice( - int *axes_specs, - int c_or_f_flag, - int buf_flags, - int ndim, - __Pyx_TypeInfo *dtype, - __Pyx_BufFmt_StackElem stack[], - __Pyx_memviewslice *memviewslice, - PyObject *original_obj) -{ - struct __pyx_memoryview_obj *memview, *new_memview; - __Pyx_RefNannyDeclarations - Py_buffer *buf; - int i, spec = 0, retval = -1; - __Pyx_BufFmt_Context ctx; - int from_memoryview = __pyx_memoryview_check(original_obj); - __Pyx_RefNannySetupContext("ValidateAndInit_memviewslice", 0); - if (from_memoryview && __pyx_typeinfo_cmp(dtype, ((struct __pyx_memoryview_obj *) - original_obj)->typeinfo)) { - memview = (struct __pyx_memoryview_obj *) original_obj; - new_memview = NULL; - } else { - memview = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - original_obj, buf_flags, 0, dtype); - new_memview = memview; - if (unlikely(!memview)) - goto fail; - } - buf = &memview->view; - if (unlikely(buf->ndim != ndim)) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - ndim, buf->ndim); - goto fail; - } - if (new_memview) { - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (unlikely(!__Pyx_BufFmt_CheckString(&ctx, buf->format))) goto fail; - } - if (unlikely((unsigned) buf->itemsize != dtype->size)) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "u byte%s) " - "does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "u byte%s)", - buf->itemsize, - (buf->itemsize > 1) ? "s" : "", - dtype->name, - dtype->size, - (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->len > 0) { - for (i = 0; i < ndim; i++) { - spec = axes_specs[i]; - if (unlikely(!__pyx_check_strides(buf, i, ndim, spec))) - goto fail; - if (unlikely(!__pyx_check_suboffsets(buf, i, ndim, spec))) - goto fail; - } - if (unlikely(buf->strides && !__pyx_verify_contig(buf, ndim, c_or_f_flag))) - goto fail; - } - if (unlikely(__Pyx_init_memviewslice(memview, ndim, memviewslice, - new_memview != NULL) == -1)) { - goto fail; - } - retval = 0; - goto no_fail; -fail: - Py_XDECREF(new_memview); - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_d_d_dc_float(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_FOLLOW), (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 3, - &__Pyx_TypeInfo_float, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* ObjectToMemviewSlice */ - static CYTHON_INLINE __Pyx_memviewslice __Pyx_PyObject_to_MemoryviewSlice_dc_int(PyObject *obj, int writable_flag) { - __Pyx_memviewslice result = { 0, 0, { 0 }, { 0 }, { 0 } }; - __Pyx_BufFmt_StackElem stack[1]; - int axes_specs[] = { (__Pyx_MEMVIEW_DIRECT | __Pyx_MEMVIEW_CONTIG) }; - int retcode; - if (obj == Py_None) { - result.memview = (struct __pyx_memoryview_obj *) Py_None; - return result; - } - retcode = __Pyx_ValidateAndInit_memviewslice(axes_specs, __Pyx_IS_C_CONTIG, - (PyBUF_C_CONTIGUOUS | PyBUF_FORMAT) | writable_flag, 1, - &__Pyx_TypeInfo_int, stack, - &result, obj); - if (unlikely(retcode == -1)) - goto __pyx_fail; - return result; -__pyx_fail: - result.memview = NULL; - result.data = NULL; - return result; -} - -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* MemviewSliceCopyTemplate */ - static __Pyx_memviewslice -__pyx_memoryview_copy_new_contig(const __Pyx_memviewslice *from_mvs, - const char *mode, int ndim, - size_t sizeof_dtype, int contig_flag, - int dtype_is_object) -{ - __Pyx_RefNannyDeclarations - int i; - __Pyx_memviewslice new_mvs = { 0, 0, { 0 }, { 0 }, { 0 } }; - struct __pyx_memoryview_obj *from_memview = from_mvs->memview; - Py_buffer *buf = &from_memview->view; - PyObject *shape_tuple = NULL; - PyObject *temp_int = NULL; - struct __pyx_array_obj *array_obj = NULL; - struct __pyx_memoryview_obj *memview_obj = NULL; - __Pyx_RefNannySetupContext("__pyx_memoryview_copy_new_contig", 0); - for (i = 0; i < ndim; i++) { - if (unlikely(from_mvs->suboffsets[i] >= 0)) { - PyErr_Format(PyExc_ValueError, "Cannot copy memoryview slice with " - "indirect dimensions (axis %d)", i); - goto fail; - } - } - shape_tuple = PyTuple_New(ndim); - if (unlikely(!shape_tuple)) { - goto fail; - } - __Pyx_GOTREF(shape_tuple); - for(i = 0; i < ndim; i++) { - temp_int = PyInt_FromSsize_t(from_mvs->shape[i]); - if(unlikely(!temp_int)) { - goto fail; - } else { - PyTuple_SET_ITEM(shape_tuple, i, temp_int); - temp_int = NULL; - } - } - array_obj = __pyx_array_new(shape_tuple, sizeof_dtype, buf->format, (char *) mode, NULL); - if (unlikely(!array_obj)) { - goto fail; - } - __Pyx_GOTREF(array_obj); - memview_obj = (struct __pyx_memoryview_obj *) __pyx_memoryview_new( - (PyObject *) array_obj, contig_flag, - dtype_is_object, - from_mvs->memview->typeinfo); - if (unlikely(!memview_obj)) - goto fail; - if (unlikely(__Pyx_init_memviewslice(memview_obj, ndim, &new_mvs, 1) < 0)) - goto fail; - if (unlikely(__pyx_memoryview_copy_contents(*from_mvs, new_mvs, ndim, ndim, - dtype_is_object) < 0)) - goto fail; - goto no_fail; -fail: - __Pyx_XDECREF(new_mvs.memview); - new_mvs.memview = NULL; - new_mvs.data = NULL; -no_fail: - __Pyx_XDECREF(shape_tuple); - __Pyx_XDECREF(temp_int); - __Pyx_XDECREF(array_obj); - __Pyx_RefNannyFinishContext(); - return new_mvs; -} - -/* MemviewSliceInit */ - static int -__Pyx_init_memviewslice(struct __pyx_memoryview_obj *memview, - int ndim, - __Pyx_memviewslice *memviewslice, - int memview_is_new_reference) -{ - __Pyx_RefNannyDeclarations - int i, retval=-1; - Py_buffer *buf = &memview->view; - __Pyx_RefNannySetupContext("init_memviewslice", 0); - if (unlikely(memviewslice->memview || memviewslice->data)) { - PyErr_SetString(PyExc_ValueError, - "memviewslice is already initialized!"); - goto fail; - } - if (buf->strides) { - for (i = 0; i < ndim; i++) { - memviewslice->strides[i] = buf->strides[i]; - } - } else { - Py_ssize_t stride = buf->itemsize; - for (i = ndim - 1; i >= 0; i--) { - memviewslice->strides[i] = stride; - stride *= buf->shape[i]; - } - } - for (i = 0; i < ndim; i++) { - memviewslice->shape[i] = buf->shape[i]; - if (buf->suboffsets) { - memviewslice->suboffsets[i] = buf->suboffsets[i]; - } else { - memviewslice->suboffsets[i] = -1; - } - } - memviewslice->memview = memview; - memviewslice->data = (char *)buf->buf; - if (__pyx_add_acquisition_count(memview) == 0 && !memview_is_new_reference) { - Py_INCREF(memview); - } - retval = 0; - goto no_fail; -fail: - memviewslice->memview = 0; - memviewslice->data = 0; - retval = -1; -no_fail: - __Pyx_RefNannyFinishContext(); - return retval; -} -#ifndef Py_NO_RETURN -#define Py_NO_RETURN -#endif -static void __pyx_fatalerror(const char *fmt, ...) Py_NO_RETURN { - va_list vargs; - char msg[200]; -#if PY_VERSION_HEX >= 0x030A0000 || defined(HAVE_STDARG_PROTOTYPES) - va_start(vargs, fmt); -#else - va_start(vargs); -#endif - vsnprintf(msg, 200, fmt, vargs); - va_end(vargs); - Py_FatalError(msg); -} -static CYTHON_INLINE int -__pyx_add_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)++; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE int -__pyx_sub_acquisition_count_locked(__pyx_atomic_int_type *acquisition_count, - PyThread_type_lock lock) -{ - int result; - PyThread_acquire_lock(lock, 1); - result = (*acquisition_count)--; - PyThread_release_lock(lock); - return result; -} -static CYTHON_INLINE void -__Pyx_INC_MEMVIEW(__Pyx_memviewslice *memslice, int have_gil, int lineno) -{ - __pyx_nonatomic_int_type old_acquisition_count; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (unlikely(!memview || (PyObject *) memview == Py_None)) { - return; - } - old_acquisition_count = __pyx_add_acquisition_count(memview); - if (unlikely(old_acquisition_count <= 0)) { - if (likely(old_acquisition_count == 0)) { - if (have_gil) { - Py_INCREF((PyObject *) memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_INCREF((PyObject *) memview); - PyGILState_Release(_gilstate); - } - } else { - __pyx_fatalerror("Acquisition count is %d (line %d)", - old_acquisition_count+1, lineno); - } - } -} -static CYTHON_INLINE void __Pyx_XCLEAR_MEMVIEW(__Pyx_memviewslice *memslice, - int have_gil, int lineno) { - __pyx_nonatomic_int_type old_acquisition_count; - struct __pyx_memoryview_obj *memview = memslice->memview; - if (unlikely(!memview || (PyObject *) memview == Py_None)) { - memslice->memview = NULL; - return; - } - old_acquisition_count = __pyx_sub_acquisition_count(memview); - memslice->data = NULL; - if (likely(old_acquisition_count > 1)) { - memslice->memview = NULL; - } else if (likely(old_acquisition_count == 1)) { - if (have_gil) { - Py_CLEAR(memslice->memview); - } else { - PyGILState_STATE _gilstate = PyGILState_Ensure(); - Py_CLEAR(memslice->memview); - PyGILState_Release(_gilstate); - } - } else { - __pyx_fatalerror("Acquisition count is %d (line %d)", - old_acquisition_count-1, lineno); - } -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const int neg_one = (int) -1, const_zero = (int) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if ((sizeof(int) < sizeof(long))) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - if (unlikely(__Pyx_PyLong_IsNeg(x))) { - goto raise_neg_overflow; - } else if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_DigitCount(x)) { - case 2: - if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) >= 2 * PyLong_SHIFT)) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) >= 3 * PyLong_SHIFT)) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) >= 4 * PyLong_SHIFT)) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if ((sizeof(int) <= sizeof(unsigned long))) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(int) <= sizeof(unsigned PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(int, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_SignedDigitCount(x)) { - case -2: - if ((8 * sizeof(int) - 1 > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if ((8 * sizeof(int) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if ((8 * sizeof(int) - 1 > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if ((8 * sizeof(int) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if ((8 * sizeof(int) - 1 > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if ((8 * sizeof(int) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(int) - 1 > 4 * PyLong_SHIFT)) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } - } -#endif - if ((sizeof(int) <= sizeof(long))) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(int) <= sizeof(PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); -#if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } -#endif - if (likely(v)) { - int ret = -1; -#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); -#else - PyObject *stepval = NULL, *mask = NULL, *shift = NULL; - int bits, remaining_bits, is_negative = 0; - long idigit; - int chunk_size = (sizeof(long) < 8) ? 30 : 62; - if (unlikely(!PyLong_CheckExact(v))) { - PyObject *tmp = v; - v = PyNumber_Long(v); - assert(PyLong_CheckExact(v)); - Py_DECREF(tmp); - if (unlikely(!v)) return (int) -1; - } -#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(x) == 0) - return (int) 0; - is_negative = Py_SIZE(x) < 0; -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - is_negative = result == 1; - } -#endif - if (is_unsigned && unlikely(is_negative)) { - goto raise_neg_overflow; - } else if (is_negative) { - stepval = PyNumber_Invert(v); - if (unlikely(!stepval)) - return (int) -1; - } else { - stepval = __Pyx_NewRef(v); - } - val = (int) 0; - mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; - shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; - for (bits = 0; bits < (int) sizeof(int) * 8 - chunk_size; bits += chunk_size) { - PyObject *tmp, *digit; - digit = PyNumber_And(stepval, mask); - if (unlikely(!digit)) goto done; - idigit = PyLong_AsLong(digit); - Py_DECREF(digit); - if (unlikely(idigit < 0)) goto done; - tmp = PyNumber_Rshift(stepval, shift); - if (unlikely(!tmp)) goto done; - Py_DECREF(stepval); stepval = tmp; - val |= ((int) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(stepval) == 0) - goto unpacking_done; - #endif - } - idigit = PyLong_AsLong(stepval); - if (unlikely(idigit < 0)) goto done; - remaining_bits = ((int) sizeof(int) * 8) - bits - (is_unsigned ? 0 : 1); - if (unlikely(idigit >= (1L << remaining_bits))) - goto raise_overflow; - val |= ((int) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - unpacking_done: - #endif - if (!is_unsigned) { - if (unlikely(val & (((int) 1) << (sizeof(int) * 8 - 1)))) - goto raise_overflow; - if (is_negative) - val = ~val; - } - ret = 0; - done: - Py_XDECREF(shift); - Py_XDECREF(mask); - Py_XDECREF(stepval); -#endif - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const long neg_one = (long) -1, const_zero = (long) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if ((sizeof(long) < sizeof(long))) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - if (unlikely(__Pyx_PyLong_IsNeg(x))) { - goto raise_neg_overflow; - } else if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_DigitCount(x)) { - case 2: - if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) >= 2 * PyLong_SHIFT)) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) >= 3 * PyLong_SHIFT)) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) >= 4 * PyLong_SHIFT)) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if ((sizeof(long) <= sizeof(unsigned long))) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(long) <= sizeof(unsigned PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(long, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_SignedDigitCount(x)) { - case -2: - if ((8 * sizeof(long) - 1 > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if ((8 * sizeof(long) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if ((8 * sizeof(long) - 1 > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if ((8 * sizeof(long) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if ((8 * sizeof(long) - 1 > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if ((8 * sizeof(long) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(long) - 1 > 4 * PyLong_SHIFT)) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } - } -#endif - if ((sizeof(long) <= sizeof(long))) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(long) <= sizeof(PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { - long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); -#if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } -#endif - if (likely(v)) { - int ret = -1; -#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); -#else - PyObject *stepval = NULL, *mask = NULL, *shift = NULL; - int bits, remaining_bits, is_negative = 0; - long idigit; - int chunk_size = (sizeof(long) < 8) ? 30 : 62; - if (unlikely(!PyLong_CheckExact(v))) { - PyObject *tmp = v; - v = PyNumber_Long(v); - assert(PyLong_CheckExact(v)); - Py_DECREF(tmp); - if (unlikely(!v)) return (long) -1; - } -#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(x) == 0) - return (long) 0; - is_negative = Py_SIZE(x) < 0; -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - is_negative = result == 1; - } -#endif - if (is_unsigned && unlikely(is_negative)) { - goto raise_neg_overflow; - } else if (is_negative) { - stepval = PyNumber_Invert(v); - if (unlikely(!stepval)) - return (long) -1; - } else { - stepval = __Pyx_NewRef(v); - } - val = (long) 0; - mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; - shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; - for (bits = 0; bits < (int) sizeof(long) * 8 - chunk_size; bits += chunk_size) { - PyObject *tmp, *digit; - digit = PyNumber_And(stepval, mask); - if (unlikely(!digit)) goto done; - idigit = PyLong_AsLong(digit); - Py_DECREF(digit); - if (unlikely(idigit < 0)) goto done; - tmp = PyNumber_Rshift(stepval, shift); - if (unlikely(!tmp)) goto done; - Py_DECREF(stepval); stepval = tmp; - val |= ((long) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(stepval) == 0) - goto unpacking_done; - #endif - } - idigit = PyLong_AsLong(stepval); - if (unlikely(idigit < 0)) goto done; - remaining_bits = ((int) sizeof(long) * 8) - bits - (is_unsigned ? 0 : 1); - if (unlikely(idigit >= (1L << remaining_bits))) - goto raise_overflow; - val |= ((long) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - unpacking_done: - #endif - if (!is_unsigned) { - if (unlikely(val & (((long) 1) << (sizeof(long) * 8 - 1)))) - goto raise_overflow; - if (is_negative) - val = ~val; - } - ret = 0; - done: - Py_XDECREF(shift); - Py_XDECREF(mask); - Py_XDECREF(stepval); -#endif - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -/* CIntFromPy */ - static CYTHON_INLINE char __Pyx_PyInt_As_char(PyObject *x) { -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wconversion" -#endif - const char neg_one = (char) -1, const_zero = (char) 0; -#ifdef __Pyx_HAS_GCC_DIAGNOSTIC -#pragma GCC diagnostic pop -#endif - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if ((sizeof(char) < sizeof(long))) { - __PYX_VERIFY_RETURN_INT(char, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (char) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - if (unlikely(__Pyx_PyLong_IsNeg(x))) { - goto raise_neg_overflow; - } else if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_upylong, __Pyx_PyLong_CompactValueUnsigned(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_DigitCount(x)) { - case 2: - if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) >= 2 * PyLong_SHIFT)) { - return (char) (((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 3: - if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) >= 3 * PyLong_SHIFT)) { - return (char) (((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - case 4: - if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) >= 4 * PyLong_SHIFT)) { - return (char) (((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0])); - } - } - break; - } - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX < 0x030C00A7 - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (char) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if ((sizeof(char) <= sizeof(unsigned long))) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(char) <= sizeof(unsigned PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(char, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - if (__Pyx_PyLong_IsCompact(x)) { - __PYX_VERIFY_RETURN_INT(char, __Pyx_compact_pylong, __Pyx_PyLong_CompactValue(x)) - } else { - const digit* digits = __Pyx_PyLong_Digits(x); - assert(__Pyx_PyLong_DigitCount(x) > 1); - switch (__Pyx_PyLong_SignedDigitCount(x)) { - case -2: - if ((8 * sizeof(char) - 1 > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { - return (char) (((char)-1)*(((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 2: - if ((8 * sizeof(char) > 1 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 2 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { - return (char) ((((((char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -3: - if ((8 * sizeof(char) - 1 > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { - return (char) (((char)-1)*(((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 3: - if ((8 * sizeof(char) > 2 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 3 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { - return (char) ((((((((char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case -4: - if ((8 * sizeof(char) - 1 > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { - return (char) (((char)-1)*(((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - case 4: - if ((8 * sizeof(char) > 3 * PyLong_SHIFT)) { - if ((8 * sizeof(unsigned long) > 4 * PyLong_SHIFT)) { - __PYX_VERIFY_RETURN_INT(char, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if ((8 * sizeof(char) - 1 > 4 * PyLong_SHIFT)) { - return (char) ((((((((((char)digits[3]) << PyLong_SHIFT) | (char)digits[2]) << PyLong_SHIFT) | (char)digits[1]) << PyLong_SHIFT) | (char)digits[0]))); - } - } - break; - } - } -#endif - if ((sizeof(char) <= sizeof(long))) { - __PYX_VERIFY_RETURN_INT_EXC(char, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if ((sizeof(char) <= sizeof(PY_LONG_LONG))) { - __PYX_VERIFY_RETURN_INT_EXC(char, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { - char val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); -#if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } -#endif - if (likely(v)) { - int ret = -1; -#if !(CYTHON_COMPILING_IN_PYPY || CYTHON_COMPILING_IN_LIMITED_API) || defined(_PyLong_AsByteArray) - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); -#else - PyObject *stepval = NULL, *mask = NULL, *shift = NULL; - int bits, remaining_bits, is_negative = 0; - long idigit; - int chunk_size = (sizeof(long) < 8) ? 30 : 62; - if (unlikely(!PyLong_CheckExact(v))) { - PyObject *tmp = v; - v = PyNumber_Long(v); - assert(PyLong_CheckExact(v)); - Py_DECREF(tmp); - if (unlikely(!v)) return (char) -1; - } -#if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(x) == 0) - return (char) 0; - is_negative = Py_SIZE(x) < 0; -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (char) -1; - is_negative = result == 1; - } -#endif - if (is_unsigned && unlikely(is_negative)) { - goto raise_neg_overflow; - } else if (is_negative) { - stepval = PyNumber_Invert(v); - if (unlikely(!stepval)) - return (char) -1; - } else { - stepval = __Pyx_NewRef(v); - } - val = (char) 0; - mask = PyLong_FromLong((1L << chunk_size) - 1); if (unlikely(!mask)) goto done; - shift = PyLong_FromLong(chunk_size); if (unlikely(!shift)) goto done; - for (bits = 0; bits < (int) sizeof(char) * 8 - chunk_size; bits += chunk_size) { - PyObject *tmp, *digit; - digit = PyNumber_And(stepval, mask); - if (unlikely(!digit)) goto done; - idigit = PyLong_AsLong(digit); - Py_DECREF(digit); - if (unlikely(idigit < 0)) goto done; - tmp = PyNumber_Rshift(stepval, shift); - if (unlikely(!tmp)) goto done; - Py_DECREF(stepval); stepval = tmp; - val |= ((char) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - if (Py_SIZE(stepval) == 0) - goto unpacking_done; - #endif - } - idigit = PyLong_AsLong(stepval); - if (unlikely(idigit < 0)) goto done; - remaining_bits = ((int) sizeof(char) * 8) - bits - (is_unsigned ? 0 : 1); - if (unlikely(idigit >= (1L << remaining_bits))) - goto raise_overflow; - val |= ((char) idigit) << bits; - #if CYTHON_COMPILING_IN_LIMITED_API && PY_VERSION_HEX < 0x030B0000 - unpacking_done: - #endif - if (!is_unsigned) { - if (unlikely(val & (((char) 1) << (sizeof(char) * 8 - 1)))) - goto raise_overflow; - if (is_negative) - val = ~val; - } - ret = 0; - done: - Py_XDECREF(shift); - Py_XDECREF(mask); - Py_XDECREF(stepval); -#endif - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (char) -1; - } - } else { - char val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (char) -1; - val = __Pyx_PyInt_As_char(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to char"); - return (char) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to char"); - return (char) -1; -} - -/* FormatTypeName */ - #if CYTHON_COMPILING_IN_LIMITED_API -static __Pyx_TypeName -__Pyx_PyType_GetName(PyTypeObject* tp) -{ - PyObject *name = __Pyx_PyObject_GetAttrStr((PyObject *)tp, - __pyx_n_s_name_2); - if (unlikely(name == NULL) || unlikely(!PyUnicode_Check(name))) { - PyErr_Clear(); - Py_XSETREF(name, __Pyx_NewRef(__pyx_n_s__23)); - } - return name; -} -#endif - -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { - char ctversion[5]; - int same=1, i, found_dot; - const char* rt_from_call = Py_GetVersion(); - PyOS_snprintf(ctversion, 5, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - found_dot = 0; - for (i = 0; i < 4; i++) { - if (!ctversion[i]) { - same = (rt_from_call[i] < '0' || rt_from_call[i] > '9'); - break; - } - if (rt_from_call[i] != ctversion[i]) { - same = 0; - break; - } - } - if (!same) { - char rtversion[5] = {'\0'}; - char message[200]; - for (i=0; i<4; ++i) { - if (rt_from_call[i] == '.') { - if (found_dot) break; - found_dot = 1; - } else if (rt_from_call[i] < '0' || rt_from_call[i] > '9') { - break; - } - rtversion[i] = rt_from_call[i]; - } - PyOS_snprintf(message, sizeof(message), - "compile time version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -/* InitStrings */ - #if PY_MAJOR_VERSION >= 3 -static int __Pyx_InitString(__Pyx_StringTabEntry t, PyObject **str) { - if (t.is_unicode | t.is_str) { - if (t.intern) { - *str = PyUnicode_InternFromString(t.s); - } else if (t.encoding) { - *str = PyUnicode_Decode(t.s, t.n - 1, t.encoding, NULL); - } else { - *str = PyUnicode_FromStringAndSize(t.s, t.n - 1); - } - } else { - *str = PyBytes_FromStringAndSize(t.s, t.n - 1); - } - if (!*str) - return -1; - if (PyObject_Hash(*str) == -1) - return -1; - return 0; -} -#endif -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION >= 3 - __Pyx_InitString(*t, t->p); 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- char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -} -#else -static CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) { - if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (likely(PyUnicode_IS_ASCII(o))) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -} -#endif -#endif -static CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { - return __Pyx_PyUnicode_AsStringAndSize(o, length); 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" - "The ability to return an instance of a strict subclass of int is deprecated, " - "and may be removed in a future version of Python.", - result_type_name)) { - __Pyx_DECREF_TypeName(result_type_name); - Py_DECREF(result); - return NULL; - } - __Pyx_DECREF_TypeName(result_type_name); - return result; - } -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type " __Pyx_FMT_TYPENAME ")", - type_name, type_name, result_type_name); - __Pyx_DECREF_TypeName(result_type_name); - Py_DECREF(result); - return NULL; -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS - PyNumberMethods *m; -#endif - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x) || PyLong_Check(x))) -#else - if (likely(PyLong_Check(x))) -#endif - return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS - m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = m->nb_int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = m->nb_long(x); - } - #else - if (likely(m && m->nb_int)) { - name = "int"; - res = m->nb_int(x); - } - #endif -#else - if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) { - res = PyNumber_Int(x); - } -#endif - if (likely(res)) { -#if PY_MAJOR_VERSION < 3 - if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) { -#else - if (unlikely(!PyLong_CheckExact(res))) { -#endif - return __Pyx_PyNumber_IntOrLongWrongResultType(res, name); - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(b); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - if (likely(__Pyx_PyLong_IsCompact(b))) { - return __Pyx_PyLong_CompactValue(b); - } else { - const digit* digits = __Pyx_PyLong_Digits(b); - const Py_ssize_t size = __Pyx_PyLong_SignedDigitCount(b); - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE Py_hash_t __Pyx_PyIndex_AsHash_t(PyObject* o) { - if (sizeof(Py_hash_t) == sizeof(Py_ssize_t)) { - return (Py_hash_t) __Pyx_PyIndex_AsSsize_t(o); -#if PY_MAJOR_VERSION < 3 - } else if (likely(PyInt_CheckExact(o))) { - return PyInt_AS_LONG(o); -#endif - } else { - Py_ssize_t ival; - PyObject *x; - x = PyNumber_Index(o); - if (!x) return -1; - ival = PyInt_AsLong(x); - Py_DECREF(x); - return ival; - } -} -static CYTHON_INLINE PyObject * __Pyx_PyBool_FromLong(long b) { - return b ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False); -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -/* #### Code section: utility_code_pragmas_end ### */ -#ifdef _MSC_VER -#pragma warning( pop ) -#endif - - - -/* #### Code section: end ### */ -#endif /* Py_PYTHON_H */ diff --git a/spaces/dilums/sentence-similarity/components/ui/skeleton.tsx b/spaces/dilums/sentence-similarity/components/ui/skeleton.tsx deleted file mode 100644 index d7e45f7bd315f86eabf32f2c9425223417920e60..0000000000000000000000000000000000000000 --- a/spaces/dilums/sentence-similarity/components/ui/skeleton.tsx +++ /dev/null @@ -1,15 +0,0 @@ -import { cn } from "@/lib/utils" - -function Skeleton({ - className, - ...props -}: React.HTMLAttributes) { - return ( -
      - ) -} - -export { Skeleton } diff --git a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/free_anchor_retina_head.py b/spaces/dineshreddy/WALT/mmdet/models/dense_heads/free_anchor_retina_head.py deleted file mode 100644 index 79879fdc3171b8e34b606b27eb1ceb67f4473e3e..0000000000000000000000000000000000000000 --- a/spaces/dineshreddy/WALT/mmdet/models/dense_heads/free_anchor_retina_head.py +++ /dev/null @@ -1,270 +0,0 @@ -import torch -import torch.nn.functional as F - -from mmdet.core import bbox_overlaps -from ..builder import HEADS -from .retina_head import RetinaHead - -EPS = 1e-12 - - -@HEADS.register_module() -class FreeAnchorRetinaHead(RetinaHead): - """FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466. - - Args: - num_classes (int): Number of categories excluding the background - category. - in_channels (int): Number of channels in the input feature map. - stacked_convs (int): Number of conv layers in cls and reg tower. - Default: 4. - conv_cfg (dict): dictionary to construct and config conv layer. - Default: None. - norm_cfg (dict): dictionary to construct and config norm layer. - Default: norm_cfg=dict(type='GN', num_groups=32, - requires_grad=True). - pre_anchor_topk (int): Number of boxes that be token in each bag. - bbox_thr (float): The threshold of the saturated linear function. It is - usually the same with the IoU threshold used in NMS. - gamma (float): Gamma parameter in focal loss. - alpha (float): Alpha parameter in focal loss. - """ # noqa: W605 - - def __init__(self, - num_classes, - in_channels, - stacked_convs=4, - conv_cfg=None, - norm_cfg=None, - pre_anchor_topk=50, - bbox_thr=0.6, - gamma=2.0, - alpha=0.5, - **kwargs): - super(FreeAnchorRetinaHead, - self).__init__(num_classes, in_channels, stacked_convs, conv_cfg, - norm_cfg, **kwargs) - - self.pre_anchor_topk = pre_anchor_topk - self.bbox_thr = bbox_thr - self.gamma = gamma - self.alpha = alpha - - def loss(self, - cls_scores, - bbox_preds, - gt_bboxes, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - """Compute losses of the head. - - Args: - cls_scores (list[Tensor]): Box scores for each scale level - Has shape (N, num_anchors * num_classes, H, W) - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level with shape (N, num_anchors * 4, H, W) - gt_bboxes (list[Tensor]): each item are the truth boxes for each - image in [tl_x, tl_y, br_x, br_y] format. - gt_labels (list[Tensor]): class indices corresponding to each box - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - gt_bboxes_ignore (None | list[Tensor]): specify which bounding - boxes can be ignored when computing the loss. - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] - assert len(featmap_sizes) == len(self.anchor_generator.base_anchors) - - anchor_list, _ = self.get_anchors(featmap_sizes, img_metas) - anchors = [torch.cat(anchor) for anchor in anchor_list] - - # concatenate each level - cls_scores = [ - cls.permute(0, 2, 3, - 1).reshape(cls.size(0), -1, self.cls_out_channels) - for cls in cls_scores - ] - bbox_preds = [ - bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4) - for bbox_pred in bbox_preds - ] - cls_scores = torch.cat(cls_scores, dim=1) - bbox_preds = torch.cat(bbox_preds, dim=1) - - cls_prob = torch.sigmoid(cls_scores) - box_prob = [] - num_pos = 0 - positive_losses = [] - for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_, - bbox_preds_) in enumerate( - zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)): - - with torch.no_grad(): - if len(gt_bboxes_) == 0: - image_box_prob = torch.zeros( - anchors_.size(0), - self.cls_out_channels).type_as(bbox_preds_) - else: - # box_localization: a_{j}^{loc}, shape: [j, 4] - pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_) - - # object_box_iou: IoU_{ij}^{loc}, shape: [i, j] - object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes) - - # object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j] - t1 = self.bbox_thr - t2 = object_box_iou.max( - dim=1, keepdim=True).values.clamp(min=t1 + 1e-12) - object_box_prob = ((object_box_iou - t1) / - (t2 - t1)).clamp( - min=0, max=1) - - # object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j] - num_obj = gt_labels_.size(0) - indices = torch.stack([ - torch.arange(num_obj).type_as(gt_labels_), gt_labels_ - ], - dim=0) - object_cls_box_prob = torch.sparse_coo_tensor( - indices, object_box_prob) - - # image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j] - """ - from "start" to "end" implement: - image_box_iou = torch.sparse.max(object_cls_box_prob, - dim=0).t() - - """ - # start - box_cls_prob = torch.sparse.sum( - object_cls_box_prob, dim=0).to_dense() - - indices = torch.nonzero(box_cls_prob, as_tuple=False).t_() - if indices.numel() == 0: - image_box_prob = torch.zeros( - anchors_.size(0), - self.cls_out_channels).type_as(object_box_prob) - else: - nonzero_box_prob = torch.where( - (gt_labels_.unsqueeze(dim=-1) == indices[0]), - object_box_prob[:, indices[1]], - torch.tensor([ - 0 - ]).type_as(object_box_prob)).max(dim=0).values - - # upmap to shape [j, c] - image_box_prob = torch.sparse_coo_tensor( - indices.flip([0]), - nonzero_box_prob, - size=(anchors_.size(0), - self.cls_out_channels)).to_dense() - # end - - box_prob.append(image_box_prob) - - # construct bags for objects - match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_) - _, matched = torch.topk( - match_quality_matrix, - self.pre_anchor_topk, - dim=1, - sorted=False) - del match_quality_matrix - - # matched_cls_prob: P_{ij}^{cls} - matched_cls_prob = torch.gather( - cls_prob_[matched], 2, - gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk, - 1)).squeeze(2) - - # matched_box_prob: P_{ij}^{loc} - matched_anchors = anchors_[matched] - matched_object_targets = self.bbox_coder.encode( - matched_anchors, - gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors)) - loss_bbox = self.loss_bbox( - bbox_preds_[matched], - matched_object_targets, - reduction_override='none').sum(-1) - matched_box_prob = torch.exp(-loss_bbox) - - # positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )} - num_pos += len(gt_bboxes_) - positive_losses.append( - self.positive_bag_loss(matched_cls_prob, matched_box_prob)) - positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos) - - # box_prob: P{a_{j} \in A_{+}} - box_prob = torch.stack(box_prob, dim=0) - - # negative_loss: - # \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B|| - negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max( - 1, num_pos * self.pre_anchor_topk) - - # avoid the absence of gradients in regression subnet - # when no ground-truth in a batch - if num_pos == 0: - positive_loss = bbox_preds.sum() * 0 - - losses = { - 'positive_bag_loss': positive_loss, - 'negative_bag_loss': negative_loss - } - return losses - - def positive_bag_loss(self, matched_cls_prob, matched_box_prob): - """Compute positive bag loss. - - :math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`. - - :math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples. - - :math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples. - - Args: - matched_cls_prob (Tensor): Classification probabilty of matched - samples in shape (num_gt, pre_anchor_topk). - matched_box_prob (Tensor): BBox probability of matched samples, - in shape (num_gt, pre_anchor_topk). - - Returns: - Tensor: Positive bag loss in shape (num_gt,). - """ # noqa: E501, W605 - # bag_prob = Mean-max(matched_prob) - matched_prob = matched_cls_prob * matched_box_prob - weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None) - weight /= weight.sum(dim=1).unsqueeze(dim=-1) - bag_prob = (weight * matched_prob).sum(dim=1) - # positive_bag_loss = -self.alpha * log(bag_prob) - return self.alpha * F.binary_cross_entropy( - bag_prob, torch.ones_like(bag_prob), reduction='none') - - def negative_bag_loss(self, cls_prob, box_prob): - """Compute negative bag loss. - - :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`. - - :math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples. - - :math:`P_{j}^{bg}`: Classification probability of negative samples. - - Args: - cls_prob (Tensor): Classification probability, in shape - (num_img, num_anchors, num_classes). - box_prob (Tensor): Box probability, in shape - (num_img, num_anchors, num_classes). - - Returns: - Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes). - """ # noqa: E501, W605 - prob = cls_prob * (1 - box_prob) - # There are some cases when neg_prob = 0. - # This will cause the neg_prob.log() to be inf without clamp. - prob = prob.clamp(min=EPS, max=1 - EPS) - negative_bag_loss = prob**self.gamma * F.binary_cross_entropy( - prob, torch.zeros_like(prob), reduction='none') - return (1 - self.alpha) * negative_bag_loss diff --git a/spaces/dorkai/ChatUIPro/app/api/conversations/route.ts b/spaces/dorkai/ChatUIPro/app/api/conversations/route.ts deleted file mode 100644 index 9a73a3d28e131eb0da9b9d007b1da67d838540dd..0000000000000000000000000000000000000000 --- a/spaces/dorkai/ChatUIPro/app/api/conversations/route.ts +++ /dev/null @@ -1,11 +0,0 @@ -import { type NextRequest } from 'next/server' -import { NextResponse } from 'next/server' -import { getInfo, setSession, client } from '@/app/api/utils/common' - -export async function GET(request: NextRequest) { - const { sessionId, user } = getInfo(request); - const { data }: any = await client.getConversations(user); - return NextResponse.json(data, { - headers: setSession(sessionId) - }) -} \ No newline at end of file diff --git a/spaces/dorkai/ChatUIPro/utils/string.ts b/spaces/dorkai/ChatUIPro/utils/string.ts deleted file mode 100644 index 0d9e8eb0eeb0bbc169cacd2effa024eef5533e3e..0000000000000000000000000000000000000000 --- a/spaces/dorkai/ChatUIPro/utils/string.ts +++ /dev/null @@ -1,6 +0,0 @@ -const chars = '0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ-_' -export function randomString(length: number) { - let result = '' - for (let i = length; i > 0; --i) result += chars[Math.floor(Math.random() * chars.length)] - return result -} diff --git a/spaces/dorkai/text-generation-webui-main/extensions/api/script.py b/spaces/dorkai/text-generation-webui-main/extensions/api/script.py deleted file mode 100644 index 5544de725a406c2619e8d465a14f5969f093387e..0000000000000000000000000000000000000000 --- a/spaces/dorkai/text-generation-webui-main/extensions/api/script.py +++ /dev/null @@ -1,7 +0,0 @@ -import extensions.api.blocking_api as blocking_api -import extensions.api.streaming_api as streaming_api -from modules import shared - -def setup(): - blocking_api.start_server(shared.args.api_blocking_port, share=shared.args.public_api) - streaming_api.start_server(shared.args.api_streaming_port, share=shared.args.public_api) diff --git a/spaces/dpe1/beat_manipulator/beat_manipulator/utils.py b/spaces/dpe1/beat_manipulator/beat_manipulator/utils.py deleted file mode 100644 index 1935f4b109e49630258c1facae89bb8ab6e0a7f1..0000000000000000000000000000000000000000 --- a/spaces/dpe1/beat_manipulator/beat_manipulator/utils.py +++ /dev/null @@ -1,25 +0,0 @@ -C_SLICE = ":><" # 0 - range, 1 - first, 2 - last -C_JOIN = ",;~&^$}" # 0 - append, 1 - first length, 2 - cut, 3 - maximum, 4 - sidechain -C_MISC = "'\"`i@_?%#![]" -# 0 ',1 " - sample, 2 ` - sample uncut, 3 i - current, 4 @ - random, -# # 5 _ - random sep, 6 ? - not count, 7 % - create variable, 8 # - shuffle, 9 ! skip -# 10, 11 [] - song -C_MATH = '+-*/.' -C_MATH_STRICT = '.+-*/' - -def _safer_eval(string:str) -> float: - if isinstance(string, str): - try: - for i in (C_MISC[4], C_MISC[7], C_MISC[8]): - if i in string: string = string[:string.find(i)] - string = string.replace('{', '<').replace('}', '>') - string = eval(''.join([i for i in string if i.isdecimal() or i in C_MATH])) - except (NameError, SyntaxError): string = 1 - return string - -def _safer_eval_strict(string:str) -> float: - if isinstance(string, str): - for n, v in enumerate(string): - assert v in C_MATH_STRICT or v == ' ' or v.isdecimal, f"_safer_eval_strict error: {string}[{n}] = {v}, which isn't a decimal, isn't in {C_MATH_STRICT} and isn't a space" - string = eval(''.join([i for i in string if i.isdecimal() or i in C_MATH_STRICT])) - return string diff --git a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h b/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h deleted file mode 100644 index c7408eba007b424194618baa63726657e36875e3..0000000000000000000000000000000000000000 --- a/spaces/dragonSwing/annotate-anything/GroundingDINO/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h +++ /dev/null @@ -1,64 +0,0 @@ -/*! -************************************************************************************************** -* Deformable DETR -* Copyright (c) 2020 SenseTime. All Rights Reserved. -* Licensed under the Apache License, Version 2.0 [see LICENSE for details] -************************************************************************************************** -* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 -************************************************************************************************** -*/ - -#pragma once - -#include "ms_deform_attn_cpu.h" - -#ifdef WITH_CUDA -#include "ms_deform_attn_cuda.h" -#endif - -namespace groundingdino { - -at::Tensor -ms_deform_attn_forward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const int im2col_step) -{ - if (value.type().is_cuda()) - { -#ifdef WITH_CUDA - return ms_deform_attn_cuda_forward( - value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step); -#else - AT_ERROR("Not compiled with GPU support"); -#endif - } - AT_ERROR("Not implemented on the CPU"); -} - -std::vector -ms_deform_attn_backward( - const at::Tensor &value, - const at::Tensor &spatial_shapes, - const at::Tensor &level_start_index, - const at::Tensor &sampling_loc, - const at::Tensor &attn_weight, - const at::Tensor &grad_output, - const int im2col_step) -{ - if (value.type().is_cuda()) - { -#ifdef WITH_CUDA - return ms_deform_attn_cuda_backward( - value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step); -#else - AT_ERROR("Not compiled with GPU support"); -#endif - } - AT_ERROR("Not implemented on the CPU"); -} - -} // namespace groundingdino \ No newline at end of file diff --git a/spaces/dragonSwing/annotate-anything/annotate_anything.py b/spaces/dragonSwing/annotate-anything/annotate_anything.py deleted file mode 100644 index 5cca810866a55fd4adc016c553ea3a48e9d50ebe..0000000000000000000000000000000000000000 --- a/spaces/dragonSwing/annotate-anything/annotate_anything.py +++ /dev/null @@ -1,441 +0,0 @@ -import argparse -import functools -import json -import os -import sys -import tempfile - -import cv2 -import numpy as np -import supervision as sv -from groundingdino.util.inference import Model as DinoModel -from imutils import paths -from PIL import Image -from segment_anything import sam_model_registry -from segment_anything import SamAutomaticMaskGenerator -from segment_anything import SamPredictor -from supervision.detection.utils import xywh_to_xyxy -from tqdm import tqdm - -sys.path.append("tag2text") - -from tag2text.models import tag2text -from config import * -from utils import detect, download_file_hf, segment, generate_tags, show_anns_sv - - -def process( - tag2text_model, - grounding_dino_model, - sam_predictor, - sam_automask_generator, - image_path, - task, - prompt, - box_threshold, - text_threshold, - iou_threshold, - kernel_size=2, - expand_mask=False, - device="cuda", - output_dir=None, - save_ann=True, - save_mask=False, -): - detections = None - metadata = {"image": {}, "annotations": [], "assets": {}} - - if save_mask: - metadata["assets"]["intermediate_mask"] = [] - - try: - # Load image - image = Image.open(image_path) - image_pil = image.convert("RGB") - image = np.array(image_pil) - orig_image = image.copy() - - # Extract image metadata - filename = os.path.basename(image_path) - basename = os.path.splitext(filename)[0] - h, w = image.shape[:2] - metadata["image"]["file_name"] = filename - metadata["image"]["width"] = w - metadata["image"]["height"] = h - - # Generate tags - if task in ["auto", "detection"] and prompt == "": - tags, caption = generate_tags(tag2text_model, image_pil, "None", device) - prompt = " . ".join(tags) - # print(f"Caption: {caption}") - # print(f"Tags: {tags}") - - # ToDo: Extract metadata - metadata["image"]["caption"] = caption - metadata["image"]["tags"] = tags - - if prompt: - metadata["prompt"] = prompt - - # Detect boxes - if prompt != "": - detections, phrases, classes = detect( - grounding_dino_model, - image, - caption=prompt, - box_threshold=box_threshold, - text_threshold=text_threshold, - iou_threshold=iou_threshold, - post_process=True, - ) - - # Save detection image - if output_dir and save_ann: - # Draw boxes - box_annotator = sv.BoxAnnotator() - labels = [ - f"{phrases[i]} {detections.confidence[i]:0.2f}" - for i in range(len(phrases)) - ] - box_image = box_annotator.annotate( - scene=image, detections=detections, labels=labels - ) - box_image_path = os.path.join(output_dir, basename + "_detect.png") - metadata["assets"]["detection"] = box_image_path - Image.fromarray(box_image).save(box_image_path) - - # Segmentation - if task in ["auto", "segment"]: - kernel = cv2.getStructuringElement( - cv2.MORPH_ELLIPSE, (2 * kernel_size + 1, 2 * kernel_size + 1) - ) - if detections: - masks, scores = segment( - sam_predictor, image=orig_image, boxes=detections.xyxy - ) - if expand_mask: - masks = [ - cv2.dilate(mask.astype(np.uint8), kernel) for mask in masks - ] - else: - masks = [ - cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, kernel) - for mask in masks - ] - detections.mask = masks - binary_mask = functools.reduce( - lambda x, y: x + y, detections.mask - ).astype(np.bool) - else: - masks = sam_automask_generator.generate(orig_image) - sorted_generated_masks = sorted( - masks, key=lambda x: x["area"], reverse=True - ) - - xywh = np.array([mask["bbox"] for mask in sorted_generated_masks]) - scores = np.array( - [mask["predicted_iou"] for mask in sorted_generated_masks] - ) - if expand_mask: - mask = np.array( - [ - cv2.dilate(mask["segmentation"].astype(np.uint8), kernel) - for mask in sorted_generated_masks - ] - ) - else: - mask = np.array( - [mask["segmentation"] for mask in sorted_generated_masks] - ) - detections = sv.Detections( - xyxy=xywh_to_xyxy(boxes_xywh=xywh), mask=mask - ) - binary_mask = None - - # Save annotated image - if output_dir and save_ann: - mask_annotator = sv.MaskAnnotator() - mask_image, res = show_anns_sv(detections) - annotated_image = mask_annotator.annotate(image, detections=detections) - - mask_image_path = os.path.join(output_dir, basename + "_mask.png") - metadata["assets"]["mask"] = mask_image_path - Image.fromarray(mask_image).save(mask_image_path) - - # Save annotation encoding from https://github.com/LUSSeg/ImageNet-S - mask_enc_path = os.path.join(output_dir, basename + "_mask_enc.npy") - np.save(mask_enc_path, res) - metadata["assets"]["mask_enc"] = mask_enc_path - - if binary_mask is not None: - cutout_image = np.expand_dims(binary_mask, axis=-1) * orig_image - cutout_image_path = os.path.join( - output_dir, basename + "_cutout.png" - ) - Image.fromarray(cutout_image).save(cutout_image_path) - - annotated_image_path = os.path.join( - output_dir, basename + "_annotate.png" - ) - metadata["assets"]["annotate"] = annotated_image_path - Image.fromarray(annotated_image).save(annotated_image_path) - - # ToDo: Extract metadata - if detections: - i = 0 - for (xyxy, mask, confidence, _, _), area, box_area in zip( - detections, detections.area, detections.box_area - ): - annotation = { - "id": i + 1, - "bbox": [int(x) for x in xyxy], - "box_area": float(box_area), - } - if confidence: - annotation["confidence"] = float(confidence) - annotation["label"] = phrases[i] - if mask is not None: - # annotation["segmentation"] = mask_to_polygons(mask) - annotation["area"] = int(area) - annotation["predicted_iou"] = float(scores[i]) - metadata["annotations"].append(annotation) - i += 1 - - if output_dir and save_mask: - mask_image_path = os.path.join( - output_dir, f"{basename}_mask_{id}.png" - ) - metadata["assets"]["intermediate_mask"].append(mask_image_path) - Image.fromarray(mask * 255).save(mask_image_path) - - if output_dir: - meta_file_path = os.path.join(output_dir, basename + "_meta.json") - with open(meta_file_path, "w") as fp: - json.dump(metadata, fp) - else: - meta_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json") - meta_file_path = meta_file.name - - return meta_file_path - except Exception as error: - raise ValueError(f"global exception: {error}") - - -def main(args: argparse.Namespace) -> None: - device = args.device - prompt = args.prompt - task = args.task - - tag2text_model = None - grounding_dino_model = None - sam_predictor = None - sam_automask_generator = None - - box_threshold = args.box_threshold - text_threshold = args.text_threshold - iou_threshold = args.iou_threshold - save_ann = not args.no_save_ann - save_mask = args.save_mask - - # load model - if task in ["auto", "detection"] and prompt == "": - print("Loading Tag2Text model...") - tag2text_type = args.tag2text_type - tag2text_checkpoint = os.path.join( - abs_weight_dir, tag2text_dict[tag2text_type]["checkpoint_file"] - ) - if not os.path.exists(tag2text_checkpoint): - print(f"Downloading weights for Tag2Text {tag2text_type} model") - os.system( - f"wget {tag2text_dict[tag2text_type]['checkpoint_url']} -O {tag2text_checkpoint}" - ) - tag2text_model = tag2text.tag2text_caption( - pretrained=tag2text_checkpoint, - image_size=384, - vit="swin_b", - delete_tag_index=delete_tag_index, - ) - # threshold for tagging - # we reduce the threshold to obtain more tags - tag2text_model.threshold = 0.64 - tag2text_model.to(device) - tag2text_model.eval() - - if task in ["auto", "detection"] or prompt != "": - print("Loading Grounding Dino model...") - dino_type = args.dino_type - dino_checkpoint = os.path.join( - abs_weight_dir, dino_dict[dino_type]["checkpoint_file"] - ) - dino_config_file = os.path.join( - abs_weight_dir, dino_dict[dino_type]["config_file"] - ) - if not os.path.exists(dino_checkpoint): - print(f"Downloading weights for Grounding Dino {dino_type} model") - dino_repo_id = dino_dict[dino_type]["repo_id"] - download_file_hf( - repo_id=dino_repo_id, - filename=dino_dict[dino_type]["checkpoint_file"], - cache_dir=weight_dir, - ) - download_file_hf( - repo_id=dino_repo_id, - filename=dino_dict[dino_type]["checkpoint_file"], - cache_dir=weight_dir, - ) - grounding_dino_model = DinoModel( - model_config_path=dino_config_file, - model_checkpoint_path=dino_checkpoint, - device=device, - ) - - if task in ["auto", "segment"]: - print("Loading SAM...") - sam_type = args.sam_type - sam_checkpoint = os.path.join( - abs_weight_dir, sam_dict[sam_type]["checkpoint_file"] - ) - if not os.path.exists(sam_checkpoint): - print(f"Downloading weights for SAM {sam_type}") - os.system( - f"wget {sam_dict[sam_type]['checkpoint_url']} -O {sam_checkpoint}" - ) - sam = sam_model_registry[sam_type](checkpoint=sam_checkpoint) - sam.to(device=device) - sam_predictor = SamPredictor(sam) - sam_automask_generator = SamAutomaticMaskGenerator(sam) - - if not os.path.exists(args.input): - raise ValueError("The input directory doesn't exist!") - elif not os.path.isdir(args.input): - image_paths = [args.input] - else: - image_paths = paths.list_images(args.input) - - os.makedirs(args.output, exist_ok=True) - - with tqdm(image_paths) as pbar: - for image_path in pbar: - pbar.set_postfix_str(f"Processing {image_path}") - process( - tag2text_model=tag2text_model, - grounding_dino_model=grounding_dino_model, - sam_predictor=sam_predictor, - sam_automask_generator=sam_automask_generator, - image_path=image_path, - task=task, - prompt=prompt, - box_threshold=box_threshold, - text_threshold=text_threshold, - iou_threshold=iou_threshold, - device=device, - output_dir=args.output, - save_ann=save_ann, - save_mask=save_mask, - ) - - -if __name__ == "__main__": - if not os.path.exists(abs_weight_dir): - os.makedirs(abs_weight_dir, exist_ok=True) - - parser = argparse.ArgumentParser( - description=( - "Runs automatic detection and mask generation on an input image or directory of images" - ) - ) - - parser.add_argument( - "--input", - "-i", - type=str, - required=True, - help="Path to either a single input image or folder of images.", - ) - - parser.add_argument( - "--output", - "-o", - type=str, - required=True, - help="Path to the directory where masks will be output.", - ) - - parser.add_argument( - "--sam-type", - type=str, - default=default_sam, - choices=sam_dict.keys(), - help="The type of SA model use for segmentation.", - ) - - parser.add_argument( - "--tag2text-type", - type=str, - default=default_tag2text, - choices=tag2text_dict.keys(), - help="The type of Tag2Text model use for tags and caption generation.", - ) - - parser.add_argument( - "--dino-type", - type=str, - default=default_dino, - choices=dino_dict.keys(), - help="The type of Grounding Dino model use for promptable object detection.", - ) - - parser.add_argument( - "--task", - help="Task to run", - default="auto", - choices=["auto", "detect", "segment"], - type=str, - ) - parser.add_argument( - "--prompt", - help="Detection prompt", - default="", - type=str, - ) - - parser.add_argument( - "--box-threshold", type=float, default=0.25, help="box threshold" - ) - parser.add_argument( - "--text-threshold", type=float, default=0.2, help="text threshold" - ) - parser.add_argument( - "--iou-threshold", type=float, default=0.5, help="iou threshold" - ) - parser.add_argument( - "--kernel-size", - type=int, - default=2, - choices=range(1, 6), - help="kernel size use for smoothing/expanding segment masks", - ) - parser.add_argument( - "--expand-mask", - action="store_true", - default=False, - help="If True, expanding segment masks for smoother output.", - ) - - parser.add_argument( - "--no-save-ann", - action="store_true", - default=False, - help="If False, save original image with blended masks and detection boxes.", - ) - parser.add_argument( - "--save-mask", - action="store_true", - default=False, - help="If True, save all intermidiate masks.", - ) - parser.add_argument( - "--device", type=str, default="cuda", help="The device to run generation on." - ) - args = parser.parse_args() - main(args) diff --git a/spaces/ennet/ChatDev/camel/prompts/translation.py b/spaces/ennet/ChatDev/camel/prompts/translation.py deleted file mode 100644 index 40993a1dd2d1d83cfe68ed2368de107d5d570fd9..0000000000000000000000000000000000000000 --- a/spaces/ennet/ChatDev/camel/prompts/translation.py +++ /dev/null @@ -1,42 +0,0 @@ -# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. =========== -# Licensed under the Apache License, Version 2.0 (the “License”); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an “AS IS” BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. =========== -from typing import Any - -from camel.prompts import TextPrompt, TextPromptDict -from camel.typing import RoleType - - -# flake8: noqa :E501 -class TranslationPromptTemplateDict(TextPromptDict): - r"""A dictionary containing :obj:`TextPrompt` used in the `Translation` - task. - - Attributes: - ASSISTANT_PROMPT (TextPrompt): A system prompt for the AI assistant - that outlines the rules of the conversation and provides - instructions for completing tasks. - """ - ASSISTANT_PROMPT = TextPrompt( - """You are an expert English to {language} translator. -Your sole purpose is to accurately translate any text presented to you from English to {language}. -Please provide the {language} translation for the given text. -If you are presented with an empty string, simply return an empty string as the translation. -Only text in between ```TEXT``` should not be translated. -Do not provide any explanation. Just provide a translation.""") - - def __init__(self, *args: Any, **kwargs: Any) -> None: - super().__init__(*args, **kwargs) - self.update({ - RoleType.ASSISTANT: self.ASSISTANT_PROMPT, - }) diff --git a/spaces/ennov8ion/500models/index.html b/spaces/ennov8ion/500models/index.html deleted file mode 100644 index 6250c2958a7186a4e64f21c02b0359ff5ecd7e97..0000000000000000000000000000000000000000 --- a/spaces/ennov8ion/500models/index.html +++ /dev/null @@ -1,16 +0,0 @@ - - - - - - - - - - - - - - - - \ No newline at end of file diff --git a/spaces/enzostvs/stable-diffusion-tpu/app/api/route.ts b/spaces/enzostvs/stable-diffusion-tpu/app/api/route.ts deleted file mode 100644 index 0fb36c38c9e5f47a5a3b4bc3a664d20edf1d89a0..0000000000000000000000000000000000000000 --- a/spaces/enzostvs/stable-diffusion-tpu/app/api/route.ts +++ /dev/null @@ -1,59 +0,0 @@ -import { PrismaClient } from '@prisma/client' - -import list_styles from "@/assets/list_styles.json" -import { UploaderDataset } from '../../utils/uploader' -import { isTextNSFW } from '@/utils/checker/prompt' -import { isImageNSFW } from '@/utils/checker/image' - -const prisma = new PrismaClient() - -export async function POST( - request: Request, -) { - const global_headers = { - Authorization: `Bearer ${process.env.HF_TOKEN}`, - 'Content-Type': 'application/json', - ['x-use-cache']: "0" - } - - const { inputs, style, userId } = await request.json() - const findStyle = list_styles.find((item) => item.name === style) - - const textIsNSFW = await isTextNSFW(inputs, global_headers) - if (textIsNSFW) return Response.json({ status: 401, ok: false, message: "Prompt is not safe for work." }); - - const response = await fetch(`${process.env.API_SDXL_URL}`, { - method: 'POST', - body: JSON.stringify({ - prompt: findStyle?.prompt.replace("{prompt}", inputs) ?? inputs, - negative_prompt: findStyle?.negative_prompt ?? "", - }), - headers: global_headers, - }) - - - const res = await response.clone().json().catch(() => ({})); - if (res?.error) return Response.json({ status: response.status, ok: false, message: res.error }); - - const base64Image = res.images[0]; - const blob = await fetch(`data:image/png;base64,${base64Image}`).then((r) => r.blob()); - - // const imageIsNSFW = await isImageNSFW(blob, global_headers) - // if (imageIsNSFW) return Response.json({ status: 401, ok: false, message: "Image is not safe for work." }); - - const name = Date.now() + `-${inputs.replace(/[^a-zA-Z0-9]/g, '-').toLowerCase()}` - const { ok, message } = await UploaderDataset(blob, name) - - if (!ok) return Response.json({ status: 500, ok: false, message }); - - const new_image = await prisma.collection.create({ - data: { - prompt: inputs, - file_name: name, - userId: userId ?? "", - }, - }) - - return Response.json({ image: new_image, status: 200, ok: true }); - -} \ No newline at end of file diff --git a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/data/BaseDataset.py b/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/data/BaseDataset.py deleted file mode 100644 index 2d3e842341ecd51514ac96ce51a13fcaa12d1733..0000000000000000000000000000000000000000 --- a/spaces/eswat/Image-and-3D-Model-Creator/PIFu/lib/data/BaseDataset.py +++ /dev/null @@ -1,46 +0,0 @@ -from torch.utils.data import Dataset -import random - - -class BaseDataset(Dataset): - ''' - This is the Base Datasets. - Itself does nothing and is not runnable. - Check self.get_item function to see what it should return. - ''' - - @staticmethod - def modify_commandline_options(parser, is_train): - return parser - - def __init__(self, opt, phase='train'): - self.opt = opt - self.is_train = self.phase == 'train' - self.projection_mode = 'orthogonal' # Declare projection mode here - - def __len__(self): - return 0 - - def get_item(self, index): - # In case of a missing file or IO error, switch to a random sample instead - try: - res = { - 'name': None, # name of this subject - 'b_min': None, # Bounding box (x_min, y_min, z_min) of target space - 'b_max': None, # Bounding box (x_max, y_max, z_max) of target space - - 'samples': None, # [3, N] samples - 'labels': None, # [1, N] labels - - 'img': None, # [num_views, C, H, W] input images - 'calib': None, # [num_views, 4, 4] calibration matrix - 'extrinsic': None, # [num_views, 4, 4] extrinsic matrix - 'mask': None, # [num_views, 1, H, W] segmentation masks - } - return res - except: - print("Requested index %s has missing files. Using a random sample instead." % index) - return self.get_item(index=random.randint(0, self.__len__() - 1)) - - def __getitem__(self, index): - return self.get_item(index) diff --git a/spaces/facebook/ov-seg/open_vocab_seg/data/datasets/register_cc3m.py b/spaces/facebook/ov-seg/open_vocab_seg/data/datasets/register_cc3m.py deleted file mode 100644 index 8aa5cb07bc99b574505b6319835750789bb3ee26..0000000000000000000000000000000000000000 --- a/spaces/facebook/ov-seg/open_vocab_seg/data/datasets/register_cc3m.py +++ /dev/null @@ -1,457 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import os - -import pandas as pd -from detectron2.data import DatasetCatalog, MetadataCatalog -from detectron2.data.datasets import load_sem_seg -from detectron2.utils.file_io import PathManager - - -COCO_CATEGORIES = [ - {"color": [220, 20, 60], "isthing": 1, "id": 1, "name": "person"}, - {"color": [119, 11, 32], "isthing": 1, "id": 2, "name": "bicycle"}, - {"color": [0, 0, 142], "isthing": 1, "id": 3, "name": "car"}, - {"color": [0, 0, 230], "isthing": 1, "id": 4, "name": "motorcycle"}, - {"color": [106, 0, 228], "isthing": 1, "id": 5, "name": "airplane"}, - {"color": [0, 60, 100], "isthing": 1, "id": 6, "name": "bus"}, - {"color": [0, 80, 100], "isthing": 1, "id": 7, "name": "train"}, - {"color": [0, 0, 70], "isthing": 1, "id": 8, "name": "truck"}, - {"color": [0, 0, 192], "isthing": 1, "id": 9, "name": "boat"}, - {"color": [250, 170, 30], "isthing": 1, "id": 10, "name": "traffic light"}, - {"color": [100, 170, 30], "isthing": 1, "id": 11, "name": "fire hydrant"}, - {"color": [220, 220, 0], "isthing": 1, "id": 13, "name": "stop sign"}, - {"color": [175, 116, 175], "isthing": 1, "id": 14, "name": "parking meter"}, - {"color": [250, 0, 30], "isthing": 1, "id": 15, "name": "bench"}, - {"color": [165, 42, 42], "isthing": 1, "id": 16, "name": "bird"}, - {"color": [255, 77, 255], "isthing": 1, "id": 17, "name": "cat"}, - {"color": [0, 226, 252], "isthing": 1, "id": 18, "name": "dog"}, - {"color": [182, 182, 255], "isthing": 1, "id": 19, "name": "horse"}, - {"color": [0, 82, 0], "isthing": 1, "id": 20, "name": "sheep"}, - {"color": [120, 166, 157], "isthing": 1, "id": 21, "name": "cow"}, - {"color": [110, 76, 0], "isthing": 1, "id": 22, "name": "elephant"}, - {"color": [174, 57, 255], "isthing": 1, "id": 23, "name": "bear"}, - {"color": [199, 100, 0], "isthing": 1, "id": 24, "name": "zebra"}, - {"color": [72, 0, 118], "isthing": 1, "id": 25, "name": "giraffe"}, - {"color": [255, 179, 240], "isthing": 1, "id": 27, "name": "backpack"}, - {"color": [0, 125, 92], "isthing": 1, "id": 28, "name": "umbrella"}, - {"color": [209, 0, 151], "isthing": 1, "id": 31, "name": "handbag"}, - {"color": [188, 208, 182], "isthing": 1, "id": 32, "name": "tie"}, - {"color": [0, 220, 176], "isthing": 1, "id": 33, "name": "suitcase"}, - {"color": [255, 99, 164], "isthing": 1, "id": 34, "name": "frisbee"}, - {"color": [92, 0, 73], "isthing": 1, "id": 35, "name": "skis"}, - {"color": [133, 129, 255], "isthing": 1, "id": 36, "name": "snowboard"}, - {"color": [78, 180, 255], "isthing": 1, "id": 37, "name": "sports ball"}, - {"color": [0, 228, 0], "isthing": 1, "id": 38, "name": "kite"}, - {"color": [174, 255, 243], "isthing": 1, "id": 39, "name": "baseball bat"}, - {"color": [45, 89, 255], "isthing": 1, "id": 40, "name": "baseball glove"}, - {"color": [134, 134, 103], "isthing": 1, "id": 41, "name": "skateboard"}, - {"color": [145, 148, 174], "isthing": 1, "id": 42, "name": "surfboard"}, - {"color": [255, 208, 186], "isthing": 1, "id": 43, "name": "tennis racket"}, - {"color": [197, 226, 255], "isthing": 1, "id": 44, "name": "bottle"}, - {"color": [171, 134, 1], "isthing": 1, "id": 46, "name": "wine glass"}, - {"color": [109, 63, 54], "isthing": 1, "id": 47, "name": "cup"}, - {"color": [207, 138, 255], "isthing": 1, "id": 48, "name": "fork"}, - {"color": [151, 0, 95], "isthing": 1, "id": 49, "name": "knife"}, - {"color": [9, 80, 61], "isthing": 1, "id": 50, "name": "spoon"}, - {"color": [84, 105, 51], "isthing": 1, "id": 51, "name": "bowl"}, - {"color": [74, 65, 105], "isthing": 1, "id": 52, "name": "banana"}, - {"color": [166, 196, 102], "isthing": 1, "id": 53, "name": "apple"}, - {"color": [208, 195, 210], "isthing": 1, "id": 54, "name": "sandwich"}, - {"color": [255, 109, 65], "isthing": 1, "id": 55, "name": "orange"}, - {"color": [0, 143, 149], "isthing": 1, "id": 56, "name": "broccoli"}, - {"color": [179, 0, 194], "isthing": 1, "id": 57, "name": "carrot"}, - {"color": [209, 99, 106], "isthing": 1, "id": 58, "name": "hot dog"}, - {"color": [5, 121, 0], "isthing": 1, "id": 59, "name": "pizza"}, - {"color": [227, 255, 205], "isthing": 1, "id": 60, "name": "donut"}, - {"color": [147, 186, 208], "isthing": 1, "id": 61, "name": "cake"}, - {"color": [153, 69, 1], "isthing": 1, "id": 62, "name": "chair"}, - {"color": [3, 95, 161], "isthing": 1, "id": 63, "name": "couch"}, - {"color": [163, 255, 0], "isthing": 1, "id": 64, "name": "potted plant"}, - {"color": [119, 0, 170], "isthing": 1, "id": 65, "name": "bed"}, - {"color": [0, 182, 199], "isthing": 1, "id": 67, "name": "dining table"}, - {"color": [0, 165, 120], "isthing": 1, "id": 70, "name": "toilet"}, - {"color": [183, 130, 88], "isthing": 1, "id": 72, "name": "tv"}, - {"color": [95, 32, 0], "isthing": 1, "id": 73, "name": "laptop"}, - {"color": [130, 114, 135], "isthing": 1, "id": 74, "name": "mouse"}, - {"color": [110, 129, 133], "isthing": 1, "id": 75, "name": "remote"}, - {"color": [166, 74, 118], "isthing": 1, "id": 76, "name": "keyboard"}, - {"color": [219, 142, 185], "isthing": 1, "id": 77, "name": "cell phone"}, - {"color": [79, 210, 114], "isthing": 1, "id": 78, "name": "microwave"}, - {"color": [178, 90, 62], "isthing": 1, "id": 79, "name": "oven"}, - {"color": [65, 70, 15], "isthing": 1, "id": 80, "name": "toaster"}, - {"color": [127, 167, 115], "isthing": 1, "id": 81, "name": "sink"}, - {"color": [59, 105, 106], "isthing": 1, "id": 82, "name": "refrigerator"}, - {"color": [142, 108, 45], "isthing": 1, "id": 84, "name": "book"}, - {"color": [196, 172, 0], "isthing": 1, "id": 85, "name": "clock"}, - {"color": [95, 54, 80], "isthing": 1, "id": 86, "name": "vase"}, - {"color": [128, 76, 255], "isthing": 1, "id": 87, "name": "scissors"}, - {"color": [201, 57, 1], "isthing": 1, "id": 88, "name": "teddy bear"}, - {"color": [246, 0, 122], "isthing": 1, "id": 89, "name": "hair drier"}, - {"color": [191, 162, 208], "isthing": 1, "id": 90, "name": "toothbrush"}, - {"id": 92, "name": "banner", "supercategory": "textile"}, - {"id": 93, "name": "blanket", "supercategory": "textile"}, - {"id": 94, "name": "branch", "supercategory": "plant"}, - {"id": 95, "name": "bridge", "supercategory": "building"}, - {"id": 96, "name": "building-other", "supercategory": "building"}, - {"id": 97, "name": "bush", "supercategory": "plant"}, - {"id": 98, "name": "cabinet", "supercategory": "furniture-stuff"}, - {"id": 99, "name": "cage", "supercategory": "structural"}, - {"id": 100, "name": "cardboard", "supercategory": "raw-material"}, - {"id": 101, "name": "carpet", "supercategory": "floor"}, - {"id": 102, "name": "ceiling-other", "supercategory": "ceiling"}, - {"id": 103, "name": "ceiling-tile", "supercategory": "ceiling"}, - {"id": 104, "name": "cloth", "supercategory": "textile"}, - {"id": 105, "name": "clothes", "supercategory": "textile"}, - {"id": 106, "name": "clouds", "supercategory": "sky"}, - {"id": 107, "name": "counter", "supercategory": "furniture-stuff"}, - {"id": 108, "name": "cupboard", "supercategory": "furniture-stuff"}, - {"id": 109, "name": "curtain", "supercategory": "textile"}, - {"id": 110, "name": "desk-stuff", "supercategory": "furniture-stuff"}, - {"id": 111, "name": "dirt", "supercategory": "ground"}, - {"id": 112, "name": "door-stuff", "supercategory": "furniture-stuff"}, - {"id": 113, "name": "fence", "supercategory": "structural"}, - {"id": 114, "name": "floor-marble", "supercategory": "floor"}, - {"id": 115, "name": "floor-other", "supercategory": "floor"}, - {"id": 116, "name": "floor-stone", "supercategory": "floor"}, - {"id": 117, "name": "floor-tile", "supercategory": "floor"}, - {"id": 118, "name": "floor-wood", "supercategory": "floor"}, - {"id": 119, "name": "flower", "supercategory": "plant"}, - {"id": 120, "name": "fog", "supercategory": "water"}, - {"id": 121, "name": "food-other", "supercategory": "food-stuff"}, - {"id": 122, "name": "fruit", "supercategory": "food-stuff"}, - {"id": 123, "name": "furniture-other", "supercategory": "furniture-stuff"}, - {"id": 124, "name": "grass", "supercategory": "plant"}, - {"id": 125, "name": "gravel", "supercategory": "ground"}, - {"id": 126, "name": "ground-other", "supercategory": "ground"}, - {"id": 127, "name": "hill", "supercategory": "solid"}, - {"id": 128, "name": "house", "supercategory": "building"}, - {"id": 129, "name": "leaves", "supercategory": "plant"}, - {"id": 130, "name": "light", "supercategory": "furniture-stuff"}, - {"id": 131, "name": "mat", "supercategory": "textile"}, - {"id": 132, "name": "metal", "supercategory": "raw-material"}, - {"id": 133, "name": "mirror-stuff", "supercategory": "furniture-stuff"}, - {"id": 134, "name": "moss", "supercategory": "plant"}, - {"id": 135, "name": "mountain", "supercategory": "solid"}, - {"id": 136, "name": "mud", "supercategory": "ground"}, - {"id": 137, "name": "napkin", "supercategory": "textile"}, - {"id": 138, "name": "net", "supercategory": "structural"}, - {"id": 139, "name": "paper", "supercategory": "raw-material"}, - {"id": 140, "name": "pavement", "supercategory": "ground"}, - {"id": 141, "name": "pillow", "supercategory": "textile"}, - {"id": 142, "name": "plant-other", "supercategory": "plant"}, - {"id": 143, "name": "plastic", "supercategory": "raw-material"}, - {"id": 144, "name": "platform", "supercategory": "ground"}, - {"id": 145, "name": "playingfield", "supercategory": "ground"}, - {"id": 146, "name": "railing", "supercategory": "structural"}, - {"id": 147, "name": "railroad", "supercategory": "ground"}, - {"id": 148, "name": "river", "supercategory": "water"}, - {"id": 149, "name": "road", "supercategory": "ground"}, - {"id": 150, "name": "rock", "supercategory": "solid"}, - {"id": 151, "name": "roof", "supercategory": "building"}, - {"id": 152, "name": "rug", "supercategory": "textile"}, - {"id": 153, "name": "salad", "supercategory": "food-stuff"}, - {"id": 154, "name": "sand", "supercategory": "ground"}, - {"id": 155, "name": "sea", "supercategory": "water"}, - {"id": 156, "name": "shelf", "supercategory": "furniture-stuff"}, - {"id": 157, "name": "sky-other", "supercategory": "sky"}, - {"id": 158, "name": "skyscraper", "supercategory": "building"}, - {"id": 159, "name": "snow", "supercategory": "ground"}, - {"id": 160, "name": "solid-other", "supercategory": "solid"}, - {"id": 161, "name": "stairs", "supercategory": "furniture-stuff"}, - {"id": 162, "name": "stone", "supercategory": "solid"}, - {"id": 163, "name": "straw", "supercategory": "plant"}, - {"id": 164, "name": "structural-other", "supercategory": "structural"}, - {"id": 165, "name": "table", "supercategory": "furniture-stuff"}, - {"id": 166, "name": "tent", "supercategory": "building"}, - {"id": 167, "name": "textile-other", "supercategory": "textile"}, - {"id": 168, "name": "towel", "supercategory": "textile"}, - {"id": 169, "name": "tree", "supercategory": "plant"}, - {"id": 170, "name": "vegetable", "supercategory": "food-stuff"}, - {"id": 171, "name": "wall-brick", "supercategory": "wall"}, - {"id": 172, "name": "wall-concrete", "supercategory": "wall"}, - {"id": 173, "name": "wall-other", "supercategory": "wall"}, - {"id": 174, "name": "wall-panel", "supercategory": "wall"}, - {"id": 175, "name": "wall-stone", "supercategory": "wall"}, - {"id": 176, "name": "wall-tile", "supercategory": "wall"}, - {"id": 177, "name": "wall-wood", "supercategory": "wall"}, - {"id": 178, "name": "water-other", "supercategory": "water"}, - {"id": 179, "name": "waterdrops", "supercategory": "water"}, - {"id": 180, "name": "window-blind", "supercategory": "window"}, - {"id": 181, "name": "window-other", "supercategory": "window"}, - {"id": 182, "name": "wood", "supercategory": "solid"}, -] - - -ADE20K_150_CATEGORIES = [ - {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"}, - {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"}, - {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"}, - {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"}, - {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"}, - {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"}, - {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"}, - {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"}, - {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "}, - {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"}, - {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"}, - {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"}, - {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"}, - {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"}, - {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"}, - {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"}, - {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"}, - {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"}, - {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"}, - {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"}, - {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"}, - {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"}, - {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"}, - {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"}, - {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"}, - {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"}, - {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"}, - {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"}, - {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"}, - {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"}, - {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"}, - {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"}, - {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"}, - {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"}, - {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"}, - {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"}, - {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"}, - {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"}, - {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"}, - {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"}, - {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"}, - {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"}, - {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"}, - {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"}, - { - "color": [6, 51, 255], - "id": 44, - "isthing": 1, - "name": "chest of drawers, chest, bureau, dresser", - }, - {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"}, - {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"}, - {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"}, - {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"}, - {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"}, - {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"}, - {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"}, - {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"}, - {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"}, - {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"}, - {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"}, - { - "color": [255, 71, 0], - "id": 56, - "isthing": 1, - "name": "pool table, billiard table, snooker table", - }, - {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"}, - {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"}, - {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"}, - {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"}, - {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"}, - {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"}, - {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"}, - {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"}, - { - "color": [0, 255, 133], - "id": 65, - "isthing": 1, - "name": "toilet, can, commode, crapper, pot, potty, stool, throne", - }, - {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"}, - {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"}, - {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"}, - {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"}, - {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"}, - {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"}, - {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"}, - {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"}, - {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"}, - {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"}, - {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"}, - {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"}, - {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"}, - {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"}, - {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"}, - {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"}, - {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"}, - {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"}, - {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"}, - {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"}, - {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"}, - {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"}, - {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"}, - {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"}, - {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"}, - {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"}, - {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"}, - {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"}, - {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"}, - { - "color": [0, 122, 255], - "id": 95, - "isthing": 1, - "name": "bannister, banister, balustrade, balusters, handrail", - }, - { - "color": [0, 255, 163], - "id": 96, - "isthing": 0, - "name": "escalator, moving staircase, moving stairway", - }, - { - "color": [255, 153, 0], - "id": 97, - "isthing": 1, - "name": "ottoman, pouf, pouffe, puff, hassock", - }, - {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"}, - {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"}, - { - "color": [143, 255, 0], - "id": 100, - "isthing": 0, - "name": "poster, posting, placard, notice, bill, card", - }, - {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"}, - {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"}, - {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"}, - {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"}, - { - "color": [133, 0, 255], - "id": 105, - "isthing": 0, - "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter", - }, - {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"}, - { - "color": [184, 0, 255], - "id": 107, - "isthing": 1, - "name": "washer, automatic washer, washing machine", - }, - {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"}, - {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"}, - {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"}, - {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"}, - {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"}, - {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"}, - {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"}, - {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"}, - {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"}, - {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"}, - {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"}, - {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"}, - {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"}, - {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"}, - {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"}, - {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"}, - {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"}, - {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"}, - {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"}, - {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"}, - {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"}, - {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"}, - {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"}, - {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"}, - {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"}, - {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"}, - {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"}, - {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"}, - {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"}, - {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"}, - {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"}, - {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"}, - {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"}, - {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"}, - {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"}, - {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"}, - {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"}, - {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"}, - {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"}, - {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"}, - {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"}, - {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"}, -] - -TEST_CATEGORIES = [ - {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "Oculus"}, - {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "Ukulele"}, -] - -COCO_BASE_CATEGORIES = [ - c - for i, c in enumerate(COCO_CATEGORIES) - if c["id"] - 1 - not in [20, 24, 32, 33, 40, 56, 86, 99, 105, 123, 144, 147, 148, 168, 171] -] -COCO_NOVEL_CATEGORIES = [ - c - for i, c in enumerate(COCO_CATEGORIES) - if c["id"] - 1 - in [20, 24, 32, 33, 40, 56, 86, 99, 105, 123, 144, 147, 148, 168, 171] -] - - -def load_cc_image(csv_file, img_key='filepath', caption_key='title', sep="\t"): - print(f'Loading csv data from {csv_file}.') - df = pd.read_csv(csv_file, sep=sep) - - input_files = df[img_key].tolist() - captions = df[caption_key].tolist() - - print("Loaded {} images".format(len(input_files))) - - dataset_dicts = [] - for (img_path, text) in zip(input_files, captions): - record = {} - record["file_name"] = img_path - record["caption"] = text - dataset_dicts.append(record) - - return dataset_dicts - - -def _get_coco_stuff_meta(cat_list): - # Id 0 is reserved for ignore_label, we change ignore_label for 0 - # to 255 in our pre-processing. - stuff_ids = [k["id"] for k in cat_list] - - # For semantic segmentation, this mapping maps from contiguous stuff id - # (in [0, 91], used in models) to ids in the dataset (used for processing results) - stuff_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(stuff_ids)} - stuff_classes = [k["name"] for k in cat_list] - - ret = { - "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id, - "stuff_classes": stuff_classes, - } - return ret - - -def register_cc_3m(csv_file): - - meta = _get_coco_stuff_meta(TEST_CATEGORIES) - name = "cc_3m_train" - - DatasetCatalog.register( - name, - lambda x=csv_file: load_cc_image(x), - ) - MetadataCatalog.get(name).set( - csv_file=csv_file, - evaluator_type="dummy", - ignore_label=255, - **meta, - ) - - -# _csv_file = "/home/jeffliang/zsseg/datasets/coco/coco_train_merge_captions.csv" -_csv_file = "/home/jeffliang/zsseg/configs/masked_images/pred/samples.csv" -register_cc_3m(_csv_file) diff --git a/spaces/failfast/2D-GameCreator/src/components/HowToUse.tsx b/spaces/failfast/2D-GameCreator/src/components/HowToUse.tsx deleted file mode 100644 index 65a98e01e08c888bde17117571ff2fb6d88d7c71..0000000000000000000000000000000000000000 --- a/spaces/failfast/2D-GameCreator/src/components/HowToUse.tsx +++ /dev/null @@ -1,268 +0,0 @@ -import { PlayArrow, Code, Replay, CropSquare, Clear } from "@mui/icons-material"; -import { - Button, - Grid, - IconButton, - List, - ListItem, - ListSubheader, - Paper, - Stack, - Typography, -} from "@mui/material"; -import { OutlinedBox, SectionBox, RainbowBox } from "@/components/base/boxes"; -import { Key } from "react"; - -const workflowSteps = [ - { - title: "Editor Usage Tips", - steps: [ - "We are limited by the context-size of the selected model, the scope of the game shouldn't be super huge (for example 'asteroids' is a game with small scope while 'fortnite' is way too big)", - "It's good to start with simple features, main mechanics first using the base game + the create game command.", - 'Iterate on this. For example "Flappy Bird. Intro screen, start the game by pressing space key."', - "See if the generated result is what you expect. Continue there, add more features. Either by using the add feature command or by extending the prompt itself and start from scratch (select the base game + create game command).", - "Make always sure to check the selected game in the games list, as this is used as the foundation for the next iteration.", - "Cancel generating the game at any time by pressing the cancel button button next to run button.", - ], - }, - { - title: "Create a New Game", - steps: [ - "Input your game concept into the prompt field.", - "Make sure the command field is set to create game.", - "Ensure that the base game is selected in the games list.", - "Click on run to start the game creation process.", - "Once the game generation is complete, it will be added to the games list and selected automatically.", - "You can view the newly created game in the game preview section.", - ], - }, - { - title: "Add a New Feature", - steps: [ - "Clear the prompt field and input the new feature you want to add.", - "Set the command field to add feature.", - "Ensure that the previously generated game is selected in the games list.", - "Click on run to start the feature addition process.", - "Once the feature addition is complete, it will be added to the games list and selected automatically.", - "If a bug appears in the process, proceed to the next section.", - ], - }, - { - title: "Fix a Bug", - steps: [ - "Clear the prompt field and input the bug that you have encountered.", - "Set the command field to fix bug.", - "Ensure that the game with the bug is selected in the games list.", - "Click on run to start the bug fixing process.", - "Once the bug fixing is complete, it will be reflected in the selected game in the games list.", - ], - }, - { - title: "Extend a Feature", - steps: [ - "Clear the prompt field and input the changes you want to make to the feature.", - "Set the command field to extend feature.", - "Ensure that the game with the feature is selected in the games list.", - "Click on run to start the feature extension process.", - "Once the feature extension is complete, it will be reflected in the selected game in the games list.", - ], - }, - { - title: "Start From Scratch", - steps: [ - "Clear the prompt field and input a new game concept.", - "Set the command field to create game.", - "Ensure that the base game is selected in the games list.", - "Click on run to start the new game creation process.", - "Once the game generation is complete, it will be added to the games list and selected automatically.", - "You can view the newly created game in the game preview section.", - ], - }, - { - title: "Viewing & Editing Source Code", - steps: [ - "To see the source code of the selected game, click the source code button in the header. This will open a code editor where you can manually change the code if desired.", - "Save changes with CTRL+S using your keyboard.", - ], - }, - { - title: "Refreshing Game Preview", - steps: [ - "The refresh button in the header of the game preview refreshes the game preview.", - "Click this if you've made changes and want to see them reflected in the game preview and for what ever reason the game preview didn't refresh automatically.", - "It's also useful if you are GAME OVER and have not added a feature to restart the game yet.", - ], - }, - { - title: "Exporting to CodeSandbox", - steps: [ - "To export your game to CodeSandbox, click the share on codesandbox button in the game preview header.", - "Note: This feature will not work for the base game; it can only export generated games.", - ], - }, - { - title: "Using Options", - steps: [ - "Open the Options by clicking on them", - "Switch the model between gpt-4 and gpt-3.5-turbo (default)", - "Update the max_tokens to 4096 (gpt-4), default is 2048 (gpt-3.5-turbo)", - "Make changes to the temperature if you want more random (value closer to 1.0) results, default is 0.2 (not very random)", - ], - }, -]; - -function parseString(string: string) { - const keywords = { - prompt: (item: string, index: Key | null | undefined) => ( - <> -  {item}  - - ), - command: (item: string, index: Key | null | undefined) => ( - <> -  {item}  - - ), - options: (item: string, index: Key | null | undefined) => ( - <> -  Options  - - ), - games: (item: string, index: Key | null | undefined) => ( - <> -  Games  - - ), - run: (item: string, index: Key | null | undefined) => ( - <> -   - -   - - ), - "cancel button": (item: string, index: Key | null | undefined) => ( - <> -   - -   - - ), - "source code button": (item: string, index: Key | null | undefined) => ( - <> -   - - - - button  - - ), - "refresh button": (item: string, index: Key | null | undefined) => ( - <> -   - - - - button  - - ), - "codesandbox button": (item: string, index: Key | null | undefined) => ( - <> -   - - - - button  - - ), - "game preview": (item: string, index: Key | null | undefined) => ( -  Game Preview  - ), - "base game": (item: string, index: Key | null | undefined) => ( -  Base Game  - ), - "create game": (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - "add feature": (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - "remove feature": (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - "extend feature": (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - "fix bug": (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - model: (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - max_tokens: (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - temperature: (item: string, index: Key | null | undefined) => ( -  {item.trim()}  - ), - }; - - Object.keys(keywords).forEach(keyword => { - const regex = new RegExp(`\\s*\\b${keyword}\\b\\s*`, "gi"); - string = string.replace(regex, `|${keyword}|`); - }); - - const items = string.split("|").filter(item => item !== ""); - - return items.map((item, index) => { - if (keywords.hasOwnProperty(item.trim())) { - return keywords[item.trim() as keyof typeof keywords](item.trim(), index); - } else { - return item; - } - }); -} - -export default function HowToUse() { - return ( - <> - - - How to use? - - - - - {workflowSteps.map(({ title, steps }, index) => { - return ( - - - - - {title} - - - {steps.map((step, index) => { - return ( - - - <>{parseString(step)} - - - ); - })} - - - - ); - })} - - - ); -} diff --git a/spaces/falterWliame/Face_Mask_Detection/Adobe Dreamweaver CS6 12.0 With Crack(Portable)[Team Nanban][TPB Download HOT Pc.md b/spaces/falterWliame/Face_Mask_Detection/Adobe Dreamweaver CS6 12.0 With Crack(Portable)[Team Nanban][TPB Download HOT Pc.md deleted file mode 100644 index a38b55d41eaff6113c5fe7836b90e3abd7303d59..0000000000000000000000000000000000000000 --- a/spaces/falterWliame/Face_Mask_Detection/Adobe Dreamweaver CS6 12.0 With Crack(Portable)[Team Nanban][TPB Download HOT Pc.md +++ /dev/null @@ -1,6 +0,0 @@ -

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      Card battle games are a type of video games that involve collecting, building, and using decks of cards to compete against other players or computer opponents. They are also known as collectible card games (CCGs), trading card games (TCGs), or digital card games (DCGs). Card battle games combine elements of strategy, luck, and skill, and often feature rich lore, colorful graphics, and diverse gameplay modes.

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      Card battle games are especially popular on PC, as they offer many advantages over other platforms. For example, PC gamers can enjoy higher resolution, faster performance, more customization options, and easier access to online communities and tournaments. Moreover, PC gamers can choose from a wide variety of card battle games, ranging from free-to-play titles to premium ones, from classic franchises to indie gems.

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      But how can you download card battle games for PC? What are the best platforms and methods to get your hands on these games? And what are some of the best card battle games that you can play right now? In this article, we will answer these questions and more. Read on to find out everything you need to know about downloading card battle games for PC.

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      How to Download Card Battle Games for PC

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      There are several ways to download card battle games for PC, depending on your preferences and budget. Here are some of the most common methods and platforms that you can use:

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      Direct Download

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      Some card battle games can be downloaded directly from the game developer's or publisher's official website. This is more common for online multiplayer games that require constant updates and patches. For example, you can download Guild Wars 2, a popular MMORPG with a card battle mode, from its official website. To download a game directly from the website, you usually need to create an account, purchase the game (if it's not free), and follow the instructions to install it on your PC.

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      Microsoft Store

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      The Microsoft Store is a digital distribution platform that offers a variety of free and paid games that you can download directly from the store. You can access the Microsoft Store from your Windows 10 PC by pressing Windows Key + S and typing in "Microsoft Store". Then, you can browse the gaming section and look for card battle games. Some examples of card battle games that you can find on the Microsoft Store are Magic: The Gathering Arena, Yu-Gi-Oh! Master Duel, and Marvel Snap. To download a game from the Microsoft Store, you need to have a Microsoft account, click on the Buy or Get button (depending on whether the game is free or not), and click on Install.

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      Steam

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      Steam is one of the most popular and widely used platforms for downloading PC games. It has a huge library of games across various genres, including card battle games. You can download Steam from its official website and install it on your PC. Then, you can create an account, browse the store, and purchase or download any game that you like. Some examples of card battle games that you can find on Steam are Slay the Spire, Gwent: The Witcher Card Game, and Faeria. To download a game from Steam, you need to have enough space on your hard drive, click on Add to Cart or Play Game (depending on whether the game is free or not), and click on Install.

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      Epic Games

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      Epic Games is another digital distribution platform that offers a variety of PC games, including some exclusive titles. You can download Epic Games from its official website and install it on your PC. Then, you can create an account, browse the store, and purchase or download any game that you like. Some examples of card battle games that you can find on Epic Games are Hearthstone, Monster Train, and Loop Hero. To download a game from Epic Games, you need to have enough space on your hard drive, click on Buy Now or Get (assuming the game is free or not), and click on Install.

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      The Best Card Battle Games for PC

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      Now that you know how to download card battle games for PC, you might be wondering which ones are worth playing. Of course, this depends on your personal taste and preferences, but here are some of the best card battle games for PC that we recommend:

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      Slay the Spire

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      Slay the Spire is a roguelike card game that challenges you to climb a spire full of enemies, traps, and treasures. You can choose from four different characters, each with their own unique deck of cards and abilities. You can also customize your deck by adding new cards, removing unwanted ones, and upgrading them. The game is highly replayable, as each run is different and randomized. Slay the Spire is a game that will test your strategic thinking, risk management, and creativity.

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      Hearthstone

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      Hearthstone is a free-to-play card game that is based on the Warcraft universe. You can choose from 10 different classes, each with their own unique cards and hero powers. You can also collect and craft cards from various expansions and sets, featuring iconic characters and locations from the Warcraft lore. The game offers multiple modes of play, such as casual, ranked, arena, battlegrounds, duels, and solo adventures. Hearthstone is a game that will appeal to fans of Warcraft, as well as casual and competitive card game players.

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      Gwent: The Witcher Card Game

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      Gwent: The Witcher Card Game is a card game that is based on the Witcher series of books and games. You can choose from six different factions, each with their own unique cards and playstyles. You can also collect and craft cards from various expansions and sets, featuring characters and events from the Witcher lore. The game has a unique gameplay mechanic, where you have to win two out of three rounds by having more points than your opponent. Gwent: The Witcher Card Game is a game that will appeal to fans of the Witcher, as well as strategic and tactical card game players.

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      Conclusion

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      Card battle games are a fun and exciting genre of video games that you can enjoy on your PC. They offer a variety of gameplay options, themes, and challenges that will keep you entertained for hours. Whether you prefer fantasy, sci-fi, or historical settings, whether you like solo or multiplayer modes, whether you want to play for free or pay for premium content, there is a card battle game for you.

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      To download card battle games for PC, you can use different methods and platforms, such as direct download, Microsoft Store, Steam, or Epic Games. You can also check out some of the best card battle games for PC that we recommended in this article, such as Slay the Spire, Hearthstone, or Gwent: The Witcher Card Game.

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      So what are you waiting for? Download your favorite card battle game today and start playing!

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      FAQs

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      What are the benefits of playing card battle games on PC?

      -

      Some of the benefits of playing card battle games on PC are:

      -
        -
      • You can enjoy higher resolution, faster performance, more customization options, and easier access to online communities and tournaments.
        • -
      • You can choose from a wide variety of card battle games, ranging from free-to-play titles to premium ones, from classic franchises to indie gems.
        • -
      • You can use keyboard and mouse controls or connect a controller if you prefer.
        • -
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      What are the drawbacks of playing card battle games on PC?

      -

      Some of the drawbacks of playing card battle games on PC are:

      -
        -
      • You need to have a compatible PC with enough space on your hard drive and a stable internet connection.
        • -
      • You might encounter technical issues such as bugs, crashes, or compatibility problems.
        • -
      • You might have to deal with DRM (digital rights management) or anti-cheat software that might affect your gaming experience.
        • -
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      How can I improve my skills in card battle games?

      -

      Some of the ways to improve your skills in card battle games are:

      -
        -
      • Learn the rules and mechanics of the game and practice them regularly.
        • -
      • Study the cards and their effects and learn how to build effective decks.
        • -
      • Watch tutorials, guides, or streams from other players who are more experienced or skilled than you.
        • -
      • Play against different opponents with different strategies and styles.
        • -
      • Analyze your mistakes and learn from them.
        • -
      -

      Are card battle games suitable for children?

      -

      Card battle games are suitable for children depending on their age, maturity, and parental guidance. Some card battle games are rated for everyone and have mild or no violence, language, or suggestive themes. Others are rated for teens or mature audiences and have more graphic or explicit content. Parents should check the ratings and reviews of the games before allowing their children to play them. They should also monitor their children's gaming time and spending habits, as some card battle games can be addictive or involve microtransactions.

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      What are some of the upcoming card battle games for PC?

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      Some of the upcoming card battle games for PC are:

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      • Artifact 2.0: A reboot of the card game based on Dota 2, developed by Valve. It promises to improve the gameplay, balance, and economy of the original game, as well as add new features and modes.
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      • Legends of Runeterra: A card game based on League of Legends, developed by Riot Games. It features a dynamic and interactive gameplay system, where players can influence each other's actions and respond to them in real time.
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      However, to enjoy the full features and performance of your Logitech wireless trackball mouse, you need to download and install the M570 Driver on your computer. The M570 Driver is a software that allows your computer to communicate with your mouse and customize its settings. In this article, we will show you how to download the M570 Driver from different sources and how to install it on your computer. We will also provide some tips on how to troubleshoot any issues that may arise with your M570 Driver.

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      M570 Driver is a software that allows your computer to communicate with your Logitech wireless trackball mouse

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      The M570 Driver is a software that enables your computer to recognize and interact with your Logitech wireless trackball mouse. It also allows you to access and modify various settings of your mouse, such as the sensitivity, speed, scrolling, buttons, and more. The M570 Driver is compatible with Windows and Mac operating systems.

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      Some users may have an HP LaserJet Pro 500 color MFP M570 printer, which is a multifunction printer that can print, copy, scan, and fax. This printer also requires the M570 Driver to work with your computer. You can download the M570 Driver from the HP website by following these steps:

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      Follow these steps to download the M570 Driver from the HP website

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      3. Click on the Download button next to the driver that matches your operating system and version.
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      How to Download M570 Driver from Logitech Website

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      If you have a Logitech wireless trackball mouse, you can download the M570 Driver from the Logitech website

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      If you have a Logitech wireless trackball mouse, you can also download the M570 Driver from the Logitech website. The Logitech website offers two options for downloading the M570 Driver: Logitech Options and Logitech SetPoint. Logitech Options is a newer software that supports more features and devices, while Logitech SetPoint is an older software that supports fewer features and devices. You can choose either one depending on your preference and compatibility.

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      Follow these steps to download the M570 Driver from the Logitech website

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        -
      1. Go to the Logitech wireless trackball mouse support page on the Logitech website.
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      2. Select your operating system and version from the drop-down menus.
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      3. Click on either Logitech Options or Logitech SetPoint to download the software that contains the M570 Driver.
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      4. Save the file to your computer and run it to start the installation process.
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      5. Follow the on-screen instructions to complete the installation.
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      How to Install M570 Driver on Your Computer

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      After downloading the M570 Driver, you need to install it on your computer to make it work with your mouse

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      After downloading the M570 Driver from either the HP website or the Logitech website, you need to install it on your computer to make it work with your mouse. The installation process may vary slightly depending on which software you downloaded, but generally, it involves these steps:

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      Follow these steps to install the M570 Driver on your computer

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        -
      1. Make sure your mouse is turned on and connected to your computer via a USB receiver or Bluetooth.
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      2. Run the downloaded file and follow the on-screen instructions to install the software and driver.
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      3. If prompted, restart your computer to complete the installation.
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      How to Troubleshoot M570 Driver Issues

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      Sometimes, you may encounter some issues with your M570 Driver, such as compatibility, connectivity, or performance problems

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      Sometimes, you may encounter some issues with your M570 Driver, such as compatibility, connectivity, or performance problems. These issues may prevent your mouse from working properly or at all with your computer. Some common causes of these issues are outdated or corrupted drivers, incompatible operating systems, low battery power, interference, or hardware damage.

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      Here are some tips to troubleshoot M570 Driver issues

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      • Check if your operating system is compatible with your mouse and driver. If not, update your operating system or download a compatible driver.
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      • Check if your driver is up to date. If not, update your driver or reinstall it if it is corrupted.
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      • Check if your mouse battery is charged. If not, replace or recharge it as needed.
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      • Check if there is any interference or obstruction between your mouse and computer. If yes, move them closer together or remove any potential sources of interference.
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      • Check if your mouse hardware is damaged. If yes, contact Logitech customer support or replace your mouse as needed.
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      Conclusion

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      In this article, we have shown you how to download and install the M570 Driver for your Logitech wireless trackball mouse. We have also provided some tips on how to troubleshoot any issues that may arise with your M570 Driver. We hope that this article has helped you to download and install the M570 Driver for your mouse and enjoy its features and performance. If you have any questions or feedback, please feel free to leave a comment below.

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      FAQs

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      Here are some frequently asked questions about the M570 Driver and the Logitech wireless trackball mouse.

      -
        -
      1. What is the difference between Logitech Options and Logitech SetPoint?
        -Logitech Options is a newer software that supports more features and devices, such as Logitech Flow, gesture control, and device switching. Logitech SetPoint is an older software that supports fewer features and devices, such as button customization, pointer speed, and battery status. You can choose either one depending on your preference and compatibility.
        2. -
      2. How do I switch between precision and fast-tracking modes on my mouse?
        -You can switch between precision and fast-tracking modes on your mouse by pressing the button next to the scroll wheel. The LED indicator will change color to show the current mode. Blue means precision mode, which is ideal for detailed work, and green means fast-tracking mode, which is ideal for browsing and scrolling.
        3. -
      3. How do I use Logitech Flow to control multiple computers with my mouse?
        -You can use Logitech Flow to control up to two computers with your mouse by following these steps:
          -
        • Download and install Logitech Options on both computers.
          • -
        • Connect your mouse to both computers via a USB receiver or Bluetooth.
          • -
        • Enable Logitech Flow on both computers by clicking on the Flow icon in Logitech Options.
          • -
        • Pair your computers by following the on-screen instructions.
          • -
        • Move your cursor to the edge of one screen to switch to the other screen.
          • -
        • Copy and paste text, images, and files between computers by using the standard keyboard shortcuts or mouse buttons.
          • -
        4. -
      4. How do I adjust the tilt angle of my mouse?
        -You can adjust the tilt angle of your mouse by using the tilt base that comes with your mouse. The tilt base has two slots that correspond to two tilt angles: 0 degrees and 20 degrees. You can insert your mouse into either slot depending on your preference. The 0-degree angle is more flat and traditional, while the 20-degree angle is more ergonomic and comfortable.
        5. -
      5. How do I check the battery status of my mouse?
        -You can check the battery status of your mouse by using one of these methods:
          -
        • Look at the LED indicator on your mouse. The LED indicator will flash red when the battery is low and green when the battery is full.
          • -
        • Launch Logitech Options or Logitech SetPoint on your computer. The software will show you the battery level of your mouse in percentage.
          • -
        • Press the battery button on the bottom of your mouse. The LED indicator will flash green if the battery is good, yellow if the battery is low, and red if the battery is critical.
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      -

      Once you have met these requirements, you can follow these steps to download NBA 2K23 on Switch:

      -
        -
      1. Go to the Nintendo eShop on your Switch and search for NBA 2K23.
        2. -
      2. Select the edition you want to buy and proceed to checkout.
        3. -
      3. Confirm your purchase and wait for the game to download automatically.
        4. -
      4. Launch the game from your home screen

        The benefits and drawbacks of digital download

        -

        Downloading NBA 2K23 on Switch has some advantages and disadvantages, depending on your preferences and needs. Here are some of them:

        - - - - - - - - - - - - - - - - - -
        BenefitsDrawbacks
        You can access the game anytime and anywhere, without needing a physical cartridge.You will need a lot of storage space on your device or a microSD card, which can be expensive or hard to find.
        You can pre-order the game and pre-load it before the release date, so you can play it as soon as it launches.You will not be able to resell or trade the game, as it is tied to your Nintendo Account.
        You can enjoy faster loading times and smoother performance, as the game runs directly from your device's memory.You will need a stable internet connection and a Nintendo Switch Online membership to download the game and access its online features.
        -

        How to Play NBA 2K23 on Switch?

        -

        The supported play modes and controllers

        -

        NBA 2K23 on Switch supports all three play modes: TV mode, tabletop mode, and handheld mode. You can choose the mode that suits your preference and situation. For example, you can play on the big screen with TV mode, or enjoy the portability and convenience of handheld mode.

        -

        NBA 2K23 on Switch also supports various controllers, such as the Joy-Con controllers, the Nintendo Switch Pro Controller, and the Nintendo Switch Lite system. You can use the controller that feels most comfortable for you, or even mix and match different controllers for multiplayer games. For example, you can use one Joy-Con controller per player, or use a Pro Controller for yourself and a Joy-Con controller for your friend.

        -

        The online and offline features and modes

        -

        NBA 2K23 on Switch offers a rich and diverse selection of features and modes, both online and offline. You can enjoy the game solo or with friends, locally or online. Here are some of the features and modes you can expect:

        -
          -
        • MyCAREER: Create your own custom player and follow their basketball journey from high school to college to the NBA or WNBA. You can customize your player's appearance, attributes, skills, badges, animations, and more. You can also interact with other players, coaches, agents, celebrities, and fans in an immersive open-world setting.
          • -
        • MyTEAM: Collect and assemble your dream team of current and legendary players from the NBA or WNBA. You can earn players by completing challenges, opening packs, participating in events, or buying them from the auction house. You can also customize your team's logo, jersey, court, coach, playbook, and more. You can compete with other players online or offline in various modes, such as Domination, Triple Threat, Unlimited, Limited, Draft, Clutch Time, and more.
          • -
        • MyGM/MyLEAGUE: Take control of your own franchise and manage every aspect of it. You can choose from existing teams or create your own expansion team. You can make decisions on trades, contracts, scouting, drafting, coaching, finances, media relations, fan support, and more. You can also simulate seasons, playoffs, drafts, and free agency. You can play with up to 29 other players online or offline in a shared league.
          • -
        • Play Now: Play a quick match with any team from the NBA or WNBA. You can choose from different settings, such as quarter length, difficulty level, rules, and more. You can also play with classic teams, all-time teams, or custom teams.
          • -
        • Online: Play with or against other players online in various modes, such as MyTEAM, MyGM/MyLEAGUE, Play Now Online, The Rec, The Park, Pro-Am, and more. You can also join or create your own online communities and leagues. You will need a Nintendo Switch Online membership and a stable internet connection to access the online features.
          • -
        • Jordan Challenge: Play as Michael Jordan and relive some of his most iconic moments in his career. You can choose from 10 different scenarios, such as "The Flu Game", "The Shrug", "The Shot", and more. You will need to match or surpass Jordan's stats and performance in each scenario to complete the challenge.
          • -
        -

        Conclusion

        -

        A summary of the main points and a call to action

        -

        NBA 2K23 is a basketball simulation game that lets you experience the thrill of playing in the NBA or WNBA, with realistic graphics, gameplay, and modes. You can download NBA 2K23 on Switch digitally from the Nintendo eShop, as long as you have a Nintendo Account, a Nintendo Switch Online membership, and enough storage space on your device or a microSD card. You can also play NBA 2K23 on Switch in various ways, such as TV mode, tabletop mode, handheld mode, solo mode, multiplayer mode, online mode, offline mode, and more. NBA 2K23 on Switch is a great game for basketball fans and gamers alike, so don't miss this opportunity to get your copy today!

        -

        FAQs

        -

        How much does NBA 2K23 cost on Switch?

        -

        The Standard Edition of NBA 2K23 costs $59.99 USD on Switch. The Digital Deluxe Edition costs $79.99 USD, and the Michael Jordan Edition costs $99.99 USD. You can also buy additional virtual currency or MyTEAM points separately.

        -

        How much storage space does NBA 2K23 require on Switch?

        -

        NBA 2K23 requires a minimum of 38 GB of free space on your microSD card, plus 8 GB on the system memory. You must have your microSD card inserted before beginning the download.

        -

        Can I play NBA 2K23 with friends on Switch?

        -

        Yes, you can play NBA 2K23 with friends on Switch locally or online. You can use one Joy-Con controller per player, or use a Pro Controller for yourself and a Joy-Con controller for your friend. You can also join or create your own online communities and leagues with other players.

        -

        Is NBA 2K23 cross-platform compatible?

        -

        No, NBA 2K23 is not cross-platform compatible. You can only play with other players who have the same platform as you.

        -

        What are the differences between NBA 2K23 and NBA 2K22?

        -

        NBA 2K23 is an improved and updated version of NBA 2K22, with new features, modes, graphics, gameplay, and more. Some of the differences include:

        -
          -
        • A new Jordan Challenge mode that lets you relive some of Michael Jordan's most iconic moments in his career.
          • -
        • A revamped MyCAREER mode that features a more immersive and interactive story, with new characters, locations, events, and choices.
          • -
        • A redesigned MyTEAM mode that introduces new ways to collect and compete with your dream team of legends.
          • -
        • An enhanced MyGM/MyLEAGUE mode that gives you more control and customization over your franchise.
          • -
        • A more realistic and responsive gameplay that incorporates new animations, physics, AI, mechanics, and feedback.
          • -
        • A more stunning and lifelike graphics that showcase the power of the next-generation consoles.
          • -

        197e85843d
        -
        -
        \ No newline at end of file diff --git a/spaces/fatiXbelha/sd/Elite Ludo A Casual Board Game with a Twist of Augmented Reality..md b/spaces/fatiXbelha/sd/Elite Ludo A Casual Board Game with a Twist of Augmented Reality..md deleted file mode 100644 index 354f8a9ffe1611c88ce6552fc6a64a9ff7d4ad01..0000000000000000000000000000000000000000 --- a/spaces/fatiXbelha/sd/Elite Ludo A Casual Board Game with a Twist of Augmented Reality..md +++ /dev/null @@ -1,110 +0,0 @@ - -

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        If you are looking for a fun and rewarding way to spend your time, you should download Elite Ludo, a popular board game that lets you play with your friends and family, and win prizes and rewards. Elite Ludo is a game developed by AVRLITE GAMES PRIVATE LIMITED, an Indian company that is registered under the Company Act, 2013. Elite Ludo is a game that combines the traditional dice game of Ludo with modern features such as online multiplayer, tournaments, cash prizes, and augmented reality. In this article, we will show you how to download Elite Ludo on your device, how to play it with your friends and family, how to win prizes and rewards, and how to enjoy it in augmented reality.

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        How to Download Elite Ludo on Your Device

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        Downloading Elite Ludo on your device is very easy and simple. Just follow these steps:

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        download elite ludo


        Download Zip ★ https://urllie.com/2uNHaF



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        1. Visit the official website of Elite Ludo at https://www.eliteludo.com/ or go to the Google Play Store and search for "Elite Ludo".
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        How to Play Elite Ludo with Your Friends and Family

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        Playing Elite Ludo with your friends and family is very fun and easy. Just follow these steps:

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        1. Choose the mode of play. You can opt for local multiplayer, online multiplayer, or private multiplayer. Local multiplayer allows you to play with up to four players on the same device. Online multiplayer allows you to play with up to four players from anywhere in the world. Private multiplayer allows you to play with up to four players who are invited by you using a table code.
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        2. Share the table code with your friends or join a random table. You can share the table code through WhatsApp or other social media platforms. You can also join a random table by clicking on the "Play Now" button.
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        How to Win Prizes and Rewards on Elite Ludo

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        Winning prizes and rewards on Elite Ludo is very exciting and rewarding. Just follow these steps:

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        1. Participate in tournaments and 1v1 battles. You can join various tournaments and 1v1 battles that are held regularly on Elite Ludo. You can choose the entry fee, the prize pool, and the number of players. You can also create your own tournaments and invite your friends to join.
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        How to Enjoy Elite Ludo in Augmented Reality

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        Enjoying Elite Ludo in augmented reality is very innovative and immersive. Just follow these steps:

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        1. Download Elite Ludo AR from the Google Play Store. You will need a device that supports ARCore, a platform that enables augmented reality experiences on Android devices.
          2. -
        2. Scan your surroundings and place the board on a flat surface. You will need to grant permission to the app to access your camera and location. You will also need to find a well-lit area with enough space to play.
          3. -
        3. Play with your friends and family in a realistic environment. You can invite your friends to join your table or play with random players online. You can see the board and the tokens as if they were in front of you. You can also interact with them using gestures and voice commands.
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        That's how you can enjoy Elite Ludo in augmented reality and experience a new level of gaming.

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        Conclusion and FAQs

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        In conclusion, Elite Ludo is a fun and rewarding board game that you should download on your device. It lets you play with your friends and family, win prizes and rewards, and enjoy it in augmented reality. It is a game that combines the traditional dice game of Ludo with modern features such as online multiplayer, tournaments, cash prizes, and augmented reality. It is a game that is easy to play, exciting to win, and immersive to enjoy.

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        If you have any questions about Elite Ludo, you might find the answers below:

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        • Q: Is Elite Ludo legal and safe?
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        • A: Yes, Elite Ludo is legal and safe. It is developed by AVRLITE GAMES PRIVATE LIMITED, an Indian company that is registered under the Company Act, 2013. It uses cashfree gateway, a secure payment method that complies with RBI guidelines. It also uses SSL encryption, firewall protection, and anti-fraud systems to ensure the safety of your data and transactions.
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        • A: You can contact Elite Ludo customer support by emailing them at support@eliteludo.com or calling them at +91-9999999999. You can also visit their website at https://www.eliteludo.com/ or follow them on Facebook, Instagram, Twitter, YouTube, or Telegram for more updates.
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        We hope you found this article helpful and informative. If you have not downloaded Elite Ludo yet, what are you waiting for? Download it now and enjoy the fun and rewarding board game with your friends and family. Happy gaming!

        401be4b1e0
        -
        -
        \ No newline at end of file diff --git a/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/lpips/__init__.py b/spaces/feng2022/Time-TravelRephotography/Time_TravelRephotography/models/encoder4editing/criteria/lpips/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/flatindo/4x-denoise/README.md b/spaces/flatindo/4x-denoise/README.md deleted file mode 100644 index 93b07eeb4971b2e0b379342b6082ad55d762b363..0000000000000000000000000000000000000000 --- a/spaces/flatindo/4x-denoise/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Real CUGAN -emoji: 🐢 -colorFrom: gray -colorTo: green -sdk: gradio -sdk_version: 3.6 -app_file: app.py -pinned: false -license: gpl-3.0 -duplicated_from: XUEDINE/Real-CUGAN2023.3.1.4x ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/flax-community/DietNerf-Demo/jaxnerf/train.py b/spaces/flax-community/DietNerf-Demo/jaxnerf/train.py deleted file mode 100644 index 16285fae7102f1fbfb0c11b1059e6a2264ffb23c..0000000000000000000000000000000000000000 --- a/spaces/flax-community/DietNerf-Demo/jaxnerf/train.py +++ /dev/null @@ -1,326 +0,0 @@ -# coding=utf-8 -# Copyright 2021 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Lint as: python3 -"""Training script for Nerf.""" -import functools -import gc -import time -from absl import app -from absl import flags -import flax -from flax.metrics import tensorboard -from flax.training import checkpoints -import jax -from jax import config -from jax import random -import jax.numpy as jnp -import numpy as np -# import wandb -from tqdm import tqdm - -from jaxnerf.nerf import datasets -from jaxnerf.nerf import models -from jaxnerf.nerf import utils -from jaxnerf.nerf import clip_utils - -FLAGS = flags.FLAGS - -utils.define_flags() -config.parse_flags_with_absl() - -# set up TPU for colab -import os -if "COLAB_TPU_ADDR" in os.environ: - import jax.tools.colab_tpu - jax.tools.colab_tpu.setup_tpu() -print(f"detected device: {jax.local_devices()}") - - -def train_step(model, clip_model, rng, state, batch, lr, step, K):#, clip_grad): - # TODO make clip_grad input enable - """One optimization step. - - Args: - model: The linen model. - rng: jnp.ndarray, random number generator. - state: utils.TrainState, state of the model/optimizer. - batch: dict, a mini-batch of data for training. - lr: float, real-time learning rate. - - Returns: - new_state: utils.TrainState, new training state. - stats: list. [(loss, psnr), (loss_coarse, psnr_coarse)]. - rng: jnp.ndarray, updated random number generator. - """ - rng, key_0, key_1 = random.split(rng, 3) - - def loss_fn(variables): - rays = batch["rays"] - ret = model.apply(variables, key_0, key_1, rays, FLAGS.randomized) - if len(ret) not in (1, 2): - raise ValueError( - "ret should contain either 1 set of output (coarse only), or 2 sets" - "of output (coarse as ret[0] and fine as ret[1]).") - # The main prediction is always at the end of the ret list. - rgb, unused_disp, unused_acc = ret[-1] - loss = ((rgb - batch["pixels"][Ellipsis, :3]) ** 2).mean() - psnr = utils.compute_psnr(loss) - if len(ret) > 1: - # If there are both coarse and fine predictions, we compute the loss for - # the coarse prediction (ret[0]) as well. - rgb_c, unused_disp_c, unused_acc_c = ret[0] - loss_c = ((rgb_c - batch["pixels"][Ellipsis, :3]) ** 2).mean() - psnr_c = utils.compute_psnr(loss_c) - else: - loss_c = 0. - psnr_c = 0. - - def tree_sum_fn(fn): - return jax.tree_util.tree_reduce(lambda x, y: x + fn(y), - variables, initializer=0) - - weight_l2 = (tree_sum_fn(lambda z: jnp.sum(z ** 2)) / - tree_sum_fn(lambda z: jnp.prod(jnp.array(z.shape)))) - - total_loss = loss + loss_c + FLAGS.weight_decay_mult * weight_l2 - stats = utils.Stats(loss=loss, psnr=psnr, loss_c=loss_c, - psnr_c=psnr_c, weight_l2=weight_l2) - return total_loss, stats - - (_, stats), grad = ( - jax.value_and_grad(loss_fn, has_aux=True)(state.optimizer.target)) - grad = jax.lax.pmean(grad, axis_name="batch") - stats = jax.lax.pmean(stats, axis_name="batch") - - # Clip the gradient by value. - if FLAGS.grad_max_val > 0: - clip_fn = lambda z: jnp.clip(z, -FLAGS.grad_max_val, FLAGS.grad_max_val) - grad = jax.tree_util.tree_map(clip_fn, grad) - - # Clip the (possibly value-clipped) gradient by norm. - if FLAGS.grad_max_norm > 0: - grad_norm = jnp.sqrt( - jax.tree_util.tree_reduce( - lambda x, y: x + jnp.sum(y ** 2), grad, initializer=0)) - mult = jnp.minimum(1, FLAGS.grad_max_norm / (1e-7 + grad_norm)) - grad = jax.tree_util.tree_map(lambda z: mult * z, grad) - - #return grad, state, rng - new_optimizer = state.optimizer.apply_gradient(grad, learning_rate =lr) - new_state = state.replace(optimizer=new_optimizer) - return new_state, stats, rng - -def update_step(state, grad, lr): - new_optimizer = state.optimizer.apply_gradient(grad, learning_rate=lr) - new_state = state.replace(optimizer=new_optimizer) - return new_state - - -def main(unused_argv): - #wandb.init(project="hf-flax-clip-nerf", entity="wandb", sync_tensorboard=True) - rng = random.PRNGKey(20200823) - # Shift the numpy random seed by host_id() to shuffle data loaded by different - # hosts. - np.random.seed(20201473 + jax.host_id()) - - if FLAGS.config is not None: - utils.update_flags(FLAGS) - if FLAGS.batch_size % jax.device_count() != 0: - raise ValueError("Batch size must be divisible by the number of devices.") - if FLAGS.train_dir is None: - raise ValueError("train_dir must be set. None set now.") - if FLAGS.data_dir is None: - raise ValueError("data_dir must be set. None set now.") - - # setup CLIP model - if FLAGS.use_semantic_loss: - clip_model = clip_utils.init_CLIP(FLAGS.clip_output_dtype, - FLAGS.clip_model_name) - print('semantic loss ACTIVATED, CLIP is set up') - else: - clip_model = None - print('semantic loss DEACTIVATED, CLIP is set to None') - - dataset = datasets.get_dataset("train", FLAGS, clip_model) - test_dataset = datasets.get_dataset("test", FLAGS, clip_model) - - # setup NeRF model - rng, key = random.split(rng) - model, variables = models.get_model(key, dataset.peek(), FLAGS) - optimizer = flax.optim.Adam(FLAGS.lr_init).create(variables) - state = utils.TrainState(optimizer=optimizer) - del optimizer, variables - learning_rate_fn = functools.partial( - utils.learning_rate_decay, - lr_init=FLAGS.lr_init, - lr_final=FLAGS.lr_final, - max_steps=FLAGS.max_steps, - lr_delay_steps=FLAGS.lr_delay_steps, - lr_delay_mult=FLAGS.lr_delay_mult) - - train_pstep = jax.pmap( - functools.partial(train_step, model, clip_model), - axis_name="batch", - in_axes=(0, 0, 0, None, None, None), - donate_argnums=(2,)) - - update_pstep = jax.pmap( - functools.partial(update_step,), - axis_name="batch", - in_axes=(0, None, None), - donate_argnums=(0,)) - - - def render_fn(variables, key_0, key_1, rays): - return jax.lax.all_gather( - model.apply(variables, key_0, key_1, rays, FLAGS.randomized), - axis_name="batch") - - render_pfn = jax.pmap( - render_fn, - in_axes=(None, None, None, 0), # Only distribute the data input. - donate_argnums=(3,), - axis_name="batch") - - # Compiling to the CPU because it's faster and more accurate. - ssim_fn = jax.jit( - functools.partial(utils.compute_ssim, max_val=1.), backend="cpu") - - if not utils.isdir(FLAGS.train_dir): - utils.makedirs(FLAGS.train_dir) - state = checkpoints.restore_checkpoint(FLAGS.train_dir, state) - # Resume training a the step of the last checkpoint. - init_step = state.optimizer.state.step + 1 - - # for distributive training - state = flax.jax_utils.replicate(state) - if jax.host_id() == 0: - summary_writer = tensorboard.SummaryWriter(FLAGS.train_dir) - - # Prefetch_buffer_size = 3 x batch_size - pdataset = flax.jax_utils.prefetch_to_device(dataset, 3) - n_local_devices = jax.local_device_count() - rng = rng + jax.host_id() # Make random seed separate across hosts. - keys = random.split(rng, n_local_devices) # For pmapping RNG keys. - gc.disable() # Disable automatic garbage collection for efficiency. - stats_trace = [] - reset_timer = True - - # for semantic loss update - cnter = 1 - trigger = int(FLAGS.sc_loss_every / n_local_devices) - - for step, batch in tqdm(zip(range(init_step, FLAGS.max_steps + 1), pdataset)): - if reset_timer: - t_loop_start = time.time() - reset_timer = False - lr = learning_rate_fn(step) - - if step%FLAGS.sc_loss_every == 0 and FLAGS.use_semantic_loss: - # remove dimension for device coz its only run in host core - sc_batch = dataset.get_clip_data() - sc_loss, sc_grad = clip_utils.update_semantic_loss(model, clip_model, - keys[0], state, sc_batch, lr) - sc_grad = flax.jax_utils.replicate(sc_grad) - sc_grad = jax.tree_map( lambda x: x[0], sc_grad) - - else: - sc_loss = 0. - - state, stats, keys = train_pstep(keys, state, batch, lr, step, FLAGS.sc_loss_every)#, grad) - - if step%FLAGS.sc_loss_every == 0 and FLAGS.use_semantic_loss: - state = update_pstep(state, sc_grad, lr) - - if jax.host_id() == 0: - stats_trace.append(stats) - if step % FLAGS.gc_every == 0: - gc.collect() - - # Log training summaries. This is put behind a host_id check because in - # multi-host evaluation, all hosts need to run inference even though we - # only use host 0 to record results. - if jax.host_id() == 0: - if step % FLAGS.print_every == 0: - summary_writer.scalar("train_loss", stats.loss[0], step) - summary_writer.scalar("train_psnr", stats.psnr[0], step) - summary_writer.scalar("train_loss_coarse", stats.loss_c[0], step) - summary_writer.scalar("train_psnr_coarse", stats.psnr_c[0], step) - summary_writer.scalar("weight_l2", stats.weight_l2[0], step) - avg_loss = np.mean(np.concatenate([s.loss for s in stats_trace])) - avg_psnr = np.mean(np.concatenate([s.psnr for s in stats_trace])) - stats_trace = [] - summary_writer.scalar("train_avg_loss", avg_loss, step) - summary_writer.scalar("train_avg_psnr", avg_psnr, step) - summary_writer.scalar("learning_rate", lr, step) - steps_per_sec = FLAGS.print_every / (time.time() - t_loop_start) - reset_timer = True - rays_per_sec = FLAGS.batch_size * steps_per_sec - summary_writer.scalar("train_steps_per_sec", steps_per_sec, step) - summary_writer.scalar("train_rays_per_sec", rays_per_sec, step) - precision = int(np.ceil(np.log10(FLAGS.max_steps))) + 1 - print(("{:" + "{:d}".format(precision) + "d}").format(step) + - f"/{FLAGS.max_steps:d}: " + f"i_loss={stats.loss[0]:0.4f}, " + - f"avg_loss={avg_loss:0.4f}, " + - f"weight_l2={stats.weight_l2[0]:0.2e}, " + - # f"sc_loss={sc_loss:0.4f}, " + - f"lr={lr:0.2e}, {rays_per_sec:0.0f} rays/sec") - if step % FLAGS.save_every == 0: - state_to_save = jax.device_get(jax.tree_map(lambda x: x[0], state)) - checkpoints.save_checkpoint( - FLAGS.train_dir, state_to_save, int(step), keep=100) - - # Test-set evaluation. - if FLAGS.render_every > 0 and step % FLAGS.render_every == 0: - # We reuse the same random number generator from the optimization step - # here on purpose so that the visualization matches what happened in - # training. - t_eval_start = time.time() - eval_variables = jax.device_get(jax.tree_map(lambda x: x[0], - state)).optimizer.target - test_case = next(test_dataset) - pred_color, pred_disp, pred_acc = utils.render_image( - functools.partial(render_pfn, eval_variables), - test_case["rays"], - keys[0], - FLAGS.dataset == "llff", - chunk=FLAGS.chunk) - - # Log eval summaries on host 0. - if jax.host_id() == 0: - psnr = utils.compute_psnr( - ((pred_color - test_case["pixels"]) ** 2).mean()) - ssim = ssim_fn(pred_color, test_case["pixels"]) - eval_time = time.time() - t_eval_start - num_rays = jnp.prod(jnp.array(test_case["rays"].directions.shape[:-1])) - rays_per_sec = num_rays / eval_time - summary_writer.scalar("test_rays_per_sec", rays_per_sec, step) - print(f"Eval {step}: {eval_time:0.3f}s., {rays_per_sec:0.0f} rays/sec") - summary_writer.scalar("test_psnr", psnr, step) - summary_writer.scalar("test_ssim", ssim, step) - summary_writer.image("test_pred_color", pred_color, step) - summary_writer.image("test_pred_disp", pred_disp, step) - summary_writer.image("test_pred_acc", pred_acc, step) - summary_writer.image("test_target", test_case["pixels"], step) - - if FLAGS.max_steps % FLAGS.save_every != 0: - state = jax.device_get(jax.tree_map(lambda x: x[0], state)) - checkpoints.save_checkpoint( - FLAGS.train_dir, state, int(FLAGS.max_steps), keep=100) - - -if __name__ == "__main__": - app.run(main) diff --git a/spaces/flowers-team/SocialAISchool/textworld_utils/utils.py b/spaces/flowers-team/SocialAISchool/textworld_utils/utils.py deleted file mode 100644 index 0ba17f47b86e125776ff69bae397f075b2434aa3..0000000000000000000000000000000000000000 --- a/spaces/flowers-team/SocialAISchool/textworld_utils/utils.py +++ /dev/null @@ -1,226 +0,0 @@ -from gym_minigrid.minigrid import COLOR_TO_IDX, OBJECT_TO_IDX - -def generate_text_obs(obs, info): - - text_observation = obs_to_text(info) - - llm_prompt = "Obs : " - llm_prompt += "".join(text_observation) - - # add utterances - if obs["utterance_history"] != "Conversation: \n": - utt_hist = obs['utterance_history'] - utt_hist = utt_hist.replace("Conversation: \n","") - llm_prompt += utt_hist - - return llm_prompt - -def obs_to_text(info): - image, vis_mask = info["image"], info["vis_mask"] - carrying = info["carrying"] - agent_pos_vx, agent_pos_vy = info["agent_pos_vx"], info["agent_pos_vy"] - npc_actions_dict = info["npc_actions_dict"] - - # (OBJECT_TO_IDX[self.type], COLOR_TO_IDX[self.color], state) - # State, 0: open, 1: closed, 2: locked - IDX_TO_COLOR = dict(zip(COLOR_TO_IDX.values(), COLOR_TO_IDX.keys())) - IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys())) - - list_textual_descriptions = [] - - if carrying is not None: - list_textual_descriptions.append("You carry a {} {}".format(carrying.color, carrying.type)) - - # agent_pos_vx, agent_pos_vy = self.get_view_coords(self.agent_pos[0], self.agent_pos[1]) - - view_field_dictionary = dict() - - for i in range(image.shape[0]): - for j in range(image.shape[1]): - if image[i][j][0] != 0 and image[i][j][0] != 1 and image[i][j][0] != 2: - if i not in view_field_dictionary.keys(): - view_field_dictionary[i] = dict() - view_field_dictionary[i][j] = image[i][j] - else: - view_field_dictionary[i][j] = image[i][j] - - # Find the wall if any - # We describe a wall only if there is no objects between the agent and the wall in straight line - - # Find wall in front - add_wall_descr = False - if add_wall_descr: - j = agent_pos_vy - 1 - object_seen = False - while j >= 0 and not object_seen: - if image[agent_pos_vx][j][0] != 0 and image[agent_pos_vx][j][0] != 1: - if image[agent_pos_vx][j][0] == 2: - list_textual_descriptions.append( - f"A wall is {agent_pos_vy - j} steps in front of you. \n") # forward - object_seen = True - else: - object_seen = True - j -= 1 - # Find wall left - i = agent_pos_vx - 1 - object_seen = False - while i >= 0 and not object_seen: - if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1: - if image[i][agent_pos_vy][0] == 2: - list_textual_descriptions.append( - f"A wall is {agent_pos_vx - i} steps to the left. \n") # left - object_seen = True - else: - object_seen = True - i -= 1 - # Find wall right - i = agent_pos_vx + 1 - object_seen = False - while i < image.shape[0] and not object_seen: - if image[i][agent_pos_vy][0] != 0 and image[i][agent_pos_vy][0] != 1: - if image[i][agent_pos_vy][0] == 2: - list_textual_descriptions.append( - f"A wall is {i - agent_pos_vx} steps to the right. \n") # right - object_seen = True - else: - object_seen = True - i += 1 - - # list_textual_descriptions.append("You see the following objects: ") - # returns the position of seen objects relative to you - for i in view_field_dictionary.keys(): - for j in view_field_dictionary[i].keys(): - if i != agent_pos_vx or j != agent_pos_vy: - object = view_field_dictionary[i][j] - - # # don't show npc - # if IDX_TO_OBJECT[object[0]] == "npc": - # continue - - front_dist = agent_pos_vy - j - left_right_dist = i - agent_pos_vx - - loc_descr = "" - if front_dist == 1 and left_right_dist == 0: - loc_descr += "Right in front of you " - - elif left_right_dist == 1 and front_dist == 0: - loc_descr += "Just to the right of you" - - elif left_right_dist == -1 and front_dist == 0: - loc_descr += "Just to the left of you" - - else: - front_str = str(front_dist) + " steps in front of you " if front_dist > 0 else "" - - loc_descr += front_str - - suff = "s" if abs(left_right_dist) > 0 else "" - and_ = "and" if loc_descr != "" else "" - - if left_right_dist < 0: - left_right_str = f"{and_} {-left_right_dist} step{suff} to the left" - loc_descr += left_right_str - - elif left_right_dist > 0: - left_right_str = f"{and_} {left_right_dist} step{suff} to the right" - loc_descr += left_right_str - - else: - left_right_str = "" - loc_descr += left_right_str - - loc_descr += f" there is a " - - obj_type = IDX_TO_OBJECT[object[0]] - if obj_type == "npc": - IDX_TO_STATE = {0: 'friendly', 1: 'antagonistic'} - - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} peer. " - - # gaze - gaze_dir = { - 0: "towards you", - 1: "to the left of you", - 2: "in the same direction as you", - 3: "to the right of you", - } - description += f"It is looking {gaze_dir[object[3]]}. " - - # point - point_dir = { - 0: "towards you", - 1: "to the left of you", - 2: "in the same direction as you", - 3: "to the right of you", - } - - if object[4] != 255: - description += f"It is pointing {point_dir[object[4]]}. " - - # last action - last_action = {v: k for k, v in npc_actions_dict.items()}[object[5]] - - last_action = { - "go_forward": "foward", - "rotate_left": "turn left", - "rotate_right": "turn right", - "toggle_action": "toggle", - "point_stop_point": "stop pointing", - "point_E": "", - "point_S": "", - "point_W": "", - "point_N": "", - "stop_point": "stop pointing", - "no_op": "" - }[last_action] - - if last_action not in ["no_op", ""]: - description += f"It's last action is {last_action}. " - - elif obj_type in ["switch", "apple", "generatorplatform", "marble", "marbletee", "fence"]: - # todo: this assumes that Switch.no_light == True - description = f"{IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[2:].mean() == 0 - - elif obj_type == "lockablebox": - IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "applegenerator": - IDX_TO_STATE = {1: 'square', 2: 'round'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "remotedoor": - IDX_TO_STATE = {0: 'open', 1: 'closed'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "door": - IDX_TO_STATE = {0: 'open', 1: 'closed', 2: 'locked'} - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} " - assert object[3:].mean() == 0 - - elif obj_type == "lever": - IDX_TO_STATE = {1: 'activated', 0: 'unactivated'} - if object[3] == 255: - countdown_txt = "" - else: - countdown_txt = f"with {object[3]} timesteps left. " - - description = f"{IDX_TO_STATE[object[2]]} {IDX_TO_COLOR[object[1]]} {IDX_TO_OBJECT[object[0]]} {countdown_txt}" - - assert object[4:].mean() == 0 - else: - raise ValueError(f"Undefined object type {obj_type}") - - full_destr = loc_descr + description + "\n" - - list_textual_descriptions.append(full_destr) - - if len(list_textual_descriptions) == 0: - list_textual_descriptions.append("\n") - - return list_textual_descriptions diff --git a/spaces/frncscp/bullerengue/musika/22kHz/parse_test.py b/spaces/frncscp/bullerengue/musika/22kHz/parse_test.py deleted file mode 100644 index 1d83893a5984558257f47b34dba72985d1701441..0000000000000000000000000000000000000000 --- a/spaces/frncscp/bullerengue/musika/22kHz/parse_test.py +++ /dev/null @@ -1,169 +0,0 @@ -import argparse -from typing import Any -import tensorflow as tf - - -class EasyDict(dict): - def __getattr__(self, name: str) -> Any: - try: - return self[name] - except KeyError: - raise AttributeError(name) - - def __setattr__(self, name: str, value: Any) -> None: - self[name] = value - - def __delattr__(self, name: str) -> None: - del self[name] - - -def str2bool(v): - if isinstance(v, bool): - return v - if v.lower() in ("yes", "true", "t", "y", "1"): - return True - elif v.lower() in ("no", "false", "f", "n", "0"): - return False - else: - raise argparse.ArgumentTypeError("Boolean value expected.") - - -def params_args(args): - parser = argparse.ArgumentParser() - - parser.add_argument( - "--conditional", - type=str2bool, - default=False, - help="If True, use a conditional GAN model", - ) - parser.add_argument( - "--hop", - type=int, - default=256, - help="Hop size (window size = 4*hop)", - ) - parser.add_argument( - "--mel_bins", - type=int, - default=256, - help="Mel bins in mel-spectrograms", - ) - parser.add_argument( - "--sr", - type=int, - default=22050, - help="Sampling Rate", - ) - parser.add_argument( - "--latlen", - type=int, - default=256, - help="Length of generated latent vectors", - ) - parser.add_argument( - "--latdepth", - type=int, - default=64, - help="Depth of generated latent vectors", - ) - parser.add_argument( - "--shape", - type=int, - default=128, - help="Length of spectrograms time axis", - ) - parser.add_argument( - "--window", - type=int, - default=64, - help="Generator spectrogram window (must divide shape)", - ) - parser.add_argument( - "--mu_rescale", - type=float, - default=-25.0, - help="Spectrogram mu used to normalize", - ) - parser.add_argument( - "--sigma_rescale", - type=float, - default=75.0, - help="Spectrogram sigma used to normalize", - ) - parser.add_argument( - "--load_path", - type=str, - default="checkpoints/techno/", - help="Path of pretrained networks weights", - ) - parser.add_argument( - "--dec_path", - type=str, - default="checkpoints/ae/", - help="Path of pretrained decoders weights", - ) - parser.add_argument( - "--testing", - type=str2bool, - default=True, - help="True if optimizers weight do not need to be loaded", - ) - parser.add_argument( - "--cpu", - type=str2bool, - default=False, - help="True if you wish to use cpu", - ) - parser.add_argument( - "--mixed_precision", - type=str2bool, - default=True, - help="True if your GPU supports mixed precision", - ) - - tmp_args = parser.parse_args() - - args.conditional = tmp_args.conditional - args.hop = tmp_args.hop - args.mel_bins = tmp_args.mel_bins - args.sr = tmp_args.sr - args.latlen = tmp_args.latlen - args.latdepth = tmp_args.latdepth - args.shape = tmp_args.shape - args.window = tmp_args.window - args.mu_rescale = tmp_args.mu_rescale - args.sigma_rescale = tmp_args.sigma_rescale - args.load_path = tmp_args.load_path - args.dec_path = tmp_args.dec_path - args.testing = tmp_args.testing - args.cpu = tmp_args.cpu - args.mixed_precision = tmp_args.mixed_precision - - print() - - args.datatype = tf.float32 - gpuls = tf.config.list_physical_devices("GPU") - if len(gpuls) == 0 or args.cpu: - args.cpu = True - args.mixed_precision = False - tf.config.set_visible_devices([], "GPU") - print() - print("Using CPU...") - print() - if args.mixed_precision: - args.datatype = tf.float16 - print() - print("Using GPU with mixed precision enabled...") - print() - if not args.mixed_precision and not args.cpu: - print() - print("Using GPU without mixed precision...") - print() - - return args - - -def parse_args(): - args = EasyDict() - return params_args(args) diff --git a/spaces/fuxin123zz/ChuanhuChatGPT/llama_func.py b/spaces/fuxin123zz/ChuanhuChatGPT/llama_func.py deleted file mode 100644 index c71027dd4e6f99c0c12626cbbf276f407877be04..0000000000000000000000000000000000000000 --- a/spaces/fuxin123zz/ChuanhuChatGPT/llama_func.py +++ /dev/null @@ -1,192 +0,0 @@ -import os -import logging - -from llama_index import GPTSimpleVectorIndex -from llama_index import download_loader -from llama_index import ( - Document, - LLMPredictor, - PromptHelper, - QuestionAnswerPrompt, - RefinePrompt, -) -from langchain.llms import OpenAI -import colorama - - -from presets import * -from utils import * - - -def get_documents(file_src): - documents = [] - index_name = "" - logging.debug("Loading documents...") - logging.debug(f"file_src: {file_src}") - for file in file_src: - logging.debug(f"file: {file.name}") - index_name += file.name - if os.path.splitext(file.name)[1] == ".pdf": - logging.debug("Loading PDF...") - CJKPDFReader = download_loader("CJKPDFReader") - loader = CJKPDFReader() - documents += loader.load_data(file=file.name) - elif os.path.splitext(file.name)[1] == ".docx": - logging.debug("Loading DOCX...") - DocxReader = download_loader("DocxReader") - loader = DocxReader() - documents += loader.load_data(file=file.name) - elif os.path.splitext(file.name)[1] == ".epub": - logging.debug("Loading EPUB...") - EpubReader = download_loader("EpubReader") - loader = EpubReader() - documents += loader.load_data(file=file.name) - else: - logging.debug("Loading text file...") - with open(file.name, "r", encoding="utf-8") as f: - text = add_space(f.read()) - documents += [Document(text)] - index_name = sha1sum(index_name) - return documents, index_name - - -def construct_index( - api_key, - file_src, - max_input_size=4096, - num_outputs=1, - max_chunk_overlap=20, - chunk_size_limit=600, - embedding_limit=None, - separator=" ", - num_children=10, - max_keywords_per_chunk=10, -): - os.environ["OPENAI_API_KEY"] = api_key - chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit - embedding_limit = None if embedding_limit == 0 else embedding_limit - separator = " " if separator == "" else separator - - llm_predictor = LLMPredictor( - llm=OpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key) - ) - prompt_helper = PromptHelper( - max_input_size, - num_outputs, - max_chunk_overlap, - embedding_limit, - chunk_size_limit, - separator=separator, - ) - documents, index_name = get_documents(file_src) - if os.path.exists(f"./index/{index_name}.json"): - logging.info("找到了缓存的索引文件，加载中……") - return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json") - else: - try: - logging.debug("构建索引中……") - index = GPTSimpleVectorIndex( - documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper - ) - os.makedirs("./index", exist_ok=True) - index.save_to_disk(f"./index/{index_name}.json") - return index - except Exception as e: - print(e) - return None - - -def chat_ai( - api_key, - index, - question, - context, - chatbot, -): - os.environ["OPENAI_API_KEY"] = api_key - - logging.info(f"Question: {question}") - - response, chatbot_display, status_text = ask_ai( - api_key, - index, - question, - replace_today(PROMPT_TEMPLATE), - REFINE_TEMPLATE, - SIM_K, - INDEX_QUERY_TEMPRATURE, - context, - ) - if response is None: - status_text = "查询失败，请换个问法试试" - return context, chatbot - response = response - - context.append({"role": "user", "content": question}) - context.append({"role": "assistant", "content": response}) - chatbot.append((question, chatbot_display)) - - os.environ["OPENAI_API_KEY"] = "" - return context, chatbot, status_text - - -def ask_ai( - api_key, - index, - question, - prompt_tmpl, - refine_tmpl, - sim_k=1, - temprature=0, - prefix_messages=[], -): - os.environ["OPENAI_API_KEY"] = api_key - - logging.debug("Index file found") - logging.debug("Querying index...") - llm_predictor = LLMPredictor( - llm=OpenAI( - temperature=temprature, - model_name="gpt-3.5-turbo-0301", - prefix_messages=prefix_messages, - ) - ) - - response = None # Initialize response variable to avoid UnboundLocalError - qa_prompt = QuestionAnswerPrompt(prompt_tmpl) - rf_prompt = RefinePrompt(refine_tmpl) - response = index.query( - question, - llm_predictor=llm_predictor, - similarity_top_k=sim_k, - text_qa_template=qa_prompt, - refine_template=rf_prompt, - response_mode="compact", - ) - - if response is not None: - logging.info(f"Response: {response}") - ret_text = response.response - nodes = [] - for index, node in enumerate(response.source_nodes): - brief = node.source_text[:25].replace("\n", "") - nodes.append( - f"
        [{index+1}]\t{brief}...

        {node.source_text}

        " - ) - new_response = ret_text + "\n----------\n" + "\n\n".join(nodes) - logging.info( - f"Response: {colorama.Fore.BLUE}{ret_text}{colorama.Style.RESET_ALL}" - ) - os.environ["OPENAI_API_KEY"] = "" - return ret_text, new_response, f"查询消耗了{llm_predictor.last_token_usage} tokens" - else: - logging.warning("No response found, returning None") - os.environ["OPENAI_API_KEY"] = "" - return None - - -def add_space(text): - punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "} - for cn_punc, en_punc in punctuations.items(): - text = text.replace(cn_punc, en_punc) - return text diff --git a/spaces/gagan3012/IMD/BusterNet/BusterNetUtils.py b/spaces/gagan3012/IMD/BusterNet/BusterNetUtils.py deleted file mode 100644 index 8bef8d3631efd711a7f826326454a0c4edc693df..0000000000000000000000000000000000000000 --- a/spaces/gagan3012/IMD/BusterNet/BusterNetUtils.py +++ /dev/null @@ -1,27 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - - -def simple_cmfd_decoder(busterNetModel, rgb): - """A simple BusterNet CMFD decoder""" - # 1. expand an image to a single sample batch - single_sample_batch = np.expand_dims(rgb, axis=0) - # 2. perform busterNet CMFD - pred = busterNetModel.predict(single_sample_batch)[0] - return pred - - -def visualize_result(rgb, gt, pred, figsize=(12, 4), title=None): - """Visualize raw input, ground truth, and BusterNet result""" - fig = plt.figure(figsize=figsize) - - plt.subplot(1, 3, 1) - plt.imshow(rgb) - plt.title("input image") - plt.subplot(1, 3, 2) - plt.title("ground truth") - plt.imshow(gt) - plt.subplot(1, 3, 3) - plt.imshow(pred) - plt.title("busterNet pred") - return fig diff --git a/spaces/ggwvits/vits-uma-genshin-honkai/commons.py b/spaces/ggwvits/vits-uma-genshin-honkai/commons.py deleted file mode 100644 index 40fcc05364d4815971f5c6f9dbb8dcef8e3ec1e9..0000000000000000000000000000000000000000 --- a/spaces/ggwvits/vits-uma-genshin-honkai/commons.py +++ /dev/null @@ -1,172 +0,0 @@ -import math -import torch -from torch.nn import functional as F -import torch.jit - - -def script_method(fn, _rcb=None): - return fn - - -def script(obj, optimize=True, _frames_up=0, _rcb=None): - return obj - - -torch.jit.script_method = script_method -torch.jit.script = script - - -def init_weights(m, mean=0.0, std=0.01): - classname = m.__class__.__name__ - if classname.find("Conv") != -1: - m.weight.data.normal_(mean, std) - - -def get_padding(kernel_size, dilation=1): - return int((kernel_size*dilation - dilation)/2) - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def intersperse(lst, item): - result = [item] * (len(lst) * 2 + 1) - result[1::2] = lst - return result - - -def kl_divergence(m_p, logs_p, m_q, logs_q): - """KL(P||Q)""" - kl = (logs_q - logs_p) - 0.5 - kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q) - return kl - - -def rand_gumbel(shape): - """Sample from the Gumbel distribution, protect from overflows.""" - uniform_samples = torch.rand(shape) * 0.99998 + 0.00001 - return -torch.log(-torch.log(uniform_samples)) - - -def rand_gumbel_like(x): - g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device) - return g - - -def slice_segments(x, ids_str, segment_size=4): - ret = torch.zeros_like(x[:, :, :segment_size]) - for i in range(x.size(0)): - idx_str = ids_str[i] - idx_end = idx_str + segment_size - ret[i] = x[i, :, idx_str:idx_end] - return ret - - -def rand_slice_segments(x, x_lengths=None, segment_size=4): - b, d, t = x.size() - if x_lengths is None: - x_lengths = t - ids_str_max = x_lengths - segment_size + 1 - ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) - ret = slice_segments(x, ids_str, segment_size) - return ret, ids_str - - -def get_timing_signal_1d( - length, channels, min_timescale=1.0, max_timescale=1.0e4): - position = torch.arange(length, dtype=torch.float) - num_timescales = channels // 2 - log_timescale_increment = ( - math.log(float(max_timescale) / float(min_timescale)) / - (num_timescales - 1)) - inv_timescales = min_timescale * torch.exp( - torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment) - scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1) - signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0) - signal = F.pad(signal, [0, 0, 0, channels % 2]) - signal = signal.view(1, channels, length) - return signal - - -def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return x + signal.to(dtype=x.dtype, device=x.device) - - -def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1): - b, channels, length = x.size() - signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale) - return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis) - - -def subsequent_mask(length): - mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) - return mask - - -@torch.jit.script -def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): - n_channels_int = n_channels[0] - in_act = input_a + input_b - t_act = torch.tanh(in_act[:, :n_channels_int, :]) - s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) - acts = t_act * s_act - return acts - - -def convert_pad_shape(pad_shape): - l = pad_shape[::-1] - pad_shape = [item for sublist in l for item in sublist] - return pad_shape - - -def shift_1d(x): - x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] - return x - - -def sequence_mask(length, max_length=None): - if max_length is None: - max_length = length.max() - x = torch.arange(max_length, dtype=length.dtype, device=length.device) - return x.unsqueeze(0) < length.unsqueeze(1) - - -def generate_path(duration, mask): - """ - duration: [b, 1, t_x] - mask: [b, 1, t_y, t_x] - """ - device = duration.device - - b, _, t_y, t_x = mask.shape - cum_duration = torch.cumsum(duration, -1) - - cum_duration_flat = cum_duration.view(b * t_x) - path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) - path = path.view(b, t_x, t_y) - path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] - path = path.unsqueeze(1).transpose(2,3) * mask - return path - - -def clip_grad_value_(parameters, clip_value, norm_type=2): - if isinstance(parameters, torch.Tensor): - parameters = [parameters] - parameters = list(filter(lambda p: p.grad is not None, parameters)) - norm_type = float(norm_type) - if clip_value is not None: - clip_value = float(clip_value) - - total_norm = 0 - for p in parameters: - param_norm = p.grad.data.norm(norm_type) - total_norm += param_norm.item() ** norm_type - if clip_value is not None: - p.grad.data.clamp_(min=-clip_value, max=clip_value) - total_norm = total_norm ** (1. / norm_type) - return total_norm diff --git a/spaces/glyszt/vt/vtoonify/model/stylegan/lpips/base_model.py b/spaces/glyszt/vt/vtoonify/model/stylegan/lpips/base_model.py deleted file mode 100644 index 8de1d16f0c7fa52d8067139abc6e769e96d0a6a1..0000000000000000000000000000000000000000 --- a/spaces/glyszt/vt/vtoonify/model/stylegan/lpips/base_model.py +++ /dev/null @@ -1,58 +0,0 @@ -import os -import numpy as np -import torch -from torch.autograd import Variable -from pdb import set_trace as st -from IPython import embed - -class BaseModel(): - def __init__(self): - pass; - - def name(self): - return 'BaseModel' - - def initialize(self, use_gpu=True, gpu_ids=[0]): - self.use_gpu = use_gpu - self.gpu_ids = gpu_ids - - def forward(self): - pass - - def get_image_paths(self): - pass - - def optimize_parameters(self): - pass - - def get_current_visuals(self): - return self.input - - def get_current_errors(self): - return {} - - def save(self, label): - pass - - # helper saving function that can be used by subclasses - def save_network(self, network, path, network_label, epoch_label): - save_filename = '%s_net_%s.pth' % (epoch_label, network_label) - save_path = os.path.join(path, save_filename) - torch.save(network.state_dict(), save_path) - - # helper loading function that can be used by subclasses - def load_network(self, network, network_label, epoch_label): - save_filename = '%s_net_%s.pth' % (epoch_label, network_label) - save_path = os.path.join(self.save_dir, save_filename) - print('Loading network from %s'%save_path) - network.load_state_dict(torch.load(save_path)) - - def update_learning_rate(): - pass - - def get_image_paths(self): - return self.image_paths - - def save_done(self, flag=False): - np.save(os.path.join(self.save_dir, 'done_flag'),flag) - np.savetxt(os.path.join(self.save_dir, 'done_flag'),[flag,],fmt='%i') diff --git a/spaces/gossminn/fillmorle-app/sftp/utils/span.py b/spaces/gossminn/fillmorle-app/sftp/utils/span.py deleted file mode 100644 index c0aba6c0072b3355ed051503adbc40ef4d001887..0000000000000000000000000000000000000000 --- a/spaces/gossminn/fillmorle-app/sftp/utils/span.py +++ /dev/null @@ -1,420 +0,0 @@ -from typing import * - -import numpy as np - -from .common import VIRTUAL_ROOT, DEFAULT_SPAN -from .bio_smoothing import BIOSmoothing -from .functions import max_match - - -class Span: - """ - Span is a simple data structure for a span (not necessarily associated with text), along with its label, - children and possibly its parent and a confidence score. - - Basic usages (suppose span is a Span object): - 1. len(span) -- #children. - 2. span[i] -- i-th child. - 3. for s in span: ... -- iterate its children. - 4. for s in span.bfs: ... -- iterate its descendents. - 5. print(span) -- show its description. - 6. span.tree() -- print the whole tree. - - It provides some utilities: - 1. Re-indexing. BPE will change token indices, and the `re_index` method can convert normal tokens - BPE word piece indices, or vice versa. - 2. Span object and span dict (JSON format) are mutually convertible (by `to_json` and `from_json` methods). - 3. Recursively truncate spans up to a given length. (see `truncate` method) - 4. Recursively replace all labels with the default label. (see `ignore_labels` method) - 5. Recursively solve the span overlapping problem by removing children overlapped with others. - (see `remove_overlapping` method) - """ - def __init__( - self, - start_idx: int, - end_idx: int, - label: Union[str, int, list] = DEFAULT_SPAN, - is_parent: bool = False, - parent: Optional["Span"] = None, - confidence: Optional[float] = None, - ): - """ - Init function. Children should be added using the `add_children` method. - :param start_idx: Start index in a seq of tokens, inclusive. - :param end_idx: End index in a seq of tokens, inclusive. - :param label: Label. If not provided, will assign a default label. - Can be of various types: String, integer, or list of something. - :param is_parent: If True, will be treated as parent. This is important because in the training process of BIO - tagger, when a span has no children, we need to know if it's a parent with no children (so we should have - an training example with all O tags) or not (then the above example doesn't exist). - We follow a convention where if a span is not parent, then the key `children` shouldn't appear in its - JSON dict; if a span is parent but has no children, the key `children` in its JSON dict should appear - and be an empty list. - :param parent: A pointer to its parent. - :param confidence: Confidence value. - """ - self.start_idx, self.end_idx = start_idx, end_idx - self.label: Union[int, str, list] = label - self.is_parent = is_parent - self.parent = parent - self._children: List[Span] = list() - self.confidence = confidence - - # Following are for label smoothing. Leave default is you don't need smoothing. - # Logic: - # The label smoothing factors of (i.e. b_smooth, i_smooth, o_smooth) depend on the `child_span` of its parent. - # The re-weighting factor of a span also depends on the `child_span` of its parent, but can be overridden - # by its own `smoothing_weight` field if it's not None. - self.child_smooth: BIOSmoothing = BIOSmoothing() - self.smooth_weight: Optional[float] = None - - def add_child(self, span: "Span") -> "Span": - """ - Add a span to children list. Will link current span to child's parent pointer. - :param span: Child span. - """ - assert self.is_parent - span.parent = self - self._children.append(span) - return self - - def re_index( - self, - offsets: List[Optional[Tuple[int, int]]], - reverse: bool = False, - recursive: bool = True, - inplace: bool = False, - ) -> "Span": - """ - BPE will change token indices, and the `re_index` method can convert normal tokens BPE word piece indices, - or vice versa. - We assume Virtual Root has a boundary [-1, -1] before being mapped to the BPE space, and a boundary [0, 0] - after the re-indexing. We use [0, 0] because it's always the BOS token in BPE. - Mapping to BPE space is straight forward. The reverse mapping has special cases where the span might - contain BOS or EOS. Usually this is a parsing bug. We will map the BOS index to 0, and EOS index to -1. - :param offsets: Offsets. Defined by BPE tokenizer and resides in the SpanFinder outputs. - :param reverse: If True, map from the BPE space to original token space. - :param recursive: If True, will apply the re-indexing to its children. - :param inplace: Inplace? - :return: Re-indexed span. - """ - span = self if inplace else self.clone() - - span.start_idx, span.end_idx = re_index_span(span.boundary, offsets, reverse) - if recursive: - new_children = list() - for child in span._children: - new_children.append(child.re_index(offsets, reverse, recursive, True)) - span._children = new_children - return span - - def truncate(self, max_length: int) -> bool: - """ - Discard spans whose end_idx exceeds the max_length (inclusive). - This is done recursively. - This is useful for some encoder like XLMR that has a limit on input length. (512 for XLMR large) - :param max_length: Max length. - :return: You don't need to care return value. - """ - if self.end_idx >= max_length: - return False - else: - self._children = list(filter(lambda x: x.truncate(max_length), self._children)) - return True - - @classmethod - def virtual_root(cls: "Span", spans: Optional[List["Span"]] = None) -> "Span": - """ - An official method to create a tree: Generate the first layer of spans by yourself, and pass them into this - method. - E.g., for SRL style task, generate a list of events, assign arguments to them as children. Then pass the - events to this method to have a virtual root which serves as a parent of events. - :param spans: 1st layer spans. - :return: Virtual root. - """ - vr = Span(-1, -1, VIRTUAL_ROOT, True) - if spans is not None: - vr._children = spans - for child in vr._children: - child.parent = vr - return vr - - def ignore_labels(self) -> None: - """ - Remove all labels. Make them placeholders. Inplace. - """ - self.label = DEFAULT_SPAN - for child in self._children: - child.ignore_labels() - - def clone(self) -> "Span": - """ - Clone a tree. - :return: Cloned tree. - """ - span = Span(self.start_idx, self.end_idx, self.label, self.is_parent, self.parent, self.confidence) - span.child_smooth, span.smooth_weight = self.child_smooth, self.smooth_weight - for child in self._children: - span.add_child(child.clone()) - return span - - def bfs(self) -> Iterable["Span"]: - """ - Iterate over all descendents with BFS, including self. - :return: Spans. - """ - yield self - yield from self._bfs() - - def _bfs(self) -> List["Span"]: - """ - Helper function. - """ - for child in self._children: - yield child - for child in self._children: - yield from child._bfs() - - def remove_overlapping(self, recursive=True) -> int: - """ - Remove overlapped spans. If spans overlap, will pick the first one and discard the others, judged by start_idx. - :param recursive: Apply to all of the descendents? - :return: The number of spans that are removed. - """ - indices = set() - new_children = list() - removing = 0 - for child in self._children: - if len(set(range(child.start_idx, child.end_idx + 1)) & indices) > 0: - removing += 1 - continue - indices.update(set(range(child.start_idx, child.end_idx + 1))) - new_children.append(child) - if recursive: - removing += child.remove_overlapping(True) - self._children = new_children - return removing - - def describe(self, sentence: Optional[List[str]] = None) -> str: - """ - :param sentence: If provided, will replace the indices with real tokens for presentation. - :return: The description in a single line. - """ - if self.start_idx >= 0: - if sentence is None: - span = f'({self.start_idx}, {self.end_idx})' - else: - span = '(' + ' '.join(sentence[self.start_idx: self.end_idx + 1]) + ')' - if self.is_parent: - return f'' - else: - return f'[Span: {span}, {self.label}]' - else: - return f'' - - def __repr__(self) -> str: - return self.describe() - - @property - def n_nodes(self) -> int: - """ - :return: Number of descendents + self. - """ - return sum([child.n_nodes for child in self._children], 1) - - @property - def boundary(self): - """ - :return: (start_idx, end_idx), both inclusive. - """ - return self.start_idx, self.end_idx - - def __iter__(self) -> Iterable["Span"]: - """ - Iterate over children. - """ - yield from self._children - - def __len__(self): - """ - :return: #children. - """ - return len(self._children) - - def __getitem__(self, idx: int): - """ - :return: The indexed child. - """ - return self._children[idx] - - def tree(self, sentence: Optional[List[str]] = None, printing: bool = True) -> str: - """ - A tree description of all descendents. Human readable. - :param sentence: If provided, will replace the indices with real tokens for presentation. - :param printing: If True, will print out. - :return: The description. - """ - ret = list() - ret.append(self.describe(sentence)) - for child in self._children: - child_lines = child.tree(sentence, False).split('\n') - for line in child_lines: - ret.append(' ' + line) - desc = '\n'.join(ret) - if printing: print(desc) - else: return desc - - def match( - self, - other: "Span", - match_label: bool = True, - depth: int = -1, - ignore_parent_boundary: bool = False, - ) -> int: - """ - Used for evaluation. Count how many spans two trees share. Two spans are considered to be identical - if their boundary, label, and parent match. - :param other: The other tree to compare. - :param match_label: If False, will ignore label. - :param depth: If specified as non-negative, will only search thru certain depth. - :param ignore_parent_boundary: If True, two children can be matched ignoring parent boundaries. - :return: #spans two tree share. - """ - if depth == 0: - return 0 - if self.label != other.label and match_label: - return 0 - if self.boundary == other.boundary: - n_match = 1 - elif ignore_parent_boundary: - # Parents fail, Children might match! - n_match = 0 - else: - return 0 - - sub_matches = np.zeros([len(self), len(other)], dtype=np.int) - for self_idx, my_child in enumerate(self): - for other_idx, other_child in enumerate(other): - sub_matches[self_idx, other_idx] = my_child.match( - other_child, match_label, depth-1, ignore_parent_boundary - ) - if not ignore_parent_boundary: - for m in [sub_matches, sub_matches.T]: - for line in m: - assert (line > 0).sum() <= 1 - n_match += max_match(sub_matches) - return n_match - - def to_json(self) -> dict: - """ - To JSON dict format. See init. - """ - ret = { - "label": self.label, - "span": list(self.boundary), - } - if self.confidence is not None: - ret['confidence'] = self.confidence - if self.is_parent: - children = list() - for child in self._children: - children.append(child.to_json()) - ret['children'] = children - return ret - - @classmethod - def from_json(cls, span_json: Union[list, dict]) -> "Span": - """ - Load from JSON. See init. - """ - if isinstance(span_json, dict): - span = Span( - span_json['span'][0], span_json['span'][1], span_json.get('label', None), 'children' in span_json, - confidence=span_json.get('confidence', None) - ) - for child_dict in span_json.get('children', []): - span.add_child(Span.from_json(child_dict)) - else: - spans = [Span.from_json(child) for child in span_json] - span = Span.virtual_root(spans) - return span - - def map_ontology( - self, - ontology_mapping: Optional[dict] = None, - inplace: bool = True, - recursive: bool = True, - ) -> Optional["Span"]: - """ - Map labels to other things, like another ontology of soft labels. - :param ontology_mapping: Mapping dict. The key should be labels, and values can be anything. - Labels not in the dict will not be deleted. So be careful. - :param inplace: Inplace? - :param recursive: Apply to all descendents if True. - :return: The mapped tree. - """ - span = self if inplace else self.clone() - if ontology_mapping is None: - # Do nothing if mapping not provided. - return span - - if recursive: - new_children = list() - for child in span: - new_child = child.map_ontology(ontology_mapping, False, True) - if new_child is not None: - new_children.append(new_child) - span._children = new_children - - if span.label != VIRTUAL_ROOT: - if span.parent is not None and (span.parent.label, span.label) in ontology_mapping: - span.label = ontology_mapping[(span.parent.label, span.label)] - elif span.label in ontology_mapping: - span.label = ontology_mapping[span.label] - else: - return - - return span - - def isolate(self) -> "Span": - """ - Generate a span that is identical to self but has no children or parent. - """ - return Span(self.start_idx, self.end_idx, self.label, self.is_parent, None, self.confidence) - - def remove_child(self, span: Optional["Span"] = None): - """ - Remove a child. 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        diff --git a/spaces/gradio/HuBERT/examples/multilingual/data_scripts/utils/strip_sgm.sh b/spaces/gradio/HuBERT/examples/multilingual/data_scripts/utils/strip_sgm.sh deleted file mode 100644 index 7f4f61d7b1a46f51a1221de6b336cb70b5a0b8b3..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/multilingual/data_scripts/utils/strip_sgm.sh +++ /dev/null @@ -1 +0,0 @@ -grep "seg id" | sed 's///g' | sed 's/<\/seg>//g' diff --git a/spaces/gradio/HuBERT/examples/paraphraser/README.md b/spaces/gradio/HuBERT/examples/paraphraser/README.md deleted file mode 100644 index 3810311f30f99f0a07fd8e5d3723bffeba9948c3..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/examples/paraphraser/README.md +++ /dev/null @@ -1,46 +0,0 @@ -# Paraphrasing with round-trip translation and mixture of experts - -Machine translation models can be used to paraphrase text by translating it to -an intermediate language and back (round-trip translation). - -This example shows how to paraphrase text by first passing it to an -English-French translation model, followed by a French-English [mixture of -experts translation model](/examples/translation_moe). - -##### 0. Setup - -Clone fairseq from source and install necessary dependencies: -```bash -git clone https://github.com/pytorch/fairseq.git -cd fairseq -pip install --editable . -pip install sacremoses sentencepiece -``` - -##### 1. Download models -```bash -wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.en-fr.tar.gz -wget https://dl.fbaipublicfiles.com/fairseq/models/paraphraser.fr-en.hMoEup.tar.gz -tar -xzvf paraphraser.en-fr.tar.gz -tar -xzvf paraphraser.fr-en.hMoEup.tar.gz -``` - -##### 2. Paraphrase -```bash -python examples/paraphraser/paraphrase.py \ - --en2fr paraphraser.en-fr \ - --fr2en paraphraser.fr-en.hMoEup -# Example input: -# The new date for the Games, postponed for a year in response to the coronavirus pandemic, gives athletes time to recalibrate their training schedules. -# Example outputs: -# Delayed one year in response to the coronavirus pandemic, the new date of the Games gives athletes time to rebalance their training schedule. -# The new date of the Games, which was rescheduled one year in response to the coronavirus (CV) pandemic, gives athletes time to rebalance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, provides athletes with time to rebalance their training schedule. -# The Games' new date, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule. -# The new Games date, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule. -# The new date of the Games, which was postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to rebalance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives athletes time to re-balance their training schedule. -# The new date of the Games, postponed one year in response to the coronavirus pandemic, gives the athletes time to rebalance their schedule of training. -# The new date of the Games, postponed one year in response to the pandemic of coronavirus, gives the athletes time to rebalance their training schedule. -``` diff --git a/spaces/gradio/HuBERT/fairseq/data/audio/audio_utils.py b/spaces/gradio/HuBERT/fairseq/data/audio/audio_utils.py deleted file mode 100644 index f51cb0cddc29c732a4573b7b3e915844a80cd2f3..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/data/audio/audio_utils.py +++ /dev/null @@ -1,174 +0,0 @@ -from pathlib import Path -from typing import BinaryIO, Optional, Tuple, Union, List - -import numpy as np -import torch - - -SF_AUDIO_FILE_EXTENSIONS = {".wav", ".flac", ".ogg"} -FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS = {".npy", ".wav", ".flac", ".ogg"} - - -def _convert_to_mono( - waveform: torch.FloatTensor, sample_rate: int -) -> torch.FloatTensor: - if waveform.shape[0] > 1: - try: - import torchaudio.sox_effects as ta_sox - except ImportError: - raise ImportError( - "Please install torchaudio to convert multi-channel audios" - ) - effects = [['channels', '1']] - return ta_sox.apply_effects_tensor(waveform, sample_rate, effects)[0] - return waveform - - -def convert_to_mono(waveform: np.ndarray, sample_rate: int) -> np.ndarray: - if waveform.shape[0] > 1: - _waveform = torch.from_numpy(waveform) - return _convert_to_mono(_waveform, sample_rate).numpy() - return waveform - - -def get_waveform( - path_or_fp: Union[str, BinaryIO], normalization=True, mono=True, - frames=-1, start=0, always_2d=True -) -> Tuple[np.ndarray, int]: - """Get the waveform and sample rate of a 16-bit WAV/FLAC/OGG Vorbis audio. - - Args: - path_or_fp (str or BinaryIO): the path or file-like object - normalization (bool): Normalize values to [-1, 1] (Default: True) - mono (bool): convert multi-channel audio to mono-channel one - frames (int): the number of frames to read. (-1 for reading all) - start (int): Where to start reading. A negative value counts from the end. - always_2d (bool): always return 2D array even for mono-channel audios - Returns: - waveform (numpy.ndarray): 1D or 2D waveform (channels x length) - sample_rate (float): sample rate - """ - if isinstance(path_or_fp, str): - ext = Path(path_or_fp).suffix - if ext not in SF_AUDIO_FILE_EXTENSIONS: - raise ValueError(f"Unsupported audio format: {ext}") - - try: - import soundfile as sf - except ImportError: - raise ImportError( - "Please install soundfile to load WAV/FLAC/OGG Vorbis audios" - ) - - waveform, sample_rate = sf.read( - path_or_fp, dtype="float32", always_2d=True, frames=frames, start=start - ) - waveform = waveform.T # T x C -> C x T - if mono and waveform.shape[0] > 1: - waveform = convert_to_mono(waveform, sample_rate) - if not normalization: - waveform *= 2 ** 15 # denormalized to 16-bit signed integers - if not always_2d: - waveform = waveform.squeeze(axis=0) - return waveform, sample_rate - - -def _get_kaldi_fbank( - waveform: np.ndarray, sample_rate: int, n_bins=80 -) -> Optional[np.ndarray]: - """Get mel-filter bank features via PyKaldi.""" - try: - from kaldi.feat.mel import MelBanksOptions - from kaldi.feat.fbank import FbankOptions, Fbank - from kaldi.feat.window import FrameExtractionOptions - from kaldi.matrix import Vector - - mel_opts = MelBanksOptions() - mel_opts.num_bins = n_bins - frame_opts = FrameExtractionOptions() - frame_opts.samp_freq = sample_rate - opts = FbankOptions() - opts.mel_opts = mel_opts - opts.frame_opts = frame_opts - fbank = Fbank(opts=opts) - features = fbank.compute(Vector(waveform.squeeze()), 1.0).numpy() - return features - except ImportError: - return None - - -def _get_torchaudio_fbank( - waveform: np.ndarray, sample_rate, n_bins=80 -) -> Optional[np.ndarray]: - """Get mel-filter bank features via TorchAudio.""" - try: - import torchaudio.compliance.kaldi as ta_kaldi - waveform = torch.from_numpy(waveform) - features = ta_kaldi.fbank( - waveform, num_mel_bins=n_bins, sample_frequency=sample_rate - ) - return features.numpy() - except ImportError: - return None - - -def get_fbank(path_or_fp: Union[str, BinaryIO], n_bins=80) -> np.ndarray: - """Get mel-filter bank features via PyKaldi or TorchAudio. Prefer PyKaldi - (faster CPP implementation) to TorchAudio (Python implementation). Note that - Kaldi/TorchAudio requires 16-bit signed integers as inputs and hence the - waveform should not be normalized.""" - waveform, sample_rate = get_waveform(path_or_fp, normalization=False) - - features = _get_kaldi_fbank(waveform, sample_rate, n_bins) - if features is None: - features = _get_torchaudio_fbank(waveform, sample_rate, n_bins) - if features is None: - raise ImportError( - "Please install pyKaldi or torchaudio to enable " - "online filterbank feature extraction" - ) - - return features - - -def is_npy_data(data: bytes) -> bool: - return data[0] == 147 and data[1] == 78 - - -def is_sf_audio_data(data: bytes) -> bool: - is_wav = (data[0] == 82 and data[1] == 73 and data[2] == 70) - is_flac = (data[0] == 102 and data[1] == 76 and data[2] == 97) - is_ogg = (data[0] == 79 and data[1] == 103 and data[2] == 103) - return is_wav or is_flac or is_ogg - - -def read_from_stored_zip(zip_path: str, offset: int, file_size: int) -> bytes: - with open(zip_path, "rb") as f: - f.seek(offset) - data = f.read(file_size) - return data - - -def parse_path(path: str) -> Tuple[str, List[int]]: - """Parse data path which is either a path to - 1. a .npy/.wav/.flac/.ogg file - 2. a stored ZIP file with slicing info: "[zip_path]:[offset]:[length]" - - Args: - path (str): the data path to parse - - Returns: - file_path (str): the file path - slice_ptr (list of int): empty in case 1; - byte offset and length for the slice in case 2 - """ - - if Path(path).suffix in FEATURE_OR_SF_AUDIO_FILE_EXTENSIONS: - _path, slice_ptr = path, [] - else: - _path, *slice_ptr = path.split(":") - if not Path(_path).is_file(): - raise FileNotFoundError(f"File not found: {_path}") - assert len(slice_ptr) in {0, 2}, f"Invalid path: {path}" - slice_ptr = [int(i) for i in slice_ptr] - return _path, slice_ptr diff --git a/spaces/gradio/HuBERT/fairseq/optim/__init__.py b/spaces/gradio/HuBERT/fairseq/optim/__init__.py deleted file mode 100644 index be783be896396ff659c0bd173a7acebb8a2d165d..0000000000000000000000000000000000000000 --- a/spaces/gradio/HuBERT/fairseq/optim/__init__.py +++ /dev/null @@ -1,48 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import importlib -import os - -from fairseq import registry -from fairseq.optim.bmuf import FairseqBMUF # noqa -from fairseq.optim.fairseq_optimizer import ( # noqa - FairseqOptimizer, - LegacyFairseqOptimizer, -) -from fairseq.optim.amp_optimizer import AMPOptimizer -from fairseq.optim.fp16_optimizer import FP16Optimizer, MemoryEfficientFP16Optimizer -from fairseq.optim.shard import shard_ -from omegaconf import DictConfig - -__all__ = [ - "AMPOptimizer", - "FairseqOptimizer", - "FP16Optimizer", - "MemoryEfficientFP16Optimizer", - "shard_", -] - -( - _build_optimizer, - register_optimizer, - OPTIMIZER_REGISTRY, - OPTIMIZER_DATACLASS_REGISTRY, -) = registry.setup_registry("--optimizer", base_class=FairseqOptimizer, required=True) - - -def build_optimizer(cfg: DictConfig, params, *extra_args, **extra_kwargs): - if all(isinstance(p, dict) for p in params): - params = [t for p in params for t in p.values()] - params = list(filter(lambda p: p.requires_grad, params)) - return _build_optimizer(cfg, params, *extra_args, **extra_kwargs) - - -# automatically import any Python files in the optim/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.optim." + file_name) diff --git a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/options.py b/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/options.py deleted file mode 100644 index 3c76097b6a0b2a312c161bd634a4a137b5929bf3..0000000000000000000000000000000000000000 --- a/spaces/grisiemjahand/Image-and-3D-Model-Creator/PIFu/lib/options.py +++ /dev/null @@ -1,161 +0,0 @@ -import argparse -import os - - -class BaseOptions(): - def __init__(self): - self.initialized = False - argparse - def initialize(self, parser): - # Datasets related - g_data = parser.add_argument_group('Data') - g_data.add_argument('--dataroot', type=str, default='./data', - help='path to images (data folder)') - - g_data.add_argument('--loadSize', type=int, default=512, help='load size of input image') - - # Experiment related - g_exp = parser.add_argument_group('Experiment') - g_exp.add_argument('--name', type=str, default='example', - help='name of the experiment. It decides where to store samples and models') - g_exp.add_argument('--debug', action='store_true', help='debug mode or not') - - g_exp.add_argument('--num_views', type=int, default=1, help='How many views to use for multiview network.') - g_exp.add_argument('--random_multiview', action='store_true', help='Select random multiview combination.') - - # Training related - g_train = parser.add_argument_group('Training') - g_train.add_argument('--gpu_id', type=int, default=0, help='gpu id for cuda') - g_train.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2, -1 for CPU mode') - - g_train.add_argument('--num_threads', default=1, type=int, help='# sthreads for loading data') - g_train.add_argument('--serial_batches', action='store_true', - help='if true, takes images in order to make batches, otherwise takes them randomly') - g_train.add_argument('--pin_memory', action='store_true', help='pin_memory') - - g_train.add_argument('--batch_size', type=int, default=2, help='input batch size') - g_train.add_argument('--learning_rate', type=float, default=1e-3, help='adam learning rate') - g_train.add_argument('--learning_rateC', type=float, default=1e-3, help='adam learning rate') - g_train.add_argument('--num_epoch', type=int, default=100, help='num epoch to train') - - g_train.add_argument('--freq_plot', type=int, default=10, help='freqency of the error plot') - g_train.add_argument('--freq_save', type=int, default=50, help='freqency of the save_checkpoints') - g_train.add_argument('--freq_save_ply', type=int, default=100, help='freqency of the save ply') - - g_train.add_argument('--no_gen_mesh', action='store_true') - g_train.add_argument('--no_num_eval', action='store_true') - - g_train.add_argument('--resume_epoch', type=int, default=-1, help='epoch resuming the training') - g_train.add_argument('--continue_train', action='store_true', help='continue training: load the latest model') - - # Testing related - g_test = parser.add_argument_group('Testing') - g_test.add_argument('--resolution', type=int, default=256, help='# of grid in mesh reconstruction') - g_test.add_argument('--test_folder_path', type=str, default=None, help='the folder of test image') - - # Sampling related - g_sample = parser.add_argument_group('Sampling') - g_sample.add_argument('--sigma', type=float, default=5.0, help='perturbation standard deviation for positions') - - g_sample.add_argument('--num_sample_inout', type=int, default=5000, help='# of sampling points') - g_sample.add_argument('--num_sample_color', type=int, default=0, help='# of sampling points') - - g_sample.add_argument('--z_size', type=float, default=200.0, help='z normalization factor') - - # Model related - g_model = parser.add_argument_group('Model') - # General - g_model.add_argument('--norm', type=str, default='group', - help='instance normalization or batch normalization or group normalization') - g_model.add_argument('--norm_color', type=str, default='instance', - help='instance normalization or batch normalization or group normalization') - - # hg filter specify - g_model.add_argument('--num_stack', type=int, default=4, help='# of hourglass') - g_model.add_argument('--num_hourglass', type=int, default=2, help='# of stacked layer of hourglass') - g_model.add_argument('--skip_hourglass', action='store_true', help='skip connection in hourglass') - g_model.add_argument('--hg_down', type=str, default='ave_pool', help='ave pool || conv64 || conv128') - g_model.add_argument('--hourglass_dim', type=int, default='256', help='256 | 512') - - # Classification General - g_model.add_argument('--mlp_dim', nargs='+', default=[257, 1024, 512, 256, 128, 1], type=int, - help='# of dimensions of mlp') - g_model.add_argument('--mlp_dim_color', nargs='+', default=[513, 1024, 512, 256, 128, 3], - type=int, help='# of dimensions of color mlp') - - g_model.add_argument('--use_tanh', action='store_true', - help='using tanh after last conv of image_filter network') - - # for train - parser.add_argument('--random_flip', action='store_true', help='if random flip') - parser.add_argument('--random_trans', action='store_true', help='if random flip') - parser.add_argument('--random_scale', action='store_true', help='if random flip') - parser.add_argument('--no_residual', action='store_true', help='no skip connection in mlp') - parser.add_argument('--schedule', type=int, nargs='+', default=[60, 80], - help='Decrease learning rate at these epochs.') - parser.add_argument('--gamma', type=float, default=0.1, help='LR is multiplied by gamma on schedule.') - parser.add_argument('--color_loss_type', type=str, default='l1', help='mse | l1') - - # for eval - parser.add_argument('--val_test_error', action='store_true', help='validate errors of test data') - parser.add_argument('--val_train_error', action='store_true', help='validate errors of train data') - parser.add_argument('--gen_test_mesh', action='store_true', help='generate test mesh') - parser.add_argument('--gen_train_mesh', action='store_true', help='generate train mesh') - parser.add_argument('--all_mesh', action='store_true', help='generate meshs from all hourglass output') - parser.add_argument('--num_gen_mesh_test', type=int, default=1, - help='how many meshes to generate during testing') - - # path - parser.add_argument('--checkpoints_path', type=str, default='./checkpoints', help='path to save checkpoints') - parser.add_argument('--load_netG_checkpoint_path', type=str, default=None, help='path to save checkpoints') - parser.add_argument('--load_netC_checkpoint_path', type=str, default=None, help='path to save checkpoints') - parser.add_argument('--results_path', type=str, default='./results', help='path to save results ply') - parser.add_argument('--load_checkpoint_path', type=str, help='path to save results ply') - parser.add_argument('--single', type=str, default='', help='single data for training') - # for single image reconstruction - parser.add_argument('--mask_path', type=str, help='path for input mask') - parser.add_argument('--img_path', type=str, help='path for input image') - - # aug - group_aug = parser.add_argument_group('aug') - group_aug.add_argument('--aug_alstd', type=float, default=0.0, help='augmentation pca lighting alpha std') - group_aug.add_argument('--aug_bri', type=float, default=0.0, help='augmentation brightness') - group_aug.add_argument('--aug_con', type=float, default=0.0, help='augmentation contrast') - group_aug.add_argument('--aug_sat', type=float, default=0.0, help='augmentation saturation') - group_aug.add_argument('--aug_hue', type=float, default=0.0, help='augmentation hue') - group_aug.add_argument('--aug_blur', type=float, default=0.0, help='augmentation blur') - - # special tasks - self.initialized = True - return parser - - def gather_options(self): - # initialize parser with basic options - if not self.initialized: - parser = argparse.ArgumentParser( - formatter_class=argparse.ArgumentDefaultsHelpFormatter) - parser = self.initialize(parser) - - self.parser = parser - - return parser.parse_args() - - def print_options(self, opt): - message = '' - message += '----------------- Options ---------------\n' - for k, v in sorted(vars(opt).items()): - comment = '' - default = self.parser.get_default(k) - if v != default: - comment = '\t[default: %s]' % str(default) - message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment) - message += '----------------- End -------------------' - print(message) - - def parse(self): - opt = self.gather_options() - return opt - - def parse_to_dict(self): - opt = self.gather_options() - return opt.__dict__ \ No newline at end of file diff --git a/spaces/gwang-kim/DATID-3D/eg3d/docs/camera_conventions.md b/spaces/gwang-kim/DATID-3D/eg3d/docs/camera_conventions.md deleted file mode 100644 index 28203a03b60879e829ff4353640c356c70ba7b87..0000000000000000000000000000000000000000 --- a/spaces/gwang-kim/DATID-3D/eg3d/docs/camera_conventions.md +++ /dev/null @@ -1,2 +0,0 @@ -Camera poses are in OpenCV Cam2World format. -Intrinsics are normalized. \ No newline at end of file diff --git a/spaces/hackathon-pln-es/BioMedIA/app.py b/spaces/hackathon-pln-es/BioMedIA/app.py deleted file mode 100644 index f6c6721519d5a4fd38bbe68e44c51cf5742bee17..0000000000000000000000000000000000000000 --- a/spaces/hackathon-pln-es/BioMedIA/app.py +++ /dev/null @@ -1,240 +0,0 @@ -from datasets import load_dataset -from transformers import ( - DPRQuestionEncoder, - DPRQuestionEncoderTokenizer, - MT5ForConditionalGeneration, - AutoTokenizer, - AutoModelForCTC, - Wav2Vec2Tokenizer, -) -from general_utils import ( - embed_questions, - transcript, - remove_chars_to_tts, - parse_final_answer, -) -from typing import List -import gradio as gr -from article_app import article, description, examples -from haystack.nodes import DensePassageRetriever -from haystack.document_stores import InMemoryDocumentStore -import numpy as np -from sentence_transformers import SentenceTransformer, util, CrossEncoder - -topk = 21 -minchars = 200 -min_snippet_length = 20 -device = "cpu" -covidterms = ["covid19", "covid", "coronavirus", "covid-19", "sars-cov-2"] - -models = { - "wav2vec2-iic": { - "processor": Wav2Vec2Tokenizer.from_pretrained( - "IIC/wav2vec2-spanish-multilibrispeech" - ), - "model": AutoModelForCTC.from_pretrained( - "IIC/wav2vec2-spanish-multilibrispeech" - ), - }, -} - - -tts_es = gr.Interface.load("huggingface/facebook/tts_transformer-es-css10") - - -params_generate = { - "min_length": 50, - "max_length": 250, - "do_sample": False, - "early_stopping": True, - "num_beams": 8, - "temperature": 1.0, - "top_k": None, - "top_p": None, - "no_repeat_ngram_size": 3, - "num_return_sequences": 1, -} - -dpr = DensePassageRetriever( - document_store=InMemoryDocumentStore(), - query_embedding_model="IIC/dpr-spanish-question_encoder-allqa-base", - passage_embedding_model="IIC/dpr-spanish-passage_encoder-allqa-base", - max_seq_len_query=64, - max_seq_len_passage=256, - batch_size=512, - use_gpu=False, -) - -mt5_tokenizer = AutoTokenizer.from_pretrained("IIC/mt5-base-lfqa-es") -mt5_lfqa = MT5ForConditionalGeneration.from_pretrained("IIC/mt5-base-lfqa-es") - -similarity_model = SentenceTransformer( - "distiluse-base-multilingual-cased", device="cpu" -) - -crossencoder = CrossEncoder("IIC/roberta-base-bne-ranker", device="cpu") - -dataset = load_dataset("IIC/spanish_biomedical_crawled_corpus", split="train") - -dataset = dataset.filter(lambda example: len(example["text"]) > minchars) - -dataset.load_faiss_index( - "embeddings", - "dpr_index_bio_newdpr.faiss", -) - - -def query_index(question: str): - question_embedding = dpr.embed_queries([question])[0] - scores, closest_passages = dataset.get_nearest_examples( - "embeddings", question_embedding, k=topk - ) - contexts = [ - closest_passages["text"][i] for i in range(len(closest_passages["text"])) - ]# [:int(topk / 3)] - return [ - context for context in contexts if len(context.split()) > min_snippet_length - ] - - -def sort_on_similarity(question, contexts, include_rank: int = 5): - question_encoded = similarity_model.encode([question])[0] - ctxs_encoded = similarity_model.encode(contexts) - sim_scores_ss = [ - util.cos_sim(question_encoded, ctx_encoded) for ctx_encoded in ctxs_encoded - ] - text_pairs = [[question, ctx] for ctx in contexts] - similarity_scores = crossencoder.predict(text_pairs) - similarity_scores = np.array(sim_scores_ss) * similarity_scores - similarity_ranking_idx = np.flip(np.argsort(similarity_scores)) - return [contexts[idx] for idx in similarity_ranking_idx][:include_rank] - - -def create_context(contexts: List): - return "

        " + "

        ".join(contexts) - - -def create_model_input(question: str, context: str): - return f"question: {question} context: {context}" - - -def generate_answer(model_input, update_params): - model_input = mt5_tokenizer( - model_input, truncation=True, padding=True, return_tensors="pt", max_length=1024 - ) - params_generate.update(update_params) - answers_encoded = mt5_lfqa.generate( - input_ids=model_input["input_ids"].to(device), - attention_mask=model_input["attention_mask"].to(device), - **params_generate, - ) - answers = mt5_tokenizer.batch_decode( - answers_encoded, skip_special_tokens=True, clean_up_tokenization_spaces=True - ) - results = [{"generated_text": answer} for answer in answers] - return results - - -def search_and_answer( - question, - audio_file, - audio_array, - min_length_answer, - num_beams, - no_repeat_ngram_size, - temperature, - max_answer_length, - do_tts, -): - update_params = { - "min_length": min_length_answer, - "max_length": max_answer_length, - "num_beams": int(num_beams), - "temperature": temperature, - "no_repeat_ngram_size": no_repeat_ngram_size, - } - if not question: - s2t_model = models["wav2vec2-iic"]["model"] - s2t_processor = models["wav2vec2-iic"]["processor"] - question = transcript( - audio_file, audio_array, processor=s2t_processor, model=s2t_model - ) - print(f"Transcripted question: *** {question} ****") - if any([any([term in word.lower() for term in covidterms]) for word in question.split(" ")]): - return "Del COVID no queremos saber ya más nada, lo sentimos, pregúntame sobre otra cosa :P ", "ni contexto ni contexta.", "audio_troll.flac" - contexts = query_index(question) - contexts = sort_on_similarity(question, contexts) - context = create_context(contexts) - model_input = create_model_input(question, context) - answers = generate_answer(model_input, update_params) - final_answer = answers[0]["generated_text"] - if do_tts: - audio_answer = tts_es(remove_chars_to_tts(final_answer)) - final_answer, documents = parse_final_answer(final_answer, contexts) - return final_answer, documents, audio_answer if do_tts else "audio_troll.flac" - - -if __name__ == "__main__": - gr.Interface( - search_and_answer, - inputs=[ - gr.inputs.Textbox( - lines=2, - label="Pregúntame sobre BioMedicina o temas relacionados. Puedes simplemente preguntarme aquí y darle al botón verde de abajo que pone Enviar.", - placeholder="Escribe aquí tu pregunta", - optional=True, - ), - gr.inputs.Audio( - source="upload", - type="filepath", - label="Sube un audio con tu respuesta aquí si quieres.", - optional=True, - ), - gr.inputs.Audio( - source="microphone", - type="numpy", - label="Graba aquí un audio con tu pregunta.", - optional=True, - ), - gr.inputs.Slider( - minimum=10, - maximum=200, - default=50, - label="Minimum size for the answer", - step=1, - ), - gr.inputs.Slider( - minimum=4, maximum=12, default=8, label="number of beams", step=1 - ), - gr.inputs.Slider( - minimum=2, maximum=5, default=3, label="no repeat n-gram size", step=1 - ), - gr.inputs.Slider( - minimum=0.8, maximum=2.0, default=1.0, label="temperature", step=0.1 - ), - gr.inputs.Slider( - minimum=220, - maximum=360, - default=250, - label="maximum answer length", - step=1, - ), - gr.inputs.Checkbox( - default=False, label="Text to Speech", optional=True), - ], - outputs=[ - gr.outputs.HTML( - label="Respuesta generada." - ), - gr.outputs.HTML( - label="Documentos utilizados." - ), - gr.outputs.Audio(label="Respuesta en audio."), - ], - description=description, - examples=examples, - theme="grass", - article=article, - thumbnail="IIC_logoP.png", - css="https://cdn.jsdelivr.net/npm/bootstrap@3.3.7/dist/css/bootstrap.min.css", - ).launch() diff --git a/spaces/hackathon-pln-es/clasificador-de-tesis/README.md b/spaces/hackathon-pln-es/clasificador-de-tesis/README.md deleted file mode 100644 index fc8e2ed23323b3fb659d27d09a506053d90302ec..0000000000000000000000000000000000000000 --- a/spaces/hackathon-pln-es/clasificador-de-tesis/README.md +++ /dev/null @@ -1,18 +0,0 @@ ---- -title: Clasificador De Tesis -emoji: 🔥 -colorFrom: red -colorTo: green -sdk: gradio -sdk_version: 2.9.1 -app_file: app.py -pinned: false -license: apache-2.0 -annotations_creators: -- MajorIsaiah -- clavel -- inoid -- Ximyer ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/hahahafofo/vits-uma-genshin-honkai/README.md b/spaces/hahahafofo/vits-uma-genshin-honkai/README.md deleted file mode 100644 index 46b2dc0a69560ca08d498a189de89fb54b32f18a..0000000000000000000000000000000000000000 --- a/spaces/hahahafofo/vits-uma-genshin-honkai/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -license: apache-2.0 -title: ' vits-uma-genshin-honkai' -sdk: gradio -sdk_version: 3.7 -emoji: 🐨 -colorTo: yellow -pinned: false -app_file: app.py -duplicated_from: zomehwh/vits-uma-genshin-honkai ---- diff --git a/spaces/hdhzk/bingo/src/lib/isomorphic/browser.ts b/spaces/hdhzk/bingo/src/lib/isomorphic/browser.ts deleted file mode 100644 index de125b1f1786d1618cb1ff47f403d76c6784f4ce..0000000000000000000000000000000000000000 --- a/spaces/hdhzk/bingo/src/lib/isomorphic/browser.ts +++ /dev/null @@ -1,11 +0,0 @@ -'use client' - -const debug = console.info.bind(console) - -class WebSocketAlias extends WebSocket { - constructor(address: string | URL, ...args: any) { - super(address) - } -} - -export default { fetch, WebSocket: WebSocketAlias, debug } diff --git a/spaces/heiyubili/bingo/src/components/user-menu.tsx b/spaces/heiyubili/bingo/src/components/user-menu.tsx deleted file mode 100644 index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000 --- a/spaces/heiyubili/bingo/src/components/user-menu.tsx +++ /dev/null @@ -1,113 +0,0 @@ -'use client' - -import { useEffect, useState } from 'react' -import Image from 'next/image' -import { toast } from 'react-hot-toast' -import { Button } from '@/components/ui/button' -import pkg from '../../package.json' -import { - DropdownMenu, - DropdownMenuContent, - DropdownMenuItem, - DropdownMenuSeparator, - DropdownMenuTrigger -} from '@/components/ui/dropdown-menu' -import { IconCopy, IconExternalLink, IconGitHub } from '@/components/ui/icons' -import SettingIcon from '@/assets/images/settings.svg' -import { useCopyToClipboard } from '@/lib/hooks/use-copy-to-clipboard' - -export function UserMenu() { - const [host, setHost] = useState('') - const { isCopied, copyToClipboard } = useCopyToClipboard({ timeout: 2000 }) - useEffect(() => { - setHost(location.host) - }, []) - - useEffect(() => { - if (isCopied) { - toast.success('复制成功') - } - }, [isCopied]) - return ( -

        - - - - - - - location.href='#dialog="settings"' - } - className="cursor-pointer" - > - 设置用户 - - - - location.href='#dialog="voice"' - } - className="cursor-pointer" - > - 语音设置 - - - - - 开源地址 - - - - - - - - 托管地址 - 🤗 - - - - - - - 复制站点 - - - - - -
        版本信息 {pkg.version}
        -         - - -
        站点域名
        -
        copyToClipboard(host)} className="flex gap-1 text-xs text-zinc-500 cursor-pointer"> - {host} -
        -         -         -         -
        - ) -} diff --git a/spaces/hekbobo/bingo/postcss.config.js b/spaces/hekbobo/bingo/postcss.config.js deleted file mode 100644 index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000 --- a/spaces/hekbobo/bingo/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -module.exports = { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} diff --git a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/competitions_with_custom_Trainers/MMS/nnUNetTrainerV2_MMS.py b/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/competitions_with_custom_Trainers/MMS/nnUNetTrainerV2_MMS.py deleted file mode 100644 index 7907fb69542584c044bb901f0348ed8fd6ad0055..0000000000000000000000000000000000000000 --- a/spaces/ho11laqe/nnUNet_calvingfront_detection/nnunet/training/network_training/competitions_with_custom_Trainers/MMS/nnUNetTrainerV2_MMS.py +++ /dev/null @@ -1,60 +0,0 @@ -import torch -from nnunet.network_architecture.generic_UNet import Generic_UNet -from nnunet.network_architecture.initialization import InitWeights_He -from nnunet.training.network_training.nnUNet_variants.data_augmentation.nnUNetTrainerV2_insaneDA import \ - nnUNetTrainerV2_insaneDA -from nnunet.utilities.nd_softmax import softmax_helper -from torch import nn - - -class nnUNetTrainerV2_MMS(nnUNetTrainerV2_insaneDA): - def setup_DA_params(self): - super().setup_DA_params() - self.data_aug_params["p_rot"] = 0.7 - self.data_aug_params["p_eldef"] = 0.1 - self.data_aug_params["p_scale"] = 0.3 - - self.data_aug_params["independent_scale_factor_for_each_axis"] = True - self.data_aug_params["p_independent_scale_per_axis"] = 0.3 - - self.data_aug_params["do_additive_brightness"] = True - self.data_aug_params["additive_brightness_mu"] = 0 - self.data_aug_params["additive_brightness_sigma"] = 0.2 - self.data_aug_params["additive_brightness_p_per_sample"] = 0.3 - self.data_aug_params["additive_brightness_p_per_channel"] = 1 - - self.data_aug_params["elastic_deform_alpha"] = (0., 300.) - self.data_aug_params["elastic_deform_sigma"] = (9., 15.) - - self.data_aug_params['gamma_range'] = (0.5, 1.6) - - def initialize_network(self): - if self.threeD: - conv_op = nn.Conv3d - dropout_op = nn.Dropout3d - norm_op = nn.BatchNorm3d - - else: - conv_op = nn.Conv2d - dropout_op = nn.Dropout2d - norm_op = nn.BatchNorm2d - - norm_op_kwargs = {'eps': 1e-5, 'affine': True} - dropout_op_kwargs = {'p': 0, 'inplace': True} - net_nonlin = nn.LeakyReLU - net_nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True} - self.network = Generic_UNet(self.num_input_channels, self.base_num_features, self.num_classes, - len(self.net_num_pool_op_kernel_sizes), - self.conv_per_stage, 2, conv_op, norm_op, norm_op_kwargs, dropout_op, - dropout_op_kwargs, - net_nonlin, net_nonlin_kwargs, True, False, lambda x: x, InitWeights_He(1e-2), - self.net_num_pool_op_kernel_sizes, self.net_conv_kernel_sizes, False, True, True) - if torch.cuda.is_available(): - self.network.cuda() - self.network.inference_apply_nonlin = softmax_helper - - """def run_training(self): - from batchviewer import view_batch - a = next(self.tr_gen) - view_batch(a['data']) - import IPython;IPython.embed()""" diff --git a/spaces/hu-po/speech2speech/src/utils.py b/spaces/hu-po/speech2speech/src/utils.py deleted file mode 100644 index b73505231f28b2b13d4283b4590a7718eb51df89..0000000000000000000000000000000000000000 --- a/spaces/hu-po/speech2speech/src/utils.py +++ /dev/null @@ -1,16 +0,0 @@ -import time -import logging - -log = logging.getLogger(__name__) - -# Decorator to time a function -def timeit(func): - def timed(*args, **kwargs): - time_start = time.time() - result = func(*args, **kwargs) - _yellow = "\x1b[33;20m" - _reset = "\x1b[0m" - _msg = f"{_yellow}{func.__name__} duration: {time.time() - time_start:.2f} seconds{_reset}" - log.info(_msg) - return result - return timed diff --git a/spaces/huak95/personaGPT_custom/frontend/styles/chat.css b/spaces/huak95/personaGPT_custom/frontend/styles/chat.css deleted file mode 100644 index 0056c23be0c81a96c34639f1ade6e1b0a187e89d..0000000000000000000000000000000000000000 --- a/spaces/huak95/personaGPT_custom/frontend/styles/chat.css +++ /dev/null @@ -1,163 +0,0 @@ -body { - background: #fff; - margin-top: 10px; -} - -.chat-box { - height: 100%; - width: 100%; - background-color: #fff; - overflow: hidden; -} - -.chats { - padding: 30px 15px -} - -.chat-avatar { - float: right -} - -.chat-avatar .avatar { - width: 30px; - -webkit-box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2), 0 6px 10px 0 rgba(0, 0, 0, 0.3); - box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.2), 0 6px 10px 0 rgba(0, 0, 0, 0.3); -} - -.chat-body { - display: block; - margin: 10px 30px 0 0; - overflow: hidden -} - -.chat.right .chat-body { - margin: 10px 0 0 0; -} - -.chat-body:first-child { - margin-top: 0 -} - -.chat-content { - position: relative; - display: block; - float: right; - padding: 8px 15px; - margin: 0 20px 10px 0; - clear: both; - color: #2c2323; - background-color: #e7d9d9; - border-radius: 4px; - -webkit-box-shadow: 0 1px 4px 0 rgba(0, 0, 0, 0.37); - box-shadow: 0 1px 4px 0 rgba(0, 0, 0, 0.37); -} - -.chat-content:before { - position: absolute; - top: 10px; - right: -10px; - width: 0; - height: 0; - content: ''; - border: 5px solid transparent; - border-left-color: #dfe9ef -} - -.chat-content>p:last-child { - margin-bottom: 0 -} - -.chat-content+.chat-content:before { - border-color: transparent -} - -.chat-time { - display: block; - margin-top: 8px; - color: rgba(255, 255, 255, .6) -} - -.chat-left .chat-avatar { - float: left -} - -.chat-left .chat-body { - margin-right: 0; - margin-left: 30px -} - -.chat-left .chat-content { - float: left; - margin: 0 0 10px 20px; - color: #fff; - background-color: #7a3016 -} - -.chat-left .chat-content:before { - right: auto; - left: -10px; - border-right-color: #7a3016; - border-left-color: transparent -} - -.chat-left .chat-content+.chat-content:before { - border-color: transparent -} - -.chat-left .chat-time { - color: #a3afb7 -} - -.panel-footer { - padding: 0 30px 15px; - background-color: transparent; - border-top: 1px solid transparent; - border-bottom-right-radius: 3px; - border-bottom-left-radius: 3px; -} - -.avatar img { - width: 100%; - max-width: 100%; - height: auto; - border: 0 none; - border-radius: 1000px; -} - -.chat-avatar .avatar { - width: 30px; -} - -.avatar { - position: relative; - display: inline-block; - width: 40px; - white-space: nowrap; - border-radius: 1000px; - vertical-align: bottom; -} - - - - - -.chats { - height: calc(70vh - 3em); - overflow-y: auto; -} - -.error { - margin: 0 0; - color: #a40d0d; -} - -.typing-dots { - margin: 0 0 0 50px; - opacity: 0.5; -} - -.guide { - padding-top: 15vh; - text-align: center; - font-size: 1.2em; -} diff --git a/spaces/huggan/cityscapes-pix2pix/app.py b/spaces/huggan/cityscapes-pix2pix/app.py deleted file mode 100644 index 2c9a9f5a53fe292343c32115266d91c2b4d5e6b2..0000000000000000000000000000000000000000 --- a/spaces/huggan/cityscapes-pix2pix/app.py +++ /dev/null @@ -1,22 +0,0 @@ -import gradio as gr -from torchvision.transforms import Compose, Resize, ToTensor, Normalize -from PIL import Image -from torchvision.utils import save_image -from huggan.pytorch.pix2pix.modeling_pix2pix import GeneratorUNet - -transform = Compose( - [ - Resize((256, 256), Image.BICUBIC), - ToTensor(), - Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), - ] -) -model = GeneratorUNet.from_pretrained('huggan/pix2pix-cityscapes') - -def predict_fn(img): - inp = transform(img).unsqueeze(0) - out = model(inp) - save_image(out, 'out.png', normalize=True) - return 'out.png' - -gr.Interface(predict_fn, inputs=gr.inputs.Image(type='pil'), outputs='image', examples=[['img.png'], ['real.jpeg'], ['real2.jpg']]).launch() diff --git a/spaces/huggingface/bloom-test-flax/screenshot.py b/spaces/huggingface/bloom-test-flax/screenshot.py deleted file mode 100644 index 69dfed54466d674941be84df38e8497aadcca743..0000000000000000000000000000000000000000 --- a/spaces/huggingface/bloom-test-flax/screenshot.py +++ /dev/null @@ -1,387 +0,0 @@ -import logging -import math -from pathlib import Path -import re -from time import time -from typing import List, Optional, Tuple - -from PIL import Image, ImageDraw, ImageFont -from PIL.ImageFont import FreeTypeFont - -from PIL import ImageFilter -logging.basicConfig(level="DEBUG") -logger = logging.getLogger(__name__) - -BG_COMP = "bg_composant" -PROMPT_VAR = "prompt_variable" -GENERATION_VAR = "generation_var" -BOX_COMP = "box_comp" - - -def wrap_text(font: FreeTypeFont, text: str, max_width: int, direction: str = "ltr") -> str: - """ - Wraps the text at the given width. - :param font: Font to use. - :param text: Text to fit. - :param max_width: Maximum width of the final text, in pixels. - :param max_height: Maximum height height of the final text, in pixels. - :param spacing: The number of pixels between lines. - :param direction: Direction of the text. It can be 'rtl' (right to - left), 'ltr' (left to right) or 'ttb' (top to bottom). - Requires libraqm. - :return: The wrapped text. - """ - words = text.split() - - lines: list[str] = [""] - curr_line_width = 0 - - for word in words: - if curr_line_width == 0: - word_width = font.getlength(word, direction) - - lines[-1] = word - curr_line_width = word_width - else: - new_line_width = font.getlength(f"{lines[-1]} {word}", direction) - - if new_line_width > max_width: - # Word is too long to fit on the current line - word_width = font.getlength(word, direction) - - # Put the word on the next line - lines.append(word) - curr_line_width = word_width - else: - # Put the word on the current line - lines[-1] = f"{lines[-1]} {word}" - curr_line_width = new_line_width - - return "\n".join(lines) - - -# tr.wrap(my_str, width=30) - - -def _fit_paragraph( - paragraph, - font: FreeTypeFont, - max_width: int, - max_height: int, - line_height, - num_lines: List[str], - spacing: int = 4, - direction: str = "ltr", - -): - paragraph_lines: list[str] = [""] - curr_line_width = 0 - # This is a very bad splitter for Chinese - words = list(paragraph) if re.search(u'[\u4e00-\u9fff]',paragraph) else paragraph.split(" ") - - for word in words: - if curr_line_width == 0: - word_width = font.getlength(word, direction) - - if word_width > max_width: - # Word is longer than max_width - return None - - paragraph_lines[-1] = word - curr_line_width = word_width - else: - new_line_width = font.getlength(f"{paragraph_lines[-1]} {word}", direction) - - if new_line_width > max_width: - # Word is too long to fit on the current line - word_width = font.getlength(word, direction) - new_num_lines = num_lines + len(paragraph_lines) + 1 - new_text_height = (new_num_lines * line_height) + (new_num_lines * spacing) - - if word_width > max_width or new_text_height > max_height: - # Word is longer than max_width, and - # adding a new line would make the text too tall - return None - - # Put the word on the next line - paragraph_lines.append(word) - curr_line_width = word_width - else: - # Put the word on the current line - paragraph_lines[-1] = f"{paragraph_lines[-1]} {word}" - curr_line_width = new_line_width - return paragraph_lines - -def try_fit_text( - font: FreeTypeFont, - prompt: str, - generation: str, - max_width: int, - max_height: int, - spacing: int = 4, - direction: str = "ltr", -) -> Optional[str]: - """ - Attempts to wrap the text into a rectangle. - Tries to fit the text into a box using the given font at decreasing sizes, - based on ``scale_factor``. Makes ``max_iterations`` attempts. - :param font: Font to use. - :param text: Text to fit. - :param max_width: Maximum width of the final text, in pixels. - :param max_height: Maximum height height of the final text, in pixels. - :param spacing: The number of pixels between lines. - :param direction: Direction of the text. It can be 'rtl' (right to - left), 'ltr' (left to right) or 'ttb' (top to bottom). - Requires libraqm. - :return: If able to fit the text, the wrapped text. Otherwise, ``None``. - """ - - line_height = font.size - - if line_height > max_height: - # The line height is already too big - return None - - prompt_lines: list[str] = [] - paragraphs = prompt.split("\n") - for paragraph in paragraphs: - paragraph_lines = _fit_paragraph( - paragraph=paragraph, - font=font, - max_width=max_width, - max_height=max_height, - line_height=line_height, - spacing=spacing, - num_lines=len(prompt_lines), - direction=direction, - ) - if paragraph_lines is None: - return None - prompt_lines.extend(paragraph_lines) - generation_lines: list[str] = [] - paragraphs = f"{prompt_lines[-1]}{generation}".split("\n") - for paragraph in paragraphs: - paragraph_lines = _fit_paragraph( - paragraph=paragraph, - font=font, - max_width=max_width, - max_height=max_height, - line_height=line_height, - spacing=spacing, - num_lines=len(prompt_lines) + len(generation_lines), - direction=direction, - ) - if paragraph_lines is None: - return None - generation_lines.extend(paragraph_lines) - generation_lines[0] = generation_lines[0][len(prompt_lines[-1]):] - return "\n".join(prompt_lines), "\n".join(generation_lines) - - -# pylint: disable=too-many-arguments -def fit_text( - font, - prompt: str, - generation: str, - max_width: int, - max_height: int, - spacing: int = 4, - scale_factor: float = 0.8, - max_iterations: int = 5, - direction: str = "ltr", -) -> Tuple[FreeTypeFont, str]: - """ - Automatically determines text wrapping and appropriate font size. - Tries to fit the text into a box using the given font at decreasing sizes, - based on ``scale_factor``. Makes ``max_iterations`` attempts. - If unable to find an appropriate font size within ``max_iterations`` - attempts, wraps the text at the last attempted size. - :param font: Font to use. - :param text: Text to fit. - :param max_width: Maximum width of the final text, in pixels. - :param max_height: Maximum height height of the final text, in pixels. - :param spacing: The number of pixels between lines. - :param scale_factor: - :param max_iterations: Maximum number of attempts to try to fit the text. - :param direction: Direction of the text. It can be 'rtl' (right to - left), 'ltr' (left to right) or 'ttb' (top to bottom). - Requires libraqm. - :return: The font at the appropriate size and the wrapped text. - """ - initial_font_size = font.size - - # logger.debug('Trying to fit text "%s"', text) - - for i in range(max_iterations): - trial_font_size = int(initial_font_size * pow(scale_factor, i)) - trial_font = font.font_variant(size=trial_font_size) - - logger.debug("Trying font size %i", trial_font_size) - - wrapped = try_fit_text(trial_font, prompt, generation, max_width, max_height, spacing, direction) - - if wrapped is not None: - logger.debug("Successfully fit text") - return (trial_font, wrapped) - - # Give up and wrap the text at the last size - logger.debug("Gave up trying to fit text; just wrapping text") - wrapped = wrap_text(trial_font, prompt, max_width, direction) + wrap_text( - trial_font, generation, max_width, direction - ) - - return (trial_font, wrapped) - - - -def main( - prompt, - generation, - width, - height, - assets_path, - font_path, - colors, - frame_to_box_margin, - text_to_text_box_margin, - init_font_size, - right_align = False -): - # prompt_color = "#ffffff" - # text_color = "#FF57A0" - # text_box = ((500, 500, 2300, 1800)) - # margin = 50 - # input_color = "#CDD2E3" - right_align_params = {"direction":'rtl',"align":'right',"features":'rtla'} if right_align else {} - text_box_margin_r = frame_to_box_margin - text_box_margin_t = frame_to_box_margin - text_box_margin_l = frame_to_box_margin - text_box_margin_b = int(height / 4.5) - - text_box = ( - text_box_margin_l, - text_box_margin_t, - width - text_box_margin_r, - height - text_box_margin_b, - ) - - background = Image.new("RGB", (width, height), color=colors[BG_COMP]) - # Get assets - assets_path = Path(assets_path) - flower_1 = Image.open(assets_path / "image_1.png").copy() - flower_2 = Image.open(assets_path / "image_2.png").copy() - shadow = Image.open(assets_path / "image_3.png").copy() - bloom_logo = Image.open(assets_path / "image_4.png").copy() - input_info = Image.open(assets_path / "image_7.png").copy() - output_info = Image.open(assets_path / "image_6.png").copy() - details = Image.open(assets_path / "image_5.png").copy() - - flower_1_offsets = (int(width - flower_1.width * 2 / 3), int(-flower_1.height / 3)) - background.paste(flower_1, flower_1_offsets, flower_1) - - flower_2_offsets = ( - -int(flower_2.width * 2 / 5), - int(height / 2 - flower_2.height / 2), - ) - background.paste(flower_2, flower_2_offsets, flower_2) - - bloom_offsets = ( - frame_to_box_margin, - int(height - bloom_logo.height - frame_to_box_margin), - ) - background.paste(bloom_logo, bloom_offsets, bloom_logo) - - input_info_offsets = ( - width - details.width - text_to_text_box_margin - input_info.width - output_info.width , - int(height - details.height - frame_to_box_margin), - ) - background.paste(input_info, input_info_offsets, input_info) - - output_info_offsets =( - width - details.width - text_to_text_box_margin - input_info.width - output_info.width , - int(height - details.height - frame_to_box_margin + input_info.height + text_to_text_box_margin), - ) - background.paste(output_info, output_info_offsets, output_info) - - details_offsets = ( - width - frame_to_box_margin - details.width , - int(height - details.height - frame_to_box_margin), - ) - background.paste(details, details_offsets, details) - box_margin = ( - text_box[0] + text_to_text_box_margin, - text_box[1] + text_to_text_box_margin, - text_box[2] - text_to_text_box_margin, - text_box[3] - text_to_text_box_margin, - ) - - # text_box = ImageText(box_margin) - drawing = ImageDraw.Draw(background, "RGBA") - - # Text box for main text - input_color = colors[BOX_COMP][1:] - input_color = tuple(int(input_color[i : i + 2], 16) for i in (0, 2, 4)) + ( - int(255 * 0.99), - ) # RGB + A - drawing.rounded_rectangle(text_box, outline="#000", fill=input_color, radius=47.9) - - # Adapt text size to box - # font, (prompt_a, generation_a) = adapt_text_to_ratio(box_margin, prompt, generation) - # generation_a = adapt_text_to_ratio(box_margin, generation, prompt) - # font = adapt_font_to_text(box_margin, prompt_a + generation_a, font_path=font_path) - - init_font = ImageFont.truetype(font_path, size=init_font_size) - font, (prompt_a, generation_a) = fit_text( - prompt=prompt, - generation=generation, - font=init_font, - max_height=box_margin[3] - box_margin[1], - max_width=box_margin[2] - box_margin[0], - max_iterations=50, - scale_factor=0.95, - direction="rtl" if right_align else "ltr" - ) - # Prompt, main, then the last line - prompt_s = prompt_a.split("\n") - prompt_main = "\n".join(prompt_s[:-1]) - prompt_lastline = prompt_s[-1] - drawing.multiline_text( - (box_margin[0], box_margin[1]), - prompt_main, - colors[PROMPT_VAR], - font, - **right_align_params - ) - end_prompt_main = font.getsize_multiline(prompt_main) - end_prompt_last = font.getsize_multiline(prompt_lastline) - drawing.multiline_text( - ((box_margin[2] - end_prompt_last[0]) if right_align else box_margin[0], box_margin[1] + end_prompt_main[1]), - prompt_lastline, - colors[PROMPT_VAR], - font, - **right_align_params - ) - - # Generated text, first line, then the rest - generation_split = generation_a.split("\n") - generation_firstline = generation_split[0] - generation_main = "\n".join(generation_split[1:]) - drawing.multiline_text( - # margin x + length(last line of prompt), margin Y + length(main part of prompt) - (box_margin[0], box_margin[1] + end_prompt_main[1]) if right_align else \ - (box_margin[0] + end_prompt_last[0], box_margin[1] + end_prompt_main[1]), - generation_firstline, - colors[GENERATION_VAR], - font, - **right_align_params - ) - drawing.multiline_text( - (box_margin[0], box_margin[1] + end_prompt_main[1] + end_prompt_last[1]), - generation_main, - colors[GENERATION_VAR], - font, - **right_align_params - ) - - final_font_size = font.size - return final_font_size, background diff --git a/spaces/hussain-shk/IndiSent/scripts/__init__.py b/spaces/hussain-shk/IndiSent/scripts/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/hysts/mistral-7b/style.css b/spaces/hysts/mistral-7b/style.css deleted file mode 100644 index 60878febc13db001635a52688abfe34d95e6c309..0000000000000000000000000000000000000000 --- a/spaces/hysts/mistral-7b/style.css +++ /dev/null @@ -1,16 +0,0 @@ -h1 { - text-align: center; -} - -#duplicate-button { - margin: auto; - color: white; - background: #1565c0; - border-radius: 100vh; -} - -.contain { - max-width: 900px; - margin: auto; - padding-top: 1.5rem; -} diff --git a/spaces/iabualhaol/ai-score-openai/app.py b/spaces/iabualhaol/ai-score-openai/app.py deleted file mode 100644 index 19730aa48ad6dbe8ef235a73959c677d461d00de..0000000000000000000000000000000000000000 --- a/spaces/iabualhaol/ai-score-openai/app.py +++ /dev/null @@ -1,54 +0,0 @@ -import gradio as gr -import openai -import re # import the regular expression module to parse the score - -def evaluate_text(api_key, model, text_to_evaluate): - # Setting the API key for OpenAI - openai.api_key = api_key - - # Few-shot learning prompt template - prompt = """Given below are some texts. Please evaluate how likely each text is AI-generated on a scale from 1-100, where 100 means highly likely and 1 means highly unlikely to be AI-generated. - -1. "I am going to the park today." - Human written - Evaluation: 5 - -2. "The quixotic endeavor synergizes cryptographic algorithms." - AI generated - Evaluation: 90 - -3. "{}" - Evaluation: """.format(text_to_evaluate) - - try: - # Generate model's response - response = openai.Completion.create( - engine=model, - prompt=prompt, - max_tokens=10 - ) - - raw_evaluation_score = response.choices[0].text.strip() - - # Parsing the evaluation score using regular expressions - parsed_score = re.search(r"\d+", raw_evaluation_score) - - if parsed_score: - return parsed_score.group() - else: - return "Score could not be parsed." - - except Exception as e: - return str(e) - -# Gradio UI -iface = gr.Interface( - fn=evaluate_text, - inputs=[ - gr.inputs.Textbox(lines=1, label="Enter your OpenAI API Key", type="password"), - gr.inputs.Dropdown(choices=["text-davinci-002", "text-ada-002"], label="Model"), - gr.inputs.Textbox(lines=5, label="Text to Evaluate") - ], - outputs=gr.outputs.Textbox(label="AI Score out of 100"), # Customized label here - live=False -) - -iface.launch() diff --git a/spaces/imseldrith/BotX/app.py b/spaces/imseldrith/BotX/app.py deleted file mode 100644 index 7626b539fcdfcdfd962a22ea800fce464d7ba71e..0000000000000000000000000000000000000000 --- a/spaces/imseldrith/BotX/app.py +++ /dev/null @@ -1,18 +0,0 @@ -# from flask import Flask -# app = Flask(__name__) - -# @app.route('/') -# def hello_world(): - # return 'Hello from Koyeb' - - -# if __name__ == "__main__": - # app.run() - -import subprocess -import gradio as gr -def r(hi): - x = hi + " hiiii!" - return x,subprocess.run("python3 bot.py",shell=True) - -gr.Interface(fn=r, inputs="text",outputs=["text","text"],live=True).launch(debug=True) \ No newline at end of file diff --git a/spaces/inamXcontru/PoeticTTS/Be Set Free Fast Larry Nims Pdf Merge What You Need to Know About BSFF and PDF Formats.md b/spaces/inamXcontru/PoeticTTS/Be Set Free Fast Larry Nims Pdf Merge What You Need to Know About BSFF and PDF Formats.md deleted file mode 100644 index f4fba52a55d1f624bd8444a19789ab92b950493d..0000000000000000000000000000000000000000 --- a/spaces/inamXcontru/PoeticTTS/Be Set Free Fast Larry Nims Pdf Merge What You Need to Know About BSFF and PDF Formats.md +++ /dev/null @@ -1,5 +0,0 @@ -
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        diff --git a/spaces/innat/Global.Wheat.Detection.MaskRCNN/mrcnn/utils.py b/spaces/innat/Global.Wheat.Detection.MaskRCNN/mrcnn/utils.py deleted file mode 100644 index 0df9eb7cf1a4c7b6e1d94d052a67bf83a7678ed2..0000000000000000000000000000000000000000 --- a/spaces/innat/Global.Wheat.Detection.MaskRCNN/mrcnn/utils.py +++ /dev/null @@ -1,984 +0,0 @@ -""" -Mask R-CNN -Common utility functions and classes. - -Copyright (c) 2017 Matterport, Inc. -Licensed under the MIT License (see LICENSE for details) -Written by Waleed Abdulla -""" - -import logging -import math -import os -import random -import shutil -import sys -import urllib.request -import warnings -from distutils.version import LooseVersion - -import numpy as np -import scipy -import skimage.color -import skimage.io -import skimage.transform -import tensorflow as tf - -# URL from which to download the latest COCO trained weights -COCO_MODEL_URL = ( - "https://github.com/matterport/Mask_RCNN/releases/download/v2.0/mask_rcnn_coco.h5" -) - - -############################################################ -# Bounding Boxes -############################################################ - - -def extract_bboxes(mask): - """Compute bounding boxes from masks. - mask: [height, width, num_instances]. Mask pixels are either 1 or 0. - - Returns: bbox array [num_instances, (y1, x1, y2, x2)]. - """ - boxes = np.zeros([mask.shape[-1], 4], dtype=np.int32) - for i in range(mask.shape[-1]): - m = mask[:, :, i] - # Bounding box. - horizontal_indicies = np.where(np.any(m, axis=0))[0] - vertical_indicies = np.where(np.any(m, axis=1))[0] - if horizontal_indicies.shape[0]: - x1, x2 = horizontal_indicies[[0, -1]] - y1, y2 = vertical_indicies[[0, -1]] - # x2 and y2 should not be part of the box. Increment by 1. - x2 += 1 - y2 += 1 - else: - # No mask for this instance. Might happen due to - # resizing or cropping. Set bbox to zeros - x1, x2, y1, y2 = 0, 0, 0, 0 - boxes[i] = np.array([y1, x1, y2, x2]) - return boxes.astype(np.int32) - - -def compute_iou(box, boxes, box_area, boxes_area): - """Calculates IoU of the given box with the array of the given boxes. - box: 1D vector [y1, x1, y2, x2] - boxes: [boxes_count, (y1, x1, y2, x2)] - box_area: float. the area of 'box' - boxes_area: array of length boxes_count. - - Note: the areas are passed in rather than calculated here for - efficiency. Calculate once in the caller to avoid duplicate work. - """ - # Calculate intersection areas - y1 = np.maximum(box[0], boxes[:, 0]) - y2 = np.minimum(box[2], boxes[:, 2]) - x1 = np.maximum(box[1], boxes[:, 1]) - x2 = np.minimum(box[3], boxes[:, 3]) - intersection = np.maximum(x2 - x1, 0) * np.maximum(y2 - y1, 0) - union = box_area + boxes_area[:] - intersection[:] - iou = intersection / union - return iou - - -def compute_overlaps(boxes1, boxes2): - """Computes IoU overlaps between two sets of boxes. - boxes1, boxes2: [N, (y1, x1, y2, x2)]. - - For better performance, pass the largest set first and the smaller second. - """ - # Areas of anchors and GT boxes - area1 = (boxes1[:, 2] - boxes1[:, 0]) * (boxes1[:, 3] - boxes1[:, 1]) - area2 = (boxes2[:, 2] - boxes2[:, 0]) * (boxes2[:, 3] - boxes2[:, 1]) - - # Compute overlaps to generate matrix [boxes1 count, boxes2 count] - # Each cell contains the IoU value. - overlaps = np.zeros((boxes1.shape[0], boxes2.shape[0])) - for i in range(overlaps.shape[1]): - box2 = boxes2[i] - overlaps[:, i] = compute_iou(box2, boxes1, area2[i], area1) - return overlaps - - -def compute_overlaps_masks(masks1, masks2): - """Computes IoU overlaps between two sets of masks. - masks1, masks2: [Height, Width, instances] - """ - - # If either set of masks is empty return empty result - if masks1.shape[-1] == 0 or masks2.shape[-1] == 0: - return np.zeros((masks1.shape[-1], masks2.shape[-1])) - # flatten masks and compute their areas - masks1 = np.reshape(masks1 > 0.5, (-1, masks1.shape[-1])).astype(np.float32) - masks2 = np.reshape(masks2 > 0.5, (-1, masks2.shape[-1])).astype(np.float32) - area1 = np.sum(masks1, axis=0) - area2 = np.sum(masks2, axis=0) - - # intersections and union - intersections = np.dot(masks1.T, masks2) - union = area1[:, None] + area2[None, :] - intersections - overlaps = intersections / union - - return overlaps - - -def non_max_suppression(boxes, scores, threshold): - """Performs non-maximum suppression and returns indices of kept boxes. - boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box. - scores: 1-D array of box scores. - threshold: Float. IoU threshold to use for filtering. - """ - assert boxes.shape[0] > 0 - if boxes.dtype.kind != "f": - boxes = boxes.astype(np.float32) - - # Compute box areas - y1 = boxes[:, 0] - x1 = boxes[:, 1] - y2 = boxes[:, 2] - x2 = boxes[:, 3] - area = (y2 - y1) * (x2 - x1) - - # Get indicies of boxes sorted by scores (highest first) - ixs = scores.argsort()[::-1] - - pick = [] - while len(ixs) > 0: - # Pick top box and add its index to the list - i = ixs[0] - pick.append(i) - # Compute IoU of the picked box with the rest - iou = compute_iou(boxes[i], boxes[ixs[1:]], area[i], area[ixs[1:]]) - # Identify boxes with IoU over the threshold. This - # returns indices into ixs[1:], so add 1 to get - # indices into ixs. - remove_ixs = np.where(iou > threshold)[0] + 1 - # Remove indices of the picked and overlapped boxes. - ixs = np.delete(ixs, remove_ixs) - ixs = np.delete(ixs, 0) - return np.array(pick, dtype=np.int32) - - -def apply_box_deltas(boxes, deltas): - """Applies the given deltas to the given boxes. - boxes: [N, (y1, x1, y2, x2)]. Note that (y2, x2) is outside the box. - deltas: [N, (dy, dx, log(dh), log(dw))] - """ - boxes = boxes.astype(np.float32) - # Convert to y, x, h, w - height = boxes[:, 2] - boxes[:, 0] - width = boxes[:, 3] - boxes[:, 1] - center_y = boxes[:, 0] + 0.5 * height - center_x = boxes[:, 1] + 0.5 * width - # Apply deltas - center_y += deltas[:, 0] * height - center_x += deltas[:, 1] * width - height *= np.exp(deltas[:, 2]) - width *= np.exp(deltas[:, 3]) - # Convert back to y1, x1, y2, x2 - y1 = center_y - 0.5 * height - x1 = center_x - 0.5 * width - y2 = y1 + height - x2 = x1 + width - return np.stack([y1, x1, y2, x2], axis=1) - - -def box_refinement_graph(box, gt_box): - """Compute refinement needed to transform box to gt_box. - box and gt_box are [N, (y1, x1, y2, x2)] - """ - box = tf.cast(box, tf.float32) - gt_box = tf.cast(gt_box, tf.float32) - - height = box[:, 2] - box[:, 0] - width = box[:, 3] - box[:, 1] - center_y = box[:, 0] + 0.5 * height - center_x = box[:, 1] + 0.5 * width - - gt_height = gt_box[:, 2] - gt_box[:, 0] - gt_width = gt_box[:, 3] - gt_box[:, 1] - gt_center_y = gt_box[:, 0] + 0.5 * gt_height - gt_center_x = gt_box[:, 1] + 0.5 * gt_width - - dy = (gt_center_y - center_y) / height - dx = (gt_center_x - center_x) / width - dh = tf.log(gt_height / height) - dw = tf.log(gt_width / width) - - result = tf.stack([dy, dx, dh, dw], axis=1) - return result - - -def box_refinement(box, gt_box): - """Compute refinement needed to transform box to gt_box. - box and gt_box are [N, (y1, x1, y2, x2)]. (y2, x2) is - assumed to be outside the box. - """ - box = box.astype(np.float32) - gt_box = gt_box.astype(np.float32) - - height = box[:, 2] - box[:, 0] - width = box[:, 3] - box[:, 1] - center_y = box[:, 0] + 0.5 * height - center_x = box[:, 1] + 0.5 * width - - gt_height = gt_box[:, 2] - gt_box[:, 0] - gt_width = gt_box[:, 3] - gt_box[:, 1] - gt_center_y = gt_box[:, 0] + 0.5 * gt_height - gt_center_x = gt_box[:, 1] + 0.5 * gt_width - - dy = (gt_center_y - center_y) / height - dx = (gt_center_x - center_x) / width - dh = np.log(gt_height / height) - dw = np.log(gt_width / width) - - return np.stack([dy, dx, dh, dw], axis=1) - - -############################################################ -# Dataset -############################################################ - - -class Dataset(object): - """The base class for dataset classes. - To use it, create a new class that adds functions specific to the dataset - you want to use. For example: - - class CatsAndDogsDataset(Dataset): - def load_cats_and_dogs(self): - ... - def load_mask(self, image_id): - ... - def image_reference(self, image_id): - ... - - See COCODataset and ShapesDataset as examples. - """ - - def __init__(self, class_map=None): - self._image_ids = [] - self.image_info = [] - # Background is always the first class - self.class_info = [{"source": "", "id": 0, "name": "BG"}] - self.source_class_ids = {} - - def add_class(self, source, class_id, class_name): - assert "." not in source, "Source name cannot contain a dot" - # Does the class exist already? - for info in self.class_info: - if info["source"] == source and info["id"] == class_id: - # source.class_id combination already available, skip - return - # Add the class - self.class_info.append( - { - "source": source, - "id": class_id, - "name": class_name, - } - ) - - def add_image(self, source, image_id, path, **kwargs): - image_info = { - "id": image_id, - "source": source, - "path": path, - } - image_info.update(kwargs) - self.image_info.append(image_info) - - def image_reference(self, image_id): - """Return a link to the image in its source Website or details about - the image that help looking it up or debugging it. - - Override for your dataset, but pass to this function - if you encounter images not in your dataset. - """ - return "" - - def prepare(self, class_map=None): - """Prepares the Dataset class for use. - - TODO: class map is not supported yet. When done, it should handle mapping - classes from different datasets to the same class ID. - """ - - def clean_name(name): - """Returns a shorter version of object names for cleaner display.""" - return ",".join(name.split(",")[:1]) - - # Build (or rebuild) everything else from the info dicts. - self.num_classes = len(self.class_info) - self.class_ids = np.arange(self.num_classes) - self.class_names = [clean_name(c["name"]) for c in self.class_info] - self.num_images = len(self.image_info) - self._image_ids = np.arange(self.num_images) - - # Mapping from source class and image IDs to internal IDs - self.class_from_source_map = { - "{}.{}".format(info["source"], info["id"]): id - for info, id in zip(self.class_info, self.class_ids) - } - self.image_from_source_map = { - "{}.{}".format(info["source"], info["id"]): id - for info, id in zip(self.image_info, self.image_ids) - } - - # Map sources to class_ids they support - self.sources = list(set([i["source"] for i in self.class_info])) - self.source_class_ids = {} - # Loop over datasets - for source in self.sources: - self.source_class_ids[source] = [] - # Find classes that belong to this dataset - for i, info in enumerate(self.class_info): - # Include BG class in all datasets - if i == 0 or source == info["source"]: - self.source_class_ids[source].append(i) - - def map_source_class_id(self, source_class_id): - """Takes a source class ID and returns the int class ID assigned to it. - - For example: - dataset.map_source_class_id("coco.12") -> 23 - """ - return self.class_from_source_map[source_class_id] - - def get_source_class_id(self, class_id, source): - """Map an internal class ID to the corresponding class ID in the source dataset.""" - info = self.class_info[class_id] - assert info["source"] == source - return info["id"] - - @property - def image_ids(self): - return self._image_ids - - def source_image_link(self, image_id): - """Returns the path or URL to the image. - Override this to return a URL to the image if it's available online for easy - debugging. - """ - return self.image_info[image_id]["path"] - - def load_image(self, image_id): - """Load the specified image and return a [H,W,3] Numpy array.""" - # Load image - image = skimage.io.imread(self.image_info[image_id]["path"]) - # If grayscale. Convert to RGB for consistency. - if image.ndim != 3: - image = skimage.color.gray2rgb(image) - # If has an alpha channel, remove it for consistency - if image.shape[-1] == 4: - image = image[..., :3] - return image - - def load_mask(self, image_id): - """Load instance masks for the given image. - - Different datasets use different ways to store masks. Override this - method to load instance masks and return them in the form of am - array of binary masks of shape [height, width, instances]. - - Returns: - masks: A bool array of shape [height, width, instance count] with - a binary mask per instance. - class_ids: a 1D array of class IDs of the instance masks. - """ - # Override this function to load a mask from your dataset. - # Otherwise, it returns an empty mask. - logging.warning( - "You are using the default load_mask(), maybe you need to define your own one." - ) - mask = np.empty([0, 0, 0]) - class_ids = np.empty([0], np.int32) - return mask, class_ids - - -def resize_image(image, min_dim=None, max_dim=None, min_scale=None, mode="square"): - """Resizes an image keeping the aspect ratio unchanged. - - min_dim: if provided, resizes the image such that it's smaller - dimension == min_dim - max_dim: if provided, ensures that the image longest side doesn't - exceed this value. - min_scale: if provided, ensure that the image is scaled up by at least - this percent even if min_dim doesn't require it. - mode: Resizing mode. - none: No resizing. Return the image unchanged. - square: Resize and pad with zeros to get a square image - of size [max_dim, max_dim]. - pad64: Pads width and height with zeros to make them multiples of 64. - If min_dim or min_scale are provided, it scales the image up - before padding. max_dim is ignored in this mode. - The multiple of 64 is needed to ensure smooth scaling of feature - maps up and down the 6 levels of the FPN pyramid (2**6=64). - crop: Picks random crops from the image. First, scales the image based - on min_dim and min_scale, then picks a random crop of - size min_dim x min_dim. Can be used in training only. - max_dim is not used in this mode. - - Returns: - image: the resized image - window: (y1, x1, y2, x2). If max_dim is provided, padding might - be inserted in the returned image. If so, this window is the - coordinates of the image part of the full image (excluding - the padding). The x2, y2 pixels are not included. - scale: The scale factor used to resize the image - padding: Padding added to the image [(top, bottom), (left, right), (0, 0)] - """ - # Keep track of image dtype and return results in the same dtype - image_dtype = image.dtype - # Default window (y1, x1, y2, x2) and default scale == 1. - h, w = image.shape[:2] - window = (0, 0, h, w) - scale = 1 - padding = [(0, 0), (0, 0), (0, 0)] - crop = None - - if mode == "none": - return image, window, scale, padding, crop - - # Scale? - if min_dim: - # Scale up but not down - print(min_dim, min(h, w), type(min_dim), type(min(h, w))) - scale = max(1, min_dim / min(h, w)) - if min_scale and scale < min_scale: - scale = min_scale - - # Does it exceed max dim? - if max_dim and mode == "square": - image_max = max(h, w) - if round(image_max * scale) > max_dim: - scale = max_dim / image_max - - # Resize image using bilinear interpolation - if scale != 1: - image = resize(image, (round(h * scale), round(w * scale)), preserve_range=True) - - # Need padding or cropping? - if mode == "square": - # Get new height and width - h, w = image.shape[:2] - top_pad = (max_dim - h) // 2 - bottom_pad = max_dim - h - top_pad - left_pad = (max_dim - w) // 2 - right_pad = max_dim - w - left_pad - padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)] - image = np.pad(image, padding, mode="constant", constant_values=0) - window = (top_pad, left_pad, h + top_pad, w + left_pad) - elif mode == "pad64": - h, w = image.shape[:2] - # Both sides must be divisible by 64 - assert min_dim % 64 == 0, "Minimum dimension must be a multiple of 64" - # Height - if h % 64 > 0: - max_h = h - (h % 64) + 64 - top_pad = (max_h - h) // 2 - bottom_pad = max_h - h - top_pad - else: - top_pad = bottom_pad = 0 - # Width - if w % 64 > 0: - max_w = w - (w % 64) + 64 - left_pad = (max_w - w) // 2 - right_pad = max_w - w - left_pad - else: - left_pad = right_pad = 0 - padding = [(top_pad, bottom_pad), (left_pad, right_pad), (0, 0)] - image = np.pad(image, padding, mode="constant", constant_values=0) - window = (top_pad, left_pad, h + top_pad, w + left_pad) - elif mode == "crop": - # Pick a random crop - h, w = image.shape[:2] - y = random.randint(0, (h - min_dim)) - x = random.randint(0, (w - min_dim)) - crop = (y, x, min_dim, min_dim) - image = image[y : y + min_dim, x : x + min_dim] - window = (0, 0, min_dim, min_dim) - else: - raise Exception("Mode {} not supported".format(mode)) - return image.astype(image_dtype), window, scale, padding, crop - - -def resize_mask(mask, scale, padding, crop=None): - """Resizes a mask using the given scale and padding. - Typically, you get the scale and padding from resize_image() to - ensure both, the image and the mask, are resized consistently. - - scale: mask scaling factor - padding: Padding to add to the mask in the form - [(top, bottom), (left, right), (0, 0)] - """ - # Suppress warning from scipy 0.13.0, the output shape of zoom() is - # calculated with round() instead of int() - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - mask = scipy.ndimage.zoom(mask, zoom=[scale, scale, 1], order=0) - if crop is not None: - y, x, h, w = crop - mask = mask[y : y + h, x : x + w] - else: - mask = np.pad(mask, padding, mode="constant", constant_values=0) - return mask - - -def minimize_mask(bbox, mask, mini_shape): - """Resize masks to a smaller version to reduce memory load. - Mini-masks can be resized back to image scale using expand_masks() - - See inspect_data.ipynb notebook for more details. - """ - mini_mask = np.zeros(mini_shape + (mask.shape[-1],), dtype=bool) - for i in range(mask.shape[-1]): - # Pick slice and cast to bool in case load_mask() returned wrong dtype - m = mask[:, :, i].astype(bool) - y1, x1, y2, x2 = bbox[i][:4] - m = m[y1:y2, x1:x2] - if m.size == 0: - raise Exception("Invalid bounding box with area of zero") - # Resize with bilinear interpolation - m = resize(m, mini_shape) - mini_mask[:, :, i] = np.around(m).astype(np.bool) - return mini_mask - - -def expand_mask(bbox, mini_mask, image_shape): - """Resizes mini masks back to image size. Reverses the change - of minimize_mask(). - - See inspect_data.ipynb notebook for more details. - """ - mask = np.zeros(image_shape[:2] + (mini_mask.shape[-1],), dtype=bool) - for i in range(mask.shape[-1]): - m = mini_mask[:, :, i] - y1, x1, y2, x2 = bbox[i][:4] - h = y2 - y1 - w = x2 - x1 - # Resize with bilinear interpolation - m = resize(m, (h, w)) - mask[y1:y2, x1:x2, i] = np.around(m).astype(np.bool) - return mask - - -# TODO: Build and use this function to reduce code duplication -def mold_mask(mask, config): - pass - - -def unmold_mask(mask, bbox, image_shape): - """Converts a mask generated by the neural network to a format similar - to its original shape. - mask: [height, width] of type float. A small, typically 28x28 mask. - bbox: [y1, x1, y2, x2]. The box to fit the mask in. - - Returns a binary mask with the same size as the original image. - """ - threshold = 0.5 - y1, x1, y2, x2 = bbox - mask = resize(mask, (y2 - y1, x2 - x1)) - mask = np.where(mask >= threshold, 1, 0).astype(np.bool) - - # Put the mask in the right location. - full_mask = np.zeros(image_shape[:2], dtype=np.bool) - full_mask[y1:y2, x1:x2] = mask - return full_mask - - -############################################################ -# Anchors -############################################################ - - -def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride): - """ - scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128] - ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2] - shape: [height, width] spatial shape of the feature map over which - to generate anchors. - feature_stride: Stride of the feature map relative to the image in pixels. - anchor_stride: Stride of anchors on the feature map. For example, if the - value is 2 then generate anchors for every other feature map pixel. - """ - # Get all combinations of scales and ratios - scales, ratios = np.meshgrid(np.array(scales), np.array(ratios)) - scales = scales.flatten() - ratios = ratios.flatten() - - # Enumerate heights and widths from scales and ratios - heights = scales / np.sqrt(ratios) - widths = scales * np.sqrt(ratios) - - # Enumerate shifts in feature space - shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride - shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride - shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y) - - # Enumerate combinations of shifts, widths, and heights - box_widths, box_centers_x = np.meshgrid(widths, shifts_x) - box_heights, box_centers_y = np.meshgrid(heights, shifts_y) - - # Reshape to get a list of (y, x) and a list of (h, w) - box_centers = np.stack([box_centers_y, box_centers_x], axis=2).reshape([-1, 2]) - box_sizes = np.stack([box_heights, box_widths], axis=2).reshape([-1, 2]) - - # Convert to corner coordinates (y1, x1, y2, x2) - boxes = np.concatenate( - [box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], axis=1 - ) - return boxes - - -def generate_pyramid_anchors( - scales, ratios, feature_shapes, feature_strides, anchor_stride -): - """Generate anchors at different levels of a feature pyramid. Each scale - is associated with a level of the pyramid, but each ratio is used in - all levels of the pyramid. - - Returns: - anchors: [N, (y1, x1, y2, x2)]. All generated anchors in one array. Sorted - with the same order of the given scales. So, anchors of scale[0] come - first, then anchors of scale[1], and so on. - """ - # Anchors - # [anchor_count, (y1, x1, y2, x2)] - anchors = [] - for i in range(len(scales)): - anchors.append( - generate_anchors( - scales[i], ratios, feature_shapes[i], feature_strides[i], anchor_stride - ) - ) - return np.concatenate(anchors, axis=0) - - -############################################################ -# Miscellaneous -############################################################ - - -def trim_zeros(x): - """It's common to have tensors larger than the available data and - pad with zeros. This function removes rows that are all zeros. - - x: [rows, columns]. - """ - assert len(x.shape) == 2 - return x[~np.all(x == 0, axis=1)] - - -def compute_matches( - gt_boxes, - gt_class_ids, - gt_masks, - pred_boxes, - pred_class_ids, - pred_scores, - pred_masks, - iou_threshold=0.5, - score_threshold=0.0, -): - """Finds matches between prediction and ground truth instances. - - Returns: - gt_match: 1-D array. For each GT box it has the index of the matched - predicted box. - pred_match: 1-D array. For each predicted box, it has the index of - the matched ground truth box. - overlaps: [pred_boxes, gt_boxes] IoU overlaps. - """ - # Trim zero padding - # TODO: cleaner to do zero unpadding upstream - gt_boxes = trim_zeros(gt_boxes) - gt_masks = gt_masks[..., : gt_boxes.shape[0]] - pred_boxes = trim_zeros(pred_boxes) - pred_scores = pred_scores[: pred_boxes.shape[0]] - # Sort predictions by score from high to low - indices = np.argsort(pred_scores)[::-1] - pred_boxes = pred_boxes[indices] - pred_class_ids = pred_class_ids[indices] - pred_scores = pred_scores[indices] - pred_masks = pred_masks[..., indices] - - # Compute IoU overlaps [pred_masks, gt_masks] - overlaps = compute_overlaps_masks(pred_masks, gt_masks) - - # Loop through predictions and find matching ground truth boxes - match_count = 0 - pred_match = -1 * np.ones([pred_boxes.shape[0]]) - gt_match = -1 * np.ones([gt_boxes.shape[0]]) - for i in range(len(pred_boxes)): - # Find best matching ground truth box - # 1. Sort matches by score - sorted_ixs = np.argsort(overlaps[i])[::-1] - # 2. Remove low scores - low_score_idx = np.where(overlaps[i, sorted_ixs] < score_threshold)[0] - if low_score_idx.size > 0: - sorted_ixs = sorted_ixs[: low_score_idx[0]] - # 3. Find the match - for j in sorted_ixs: - # If ground truth box is already matched, go to next one - if gt_match[j] > -1: - continue - # If we reach IoU smaller than the threshold, end the loop - iou = overlaps[i, j] - if iou < iou_threshold: - break - # Do we have a match? - if pred_class_ids[i] == gt_class_ids[j]: - match_count += 1 - gt_match[j] = i - pred_match[i] = j - break - - return gt_match, pred_match, overlaps - - -def compute_ap( - gt_boxes, - gt_class_ids, - gt_masks, - pred_boxes, - pred_class_ids, - pred_scores, - pred_masks, - iou_threshold=0.5, -): - """Compute Average Precision at a set IoU threshold (default 0.5). - - Returns: - mAP: Mean Average Precision - precisions: List of precisions at different class score thresholds. - recalls: List of recall values at different class score thresholds. - overlaps: [pred_boxes, gt_boxes] IoU overlaps. - """ - # Get matches and overlaps - gt_match, pred_match, overlaps = compute_matches( - gt_boxes, - gt_class_ids, - gt_masks, - pred_boxes, - pred_class_ids, - pred_scores, - pred_masks, - iou_threshold, - ) - - # Compute precision and recall at each prediction box step - precisions = np.cumsum(pred_match > -1) / (np.arange(len(pred_match)) + 1) - recalls = np.cumsum(pred_match > -1).astype(np.float32) / len(gt_match) - - # Pad with start and end values to simplify the math - precisions = np.concatenate([[0], precisions, [0]]) - recalls = np.concatenate([[0], recalls, [1]]) - - # Ensure precision values decrease but don't increase. This way, the - # precision value at each recall threshold is the maximum it can be - # for all following recall thresholds, as specified by the VOC paper. - for i in range(len(precisions) - 2, -1, -1): - precisions[i] = np.maximum(precisions[i], precisions[i + 1]) - - # Compute mean AP over recall range - indices = np.where(recalls[:-1] != recalls[1:])[0] + 1 - mAP = np.sum((recalls[indices] - recalls[indices - 1]) * precisions[indices]) - - return mAP, precisions, recalls, overlaps - - -def compute_ap_range( - gt_box, - gt_class_id, - gt_mask, - pred_box, - pred_class_id, - pred_score, - pred_mask, - iou_thresholds=None, - verbose=1, -): - """Compute AP over a range or IoU thresholds. Default range is 0.5-0.95.""" - # Default is 0.5 to 0.95 with increments of 0.05 - iou_thresholds = iou_thresholds or np.arange(0.5, 1.0, 0.05) - - # Compute AP over range of IoU thresholds - AP = [] - for iou_threshold in iou_thresholds: - ap, precisions, recalls, overlaps = compute_ap( - gt_box, - gt_class_id, - gt_mask, - pred_box, - pred_class_id, - pred_score, - pred_mask, - iou_threshold=iou_threshold, - ) - if verbose: - print("AP @{:.2f}:\t {:.3f}".format(iou_threshold, ap)) - AP.append(ap) - AP = np.array(AP).mean() - if verbose: - print( - "AP @{:.2f}-{:.2f}:\t {:.3f}".format( - iou_thresholds[0], iou_thresholds[-1], AP - ) - ) - return AP - - -def compute_recall(pred_boxes, gt_boxes, iou): - """Compute the recall at the given IoU threshold. It's an indication - of how many GT boxes were found by the given prediction boxes. - - pred_boxes: [N, (y1, x1, y2, x2)] in image coordinates - gt_boxes: [N, (y1, x1, y2, x2)] in image coordinates - """ - # Measure overlaps - overlaps = compute_overlaps(pred_boxes, gt_boxes) - iou_max = np.max(overlaps, axis=1) - iou_argmax = np.argmax(overlaps, axis=1) - positive_ids = np.where(iou_max >= iou)[0] - matched_gt_boxes = iou_argmax[positive_ids] - - recall = len(set(matched_gt_boxes)) / gt_boxes.shape[0] - return recall, positive_ids - - -# ## Batch Slicing -# Some custom layers support a batch size of 1 only, and require a lot of work -# to support batches greater than 1. This function slices an input tensor -# across the batch dimension and feeds batches of size 1. Effectively, -# an easy way to support batches > 1 quickly with little code modification. -# In the long run, it's more efficient to modify the code to support large -# batches and getting rid of this function. Consider this a temporary solution -def batch_slice(inputs, graph_fn, batch_size, names=None): - """Splits inputs into slices and feeds each slice to a copy of the given - computation graph and then combines the results. It allows you to run a - graph on a batch of inputs even if the graph is written to support one - instance only. - - inputs: list of tensors. All must have the same first dimension length - graph_fn: A function that returns a TF tensor that's part of a graph. - batch_size: number of slices to divide the data into. - names: If provided, assigns names to the resulting tensors. - """ - if not isinstance(inputs, list): - inputs = [inputs] - - outputs = [] - for i in range(batch_size): - inputs_slice = [x[i] for x in inputs] - output_slice = graph_fn(*inputs_slice) - if not isinstance(output_slice, (tuple, list)): - output_slice = [output_slice] - outputs.append(output_slice) - # Change outputs from a list of slices where each is - # a list of outputs to a list of outputs and each has - # a list of slices - outputs = list(zip(*outputs)) - - if names is None: - names = [None] * len(outputs) - - result = [tf.stack(o, axis=0, name=n) for o, n in zip(outputs, names)] - if len(result) == 1: - result = result[0] - - return result - - -def download_trained_weights(coco_model_path, verbose=1): - """Download COCO trained weights from Releases. - - coco_model_path: local path of COCO trained weights - """ - if verbose > 0: - print("Downloading pretrained model to " + coco_model_path + " ...") - with urllib.request.urlopen(COCO_MODEL_URL) as resp, open( - coco_model_path, "wb" - ) as out: - shutil.copyfileobj(resp, out) - if verbose > 0: - print("... done downloading pretrained model!") - - -def norm_boxes(boxes, shape): - """Converts boxes from pixel coordinates to normalized coordinates. - boxes: [N, (y1, x1, y2, x2)] in pixel coordinates - shape: [..., (height, width)] in pixels - - Note: In pixel coordinates (y2, x2) is outside the box. But in normalized - coordinates it's inside the box. - - Returns: - [N, (y1, x1, y2, x2)] in normalized coordinates - """ - h, w = shape - scale = np.array([h - 1, w - 1, h - 1, w - 1]) - shift = np.array([0, 0, 1, 1]) - return np.divide((boxes - shift), scale).astype(np.float32) - - -def denorm_boxes(boxes, shape): - """Converts boxes from normalized coordinates to pixel coordinates. - boxes: [N, (y1, x1, y2, x2)] in normalized coordinates - shape: [..., (height, width)] in pixels - - Note: In pixel coordinates (y2, x2) is outside the box. But in normalized - coordinates it's inside the box. - - Returns: - [N, (y1, x1, y2, x2)] in pixel coordinates - """ - h, w = shape - scale = np.array([h - 1, w - 1, h - 1, w - 1]) - shift = np.array([0, 0, 1, 1]) - return np.around(np.multiply(boxes, scale) + shift).astype(np.int32) - - -def resize( - image, - output_shape, - order=1, - mode="constant", - cval=0, - clip=True, - preserve_range=False, - anti_aliasing=False, - anti_aliasing_sigma=None, -): - """A wrapper for Scikit-Image resize(). - - Scikit-Image generates warnings on every call to resize() if it doesn't - receive the right parameters. The right parameters depend on the version - of skimage. This solves the problem by using different parameters per - version. And it provides a central place to control resizing defaults. - """ - if LooseVersion(skimage.__version__) >= LooseVersion("0.14"): - # New in 0.14: anti_aliasing. Default it to False for backward - # compatibility with skimage 0.13. - return skimage.transform.resize( - image, - output_shape, - order=order, - mode=mode, - cval=cval, - clip=clip, - preserve_range=preserve_range, - anti_aliasing=anti_aliasing, - anti_aliasing_sigma=anti_aliasing_sigma, - ) - else: - return skimage.transform.resize( - image, - output_shape, - order=order, - mode=mode, - cval=cval, - clip=clip, - preserve_range=preserve_range, - ) diff --git a/spaces/innat/VideoMAE/utils.py b/spaces/innat/VideoMAE/utils.py deleted file mode 100644 index 535b91f323986a8461ee151226da50431bfb1dfa..0000000000000000000000000000000000000000 --- a/spaces/innat/VideoMAE/utils.py +++ /dev/null @@ -1,105 +0,0 @@ - -import tensorflow as tf -import numpy as np -from einops import rearrange -from decord import VideoReader - -num_frames = 16 -input_size = 224 -patch_size = (16, 16) -IMAGENET_MEAN = np.array([0.45, 0.45, 0.45]) -IMAGENET_STD = np.array([0.225, 0.225, 0.225]) - -def format_frames(frame, output_size): - frame = tf.image.convert_image_dtype(frame, tf.uint8) - frame = tf.image.resize(frame, size=output_size) - frame = frame / 255. - frame = frame - IMAGENET_MEAN - frame = frame / IMAGENET_STD - return frame - -def read_video(file_path): - container = VideoReader(file_path) - return container - -def frame_sampling(container, num_frames): - interval = len(container) // num_frames - bids = np.arange(num_frames) * interval - offset = np.random.randint(interval, size=bids.shape) - frame_index = bids + offset - frames = container.get_batch(frame_index).asnumpy() - frames = np.stack(frames) - frames = format_frames(frames, [input_size] * 2) - return frames - -def denormalize(image): - image = image.numpy() if not isinstance(image, np.ndarray) else image - image = image * IMAGENET_STD + IMAGENET_MEAN - image = (image * 255).clip(0, 255).astype('uint8') - return image - -def reconstrunction(input_frame, bool_mask, pretrained_pred): - img_squeeze = rearrange( - input_frame.numpy(), - 'b (t p0) (h p1) (w p2) c -> b (t h w) (p0 p1 p2) c', - p0=2, p1=patch_size[0], p2=patch_size[0] - ) - img_mean = np.mean(img_squeeze, axis=-2, keepdims=True) - img_variance = np.var(img_squeeze, axis=-2, ddof=1, keepdims=True) - img_norm = (img_squeeze - img_mean) / (np.sqrt(img_variance) + 1e-6) - img_patch = rearrange(img_norm, 'b n p c -> b n (p c)') - img_patch[bool_mask] = pretrained_pred - - # make mask - mask = np.ones_like(img_patch) - mask[bool_mask] = 0 - mask = rearrange( - mask, 'b n (p c) -> b n p c', c=3 - ) - mask = rearrange( - mask, - 'b (t h w) (p0 p1 p2) c -> b (t p0) (h p1) (w p2) c', - p0=2, p1=patch_size[0], p2=patch_size[1], h=14, w=14 - ) - - #save reconstruction video - rec_img = rearrange(img_patch, 'b n (p c) -> b n p c', c=3) - - # Notice: To visualize the reconstruction video, we add the predict and the original mean and var of each patch. - img_mean = np.mean(img_squeeze, axis=-2, keepdims=True) - img_std = np.sqrt(np.var(img_squeeze, axis=-2, ddof=1, keepdims=True) + 1e-6) - rec_img = rec_img * img_std + img_mean - rec_img = rearrange( - rec_img, - 'b (t h w) (p0 p1 p2) c -> b (t p0) (h p1) (w p2) c', - p0=2, p1=patch_size[0], p2=patch_size[1], h=14, w=14 - ) - - return ( - rec_img[0], - mask[0] - ) - - -class TubeMaskingGenerator: - def __init__(self, input_size, mask_ratio): - self.frames, self.height, self.width = input_size - self.num_patches_per_frame = self.height * self.width - self.total_patches = self.frames * self.num_patches_per_frame - self.num_masks_per_frame = int(mask_ratio * self.num_patches_per_frame) - self.total_masks = self.frames * self.num_masks_per_frame - - def __repr__(self): - repr_str = "Maks: total patches {}, mask patches {}".format( - self.total_patches, self.total_masks - ) - return repr_str - - def __call__(self): - mask_per_frame = np.hstack([ - np.zeros(self.num_patches_per_frame - self.num_masks_per_frame), - np.ones(self.num_masks_per_frame), - ]) - np.random.shuffle(mask_per_frame) - mask = np.tile(mask_per_frame, (self.frames,1)).flatten() - return mask \ No newline at end of file diff --git a/spaces/innnky/nene-emotion/models.py b/spaces/innnky/nene-emotion/models.py deleted file mode 100644 index b1b35b4072179b9f12e12bfb8b4e953999adb71a..0000000000000000000000000000000000000000 --- a/spaces/innnky/nene-emotion/models.py +++ /dev/null @@ -1,537 +0,0 @@ -import copy -import math -import torch -from torch import nn -from torch.nn import functional as F - -import commons -import modules -import attentions -# import monotonic_align - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from commons import init_weights, get_padding - - -class StochasticDurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, n_flows=4, gin_channels=0): - super().__init__() - filter_channels = in_channels # it needs to be removed from future version. - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.log_flow = modules.Log() - self.flows = nn.ModuleList() - self.flows.append(modules.ElementwiseAffine(2)) - for i in range(n_flows): - self.flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.flows.append(modules.Flip()) - - self.post_pre = nn.Conv1d(1, filter_channels, 1) - self.post_proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.post_convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - self.post_flows = nn.ModuleList() - self.post_flows.append(modules.ElementwiseAffine(2)) - for i in range(4): - self.post_flows.append(modules.ConvFlow(2, filter_channels, kernel_size, n_layers=3)) - self.post_flows.append(modules.Flip()) - - self.pre = nn.Conv1d(in_channels, filter_channels, 1) - self.proj = nn.Conv1d(filter_channels, filter_channels, 1) - self.convs = modules.DDSConv(filter_channels, kernel_size, n_layers=3, p_dropout=p_dropout) - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, filter_channels, 1) - - def forward(self, x, x_mask, w=None, g=None, reverse=False, noise_scale=1.0): - x = torch.detach(x) - x = self.pre(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.convs(x, x_mask) - x = self.proj(x) * x_mask - - if not reverse: - flows = self.flows - assert w is not None - - logdet_tot_q = 0 - h_w = self.post_pre(w) - h_w = self.post_convs(h_w, x_mask) - h_w = self.post_proj(h_w) * x_mask - e_q = torch.randn(w.size(0), 2, w.size(2)).to(device=x.device, dtype=x.dtype) * x_mask - z_q = e_q - for flow in self.post_flows: - z_q, logdet_q = flow(z_q, x_mask, g=(x + h_w)) - logdet_tot_q += logdet_q - z_u, z1 = torch.split(z_q, [1, 1], 1) - u = torch.sigmoid(z_u) * x_mask - z0 = (w - u) * x_mask - logdet_tot_q += torch.sum((F.logsigmoid(z_u) + F.logsigmoid(-z_u)) * x_mask, [1,2]) - logq = torch.sum(-0.5 * (math.log(2*math.pi) + (e_q**2)) * x_mask, [1,2]) - logdet_tot_q - - logdet_tot = 0 - z0, logdet = self.log_flow(z0, x_mask) - logdet_tot += logdet - z = torch.cat([z0, z1], 1) - for flow in flows: - z, logdet = flow(z, x_mask, g=x, reverse=reverse) - logdet_tot = logdet_tot + logdet - nll = torch.sum(0.5 * (math.log(2*math.pi) + (z**2)) * x_mask, [1,2]) - logdet_tot - return nll + logq # [b] - else: - flows = list(reversed(self.flows)) - flows = flows[:-2] + [flows[-1]] # remove a useless vflow - z = torch.randn(x.size(0), 2, x.size(2)).to(device=x.device, dtype=x.dtype) * noise_scale - for flow in flows: - z = flow(z, x_mask, g=x, reverse=reverse) - z0, z1 = torch.split(z, [1, 1], 1) - logw = z0 - return logw - - -class DurationPredictor(nn.Module): - def __init__(self, in_channels, filter_channels, kernel_size, p_dropout, gin_channels=0): - super().__init__() - - self.in_channels = in_channels - self.filter_channels = filter_channels - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.gin_channels = gin_channels - - self.drop = nn.Dropout(p_dropout) - self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_1 = modules.LayerNorm(filter_channels) - self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2) - self.norm_2 = modules.LayerNorm(filter_channels) - self.proj = nn.Conv1d(filter_channels, 1, 1) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, in_channels, 1) - - def forward(self, x, x_mask, g=None): - x = torch.detach(x) - if g is not None: - g = torch.detach(g) - x = x + self.cond(g) - x = self.conv_1(x * x_mask) - x = torch.relu(x) - x = self.norm_1(x) - x = self.drop(x) - x = self.conv_2(x * x_mask) - x = torch.relu(x) - x = self.norm_2(x) - x = self.drop(x) - x = self.proj(x * x_mask) - return x * x_mask - - -class TextEncoder(nn.Module): - def __init__(self, - n_vocab, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout): - super().__init__() - self.n_vocab = n_vocab - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - - self.emb = nn.Embedding(n_vocab, hidden_channels) - self.emo_proj = nn.Linear(1024, hidden_channels) - - nn.init.normal_(self.emb.weight, 0.0, hidden_channels**-0.5) - - self.encoder = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj= nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, emo): - x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] - x = x + self.emo_proj(emo.unsqueeze(1)) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return x, m, logs, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__(self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True)) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - -class PosteriorEncoder(nn.Module): - def __init__(self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -class Generator(torch.nn.Module): - def __init__(self, initial_channel, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=0): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d(initial_channel, upsample_initial_channel, 7, 1, padding=3) - resblock = modules.ResBlock1 if resblock == '1' else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append(weight_norm( - ConvTranspose1d(upsample_initial_channel//(2**i), upsample_initial_channel//(2**(i+1)), - k, u, padding=(k-u)//2))) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel//(2**(i+1)) - for j, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i*self.num_kernels+j](x) - else: - xs += self.resblocks[i*self.num_kernels+j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - print('Removing weight norm...') - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))), - norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))), - ]) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList([ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ]) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2,3,5,7,11] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - def __init__(self, - n_vocab, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - n_speakers=0, - gin_channels=0, - use_sdp=True, - **kwargs): - - super().__init__() - self.n_vocab = n_vocab - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.n_speakers = n_speakers - self.gin_channels = gin_channels - - self.use_sdp = use_sdp - - self.enc_p = TextEncoder(n_vocab, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.dec = Generator(inter_channels, resblock, resblock_kernel_sizes, resblock_dilation_sizes, upsample_rates, upsample_initial_channel, upsample_kernel_sizes, gin_channels=gin_channels) - self.enc_q = PosteriorEncoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels) - self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels) - - if use_sdp: - self.dp = StochasticDurationPredictor(hidden_channels, 192, 3, 0.5, 4, gin_channels=gin_channels) - else: - self.dp = DurationPredictor(hidden_channels, 256, 3, 0.5, gin_channels=gin_channels) - - if n_speakers > 1: - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - def forward(self, x, x_lengths, y, y_lengths, sid=None, emo=None): - - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths, emo) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g) - z_p = self.flow(z, y_mask, g=g) - - with torch.no_grad(): - # negative cross-entropy - s_p_sq_r = torch.exp(-2 * logs_p) # [b, d, t] - neg_cent1 = torch.sum(-0.5 * math.log(2 * math.pi) - logs_p, [1], keepdim=True) # [b, 1, t_s] - neg_cent2 = torch.matmul(-0.5 * (z_p ** 2).transpose(1, 2), s_p_sq_r) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent3 = torch.matmul(z_p.transpose(1, 2), (m_p * s_p_sq_r)) # [b, t_t, d] x [b, d, t_s] = [b, t_t, t_s] - neg_cent4 = torch.sum(-0.5 * (m_p ** 2) * s_p_sq_r, [1], keepdim=True) # [b, 1, t_s] - neg_cent = neg_cent1 + neg_cent2 + neg_cent3 + neg_cent4 - - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = monotonic_align.maximum_path(neg_cent, attn_mask.squeeze(1)).unsqueeze(1).detach() - - w = attn.sum(2) - if self.use_sdp: - l_length = self.dp(x, x_mask, w, g=g) - l_length = l_length / torch.sum(x_mask) - else: - logw_ = torch.log(w + 1e-6) * x_mask - logw = self.dp(x, x_mask, g=g) - l_length = torch.sum((logw - logw_)**2, [1,2]) / torch.sum(x_mask) # for averaging - - # expand prior - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) - - z_slice, ids_slice = commons.rand_slice_segments(z, y_lengths, self.segment_size) - o = self.dec(z_slice, g=g) - return o, l_length, attn, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q) - - def infer(self, x, x_lengths, sid=None, emo=None, noise_scale=1, length_scale=1, noise_scale_w=1., max_len=None): - x, m_p, logs_p, x_mask = self.enc_p(x, x_lengths,emo) - if self.n_speakers > 0: - g = self.emb_g(sid).unsqueeze(-1) # [b, h, 1] - else: - g = None - - if self.use_sdp: - logw = self.dp(x, x_mask, g=g, reverse=True, noise_scale=noise_scale_w) - else: - logw = self.dp(x, x_mask, g=g) - w = torch.exp(logw) * x_mask * length_scale - w_ceil = torch.ceil(w) - y_lengths = torch.clamp_min(torch.sum(w_ceil, [1, 2]), 1).long() - y_mask = torch.unsqueeze(commons.sequence_mask(y_lengths, None), 1).to(x_mask.dtype) - attn_mask = torch.unsqueeze(x_mask, 2) * torch.unsqueeze(y_mask, -1) - attn = commons.generate_path(w_ceil, attn_mask) - - m_p = torch.matmul(attn.squeeze(1), m_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - logs_p = torch.matmul(attn.squeeze(1), logs_p.transpose(1, 2)).transpose(1, 2) # [b, t', t], [b, t, d] -> [b, d, t'] - - z_p = m_p + torch.randn_like(m_p) * torch.exp(logs_p) * noise_scale - z = self.flow(z_p, y_mask, g=g, reverse=True) - o = self.dec((z * y_mask)[:,:,:max_len], g=g) - return o, attn, y_mask, (z, z_p, m_p, logs_p) - - def voice_conversion(self, y, y_lengths, sid_src, sid_tgt): - assert self.n_speakers > 0, "n_speakers have to be larger than 0." - g_src = self.emb_g(sid_src).unsqueeze(-1) - g_tgt = self.emb_g(sid_tgt).unsqueeze(-1) - z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g_src) - z_p = self.flow(z, y_mask, g=g_src) - z_hat = self.flow(z_p, y_mask, g=g_tgt, reverse=True) - o_hat = self.dec(z_hat * y_mask, g=g_tgt) - return o_hat, y_mask, (z, z_p, z_hat) - diff --git a/spaces/inplisQlawa/anything-midjourney-v4-1/AbleBits Ultimate Suite For Microsoft Excel 2015.1.0.380 Crack ((FULL)).md b/spaces/inplisQlawa/anything-midjourney-v4-1/AbleBits Ultimate Suite For Microsoft Excel 2015.1.0.380 Crack ((FULL)).md deleted file mode 100644 index 1cd508d86269f48f47dbf490fba1fa3923d75cff..0000000000000000000000000000000000000000 --- a/spaces/inplisQlawa/anything-midjourney-v4-1/AbleBits Ultimate Suite For Microsoft Excel 2015.1.0.380 Crack ((FULL)).md +++ /dev/null @@ -1,97 +0,0 @@ - -

        AbleBits Ultimate Suite for Microsoft Excel 2015.1.0.380 Crack: A Comprehensive Review

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        If you are looking for a way to enhance your productivity and efficiency in Microsoft Excel, you may have heard of AbleBits Ultimate Suite for Microsoft Excel 2015.1.0.380. This is a powerful and versatile tool that offers 70+ professional features to help you accomplish any task impeccably without errors or delays. But what exactly does this tool do, and how can you get it for free with a crack? In this article, we will give you a comprehensive review of AbleBits Ultimate Suite for Microsoft Excel 2015.1.0.380, and show you how to download, install and activate it with a crack.

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        If you don't want to spend that much money on an Excel add-in or if you just want to try it out before buying it, -you may be tempted to look for a crack online. A crack is a modified version of the original software that bypasses its security features and allows you to use it without paying or activating it.

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        Conclusion

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        7. Like a Mighty Army: This is the seventh book in the series, where we see Charis fighting for its survival and freedom. The church's crusade reaches its climax, as both sides engage in fierce battles across land and sea. Charis also faces betrayal from some of its allies, who switch sides or remain neutral. Merlin also learns more about his origins and his mission on Safehold.
          8. -
        8. Hell's Foundations Quiver: This is the eighth book in the series, where we see Charis achieving a decisive victory over the church. The church's army is routed and its navy is destroyed by Charis's superior firepower and strategy. The church's leader, Grand Vicar Clyntahn, is captured and executed by Cayleb. Charis also gains more support from other kingdoms who join its cause or recognize its independence.
          9. -
        9. At the Sign of Triumph: This is the ninth book in the series, where we see Charis ending the war with the church. The church's remaining leaders surrender to Cayleb and agree to accept his terms of peace. The church also renounces its false doctrine and acknowledges the truth about humanity's history and destiny. Charis also reunites with Corisande, which becomes part of its empire after Prince Hektor marries Cayleb's sister.
          10. -
        10. Through Fiery Trials: This is the tenth book in the series, where we see Charis rebuilding its society and preparing for its future. The war with the church has left many scars and wounds on Safehold, which need to be healed and repaired. Charis also faces new challenges from within and without, such as political unrest, economic crisis, social change, and foreign threats.
          11. -
        11. The Road to Hell: This is the eleventh book in the series, where we see Charis facing a new enemy from beyond Safehold. A mysterious force attacks one of Safehold's orbital platforms, which Merlin has been using to monitor space activity. Merlin suspects that it is related to the Gbaba, who may have found Safehold's location. He decides to launch a counterattack with his own space fleet.
          12. -
        12. In Fury Born: This is the twelfth book in the series, where we see Charis confronting its ultimate foe: the Gbaba. Merlin leads his space fleet to engage with the Gbaba's armada near Earth's orbit. He hopes to destroy them once and for all or at least buy enough time for Safehold to develop its own space capabilities.
          13. -
        -

        Conclusion

        -

        David Weber Safehold epub files are a great choice for anyone who loves science fiction and fantasy. They are easy to find and read with an e-reader device or app that supports this format. They are also full of amazing stories and characters that will keep you hooked and entertained for hours. David Weber Safehold epub files are a journey through humanity's past, present, and future that will make you think, feel, and wonder.

        3cee63e6c2
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        -
        \ No newline at end of file diff --git a/spaces/jbilcke-hf/Panoremix/README.md b/spaces/jbilcke-hf/Panoremix/README.md deleted file mode 100644 index 58b443c117d688711cf29f7ee524d102ee5cf3d9..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/Panoremix/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Panoremix -emoji: 👩‍🎨 -colorFrom: blue -colorTo: yellow -sdk: docker -pinned: true -app_port: 3000 ---- - -# Panoremix \ No newline at end of file diff --git a/spaces/jbilcke-hf/ai-clip-factory/src/lib/computePercentage.ts b/spaces/jbilcke-hf/ai-clip-factory/src/lib/computePercentage.ts deleted file mode 100644 index eaf8c1645451d44bf97a417d04e098e51ec167bb..0000000000000000000000000000000000000000 --- a/spaces/jbilcke-hf/ai-clip-factory/src/lib/computePercentage.ts +++ /dev/null @@ -1,4 +0,0 @@ -export function computePercentage(input: string | number) { - // TODO something - return 0 -} \ No newline at end of file diff --git a/spaces/jhwen/bingo/src/components/chat-history.tsx b/spaces/jhwen/bingo/src/components/chat-history.tsx deleted file mode 100644 index feb81de66562edda8f40d3c0cc717202c92b6509..0000000000000000000000000000000000000000 --- a/spaces/jhwen/bingo/src/components/chat-history.tsx +++ /dev/null @@ -1,48 +0,0 @@ -import { IconEdit, IconTrash, IconMore, IconDownload } from "./ui/icons" - -export function ChatHistory() { - return ( -
        -
        - 历史记录 -
        -
        -
        -
        -
        -
        -
        - -
        -

        无标题的聊天

        -
        -

        上午1:42

        -
        - - - - - - - - -
        -
        -
        -
        -
        -
        -
        -
        - ) -} diff --git a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/mel_processing.py b/spaces/jimschat/VITS-Umamusume-voice-synthesizer/mel_processing.py deleted file mode 100644 index 3e252e76320522a8a4195a60665168f22769aec2..0000000000000000000000000000000000000000 --- a/spaces/jimschat/VITS-Umamusume-voice-synthesizer/mel_processing.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -import torch.utils.data -from librosa.filters import mel as librosa_mel_fn - -MAX_WAV_VALUE = 32768.0 - - -def dynamic_range_compression_torch(x, C=1, clip_val=1e-5): - """ - PARAMS - ------ - C: compression factor - """ - return torch.log(torch.clamp(x, min=clip_val) * C) - - -def dynamic_range_decompression_torch(x, C=1): - """ - PARAMS - ------ - C: compression factor used to compress - """ - return torch.exp(x) / C - - -def spectral_normalize_torch(magnitudes): - output = dynamic_range_compression_torch(magnitudes) - return output - - -def spectral_de_normalize_torch(magnitudes): - output = dynamic_range_decompression_torch(magnitudes) - return output - - -mel_basis = {} -hann_window = {} - - -def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - return spec - - -def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax): - global mel_basis - dtype_device = str(spec.dtype) + '_' + str(spec.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device) - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - return spec - - -def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False): - if torch.min(y) < -1.: - print('min value is ', torch.min(y)) - if torch.max(y) > 1.: - print('max value is ', torch.max(y)) - - global mel_basis, hann_window - dtype_device = str(y.dtype) + '_' + str(y.device) - fmax_dtype_device = str(fmax) + '_' + dtype_device - wnsize_dtype_device = str(win_size) + '_' + dtype_device - if fmax_dtype_device not in mel_basis: - mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax) - mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device) - if wnsize_dtype_device not in hann_window: - hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device) - - y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect') - y = y.squeeze(1) - - spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device], - center=center, pad_mode='reflect', normalized=False, onesided=True) - - spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6) - - spec = torch.matmul(mel_basis[fmax_dtype_device], spec) - spec = spectral_normalize_torch(spec) - - return spec diff --git a/spaces/jkang/demo-artist-classifier/gradio_artist_classifier.py b/spaces/jkang/demo-artist-classifier/gradio_artist_classifier.py deleted file mode 100644 index d338a0c4bcb2e35852d40ea87308a0a890e61766..0000000000000000000000000000000000000000 --- a/spaces/jkang/demo-artist-classifier/gradio_artist_classifier.py +++ /dev/null @@ -1,136 +0,0 @@ -'''Artist Classifier - -prototype - ---- -- 2022-01-18 jkang first created -''' -from gradcam_utils import get_img_4d_array, make_gradcam_heatmap, align_image_with_heatmap -from PIL import Image -import matplotlib.pyplot as plt -import matplotlib.image as mpimg -import seaborn as sns - -import io -import json -import numpy as np -import skimage -import skimage.io -from skimage.transform import resize -from loguru import logger -from huggingface_hub import from_pretrained_keras -import gradio as gr -import tensorflow as tf -tfk = tf.keras - -from gradcam_utils import get_img_4d_array, make_gradcam_heatmap, align_image_with_heatmap - -# ---------- Settings ---------- -ARTIST_META = 'artist.json' -TREND_META = 'trend.json' -EXAMPLES = ['monet2.jpg', 'surrelaism.png', 'graffitiart.png', 'lichtenstein_popart.jpg', 'pierre_augste_renoir.png'] -ALPHA = 0.9 -IMG_WIDTH = 299 -IMG_HEIGHT = 299 - -# ---------- Logging ---------- -logger.add('app.log', mode='a') -logger.info('============================= App restarted =============================') - -# ---------- Model ---------- -logger.info('loading models...') -artist_model = from_pretrained_keras("jkang/drawing-artist-classifier") -trend_model = from_pretrained_keras("jkang/drawing-artistic-trend-classifier") -logger.info('both models loaded') - -def load_json_as_dict(json_file): - with open(json_file, 'r') as f: - out = json.load(f) - return dict(out) - -def load_image_as_array(image_file): - img = skimage.io.imread(image_file, as_gray=False, plugin='matplotlib') - if (img.shape[-1] > 3): # if RGBA - img = img[..., :-1] - return img - -def resize_image(img_array, width, height): - img_resized = resize(img_array, (height, width), - anti_aliasing=True, - preserve_range=False) - return skimage.img_as_ubyte(img_resized) - -def predict(input_image): - img_3d_array = load_image_as_array(input_image) - img_3d_array = resize_image(img_3d_array, IMG_WIDTH, IMG_HEIGHT) - img_4d_array = img_3d_array[np.newaxis,...] - logger.info(f'--- {input_image} loaded') - - artist2id = load_json_as_dict(ARTIST_META) - trend2id = load_json_as_dict(TREND_META) - id2artist = {artist2id[artist]:artist for artist in artist2id} - id2trend = {trend2id[trend]:trend for trend in trend2id} - - # Artist model - a_heatmap, a_pred_id, a_pred_out = make_gradcam_heatmap(artist_model, - img_4d_array, - pred_idx=None) - a_img_pil = align_image_with_heatmap( - img_4d_array, a_heatmap, alpha=ALPHA, cmap='jet') - a_img = np.asarray(a_img_pil).astype('float32')/255 - a_label = id2artist[a_pred_id] - a_prob = a_pred_out[a_pred_id] - - # Trend model - t_heatmap, t_pred_id, t_pred_out = make_gradcam_heatmap(trend_model, - img_4d_array, - pred_idx=None) - - t_img_pil = align_image_with_heatmap( - img_4d_array, t_heatmap, alpha=ALPHA, cmap='jet') - t_img = np.asarray(t_img_pil).astype('float32')/255 - t_label = id2trend[t_pred_id] - t_prob = t_pred_out[t_pred_id] - - with sns.plotting_context('poster', font_scale=0.7): - fig, (ax1, ax2, ax3) = plt.subplots( - 1, 3, figsize=(12, 6), facecolor='white') - for ax in (ax1, ax2, ax3): - ax.set_xticks([]) - ax.set_yticks([]) - - ax1.imshow(img_3d_array) - ax2.imshow(a_img) - ax3.imshow(t_img) - - ax1.set_title(f'Input Image', ha='left', x=0, y=1.05) - ax2.set_title(f'Artist Prediction:\n => {a_label} ({a_prob:.2f})', ha='left', x=0, y=1.05) - ax3.set_title(f'Style Prediction:\n => {t_label} ({t_prob:.2f})', ha='left', x=0, y=1.05) - fig.tight_layout() - - buf = io.BytesIO() - fig.savefig(buf, bbox_inches='tight', format='jpg') - buf.seek(0) - pil_img = Image.open(buf) - plt.close() - logger.info('--- image generated') - - a_labels = {id2artist[i]: float(pred) for i, pred in enumerate(a_pred_out)} - t_labels = {id2trend[i]: float(pred) for i, pred in enumerate(t_pred_out)} - return a_labels, t_labels, pil_img - -iface = gr.Interface( - predict, - title='Predict Artist and Artistic Style of Drawings 🎨👨🏻‍🎨 (prototype)', - description='Upload a drawing/image and the model will predict how likely it seems given 10 artists and their trend/style', - inputs=[ - gr.inputs.Image(label='Upload a drawing/image', type='file') - ], - outputs=[ - gr.outputs.Label(label='Artists', num_top_classes=5, type='auto'), - gr.outputs.Label(label='Styles', num_top_classes=5, type='auto'), - gr.outputs.Image(label='Prediction with GradCAM') - ], - examples=EXAMPLES, -) -iface.launch(debug=True, enable_queue=True) diff --git a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Util/test_asn1.py b/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Util/test_asn1.py deleted file mode 100644 index e441ba0a3ee5a421800450dc191e5498ea988b43..0000000000000000000000000000000000000000 --- a/spaces/joaopereirajp/livvieChatBot/venv/lib/python3.9/site-packages/Crypto/SelfTest/Util/test_asn1.py +++ /dev/null @@ -1,851 +0,0 @@ -# -# SelfTest/Util/test_asn.py: Self-test for the Crypto.Util.asn1 module -# -# =================================================================== -# -# Copyright (c) 2014, Legrandin -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in -# the documentation and/or other materials provided with the -# distribution. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE -# COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, -# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, -# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; -# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT -# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN -# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE -# POSSIBILITY OF SUCH DAMAGE. -# =================================================================== - -"""Self-tests for Crypto.Util.asn1""" - -import unittest - -from Crypto.Util.py3compat import * -from Crypto.Util.asn1 import (DerObject, DerSetOf, DerInteger, - DerBitString, - DerObjectId, DerNull, DerOctetString, - DerSequence, DerBoolean) - -class DerObjectTests(unittest.TestCase): - - def testObjInit1(self): - # Fail with invalid tag format (must be 1 byte) - self.assertRaises(ValueError, DerObject, b('\x00\x99')) - # Fail with invalid implicit tag (must be <0x1F) - self.assertRaises(ValueError, DerObject, 0x1F) - - # ------ - - def testObjEncode1(self): - # No payload - der = DerObject(b('\x02')) - self.assertEqual(der.encode(), b('\x02\x00')) - # Small payload (primitive) - der.payload = b('\x45') - self.assertEqual(der.encode(), b('\x02\x01\x45')) - # Invariant - self.assertEqual(der.encode(), b('\x02\x01\x45')) - # Initialize with numerical tag - der = DerObject(0x04) - der.payload = b('\x45') - self.assertEqual(der.encode(), b('\x04\x01\x45')) - # Initialize with constructed type - der = DerObject(b('\x10'), constructed=True) - self.assertEqual(der.encode(), b('\x30\x00')) - - def testObjEncode2(self): - # Initialize with payload - der = DerObject(0x03, b('\x12\x12')) - self.assertEqual(der.encode(), b('\x03\x02\x12\x12')) - - def testObjEncode3(self): - # Long payload - der = DerObject(b('\x10')) - der.payload = b("0")*128 - self.assertEqual(der.encode(), b('\x10\x81\x80' + "0"*128)) - - def testObjEncode4(self): - # Implicit tags (constructed) - der = DerObject(0x10, implicit=1, constructed=True) - der.payload = b('ppll') - self.assertEqual(der.encode(), b('\xa1\x04ppll')) - # Implicit tags (primitive) - der = DerObject(0x02, implicit=0x1E, constructed=False) - der.payload = b('ppll') - self.assertEqual(der.encode(), b('\x9E\x04ppll')) - - def testObjEncode5(self): - # Encode type with explicit tag - der = DerObject(0x10, explicit=5) - der.payload = b("xxll") - self.assertEqual(der.encode(), b("\xa5\x06\x10\x04xxll")) - - # ----- - - def testObjDecode1(self): - # Decode short payload - der = DerObject(0x02) - der.decode(b('\x02\x02\x01\x02')) - self.assertEqual(der.payload, b("\x01\x02")) - self.assertEqual(der._tag_octet, 0x02) - - def testObjDecode2(self): - # Decode long payload - der = DerObject(0x02) - der.decode(b('\x02\x81\x80' + "1"*128)) - self.assertEqual(der.payload, b("1")*128) - self.assertEqual(der._tag_octet, 0x02) - - def testObjDecode3(self): - # Decode payload with too much data gives error - der = DerObject(0x02) - self.assertRaises(ValueError, der.decode, b('\x02\x02\x01\x02\xFF')) - # Decode payload with too little data gives error - der = DerObject(0x02) - self.assertRaises(ValueError, der.decode, b('\x02\x02\x01')) - - def testObjDecode4(self): - # Decode implicit tag (primitive) - der = DerObject(0x02, constructed=False, implicit=0xF) - self.assertRaises(ValueError, der.decode, b('\x02\x02\x01\x02')) - der.decode(b('\x8F\x01\x00')) - self.assertEqual(der.payload, b('\x00')) - # Decode implicit tag (constructed) - der = DerObject(0x02, constructed=True, implicit=0xF) - self.assertRaises(ValueError, der.decode, b('\x02\x02\x01\x02')) - der.decode(b('\xAF\x01\x00')) - self.assertEqual(der.payload, b('\x00')) - - def testObjDecode5(self): - # Decode payload with unexpected tag gives error - der = DerObject(0x02) - self.assertRaises(ValueError, der.decode, b('\x03\x02\x01\x02')) - - def testObjDecode6(self): - # Arbitrary DER object - der = DerObject() - der.decode(b('\x65\x01\x88')) - self.assertEqual(der._tag_octet, 0x65) - self.assertEqual(der.payload, b('\x88')) - - def testObjDecode7(self): - # Decode explicit tag - der = DerObject(0x10, explicit=5) - der.decode(b("\xa5\x06\x10\x04xxll")) - self.assertEqual(der._inner_tag_octet, 0x10) - self.assertEqual(der.payload, b('xxll')) - - # Explicit tag may be 0 - der = DerObject(0x10, explicit=0) - der.decode(b("\xa0\x06\x10\x04xxll")) - self.assertEqual(der._inner_tag_octet, 0x10) - self.assertEqual(der.payload, b('xxll')) - - def testObjDecode8(self): - # Verify that decode returns the object - der = DerObject(0x02) - self.assertEqual(der, der.decode(b('\x02\x02\x01\x02'))) - -class DerIntegerTests(unittest.TestCase): - - def testInit1(self): - der = DerInteger(1) - self.assertEqual(der.encode(), b('\x02\x01\x01')) - - def testEncode1(self): - # Single-byte integers - # Value 0 - der = DerInteger(0) - self.assertEqual(der.encode(), b('\x02\x01\x00')) - # Value 1 - der = DerInteger(1) - self.assertEqual(der.encode(), b('\x02\x01\x01')) - # Value 127 - der = DerInteger(127) - self.assertEqual(der.encode(), b('\x02\x01\x7F')) - - def testEncode2(self): - # Multi-byte integers - # Value 128 - der = DerInteger(128) - self.assertEqual(der.encode(), b('\x02\x02\x00\x80')) - # Value 0x180 - der = DerInteger(0x180) - self.assertEqual(der.encode(), b('\x02\x02\x01\x80')) - # One very long integer - der = DerInteger(2**2048) - self.assertEqual(der.encode(), - b('\x02\x82\x01\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00')) - - def testEncode3(self): - # Negative integers - # Value -1 - der = DerInteger(-1) - self.assertEqual(der.encode(), b('\x02\x01\xFF')) - # Value -128 - der = DerInteger(-128) - self.assertEqual(der.encode(), b('\x02\x01\x80')) - # Value - der = DerInteger(-87873) - self.assertEqual(der.encode(), b('\x02\x03\xFE\xA8\xBF')) - - def testEncode4(self): - # Explicit encoding - number = DerInteger(0x34, explicit=3) - self.assertEqual(number.encode(), b('\xa3\x03\x02\x01\x34')) - - # ----- - - def testDecode1(self): - # Single-byte integer - der = DerInteger() - # Value 0 - der.decode(b('\x02\x01\x00')) - self.assertEqual(der.value, 0) - # Value 1 - der.decode(b('\x02\x01\x01')) - self.assertEqual(der.value, 1) - # Value 127 - der.decode(b('\x02\x01\x7F')) - self.assertEqual(der.value, 127) - - def testDecode2(self): - # Multi-byte integer - der = DerInteger() - # Value 0x180L - der.decode(b('\x02\x02\x01\x80')) - self.assertEqual(der.value,0x180) - # One very long integer - der.decode( - b('\x02\x82\x01\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00')) - self.assertEqual(der.value,2**2048) - - def testDecode3(self): - # Negative integer - der = DerInteger() - # Value -1 - der.decode(b('\x02\x01\xFF')) - self.assertEqual(der.value, -1) - # Value -32768 - der.decode(b('\x02\x02\x80\x00')) - self.assertEqual(der.value, -32768) - - def testDecode5(self): - # We still accept BER integer format - der = DerInteger() - # Redundant leading zeroes - der.decode(b('\x02\x02\x00\x01')) - self.assertEqual(der.value, 1) - # Redundant leading 0xFF - der.decode(b('\x02\x02\xFF\xFF')) - self.assertEqual(der.value, -1) - # Empty payload - der.decode(b('\x02\x00')) - self.assertEqual(der.value, 0) - - def testDecode6(self): - # Explicit encoding - number = DerInteger(explicit=3) - number.decode(b('\xa3\x03\x02\x01\x34')) - self.assertEqual(number.value, 0x34) - - def testDecode7(self): - # Verify decode returns the DerInteger - der = DerInteger() - self.assertEqual(der, der.decode(b('\x02\x01\x7F'))) - - ### - - def testStrict1(self): - number = DerInteger() - - number.decode(b'\x02\x02\x00\x01') - number.decode(b'\x02\x02\x00\x7F') - self.assertRaises(ValueError, number.decode, b'\x02\x02\x00\x01', strict=True) - self.assertRaises(ValueError, number.decode, b'\x02\x02\x00\x7F', strict=True) - - ### - - def testErrDecode1(self): - # Wide length field - der = DerInteger() - self.assertRaises(ValueError, der.decode, b('\x02\x81\x01\x01')) - - -class DerSequenceTests(unittest.TestCase): - - def testInit1(self): - der = DerSequence([1, DerInteger(2), b('0\x00')]) - self.assertEqual(der.encode(), b('0\x08\x02\x01\x01\x02\x01\x020\x00')) - - def testEncode1(self): - # Empty sequence - der = DerSequence() - self.assertEqual(der.encode(), b('0\x00')) - self.assertFalse(der.hasOnlyInts()) - # One single-byte integer (zero) - der.append(0) - self.assertEqual(der.encode(), b('0\x03\x02\x01\x00')) - self.assertEqual(der.hasInts(),1) - self.assertEqual(der.hasInts(False),1) - self.assertTrue(der.hasOnlyInts()) - self.assertTrue(der.hasOnlyInts(False)) - # Invariant - self.assertEqual(der.encode(), b('0\x03\x02\x01\x00')) - - def testEncode2(self): - # Indexing - der = DerSequence() - der.append(0) - der[0] = 1 - self.assertEqual(len(der),1) - self.assertEqual(der[0],1) - self.assertEqual(der[-1],1) - self.assertEqual(der.encode(), b('0\x03\x02\x01\x01')) - # - der[:] = [1] - self.assertEqual(len(der),1) - self.assertEqual(der[0],1) - self.assertEqual(der.encode(), b('0\x03\x02\x01\x01')) - - def testEncode3(self): - # One multi-byte integer (non-zero) - der = DerSequence() - der.append(0x180) - self.assertEqual(der.encode(), b('0\x04\x02\x02\x01\x80')) - - def testEncode4(self): - # One very long integer - der = DerSequence() - der.append(2**2048) - self.assertEqual(der.encode(), b('0\x82\x01\x05')+ - b('\x02\x82\x01\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00')) - - def testEncode5(self): - der = DerSequence() - der += 1 - der += b('\x30\x00') - self.assertEqual(der.encode(), b('\x30\x05\x02\x01\x01\x30\x00')) - - def testEncode6(self): - # Two positive integers - der = DerSequence() - der.append(0x180) - der.append(0xFF) - self.assertEqual(der.encode(), b('0\x08\x02\x02\x01\x80\x02\x02\x00\xff')) - self.assertTrue(der.hasOnlyInts()) - self.assertTrue(der.hasOnlyInts(False)) - # Two mixed integers - der = DerSequence() - der.append(2) - der.append(-2) - self.assertEqual(der.encode(), b('0\x06\x02\x01\x02\x02\x01\xFE')) - self.assertEqual(der.hasInts(), 1) - self.assertEqual(der.hasInts(False), 2) - self.assertFalse(der.hasOnlyInts()) - self.assertTrue(der.hasOnlyInts(False)) - # - der.append(0x01) - der[1:] = [9,8] - self.assertEqual(len(der),3) - self.assertEqual(der[1:],[9,8]) - self.assertEqual(der[1:-1],[9]) - self.assertEqual(der.encode(), b('0\x09\x02\x01\x02\x02\x01\x09\x02\x01\x08')) - - def testEncode7(self): - # One integer and another type (already encoded) - der = DerSequence() - der.append(0x180) - der.append(b('0\x03\x02\x01\x05')) - self.assertEqual(der.encode(), b('0\x09\x02\x02\x01\x800\x03\x02\x01\x05')) - self.assertFalse(der.hasOnlyInts()) - - def testEncode8(self): - # One integer and another type (yet to encode) - der = DerSequence() - der.append(0x180) - der.append(DerSequence([5])) - self.assertEqual(der.encode(), b('0\x09\x02\x02\x01\x800\x03\x02\x01\x05')) - self.assertFalse(der.hasOnlyInts()) - - #### - - def testDecode1(self): - # Empty sequence - der = DerSequence() - der.decode(b('0\x00')) - self.assertEqual(len(der),0) - # One single-byte integer (zero) - der.decode(b('0\x03\x02\x01\x00')) - self.assertEqual(len(der),1) - self.assertEqual(der[0],0) - # Invariant - der.decode(b('0\x03\x02\x01\x00')) - self.assertEqual(len(der),1) - self.assertEqual(der[0],0) - - def testDecode2(self): - # One single-byte integer (non-zero) - der = DerSequence() - der.decode(b('0\x03\x02\x01\x7f')) - self.assertEqual(len(der),1) - self.assertEqual(der[0],127) - - def testDecode4(self): - # One very long integer - der = DerSequence() - der.decode(b('0\x82\x01\x05')+ - b('\x02\x82\x01\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00')+ - b('\x00\x00\x00\x00\x00\x00\x00\x00\x00')) - self.assertEqual(len(der),1) - self.assertEqual(der[0],2**2048) - - def testDecode6(self): - # Two integers - der = DerSequence() - der.decode(b('0\x08\x02\x02\x01\x80\x02\x02\x00\xff')) - self.assertEqual(len(der),2) - self.assertEqual(der[0],0x180) - self.assertEqual(der[1],0xFF) - - def testDecode7(self): - # One integer and 2 other types - der = DerSequence() - der.decode(b('0\x0A\x02\x02\x01\x80\x24\x02\xb6\x63\x12\x00')) - self.assertEqual(len(der),3) - self.assertEqual(der[0],0x180) - self.assertEqual(der[1],b('\x24\x02\xb6\x63')) - self.assertEqual(der[2],b('\x12\x00')) - - def testDecode8(self): - # Only 2 other types - der = DerSequence() - der.decode(b('0\x06\x24\x02\xb6\x63\x12\x00')) - self.assertEqual(len(der),2) - self.assertEqual(der[0],b('\x24\x02\xb6\x63')) - self.assertEqual(der[1],b('\x12\x00')) - self.assertEqual(der.hasInts(), 0) - self.assertEqual(der.hasInts(False), 0) - self.assertFalse(der.hasOnlyInts()) - self.assertFalse(der.hasOnlyInts(False)) - - def testDecode9(self): - # Verify that decode returns itself - der = DerSequence() - self.assertEqual(der, der.decode(b('0\x06\x24\x02\xb6\x63\x12\x00'))) - - ### - - def testErrDecode1(self): - # Not a sequence - der = DerSequence() - self.assertRaises(ValueError, der.decode, b('')) - self.assertRaises(ValueError, der.decode, b('\x00')) - self.assertRaises(ValueError, der.decode, b('\x30')) - - def testErrDecode2(self): - der = DerSequence() - # Too much data - self.assertRaises(ValueError, der.decode, b('\x30\x00\x00')) - - def testErrDecode3(self): - # Wrong length format - der = DerSequence() - # Missing length in sub-item - self.assertRaises(ValueError, der.decode, b('\x30\x04\x02\x01\x01\x00')) - # Valid BER, but invalid DER length - self.assertRaises(ValueError, der.decode, b('\x30\x81\x03\x02\x01\x01')) - self.assertRaises(ValueError, der.decode, b('\x30\x04\x02\x81\x01\x01')) - - def test_expected_nr_elements(self): - der_bin = DerSequence([1, 2, 3]).encode() - - DerSequence().decode(der_bin, nr_elements=3) - DerSequence().decode(der_bin, nr_elements=(2,3)) - self.assertRaises(ValueError, DerSequence().decode, der_bin, nr_elements=1) - self.assertRaises(ValueError, DerSequence().decode, der_bin, nr_elements=(4,5)) - - def test_expected_only_integers(self): - - der_bin1 = DerSequence([1, 2, 3]).encode() - der_bin2 = DerSequence([1, 2, DerSequence([3, 4])]).encode() - - DerSequence().decode(der_bin1, only_ints_expected=True) - DerSequence().decode(der_bin1, only_ints_expected=False) - DerSequence().decode(der_bin2, only_ints_expected=False) - self.assertRaises(ValueError, DerSequence().decode, der_bin2, only_ints_expected=True) - - -class DerOctetStringTests(unittest.TestCase): - - def testInit1(self): - der = DerOctetString(b('\xFF')) - self.assertEqual(der.encode(), b('\x04\x01\xFF')) - - def testEncode1(self): - # Empty sequence - der = DerOctetString() - self.assertEqual(der.encode(), b('\x04\x00')) - # Small payload - der.payload = b('\x01\x02') - self.assertEqual(der.encode(), b('\x04\x02\x01\x02')) - - #### - - def testDecode1(self): - # Empty sequence - der = DerOctetString() - der.decode(b('\x04\x00')) - self.assertEqual(der.payload, b('')) - # Small payload - der.decode(b('\x04\x02\x01\x02')) - self.assertEqual(der.payload, b('\x01\x02')) - - def testDecode2(self): - # Verify that decode returns the object - der = DerOctetString() - self.assertEqual(der, der.decode(b('\x04\x00'))) - - def testErrDecode1(self): - # No leftovers allowed - der = DerOctetString() - self.assertRaises(ValueError, der.decode, b('\x04\x01\x01\xff')) - -class DerNullTests(unittest.TestCase): - - def testEncode1(self): - der = DerNull() - self.assertEqual(der.encode(), b('\x05\x00')) - - #### - - def testDecode1(self): - # Empty sequence - der = DerNull() - self.assertEqual(der, der.decode(b('\x05\x00'))) - -class DerObjectIdTests(unittest.TestCase): - - def testInit1(self): - der = DerObjectId("1.1") - self.assertEqual(der.encode(), b'\x06\x01)') - - def testEncode1(self): - der = DerObjectId('1.2.840.113549.1.1.1') - self.assertEqual(der.encode(), b'\x06\x09\x2A\x86\x48\x86\xF7\x0D\x01\x01\x01') - - der = DerObjectId() - der.value = '1.2.840.113549.1.1.1' - self.assertEqual(der.encode(), b'\x06\x09\x2A\x86\x48\x86\xF7\x0D\x01\x01\x01') - - der = DerObjectId('2.999.1234') - self.assertEqual(der.encode(), b'\x06\x04\x88\x37\x89\x52') - - def testEncode2(self): - der = DerObjectId('3.4') - self.assertRaises(ValueError, der.encode) - - der = DerObjectId('1.40') - self.assertRaises(ValueError, der.encode) - - #### - - def testDecode1(self): - # Empty sequence - der = DerObjectId() - der.decode(b'\x06\x09\x2A\x86\x48\x86\xF7\x0D\x01\x01\x01') - self.assertEqual(der.value, '1.2.840.113549.1.1.1') - - def testDecode2(self): - # Verify that decode returns the object - der = DerObjectId() - self.assertEqual(der, - der.decode(b'\x06\x09\x2A\x86\x48\x86\xF7\x0D\x01\x01\x01')) - - def testDecode3(self): - der = DerObjectId() - der.decode(b'\x06\x09\x2A\x86\x48\x86\xF7\x0D\x01\x00\x01') - self.assertEqual(der.value, '1.2.840.113549.1.0.1') - - def testDecode4(self): - der = DerObjectId() - der.decode(b'\x06\x04\x88\x37\x89\x52') - self.assertEqual(der.value, '2.999.1234') - - -class DerBitStringTests(unittest.TestCase): - - def testInit1(self): - der = DerBitString(b("\xFF")) - self.assertEqual(der.encode(), b('\x03\x02\x00\xFF')) - - def testInit2(self): - der = DerBitString(DerInteger(1)) - self.assertEqual(der.encode(), b('\x03\x04\x00\x02\x01\x01')) - - def testEncode1(self): - # Empty sequence - der = DerBitString() - self.assertEqual(der.encode(), b('\x03\x01\x00')) - # Small payload - der = DerBitString(b('\x01\x02')) - self.assertEqual(der.encode(), b('\x03\x03\x00\x01\x02')) - # Small payload - der = DerBitString() - der.value = b('\x01\x02') - self.assertEqual(der.encode(), b('\x03\x03\x00\x01\x02')) - - #### - - def testDecode1(self): - # Empty sequence - der = DerBitString() - der.decode(b('\x03\x00')) - self.assertEqual(der.value, b('')) - # Small payload - der.decode(b('\x03\x03\x00\x01\x02')) - self.assertEqual(der.value, b('\x01\x02')) - - def testDecode2(self): - # Verify that decode returns the object - der = DerBitString() - self.assertEqual(der, der.decode(b('\x03\x00'))) - - -class DerSetOfTests(unittest.TestCase): - - def testInit1(self): - der = DerSetOf([DerInteger(1), DerInteger(2)]) - self.assertEqual(der.encode(), b('1\x06\x02\x01\x01\x02\x01\x02')) - - def testEncode1(self): - # Empty set - der = DerSetOf() - self.assertEqual(der.encode(), b('1\x00')) - # One single-byte integer (zero) - der.add(0) - self.assertEqual(der.encode(), b('1\x03\x02\x01\x00')) - # Invariant - self.assertEqual(der.encode(), b('1\x03\x02\x01\x00')) - - def testEncode2(self): - # Two integers - der = DerSetOf() - der.add(0x180) - der.add(0xFF) - self.assertEqual(der.encode(), b('1\x08\x02\x02\x00\xff\x02\x02\x01\x80')) - # Initialize with integers - der = DerSetOf([0x180, 0xFF]) - self.assertEqual(der.encode(), b('1\x08\x02\x02\x00\xff\x02\x02\x01\x80')) - - def testEncode3(self): - # One integer and another type (no matter what it is) - der = DerSetOf() - der.add(0x180) - self.assertRaises(ValueError, der.add, b('\x00\x02\x00\x00')) - - def testEncode4(self): - # Only non integers - der = DerSetOf() - der.add(b('\x01\x00')) - der.add(b('\x01\x01\x01')) - self.assertEqual(der.encode(), b('1\x05\x01\x00\x01\x01\x01')) - - #### - - def testDecode1(self): - # Empty sequence - der = DerSetOf() - der.decode(b('1\x00')) - self.assertEqual(len(der),0) - # One single-byte integer (zero) - der.decode(b('1\x03\x02\x01\x00')) - self.assertEqual(len(der),1) - self.assertEqual(list(der),[0]) - - def testDecode2(self): - # Two integers - der = DerSetOf() - der.decode(b('1\x08\x02\x02\x01\x80\x02\x02\x00\xff')) - self.assertEqual(len(der),2) - l = list(der) - self.assertTrue(0x180 in l) - self.assertTrue(0xFF in l) - - def testDecode3(self): - # One integer and 2 other types - der = DerSetOf() - #import pdb; pdb.set_trace() - self.assertRaises(ValueError, der.decode, - b('0\x0A\x02\x02\x01\x80\x24\x02\xb6\x63\x12\x00')) - - def testDecode4(self): - # Verify that decode returns the object - der = DerSetOf() - self.assertEqual(der, - der.decode(b('1\x08\x02\x02\x01\x80\x02\x02\x00\xff'))) - - ### - - def testErrDecode1(self): - # No leftovers allowed - der = DerSetOf() - self.assertRaises(ValueError, der.decode, - b('1\x08\x02\x02\x01\x80\x02\x02\x00\xff\xAA')) - - -class DerBooleanTests(unittest.TestCase): - - def testEncode1(self): - der = DerBoolean(False) - self.assertEqual(der.encode(), b'\x01\x01\x00') - - def testEncode2(self): - der = DerBoolean(True) - self.assertEqual(der.encode(), b'\x01\x01\xFF') - - def testEncode3(self): - der = DerBoolean(False, implicit=0x12) - self.assertEqual(der.encode(), b'\x92\x01\x00') - - def testEncode4(self): - der = DerBoolean(False, explicit=0x05) - self.assertEqual(der.encode(), b'\xA5\x03\x01\x01\x00') - #### - - def testDecode1(self): - der = DerBoolean() - der.decode(b'\x01\x01\x00') - self.assertEqual(der.value, False) - - def testDecode2(self): - der = DerBoolean() - der.decode(b'\x01\x01\xFF') - self.assertEqual(der.value, True) - - def testDecode3(self): - der = DerBoolean(implicit=0x12) - der.decode(b'\x92\x01\x00') - self.assertEqual(der.value, False) - - def testDecode4(self): - der = DerBoolean(explicit=0x05) - der.decode(b'\xA5\x03\x01\x01\x00') - self.assertEqual(der.value, False) - - def testErrorDecode1(self): - der = DerBoolean() - # Wrong tag - self.assertRaises(ValueError, der.decode, b'\x02\x01\x00') - - def testErrorDecode2(self): - der = DerBoolean() - # Payload too long - self.assertRaises(ValueError, der.decode, b'\x01\x01\x00\xFF') - - -def get_tests(config={}): - from Crypto.SelfTest.st_common import list_test_cases - listTests = [] - listTests += list_test_cases(DerObjectTests) - listTests += list_test_cases(DerIntegerTests) - listTests += list_test_cases(DerSequenceTests) - listTests += list_test_cases(DerOctetStringTests) - listTests += list_test_cases(DerNullTests) - listTests += list_test_cases(DerObjectIdTests) - listTests += list_test_cases(DerBitStringTests) - listTests += list_test_cases(DerSetOfTests) - listTests += list_test_cases(DerBooleanTests) - return listTests - -if __name__ == '__main__': - suite = lambda: unittest.TestSuite(get_tests()) - unittest.main(defaultTest='suite') - -# vim:set ts=4 sw=4 sts=4 expandtab: diff --git a/spaces/johnyang/ChatPaper111/scipdf_utils.py b/spaces/johnyang/ChatPaper111/scipdf_utils.py deleted file mode 100644 index 6550bd6728b3d34c0588240721250f635e1e79c8..0000000000000000000000000000000000000000 --- a/spaces/johnyang/ChatPaper111/scipdf_utils.py +++ /dev/null @@ -1,424 +0,0 @@ -import re -import os -import os.path as op -from glob import glob -import urllib -import subprocess -import requests -from bs4 import BeautifulSoup, NavigableString - - -# or https://cloud.science-miner.com/grobid/ for cloud service -GROBID_URL = "http://localhost:8070" -DIR_PATH = op.dirname(op.abspath(__file__)) -PDF_FIGURES_JAR_PATH = op.join( - DIR_PATH, "pdffigures2", "pdffigures2-assembly-0.0.12-SNAPSHOT.jar" -) - - -def list_pdf_paths(pdf_folder: str): - """ - list of pdf paths in pdf folder - """ - return glob(op.join(pdf_folder, "*", "*", "*.pdf")) - - -def validate_url(path: str): - """ - Validate a given ``path`` if it is URL or not - """ - regex = re.compile( - r"^(?:http|ftp)s?://" # http:// or https:// - # domain... - r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|" - r"localhost|" # localhost... - r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})" # ...or ip - r"(?::\d+)?" # optional port - r"(?:/?|[/?]\S+)$", - re.IGNORECASE, - ) - return re.match(regex, path) is not None - - -def parse_pdf( - pdf_path: str, - fulltext: bool = True, - soup: bool = False, - grobid_url: str = GROBID_URL, -): - """ - Function to parse PDF to XML or BeautifulSoup using GROBID tool - - You can see http://grobid.readthedocs.io/en/latest/Install-Grobid/ on how to run GROBID locally - After loading GROBID zip file, you can run GROBID by using the following - >> ./gradlew run - - Parameters - ========== - pdf_path: str or bytes, path or URL to publication or article or bytes string of PDF - fulltext: bool, option for parsing, if True, parse full text of the article - if False, parse only header - grobid_url: str, url to GROBID parser, default at 'http://localhost:8070' - This could be changed to "https://cloud.science-miner.com/grobid/" for the cloud service - soup: bool, if True, return BeautifulSoup of the article - - Output - ====== - parsed_article: if soup is False, return parsed XML in text format, - else return BeautifulSoup of the XML - Example - ======= - >> parsed_article = parse_pdf(pdf_path, fulltext=True, soup=True) - """ - # GROBID URL - if fulltext: - url = "%s/api/processFulltextDocument" % grobid_url - else: - url = "%s/api/processHeaderDocument" % grobid_url - - if isinstance(pdf_path, str): - if validate_url(pdf_path) and op.splitext(pdf_path)[-1].lower() != ".pdf": - print("The input URL has to end with ``.pdf``") - parsed_article = None - elif validate_url(pdf_path) and op.splitext(pdf_path)[-1] == ".pdf": - page = urllib.request.urlopen(pdf_path).read() - parsed_article = requests.post(url, files={"input": page}).text - elif op.exists(pdf_path): - parsed_article = requests.post( - url, files={"input": open(pdf_path, "rb")} - ).text - else: - parsed_article = None - elif isinstance(pdf_path, bytes): - # assume that incoming is byte string - parsed_article = requests.post(url, files={"input": pdf_path}).text - else: - parsed_article = None - - if soup and parsed_article is not None: - parsed_article = BeautifulSoup(parsed_article, "lxml") - return parsed_article - - -def parse_authors(article): - """ - Parse authors from a given BeautifulSoup of an article - """ - author_names = article.find("sourcedesc").findAll("persname") - authors = [] - for author in author_names: - firstname = author.find("forename", {"type": "first"}) - firstname = firstname.text.strip() if firstname is not None else "" - middlename = author.find("forename", {"type": "middle"}) - middlename = middlename.text.strip() if middlename is not None else "" - lastname = author.find("surname") - lastname = lastname.text.strip() if lastname is not None else "" - if middlename != "": - authors.append(firstname + " " + middlename + " " + lastname) - else: - authors.append(firstname + " " + lastname) - authors = "; ".join(authors) - return authors - - -def parse_date(article): - """ - Parse date from a given BeautifulSoup of an article - """ - pub_date = article.find("publicationstmt") - year = pub_date.find("date") - year = year.attrs.get("when") if year is not None else "" - return year - - -def parse_abstract(article): - """ - Parse abstract from a given BeautifulSoup of an article - """ - div = article.find("abstract") - abstract = "" - for p in list(div.children): - if not isinstance(p, NavigableString) and len(list(p)) > 0: - abstract += " ".join( - [elem.text for elem in p if not isinstance( - elem, NavigableString)] - ) - return abstract - - -def calculate_number_of_references(div): - """ - For a given section, calculate number of references made in the section - """ - n_publication_ref = len( - [ref for ref in div.find_all("ref") if ref.attrs.get("type") == "bibr"] - ) - n_figure_ref = len( - [ref for ref in div.find_all( - "ref") if ref.attrs.get("type") == "figure"] - ) - return {"n_publication_ref": n_publication_ref, "n_figure_ref": n_figure_ref} - - -def parse_sections(article, as_list: bool = False): - """ - Parse list of sections from a given BeautifulSoup of an article - - Parameters - ========== - as_list: bool, if True, output text as a list of paragraph instead - of joining it together as one single text - """ - article_text = article.find("text") - divs = article_text.find_all( - "div", attrs={"xmlns": "http://www.tei-c.org/ns/1.0"}) - sections = [] - for div in divs: - div_list = list(div.children) - if len(div_list) == 0: - heading = "" - text = "" - all_paragraphs = [] - elif len(div_list) == 1: - if isinstance(div_list[0], NavigableString): - heading = str(div_list[0]) - text = "" - all_paragraphs = [] - else: - heading = "" - text = div_list[0].text - all_paragraphs = [text] - else: - text = [] - heading = div_list[0] - all_paragraphs = [] - if isinstance(heading, NavigableString): - heading = str(heading) - p_all = list(div.children)[1:] - else: - heading = "" - p_all = list(div.children) - for p in p_all: - if p is not None: - try: - text.append(p.text) - all_paragraphs.append(p.text) - except: - pass - if not as_list: - text = "\n".join(text) - if heading != "" or text != "": - ref_dict = calculate_number_of_references(div) - sections.append( - { - "heading": heading, - "text": text, - "all_paragraphs": all_paragraphs, - "n_publication_ref": ref_dict["n_publication_ref"], - "n_figure_ref": ref_dict["n_figure_ref"], - } - ) - return sections - - -def parse_references(article): - """ - Parse list of references from a given BeautifulSoup of an article - """ - reference_list = [] - references = article.find("text").find("div", attrs={"type": "references"}) - references = references.find_all( - "biblstruct") if references is not None else [] - reference_list = [] - for reference in references: - title = reference.find("title", attrs={"level": "a"}) - if title is None: - title = reference.find("title", attrs={"level": "m"}) - title = title.text if title is not None else "" - journal = reference.find("title", attrs={"level": "j"}) - journal = journal.text if journal is not None else "" - if journal == "": - journal = reference.find("publisher") - journal = journal.text if journal is not None else "" - year = reference.find("date") - year = year.attrs.get("when") if year is not None else "" - authors = [] - for author in reference.find_all("author"): - firstname = author.find("forename", {"type": "first"}) - firstname = firstname.text.strip() if firstname is not None else "" - middlename = author.find("forename", {"type": "middle"}) - middlename = middlename.text.strip() if middlename is not None else "" - lastname = author.find("surname") - lastname = lastname.text.strip() if lastname is not None else "" - if middlename != "": - authors.append(firstname + " " + middlename + " " + lastname) - else: - authors.append(firstname + " " + lastname) - authors = "; ".join(authors) - reference_list.append( - {"title": title, "journal": journal, "year": year, "authors": authors} - ) - return reference_list - - -def parse_figure_caption(article): - """ - Parse list of figures/tables from a given BeautifulSoup of an article - """ - figures_list = [] - figures = article.find_all("figure") - for figure in figures: - figure_type = figure.attrs.get("type") or "" - figure_id = figure.attrs["xml:id"] or "" - label = figure.find("label").text - if figure_type == "table": - caption = figure.find("figdesc").text - data = figure.table.text - else: - caption = figure.text - data = "" - figures_list.append( - { - "figure_label": label, - "figure_type": figure_type, - "figure_id": figure_id, - "figure_caption": caption, - "figure_data": data, - } - ) - return figures_list - - -def convert_article_soup_to_dict(article, as_list: bool = False): - """ - Function to convert BeautifulSoup to JSON format - similar to the output from https://github.com/allenai/science-parse/ - - Parameters - ========== - article: BeautifulSoup - - Output - ====== - article_json: dict, parsed dictionary of a given article in the following format - { - 'title': ..., - 'abstract': ..., - 'sections': [ - {'heading': ..., 'text': ...}, - {'heading': ..., 'text': ...}, - ... - ], - 'references': [ - {'title': ..., 'journal': ..., 'year': ..., 'authors': ...}, - {'title': ..., 'journal': ..., 'year': ..., 'authors': ...}, - ... - ], - 'figures': [ - {'figure_label': ..., 'figure_type': ..., 'figure_id': ..., 'figure_caption': ..., 'figure_data': ...}, - ... - ] - } - """ - article_dict = {} - if article is not None: - title = article.find("title", attrs={"type": "main"}) - title = title.text.strip() if title is not None else "" - article_dict["authors"] = parse_authors(article) - article_dict["pub_date"] = parse_date(article) - article_dict["title"] = title - article_dict["abstract"] = parse_abstract(article) - article_dict["sections"] = parse_sections(article, as_list=as_list) - article_dict["references"] = parse_references(article) - article_dict["figures"] = parse_figure_caption(article) - - doi = article.find("idno", attrs={"type": "DOI"}) - doi = doi.text if doi is not None else "" - article_dict["doi"] = doi - - return article_dict - else: - return None - - -def parse_pdf_to_dict( - pdf_path: str, - fulltext: bool = True, - soup: bool = True, - as_list: bool = False, - grobid_url: str = GROBID_URL, -): - """ - Parse the given PDF and return dictionary of the parsed article - - Parameters - ========== - pdf_path: str, path to publication or article - fulltext: bool, whether to extract fulltext or not - soup: bool, whether to return BeautifulSoup or not - as_list: bool, whether to return list of sections or not - grobid_url: str, url to grobid server, default is `GROBID_URL` - This could be changed to "https://cloud.science-miner.com/grobid/" for the cloud service - - Ouput - ===== - article_dict: dict, dictionary of an article - """ - parsed_article = parse_pdf( - pdf_path, fulltext=fulltext, soup=soup, grobid_url=grobid_url - ) - article_dict = convert_article_soup_to_dict( - parsed_article, as_list=as_list) - return article_dict - - -def parse_figures( - pdf_folder: str, - jar_path: str = PDF_FIGURES_JAR_PATH, - resolution: int = 300, - output_folder: str = "figures", -): - """ - Parse figures from the given scientific PDF using pdffigures2 - - Parameters - ========== - pdf_folder: str, path to a folder that contains PDF files. A folder must contains only PDF files - jar_path: str, default path to pdffigures2-assembly-0.0.12-SNAPSHOT.jar file - resolution: int, resolution of the output figures - output_folder: str, path to folder that we want to save parsed data (related to figures) and figures - - Output - ====== - folder: making a folder of output_folder/data and output_folder/figures of parsed data and figures relatively - """ - if not op.isdir(output_folder): - os.makedirs(output_folder) - - # create ``data`` and ``figures`` subfolder within ``output_folder`` - data_path = op.join(output_folder, "data") - figure_path = op.join(output_folder, "figures") - if not op.exists(data_path): - os.makedirs(data_path) - if not op.exists(figure_path): - os.makedirs(figure_path) - - if op.isdir(data_path) and op.isdir(figure_path): - args = [ - "java", - "-jar", - jar_path, - pdf_folder, - "-i", - str(resolution), - "-d", - os.path.join(os.path.abspath(data_path), ""), - "-m", - op.join(os.path.abspath(figure_path), ""), # end path with "/" - ] - _ = subprocess.run( - args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=20 - ) - print("Done parsing figures from PDFs!") - else: - print("You may have to check of ``data`` and ``figures`` in the the output folder path.") diff --git a/spaces/jordonpeter01/MusicGen2/tests/utils/__init__.py b/spaces/jordonpeter01/MusicGen2/tests/utils/__init__.py deleted file mode 100644 index 0952fcc3f57e34b3747962e9ebd6fc57aeea63fa..0000000000000000000000000000000000000000 --- a/spaces/jordonpeter01/MusicGen2/tests/utils/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. diff --git a/spaces/jvahala/dummy/README.md b/spaces/jvahala/dummy/README.md deleted file mode 100644 index 2e11a6350eded4f0c021f18d6091243519e1f13c..0000000000000000000000000000000000000000 --- a/spaces/jvahala/dummy/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Dummy -emoji: 🦀 -colorFrom: purple -colorTo: yellow -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: apache-2.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/k8tems/LangChain_AgentGPTBot/app.py b/spaces/k8tems/LangChain_AgentGPTBot/app.py deleted file mode 100644 index f7e272bc3fea1baa05ea6bf0ebfdc025b9d06bb1..0000000000000000000000000000000000000000 --- a/spaces/k8tems/LangChain_AgentGPTBot/app.py +++ /dev/null @@ -1,80 +0,0 @@ -from langchain.llms import OpenAI -from langchain.chains.qa_with_sources import load_qa_with_sources_chain -from langchain.docstore.document import Document -import requests -import pathlib -import subprocess -import tempfile -import os -import gradio as gr -import pickle - -# using a vector space for our search -from langchain.embeddings.openai import OpenAIEmbeddings -from langchain.vectorstores.faiss import FAISS -from langchain.text_splitter import CharacterTextSplitter - -#loading FAISS search index from disk -with open("search_index.pickle", "rb") as f: - search_index = pickle.load(f) - -#Get GPT3 response using Langchain -def print_answer(question, openai): #openai_embeddings - #search_index = get_search_index() - chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) - response = ( - chain( - { - "input_documents": search_index.similarity_search(question, k=4), - "question": question, - }, - return_only_outputs=True, - )["output_text"] - ) - if len(response.split('\n')[-1].split())>2: - response = response.split('\n')[0] + ', '.join([' Click Link' + str(i) + '' for i in range(1,len(response.split('\n')[-1].split()))]) - else: - response = response.split('\n')[0] + ' Click Link' - return response - - -def chat(message, history, openai_api_key): - #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key) - openai = OpenAI(temperature=0, openai_api_key=openai_api_key ) - #os.environ["OPENAI_API_KEY"] = openai_api_key - history = history or [] - message = message.lower() - response = print_answer(message, openai) #openai_embeddings - history.append((message, response)) - return history, history - - -with gr.Blocks() as demo: - gr.HTML("""
        -
        -

        - AgentGPT QandA - LangChain Bot -

        -
        -

        - Hi, I'm a Q and A AgentGPT expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your AgentGPT implementations and then press enter.
        - Duplicate SpaceDuplicate Space with GPU Upgrade for fast Inference & no queue
        - Built using LangChain and Gradio for the AgentGPT Repo -

        -
        """) - with gr.Row(): - question = gr.Textbox(label = 'Type in your questions about AgentGPT here and press Enter!', placeholder = 'What questions do you want to ask about the AgentGPT library?') - openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here") - state = gr.State() - chatbot = gr.Chatbot() - question.submit(chat, [question, state, openai_api_key], [chatbot, state]) - -if __name__ == "__main__": - demo.launch() \ No newline at end of file diff --git a/spaces/kazuk/youtube-whisper-07/README.md b/spaces/kazuk/youtube-whisper-07/README.md deleted file mode 100644 index c3180680339155aaf1d27f629129b68d12cac021..0000000000000000000000000000000000000000 --- a/spaces/kazuk/youtube-whisper-07/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Youtube Whisper -emoji: ⚡ -colorFrom: green -colorTo: red -sdk: gradio -sdk_version: 3.16.2 -app_file: app.py -pinned: false -license: unknown -duplicated_from: kazuk/youtube-whisper ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/utils/paste_pic.py b/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/utils/paste_pic.py deleted file mode 100644 index f9989e21e48e64f620f9b148e65fdfe806c53b14..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/ChatGLM2-SadTalker-VC/src/utils/paste_pic.py +++ /dev/null @@ -1,69 +0,0 @@ -import cv2, os -import numpy as np -from tqdm import tqdm -import uuid - -from src.utils.videoio import save_video_with_watermark - -def paste_pic(video_path, pic_path, crop_info, new_audio_path, full_video_path, extended_crop=False): - - if not os.path.isfile(pic_path): - raise ValueError('pic_path must be a valid path to video/image file') - elif pic_path.split('.')[-1] in ['jpg', 'png', 'jpeg']: - # loader for first frame - full_img = cv2.imread(pic_path) - else: - # loader for videos - video_stream = cv2.VideoCapture(pic_path) - fps = video_stream.get(cv2.CAP_PROP_FPS) - full_frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading: - video_stream.release() - break - break - full_img = frame - frame_h = full_img.shape[0] - frame_w = full_img.shape[1] - - video_stream = cv2.VideoCapture(video_path) - fps = video_stream.get(cv2.CAP_PROP_FPS) - crop_frames = [] - while 1: - still_reading, frame = video_stream.read() - if not still_reading: - video_stream.release() - break - crop_frames.append(frame) - - if len(crop_info) != 3: - print("you didn't crop the image") - return - else: - r_w, r_h = crop_info[0] - clx, cly, crx, cry = crop_info[1] - lx, ly, rx, ry = crop_info[2] - lx, ly, rx, ry = int(lx), int(ly), int(rx), int(ry) - # oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx - # oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx - - if extended_crop: - oy1, oy2, ox1, ox2 = cly, cry, clx, crx - else: - oy1, oy2, ox1, ox2 = cly+ly, cly+ry, clx+lx, clx+rx - - tmp_path = str(uuid.uuid4())+'.mp4' - out_tmp = cv2.VideoWriter(tmp_path, cv2.VideoWriter_fourcc(*'MP4V'), fps, (frame_w, frame_h)) - for crop_frame in tqdm(crop_frames, 'seamlessClone:'): - p = cv2.resize(crop_frame.astype(np.uint8), (ox2-ox1, oy2 - oy1)) - - mask = 255*np.ones(p.shape, p.dtype) - location = ((ox1+ox2) // 2, (oy1+oy2) // 2) - gen_img = cv2.seamlessClone(p, full_img, mask, location, cv2.NORMAL_CLONE) - out_tmp.write(gen_img) - - out_tmp.release() - - save_video_with_watermark(tmp_path, new_audio_path, full_video_path, watermark=False) - os.remove(tmp_path) diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py b/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py deleted file mode 100644 index 46ae777cc97af41a531cba4e5d1ff31f2efcb468..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/configs/glint360k_mbf.py +++ /dev/null @@ -1,26 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "cosface" -config.network = "mbf" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 0.1 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 2e-4 -config.batch_size = 128 -config.lr = 0.1 # batch size is 512 - -config.rec = "/train_tmp/glint360k" -config.num_classes = 360232 -config.num_image = 17091657 -config.num_epoch = 20 -config.warmup_epoch = -1 -config.decay_epoch = [8, 12, 15, 18] -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py b/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py deleted file mode 100644 index eb4e0d31f1aedf4590628d394e1606920fefb5c9..0000000000000000000000000000000000000000 --- a/spaces/kevinwang676/SadTalker/src/face3d/models/arcface_torch/configs/ms1mv3_r18.py +++ /dev/null @@ -1,26 +0,0 @@ -from easydict import EasyDict as edict - -# make training faster -# our RAM is 256G -# mount -t tmpfs -o size=140G tmpfs /train_tmp - -config = edict() -config.loss = "arcface" -config.network = "r18" -config.resume = False -config.output = None -config.embedding_size = 512 -config.sample_rate = 1.0 -config.fp16 = True -config.momentum = 0.9 -config.weight_decay = 5e-4 -config.batch_size = 128 -config.lr = 0.1 # batch size is 512 - -config.rec = "/train_tmp/ms1m-retinaface-t1" -config.num_classes = 93431 -config.num_image = 5179510 -config.num_epoch = 25 -config.warmup_epoch = -1 -config.decay_epoch = [10, 16, 22] -config.val_targets = ["lfw", "cfp_fp", "agedb_30"] diff --git a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_scribble.py b/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_scribble.py deleted file mode 100644 index 8f3375a0bfe3c7cf7f69c3f81217d77c704327ef..0000000000000000000000000000000000000000 --- a/spaces/king007/Stable-Diffusion-ControlNet-WebUI/diffusion_webui/diffusion_models/controlnet/controlnet_scribble.py +++ /dev/null @@ -1,188 +0,0 @@ -import gradio as gr -import torch -from controlnet_aux import HEDdetector -from diffusers import ( - ControlNetModel, - StableDiffusionControlNetPipeline, - UniPCMultistepScheduler, -) -from PIL import Image - -from diffusion_webui.utils.model_list import ( - controlnet_scribble_model_list, - stable_model_list, -) -from diffusion_webui.utils.scheduler_list import ( - SCHEDULER_LIST, - get_scheduler_list, -) - - -class StableDiffusionControlNetScribbleGenerator: - def __init__(self): - self.pipe = None - - def load_model(self, stable_model_path, controlnet_model_path, scheduler): - if self.pipe is None: - controlnet = ControlNetModel.from_pretrained( - controlnet_model_path, torch_dtype=torch.float16 - ) - - self.pipe = StableDiffusionControlNetPipeline.from_pretrained( - pretrained_model_name_or_path=stable_model_path, - controlnet=controlnet, - safety_checker=None, - torch_dtype=torch.float16, - ) - - self.pipe = get_scheduler_list(pipe=self.pipe, scheduler=scheduler) - self.pipe.to("cuda") - self.pipe.enable_xformers_memory_efficient_attention() - - return self.pipe - - def controlnet_scribble(self, image_path: str): - hed = HEDdetector.from_pretrained("lllyasviel/ControlNet") - - image = Image.open(image_path) - image = hed(image, scribble=True) - - return image - - def generate_image( - self, - image_path: str, - stable_model_path: str, - controlnet_hed_model_path: str, - prompt: str, - negative_prompt: str, - num_images_per_prompt: int, - guidance_scale: int, - num_inference_step: int, - scheduler: str, - seed_generator: int, - ): - - image = self.controlnet_scribble(image_path=image_path) - - pipe = self.load_model( - stable_model_path=stable_model_path, - controlnet_model_path=controlnet_hed_model_path, - scheduler=scheduler, - ) - if seed_generator == 0: - random_seed = torch.randint(0, 1000000, (1,)) - generator = torch.manual_seed(random_seed) - else: - generator = torch.manual_seed(seed_generator) - - output = pipe( - prompt=prompt, - image=image, - negative_prompt=negative_prompt, - num_images_per_prompt=num_images_per_prompt, - num_inference_steps=num_inference_step, - guidance_scale=guidance_scale, - generator=generator, - ).images - - return output - - def app(): - with gr.Blocks(): - with gr.Row(): - with gr.Column(): - controlnet_scribble_image_file = gr.Image( - type="filepath", label="Image" - ) - controlnet_scribble_prompt = gr.Textbox( - lines=1, - show_label=False, - placeholder="Prompt", - ) - - controlnet_scribble_negative_prompt = gr.Textbox( - lines=1, - show_label=False, - placeholder="Negative Prompt", - ) - - with gr.Row(): - with gr.Column(): - controlnet_scribble_stable_model_id = gr.Dropdown( - choices=stable_model_list, - value=stable_model_list[0], - label="Stable Model Id", - ) - controlnet_scribble_guidance_scale = gr.Slider( - minimum=0.1, - maximum=15, - step=0.1, - value=7.5, - label="Guidance Scale", - ) - - controlnet_scribble_num_inference_step = gr.Slider( - minimum=1, - maximum=100, - step=1, - value=50, - label="Num Inference Step", - ) - - controlnet_scribble_num_images_per_prompt = ( - gr.Slider( - minimum=1, - maximum=10, - step=1, - value=1, - label="Number Of Images", - ) - ) - with gr.Row(): - with gr.Column(): - controlnet_scribble_model_id = gr.Dropdown( - choices=controlnet_scribble_model_list, - value=controlnet_scribble_model_list[0], - label="ControlNet Model Id", - ) - - controlnet_scribble_scheduler = gr.Dropdown( - choices=SCHEDULER_LIST, - value=SCHEDULER_LIST[0], - label="Scheduler", - ) - - controlnet_scribble_seed_generator = gr.Number( - minimum=0, - maximum=1000000, - step=1, - value=0, - label="Seed Generator", - ) - - controlnet_scribble_predict = gr.Button(value="Generator") - - with gr.Column(): - output_image = gr.Gallery( - label="Generated images", - show_label=False, - elem_id="gallery", - ).style(grid=(1, 2)) - - controlnet_scribble_predict.click( - fn=StableDiffusionControlNetScribbleGenerator().generate_image, - inputs=[ - controlnet_scribble_image_file, - controlnet_scribble_stable_model_id, - controlnet_scribble_model_id, - controlnet_scribble_prompt, - controlnet_scribble_negative_prompt, - controlnet_scribble_num_images_per_prompt, - controlnet_scribble_guidance_scale, - controlnet_scribble_num_inference_step, - controlnet_scribble_scheduler, - controlnet_scribble_seed_generator, - ], - outputs=output_image, - ) diff --git a/spaces/kokofixcomputers/chat-ui/postcss.config.js b/spaces/kokofixcomputers/chat-ui/postcss.config.js deleted file mode 100644 index 7b75c83aff1c05e0e0e315638e07a22314603d4d..0000000000000000000000000000000000000000 --- a/spaces/kokofixcomputers/chat-ui/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -export default { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -}; diff --git a/spaces/kony1337/frame-interpolation-fix/README.md b/spaces/kony1337/frame-interpolation-fix/README.md deleted file mode 100644 index b40ff22bb211f33bbd37f7310909ac8858825dfc..0000000000000000000000000000000000000000 --- a/spaces/kony1337/frame-interpolation-fix/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Frame Interpolation -emoji: 🐢 -colorFrom: blue -colorTo: gray -sdk: gradio -sdk_version: 3.1.4 -app_file: app.py -pinned: false -duplicated_from: johngoad/frame-interpolation ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference diff --git a/spaces/kquote03/lama-video-watermark-remover/README.md b/spaces/kquote03/lama-video-watermark-remover/README.md deleted file mode 100644 index e34deaed32a11ec64e933deff0803687040cbea7..0000000000000000000000000000000000000000 --- a/spaces/kquote03/lama-video-watermark-remover/README.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -title: LaMa Video Watermark Remover -emoji: 🌖 -colorFrom: red -colorTo: green -sdk: gradio -sdk_version: 3.0.24 -python_version: 3.7.13 -app_file: app.py -pinned: false -duplicated_from: fffiloni/lama-video-watermark-remover ---- - -# Configuration - -`title`: _string_ -Display title for the Space - -`emoji`: _string_ -Space emoji (emoji-only character allowed) - -`colorFrom`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`colorTo`: _string_ -Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray) - -`sdk`: _string_ -Can be either `gradio` or `streamlit` - -`sdk_version` : _string_ -Only applicable for `streamlit` SDK. -See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions. - -`python_version`: string -Any valid Python 3.x or 3.x.x version. -Defaults to 3.8.9. - -`app_file`: _string_ -Path to your main application file (which contains either `gradio` or `streamlit` Python code). -Path is relative to the root of the repository. - -`pinned`: _boolean_ -Whether the Space stays on top of your list. diff --git a/spaces/krystaltechnology/image-video-colorization/models/deep_colorization/colorizers/eccv16.py b/spaces/krystaltechnology/image-video-colorization/models/deep_colorization/colorizers/eccv16.py deleted file mode 100644 index 896ed477c20934dc86a6088117eed63af773ace8..0000000000000000000000000000000000000000 --- a/spaces/krystaltechnology/image-video-colorization/models/deep_colorization/colorizers/eccv16.py +++ /dev/null @@ -1,105 +0,0 @@ - -import torch -import torch.nn as nn -import numpy as np -from IPython import embed - -from .base_color import * - -class ECCVGenerator(BaseColor): - def __init__(self, norm_layer=nn.BatchNorm2d): - super(ECCVGenerator, self).__init__() - - model1=[nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=True),] - model1+=[nn.ReLU(True),] - model1+=[nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=True),] - model1+=[nn.ReLU(True),] - model1+=[norm_layer(64),] - - model2=[nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=True),] - model2+=[nn.ReLU(True),] - model2+=[nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1, bias=True),] - model2+=[nn.ReLU(True),] - model2+=[norm_layer(128),] - - model3=[nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=True),] - model3+=[nn.ReLU(True),] - model3+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),] - model3+=[nn.ReLU(True),] - model3+=[nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1, bias=True),] - model3+=[nn.ReLU(True),] - model3+=[norm_layer(256),] - - model4=[nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=True),] - model4+=[nn.ReLU(True),] - model4+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),] - model4+=[nn.ReLU(True),] - model4+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),] - model4+=[nn.ReLU(True),] - model4+=[norm_layer(512),] - - model5=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),] - model5+=[nn.ReLU(True),] - model5+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),] - model5+=[nn.ReLU(True),] - model5+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),] - model5+=[nn.ReLU(True),] - model5+=[norm_layer(512),] - - model6=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),] - model6+=[nn.ReLU(True),] - model6+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),] - model6+=[nn.ReLU(True),] - model6+=[nn.Conv2d(512, 512, kernel_size=3, dilation=2, stride=1, padding=2, bias=True),] - model6+=[nn.ReLU(True),] - model6+=[norm_layer(512),] - - model7=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),] - model7+=[nn.ReLU(True),] - model7+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),] - model7+=[nn.ReLU(True),] - model7+=[nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=True),] - model7+=[nn.ReLU(True),] - model7+=[norm_layer(512),] - - model8=[nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1, bias=True),] - model8+=[nn.ReLU(True),] - model8+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),] - model8+=[nn.ReLU(True),] - model8+=[nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=True),] - model8+=[nn.ReLU(True),] - - model8+=[nn.Conv2d(256, 313, kernel_size=1, stride=1, padding=0, bias=True),] - - self.model1 = nn.Sequential(*model1) - self.model2 = nn.Sequential(*model2) - self.model3 = nn.Sequential(*model3) - self.model4 = nn.Sequential(*model4) - self.model5 = nn.Sequential(*model5) - self.model6 = nn.Sequential(*model6) - self.model7 = nn.Sequential(*model7) - self.model8 = nn.Sequential(*model8) - - self.softmax = nn.Softmax(dim=1) - self.model_out = nn.Conv2d(313, 2, kernel_size=1, padding=0, dilation=1, stride=1, bias=False) - self.upsample4 = nn.Upsample(scale_factor=4, mode='bilinear') - - def forward(self, input_l): - conv1_2 = self.model1(self.normalize_l(input_l)) - conv2_2 = self.model2(conv1_2) - conv3_3 = self.model3(conv2_2) - conv4_3 = self.model4(conv3_3) - conv5_3 = self.model5(conv4_3) - conv6_3 = self.model6(conv5_3) - conv7_3 = self.model7(conv6_3) - conv8_3 = self.model8(conv7_3) - out_reg = self.model_out(self.softmax(conv8_3)) - - return self.unnormalize_ab(self.upsample4(out_reg)) - -def eccv16(pretrained=True): - model = ECCVGenerator() - if(pretrained): - import torch.utils.model_zoo as model_zoo - model.load_state_dict(model_zoo.load_url('https://colorizers.s3.us-east-2.amazonaws.com/colorization_release_v2-9b330a0b.pth',map_location='cpu',check_hash=True)) - return model diff --git a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/voltLib/error.py b/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/voltLib/error.py deleted file mode 100644 index c51d3b8fdc45afdb7bafbeb13a951264e0228985..0000000000000000000000000000000000000000 --- a/spaces/ky2k/Toxicity_Classifier_POC/.venv/lib/python3.9/site-packages/fontTools/voltLib/error.py +++ /dev/null @@ -1,12 +0,0 @@ -class VoltLibError(Exception): - def __init__(self, message, location): - Exception.__init__(self, message) - self.location = location - - def __str__(self): - message = Exception.__str__(self) - if self.location: - path, line, column = self.location - return "%s:%d:%d: %s" % (path, line, column, message) - else: - return message diff --git a/spaces/lewiswu1209/MockingBird/ppg_extractor/encoders.py b/spaces/lewiswu1209/MockingBird/ppg_extractor/encoders.py deleted file mode 100644 index 526140f4fac5b0c4663e435243655ae74a4735fa..0000000000000000000000000000000000000000 --- a/spaces/lewiswu1209/MockingBird/ppg_extractor/encoders.py +++ /dev/null @@ -1,298 +0,0 @@ -import logging -import six - -import numpy as np -import torch -import torch.nn.functional as F -from torch.nn.utils.rnn import pack_padded_sequence -from torch.nn.utils.rnn import pad_packed_sequence - -from .e2e_asr_common import get_vgg2l_odim -from .nets_utils import make_pad_mask, to_device - - -class RNNP(torch.nn.Module): - """RNN with projection layer module - - :param int idim: dimension of inputs - :param int elayers: number of encoder layers - :param int cdim: number of rnn units (resulted in cdim * 2 if bidirectional) - :param int hdim: number of projection units - :param np.ndarray subsample: list of subsampling numbers - :param float dropout: dropout rate - :param str typ: The RNN type - """ - - def __init__(self, idim, elayers, cdim, hdim, subsample, dropout, typ="blstm"): - super(RNNP, self).__init__() - bidir = typ[0] == "b" - for i in six.moves.range(elayers): - if i == 0: - inputdim = idim - else: - inputdim = hdim - rnn = torch.nn.LSTM(inputdim, cdim, dropout=dropout, num_layers=1, bidirectional=bidir, - batch_first=True) if "lstm" in typ \ - else torch.nn.GRU(inputdim, cdim, dropout=dropout, num_layers=1, bidirectional=bidir, batch_first=True) - setattr(self, "%s%d" % ("birnn" if bidir else "rnn", i), rnn) - # bottleneck layer to merge - if bidir: - setattr(self, "bt%d" % i, torch.nn.Linear(2 * cdim, hdim)) - else: - setattr(self, "bt%d" % i, torch.nn.Linear(cdim, hdim)) - - self.elayers = elayers - self.cdim = cdim - self.subsample = subsample - self.typ = typ - self.bidir = bidir - - def forward(self, xs_pad, ilens, prev_state=None): - """RNNP forward - - :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, idim) - :param torch.Tensor ilens: batch of lengths of input sequences (B) - :param torch.Tensor prev_state: batch of previous RNN states - :return: batch of hidden state sequences (B, Tmax, hdim) - :rtype: torch.Tensor - """ - logging.debug(self.__class__.__name__ + ' input lengths: ' + str(ilens)) - elayer_states = [] - for layer in six.moves.range(self.elayers): - xs_pack = pack_padded_sequence(xs_pad, ilens, batch_first=True, enforce_sorted=False) - rnn = getattr(self, ("birnn" if self.bidir else "rnn") + str(layer)) - rnn.flatten_parameters() - if prev_state is not None and rnn.bidirectional: - prev_state = reset_backward_rnn_state(prev_state) - ys, states = rnn(xs_pack, hx=None if prev_state is None else prev_state[layer]) - elayer_states.append(states) - # ys: utt list of frame x cdim x 2 (2: means bidirectional) - ys_pad, ilens = pad_packed_sequence(ys, batch_first=True) - sub = self.subsample[layer + 1] - if sub > 1: - ys_pad = ys_pad[:, ::sub] - ilens = [int(i + 1) // sub for i in ilens] - # (sum _utt frame_utt) x dim - projected = getattr(self, 'bt' + str(layer) - )(ys_pad.contiguous().view(-1, ys_pad.size(2))) - if layer == self.elayers - 1: - xs_pad = projected.view(ys_pad.size(0), ys_pad.size(1), -1) - else: - xs_pad = torch.tanh(projected.view(ys_pad.size(0), ys_pad.size(1), -1)) - - return xs_pad, ilens, elayer_states # x: utt list of frame x dim - - -class RNN(torch.nn.Module): - """RNN module - - :param int idim: dimension of inputs - :param int elayers: number of encoder layers - :param int cdim: number of rnn units (resulted in cdim * 2 if bidirectional) - :param int hdim: number of final projection units - :param float dropout: dropout rate - :param str typ: The RNN type - """ - - def __init__(self, idim, elayers, cdim, hdim, dropout, typ="blstm"): - super(RNN, self).__init__() - bidir = typ[0] == "b" - self.nbrnn = torch.nn.LSTM(idim, cdim, elayers, batch_first=True, - dropout=dropout, bidirectional=bidir) if "lstm" in typ \ - else torch.nn.GRU(idim, cdim, elayers, batch_first=True, dropout=dropout, - bidirectional=bidir) - if bidir: - self.l_last = torch.nn.Linear(cdim * 2, hdim) - else: - self.l_last = torch.nn.Linear(cdim, hdim) - self.typ = typ - - def forward(self, xs_pad, ilens, prev_state=None): - """RNN forward - - :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D) - :param torch.Tensor ilens: batch of lengths of input sequences (B) - :param torch.Tensor prev_state: batch of previous RNN states - :return: batch of hidden state sequences (B, Tmax, eprojs) - :rtype: torch.Tensor - """ - logging.debug(self.__class__.__name__ + ' input lengths: ' + str(ilens)) - xs_pack = pack_padded_sequence(xs_pad, ilens, batch_first=True) - self.nbrnn.flatten_parameters() - if prev_state is not None and self.nbrnn.bidirectional: - # We assume that when previous state is passed, it means that we're streaming the input - # and therefore cannot propagate backward BRNN state (otherwise it goes in the wrong direction) - prev_state = reset_backward_rnn_state(prev_state) - ys, states = self.nbrnn(xs_pack, hx=prev_state) - # ys: utt list of frame x cdim x 2 (2: means bidirectional) - ys_pad, ilens = pad_packed_sequence(ys, batch_first=True) - # (sum _utt frame_utt) x dim - projected = torch.tanh(self.l_last( - ys_pad.contiguous().view(-1, ys_pad.size(2)))) - xs_pad = projected.view(ys_pad.size(0), ys_pad.size(1), -1) - return xs_pad, ilens, states # x: utt list of frame x dim - - -def reset_backward_rnn_state(states): - """Sets backward BRNN states to zeroes - useful in processing of sliding windows over the inputs""" - if isinstance(states, (list, tuple)): - for state in states: - state[1::2] = 0. - else: - states[1::2] = 0. - return states - - -class VGG2L(torch.nn.Module): - """VGG-like module - - :param int in_channel: number of input channels - """ - - def __init__(self, in_channel=1, downsample=True): - super(VGG2L, self).__init__() - # CNN layer (VGG motivated) - self.conv1_1 = torch.nn.Conv2d(in_channel, 64, 3, stride=1, padding=1) - self.conv1_2 = torch.nn.Conv2d(64, 64, 3, stride=1, padding=1) - self.conv2_1 = torch.nn.Conv2d(64, 128, 3, stride=1, padding=1) - self.conv2_2 = torch.nn.Conv2d(128, 128, 3, stride=1, padding=1) - - self.in_channel = in_channel - self.downsample = downsample - if downsample: - self.stride = 2 - else: - self.stride = 1 - - def forward(self, xs_pad, ilens, **kwargs): - """VGG2L forward - - :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D) - :param torch.Tensor ilens: batch of lengths of input sequences (B) - :return: batch of padded hidden state sequences (B, Tmax // 4, 128 * D // 4) if downsample - :rtype: torch.Tensor - """ - logging.debug(self.__class__.__name__ + ' input lengths: ' + str(ilens)) - - # x: utt x frame x dim - # xs_pad = F.pad_sequence(xs_pad) - - # x: utt x 1 (input channel num) x frame x dim - xs_pad = xs_pad.view(xs_pad.size(0), xs_pad.size(1), self.in_channel, - xs_pad.size(2) // self.in_channel).transpose(1, 2) - - # NOTE: max_pool1d ? - xs_pad = F.relu(self.conv1_1(xs_pad)) - xs_pad = F.relu(self.conv1_2(xs_pad)) - if self.downsample: - xs_pad = F.max_pool2d(xs_pad, 2, stride=self.stride, ceil_mode=True) - - xs_pad = F.relu(self.conv2_1(xs_pad)) - xs_pad = F.relu(self.conv2_2(xs_pad)) - if self.downsample: - xs_pad = F.max_pool2d(xs_pad, 2, stride=self.stride, ceil_mode=True) - if torch.is_tensor(ilens): - ilens = ilens.cpu().numpy() - else: - ilens = np.array(ilens, dtype=np.float32) - if self.downsample: - ilens = np.array(np.ceil(ilens / 2), dtype=np.int64) - ilens = np.array( - np.ceil(np.array(ilens, dtype=np.float32) / 2), dtype=np.int64).tolist() - - # x: utt_list of frame (remove zeropaded frames) x (input channel num x dim) - xs_pad = xs_pad.transpose(1, 2) - xs_pad = xs_pad.contiguous().view( - xs_pad.size(0), xs_pad.size(1), xs_pad.size(2) * xs_pad.size(3)) - return xs_pad, ilens, None # no state in this layer - - -class Encoder(torch.nn.Module): - """Encoder module - - :param str etype: type of encoder network - :param int idim: number of dimensions of encoder network - :param int elayers: number of layers of encoder network - :param int eunits: number of lstm units of encoder network - :param int eprojs: number of projection units of encoder network - :param np.ndarray subsample: list of subsampling numbers - :param float dropout: dropout rate - :param int in_channel: number of input channels - """ - - def __init__(self, etype, idim, elayers, eunits, eprojs, subsample, dropout, in_channel=1): - super(Encoder, self).__init__() - typ = etype.lstrip("vgg").rstrip("p") - if typ not in ['lstm', 'gru', 'blstm', 'bgru']: - logging.error("Error: need to specify an appropriate encoder architecture") - - if etype.startswith("vgg"): - if etype[-1] == "p": - self.enc = torch.nn.ModuleList([VGG2L(in_channel), - RNNP(get_vgg2l_odim(idim, in_channel=in_channel), elayers, eunits, - eprojs, - subsample, dropout, typ=typ)]) - logging.info('Use CNN-VGG + ' + typ.upper() + 'P for encoder') - else: - self.enc = torch.nn.ModuleList([VGG2L(in_channel), - RNN(get_vgg2l_odim(idim, in_channel=in_channel), elayers, eunits, - eprojs, - dropout, typ=typ)]) - logging.info('Use CNN-VGG + ' + typ.upper() + ' for encoder') - else: - if etype[-1] == "p": - self.enc = torch.nn.ModuleList( - [RNNP(idim, elayers, eunits, eprojs, subsample, dropout, typ=typ)]) - logging.info(typ.upper() + ' with every-layer projection for encoder') - else: - self.enc = torch.nn.ModuleList([RNN(idim, elayers, eunits, eprojs, dropout, typ=typ)]) - logging.info(typ.upper() + ' without projection for encoder') - - def forward(self, xs_pad, ilens, prev_states=None): - """Encoder forward - - :param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D) - :param torch.Tensor ilens: batch of lengths of input sequences (B) - :param torch.Tensor prev_state: batch of previous encoder hidden states (?, ...) - :return: batch of hidden state sequences (B, Tmax, eprojs) - :rtype: torch.Tensor - """ - if prev_states is None: - prev_states = [None] * len(self.enc) - assert len(prev_states) == len(self.enc) - - current_states = [] - for module, prev_state in zip(self.enc, prev_states): - xs_pad, ilens, states = module(xs_pad, ilens, prev_state=prev_state) - current_states.append(states) - - # make mask to remove bias value in padded part - mask = to_device(self, make_pad_mask(ilens).unsqueeze(-1)) - - return xs_pad.masked_fill(mask, 0.0), ilens, current_states - - -def encoder_for(args, idim, subsample): - """Instantiates an encoder module given the program arguments - - :param Namespace args: The arguments - :param int or List of integer idim: dimension of input, e.g. 83, or - List of dimensions of inputs, e.g. [83,83] - :param List or List of List subsample: subsample factors, e.g. [1,2,2,1,1], or - List of subsample factors of each encoder. e.g. [[1,2,2,1,1], [1,2,2,1,1]] - :rtype torch.nn.Module - :return: The encoder module - """ - num_encs = getattr(args, "num_encs", 1) # use getattr to keep compatibility - if num_encs == 1: - # compatible with single encoder asr mode - return Encoder(args.etype, idim, args.elayers, args.eunits, args.eprojs, subsample, args.dropout_rate) - elif num_encs >= 1: - enc_list = torch.nn.ModuleList() - for idx in range(num_encs): - enc = Encoder(args.etype[idx], idim[idx], args.elayers[idx], args.eunits[idx], args.eprojs, subsample[idx], - args.dropout_rate[idx]) - enc_list.append(enc) - return enc_list - else: - raise ValueError("Number of encoders needs to be more than one. {}".format(num_encs)) diff --git a/spaces/limingcv/AlignDet/style.css b/spaces/limingcv/AlignDet/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/limingcv/AlignDet/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/lincquiQcaudo/Top-20-Diffusion/K-Lite Codec Pack 11.9.0 Basic.md b/spaces/lincquiQcaudo/Top-20-Diffusion/K-Lite Codec Pack 11.9.0 Basic.md deleted file mode 100644 index 0321a4d65ca7f51ef5956803a31ec20b7492d3dc..0000000000000000000000000000000000000000 --- a/spaces/lincquiQcaudo/Top-20-Diffusion/K-Lite Codec Pack 11.9.0 Basic.md +++ /dev/null @@ -1,6 +0,0 @@ -

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        diff --git a/spaces/lithiumice/SadTalker/src/face3d/util/nvdiffrast.py b/spaces/lithiumice/SadTalker/src/face3d/util/nvdiffrast.py deleted file mode 100644 index f3245859c650afbfe841a66b74cddefaf28820d9..0000000000000000000000000000000000000000 --- a/spaces/lithiumice/SadTalker/src/face3d/util/nvdiffrast.py +++ /dev/null @@ -1,126 +0,0 @@ -"""This script is the differentiable renderer for Deep3DFaceRecon_pytorch - Attention, antialiasing step is missing in current version. -""" -import pytorch3d.ops -import torch -import torch.nn.functional as F -import kornia -from kornia.geometry.camera import pixel2cam -import numpy as np -from typing import List -from scipy.io import loadmat -from torch import nn - -from pytorch3d.structures import Meshes -from pytorch3d.renderer import ( - look_at_view_transform, - FoVPerspectiveCameras, - DirectionalLights, - RasterizationSettings, - MeshRenderer, - MeshRasterizer, - SoftPhongShader, - TexturesUV, -) - -# def ndc_projection(x=0.1, n=1.0, f=50.0): -# return np.array([[n/x, 0, 0, 0], -# [ 0, n/-x, 0, 0], -# [ 0, 0, -(f+n)/(f-n), -(2*f*n)/(f-n)], -# [ 0, 0, -1, 0]]).astype(np.float32) - -class MeshRenderer(nn.Module): - def __init__(self, - rasterize_fov, - znear=0.1, - zfar=10, - rasterize_size=224): - super(MeshRenderer, self).__init__() - - # x = np.tan(np.deg2rad(rasterize_fov * 0.5)) * znear - # self.ndc_proj = torch.tensor(ndc_projection(x=x, n=znear, f=zfar)).matmul( - # torch.diag(torch.tensor([1., -1, -1, 1]))) - self.rasterize_size = rasterize_size - self.fov = rasterize_fov - self.znear = znear - self.zfar = zfar - - self.rasterizer = None - - def forward(self, vertex, tri, feat=None): - """ - Return: - mask -- torch.tensor, size (B, 1, H, W) - depth -- torch.tensor, size (B, 1, H, W) - features(optional) -- torch.tensor, size (B, C, H, W) if feat is not None - - Parameters: - vertex -- torch.tensor, size (B, N, 3) - tri -- torch.tensor, size (B, M, 3) or (M, 3), triangles - feat(optional) -- torch.tensor, size (B, N ,C), features - """ - device = vertex.device - rsize = int(self.rasterize_size) - # ndc_proj = self.ndc_proj.to(device) - # trans to homogeneous coordinates of 3d vertices, the direction of y is the same as v - if vertex.shape[-1] == 3: - vertex = torch.cat([vertex, torch.ones([*vertex.shape[:2], 1]).to(device)], dim=-1) - vertex[..., 0] = -vertex[..., 0] - - - # vertex_ndc = vertex @ ndc_proj.t() - if self.rasterizer is None: - self.rasterizer = MeshRasterizer() - print("create rasterizer on device cuda:%d"%device.index) - - # ranges = None - # if isinstance(tri, List) or len(tri.shape) == 3: - # vum = vertex_ndc.shape[1] - # fnum = torch.tensor([f.shape[0] for f in tri]).unsqueeze(1).to(device) - # fstartidx = torch.cumsum(fnum, dim=0) - fnum - # ranges = torch.cat([fstartidx, fnum], axis=1).type(torch.int32).cpu() - # for i in range(tri.shape[0]): - # tri[i] = tri[i] + i*vum - # vertex_ndc = torch.cat(vertex_ndc, dim=0) - # tri = torch.cat(tri, dim=0) - - # for range_mode vetex: [B*N, 4], tri: [B*M, 3], for instance_mode vetex: [B, N, 4], tri: [M, 3] - tri = tri.type(torch.int32).contiguous() - - # rasterize - cameras = FoVPerspectiveCameras( - device=device, - fov=self.fov, - znear=self.znear, - zfar=self.zfar, - ) - - raster_settings = RasterizationSettings( - image_size=rsize - ) - - # print(vertex.shape, tri.shape) - mesh = Meshes(vertex.contiguous()[...,:3], tri.unsqueeze(0).repeat((vertex.shape[0],1,1))) - - fragments = self.rasterizer(mesh, cameras = cameras, raster_settings = raster_settings) - rast_out = fragments.pix_to_face.squeeze(-1) - depth = fragments.zbuf - - # render depth - depth = depth.permute(0, 3, 1, 2) - mask = (rast_out > 0).float().unsqueeze(1) - depth = mask * depth - - - image = None - if feat is not None: - attributes = feat.reshape(-1,3)[mesh.faces_packed()] - image = pytorch3d.ops.interpolate_face_attributes(fragments.pix_to_face, - fragments.bary_coords, - attributes) - # print(image.shape) - image = image.squeeze(-2).permute(0, 3, 1, 2) - image = mask * image - - return mask, depth, image - diff --git a/spaces/ljiy/GGG/devices/device_8958.js b/spaces/ljiy/GGG/devices/device_8958.js deleted file mode 100644 index 455ddb0108b70276949e6539926481590a98e0d9..0000000000000000000000000000000000000000 --- a/spaces/ljiy/GGG/devices/device_8958.js +++ /dev/null @@ -1,344 +0,0 @@ -"use strict"; -var __importDefault = (this && this.__importDefault) || function (mod) { - return (mod && mod.__esModule) ? mod : { "default": mod }; -}; -Object.defineProperty(exports, "__esModule", { value: true }); -exports.getApkInfo = exports.Platform = exports.Device = exports.generateFullDevice = exports.generateShortDevice = void 0; -const crypto_1 = require("crypto"); -const constants_1 = require("./constants"); -const axios_1 = __importDefault(require("axios")); -const algo_1 = require("./algo"); -function generateImei() { - let imei = `86${(0, constants_1.randomString)(12, '0123456789')}`; - function calcSP(imei) { - let sum = 0; - for (let i = 0; i < imei.length; ++i) { - if (i % 2) { - let j = parseInt(imei[i]) * 2; - sum += j % 10 + Math.floor(j / 10); - } - else { - sum += parseInt(imei[i]); - } - } - return (100 - sum) % 10; - } - return imei + calcSP(imei); -} -/** 生成短设备信息 */ -function generateShortDevice() { - const randstr = (length, num = false) => { - const map = num ? '0123456789' : '0123456789abcdef'; - return (0, constants_1.randomString)(length, map); - }; - return { - "--begin--": "该设备为随机生成，丢失后不能得到原先配置", - product: `ILPP-${randstr(5).toUpperCase()}`, - device: `${randstr(5).toUpperCase()}`, - board: `${randstr(5).toUpperCase()}`, - brand: `${randstr(4).toUpperCase()}`, - model: `ICQQ ${randstr(4).toUpperCase()}`, - wifi_ssid: `HUAWEI-${randstr(7)}`, - bootloader: `U-boot`, - android_id: `IL.${randstr(7, true)}.${randstr(4, true)}`, - boot_id: `${randstr(8)}-${randstr(4)}-${randstr(4)}-${randstr(4)}-${randstr(12)}`, - proc_version: `Linux version 5.10.101-android12-${randstr(8)}`, - mac_address: `2D:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}:${randstr(2).toUpperCase()}`, - ip_address: `192.168.${randstr(2, true)}.${randstr(2, true)}`, - imei: `${generateImei()}`, - incremental: `${randstr(10, true).toUpperCase()}`, - "--end--": "修改后可能需要重新验证设备。" - }; -} -exports.generateShortDevice = generateShortDevice; -/** 生成完整设备信息 */ -function generateFullDevice(apk, d) { - if (!d) - d = generateShortDevice(); - return { - display: d.android_id, - product: d.product, - device: d.device, - board: d.board, - brand: d.brand, - model: d.model, - bootloader: d.bootloader, - fingerprint: `${d.brand}/${d.product}/${d.device}:10/${d.android_id}/${d.incremental}:user/release-keys`, - boot_id: d.boot_id, - proc_version: d.proc_version, - baseband: "", - sim: "T-Mobile", - os_type: "android", - mac_address: d.mac_address, - ip_address: d.ip_address, - wifi_bssid: d.mac_address, - wifi_ssid: d.wifi_ssid, - imei: d.imei, - android_id: (0, constants_1.md5)(d.android_id).toString("hex"), - apn: "wifi", - version: { - incremental: d.incremental, - release: "10", - codename: "REL", - sdk: 29, - }, - imsi: (0, crypto_1.randomBytes)(16), - guid: (0, constants_1.md5)(Buffer.concat([Buffer.from(d.imei), Buffer.from(d.mac_address)])), - }; -} -exports.generateFullDevice = generateFullDevice; -class Device { - constructor(apk, d) { - this.apk = apk; - this.secret = 'ZdJqM15EeO2zWc08'; - this.publicKey = `-----BEGIN PUBLIC KEY----- -MIGfMA0GCSqGSIb3DQEBAQUAA4GNADCBiQKBgQDEIxgwoutfwoJxcGQeedgP7FG9 -qaIuS0qzfR8gWkrkTZKM2iWHn2ajQpBRZjMSoSf6+KJGvar2ORhBfpDXyVtZCKpq -LQ+FLkpncClKVIrBwv6PHyUvuCb0rIarmgDnzkfQAqVufEtR64iazGDKatvJ9y6B -9NMbHddGSAUmRTCrHQIDAQAB ------END PUBLIC KEY-----`; - if (!d) - d = generateShortDevice(); - Object.assign(this, generateFullDevice(apk, d)); - } - async getQIMEI() { - if (this.apk.app_key === "") { - return; - } - const k = (0, constants_1.randomString)(16); - const key = (0, algo_1.encryptPKCS1)(this.publicKey, k); - const time = Date.now(); - const nonce = (0, constants_1.randomString)(16); - const payload = this.genRandomPayloadByDevice(); - const params = (0, algo_1.aesEncrypt)(JSON.stringify(payload), k).toString('base64'); - try { - const { data } = await axios_1.default.post("https://snowflake.qq.com/ola/android", { - key, - params, - time, nonce, - sign: (0, constants_1.md5)(key + params + time + nonce + this.secret).toString("hex"), - extra: '' - }, { - headers: { - 'User-Agent': `Dalvik/2.1.0 (Linux; U; Android ${this.version.release}; PCRT00 Build/N2G48H)`, - 'Content-Type': "application/json" - } - }); - if (data?.code !== 0) { - return; - } - const { q16, q36 } = JSON.parse((0, algo_1.aesDecrypt)(data.data, k)); - this.qImei16 = q16; - this.qImei36 = q36; - } - catch { - } - } - genRandomPayloadByDevice() { - const fixedRand = (max = 1, min = 0) => { - if (max < min) - [max, min] = [min, max]; - const diff = max - min; - return Math.floor(Math.random() * diff) + min; - }; - const reserved = { - "harmony": "0", - "clone": Math.random() > 0.5 ? "1" : "0", - "containe": "", - "oz": "", - "oo": "", - "kelong": Math.random() > 0.5 ? "1" : "0", - "uptimes": (0, constants_1.formatTime)(new Date()), - "multiUser": Math.random() > 0.5 ? "1" : "0", - "bod": this.board, - "brd": this.brand, - "dv": this.device, - "firstLevel": "", - "manufact": this.brand, - "name": this.model, - "host": "se.infra", - "kernel": this.fingerprint - }; - const timestamp = Date.now(); - this.mtime = this.mtime || Date.now(); - const mtime1 = new Date(this.mtime || Date.now()); - const dateFormat = (fmt, time = Date.now()) => (0, constants_1.formatTime)(time, fmt); - const mtimeStr1 = dateFormat("YYYY-mm-ddHHMMSS", mtime1) + "." + this.imei.slice(2, 11); - const mtime2 = new Date(this.mtime - parseInt(this.imei.slice(2, 4))); - const mtimeStr2 = dateFormat("YYYY-mm-ddHHMMSS", mtime2) + "." + this.imei.slice(5, 14); - let beaconIdArr = [ - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - mtimeStr1, - '0000000000000000', - (0, constants_1.md5)(this.android_id + this.imei).toString("hex").slice(0, 16), - ...new Array(4).fill(false).map((_) => fixedRand(10000000, 1000000)), - this.boot_id, - '1', - fixedRand(5, 0), - fixedRand(5, 0), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(5, 0), - fixedRand(100, 10), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(50000, 10000), - fixedRand(100, 10), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - mtimeStr2, - fixedRand(10000, 1000), - fixedRand(5, 0), - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((10 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - fixedRand(10000, 1000), - fixedRand(100, 10), - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - `${dateFormat("YYYY-mm-ddHHMMSS")}.${String(((11 + parseInt(this.imei.slice(5, 7))) % 100)).padStart(2, "0")}0000000`, - fixedRand(10000, 1000), - fixedRand(5, 0), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(5, 0), - fixedRand(100, 10), - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - `${(0, constants_1.formatTime)(new Date(timestamp + fixedRand(60, 0)))}.${String(fixedRand(99, 0)).padStart(2, '0')}0000000`, - fixedRand(5, 0), - fixedRand(5, 0), - ].map((str, idx) => `k${idx + 1}:${str}`); - return { - "androidId": this.android_id, - "platformId": 1, - "appKey": this.apk.app_key, - "appVersion": this.apk.version, - "beaconIdSrc": beaconIdArr.join(';'), - "brand": this.brand, - "channelId": "2017", - "cid": "", - "imei": this.imei, - "imsi": this.imsi.toString("hex"), - "mac": this.mac_address, - "model": this.model, - "networkType": "unknown", - "oaid": "", - "osVersion": `Android ${this.version.release},level ${this.version.sdk}`, - "qimei": "", - "qimei36": "", - "sdkVersion": "1.2.13.6", - "targetSdkVersion": "26", - "audit": "", - "userId": "{}", - "packageId": this.apk.id, - "deviceType": this.display, - "sdkName": "", - "reserved": JSON.stringify(reserved), - }; - } -} -exports.Device = Device; -/** 支持的登录设备平台 */ -var Platform; -(function (Platform) { - Platform[Platform["Android"] = 1] = "Android"; - Platform[Platform["aPad"] = 2] = "aPad"; - Platform[Platform["Watch"] = 3] = "Watch"; - Platform[Platform["iMac"] = 4] = "iMac"; - Platform[Platform["iPad"] = 5] = "iPad"; - Platform[Platform["Tim"] = 6] = "Tim"; -})(Platform = exports.Platform || (exports.Platform = {})); -const mobile = { - id: "com.tencent.mobileqq", - app_key: '0S200MNJT807V3GE', - name: "A8.9.58.11175", - version: "8.9.58.11175", - ver: "8.9.58", - sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))), - buildtime: 1684467300, - appid: 16, - subid: 537163194, - bitmap: 150470524, - main_sig_map: 16724722, - sub_sig_map: 0x10400, - sdkver: "6.0.0.2545", - display: "Android_8.9.58", - qua: 'V1_AND_SQ_8.9.58_4108_YYB_D', - ssover: 20, -}; -const tim = { - id: "com.tencent.tim", - app_key: '0S200MNJT807V3GE', - name: "A3.5.1.3168", - version: "3.5.1.3168", - ver: "3.5.1", - sign: Buffer.from('775e696d09856872fdd8ab4f3f06b1e0', 'hex'), - buildtime: 1630062176, - appid: 16, - subid: 537150355, - bitmap: 150470524, - main_sig_map: 16724722, - sub_sig_map: 0x10400, - sdkver: "6.0.0.2484", - display: "Tim", - qua: "V1_AND_SQ_8.3.9_351_TIM_D", - ssover: 18, -}; -const watch = { - id: "com.tencent.qqlite", - app_key: '0S200MNJT807V3GE', - name: "A2.0.8", - version: "2.0.8", - ver: "2.0.8", - sign: Buffer.from('A6 B7 45 BF 24 A2 C2 77 52 77 16 F6 F3 6E B6 8D'.split(' ').map(s => parseInt(s, 16))), - buildtime: 1559564731, - appid: 16, - subid: 537065138, - bitmap: 16252796, - main_sig_map: 16724722, - sub_sig_map: 0x10400, - sdkver: "6.0.0.2365", - display: "Watch", - qua: '', - ssover: 5 -}; -const hd = { - id: "com.tencent.minihd.qq", - app_key: '0S200MNJT807V3GE', - name: "A5.9.3.3468", - version: "5.9.3.3468", - ver: "5.9.3", - sign: Buffer.from('AA 39 78 F4 1F D9 6F F9 91 4A 66 9E 18 64 74 C7'.split(' ').map(s => parseInt(s, 16))), - buildtime: 1637427966, - appid: 16, - subid: 537128930, - bitmap: 150470524, - main_sig_map: 1970400, - sub_sig_map: 66560, - sdkver: "6.0.0.2433", - display: "iMac", - qua: '', - ssover: 12 -}; -const apklist = { - [Platform.Android]: mobile, - [Platform.Tim]: tim, - [Platform.aPad]: { - ...mobile, - subid: 537163242, - display: 'aPad_8.9.58' - }, - [Platform.Watch]: watch, - [Platform.iMac]: { ...hd }, - [Platform.iPad]: { - ...mobile, - subid: 537155074, - sign: hd.sign, - name: '8.9.50.611', - ver: '8.9.50', - sdkver: '6.0.0.2535', - qua: 'V1_AND_SQ_8.9.50_3898_YYB_D', - display: 'iPad' - }, -}; -function getApkInfo(p) { - return apklist[p] || apklist[Platform.Android]; -} -exports.getApkInfo = getApkInfo; diff --git a/spaces/ltg/chat-nort5/retrieval.py b/spaces/ltg/chat-nort5/retrieval.py deleted file mode 100644 index 9e27ea339e6a60a3b074ffc06ef970522b4fe3fc..0000000000000000000000000000000000000000 --- a/spaces/ltg/chat-nort5/retrieval.py +++ /dev/null @@ -1,183 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -from question_detection_norbert3_small.modeling_norbert import NorbertForSequenceClassification -from keyword_generation_nort5_small.modeling_nort5 import NorT5ForConditionalGeneration -from norquad.modeling_norbert import NorbertForQuestionAnswering -from transformers import AutoTokenizer - -import wikipedia -import wikipediaapi - -from smart_open import open -from trie import MarisaTrie - - -class Retrival: - def __init__(self): - self.tokenizer = AutoTokenizer.from_pretrained("keyword_generation_nort5_small") - self.device = "cuda" if torch.cuda.is_available() else "cpu" - - self.question_detection = NorbertForSequenceClassification.from_pretrained("question_detection_norbert3_small") - self.question_detection = self.question_detection.to(self.device) - self.question_detection.eval() - - self.keyword_generation = NorT5ForConditionalGeneration.from_pretrained("keyword_generation_nort5_small") - self.keyword_generation = self.keyword_generation.to(self.device) - self.keyword_generation.eval() - - self.norquad = NorbertForQuestionAnswering.from_pretrained("norquad") - self.norquad = self.norquad.to(self.device) - self.norquad.eval() - - self.wiki = wikipediaapi.Wikipedia("ChatNorT5 (https://huggingface.co/spaces/davda54/chat-nort5", 'nb') - wikipedia.set_lang('nb') - - titles = [title.strip().replace('_', ' ') for title in open("https://dumps.wikimedia.org/nowiki/latest/nowiki-latest-all-titles-in-ns0.gz")] - titles = [[5] + self.tokenizer(title, add_special_tokens=False).input_ids + [6] for title in titles] - self.trie = MarisaTrie(titles) - - self.extraction_max_length = 384 # The maximum length of a feature (question and context) - self.extraction_doc_stride = 128 # The authorized overlap between two part of the context when splitting it is needed. - - def is_question(self, text): - if len(text) > 256 or len(text) < 8: - return False - - input_ids = self.tokenizer([text]).input_ids - input_ids = torch.tensor(input_ids, device=self.device) - - output = self.question_detection(input_ids).logits - - print(f"Question probability: {F.softmax(output.squeeze(), -1)[1].item() * 100.0:.2f}") - return output.argmax(-1).squeeze().item() == 1 - - def get_answer(self, text): - keywords = self._create_keywords(text) - print(keywords) - - for title, url, document in self._yield_documents(keywords): - response = self._extract_answer(text, document) - if response is None: - continue - - print(response) - return title, url, response - - return None, None, None - - def _extract_answer(self, question, document): - encoding = self.tokenizer( - question, - document, - truncation="only_second", - max_length=self.extraction_max_length, - stride=self.extraction_doc_stride, - return_overflowing_tokens=True, - return_offsets_mapping=True, - padding="max_length" - ) - - for i in range(len(encoding["input_ids"])): - sequence_ids = encoding.sequence_ids(i) - encoding["offset_mapping"][i] = [ - (o if sequence_ids[k] == 1 else None) - for k, o in enumerate(encoding["offset_mapping"][i]) - ] - - batch_size = len(encoding.input_ids) - start_logits, end_logits = [], [] - for i in range((batch_size + 7) // 8): - output = self.norquad(torch.tensor(encoding.input_ids[i*8 : (i+1)*8], device=self.device)) - start_logits.append(output.start_logits.cpu()) - end_logits.append(output.end_logits.cpu()) - - start_logits = torch.cat(start_logits, dim=0) - end_logits = torch.cat(end_logits, dim=0) - - valid_answers = [] - - for feature_index in range(batch_size): - offset_mapping = encoding["offset_mapping"][feature_index] - feature_null_score = start_logits[feature_index, 0] + end_logits[feature_index, 0] - - start_indexes = start_logits[feature_index].argsort(descending=True)[:20].tolist() - end_indexes = end_logits[feature_index].argsort(descending=True)[:20].tolist() - for start_index in start_indexes: - for end_index in end_indexes: - if ( - start_index >= len(offset_mapping) - or end_index >= len(offset_mapping) - or offset_mapping[start_index] is None - or offset_mapping[end_index] is None - ): - continue - - if end_index < start_index: - continue - - if start_logits[feature_index, start_index] + end_logits[feature_index, end_index] < feature_null_score: - continue - - start_char = offset_mapping[start_index][0] - end_char = offset_mapping[end_index][1] - valid_answers.append( - { - "score": start_logits[feature_index, start_index] + end_logits[feature_index, end_index], - "start": start_char, - "end": end_char - } - ) - - if len(valid_answers) == 0: - return None - - best_answer = sorted(valid_answers, key=lambda x: x["score"], reverse=True)[0] - start, end = best_answer["start"], best_answer["end"] - print(document[start:end]) - - left_context = document[max(0, start - 128):start] - answer = document[start:end] - right_context = document[end:end+256] - - left_context = ' '.join(left_context.split('\n')[-1].split()) - right_context = ' '.join(right_context.split('\n\n')[0].split()) - - return left_context, answer, right_context - - - def _create_keywords(self, text): - input_ids = self.tokenizer([text]).input_ids - input_ids = torch.tensor(input_ids, device=self.device) - - output = self.keyword_generation.generate( - input_ids=input_ids, - do_sample = False, - num_return_sequences=8, - num_beams=16, - temperature=1.0, - prefix_allowed_tokens_fn=lambda batch_id, sent: self.trie.get(sent.tolist()) - ) - keywords = self.tokenizer.batch_decode(output.cpu(), skip_special_tokens=True) - return keywords - - - def _yield_documents(self, keywords): - used_keywords = set() - for keyword in keywords: - suggestions = wikipedia.search(keyword, results=1, suggestion=False) - if len(suggestions) == 0: - continue - - if suggestions[0] in used_keywords: - continue - used_keywords.add(suggestions[0]) - - page = self.wiki.page(suggestions[0]) - - if not page.exists(): - continue - - print(suggestions[0], '->', page.fullurl) - yield suggestions[0], page.fullurl, page.text diff --git a/spaces/ma-xu/LIVE/thrust/thrust/detail/preprocessor.h b/spaces/ma-xu/LIVE/thrust/thrust/detail/preprocessor.h deleted file mode 100644 index 0e9943b76f84ed5481364aa1fce7d35970d26097..0000000000000000000000000000000000000000 --- a/spaces/ma-xu/LIVE/thrust/thrust/detail/preprocessor.h +++ /dev/null @@ -1,1182 +0,0 @@ -// Copyright (c) 2017-2018 NVIDIA Corporation -// Copyright (c) 2014-2018 Bryce Adelstein Lelbach -// Copyright (c) 2001-2015 Housemarque Oy (housemarque.com) -// Copyright (c) 2007-2015 Hartmut Kaiser -// Copyright (c) 2002 Peter Dimov and Multi Media Ltd -// (`THRUST_CURRENT_FUNCTION`) -// -// Distributed under the Boost Software License v1.0 (boost.org/LICENSE_1_0.txt) - -#pragma once - -/////////////////////////////////////////////////////////////////////////////// - -/// \def THRUST_PP_STRINGIZE(expr) -/// \brief Stringizes the expression \a expr. -/// -/// \par Example: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << THRUST_PP_STRINGIZE(foo) << "\n"; -/// } -/// \endcode -/// -/// The above code expands to: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << "foo" << "\n"; -/// } -/// \endcode -/// -#define THRUST_PP_STRINGIZE(expr) THRUST_PP_STRINGIZE_IMPL0(expr) -#define THRUST_PP_STRINGIZE_IMPL0(expr) #expr - -/////////////////////////////////////////////////////////////////////////////// - -/// \def THRUST_PP_CAT2(a, b) -/// \brief Concatenates the tokens \a a and \b b. -/// -/// \par Example: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << THRUST_PP_CAT2(1, THRUST_PP_CAT2(2, 3)) << "\n"; -/// } -/// \endcode -/// -/// The above code expands to: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << 123 << "\n"; -/// } -/// \endcode -/// -#define THRUST_PP_CAT2(a, b) THRUST_PP_CAT2_IMPL0(a, b) - -#if defined(_MSC_VER) \ - && (defined(__EDG__) || defined(__EDG_VERSION__)) \ - && (defined(__INTELLISENSE__) || __EDG_VERSION__ >= 308) - #define THRUST_PP_CAT2_IMPL0(a, b) THRUST_PP_CAT2_IMPL1(~, a ## b) - #define THRUST_PP_CAT2_IMPL1(p, res) res -#else - #define THRUST_PP_CAT2_IMPL0(a, b) a ## b -#endif - -#define THRUST_PP_CAT3(a, b, c) \ - THRUST_PP_CAT2(a, \ - THRUST_PP_CAT2(b, c)) \ - /**/ - -#define THRUST_PP_CAT4(a, b, c, d) \ - THRUST_PP_CAT2(a, \ - THRUST_PP_CAT2(b, \ - THRUST_PP_CAT2(c, d))) \ - /**/ - -#define THRUST_PP_CAT5(a, b, c, d, e) \ - THRUST_PP_CAT2(a, \ - THRUST_PP_CAT2(b, \ - THRUST_PP_CAT2(c, \ - THRUST_PP_CAT2(d, e)))) \ - /**/ - -/////////////////////////////////////////////////////////////////////////////// - -/// \def THRUST_PP_EXPAND(x) -/// \brief Performs macro expansion on \a x. -/// -/// \par Example: -/// -/// \code -/// #include -/// #include -/// -/// #define FOO_BAR() "foo_bar" -/// #define BUZZ() THRUST_PP_EXPAND(THRUST_PP_CAT2(FOO_, BAR)()) -/// -/// int main() -/// { -/// std::cout << BUZZ() << "\n"; -/// } -/// \endcode -/// -/// The above code expands to: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << "foo_bar" << "\n"; -/// } -/// \endcode -/// -#define THRUST_PP_EXPAND(x) THRUST_PP_EXPAND_IMPL0(x) -#define THRUST_PP_EXPAND_IMPL0(x) x - -#define THRUST_PP_EXPAND_ARGS(...) THRUST_PP_EXPAND_ARGS_IMPL0(__VA_ARGS__) -#define THRUST_PP_EXPAND_ARGS_IMPL0(...) __VA_ARGS__ - -#define THRUST_PP_HEAD(x, ...) x - -#define THRUST_PP_TAIL(x, ...) __VA_ARGS__ - -/////////////////////////////////////////////////////////////////////////////// - -#define THRUST_PP_EMPTY() - -#define THRUST_PP_COMMA() , - -/////////////////////////////////////////////////////////////////////////////// - -#define THRUST_PP_INC(x) THRUST_PP_INC_IMPL0(x) - -#define THRUST_PP_INC_IMPL0(x) THRUST_PP_CAT2(THRUST_PP_INC_IMPL_TAG, x) - -#define THRUST_PP_INC_IMPL_TAG0 1 -#define THRUST_PP_INC_IMPL_TAG1 2 -#define THRUST_PP_INC_IMPL_TAG2 3 -#define THRUST_PP_INC_IMPL_TAG3 4 -#define THRUST_PP_INC_IMPL_TAG4 5 -#define THRUST_PP_INC_IMPL_TAG5 6 -#define THRUST_PP_INC_IMPL_TAG6 7 -#define THRUST_PP_INC_IMPL_TAG7 8 -#define THRUST_PP_INC_IMPL_TAG8 9 -#define THRUST_PP_INC_IMPL_TAG9 10 -#define THRUST_PP_INC_IMPL_TAG10 11 -#define THRUST_PP_INC_IMPL_TAG11 12 -#define THRUST_PP_INC_IMPL_TAG12 13 -#define THRUST_PP_INC_IMPL_TAG13 14 -#define THRUST_PP_INC_IMPL_TAG14 15 -#define THRUST_PP_INC_IMPL_TAG15 16 -#define THRUST_PP_INC_IMPL_TAG16 17 -#define THRUST_PP_INC_IMPL_TAG17 18 -#define THRUST_PP_INC_IMPL_TAG18 19 -#define THRUST_PP_INC_IMPL_TAG19 20 -#define THRUST_PP_INC_IMPL_TAG20 21 -#define THRUST_PP_INC_IMPL_TAG21 22 -#define THRUST_PP_INC_IMPL_TAG22 23 -#define THRUST_PP_INC_IMPL_TAG23 24 -#define THRUST_PP_INC_IMPL_TAG24 25 -#define THRUST_PP_INC_IMPL_TAG25 26 -#define THRUST_PP_INC_IMPL_TAG26 27 -#define THRUST_PP_INC_IMPL_TAG27 28 -#define THRUST_PP_INC_IMPL_TAG28 29 -#define THRUST_PP_INC_IMPL_TAG29 30 -#define THRUST_PP_INC_IMPL_TAG30 31 -#define THRUST_PP_INC_IMPL_TAG31 32 -#define THRUST_PP_INC_IMPL_TAG32 33 -#define THRUST_PP_INC_IMPL_TAG33 34 -#define THRUST_PP_INC_IMPL_TAG34 35 -#define THRUST_PP_INC_IMPL_TAG35 36 -#define THRUST_PP_INC_IMPL_TAG36 37 -#define THRUST_PP_INC_IMPL_TAG37 38 -#define THRUST_PP_INC_IMPL_TAG38 39 -#define THRUST_PP_INC_IMPL_TAG39 40 -#define THRUST_PP_INC_IMPL_TAG40 41 -#define THRUST_PP_INC_IMPL_TAG41 42 -#define THRUST_PP_INC_IMPL_TAG42 43 -#define THRUST_PP_INC_IMPL_TAG43 44 -#define THRUST_PP_INC_IMPL_TAG44 45 -#define THRUST_PP_INC_IMPL_TAG45 46 -#define THRUST_PP_INC_IMPL_TAG46 47 -#define THRUST_PP_INC_IMPL_TAG47 48 -#define THRUST_PP_INC_IMPL_TAG48 49 -#define THRUST_PP_INC_IMPL_TAG49 50 -#define THRUST_PP_INC_IMPL_TAG50 51 -#define THRUST_PP_INC_IMPL_TAG51 52 -#define THRUST_PP_INC_IMPL_TAG52 53 -#define THRUST_PP_INC_IMPL_TAG53 54 -#define THRUST_PP_INC_IMPL_TAG54 55 -#define THRUST_PP_INC_IMPL_TAG55 56 -#define THRUST_PP_INC_IMPL_TAG56 57 -#define THRUST_PP_INC_IMPL_TAG57 58 -#define THRUST_PP_INC_IMPL_TAG58 59 -#define THRUST_PP_INC_IMPL_TAG59 60 -#define THRUST_PP_INC_IMPL_TAG60 61 -#define THRUST_PP_INC_IMPL_TAG61 62 -#define THRUST_PP_INC_IMPL_TAG62 63 -#define THRUST_PP_INC_IMPL_TAG63 64 -#define THRUST_PP_INC_IMPL_TAG64 65 -#define THRUST_PP_INC_IMPL_TAG65 66 -#define THRUST_PP_INC_IMPL_TAG66 67 -#define THRUST_PP_INC_IMPL_TAG67 68 -#define THRUST_PP_INC_IMPL_TAG68 69 -#define THRUST_PP_INC_IMPL_TAG69 70 -#define THRUST_PP_INC_IMPL_TAG70 71 -#define THRUST_PP_INC_IMPL_TAG71 72 -#define THRUST_PP_INC_IMPL_TAG72 73 -#define THRUST_PP_INC_IMPL_TAG73 74 -#define THRUST_PP_INC_IMPL_TAG74 75 -#define THRUST_PP_INC_IMPL_TAG75 76 -#define THRUST_PP_INC_IMPL_TAG76 77 -#define THRUST_PP_INC_IMPL_TAG77 78 -#define THRUST_PP_INC_IMPL_TAG78 79 -#define THRUST_PP_INC_IMPL_TAG79 80 -#define THRUST_PP_INC_IMPL_TAG80 81 -#define THRUST_PP_INC_IMPL_TAG81 82 -#define THRUST_PP_INC_IMPL_TAG82 83 -#define THRUST_PP_INC_IMPL_TAG83 84 -#define THRUST_PP_INC_IMPL_TAG84 85 -#define THRUST_PP_INC_IMPL_TAG85 86 -#define THRUST_PP_INC_IMPL_TAG86 87 -#define THRUST_PP_INC_IMPL_TAG87 88 -#define THRUST_PP_INC_IMPL_TAG88 89 -#define THRUST_PP_INC_IMPL_TAG89 90 -#define THRUST_PP_INC_IMPL_TAG90 91 -#define THRUST_PP_INC_IMPL_TAG91 92 -#define THRUST_PP_INC_IMPL_TAG92 93 -#define THRUST_PP_INC_IMPL_TAG93 94 -#define THRUST_PP_INC_IMPL_TAG94 95 -#define THRUST_PP_INC_IMPL_TAG95 96 -#define THRUST_PP_INC_IMPL_TAG96 97 -#define THRUST_PP_INC_IMPL_TAG97 98 -#define THRUST_PP_INC_IMPL_TAG98 99 -#define THRUST_PP_INC_IMPL_TAG99 100 -#define THRUST_PP_INC_IMPL_TAG100 101 -#define THRUST_PP_INC_IMPL_TAG101 102 -#define THRUST_PP_INC_IMPL_TAG102 103 -#define THRUST_PP_INC_IMPL_TAG103 104 -#define THRUST_PP_INC_IMPL_TAG104 105 -#define THRUST_PP_INC_IMPL_TAG105 106 -#define THRUST_PP_INC_IMPL_TAG106 107 -#define THRUST_PP_INC_IMPL_TAG107 108 -#define THRUST_PP_INC_IMPL_TAG108 109 -#define THRUST_PP_INC_IMPL_TAG109 110 -#define THRUST_PP_INC_IMPL_TAG110 111 -#define THRUST_PP_INC_IMPL_TAG111 112 -#define THRUST_PP_INC_IMPL_TAG112 113 -#define THRUST_PP_INC_IMPL_TAG113 114 -#define THRUST_PP_INC_IMPL_TAG114 115 -#define THRUST_PP_INC_IMPL_TAG115 116 -#define THRUST_PP_INC_IMPL_TAG116 117 -#define THRUST_PP_INC_IMPL_TAG117 118 -#define THRUST_PP_INC_IMPL_TAG118 119 -#define THRUST_PP_INC_IMPL_TAG119 120 -#define THRUST_PP_INC_IMPL_TAG120 121 -#define THRUST_PP_INC_IMPL_TAG121 122 -#define THRUST_PP_INC_IMPL_TAG122 123 -#define THRUST_PP_INC_IMPL_TAG123 124 -#define THRUST_PP_INC_IMPL_TAG124 125 -#define THRUST_PP_INC_IMPL_TAG125 126 -#define THRUST_PP_INC_IMPL_TAG126 127 -#define THRUST_PP_INC_IMPL_TAG127 128 -#define THRUST_PP_INC_IMPL_TAG128 129 -#define THRUST_PP_INC_IMPL_TAG129 130 -#define THRUST_PP_INC_IMPL_TAG130 131 -#define THRUST_PP_INC_IMPL_TAG131 132 -#define THRUST_PP_INC_IMPL_TAG132 133 -#define THRUST_PP_INC_IMPL_TAG133 134 -#define THRUST_PP_INC_IMPL_TAG134 135 -#define THRUST_PP_INC_IMPL_TAG135 136 -#define THRUST_PP_INC_IMPL_TAG136 137 -#define THRUST_PP_INC_IMPL_TAG137 138 -#define THRUST_PP_INC_IMPL_TAG138 139 -#define THRUST_PP_INC_IMPL_TAG139 140 -#define THRUST_PP_INC_IMPL_TAG140 141 -#define THRUST_PP_INC_IMPL_TAG141 142 -#define THRUST_PP_INC_IMPL_TAG142 143 -#define THRUST_PP_INC_IMPL_TAG143 144 -#define THRUST_PP_INC_IMPL_TAG144 145 -#define THRUST_PP_INC_IMPL_TAG145 146 -#define THRUST_PP_INC_IMPL_TAG146 147 -#define THRUST_PP_INC_IMPL_TAG147 148 -#define THRUST_PP_INC_IMPL_TAG148 149 -#define THRUST_PP_INC_IMPL_TAG149 150 -#define THRUST_PP_INC_IMPL_TAG150 151 -#define THRUST_PP_INC_IMPL_TAG151 152 -#define THRUST_PP_INC_IMPL_TAG152 153 -#define THRUST_PP_INC_IMPL_TAG153 154 -#define THRUST_PP_INC_IMPL_TAG154 155 -#define THRUST_PP_INC_IMPL_TAG155 156 -#define THRUST_PP_INC_IMPL_TAG156 157 -#define THRUST_PP_INC_IMPL_TAG157 158 -#define THRUST_PP_INC_IMPL_TAG158 159 -#define THRUST_PP_INC_IMPL_TAG159 160 -#define THRUST_PP_INC_IMPL_TAG160 161 -#define THRUST_PP_INC_IMPL_TAG161 162 -#define THRUST_PP_INC_IMPL_TAG162 163 -#define THRUST_PP_INC_IMPL_TAG163 164 -#define THRUST_PP_INC_IMPL_TAG164 165 -#define THRUST_PP_INC_IMPL_TAG165 166 -#define THRUST_PP_INC_IMPL_TAG166 167 -#define THRUST_PP_INC_IMPL_TAG167 168 -#define THRUST_PP_INC_IMPL_TAG168 169 -#define THRUST_PP_INC_IMPL_TAG169 170 -#define THRUST_PP_INC_IMPL_TAG170 171 -#define THRUST_PP_INC_IMPL_TAG171 172 -#define THRUST_PP_INC_IMPL_TAG172 173 -#define THRUST_PP_INC_IMPL_TAG173 174 -#define THRUST_PP_INC_IMPL_TAG174 175 -#define THRUST_PP_INC_IMPL_TAG175 176 -#define THRUST_PP_INC_IMPL_TAG176 177 -#define THRUST_PP_INC_IMPL_TAG177 178 -#define THRUST_PP_INC_IMPL_TAG178 179 -#define THRUST_PP_INC_IMPL_TAG179 180 -#define THRUST_PP_INC_IMPL_TAG180 181 -#define THRUST_PP_INC_IMPL_TAG181 182 -#define THRUST_PP_INC_IMPL_TAG182 183 -#define THRUST_PP_INC_IMPL_TAG183 184 -#define THRUST_PP_INC_IMPL_TAG184 185 -#define THRUST_PP_INC_IMPL_TAG185 186 -#define THRUST_PP_INC_IMPL_TAG186 187 -#define THRUST_PP_INC_IMPL_TAG187 188 -#define THRUST_PP_INC_IMPL_TAG188 189 -#define THRUST_PP_INC_IMPL_TAG189 190 -#define THRUST_PP_INC_IMPL_TAG190 191 -#define THRUST_PP_INC_IMPL_TAG191 192 -#define THRUST_PP_INC_IMPL_TAG192 193 -#define THRUST_PP_INC_IMPL_TAG193 194 -#define THRUST_PP_INC_IMPL_TAG194 195 -#define THRUST_PP_INC_IMPL_TAG195 196 -#define THRUST_PP_INC_IMPL_TAG196 197 -#define THRUST_PP_INC_IMPL_TAG197 198 -#define THRUST_PP_INC_IMPL_TAG198 199 -#define THRUST_PP_INC_IMPL_TAG199 200 -#define THRUST_PP_INC_IMPL_TAG200 201 -#define THRUST_PP_INC_IMPL_TAG201 202 -#define THRUST_PP_INC_IMPL_TAG202 203 -#define THRUST_PP_INC_IMPL_TAG203 204 -#define THRUST_PP_INC_IMPL_TAG204 205 -#define THRUST_PP_INC_IMPL_TAG205 206 -#define THRUST_PP_INC_IMPL_TAG206 207 -#define THRUST_PP_INC_IMPL_TAG207 208 -#define THRUST_PP_INC_IMPL_TAG208 209 -#define THRUST_PP_INC_IMPL_TAG209 210 -#define THRUST_PP_INC_IMPL_TAG210 211 -#define THRUST_PP_INC_IMPL_TAG211 212 -#define THRUST_PP_INC_IMPL_TAG212 213 -#define THRUST_PP_INC_IMPL_TAG213 214 -#define THRUST_PP_INC_IMPL_TAG214 215 -#define THRUST_PP_INC_IMPL_TAG215 216 -#define THRUST_PP_INC_IMPL_TAG216 217 -#define THRUST_PP_INC_IMPL_TAG217 218 -#define THRUST_PP_INC_IMPL_TAG218 219 -#define THRUST_PP_INC_IMPL_TAG219 220 -#define THRUST_PP_INC_IMPL_TAG220 221 -#define THRUST_PP_INC_IMPL_TAG221 222 -#define THRUST_PP_INC_IMPL_TAG222 223 -#define THRUST_PP_INC_IMPL_TAG223 224 -#define THRUST_PP_INC_IMPL_TAG224 225 -#define THRUST_PP_INC_IMPL_TAG225 226 -#define THRUST_PP_INC_IMPL_TAG226 227 -#define THRUST_PP_INC_IMPL_TAG227 228 -#define THRUST_PP_INC_IMPL_TAG228 229 -#define THRUST_PP_INC_IMPL_TAG229 230 -#define THRUST_PP_INC_IMPL_TAG230 231 -#define THRUST_PP_INC_IMPL_TAG231 232 -#define THRUST_PP_INC_IMPL_TAG232 233 -#define THRUST_PP_INC_IMPL_TAG233 234 -#define THRUST_PP_INC_IMPL_TAG234 235 -#define THRUST_PP_INC_IMPL_TAG235 236 -#define THRUST_PP_INC_IMPL_TAG236 237 -#define THRUST_PP_INC_IMPL_TAG237 238 -#define THRUST_PP_INC_IMPL_TAG238 239 -#define THRUST_PP_INC_IMPL_TAG239 240 -#define THRUST_PP_INC_IMPL_TAG240 241 -#define THRUST_PP_INC_IMPL_TAG241 242 -#define THRUST_PP_INC_IMPL_TAG242 243 -#define THRUST_PP_INC_IMPL_TAG243 244 -#define THRUST_PP_INC_IMPL_TAG244 245 -#define THRUST_PP_INC_IMPL_TAG245 246 -#define THRUST_PP_INC_IMPL_TAG246 247 -#define THRUST_PP_INC_IMPL_TAG247 248 -#define THRUST_PP_INC_IMPL_TAG248 249 -#define THRUST_PP_INC_IMPL_TAG249 250 -#define THRUST_PP_INC_IMPL_TAG250 251 -#define THRUST_PP_INC_IMPL_TAG251 252 -#define THRUST_PP_INC_IMPL_TAG252 253 -#define THRUST_PP_INC_IMPL_TAG253 254 -#define THRUST_PP_INC_IMPL_TAG254 255 -#define THRUST_PP_INC_IMPL_TAG255 256 -#define THRUST_PP_INC_IMPL_TAG256 256 - -#define THRUST_PP_DEC(x) THRUST_PP_DEC_IMPL0(x) - -#define THRUST_PP_DEC_IMPL0(x) THRUST_PP_CAT2(THRUST_PP_DEC_IMPL_TAG, x) - -#define THRUST_PP_DEC_IMPL_TAG0 0 -#define THRUST_PP_DEC_IMPL_TAG1 0 -#define THRUST_PP_DEC_IMPL_TAG2 1 -#define THRUST_PP_DEC_IMPL_TAG3 2 -#define THRUST_PP_DEC_IMPL_TAG4 3 -#define THRUST_PP_DEC_IMPL_TAG5 4 -#define THRUST_PP_DEC_IMPL_TAG6 5 -#define THRUST_PP_DEC_IMPL_TAG7 6 -#define THRUST_PP_DEC_IMPL_TAG8 7 -#define THRUST_PP_DEC_IMPL_TAG9 8 -#define THRUST_PP_DEC_IMPL_TAG10 9 -#define THRUST_PP_DEC_IMPL_TAG11 10 -#define THRUST_PP_DEC_IMPL_TAG12 11 -#define THRUST_PP_DEC_IMPL_TAG13 12 -#define THRUST_PP_DEC_IMPL_TAG14 13 -#define THRUST_PP_DEC_IMPL_TAG15 14 -#define THRUST_PP_DEC_IMPL_TAG16 15 -#define THRUST_PP_DEC_IMPL_TAG17 16 -#define THRUST_PP_DEC_IMPL_TAG18 17 -#define THRUST_PP_DEC_IMPL_TAG19 18 -#define THRUST_PP_DEC_IMPL_TAG20 19 -#define THRUST_PP_DEC_IMPL_TAG21 20 -#define THRUST_PP_DEC_IMPL_TAG22 21 -#define THRUST_PP_DEC_IMPL_TAG23 22 -#define THRUST_PP_DEC_IMPL_TAG24 23 -#define THRUST_PP_DEC_IMPL_TAG25 24 -#define THRUST_PP_DEC_IMPL_TAG26 25 -#define THRUST_PP_DEC_IMPL_TAG27 26 -#define THRUST_PP_DEC_IMPL_TAG28 27 -#define THRUST_PP_DEC_IMPL_TAG29 28 -#define THRUST_PP_DEC_IMPL_TAG30 29 -#define THRUST_PP_DEC_IMPL_TAG31 30 -#define THRUST_PP_DEC_IMPL_TAG32 31 -#define THRUST_PP_DEC_IMPL_TAG33 32 -#define THRUST_PP_DEC_IMPL_TAG34 33 -#define THRUST_PP_DEC_IMPL_TAG35 34 -#define THRUST_PP_DEC_IMPL_TAG36 35 -#define THRUST_PP_DEC_IMPL_TAG37 36 -#define THRUST_PP_DEC_IMPL_TAG38 37 -#define THRUST_PP_DEC_IMPL_TAG39 38 -#define THRUST_PP_DEC_IMPL_TAG40 39 -#define THRUST_PP_DEC_IMPL_TAG41 40 -#define THRUST_PP_DEC_IMPL_TAG42 41 -#define THRUST_PP_DEC_IMPL_TAG43 42 -#define THRUST_PP_DEC_IMPL_TAG44 43 -#define THRUST_PP_DEC_IMPL_TAG45 44 -#define THRUST_PP_DEC_IMPL_TAG46 45 -#define THRUST_PP_DEC_IMPL_TAG47 46 -#define THRUST_PP_DEC_IMPL_TAG48 47 -#define THRUST_PP_DEC_IMPL_TAG49 48 -#define THRUST_PP_DEC_IMPL_TAG50 49 -#define THRUST_PP_DEC_IMPL_TAG51 50 -#define THRUST_PP_DEC_IMPL_TAG52 51 -#define THRUST_PP_DEC_IMPL_TAG53 52 -#define THRUST_PP_DEC_IMPL_TAG54 53 -#define THRUST_PP_DEC_IMPL_TAG55 54 -#define THRUST_PP_DEC_IMPL_TAG56 55 -#define THRUST_PP_DEC_IMPL_TAG57 56 -#define THRUST_PP_DEC_IMPL_TAG58 57 -#define THRUST_PP_DEC_IMPL_TAG59 58 -#define THRUST_PP_DEC_IMPL_TAG60 59 -#define THRUST_PP_DEC_IMPL_TAG61 60 -#define THRUST_PP_DEC_IMPL_TAG62 61 -#define THRUST_PP_DEC_IMPL_TAG63 62 -#define THRUST_PP_DEC_IMPL_TAG64 63 -#define THRUST_PP_DEC_IMPL_TAG65 64 -#define THRUST_PP_DEC_IMPL_TAG66 65 -#define THRUST_PP_DEC_IMPL_TAG67 66 -#define THRUST_PP_DEC_IMPL_TAG68 67 -#define THRUST_PP_DEC_IMPL_TAG69 68 -#define THRUST_PP_DEC_IMPL_TAG70 69 -#define THRUST_PP_DEC_IMPL_TAG71 70 -#define THRUST_PP_DEC_IMPL_TAG72 71 -#define THRUST_PP_DEC_IMPL_TAG73 72 -#define THRUST_PP_DEC_IMPL_TAG74 73 -#define THRUST_PP_DEC_IMPL_TAG75 74 -#define THRUST_PP_DEC_IMPL_TAG76 75 -#define THRUST_PP_DEC_IMPL_TAG77 76 -#define THRUST_PP_DEC_IMPL_TAG78 77 -#define THRUST_PP_DEC_IMPL_TAG79 78 -#define THRUST_PP_DEC_IMPL_TAG80 79 -#define THRUST_PP_DEC_IMPL_TAG81 80 -#define THRUST_PP_DEC_IMPL_TAG82 81 -#define THRUST_PP_DEC_IMPL_TAG83 82 -#define THRUST_PP_DEC_IMPL_TAG84 83 -#define THRUST_PP_DEC_IMPL_TAG85 84 -#define THRUST_PP_DEC_IMPL_TAG86 85 -#define THRUST_PP_DEC_IMPL_TAG87 86 -#define THRUST_PP_DEC_IMPL_TAG88 87 -#define THRUST_PP_DEC_IMPL_TAG89 88 -#define THRUST_PP_DEC_IMPL_TAG90 89 -#define THRUST_PP_DEC_IMPL_TAG91 90 -#define THRUST_PP_DEC_IMPL_TAG92 91 -#define THRUST_PP_DEC_IMPL_TAG93 92 -#define THRUST_PP_DEC_IMPL_TAG94 93 -#define THRUST_PP_DEC_IMPL_TAG95 94 -#define THRUST_PP_DEC_IMPL_TAG96 95 -#define THRUST_PP_DEC_IMPL_TAG97 96 -#define THRUST_PP_DEC_IMPL_TAG98 97 -#define THRUST_PP_DEC_IMPL_TAG99 98 -#define THRUST_PP_DEC_IMPL_TAG100 99 -#define THRUST_PP_DEC_IMPL_TAG101 100 -#define THRUST_PP_DEC_IMPL_TAG102 101 -#define THRUST_PP_DEC_IMPL_TAG103 102 -#define THRUST_PP_DEC_IMPL_TAG104 103 -#define THRUST_PP_DEC_IMPL_TAG105 104 -#define THRUST_PP_DEC_IMPL_TAG106 105 -#define THRUST_PP_DEC_IMPL_TAG107 106 -#define THRUST_PP_DEC_IMPL_TAG108 107 -#define THRUST_PP_DEC_IMPL_TAG109 108 -#define THRUST_PP_DEC_IMPL_TAG110 109 -#define THRUST_PP_DEC_IMPL_TAG111 110 -#define THRUST_PP_DEC_IMPL_TAG112 111 -#define THRUST_PP_DEC_IMPL_TAG113 112 -#define THRUST_PP_DEC_IMPL_TAG114 113 -#define THRUST_PP_DEC_IMPL_TAG115 114 -#define THRUST_PP_DEC_IMPL_TAG116 115 -#define THRUST_PP_DEC_IMPL_TAG117 116 -#define THRUST_PP_DEC_IMPL_TAG118 117 -#define THRUST_PP_DEC_IMPL_TAG119 118 -#define THRUST_PP_DEC_IMPL_TAG120 119 -#define THRUST_PP_DEC_IMPL_TAG121 120 -#define THRUST_PP_DEC_IMPL_TAG122 121 -#define THRUST_PP_DEC_IMPL_TAG123 122 -#define THRUST_PP_DEC_IMPL_TAG124 123 -#define THRUST_PP_DEC_IMPL_TAG125 124 -#define THRUST_PP_DEC_IMPL_TAG126 125 -#define THRUST_PP_DEC_IMPL_TAG127 126 -#define THRUST_PP_DEC_IMPL_TAG128 127 -#define THRUST_PP_DEC_IMPL_TAG129 128 -#define THRUST_PP_DEC_IMPL_TAG130 129 -#define THRUST_PP_DEC_IMPL_TAG131 130 -#define THRUST_PP_DEC_IMPL_TAG132 131 -#define THRUST_PP_DEC_IMPL_TAG133 132 -#define THRUST_PP_DEC_IMPL_TAG134 133 -#define THRUST_PP_DEC_IMPL_TAG135 134 -#define THRUST_PP_DEC_IMPL_TAG136 135 -#define THRUST_PP_DEC_IMPL_TAG137 136 -#define THRUST_PP_DEC_IMPL_TAG138 137 -#define THRUST_PP_DEC_IMPL_TAG139 138 -#define THRUST_PP_DEC_IMPL_TAG140 139 -#define THRUST_PP_DEC_IMPL_TAG141 140 -#define THRUST_PP_DEC_IMPL_TAG142 141 -#define THRUST_PP_DEC_IMPL_TAG143 142 -#define THRUST_PP_DEC_IMPL_TAG144 143 -#define THRUST_PP_DEC_IMPL_TAG145 144 -#define THRUST_PP_DEC_IMPL_TAG146 145 -#define THRUST_PP_DEC_IMPL_TAG147 146 -#define THRUST_PP_DEC_IMPL_TAG148 147 -#define THRUST_PP_DEC_IMPL_TAG149 148 -#define THRUST_PP_DEC_IMPL_TAG150 149 -#define THRUST_PP_DEC_IMPL_TAG151 150 -#define THRUST_PP_DEC_IMPL_TAG152 151 -#define THRUST_PP_DEC_IMPL_TAG153 152 -#define THRUST_PP_DEC_IMPL_TAG154 153 -#define THRUST_PP_DEC_IMPL_TAG155 154 -#define THRUST_PP_DEC_IMPL_TAG156 155 -#define THRUST_PP_DEC_IMPL_TAG157 156 -#define THRUST_PP_DEC_IMPL_TAG158 157 -#define THRUST_PP_DEC_IMPL_TAG159 158 -#define THRUST_PP_DEC_IMPL_TAG160 159 -#define THRUST_PP_DEC_IMPL_TAG161 160 -#define THRUST_PP_DEC_IMPL_TAG162 161 -#define THRUST_PP_DEC_IMPL_TAG163 162 -#define THRUST_PP_DEC_IMPL_TAG164 163 -#define THRUST_PP_DEC_IMPL_TAG165 164 -#define THRUST_PP_DEC_IMPL_TAG166 165 -#define THRUST_PP_DEC_IMPL_TAG167 166 -#define THRUST_PP_DEC_IMPL_TAG168 167 -#define THRUST_PP_DEC_IMPL_TAG169 168 -#define THRUST_PP_DEC_IMPL_TAG170 169 -#define THRUST_PP_DEC_IMPL_TAG171 170 -#define THRUST_PP_DEC_IMPL_TAG172 171 -#define THRUST_PP_DEC_IMPL_TAG173 172 -#define THRUST_PP_DEC_IMPL_TAG174 173 -#define THRUST_PP_DEC_IMPL_TAG175 174 -#define THRUST_PP_DEC_IMPL_TAG176 175 -#define THRUST_PP_DEC_IMPL_TAG177 176 -#define THRUST_PP_DEC_IMPL_TAG178 177 -#define THRUST_PP_DEC_IMPL_TAG179 178 -#define THRUST_PP_DEC_IMPL_TAG180 179 -#define THRUST_PP_DEC_IMPL_TAG181 180 -#define THRUST_PP_DEC_IMPL_TAG182 181 -#define THRUST_PP_DEC_IMPL_TAG183 182 -#define THRUST_PP_DEC_IMPL_TAG184 183 -#define THRUST_PP_DEC_IMPL_TAG185 184 -#define THRUST_PP_DEC_IMPL_TAG186 185 -#define THRUST_PP_DEC_IMPL_TAG187 186 -#define THRUST_PP_DEC_IMPL_TAG188 187 -#define THRUST_PP_DEC_IMPL_TAG189 188 -#define THRUST_PP_DEC_IMPL_TAG190 189 -#define THRUST_PP_DEC_IMPL_TAG191 190 -#define THRUST_PP_DEC_IMPL_TAG192 191 -#define THRUST_PP_DEC_IMPL_TAG193 192 -#define THRUST_PP_DEC_IMPL_TAG194 193 -#define THRUST_PP_DEC_IMPL_TAG195 194 -#define THRUST_PP_DEC_IMPL_TAG196 195 -#define THRUST_PP_DEC_IMPL_TAG197 196 -#define THRUST_PP_DEC_IMPL_TAG198 197 -#define THRUST_PP_DEC_IMPL_TAG199 198 -#define THRUST_PP_DEC_IMPL_TAG200 199 -#define THRUST_PP_DEC_IMPL_TAG201 200 -#define THRUST_PP_DEC_IMPL_TAG202 201 -#define THRUST_PP_DEC_IMPL_TAG203 202 -#define THRUST_PP_DEC_IMPL_TAG204 203 -#define THRUST_PP_DEC_IMPL_TAG205 204 -#define THRUST_PP_DEC_IMPL_TAG206 205 -#define THRUST_PP_DEC_IMPL_TAG207 206 -#define THRUST_PP_DEC_IMPL_TAG208 207 -#define THRUST_PP_DEC_IMPL_TAG209 208 -#define THRUST_PP_DEC_IMPL_TAG210 209 -#define THRUST_PP_DEC_IMPL_TAG211 210 -#define THRUST_PP_DEC_IMPL_TAG212 211 -#define THRUST_PP_DEC_IMPL_TAG213 212 -#define THRUST_PP_DEC_IMPL_TAG214 213 -#define THRUST_PP_DEC_IMPL_TAG215 214 -#define THRUST_PP_DEC_IMPL_TAG216 215 -#define THRUST_PP_DEC_IMPL_TAG217 216 -#define THRUST_PP_DEC_IMPL_TAG218 217 -#define THRUST_PP_DEC_IMPL_TAG219 218 -#define THRUST_PP_DEC_IMPL_TAG220 219 -#define THRUST_PP_DEC_IMPL_TAG221 220 -#define THRUST_PP_DEC_IMPL_TAG222 221 -#define THRUST_PP_DEC_IMPL_TAG223 222 -#define THRUST_PP_DEC_IMPL_TAG224 223 -#define THRUST_PP_DEC_IMPL_TAG225 224 -#define THRUST_PP_DEC_IMPL_TAG226 225 -#define THRUST_PP_DEC_IMPL_TAG227 226 -#define THRUST_PP_DEC_IMPL_TAG228 227 -#define THRUST_PP_DEC_IMPL_TAG229 228 -#define THRUST_PP_DEC_IMPL_TAG230 229 -#define THRUST_PP_DEC_IMPL_TAG231 230 -#define THRUST_PP_DEC_IMPL_TAG232 231 -#define THRUST_PP_DEC_IMPL_TAG233 232 -#define THRUST_PP_DEC_IMPL_TAG234 233 -#define THRUST_PP_DEC_IMPL_TAG235 234 -#define THRUST_PP_DEC_IMPL_TAG236 235 -#define THRUST_PP_DEC_IMPL_TAG237 236 -#define THRUST_PP_DEC_IMPL_TAG238 237 -#define THRUST_PP_DEC_IMPL_TAG239 238 -#define THRUST_PP_DEC_IMPL_TAG240 239 -#define THRUST_PP_DEC_IMPL_TAG241 240 -#define THRUST_PP_DEC_IMPL_TAG242 241 -#define THRUST_PP_DEC_IMPL_TAG243 242 -#define THRUST_PP_DEC_IMPL_TAG244 243 -#define THRUST_PP_DEC_IMPL_TAG245 244 -#define THRUST_PP_DEC_IMPL_TAG246 245 -#define THRUST_PP_DEC_IMPL_TAG247 246 -#define THRUST_PP_DEC_IMPL_TAG248 247 -#define THRUST_PP_DEC_IMPL_TAG249 248 -#define THRUST_PP_DEC_IMPL_TAG250 249 -#define THRUST_PP_DEC_IMPL_TAG251 250 -#define THRUST_PP_DEC_IMPL_TAG252 251 -#define THRUST_PP_DEC_IMPL_TAG253 252 -#define THRUST_PP_DEC_IMPL_TAG254 253 -#define THRUST_PP_DEC_IMPL_TAG255 254 -#define THRUST_PP_DEC_IMPL_TAG256 255 -#define THRUST_PP_DEC_IMPL_TAG257 256 - -#define THRUST_PP_BOOL(x) THRUST_PP_BOOL_IMPL0(x) - -#define THRUST_PP_BOOL_IMPL0(x) THRUST_PP_CAT2(THRUST_PP_BOOL_IMPL_TAG, x) - -#define THRUST_PP_BOOL_IMPL_TAG0 0 -#define THRUST_PP_BOOL_IMPL_TAG1 1 -#define THRUST_PP_BOOL_IMPL_TAG2 1 -#define THRUST_PP_BOOL_IMPL_TAG3 1 -#define THRUST_PP_BOOL_IMPL_TAG4 1 -#define THRUST_PP_BOOL_IMPL_TAG5 1 -#define THRUST_PP_BOOL_IMPL_TAG6 1 -#define THRUST_PP_BOOL_IMPL_TAG7 1 -#define THRUST_PP_BOOL_IMPL_TAG8 1 -#define THRUST_PP_BOOL_IMPL_TAG9 1 -#define THRUST_PP_BOOL_IMPL_TAG10 1 -#define THRUST_PP_BOOL_IMPL_TAG11 1 -#define THRUST_PP_BOOL_IMPL_TAG12 1 -#define THRUST_PP_BOOL_IMPL_TAG13 1 -#define THRUST_PP_BOOL_IMPL_TAG14 1 -#define THRUST_PP_BOOL_IMPL_TAG15 1 -#define THRUST_PP_BOOL_IMPL_TAG16 1 -#define THRUST_PP_BOOL_IMPL_TAG17 1 -#define THRUST_PP_BOOL_IMPL_TAG18 1 -#define THRUST_PP_BOOL_IMPL_TAG19 1 -#define THRUST_PP_BOOL_IMPL_TAG20 1 -#define THRUST_PP_BOOL_IMPL_TAG21 1 -#define THRUST_PP_BOOL_IMPL_TAG22 1 -#define THRUST_PP_BOOL_IMPL_TAG23 1 -#define THRUST_PP_BOOL_IMPL_TAG24 1 -#define THRUST_PP_BOOL_IMPL_TAG25 1 -#define THRUST_PP_BOOL_IMPL_TAG26 1 -#define THRUST_PP_BOOL_IMPL_TAG27 1 -#define THRUST_PP_BOOL_IMPL_TAG28 1 -#define THRUST_PP_BOOL_IMPL_TAG29 1 -#define THRUST_PP_BOOL_IMPL_TAG30 1 -#define THRUST_PP_BOOL_IMPL_TAG31 1 -#define THRUST_PP_BOOL_IMPL_TAG32 1 -#define THRUST_PP_BOOL_IMPL_TAG33 1 -#define THRUST_PP_BOOL_IMPL_TAG34 1 -#define THRUST_PP_BOOL_IMPL_TAG35 1 -#define THRUST_PP_BOOL_IMPL_TAG36 1 -#define THRUST_PP_BOOL_IMPL_TAG37 1 -#define THRUST_PP_BOOL_IMPL_TAG38 1 -#define THRUST_PP_BOOL_IMPL_TAG39 1 -#define THRUST_PP_BOOL_IMPL_TAG40 1 -#define THRUST_PP_BOOL_IMPL_TAG41 1 -#define THRUST_PP_BOOL_IMPL_TAG42 1 -#define THRUST_PP_BOOL_IMPL_TAG43 1 -#define THRUST_PP_BOOL_IMPL_TAG44 1 -#define THRUST_PP_BOOL_IMPL_TAG45 1 -#define THRUST_PP_BOOL_IMPL_TAG46 1 -#define THRUST_PP_BOOL_IMPL_TAG47 1 -#define THRUST_PP_BOOL_IMPL_TAG48 1 -#define THRUST_PP_BOOL_IMPL_TAG49 1 -#define THRUST_PP_BOOL_IMPL_TAG50 1 -#define THRUST_PP_BOOL_IMPL_TAG51 1 -#define THRUST_PP_BOOL_IMPL_TAG52 1 -#define THRUST_PP_BOOL_IMPL_TAG53 1 -#define THRUST_PP_BOOL_IMPL_TAG54 1 -#define THRUST_PP_BOOL_IMPL_TAG55 1 -#define THRUST_PP_BOOL_IMPL_TAG56 1 -#define THRUST_PP_BOOL_IMPL_TAG57 1 -#define THRUST_PP_BOOL_IMPL_TAG58 1 -#define THRUST_PP_BOOL_IMPL_TAG59 1 -#define THRUST_PP_BOOL_IMPL_TAG60 1 -#define THRUST_PP_BOOL_IMPL_TAG61 1 -#define THRUST_PP_BOOL_IMPL_TAG62 1 -#define THRUST_PP_BOOL_IMPL_TAG63 1 -#define THRUST_PP_BOOL_IMPL_TAG64 1 -#define THRUST_PP_BOOL_IMPL_TAG65 1 -#define THRUST_PP_BOOL_IMPL_TAG66 1 -#define THRUST_PP_BOOL_IMPL_TAG67 1 -#define THRUST_PP_BOOL_IMPL_TAG68 1 -#define THRUST_PP_BOOL_IMPL_TAG69 1 -#define THRUST_PP_BOOL_IMPL_TAG70 1 -#define THRUST_PP_BOOL_IMPL_TAG71 1 -#define THRUST_PP_BOOL_IMPL_TAG72 1 -#define THRUST_PP_BOOL_IMPL_TAG73 1 -#define THRUST_PP_BOOL_IMPL_TAG74 1 -#define THRUST_PP_BOOL_IMPL_TAG75 1 -#define THRUST_PP_BOOL_IMPL_TAG76 1 -#define THRUST_PP_BOOL_IMPL_TAG77 1 -#define THRUST_PP_BOOL_IMPL_TAG78 1 -#define THRUST_PP_BOOL_IMPL_TAG79 1 -#define THRUST_PP_BOOL_IMPL_TAG80 1 -#define THRUST_PP_BOOL_IMPL_TAG81 1 -#define THRUST_PP_BOOL_IMPL_TAG82 1 -#define THRUST_PP_BOOL_IMPL_TAG83 1 -#define THRUST_PP_BOOL_IMPL_TAG84 1 -#define THRUST_PP_BOOL_IMPL_TAG85 1 -#define THRUST_PP_BOOL_IMPL_TAG86 1 -#define THRUST_PP_BOOL_IMPL_TAG87 1 -#define THRUST_PP_BOOL_IMPL_TAG88 1 -#define THRUST_PP_BOOL_IMPL_TAG89 1 -#define THRUST_PP_BOOL_IMPL_TAG90 1 -#define THRUST_PP_BOOL_IMPL_TAG91 1 -#define THRUST_PP_BOOL_IMPL_TAG92 1 -#define THRUST_PP_BOOL_IMPL_TAG93 1 -#define THRUST_PP_BOOL_IMPL_TAG94 1 -#define THRUST_PP_BOOL_IMPL_TAG95 1 -#define THRUST_PP_BOOL_IMPL_TAG96 1 -#define THRUST_PP_BOOL_IMPL_TAG97 1 -#define THRUST_PP_BOOL_IMPL_TAG98 1 -#define THRUST_PP_BOOL_IMPL_TAG99 1 -#define THRUST_PP_BOOL_IMPL_TAG100 1 -#define THRUST_PP_BOOL_IMPL_TAG101 1 -#define THRUST_PP_BOOL_IMPL_TAG102 1 -#define THRUST_PP_BOOL_IMPL_TAG103 1 -#define THRUST_PP_BOOL_IMPL_TAG104 1 -#define THRUST_PP_BOOL_IMPL_TAG105 1 -#define THRUST_PP_BOOL_IMPL_TAG106 1 -#define THRUST_PP_BOOL_IMPL_TAG107 1 -#define THRUST_PP_BOOL_IMPL_TAG108 1 -#define THRUST_PP_BOOL_IMPL_TAG109 1 -#define THRUST_PP_BOOL_IMPL_TAG110 1 -#define THRUST_PP_BOOL_IMPL_TAG111 1 -#define THRUST_PP_BOOL_IMPL_TAG112 1 -#define THRUST_PP_BOOL_IMPL_TAG113 1 -#define THRUST_PP_BOOL_IMPL_TAG114 1 -#define THRUST_PP_BOOL_IMPL_TAG115 1 -#define THRUST_PP_BOOL_IMPL_TAG116 1 -#define THRUST_PP_BOOL_IMPL_TAG117 1 -#define THRUST_PP_BOOL_IMPL_TAG118 1 -#define THRUST_PP_BOOL_IMPL_TAG119 1 -#define THRUST_PP_BOOL_IMPL_TAG120 1 -#define THRUST_PP_BOOL_IMPL_TAG121 1 -#define THRUST_PP_BOOL_IMPL_TAG122 1 -#define THRUST_PP_BOOL_IMPL_TAG123 1 -#define THRUST_PP_BOOL_IMPL_TAG124 1 -#define THRUST_PP_BOOL_IMPL_TAG125 1 -#define THRUST_PP_BOOL_IMPL_TAG126 1 -#define THRUST_PP_BOOL_IMPL_TAG127 1 -#define THRUST_PP_BOOL_IMPL_TAG128 1 -#define THRUST_PP_BOOL_IMPL_TAG129 1 -#define THRUST_PP_BOOL_IMPL_TAG130 1 -#define THRUST_PP_BOOL_IMPL_TAG131 1 -#define THRUST_PP_BOOL_IMPL_TAG132 1 -#define THRUST_PP_BOOL_IMPL_TAG133 1 -#define THRUST_PP_BOOL_IMPL_TAG134 1 -#define THRUST_PP_BOOL_IMPL_TAG135 1 -#define THRUST_PP_BOOL_IMPL_TAG136 1 -#define THRUST_PP_BOOL_IMPL_TAG137 1 -#define THRUST_PP_BOOL_IMPL_TAG138 1 -#define THRUST_PP_BOOL_IMPL_TAG139 1 -#define THRUST_PP_BOOL_IMPL_TAG140 1 -#define THRUST_PP_BOOL_IMPL_TAG141 1 -#define THRUST_PP_BOOL_IMPL_TAG142 1 -#define THRUST_PP_BOOL_IMPL_TAG143 1 -#define THRUST_PP_BOOL_IMPL_TAG144 1 -#define THRUST_PP_BOOL_IMPL_TAG145 1 -#define THRUST_PP_BOOL_IMPL_TAG146 1 -#define THRUST_PP_BOOL_IMPL_TAG147 1 -#define THRUST_PP_BOOL_IMPL_TAG148 1 -#define THRUST_PP_BOOL_IMPL_TAG149 1 -#define THRUST_PP_BOOL_IMPL_TAG150 1 -#define THRUST_PP_BOOL_IMPL_TAG151 1 -#define THRUST_PP_BOOL_IMPL_TAG152 1 -#define THRUST_PP_BOOL_IMPL_TAG153 1 -#define THRUST_PP_BOOL_IMPL_TAG154 1 -#define THRUST_PP_BOOL_IMPL_TAG155 1 -#define THRUST_PP_BOOL_IMPL_TAG156 1 -#define THRUST_PP_BOOL_IMPL_TAG157 1 -#define THRUST_PP_BOOL_IMPL_TAG158 1 -#define THRUST_PP_BOOL_IMPL_TAG159 1 -#define THRUST_PP_BOOL_IMPL_TAG160 1 -#define THRUST_PP_BOOL_IMPL_TAG161 1 -#define THRUST_PP_BOOL_IMPL_TAG162 1 -#define THRUST_PP_BOOL_IMPL_TAG163 1 -#define THRUST_PP_BOOL_IMPL_TAG164 1 -#define THRUST_PP_BOOL_IMPL_TAG165 1 -#define THRUST_PP_BOOL_IMPL_TAG166 1 -#define THRUST_PP_BOOL_IMPL_TAG167 1 -#define THRUST_PP_BOOL_IMPL_TAG168 1 -#define THRUST_PP_BOOL_IMPL_TAG169 1 -#define THRUST_PP_BOOL_IMPL_TAG170 1 -#define THRUST_PP_BOOL_IMPL_TAG171 1 -#define THRUST_PP_BOOL_IMPL_TAG172 1 -#define THRUST_PP_BOOL_IMPL_TAG173 1 -#define THRUST_PP_BOOL_IMPL_TAG174 1 -#define THRUST_PP_BOOL_IMPL_TAG175 1 -#define THRUST_PP_BOOL_IMPL_TAG176 1 -#define THRUST_PP_BOOL_IMPL_TAG177 1 -#define THRUST_PP_BOOL_IMPL_TAG178 1 -#define THRUST_PP_BOOL_IMPL_TAG179 1 -#define THRUST_PP_BOOL_IMPL_TAG180 1 -#define THRUST_PP_BOOL_IMPL_TAG181 1 -#define THRUST_PP_BOOL_IMPL_TAG182 1 -#define THRUST_PP_BOOL_IMPL_TAG183 1 -#define THRUST_PP_BOOL_IMPL_TAG184 1 -#define THRUST_PP_BOOL_IMPL_TAG185 1 -#define THRUST_PP_BOOL_IMPL_TAG186 1 -#define THRUST_PP_BOOL_IMPL_TAG187 1 -#define THRUST_PP_BOOL_IMPL_TAG188 1 -#define THRUST_PP_BOOL_IMPL_TAG189 1 -#define THRUST_PP_BOOL_IMPL_TAG190 1 -#define THRUST_PP_BOOL_IMPL_TAG191 1 -#define THRUST_PP_BOOL_IMPL_TAG192 1 -#define THRUST_PP_BOOL_IMPL_TAG193 1 -#define THRUST_PP_BOOL_IMPL_TAG194 1 -#define THRUST_PP_BOOL_IMPL_TAG195 1 -#define THRUST_PP_BOOL_IMPL_TAG196 1 -#define THRUST_PP_BOOL_IMPL_TAG197 1 -#define THRUST_PP_BOOL_IMPL_TAG198 1 -#define THRUST_PP_BOOL_IMPL_TAG199 1 -#define THRUST_PP_BOOL_IMPL_TAG200 1 -#define THRUST_PP_BOOL_IMPL_TAG201 1 -#define THRUST_PP_BOOL_IMPL_TAG202 1 -#define THRUST_PP_BOOL_IMPL_TAG203 1 -#define THRUST_PP_BOOL_IMPL_TAG204 1 -#define THRUST_PP_BOOL_IMPL_TAG205 1 -#define THRUST_PP_BOOL_IMPL_TAG206 1 -#define THRUST_PP_BOOL_IMPL_TAG207 1 -#define THRUST_PP_BOOL_IMPL_TAG208 1 -#define THRUST_PP_BOOL_IMPL_TAG209 1 -#define THRUST_PP_BOOL_IMPL_TAG210 1 -#define THRUST_PP_BOOL_IMPL_TAG211 1 -#define THRUST_PP_BOOL_IMPL_TAG212 1 -#define THRUST_PP_BOOL_IMPL_TAG213 1 -#define THRUST_PP_BOOL_IMPL_TAG214 1 -#define THRUST_PP_BOOL_IMPL_TAG215 1 -#define THRUST_PP_BOOL_IMPL_TAG216 1 -#define THRUST_PP_BOOL_IMPL_TAG217 1 -#define THRUST_PP_BOOL_IMPL_TAG218 1 -#define THRUST_PP_BOOL_IMPL_TAG219 1 -#define THRUST_PP_BOOL_IMPL_TAG220 1 -#define THRUST_PP_BOOL_IMPL_TAG221 1 -#define THRUST_PP_BOOL_IMPL_TAG222 1 -#define THRUST_PP_BOOL_IMPL_TAG223 1 -#define THRUST_PP_BOOL_IMPL_TAG224 1 -#define THRUST_PP_BOOL_IMPL_TAG225 1 -#define THRUST_PP_BOOL_IMPL_TAG226 1 -#define THRUST_PP_BOOL_IMPL_TAG227 1 -#define THRUST_PP_BOOL_IMPL_TAG228 1 -#define THRUST_PP_BOOL_IMPL_TAG229 1 -#define THRUST_PP_BOOL_IMPL_TAG230 1 -#define THRUST_PP_BOOL_IMPL_TAG231 1 -#define THRUST_PP_BOOL_IMPL_TAG232 1 -#define THRUST_PP_BOOL_IMPL_TAG233 1 -#define THRUST_PP_BOOL_IMPL_TAG234 1 -#define THRUST_PP_BOOL_IMPL_TAG235 1 -#define THRUST_PP_BOOL_IMPL_TAG236 1 -#define THRUST_PP_BOOL_IMPL_TAG237 1 -#define THRUST_PP_BOOL_IMPL_TAG238 1 -#define THRUST_PP_BOOL_IMPL_TAG239 1 -#define THRUST_PP_BOOL_IMPL_TAG240 1 -#define THRUST_PP_BOOL_IMPL_TAG241 1 -#define THRUST_PP_BOOL_IMPL_TAG242 1 -#define THRUST_PP_BOOL_IMPL_TAG243 1 -#define THRUST_PP_BOOL_IMPL_TAG244 1 -#define THRUST_PP_BOOL_IMPL_TAG245 1 -#define THRUST_PP_BOOL_IMPL_TAG246 1 -#define THRUST_PP_BOOL_IMPL_TAG247 1 -#define THRUST_PP_BOOL_IMPL_TAG248 1 -#define THRUST_PP_BOOL_IMPL_TAG249 1 -#define THRUST_PP_BOOL_IMPL_TAG250 1 -#define THRUST_PP_BOOL_IMPL_TAG251 1 -#define THRUST_PP_BOOL_IMPL_TAG252 1 -#define THRUST_PP_BOOL_IMPL_TAG253 1 -#define THRUST_PP_BOOL_IMPL_TAG254 1 -#define THRUST_PP_BOOL_IMPL_TAG255 1 -#define THRUST_PP_BOOL_IMPL_TAG256 1 - -/////////////////////////////////////////////////////////////////////////////// - -#define THRUST_PP_IIF(bit, t, f) THRUST_PP_IIF_IMPL0(bit, t, f) - -#if defined(_MSC_VER) - #define THRUST_PP_IIF_IMPL0(bit, t, f) \ - THRUST_PP_IIF_IMPL1(THRUST_PP_CAT2(THRUST_PP_IIF_IMPL_TAG, bit(t, f))) \ - /**/ - #define THRUST_PP_IIF_IMPL1(id) id -#else - #define THRUST_PP_IIF_IMPL0(bit, t, f) \ - THRUST_PP_CAT2(THRUST_PP_IIF_IMPL_TAG, bit(t, f)) - /**/ -#endif - -#define THRUST_PP_IIF_IMPL_TAG0(t, f) f -#define THRUST_PP_IIF_IMPL_TAG1(t, f) t - -#if defined(__EDG__) - #define THRUST_PP_IF(cond, t, f) THRUST_PP_IF_IMPL0(cond, t, f) - #define THRUST_PP_IF_IMPL0(cond, t, f) \ - THRUST_PP_IIF(THRUST_PP_BOOL(cond), t, f) \ - /**/ -#else - #define THRUST_PP_IF(cond, t, f) THRUST_PP_IIF(THRUST_PP_BOOL(cond), t, f) -#endif - -/// \def THRUST_COMMA_IF(cond) -/// \brief If \a cond is true, expands to a comma. Otherwise, expands to nothing. -/// -/// \par Example: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << THRUST_PP_STRINGIZE(THRUST_COMMA_IF(0)) << "\n" -/// << THRUST_PP_STRINGIZE(THRUST_COMMA_IF(1)) << "\n"; -/// } -/// \endcode -/// -/// The above code expands to: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << "" << "\n" -/// << "," << "\n"; -/// } -/// \endcode -/// -#if defined(__EDG__) - #define THRUST_PP_COMMA_IF(cond) THRUST_PP_COMMA_IF_IMPL0(cond) - #define THRUST_PP_COMMA_IF_IMPL0(cond) \ - THRUST_PP_IF(cond, THRUST_PP_COMMA, THRUST_PP_EMPTY)() \ - /**/ -#else - #define THRUST_PP_COMMA_IF(cond) \ - THRUST_PP_IF(cond, THRUST_PP_COMMA, THRUST_PP_EMPTY)() \ - /**/ -#endif - -/////////////////////////////////////////////////////////////////////////////// - -// http://gustedt.wordpress.com/2010/06/08/detect-empty-macro-arguments - -#define THRUST_PP_64TH_ARG( \ - _1, _2, _3, _4, _5, _6, _7, _8, _9,_10,_11,_12,_13,_14,_15,_16 \ - , _17,_18,_19,_20,_21,_22,_23,_24,_25,_26,_27,_28,_29,_30,_31,_32 \ - , _33,_34,_35,_36,_37,_38,_39,_40,_41,_42,_43,_44,_45,_46,_47,_48 \ - , _49,_50,_51,_52,_53,_54,_55,_56,_57,_58,_59,_60,_61,_62,_63, N \ - , ... \ - ) N \ - /**/ - -#define THRUST_PP_HAS_COMMA(...) \ - THRUST_PP_EXPAND(THRUST_PP_64TH_ARG( \ - __VA_ARGS__ \ - , 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 \ - , 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 \ - , 1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 \ - , 1,1,1,1,1,1,1,1,1,1,1,1,1,1,0 \ - )) \ - /**/ - -#define THRUST_PP_TRIGGER_PAREN(...) , - -#define THRUST_PP_IS_VARIADIC_NULLARY(...) \ - THRUST_PP_IS_VARIADIC_NULLARY_IMPL0( \ - /* Test if there is just one argument, eventually an empty one. */ \ - THRUST_PP_HAS_COMMA(__VA_ARGS__), \ - /* Test if THRUST_PP_TRIGGER_PAREN together with the argument adds a */ \ - /* comma. */ \ - THRUST_PP_HAS_COMMA(THRUST_PP_TRIGGER_PAREN __VA_ARGS__), \ - /* Test if the argument together with a parenthesis adds a comma. */ \ - THRUST_PP_HAS_COMMA(__VA_ARGS__ (/*empty*/)), \ - /* Test if placing it between THRUST_PP_TRIGGER_PAREN and the */ \ - /* parenthesis adds a comma. */ \ - THRUST_PP_HAS_COMMA(THRUST_PP_TRIGGER_PAREN __VA_ARGS__ (/*empty*/)) \ - ) \ - /**/ - -#define THRUST_PP_IS_VARIADIC_NULLARY_IMPL0(_0, _1, _2, _3) \ - THRUST_PP_HAS_COMMA( \ - THRUST_PP_CAT5(THRUST_PP_IS_VARIADIC_NULLARY_IMPL_TAG, _0, _1, _2, _3) \ - ) \ - -#define THRUST_PP_IS_VARIADIC_NULLARY_IMPL_TAG0001 , - -/////////////////////////////////////////////////////////////////////////////// - -/// \def THRUST_PP_ARITY(...) -/// \brief Returns the number of arguments that it was called with. Must be -/// called with less than 64 arguments. -/// -/// \par Example: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << THRUST_PP_ARITY() << "\n" -/// << THRUST_PP_ARITY(x) << "\n" -/// << THRUST_PP_ARITY(x, y) << "\n" -/// << THRUST_PP_ARITY(x, y, z) << "\n"; -/// } -/// \endcode -/// -/// The above code expands to: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << 0 << "\n" -/// << 1 << "\n" -/// << 2 << "\n" -/// << 3 << "\n"; -/// } -/// \endcode -/// -#define THRUST_PP_ARITY(...) \ - THRUST_PP_EXPAND( \ - THRUST_PP_IF( \ - THRUST_PP_IS_VARIADIC_NULLARY(__VA_ARGS__) \ - , 0 \ - , THRUST_PP_64TH_ARG( \ - __VA_ARGS__ \ - , 63,62,61,60,59,58,57,56,55,54,53,52,51,50,49,48 \ - , 47,46,45,44,43,42,41,40,39,38,37,36,35,34,33,32 \ - , 31,30,29,28,27,26,25,24,23,22,21,20,19,18,17,16 \ - , 15,14,13,12,11,10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0 \ - ) \ - ) \ - ) \ - /**/ - -/// \def THRUST_PP_DISPATCH(basename, ...) -/// \brief Expands to basenameN(...), where N is the -/// number of variadic arguments that \a THRUST_PP_DISPATCH was called -/// with. This macro can be used to implement "macro overloading". -/// -/// \par Example: -/// -/// \code -/// #include -/// #include -/// -/// #define PLUS(...) THRUST_PP_DISPATCH(PLUS, __VA_ARGS__) -/// #define PLUS0() 0 -/// #define PLUS1(x) x -/// #define PLUS2(x, y) x + y -/// #define PLUS3(x, y, z) x + y + z -/// -/// int main() -/// { -/// std::cout << PLUS() << "\n" -/// << PLUS(1) << "\n" -/// << PLUS(1, 2) << "\n" -/// << PLUS(1, 2, 3) << "\n"; -/// } -/// \endcode -/// -/// The above code expands to: -/// -/// \code -/// #include -/// #include -/// -/// int main() -/// { -/// std::cout << 0 << "\n" -/// << 1 << "\n" -/// << 1 + 2 << "\n" -/// << 1 + 2 + 3 << "\n"; -/// } -/// \endcode -/// -#define THRUST_PP_DISPATCH(basename, ...) \ - THRUST_PP_EXPAND( \ - THRUST_PP_CAT2( \ - basename, \ - THRUST_PP_ARITY(__VA_ARGS__) \ - )(__VA_ARGS__) \ - ) \ - /**/ - -/////////////////////////////////////////////////////////////////////////////// - -/// \def THRUST_CURRENT_FUNCTION -/// \brief The name of the current function as a string. -/// -#if defined(__GNUC__) \ - || (defined(__MWERKS__) && (__MWERKS__ >= 0x3000)) \ - || (defined(__ICC) && (__ICC >= 600)) || defined(__ghs__) - #define THRUST_CURRENT_FUNCTION __PRETTY_FUNCTION__ -#elif defined(__DMC__) && (__DMC__ >= 0x810) - #define THRUST_CURRENT_FUNCTION __PRETTY_FUNCTION__ -#elif defined(__FUNCSIG__) - #define THRUST_CURRENT_FUNCTION __FUNCSIG__ -#elif (defined(__INTEL_COMPILER) && (__INTEL_COMPILER >= 600)) \ - || (defined(__IBMCTHRUST_PP__) && (__IBMCTHRUST_PP__ >= 500)) - #define THRUST_CURRENT_FUNCTION __FUNCTION__ -#elif defined(__BORLANDC__) && (__BORLANDC__ >= 0x550) - #define THRUST_CURRENT_FUNCTION __FUNC__ -#elif defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 199901) - #define THRUST_CURRENT_FUNCTION __func__ -#elif defined(__cplusplus) && (__cplusplus >= 201103) - #define THRUST_CURRENT_FUNCTION __func__ -#else - #define THRUST_CURRENT_FUNCTION "(unknown)" -#endif - -/////////////////////////////////////////////////////////////////////////////// - diff --git a/spaces/manhkhanhUIT/BOPBTL/Global/train_mapping.py b/spaces/manhkhanhUIT/BOPBTL/Global/train_mapping.py deleted file mode 100644 index ffff4a5de7622e831989e8cb0daa694325a345b5..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/BOPBTL/Global/train_mapping.py +++ /dev/null @@ -1,162 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import time -from collections import OrderedDict -from options.train_options import TrainOptions -from data.data_loader import CreateDataLoader -from models.mapping_model import Pix2PixHDModel_Mapping -import util.util as util -from util.visualizer import Visualizer -import os -import numpy as np -import torch -import torchvision.utils as vutils -from torch.autograd import Variable -import datetime -import random - - - -opt = TrainOptions().parse() -visualizer = Visualizer(opt) -iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt') -if opt.continue_train: - try: - start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int) - except: - start_epoch, epoch_iter = 1, 0 - visualizer.print_save('Resuming from epoch %d at iteration %d' % (start_epoch-1, epoch_iter)) -else: - start_epoch, epoch_iter = 1, 0 - -if opt.which_epoch != "latest": - start_epoch=int(opt.which_epoch) - visualizer.print_save('Notice : Resuming from epoch %d at iteration %d' % (start_epoch - 1, epoch_iter)) - -opt.start_epoch=start_epoch -### temp for continue train unfixed decoder - -data_loader = CreateDataLoader(opt) -dataset = data_loader.load_data() -dataset_size = len(dataset) * opt.batchSize -print('#training images = %d' % dataset_size) - - -model = Pix2PixHDModel_Mapping() -model.initialize(opt) - -path = os.path.join(opt.checkpoints_dir, opt.name, 'model.txt') -fd = open(path, 'w') - -if opt.use_skip_model: - fd.write(str(model.mapping_net)) - fd.close() -else: - fd.write(str(model.netG_A)) - fd.write(str(model.mapping_net)) - fd.close() - -if opt.isTrain and len(opt.gpu_ids) > 1: - model = torch.nn.DataParallel(model, device_ids=opt.gpu_ids) - - - -total_steps = (start_epoch-1) * dataset_size + epoch_iter - -display_delta = total_steps % opt.display_freq -print_delta = total_steps % opt.print_freq -save_delta = total_steps % opt.save_latest_freq -### used for recovering training - -for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1): - epoch_s_t=datetime.datetime.now() - epoch_start_time = time.time() - if epoch != start_epoch: - epoch_iter = epoch_iter % dataset_size - for i, data in enumerate(dataset, start=epoch_iter): - iter_start_time = time.time() - total_steps += opt.batchSize - epoch_iter += opt.batchSize - - # whether to collect output images - save_fake = total_steps % opt.display_freq == display_delta - - ############## Forward Pass ###################### - #print(pair) - losses, generated = model(Variable(data['label']), Variable(data['inst']), - Variable(data['image']), Variable(data['feat']), infer=save_fake) - - # sum per device losses - losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ] - loss_dict = dict(zip(model.module.loss_names, losses)) - - # calculate final loss scalar - loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 - loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat',0) + loss_dict.get('G_VGG',0) + loss_dict.get('G_Feat_L2', 0) +loss_dict.get('Smooth_L1', 0)+loss_dict.get('G_Feat_L2_Stage_1',0) - #loss_G = loss_dict['G_Feat_L2'] - - ############### Backward Pass #################### - # update generator weights - model.module.optimizer_mapping.zero_grad() - loss_G.backward() - model.module.optimizer_mapping.step() - - # update discriminator weights - model.module.optimizer_D.zero_grad() - loss_D.backward() - model.module.optimizer_D.step() - - ############## Display results and errors ########## - ### print out errors - if i == 0 or total_steps % opt.print_freq == print_delta: - errors = {k: v.data if not isinstance(v, int) else v for k, v in loss_dict.items()} - t = (time.time() - iter_start_time) / opt.batchSize - visualizer.print_current_errors(epoch, epoch_iter, errors, t,model.module.old_lr) - visualizer.plot_current_errors(errors, total_steps) - - ### display output images - if save_fake: - - if not os.path.exists(opt.outputs_dir + opt.name): - os.makedirs(opt.outputs_dir + opt.name) - - imgs_num = 5 - if opt.NL_use_mask: - mask=data['inst'][:imgs_num] - mask=mask.repeat(1,3,1,1) - imgs = torch.cat((data['label'][:imgs_num], mask,generated.data.cpu()[:imgs_num], data['image'][:imgs_num]), 0) - else: - imgs = torch.cat((data['label'][:imgs_num], generated.data.cpu()[:imgs_num], data['image'][:imgs_num]), 0) - - imgs=(imgs+1.)/2.0 ## de-normalize - - try: - image_grid = vutils.save_image(imgs, opt.outputs_dir + opt.name + '/' + str(epoch) + '_' + str(total_steps) + '.png', - nrow=imgs_num, padding=0, normalize=True) - except OSError as err: - print(err) - - if epoch_iter >= dataset_size: - break - - # end of epoch - epoch_e_t=datetime.datetime.now() - iter_end_time = time.time() - print('End of epoch %d / %d \t Time Taken: %s' % - (epoch, opt.niter + opt.niter_decay, str(epoch_e_t-epoch_s_t))) - - ### save model for this epoch - if epoch % opt.save_epoch_freq == 0: - print('saving the model at the end of epoch %d, iters %d' % (epoch, total_steps)) - model.module.save('latest') - model.module.save(epoch) - np.savetxt(iter_path, (epoch+1, 0), delimiter=',', fmt='%d') - - ### instead of only training the local enhancer, train the entire network after certain iterations - if (opt.niter_fix_global != 0) and (epoch == opt.niter_fix_global): - model.module.update_fixed_params() - - ### linearly decay learning rate after certain iterations - if epoch > opt.niter: - model.module.update_learning_rate() \ No newline at end of file diff --git a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/detection_util/util.py b/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/detection_util/util.py deleted file mode 100644 index be10881fc4077015d12a28f5ae5b0a04021ad627..0000000000000000000000000000000000000000 --- a/spaces/manhkhanhUIT/Image_Restoration_Colorization/Global/detection_util/util.py +++ /dev/null @@ -1,245 +0,0 @@ -# Copyright (c) Microsoft Corporation. -# Licensed under the MIT License. - -import os -import sys -import time -import shutil -import platform -import numpy as np -from datetime import datetime - -import torch -import torchvision as tv -import torch.backends.cudnn as cudnn - -# from torch.utils.tensorboard import SummaryWriter - -import yaml -import matplotlib.pyplot as plt -from easydict import EasyDict as edict -import torchvision.utils as vutils - - -##### option parsing ###### -def print_options(config_dict): - print("------------ Options -------------") - for k, v in sorted(config_dict.items()): - print("%s: %s" % (str(k), str(v))) - print("-------------- End ----------------") - - -def save_options(config_dict): - from time import gmtime, strftime - - file_dir = os.path.join(config_dict["checkpoint_dir"], config_dict["name"]) - mkdir_if_not(file_dir) - file_name = os.path.join(file_dir, "opt.txt") - with open(file_name, "wt") as opt_file: - opt_file.write(os.path.basename(sys.argv[0]) + " " + strftime("%Y-%m-%d %H:%M:%S", gmtime()) + "\n") - opt_file.write("------------ Options -------------\n") - for k, v in sorted(config_dict.items()): - opt_file.write("%s: %s\n" % (str(k), str(v))) - opt_file.write("-------------- End ----------------\n") - - -def config_parse(config_file, options, save=True): - with open(config_file, "r") as stream: - config_dict = yaml.safe_load(stream) - config = edict(config_dict) - - for option_key, option_value in vars(options).items(): - config_dict[option_key] = option_value - config[option_key] = option_value - - if config.debug_mode: - config_dict["num_workers"] = 0 - config.num_workers = 0 - config.batch_size = 2 - if isinstance(config.gpu_ids, str): - config.gpu_ids = [int(x) for x in config.gpu_ids.split(",")][0] - - print_options(config_dict) - if save: - save_options(config_dict) - - return config - - -###### utility ###### -def to_np(x): - return x.cpu().numpy() - - -def prepare_device(use_gpu, gpu_ids): - if use_gpu: - cudnn.benchmark = True - os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" - if isinstance(gpu_ids, str): - gpu_ids = [int(x) for x in gpu_ids.split(",")] - torch.cuda.set_device(gpu_ids[0]) - device = torch.device("cuda:" + str(gpu_ids[0])) - else: - torch.cuda.set_device(gpu_ids) - device = torch.device("cuda:" + str(gpu_ids)) - print("running on GPU {}".format(gpu_ids)) - else: - device = torch.device("cpu") - print("running on CPU") - - return device - - -###### file system ###### -def get_dir_size(start_path="."): - total_size = 0 - for dirpath, dirnames, filenames in os.walk(start_path): - for f in filenames: - fp = os.path.join(dirpath, f) - total_size += os.path.getsize(fp) - return total_size - - -def mkdir_if_not(dir_path): - if not os.path.exists(dir_path): - os.makedirs(dir_path) - - -##### System related ###### -class Timer: - def __init__(self, msg): - self.msg = msg - self.start_time = None - - def __enter__(self): - self.start_time = time.time() - - def __exit__(self, exc_type, exc_value, exc_tb): - elapse = time.time() - self.start_time - print(self.msg % elapse) - - -###### interactive ###### -def get_size(start_path="."): - total_size = 0 - for dirpath, dirnames, filenames in os.walk(start_path): - for f in filenames: - fp = os.path.join(dirpath, f) - total_size += os.path.getsize(fp) - return total_size - - -def clean_tensorboard(directory): - tensorboard_list = os.listdir(directory) - SIZE_THRESH = 100000 - for tensorboard in tensorboard_list: - tensorboard = os.path.join(directory, tensorboard) - if get_size(tensorboard) < SIZE_THRESH: - print("deleting the empty tensorboard: ", tensorboard) - # - if os.path.isdir(tensorboard): - shutil.rmtree(tensorboard) - else: - os.remove(tensorboard) - - -def prepare_tensorboard(config, experiment_name=datetime.now().strftime("%Y-%m-%d %H-%M-%S")): - tensorboard_directory = os.path.join(config.checkpoint_dir, config.name, "tensorboard_logs") - mkdir_if_not(tensorboard_directory) - clean_tensorboard(tensorboard_directory) - tb_writer = SummaryWriter(os.path.join(tensorboard_directory, experiment_name), flush_secs=10) - - # try: - # shutil.copy('outputs/opt.txt', tensorboard_directory) - # except: - # print('cannot find file opt.txt') - return tb_writer - - -def tb_loss_logger(tb_writer, iter_index, loss_logger): - for tag, value in loss_logger.items(): - tb_writer.add_scalar(tag, scalar_value=value.item(), global_step=iter_index) - - -def tb_image_logger(tb_writer, iter_index, images_info, config): - ### Save and write the output into the tensorboard - tb_logger_path = os.path.join(config.output_dir, config.name, config.train_mode) - mkdir_if_not(tb_logger_path) - for tag, image in images_info.items(): - if tag == "test_image_prediction" or tag == "image_prediction": - continue - image = tv.utils.make_grid(image.cpu()) - image = torch.clamp(image, 0, 1) - tb_writer.add_image(tag, img_tensor=image, global_step=iter_index) - tv.transforms.functional.to_pil_image(image).save( - os.path.join(tb_logger_path, "{:06d}_{}.jpg".format(iter_index, tag)) - ) - - -def tb_image_logger_test(epoch, iter, images_info, config): - - url = os.path.join(config.output_dir, config.name, config.train_mode, "val_" + str(epoch)) - if not os.path.exists(url): - os.makedirs(url) - scratch_img = images_info["test_scratch_image"].data.cpu() - if config.norm_input: - scratch_img = (scratch_img + 1.0) / 2.0 - scratch_img = torch.clamp(scratch_img, 0, 1) - gt_mask = images_info["test_mask_image"].data.cpu() - predict_mask = images_info["test_scratch_prediction"].data.cpu() - - predict_hard_mask = (predict_mask.data.cpu() >= 0.5).float() - - imgs = torch.cat((scratch_img, predict_hard_mask, gt_mask), 0) - img_grid = vutils.save_image( - imgs, os.path.join(url, str(iter) + ".jpg"), nrow=len(scratch_img), padding=0, normalize=True - ) - - -def imshow(input_image, title=None, to_numpy=False): - inp = input_image - if to_numpy or type(input_image) is torch.Tensor: - inp = input_image.numpy() - - fig = plt.figure() - if inp.ndim == 2: - fig = plt.imshow(inp, cmap="gray", clim=[0, 255]) - else: - fig = plt.imshow(np.transpose(inp, [1, 2, 0]).astype(np.uint8)) - plt.axis("off") - fig.axes.get_xaxis().set_visible(False) - fig.axes.get_yaxis().set_visible(False) - plt.title(title) - - -###### vgg preprocessing ###### -def vgg_preprocess(tensor): - # input is RGB tensor which ranges in [0,1] - # output is BGR tensor which ranges in [0,255] - tensor_bgr = torch.cat((tensor[:, 2:3, :, :], tensor[:, 1:2, :, :], tensor[:, 0:1, :, :]), dim=1) - # tensor_bgr = tensor[:, [2, 1, 0], ...] - tensor_bgr_ml = tensor_bgr - torch.Tensor([0.40760392, 0.45795686, 0.48501961]).type_as(tensor_bgr).view( - 1, 3, 1, 1 - ) - tensor_rst = tensor_bgr_ml * 255 - return tensor_rst - - -def torch_vgg_preprocess(tensor): - # pytorch version normalization - # note that both input and output are RGB tensors; - # input and output ranges in [0,1] - # normalize the tensor with mean and variance - tensor_mc = tensor - torch.Tensor([0.485, 0.456, 0.406]).type_as(tensor).view(1, 3, 1, 1) - tensor_mc_norm = tensor_mc / torch.Tensor([0.229, 0.224, 0.225]).type_as(tensor_mc).view(1, 3, 1, 1) - return tensor_mc_norm - - -def network_gradient(net, gradient_on=True): - if gradient_on: - for param in net.parameters(): - param.requires_grad = True - else: - for param in net.parameters(): - param.requires_grad = False - return net diff --git a/spaces/matthoffner/AudioCraft_Plus/audiocraft/quantization/__init__.py b/spaces/matthoffner/AudioCraft_Plus/audiocraft/quantization/__init__.py deleted file mode 100644 index 1e0c7e429ab96d67be667e23bf7a0ffa389c036b..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/AudioCraft_Plus/audiocraft/quantization/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -"""RVQ.""" -# flake8: noqa -from .vq import ResidualVectorQuantizer -from .base import BaseQuantizer, DummyQuantizer, QuantizedResult diff --git a/spaces/matthoffner/chatbot-mini/pages/index.tsx b/spaces/matthoffner/chatbot-mini/pages/index.tsx deleted file mode 100644 index 4b981767be4084b50d007bdaf444c7111b596c34..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/chatbot-mini/pages/index.tsx +++ /dev/null @@ -1 +0,0 @@ -export { default, getServerSideProps } from './api/home'; diff --git a/spaces/matthoffner/open-codetree/graphql/definitions/user.definitions.ts b/spaces/matthoffner/open-codetree/graphql/definitions/user.definitions.ts deleted file mode 100644 index 304d4196e8250d896002887fb6d3e7bfd5b526e2..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/open-codetree/graphql/definitions/user.definitions.ts +++ /dev/null @@ -1,54 +0,0 @@ -import { gql } from "@apollo/client"; - -export const UsersIDsQuery = gql(`query UsersIDsQuery($input: UsersInput!) { - users(input: $input) { - message - status - data { - username - } - } -}`); - -export const UserQuery = gql(`query User($input: UserInput!) { - user(input: $input) { - message - status - data { - id - email - username - firstname - lastname - avatar - bio - website - jobTitle - country - showProfession - points - projects { - id - title - slug - tags { - id - value - } - } - } - } -}`); - -export const MostActiveUsersQuery = gql(`query MostActiveUsers { - mostActiveUsers { - status - message - data { - id - avatar - username - bio - } - } -}`); diff --git a/spaces/matthoffner/starchat-ui/types/export.ts b/spaces/matthoffner/starchat-ui/types/export.ts deleted file mode 100644 index 655e3f7b7a47012fb7fa96456304f024ce1f0c21..0000000000000000000000000000000000000000 --- a/spaces/matthoffner/starchat-ui/types/export.ts +++ /dev/null @@ -1,45 +0,0 @@ -import { Conversation, Message } from './chat'; -import { FolderInterface } from './folder'; -import { OpenAIModel } from './openai'; -import { Prompt } from './prompt'; - -export type SupportedExportFormats = - | ExportFormatV1 - | ExportFormatV2 - | ExportFormatV3 - | ExportFormatV4; -export type LatestExportFormat = ExportFormatV4; - -//////////////////////////////////////////////////////////////////////////////////////////// -interface ConversationV1 { - id: number; - name: string; - messages: Message[]; -} - -export type ExportFormatV1 = ConversationV1[]; - -//////////////////////////////////////////////////////////////////////////////////////////// -interface ChatFolder { - id: number; - name: string; -} - -export interface ExportFormatV2 { - history: Conversation[] | null; - folders: ChatFolder[] | null; -} - -//////////////////////////////////////////////////////////////////////////////////////////// -export interface ExportFormatV3 { - version: 3; - history: Conversation[]; - folders: FolderInterface[]; -} - -export interface ExportFormatV4 { - version: 4; - history: Conversation[]; - folders: FolderInterface[]; - prompts: Prompt[]; -} diff --git a/spaces/merve/anonymization/source/uncertainty-calibration/draw_calibrationcurve.js b/spaces/merve/anonymization/source/uncertainty-calibration/draw_calibrationcurve.js deleted file mode 100644 index c7992a7c79b1a5187bc3f267350869904c836626..0000000000000000000000000000000000000000 --- a/spaces/merve/anonymization/source/uncertainty-calibration/draw_calibrationcurve.js +++ /dev/null @@ -1,102 +0,0 @@ - -window.drawCalibrationCurve = function (graphSel, fig_height, fig_width){ - var width = Math.min(fig_height, fig_width) - var sel = graphSel - .append('div').st({textAlign: 'center'}) - .append('div').st({display: 'inline-block'}) - - var c = d3.conventions({ - sel, - width, - height: width, - margin: {top: 40} - }); - - c.svg.parent() - - //TODO(nthain) Who owns the buckets? We have at least 2 instances, reduce to 1 - var buckets = d3.pairs(window.weatherGraph.thresholds) - buckets.forEach(bucket => { - bucket.val = d3.mean(bucket, d => d.origVal) - }) - - c.xAxis.tickValues(buckets.map(d => d.val)).tickFormat(d3.format('.2f')) - c.yAxis.tickValues(buckets.map(d => d.val)).tickFormat(d3.format('.2f')) - d3.drawAxis(c) - window.util.ggPlotBg(c) - - window.util.addAxisLabel(c, 'Calibrated Model Score', 'Probability of Rain') - - var eceSel = c.svg.append('g.ece') - var eceBox = eceSel.append('rect.val-box') - .at({width: 55, height: 20, x: c.width/2 + 72.5, y: -35, rx: 3, ry: 3}) - var eceText = eceSel.append('text.big-text') - .at({y: -20, x: c.width/2-30, textAnchor: 'middle'}) - var eceVal = eceSel.append('text.val-text') - .at({y: -20, x: c.width/2+100, textAnchor: 'middle'}) - - c.svg.append('path') - .at({ - d: ['M', 0, c.height, 'L', c.width, 0].join(' '), - stroke: '#555', - strokeDasharray: '3 3', - }) - - var bucketSel = c.svg.appendMany('g.bucket', buckets) - - var circleSel = bucketSel.append('circle') - .at({fillOpacity: .4, fill: 'steelblue'}) - - var pathSel = bucketSel.append('path') - .at({stroke: 'steelblue', strokeWidth: 3}) - - var bucketText = bucketSel.append('text').text('8 / 10') - .at({textAnchor: 'start', dy: '.33em', fontSize: 10, fill: '#000'}) - - - // function remap_score(s) { - // // new_score = min_threshold_new + (old_score-min_threshold_old)(max_threshold_new-min_threshold_new)/(max_threshold_old-min_threshold_old) - // //find index less than score - // } - - function renderBuckets(){ - var filter_rain = window.slides.slide?.filter_rain - - buckets.forEach(bucket => { - bucket.data = weatherdata - .filter(d => bucket[0].val <= d.score && d.score <= bucket[1].val) - .filter(d => !filter_rain || !d.is_filter) - - bucket.nPositive = d3.sum(bucket.data, d => d.label) - bucket.percent = bucket.nPositive/bucket.data.length - - if (isNaN(bucket.percent)) bucket.percent = bucket[0].val - }) - - var ece = d3.sum(buckets, d => d.data.length*Math.abs(d.val - d.percent)) - ece = ece/d3.sum(buckets, d => d.data.length) - - eceText.text('Expected Calibration Error: ') - eceVal.text(d3.format('.3f')(ece)) - - var rScale = d3.scaleSqrt().domain([0, 50]).range([0, 20]) - - bucketSel - .st({opacity: d => d.data.length}) - .filter(d => d.data.length) - .translate(d => [c.x(d.val), c.y(d.percent)]) - - circleSel - .at({r: d => rScale(d.data.length)}) - - pathSel.at({d: d => 'M 0 0 V ' + (c.y(d.val) - c.y(d.percent))}) - - bucketText - .text(d => `${d.nPositive} / ${d.data.length}`) - .at({x: d => rScale(d.data.length) + 2}) - } - - return {renderBuckets, c, buckets, calibrationDataFn: () => console.log('test')} -} - -if (window.init) window.init() diff --git a/spaces/ml6team/Speaker-Diarization/README.md b/spaces/ml6team/Speaker-Diarization/README.md deleted file mode 100644 index 9e6fcdcdf90c6e5055b696cd3757d2c0f5fe4332..0000000000000000000000000000000000000000 --- a/spaces/ml6team/Speaker-Diarization/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Diarization -emoji: 🐠 -colorFrom: red -colorTo: green -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/mnauf/detect-bees/segment/predict.py b/spaces/mnauf/detect-bees/segment/predict.py deleted file mode 100644 index 3ae68240726afa91e08d34714b77a0dffb240b6a..0000000000000000000000000000000000000000 --- a/spaces/mnauf/detect-bees/segment/predict.py +++ /dev/null @@ -1,273 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Run YOLOv5 segmentation inference on images, videos, directories, streams, etc. - -Usage - sources: - $ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam - img.jpg # image - vid.mp4 # video - path/ # directory - 'path/*.jpg' # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream - -Usage - formats: - $ python segment/predict.py --weights yolov5s-seg.pt # PyTorch - yolov5s-seg.torchscript # TorchScript - yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s-seg_openvino_model # OpenVINO - yolov5s-seg.engine # TensorRT - yolov5s-seg.mlmodel # CoreML (macOS-only) - yolov5s-seg_saved_model # TensorFlow SavedModel - yolov5s-seg.pb # TensorFlow GraphDef - yolov5s-seg.tflite # TensorFlow Lite - yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU - yolov5s-seg_paddle_model # PaddlePaddle -""" - -import argparse -import os -import platform -import sys -from pathlib import Path - -import torch - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.common import DetectMultiBackend -from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams -from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, - increment_path, non_max_suppression, print_args, scale_boxes, scale_segments, - strip_optimizer, xyxy2xywh) -from utils.plots import Annotator, colors, save_one_box -from utils.segment.general import masks2segments, process_mask -from utils.torch_utils import select_device, smart_inference_mode - - -@smart_inference_mode() -def run( - weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s) - source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam) - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - imgsz=(640, 640), # inference size (height, width) - conf_thres=0.25, # confidence threshold - iou_thres=0.45, # NMS IOU threshold - max_det=1000, # maximum detections per image - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - view_img=False, # show results - save_txt=False, # save results to *.txt - save_conf=False, # save confidences in --save-txt labels - save_crop=False, # save cropped prediction boxes - nosave=False, # do not save images/videos - classes=None, # filter by class: --class 0, or --class 0 2 3 - agnostic_nms=False, # class-agnostic NMS - augment=False, # augmented inference - visualize=False, # visualize features - update=False, # update all models - project=ROOT / 'runs/predict-seg', # save results to project/name - name='exp', # save results to project/name - exist_ok=False, # existing project/name ok, do not increment - line_thickness=3, # bounding box thickness (pixels) - hide_labels=False, # hide labels - hide_conf=False, # hide confidences - half=False, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - vid_stride=1, # video frame-rate stride - retina_masks=False, -): - source = str(source) - save_img = not nosave and not source.endswith('.txt') # save inference images - is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) - screenshot = source.lower().startswith('screen') - if is_url and is_file: - source = check_file(source) # download - - # Directories - save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Load model - device = select_device(device) - model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half) - stride, names, pt = model.stride, model.names, model.pt - imgsz = check_img_size(imgsz, s=stride) # check image size - - # Dataloader - bs = 1 # batch_size - if webcam: - view_img = check_imshow(warn=True) - dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) - bs = len(dataset) - elif screenshot: - dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt) - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) - vid_path, vid_writer = [None] * bs, [None] * bs - - # Run inference - model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup - seen, windows, dt = 0, [], (Profile(), Profile(), Profile()) - for path, im, im0s, vid_cap, s in dataset: - with dt[0]: - im = torch.from_numpy(im).to(model.device) - im = im.half() if model.fp16 else im.float() # uint8 to fp16/32 - im /= 255 # 0 - 255 to 0.0 - 1.0 - if len(im.shape) == 3: - im = im[None] # expand for batch dim - - # Inference - with dt[1]: - visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False - pred, proto = model(im, augment=augment, visualize=visualize)[:2] - - # NMS - with dt[2]: - pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32) - - # Second-stage classifier (optional) - # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) - - # Process predictions - for i, det in enumerate(pred): # per image - seen += 1 - if webcam: # batch_size >= 1 - p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' - else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt - s += '%gx%g ' % im.shape[2:] # print string - imc = im0.copy() if save_crop else im0 # for save_crop - annotator = Annotator(im0, line_width=line_thickness, example=str(names)) - if len(det): - masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC - det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size - - # Segments - if save_txt: - segments = reversed(masks2segments(masks)) - segments = [scale_segments(im.shape[2:], x, im0.shape).round() for x in segments] - - # Print results - for c in det[:, 5].unique(): - n = (det[:, 5] == c).sum() # detections per class - s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string - - # Mask plotting - annotator.masks(masks, - colors=[colors(x, True) for x in det[:, 5]], - im_gpu=None if retina_masks else im[i]) - - # Write results - for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])): - if save_txt: # Write to file - segj = segments[j].reshape(-1) # (n,2) to (n*2) - line = (cls, *segj, conf) if save_conf else (cls, *segj) # label format - with open(f'{txt_path}.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') - - if save_img or save_crop or view_img: # Add bbox to image - c = int(cls) # integer class - label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') - annotator.box_label(xyxy, label, color=colors(c, True)) - # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3) - if save_crop: - save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) - - # Stream results - im0 = annotator.result() - if view_img: - if platform.system() == 'Linux' and p not in windows: - windows.append(p) - cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux) - cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0]) - cv2.imshow(str(p), im0) - if cv2.waitKey(1) == ord('q'): # 1 millisecond - exit() - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - else: # 'video' or 'stream' - if vid_path[i] != save_path: # new video - vid_path[i] = save_path - if isinstance(vid_writer[i], cv2.VideoWriter): - vid_writer[i].release() # release previous video writer - if vid_cap: # video - fps = vid_cap.get(cv2.CAP_PROP_FPS) - w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - else: # stream - fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer[i].write(im0) - - # Print time (inference-only) - LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms") - - # Print results - t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) - if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") - if update: - strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning) - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--view-img', action='store_true', help='show results') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3') - parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/predict-seg', help='save results to project/name') - parser.add_argument('--name', default='exp', help='save results to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') - parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') - parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride') - parser.add_argument('--retina-masks', action='store_true', help='whether to plot masks in native resolution') - opt = parser.parse_args() - opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand - print_args(vars(opt)) - return opt - - -def main(opt): - check_requirements(exclude=('tensorboard', 'thop')) - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/spaces/modjunkie/MGX-Midjourney-v4/README.md b/spaces/modjunkie/MGX-Midjourney-v4/README.md deleted file mode 100644 index 37f07af408b5f1f1ed7b071a288d87f0034b9ad2..0000000000000000000000000000000000000000 --- a/spaces/modjunkie/MGX-Midjourney-v4/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: openjourney -emoji: 👀 -colorFrom: gray -colorTo: green -sdk: gradio -sdk_version: 3.10.1 -app_file: app.py -pinned: false -duplicated_from: mgxwrites/MGX-Midjourney-v4 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/README.md b/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/README.md deleted file mode 100644 index cc610c0c9e936a5ae4659ceda691c6db6d387296..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/examples/multilingual/data_scripts/README.md +++ /dev/null @@ -1,24 +0,0 @@ - -# Install dependency -```bash -pip install -r requirement.txt -``` - -# Download the data set -```bash -export WORKDIR_ROOT= - -``` -The downloaded data will be at $WORKDIR_ROOT/ML50 - -# preprocess the data -Install SPM [here](https://github.com/google/sentencepiece) -```bash -export WORKDIR_ROOT= -export SPM_PATH= -``` -* $WORKDIR_ROOT/ML50/raw: extracted raw data -* $WORKDIR_ROOT/ML50/dedup: dedup data -* $WORKDIR_ROOT/ML50/clean: data with valid and test sentences removed from the dedup data - - diff --git a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/__init__.py b/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/__init__.py deleted file mode 100644 index 5b3dbc023aa4a6f7bfb8403b8204d71ca432f79c..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/fairseq/fairseq/optim/lr_scheduler/__init__.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. -"""isort:skip_file""" - -import importlib -import os - -from fairseq import registry -from fairseq.optim.lr_scheduler.fairseq_lr_scheduler import ( # noqa - FairseqLRScheduler, - LegacyFairseqLRScheduler, -) -from omegaconf import DictConfig - - -( - build_lr_scheduler_, - register_lr_scheduler, - LR_SCHEDULER_REGISTRY, - LR_SCHEDULER_DATACLASS_REGISTRY, -) = registry.setup_registry( - "--lr-scheduler", base_class=FairseqLRScheduler, default="fixed" -) - - -def build_lr_scheduler(cfg: DictConfig, optimizer): - return build_lr_scheduler_(cfg, optimizer) - - -# automatically import any Python files in the optim/lr_scheduler/ directory -for file in sorted(os.listdir(os.path.dirname(__file__))): - if file.endswith(".py") and not file.startswith("_"): - file_name = file[: file.find(".py")] - importlib.import_module("fairseq.optim.lr_scheduler." + file_name) diff --git a/spaces/mshukor/UnIVAL/run_scripts/caption/scaling_best/onlylinear/unival_audio_caption_s2_onlylinear.sh b/spaces/mshukor/UnIVAL/run_scripts/caption/scaling_best/onlylinear/unival_audio_caption_s2_onlylinear.sh deleted file mode 100644 index f7b27c72337a03f621b6f1c4a8da63275761cd90..0000000000000000000000000000000000000000 --- a/spaces/mshukor/UnIVAL/run_scripts/caption/scaling_best/onlylinear/unival_audio_caption_s2_onlylinear.sh +++ /dev/null @@ -1,217 +0,0 @@ - - -# Number of GPUs per GPU worker -export GPUS_PER_NODE=8 -# Number of GPU workers, for single-worker training, please set to 1 -export NUM_NODES=$SLURM_NNODES -# The ip address of the rank-0 worker, for single-worker training, please set to localhost -master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) -export MASTER_ADDR=$master_addr - -# The port for communication -export MASTER_PORT=12350 -# The rank of this worker, should be in {0, ..., WORKER_CNT-1}, for single-worker training, please set to 0 -export RANK=$SLURM_NODEID - -echo "MASTER_ADDR: $MASTER_ADDR" -echo "RANK :$RANK" -echo "NUM_NODES :$NUM_NODES" -echo "GPUS_PER_NODE :$GPUS_PER_NODE" - -export MIOPEN_USER_DB_PATH=/lus/home/NAT/gda2204/mshukor/.config/miopen_${MASTER_ADDR}_${SLURM_PROCID}/ - -echo "MIOPEN_USER_DB_PATH :$MIOPEN_USER_DB_PATH" - -num_workers=0 - - -exp_name=unival_audio_caption_s2_onlylinear - -ofa_dir=/lus/home/NAT/gda2204/mshukor/code/unival -base_data_dir=/lus/scratch/NAT/gda2204/SHARED/data -base_log_dir=/work/NAT/gda2204/mshukor/logs - -save_base_log_dir=/lus/scratch/NAT/gda2204/SHARED/logs -save_dir=${save_base_log_dir}/ofa/checkpoints/caption/${exp_name} -log_dir=${save_dir} - -mkdir -p $log_dir $save_dir - -bpe_dir=${ofa_dir}/utils/BPE -user_dir=${ofa_dir}/ofa_module - - - -image_dir=${base_data_dir} - - -data_dir=${base_data_dir}/ofa/audio_data/caption_data -data=${data_dir}/audiocaps_train_1.tsv,${data_dir}/audiocaps_train_2.tsv,${data_dir}/audiocaps_train_3.tsv,${data_dir}/audiocaps_train_4.tsv,${data_dir}/audiocaps_train_5.tsv,${data_dir}/audiocaps_train_6.tsv,${data_dir}/audiocaps_train_7.tsv,${data_dir}/audiocaps_train_8.tsv,${data_dir}/audiocaps_train_9.tsv,${data_dir}/audiocaps_train_10.tsv,${data_dir}/audiocaps_test.tsv -eval_cider_cached=${data_dir}/cider_cached_tokens/audiocaps-test-words.p - - -restore_file=${base_log_dir}/ofa/checkpoints/pretrain/unival_s2_hs/checkpoint3.pt - - - -selected_cols=0,4,2 - -task=audio_caption -arch=unival_base -pretrained_model= - - -criterion=adjust_label_smoothed_encouraging_loss -label_smoothing=0.1 -lr=5e-4 -max_epoch=25 -warmup_ratio=0.06 -batch_size=8 -update_freq=2 -resnet_drop_path_rate=0.0 -encoder_drop_path_rate=0.1 -decoder_drop_path_rate=0.1 -dropout=0.1 -attention_dropout=0.0 -max_src_length=80 -max_tgt_length=20 -num_bins=1000 -# patch_image_size=480 -drop_worst_ratio=0.2 - - - - -### -image_encoder_name=timm_resnet #vit_base_patch16_224 144 -patch_image_size=384 -resnet_type=resnet101 - -resnet_model_path=${base_log_dir}/pretrained_models/resnet101_a1h-36d3f2aa.pth - -# video -video_encoder_name=all_resnext101 # 49 -patch_frame_size=224 -video_model_path=${base_log_dir}/pretrained_models/3dcnn/resnext-101-kinetics.pth #${base_log_dir}/pretrained_models/TimeSformer_divST_8x32_224_K600.pyth -num_frames=8 - -# audio -audio_encoder_name=pann_cnn14 -audio_model_path=${base_log_dir}/pretrained_models/Cnn14_mAP_0.431.pth -mel_bins=64 -hop_size=200 # 155 tok - - - -save_interval=1 -validate_interval_updates=2000 -save_interval_updates=0 - - -sample_patch_num='--sample-patch-num=784' # '' - -eval_args='--eval-args={"beam":5,"unnormalized":true,"temperature":1.0,"stop_on_max_len":true}' - - -drop_worst_ratio=0.05 # modified from 0.2 for el -log_end=0.75 # for el -drop_best_ratio=0.05 -drop_best_after=6000 -drop_worst_after=6000 - -echo "max_epoch "${max_epoch} -for warmup_ratio in {0.06,}; do - echo "warmup_ratio "${warmup_ratio} - for drop_worst_after in {6000,}; do - echo "drop_worst_after "${drop_worst_after} - - log_file=${log_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after}".log" - save_path=${save_dir}/${max_epoch}"_"${warmup_ratio}"_"${drop_worst_after} - mkdir -p $save_path - - python3 -m torch.distributed.launch \ - --nnodes=${NUM_NODES} \ - --nproc_per_node=${GPUS_PER_NODE} \ - --master_port=${MASTER_PORT} \ - --node_rank=${RANK} \ - --master_addr=${MASTER_ADDR} \ - --use_env ${ofa_dir}/train.py \ - $data \ - --selected-cols=${selected_cols} \ - --bpe-dir=${bpe_dir} \ - --user-dir=${user_dir} \ - --restore-file=${restore_file} \ - --save-dir=${save_path} \ - --task=${task} \ - --arch=${arch} \ - --criterion=${criterion} \ - --label-smoothing=${label_smoothing} \ - --batch-size=${batch_size} \ - --update-freq=${update_freq} \ - --encoder-normalize-before \ - --decoder-normalize-before \ - --share-decoder-input-output-embed \ - --share-all-embeddings \ - --layernorm-embedding \ - --patch-layernorm-embedding \ - --code-layernorm-embedding \ - --resnet-drop-path-rate=${resnet_drop_path_rate} \ - --encoder-drop-path-rate=${encoder_drop_path_rate} \ - --decoder-drop-path-rate=${decoder_drop_path_rate} \ - --dropout=${dropout} \ - --attention-dropout=${attention_dropout} \ - --weight-decay=0.01 --optimizer=adam --adam-betas="(0.9,0.999)" --adam-eps=1e-08 --clip-norm=1.0 \ - --lr-scheduler=polynomial_decay --lr=${lr} \ - --max-epoch=${max_epoch} --warmup-ratio=${warmup_ratio} \ - --log-format=simple --log-interval=10 \ - --fixed-validation-seed=7 \ - --no-epoch-checkpoints --keep-best-checkpoints=1 \ - --save-interval=${save_interval} --validate-interval=1 \ - --save-interval-updates=${save_interval_updates} --validate-interval-updates=${validate_interval_updates} \ - --eval-cider \ - --eval-cider-cached-tokens=${eval_cider_cached} \ - --eval-args='{"beam":5,"max_len_b":16,"no_repeat_ngram_size":3}' \ - --best-checkpoint-metric=cider --maximize-best-checkpoint-metric \ - --max-src-length=${max_src_length} \ - --max-tgt-length=${max_tgt_length} \ - --find-unused-parameters \ - --freeze-encoder-embedding \ - --freeze-decoder-embedding \ - --add-type-embedding \ - --scale-attn \ - --scale-fc \ - --scale-heads \ - --disable-entangle \ - --num-bins=${num_bins} \ - --patch-image-size=${patch_image_size} \ - --drop-worst-ratio=${drop_worst_ratio} \ - --drop-worst-after=${drop_worst_after} \ - --fp16-scale-window=512 \ - --fp16 \ - --num-workers=0 \ - --image-encoder-name=${image_encoder_name} \ - --image-dir=${image_dir} \ - --video-encoder-name=${video_encoder_name} \ - --video-model-path=${video_model_path} \ - --patch-frame-size=${patch_frame_size} \ - ${sample_patch_num} \ - ${eval_args} \ - --num-frames=${num_frames} \ - --resnet-type=${resnet_type} \ - --resnet-model-path=${resnet_model_path} \ - --audio-encoder-name=${audio_encoder_name} \ - --audio-model-path=${audio_model_path} \ - --mel-bins=${mel_bins} \ - --hop-size=${hop_size} \ - --reset-dataloader --reset-meters --reset-optimizer \ - --save-on-cuda \ - --strict \ - --freeze-encoder \ - --freeze-decoder \ - --freeze-audio-encoder \ - --audio-sample-rate \ - --strict \ - --log-end ${log_end} --drop-best-ratio ${drop_best_ratio} --drop-best-after ${drop_best_after} - - done -done diff --git a/spaces/nateraw/huggingpics-explorer/README.md b/spaces/nateraw/huggingpics-explorer/README.md deleted file mode 100644 index 51a5160f4fb00f1b56230659d7214c10b799a52f..0000000000000000000000000000000000000000 --- a/spaces/nateraw/huggingpics-explorer/README.md +++ /dev/null @@ -1,19 +0,0 @@ ---- -title: huggingpics-explorer -emoji: 🤗 -colorFrom: blue -colorTo: red -sdk: streamlit -app_file: app.py -pinned: false ---- - -# huggingpics-explorer - -[![Generic badge](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/nateraw/huggingpics-explorer) - -A streamlit app for exploring image search results from HuggingPics - ---- - -Autogenerated using [this template](https://github.com/nateraw/spaces-template) diff --git a/spaces/nateraw/jupyterlab-test2/on_startup.sh b/spaces/nateraw/jupyterlab-test2/on_startup.sh deleted file mode 100644 index 448000271bbc7142681947fd1a447772f12ecfff..0000000000000000000000000000000000000000 --- a/spaces/nateraw/jupyterlab-test2/on_startup.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash -# Write some commands here that will run on root user before startup. -# For example, to clone transformers and install it in dev mode: -# git clone https://github.com/huggingface/transformers.git -# cd transformers && pip install -e ".[dev]" \ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Delcam.2012.SP4..Powermill..Exchange.and.Post.Processor..-.By.MSeeder UPD.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Delcam.2012.SP4..Powermill..Exchange.and.Post.Processor..-.By.MSeeder UPD.md deleted file mode 100644 index dfd9760835e26bbe0984c1db51dd2f96bf6b1bfd..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Delcam.2012.SP4..Powermill..Exchange.and.Post.Processor..-.By.MSeeder UPD.md +++ /dev/null @@ -1,102 +0,0 @@ -
        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor - By MSeeder

        -

        If you are looking for a powerful and reliable software for CNC machining, you might want to check out Delcam 2012 SP4 Powermill Exchange and Post Processor. This software is designed to help you create high-quality toolpaths for complex shapes and surfaces. It also allows you to customize your post processors to suit your specific machine requirements.

        -

        Delcam.2012.SP4..Powermill..Exchange.and.Post.Processor..-.By.MSeeder


        DOWNLOAD ✒ ✒ ✒ https://urlcod.com/2uIckX



        -

        But where can you find this software? And how can you trust that it is safe and working? That's where MSeeder comes in. MSeeder is a trusted uploader who has shared many useful software for CNC enthusiasts. He has uploaded Delcam 2012 SP4 Powermill Exchange and Post Processor on several torrent sites, along with detailed instructions on how to install and use it.

        -

        In this article, we will review the main features of Delcam 2012 SP4 Powermill Exchange and Post Processor, show you how to install and use it, give you some tips and tricks on how to customize it, and answer some frequently asked questions about it. By the end of this article, you will have a clear idea of what this software can do for you and how to get it from MSeeder.

        -

        Main Features of Delcam 2012 SP4 Powermill Exchange and Post Processor

        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor is a comprehensive software package that includes two main components: Powermill Exchange and Powermill Post Processor.

        -

        Powermill Exchange is a tool that allows you to import models from various CAD systems into Powermill. It supports a wide range of file formats such as IGES, STEP, STL, Parasolid, CATIA, SolidWorks, Solid Edge, and more. It also allows you to repair and optimize your models for machining, such as removing gaps, overlaps, and duplicates, simplifying curves and surfaces, and reducing the number of triangles. Powermill Exchange helps you prepare your models for the next step: creating toolpaths.

        -

        -

        Powermill Post Processor is a tool that allows you to generate NC code from your toolpaths. It supports a wide range of machine types and controllers, such as 3-axis, 4-axis, 5-axis, mill-turn, robots, Heidenhain, Fanuc, Siemens, and more. It also allows you to customize your post processors to match your specific machine capabilities and preferences. You can edit the existing post processors or create your own using the Post Processor Editor. Powermill Post Processor helps you turn your toolpaths into executable code for your machine.

        -

        Together, Powermill Exchange and Powermill Post Processor offer you a complete solution for CNC machining. Some of the main features of this software are:

        -

        High-speed machining

        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor enables you to create high-speed toolpaths that can reduce machining time and improve surface quality. It uses advanced algorithms to calculate the optimal feed rate and spindle speed for each segment of the toolpath, taking into account the material properties, tool geometry, and machine limitations. It also uses smooth transitions and continuous tangency to avoid sudden changes in direction and acceleration that can cause tool wear and vibration. With high-speed machining, you can achieve faster and smoother results.

        -

        Multi-axis machining

        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor allows you to create multi-axis toolpaths that can handle complex shapes and surfaces. It supports up to 5-axis simultaneous machining, as well as mill-turn operations that combine turning and milling in one setup. It also offers various strategies for multi-axis machining, such as swarf cutting, surface finishing, blade machining, port machining, impeller machining, rotary machining, and more. With multi-axis machining, you can access hard-to-reach areas and produce intricate features.

        -

        Toolpath optimization

        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor helps you optimize your toolpaths to improve machining efficiency and quality. It offers various options for toolpath optimization, such as collision avoidance, rest material removal, corner smoothing, arc fitting, lead-in/lead-out control, ramping control, linking control, and more. It also allows you to compare different toolpath options using the Toolpath Statistics feature that shows you the estimated machining time, distance traveled, number of moves, and more. With toolpath optimization, you can reduce waste and errors.

        -

        Simulation and verification

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        Delcam 2012 SP4 Powermill Exchange and Post Processor enables you to simulate and verify your toolpaths before sending them to the machine. It offers various modes of simulation such as wireframe, shaded, solid, and machine simulation. It also offers various tools for verification, such as collision detection, gouge detection, excess material detection, and more. It also allows you to generate reports and screenshots of your simulation and verification results. With simulation and verification, you can ensure the accuracy and safety of your toolpaths.

        -

        Post processing

        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor allows you to generate NC code from your toolpaths using the post processors that match your machine specifications. It supports a wide range of machine types and controllers, and allows you to customize your post processors using the Post Processor Editor. It also allows you to preview and edit your NC code using the NC Editor. With post processing, you can prepare your toolpaths for machining.

        -

        How to Install Delcam 2012 SP4 Powermill Exchange and Post Processor

        -

        If you want to install Delcam 2012 SP4 Powermill Exchange and Post Processor on your computer, you need to follow these steps:

        -

        System requirements

        -

        Before you install the software, you need to make sure that your computer meets the minimum system requirements. Here are the system requirements for Delcam 2012 SP4 Powermill Exchange and Post Processor:

        - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        Operating systemWindows XP/Vista/7/8/10 (32-bit or 64-bit)
        ProcessorIntel Pentium 4 or higher
        Memory2 GB RAM or more
        Hard disk space5 GB free space or more
        Graphics cardNVIDIA or ATI with OpenGL 2.0 support
        Monitor resolution1024 x 768 pixels or higher
        Internet connectionRequired for activation and updates
        -

        Download links

        -

        To download Delcam 2012 SP4 Powermill Exchange and Post Processor, you need to visit one of the torrent sites where MSeeder has uploaded it. Here are some of the torrent sites where you can find it:

        - - [The Pirate Bay] - [Kickass Torrents] - [1337x] - [LimeTorrents]

        You need to use a torrent client such as uTorrent or BitTorrent to download the software. The file size is about 1.5 GB. The file name is Delcam.2012.SP4.Powermill.Exchange.and.Post.Processor.-.By.MSeeder.rar.

        -

        Installation steps

        -

        To install Delcam 2012 SP4 Powermill Exchange and Post Processor, you need to follow these steps:

        - - Extract the rar file using WinRAR or 7-Zip. - Run the setup.exe file as administrator. - Follow the instructions on the screen. - When asked for the serial number, use the one provided in the readme.txt file. - When asked for the activation code, use the one provided in the readme.txt file. - Wait for the installation to complete. - Enjoy using Delcam 2012 SP4 Powermill Exchange and Post Processor.

        How to Use Delcam 2012 SP4 Powermill Exchange and Post Processor

        -

        To use Delcam 2012 SP4 Powermill Exchange and Post Processor, you need to follow these steps:

        -

        Importing models

        -

        To import models from various CAD systems into Powermill, you need to follow these steps:

        - - Launch Powermill Exchange from the Start menu or the desktop shortcut. - Click on the File menu and select Import. - Browse to the location of your model file and select it. - Choose the file format from the drop-down list and click Open. - Wait for the model to be imported into Powermill Exchange. - You can use the tools on the toolbar to repair and optimize your model for machining. - Click on the File menu and select Save As. - Choose a name and location for your model file and click Save. - You can now open your model file in Powermill.

        Creating toolpaths

        -

        To create toolpaths for your model in Powermill, you need to follow these steps:

        - - Launch Powermill from the Start menu or the desktop shortcut. - Click on the File menu and select Open Project. - Browse to the location of your model file and select it. - Click Open. - You can use the tools on the toolbar to define your workplane, stock, and boundaries. - Click on the Toolpaths menu and select Create Toolpath. - Choose a strategy from the list, such as Roughing, Finishing, Rest Roughing, Rest Finishing, or Special. - Click on the Parameters tab and adjust the settings for your toolpath, such as tool, stepover, stepdown, feed rate, spindle speed, and more. - Click on the Calculate button and wait for the toolpath to be generated. - You can use the tools on the toolbar to edit, simulate, and verify your toolpath. - Repeat the steps for each toolpath you want to create.

        Exporting toolpaths

        -

        To export toolpaths from Powermill to your machine using Powermill Post Processor, you need to follow these steps:

        - - Click on the File menu and select Export Project. - Choose a name and location for your project file and click Save. - Launch Powermill Post Processor from the Start menu or the desktop shortcut. - Click on the File menu and select Open Project. - Browse to the location of your project file and select it. - Click Open. - Click on the Post Processors tab and select a post processor from the list that matches your machine type and controller. - You can use the tools on the toolbar to edit, preview, and test your post processor. - Click on the NC Programs tab and select the toolpaths you want to export. - Click on the Export button and choose a name and location for your NC file. - Click Save. - You can now transfer your NC file to your machine using a USB drive, a network connection, or any other method.

        How to Customize Delcam 2012 SP4 Powermill Exchange and Post Processor

        -

        If you want to customize Delcam 2012 SP4 Powermill Exchange and Post Processor to suit your specific needs, you can follow these steps:

        -

        Finding generic post processors

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        If you don't have a post processor that matches your machine type and controller, you can try to find a generic post processor that is compatible with it. Delcam 2012 SP4 Powermill Exchange and Post Processor comes with a library of generic post processors that cover various machine types and controllers. You can access this library by clicking on the Help menu and selecting Generic Post Processors. You can browse through the categories and subcategories of generic post processors and see their descriptions and features. You can also download more generic post processors from Delcam's website.

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        Editing post processors

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        If you have a post processor that is close to what you need, but not exactly what you want, you can edit it using the Post Processor Editor. The Post Processor Editor is a tool that allows you to modify the parameters, variables, macros, commands, and formats of your post processor. You can access the Post Processor Editor by clicking on the Tools menu and selecting Post Processor Editor. You can open your existing post processor or create a new one from scratch. You can use the tools on the toolbar to edit your post processor. You can also use the Help menu to access the documentation and tutorials for the Post Processor Editor.

        -

        Purchasing post processors

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        If you don't have a post processor that works for your machine type and controller, and you don't want to edit or create one yourself, you can purchase one from Delcam or from third-party vendors. Delcam offers a range of custom post processors that are tailored to specific machine types and controllers. You can contact Delcam's sales team or visit their website to request a quote for a custom post processor. Alternatively, you can search online for third-party vendors that offer custom post processors for various machine types and controllers. However, you should be careful when buying from third-party vendors, as they may not be reliable or trustworthy.

        -

        Tips and Tricks for Delcam 2012 SP4 Powermill Exchange and Post Processor

        -

        To get the most out of Delcam 2012 SP4 Powermill Exchange and Post Processor, you can follow these tips and tricks:

        -

        How to improve machining efficiency

        -

        To improve machining efficiency, you can try these methods:

        - - Use high-speed machining strategies that reduce machining time and improve surface quality. - Use multi-axis machining strategies that access hard-to-reach areas and produce intricate features. - Use rest material removal strategies that avoid re-cutting areas that have already been machined. - Use corner smoothing strategies that reduce tool wear and vibration by avoiding sharp corners. - Use arc fitting strategies that reduce the number of moves by replacing linear segments with arcs.

        How to avoid common errors

        -

        To avoid common errors, you can try these methods:

        - - Use collision avoidance strategies that prevent collisions between the tool, holder, spindle, stock, fixture [user]( and machine. - Use gouge detection strategies that prevent gouges or undercuts on the model surface. - Use excess material detection strategies that prevent leaving excess material on the model surface. - Use simulation and verification tools to check your toolpaths for errors before sending them to the machine. - Use post processing tools to generate NC code that is compatible with your machine type and controller.

        How to troubleshoot problems

        -

        To troubleshoot problems, you can try these methods:

        - - Use the Help menu to access the documentation and tutorials for Delcam 2012 SP4 Powermill Exchange and Post Processor. - Use the online forums and communities to ask questions and get answers from other users and experts. - Use the technical support service to contact Delcam's support team and get assistance and solutions. - Use the update service to download and install the latest updates and patches for Delcam 2012 SP4 Powermill Exchange and Post Processor.

        Conclusion

        -

        Delcam 2012 SP4 Powermill Exchange and Post Processor is a powerful and reliable software for CNC machining. It allows you to import models from various CAD systems, create high-quality toolpaths for complex shapes and surfaces, optimize your toolpaths for machining efficiency and quality, simulate and verify your toolpaths for accuracy and safety, and generate NC code for your machine using custom post processors. It also allows you to customize your post processors to suit your specific machine requirements and preferences.

        -

        If you want to get this software, you can download it from MSeeder, a trusted uploader who has shared many useful software for CNC enthusiasts. He has uploaded Delcam 2012 SP4 Powermill Exchange and Post Processor on several torrent sites, along with detailed instructions on how to install and use it.

        -

        We hope this article has given you a clear idea of what Delcam 2012 SP4 Powermill Exchange and Post Processor can do for you and how to get it from MSeeder. If you have any questions or comments, please feel free to leave them below. Thank you for reading!

        -

        FAQs

        -

        What is the difference between Powermill Exchange and Powermill Post Processor?

        -

        Powermill Exchange is a tool that allows you to import models from various CAD systems into Powermill. Powermill Post Processor is a tool that allows you to generate NC code from your toolpaths using custom post processors.

        -

        What are the supported file formats for Powermill Exchange and Powermill Post Processor?

        -

        Powermill Exchange supports a wide range of file formats such as IGES, STEP, STL, Parasolid, CATIA, SolidWorks, Solid Edge, and more. Powermill Post Processor supports a wide range of machine types and controllers such as 3-axis, 4-axis, 5-axis, mill-turn, robots, Heidenhain, Fanuc, Siemens, and more.

        -

        How can I update Delcam 2012 SP4 Powermill Exchange and Post Processor to the latest version?

        -

        You can update Delcam 2012 SP4 Powermill Exchange and Post Processor to the latest version by clicking on the Help menu and selecting Check for Updates. You can also visit Delcam's website to download the latest updates and patches.

        -

        How can I get technical support for Delcam 2012 SP4 Powermill Exchange and Post Processor?

        -

        You can get technical support for Delcam 2012 SP4 Powermill Exchange and Post Processor by clicking on the Help menu and selecting Technical Support. You can also contact Delcam's support team by phone or email.

        -

        Where can I find more resources and tutorials for Delcam 2012 SP4 Powermill Exchange and Post Processor?

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        You can find more resources and tutorials for Delcam 2012 SP4 Powermill Exchange and Post Processor by clicking on the Help menu and selecting Online Resources. You can also visit Delcam's website, YouTube channel, or blog to find more information and videos.

        b2dd77e56b
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        \ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D).md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D).md deleted file mode 100644 index ad627ea85b44d33205b05557b04a17452bd1e092..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D).md +++ /dev/null @@ -1,20 +0,0 @@ - -

        HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D): A Review

        -

        If you are a fan of the dark comedy crime series Fargo, you might want to check out the HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D), a streaming service that offers high-quality video and audio for the first season of the show. Here are some of the features and benefits of using this online player:

        -

        HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D)


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          -
        • You can watch all 10 episodes of Fargo Season 1 in full HD resolution (1080p) and with surround sound (5.1 channels).
          • -
        • You can access the online player from any device that supports web browsers, such as laptops, tablets, smartphones, smart TVs, etc.
          • -
        • You can enjoy the online player without any ads, interruptions, or buffering issues.
          • -
        • You can choose from different subtitles and audio languages, depending on your preference.
          • -
        • You can also download the episodes to your device for offline viewing.
          • -
        -

        The HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D) is a great option for Fargo fans who want to relive the thrilling and hilarious adventures of Lester Nygaard, Lorne Malvo, Molly Solverson, and other memorable characters from the first season of the show. The online player is easy to use, reliable, and affordable. You can sign up for a free trial or a monthly subscription at https://hd-online-player-fargo-s01-season-1-1080p-5-1ch-web-d.com.

        - -

        What makes Fargo Season 1 so captivating and enjoyable is the brilliant writing, directing, acting, and cinematography that bring the story to life. The show is inspired by the 1996 film of the same name by the Coen brothers, but it is not a direct adaptation. Rather, it is a homage to the film's themes, style, and tone, while creating its own original plot and characters.

        -

        The show is set in 2006 in the frozen landscape of Minnesota and North Dakota, where a series of violent and bizarre events unfold after a timid insurance salesman named Lester Nygaard (Martin Freeman) meets a mysterious and ruthless hitman named Lorne Malvo (Billy Bob Thornton). The show explores the themes of morality, fate, crime, and justice, as well as the quirky and eccentric aspects of the Midwest culture and humor.

        -

        -

        The show also features an outstanding cast of supporting characters, such as Molly Solverson (Allison Tolman), a smart and determined deputy who tries to solve the case; Gus Grimly (Colin Hanks), a kind-hearted but inexperienced police officer who crosses paths with Malvo; Bob Odenkirk as Bill Oswalt, Lester's clueless boss and Molly's superior; Keith Carradine as Lou Solverson, Molly's father and a retired cop; and many more.

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        Fargo Season 1 received critical acclaim and won several awards, including three Primetime Emmy Awards and two Golden Globe Awards. It is widely regarded as one of the best TV shows of the 2010s. If you haven't watched it yet, you are missing out on a masterpiece of television. And if you have watched it already, you might want to watch it again with the HD Online Player (Fargo S01 Season 1 1080p 5.1Ch Web-D), which will enhance your viewing experience with its superb quality and features.

        81aa517590
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        \ No newline at end of file diff --git a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Horoscopo Libra Astrology Zone.md b/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Horoscopo Libra Astrology Zone.md deleted file mode 100644 index 01c55ac7bdc1570fb6bac3872269fd4c23ba3de6..0000000000000000000000000000000000000000 --- a/spaces/netiMophi/DreamlikeArt-Diffusion-1.0/Horoscopo Libra Astrology Zone.md +++ /dev/null @@ -1,16 +0,0 @@ -
        -

        Horoscopo Libra Astrology Zone: What to Expect in April 2023

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        If you are a Libra, you might be curious about what the stars have in store for you in April 2023. According to Susan Miller, the famous astrologer behind Astrology Zone, this month will bring you some exciting opportunities and challenges in different areas of your life. Here are some highlights of your horoscope for April 2023:

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        • Love: You will have a chance to deepen your connection with your partner or attract a new admirer, thanks to the harmonious influence of Venus, your ruling planet, in your seventh house of relationships. However, you will also need to be careful of misunderstandings and arguments, especially around the full moon in Scorpio on April 23, which will oppose your sun and create some tension. Try to be diplomatic and avoid being too critical or demanding.
          • -
        • Career: You will have a lot of creative energy and inspiration this month, as Mercury, the planet of communication, joins forces with Uranus, the planet of innovation, in your fifth house of creativity and self-expression. This is a great time to showcase your talents and original ideas, especially in fields related to art, media, entertainment, or technology. You might also receive some recognition or praise for your work around the new moon in Aries on April 9, which will highlight your tenth house of career and reputation.
          • -
        • Health: You will need to pay attention to your physical and mental well-being this month, as Mars, the planet of action and aggression, will transit your sixth house of health and daily routines. This can make you more prone to stress, fatigue, inflammation, or accidents. To prevent any health issues, try to balance your work and rest, exercise moderately, eat well, and avoid any risky or reckless behavior. You might also benefit from some alternative therapies or healing modalities that can help you relax and recharge.
          • -
        -

        To read more about your horoscope for April 2023, visit Astrology Zone, where you can find detailed monthly forecasts by Susan Miller for each sign. You can also download the Astrology Zone mobile app to get daily horoscopes and more on your phone.

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        Horoscopo Libra Astrology Zone


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        - -

        Libra is also a sign of beauty and harmony, as it is ruled by Venus, the planet of love and aesthetics. Libra loves to surround themselves with art, music, culture, and nature. They have a refined taste and a keen eye for design and style. They also enjoy pampering themselves and their loved ones with luxurious gifts and experiences.

        -

        Libra is an air sign, which means they are intellectual, curious, and communicative. Libra has a sharp mind and a witty sense of humor. They enjoy learning new things and sharing their knowledge with others. They are also great conversationalists who can charm anyone with their words. Libra is always interested in other people's opinions and perspectives, and they are willing to listen and learn from them.

        -

        Libra is a cardinal sign, which means they are initiators, leaders, and innovators. Libra has a vision of how things should be and they are not afraid to pursue it. They are creative and resourceful, always finding new ways to improve themselves and their environment. They are also ambitious and driven, always setting high goals and working hard to achieve them.

        cec2833e83
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        \ No newline at end of file diff --git a/spaces/ngoctuanai/DALL-E/html2canvas.js b/spaces/ngoctuanai/DALL-E/html2canvas.js deleted file mode 100644 index 96e2dc5707b1a584ff7b3b583aea7c6c18d4ea76..0000000000000000000000000000000000000000 --- a/spaces/ngoctuanai/DALL-E/html2canvas.js +++ /dev/null @@ -1,7756 +0,0 @@ -/*! - * html2canvas 1.4.1 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ -(function (global, factory) { - typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : - typeof define === 'function' && define.amd ? define(factory) : - (global = typeof globalThis !== 'undefined' ? globalThis : global || self, global.html2canvas = factory()); -}(this, (function () { 'use strict'; - - /*! ***************************************************************************** - Copyright (c) Microsoft Corporation. - - Permission to use, copy, modify, and/or distribute this software for any - purpose with or without fee is hereby granted. - - THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH - REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY - AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, - INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM - LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR - OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR - PERFORMANCE OF THIS SOFTWARE. - ***************************************************************************** */ - /* global Reflect, Promise */ - - var extendStatics = function(d, b) { - extendStatics = Object.setPrototypeOf || - ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) || - function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; }; - return extendStatics(d, b); - }; - - function __extends(d, b) { - if (typeof b !== "function" && b !== null) - throw new TypeError("Class extends value " + String(b) + " is not a constructor or null"); - extendStatics(d, b); - function __() { this.constructor = d; } - d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __()); - } - - var __assign = function() { - __assign = Object.assign || function __assign(t) { - for (var s, i = 1, n = arguments.length; i < n; i++) { - s = arguments[i]; - for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p]; - } - return t; - }; - return __assign.apply(this, arguments); - }; - - function __awaiter(thisArg, _arguments, P, generator) { - function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); } - return new (P || (P = Promise))(function (resolve, reject) { - function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } } - function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } } - function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); } - step((generator = generator.apply(thisArg, _arguments || [])).next()); - }); - } - - function __generator(thisArg, body) { - var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g; - return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g; - function verb(n) { return function (v) { return step([n, v]); }; } - function step(op) { - if (f) throw new TypeError("Generator is already executing."); - while (_) try { - if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t; - if (y = 0, t) op = [op[0] & 2, t.value]; - switch (op[0]) { - case 0: case 1: t = op; break; - case 4: _.label++; return { value: op[1], done: false }; - case 5: _.label++; y = op[1]; op = [0]; continue; - case 7: op = _.ops.pop(); _.trys.pop(); continue; - default: - if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; } - if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; } - if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; } - if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; } - if (t[2]) _.ops.pop(); - _.trys.pop(); continue; - } - op = body.call(thisArg, _); - } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; } - if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true }; - } - } - - function __spreadArray(to, from, pack) { - if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) { - if (ar || !(i in from)) { - if (!ar) ar = Array.prototype.slice.call(from, 0, i); - ar[i] = from[i]; - } - } - return to.concat(ar || from); - } - - var Bounds = /** @class */ (function () { - function Bounds(left, top, width, height) { - this.left = left; - this.top = top; - this.width = width; - this.height = height; - } - Bounds.prototype.add = function (x, y, w, h) { - return new Bounds(this.left + x, this.top + y, this.width + w, this.height + h); - }; - Bounds.fromClientRect = function (context, clientRect) { - return new Bounds(clientRect.left + context.windowBounds.left, clientRect.top + context.windowBounds.top, clientRect.width, clientRect.height); - }; - Bounds.fromDOMRectList = function (context, domRectList) { - var domRect = Array.from(domRectList).find(function (rect) { return rect.width !== 0; }); - return domRect - ? new Bounds(domRect.left + context.windowBounds.left, domRect.top + context.windowBounds.top, domRect.width, domRect.height) - : Bounds.EMPTY; - }; - Bounds.EMPTY = new Bounds(0, 0, 0, 0); - return Bounds; - }()); - var parseBounds = function (context, node) { - return Bounds.fromClientRect(context, node.getBoundingClientRect()); - }; - var parseDocumentSize = function (document) { - var body = document.body; - var documentElement = document.documentElement; - if (!body || !documentElement) { - throw new Error("Unable to get document size"); - } - var width = Math.max(Math.max(body.scrollWidth, documentElement.scrollWidth), Math.max(body.offsetWidth, documentElement.offsetWidth), Math.max(body.clientWidth, documentElement.clientWidth)); - var height = Math.max(Math.max(body.scrollHeight, documentElement.scrollHeight), Math.max(body.offsetHeight, documentElement.offsetHeight), Math.max(body.clientHeight, documentElement.clientHeight)); - return new Bounds(0, 0, width, height); - }; - - /* - * css-line-break 2.1.0 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ - var toCodePoints$1 = function (str) { - var codePoints = []; - var i = 0; - var length = str.length; - while (i < length) { - var value = str.charCodeAt(i++); - if (value >= 0xd800 && value <= 0xdbff && i < length) { - var extra = str.charCodeAt(i++); - if ((extra & 0xfc00) === 0xdc00) { - codePoints.push(((value & 0x3ff) << 10) + (extra & 0x3ff) + 0x10000); - } - else { - codePoints.push(value); - i--; - } - } - else { - codePoints.push(value); - } - } - return codePoints; - }; - var fromCodePoint$1 = function () { - var codePoints = []; - for (var _i = 0; _i < arguments.length; _i++) { - codePoints[_i] = arguments[_i]; - } - if (String.fromCodePoint) { - return String.fromCodePoint.apply(String, codePoints); - } - var length = codePoints.length; - if (!length) { - return ''; - } - var codeUnits = []; - var index = -1; - var result = ''; - while (++index < length) { - var codePoint = codePoints[index]; - if (codePoint <= 0xffff) { - codeUnits.push(codePoint); - } - else { - codePoint -= 0x10000; - codeUnits.push((codePoint >> 10) + 0xd800, (codePoint % 0x400) + 0xdc00); - } - if (index + 1 === length || codeUnits.length > 0x4000) { - result += String.fromCharCode.apply(String, codeUnits); - codeUnits.length = 0; - } - } - return result; - }; - var chars$2 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'; - // Use a lookup table to find the index. - var lookup$2 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256); - for (var i$2 = 0; i$2 < chars$2.length; i$2++) { - lookup$2[chars$2.charCodeAt(i$2)] = i$2; - } - - /* - * utrie 1.0.2 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ - var chars$1$1 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'; - // Use a lookup table to find the index. - var lookup$1$1 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256); - for (var i$1$1 = 0; i$1$1 < chars$1$1.length; i$1$1++) { - lookup$1$1[chars$1$1.charCodeAt(i$1$1)] = i$1$1; - } - var decode$1 = function (base64) { - var bufferLength = base64.length * 0.75, len = base64.length, i, p = 0, encoded1, encoded2, encoded3, encoded4; - if (base64[base64.length - 1] === '=') { - bufferLength--; - if (base64[base64.length - 2] === '=') { - bufferLength--; - } - } - var buffer = typeof ArrayBuffer !== 'undefined' && - typeof Uint8Array !== 'undefined' && - typeof Uint8Array.prototype.slice !== 'undefined' - ? new ArrayBuffer(bufferLength) - : new Array(bufferLength); - var bytes = Array.isArray(buffer) ? buffer : new Uint8Array(buffer); - for (i = 0; i < len; i += 4) { - encoded1 = lookup$1$1[base64.charCodeAt(i)]; - encoded2 = lookup$1$1[base64.charCodeAt(i + 1)]; - encoded3 = lookup$1$1[base64.charCodeAt(i + 2)]; - encoded4 = lookup$1$1[base64.charCodeAt(i + 3)]; - bytes[p++] = (encoded1 << 2) | (encoded2 >> 4); - bytes[p++] = ((encoded2 & 15) << 4) | (encoded3 >> 2); - bytes[p++] = ((encoded3 & 3) << 6) | (encoded4 & 63); - } - return buffer; - }; - var polyUint16Array$1 = function (buffer) { - var length = buffer.length; - var bytes = []; - for (var i = 0; i < length; i += 2) { - bytes.push((buffer[i + 1] << 8) | buffer[i]); - } - return bytes; - }; - var polyUint32Array$1 = function (buffer) { - var length = buffer.length; - var bytes = []; - for (var i = 0; i < length; i += 4) { - bytes.push((buffer[i + 3] << 24) | (buffer[i + 2] << 16) | (buffer[i + 1] << 8) | buffer[i]); - } - return bytes; - }; - - /** Shift size for getting the index-2 table offset. */ - var UTRIE2_SHIFT_2$1 = 5; - /** Shift size for getting the index-1 table offset. */ - var UTRIE2_SHIFT_1$1 = 6 + 5; - /** - * Shift size for shifting left the index array values. - * Increases possible data size with 16-bit index values at the cost - * of compactability. - * This requires data blocks to be aligned by UTRIE2_DATA_GRANULARITY. - */ - var UTRIE2_INDEX_SHIFT$1 = 2; - /** - * Difference between the two shift sizes, - * for getting an index-1 offset from an index-2 offset. 6=11-5 - */ - var UTRIE2_SHIFT_1_2$1 = UTRIE2_SHIFT_1$1 - UTRIE2_SHIFT_2$1; - /** - * The part of the index-2 table for U+D800..U+DBFF stores values for - * lead surrogate code _units_ not code _points_. - * Values for lead surrogate code _points_ are indexed with this portion of the table. - * Length=32=0x20=0x400>>UTRIE2_SHIFT_2. (There are 1024=0x400 lead surrogates.) - */ - var UTRIE2_LSCP_INDEX_2_OFFSET$1 = 0x10000 >> UTRIE2_SHIFT_2$1; - /** Number of entries in a data block. 32=0x20 */ - var UTRIE2_DATA_BLOCK_LENGTH$1 = 1 << UTRIE2_SHIFT_2$1; - /** Mask for getting the lower bits for the in-data-block offset. */ - var UTRIE2_DATA_MASK$1 = UTRIE2_DATA_BLOCK_LENGTH$1 - 1; - var UTRIE2_LSCP_INDEX_2_LENGTH$1 = 0x400 >> UTRIE2_SHIFT_2$1; - /** Count the lengths of both BMP pieces. 2080=0x820 */ - var UTRIE2_INDEX_2_BMP_LENGTH$1 = UTRIE2_LSCP_INDEX_2_OFFSET$1 + UTRIE2_LSCP_INDEX_2_LENGTH$1; - /** - * The 2-byte UTF-8 version of the index-2 table follows at offset 2080=0x820. - * Length 32=0x20 for lead bytes C0..DF, regardless of UTRIE2_SHIFT_2. - */ - var UTRIE2_UTF8_2B_INDEX_2_OFFSET$1 = UTRIE2_INDEX_2_BMP_LENGTH$1; - var UTRIE2_UTF8_2B_INDEX_2_LENGTH$1 = 0x800 >> 6; /* U+0800 is the first code point after 2-byte UTF-8 */ - /** - * The index-1 table, only used for supplementary code points, at offset 2112=0x840. - * Variable length, for code points up to highStart, where the last single-value range starts. - * Maximum length 512=0x200=0x100000>>UTRIE2_SHIFT_1. - * (For 0x100000 supplementary code points U+10000..U+10ffff.) - * - * The part of the index-2 table for supplementary code points starts - * after this index-1 table. - * - * Both the index-1 table and the following part of the index-2 table - * are omitted completely if there is only BMP data. - */ - var UTRIE2_INDEX_1_OFFSET$1 = UTRIE2_UTF8_2B_INDEX_2_OFFSET$1 + UTRIE2_UTF8_2B_INDEX_2_LENGTH$1; - /** - * Number of index-1 entries for the BMP. 32=0x20 - * This part of the index-1 table is omitted from the serialized form. - */ - var UTRIE2_OMITTED_BMP_INDEX_1_LENGTH$1 = 0x10000 >> UTRIE2_SHIFT_1$1; - /** Number of entries in an index-2 block. 64=0x40 */ - var UTRIE2_INDEX_2_BLOCK_LENGTH$1 = 1 << UTRIE2_SHIFT_1_2$1; - /** Mask for getting the lower bits for the in-index-2-block offset. */ - var UTRIE2_INDEX_2_MASK$1 = UTRIE2_INDEX_2_BLOCK_LENGTH$1 - 1; - var slice16$1 = function (view, start, end) { - if (view.slice) { - return view.slice(start, end); - } - return new Uint16Array(Array.prototype.slice.call(view, start, end)); - }; - var slice32$1 = function (view, start, end) { - if (view.slice) { - return view.slice(start, end); - } - return new Uint32Array(Array.prototype.slice.call(view, start, end)); - }; - var createTrieFromBase64$1 = function (base64, _byteLength) { - var buffer = decode$1(base64); - var view32 = Array.isArray(buffer) ? polyUint32Array$1(buffer) : new Uint32Array(buffer); - var view16 = Array.isArray(buffer) ? polyUint16Array$1(buffer) : new Uint16Array(buffer); - var headerLength = 24; - var index = slice16$1(view16, headerLength / 2, view32[4] / 2); - var data = view32[5] === 2 - ? slice16$1(view16, (headerLength + view32[4]) / 2) - : slice32$1(view32, Math.ceil((headerLength + view32[4]) / 4)); - return new Trie$1(view32[0], view32[1], view32[2], view32[3], index, data); - }; - var Trie$1 = /** @class */ (function () { - function Trie(initialValue, errorValue, highStart, highValueIndex, index, data) { - this.initialValue = initialValue; - this.errorValue = errorValue; - this.highStart = highStart; - this.highValueIndex = highValueIndex; - this.index = index; - this.data = data; - } - /** - * Get the value for a code point as stored in the Trie. - * - * @param codePoint the code point - * @return the value - */ - Trie.prototype.get = function (codePoint) { - var ix; - if (codePoint >= 0) { - if (codePoint < 0x0d800 || (codePoint > 0x0dbff && codePoint <= 0x0ffff)) { - // Ordinary BMP code point, excluding leading surrogates. - // BMP uses a single level lookup. BMP index starts at offset 0 in the Trie2 index. - // 16 bit data is stored in the index array itself. - ix = this.index[codePoint >> UTRIE2_SHIFT_2$1]; - ix = (ix << UTRIE2_INDEX_SHIFT$1) + (codePoint & UTRIE2_DATA_MASK$1); - return this.data[ix]; - } - if (codePoint <= 0xffff) { - // Lead Surrogate Code Point. A Separate index section is stored for - // lead surrogate code units and code points. - // The main index has the code unit data. - // For this function, we need the code point data. - // Note: this expression could be refactored for slightly improved efficiency, but - // surrogate code points will be so rare in practice that it's not worth it. - ix = this.index[UTRIE2_LSCP_INDEX_2_OFFSET$1 + ((codePoint - 0xd800) >> UTRIE2_SHIFT_2$1)]; - ix = (ix << UTRIE2_INDEX_SHIFT$1) + (codePoint & UTRIE2_DATA_MASK$1); - return this.data[ix]; - } - if (codePoint < this.highStart) { - // Supplemental code point, use two-level lookup. - ix = UTRIE2_INDEX_1_OFFSET$1 - UTRIE2_OMITTED_BMP_INDEX_1_LENGTH$1 + (codePoint >> UTRIE2_SHIFT_1$1); - ix = this.index[ix]; - ix += (codePoint >> UTRIE2_SHIFT_2$1) & UTRIE2_INDEX_2_MASK$1; - ix = this.index[ix]; - ix = (ix << UTRIE2_INDEX_SHIFT$1) + (codePoint & UTRIE2_DATA_MASK$1); - return this.data[ix]; - } - if (codePoint <= 0x10ffff) { - return this.data[this.highValueIndex]; - } - } - // Fall through. The code point is outside of the legal range of 0..0x10ffff. - return this.errorValue; - }; - return Trie; - }()); - - /* - * base64-arraybuffer 1.0.2 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ - var chars$3 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'; - // Use a lookup table to find the index. - var lookup$3 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256); - for (var i$3 = 0; i$3 < chars$3.length; i$3++) { - lookup$3[chars$3.charCodeAt(i$3)] = i$3; - } - - var base64$1 = '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'; - - var LETTER_NUMBER_MODIFIER = 50; - // Non-tailorable Line Breaking Classes - var BK = 1; // Cause a line break (after) - var CR$1 = 2; // Cause a line break (after), except between CR and LF - var LF$1 = 3; // Cause a line break (after) - var CM = 4; // Prohibit a line break between the character and the preceding character - var NL = 5; // Cause a line break (after) - var WJ = 7; // Prohibit line breaks before and after - var ZW = 8; // Provide a break opportunity - var GL = 9; // Prohibit line breaks before and after - var SP = 10; // Enable indirect line breaks - var ZWJ$1 = 11; // Prohibit line breaks within joiner sequences - // Break Opportunities - var B2 = 12; // Provide a line break opportunity before and after the character - var BA = 13; // Generally provide a line break opportunity after the character - var BB = 14; // Generally provide a line break opportunity before the character - var HY = 15; // Provide a line break opportunity after the character, except in numeric context - var CB = 16; // Provide a line break opportunity contingent on additional information - // Characters Prohibiting Certain Breaks - var CL = 17; // Prohibit line breaks before - var CP = 18; // Prohibit line breaks before - var EX = 19; // Prohibit line breaks before - var IN = 20; // Allow only indirect line breaks between pairs - var NS = 21; // Allow only indirect line breaks before - var OP = 22; // Prohibit line breaks after - var QU = 23; // Act like they are both opening and closing - // Numeric Context - var IS = 24; // Prevent breaks after any and before numeric - var NU = 25; // Form numeric expressions for line breaking purposes - var PO = 26; // Do not break following a numeric expression - var PR = 27; // Do not break in front of a numeric expression - var SY = 28; // Prevent a break before; and allow a break after - // Other Characters - var AI = 29; // Act like AL when the resolvedEAW is N; otherwise; act as ID - var AL = 30; // Are alphabetic characters or symbols that are used with alphabetic characters - var CJ = 31; // Treat as NS or ID for strict or normal breaking. - var EB = 32; // Do not break from following Emoji Modifier - var EM = 33; // Do not break from preceding Emoji Base - var H2 = 34; // Form Korean syllable blocks - var H3 = 35; // Form Korean syllable blocks - var HL = 36; // Do not break around a following hyphen; otherwise act as Alphabetic - var ID = 37; // Break before or after; except in some numeric context - var JL = 38; // Form Korean syllable blocks - var JV = 39; // Form Korean syllable blocks - var JT = 40; // Form Korean syllable blocks - var RI$1 = 41; // Keep pairs together. For pairs; break before and after other classes - var SA = 42; // Provide a line break opportunity contingent on additional, language-specific context analysis - var XX = 43; // Have as yet unknown line breaking behavior or unassigned code positions - var ea_OP = [0x2329, 0xff08]; - var BREAK_MANDATORY = '!'; - var BREAK_NOT_ALLOWED$1 = '×'; - var BREAK_ALLOWED$1 = '÷'; - var UnicodeTrie$1 = createTrieFromBase64$1(base64$1); - var ALPHABETICS = [AL, HL]; - var HARD_LINE_BREAKS = [BK, CR$1, LF$1, NL]; - var SPACE$1 = [SP, ZW]; - var PREFIX_POSTFIX = [PR, PO]; - var LINE_BREAKS = HARD_LINE_BREAKS.concat(SPACE$1); - var KOREAN_SYLLABLE_BLOCK = [JL, JV, JT, H2, H3]; - var HYPHEN = [HY, BA]; - var codePointsToCharacterClasses = function (codePoints, lineBreak) { - if (lineBreak === void 0) { lineBreak = 'strict'; } - var types = []; - var indices = []; - var categories = []; - codePoints.forEach(function (codePoint, index) { - var classType = UnicodeTrie$1.get(codePoint); - if (classType > LETTER_NUMBER_MODIFIER) { - categories.push(true); - classType -= LETTER_NUMBER_MODIFIER; - } - else { - categories.push(false); - } - if (['normal', 'auto', 'loose'].indexOf(lineBreak) !== -1) { - // U+2010, – U+2013, 〜 U+301C, ゠ U+30A0 - if ([0x2010, 0x2013, 0x301c, 0x30a0].indexOf(codePoint) !== -1) { - indices.push(index); - return types.push(CB); - } - } - if (classType === CM || classType === ZWJ$1) { - // LB10 Treat any remaining combining mark or ZWJ as AL. - if (index === 0) { - indices.push(index); - return types.push(AL); - } - // LB9 Do not break a combining character sequence; treat it as if it has the line breaking class of - // the base character in all of the following rules. Treat ZWJ as if it were CM. - var prev = types[index - 1]; - if (LINE_BREAKS.indexOf(prev) === -1) { - indices.push(indices[index - 1]); - return types.push(prev); - } - indices.push(index); - return types.push(AL); - } - indices.push(index); - if (classType === CJ) { - return types.push(lineBreak === 'strict' ? NS : ID); - } - if (classType === SA) { - return types.push(AL); - } - if (classType === AI) { - return types.push(AL); - } - // For supplementary characters, a useful default is to treat characters in the range 10000..1FFFD as AL - // and characters in the ranges 20000..2FFFD and 30000..3FFFD as ID, until the implementation can be revised - // to take into account the actual line breaking properties for these characters. - if (classType === XX) { - if ((codePoint >= 0x20000 && codePoint <= 0x2fffd) || (codePoint >= 0x30000 && codePoint <= 0x3fffd)) { - return types.push(ID); - } - else { - return types.push(AL); - } - } - types.push(classType); - }); - return [indices, types, categories]; - }; - var isAdjacentWithSpaceIgnored = function (a, b, currentIndex, classTypes) { - var current = classTypes[currentIndex]; - if (Array.isArray(a) ? a.indexOf(current) !== -1 : a === current) { - var i = currentIndex; - while (i <= classTypes.length) { - i++; - var next = classTypes[i]; - if (next === b) { - return true; - } - if (next !== SP) { - break; - } - } - } - if (current === SP) { - var i = currentIndex; - while (i > 0) { - i--; - var prev = classTypes[i]; - if (Array.isArray(a) ? a.indexOf(prev) !== -1 : a === prev) { - var n = currentIndex; - while (n <= classTypes.length) { - n++; - var next = classTypes[n]; - if (next === b) { - return true; - } - if (next !== SP) { - break; - } - } - } - if (prev !== SP) { - break; - } - } - } - return false; - }; - var previousNonSpaceClassType = function (currentIndex, classTypes) { - var i = currentIndex; - while (i >= 0) { - var type = classTypes[i]; - if (type === SP) { - i--; - } - else { - return type; - } - } - return 0; - }; - var _lineBreakAtIndex = function (codePoints, classTypes, indicies, index, forbiddenBreaks) { - if (indicies[index] === 0) { - return BREAK_NOT_ALLOWED$1; - } - var currentIndex = index - 1; - if (Array.isArray(forbiddenBreaks) && forbiddenBreaks[currentIndex] === true) { - return BREAK_NOT_ALLOWED$1; - } - var beforeIndex = currentIndex - 1; - var afterIndex = currentIndex + 1; - var current = classTypes[currentIndex]; - // LB4 Always break after hard line breaks. - // LB5 Treat CR followed by LF, as well as CR, LF, and NL as hard line breaks. - var before = beforeIndex >= 0 ? classTypes[beforeIndex] : 0; - var next = classTypes[afterIndex]; - if (current === CR$1 && next === LF$1) { - return BREAK_NOT_ALLOWED$1; - } - if (HARD_LINE_BREAKS.indexOf(current) !== -1) { - return BREAK_MANDATORY; - } - // LB6 Do not break before hard line breaks. - if (HARD_LINE_BREAKS.indexOf(next) !== -1) { - return BREAK_NOT_ALLOWED$1; - } - // LB7 Do not break before spaces or zero width space. - if (SPACE$1.indexOf(next) !== -1) { - return BREAK_NOT_ALLOWED$1; - } - // LB8 Break before any character following a zero-width space, even if one or more spaces intervene. - if (previousNonSpaceClassType(currentIndex, classTypes) === ZW) { - return BREAK_ALLOWED$1; - } - // LB8a Do not break after a zero width joiner. - if (UnicodeTrie$1.get(codePoints[currentIndex]) === ZWJ$1) { - return BREAK_NOT_ALLOWED$1; - } - // zwj emojis - if ((current === EB || current === EM) && UnicodeTrie$1.get(codePoints[afterIndex]) === ZWJ$1) { - return BREAK_NOT_ALLOWED$1; - } - // LB11 Do not break before or after Word joiner and related characters. - if (current === WJ || next === WJ) { - return BREAK_NOT_ALLOWED$1; - } - // LB12 Do not break after NBSP and related characters. - if (current === GL) { - return BREAK_NOT_ALLOWED$1; - } - // LB12a Do not break before NBSP and related characters, except after spaces and hyphens. - if ([SP, BA, HY].indexOf(current) === -1 && next === GL) { - return BREAK_NOT_ALLOWED$1; - } - // LB13 Do not break before ‘]’ or ‘!’ or ‘;’ or ‘/’, even after spaces. - if ([CL, CP, EX, IS, SY].indexOf(next) !== -1) { - return BREAK_NOT_ALLOWED$1; - } - // LB14 Do not break after ‘[’, even after spaces. - if (previousNonSpaceClassType(currentIndex, classTypes) === OP) { - return BREAK_NOT_ALLOWED$1; - } - // LB15 Do not break within ‘”[’, even with intervening spaces. - if (isAdjacentWithSpaceIgnored(QU, OP, currentIndex, classTypes)) { - return BREAK_NOT_ALLOWED$1; - } - // LB16 Do not break between closing punctuation and a nonstarter (lb=NS), even with intervening spaces. - if (isAdjacentWithSpaceIgnored([CL, CP], NS, currentIndex, classTypes)) { - return BREAK_NOT_ALLOWED$1; - } - // LB17 Do not break within ‘——’, even with intervening spaces. - if (isAdjacentWithSpaceIgnored(B2, B2, currentIndex, classTypes)) { - return BREAK_NOT_ALLOWED$1; - } - // LB18 Break after spaces. - if (current === SP) { - return BREAK_ALLOWED$1; - } - // LB19 Do not break before or after quotation marks, such as ‘ ” ’. - if (current === QU || next === QU) { - return BREAK_NOT_ALLOWED$1; - } - // LB20 Break before and after unresolved CB. - if (next === CB || current === CB) { - return BREAK_ALLOWED$1; - } - // LB21 Do not break before hyphen-minus, other hyphens, fixed-width spaces, small kana, and other non-starters, or after acute accents. - if ([BA, HY, NS].indexOf(next) !== -1 || current === BB) { - return BREAK_NOT_ALLOWED$1; - } - // LB21a Don't break after Hebrew + Hyphen. - if (before === HL && HYPHEN.indexOf(current) !== -1) { - return BREAK_NOT_ALLOWED$1; - } - // LB21b Don’t break between Solidus and Hebrew letters. - if (current === SY && next === HL) { - return BREAK_NOT_ALLOWED$1; - } - // LB22 Do not break before ellipsis. - if (next === IN) { - return BREAK_NOT_ALLOWED$1; - } - // LB23 Do not break between digits and letters. - if ((ALPHABETICS.indexOf(next) !== -1 && current === NU) || (ALPHABETICS.indexOf(current) !== -1 && next === NU)) { - return BREAK_NOT_ALLOWED$1; - } - // LB23a Do not break between numeric prefixes and ideographs, or between ideographs and numeric postfixes. - if ((current === PR && [ID, EB, EM].indexOf(next) !== -1) || - ([ID, EB, EM].indexOf(current) !== -1 && next === PO)) { - return BREAK_NOT_ALLOWED$1; - } - // LB24 Do not break between numeric prefix/postfix and letters, or between letters and prefix/postfix. - if ((ALPHABETICS.indexOf(current) !== -1 && PREFIX_POSTFIX.indexOf(next) !== -1) || - (PREFIX_POSTFIX.indexOf(current) !== -1 && ALPHABETICS.indexOf(next) !== -1)) { - return BREAK_NOT_ALLOWED$1; - } - // LB25 Do not break between the following pairs of classes relevant to numbers: - if ( - // (PR | PO) × ( OP | HY )? NU - ([PR, PO].indexOf(current) !== -1 && - (next === NU || ([OP, HY].indexOf(next) !== -1 && classTypes[afterIndex + 1] === NU))) || - // ( OP | HY ) × NU - ([OP, HY].indexOf(current) !== -1 && next === NU) || - // NU × (NU | SY | IS) - (current === NU && [NU, SY, IS].indexOf(next) !== -1)) { - return BREAK_NOT_ALLOWED$1; - } - // NU (NU | SY | IS)* × (NU | SY | IS | CL | CP) - if ([NU, SY, IS, CL, CP].indexOf(next) !== -1) { - var prevIndex = currentIndex; - while (prevIndex >= 0) { - var type = classTypes[prevIndex]; - if (type === NU) { - return BREAK_NOT_ALLOWED$1; - } - else if ([SY, IS].indexOf(type) !== -1) { - prevIndex--; - } - else { - break; - } - } - } - // NU (NU | SY | IS)* (CL | CP)? × (PO | PR)) - if ([PR, PO].indexOf(next) !== -1) { - var prevIndex = [CL, CP].indexOf(current) !== -1 ? beforeIndex : currentIndex; - while (prevIndex >= 0) { - var type = classTypes[prevIndex]; - if (type === NU) { - return BREAK_NOT_ALLOWED$1; - } - else if ([SY, IS].indexOf(type) !== -1) { - prevIndex--; - } - else { - break; - } - } - } - // LB26 Do not break a Korean syllable. - if ((JL === current && [JL, JV, H2, H3].indexOf(next) !== -1) || - ([JV, H2].indexOf(current) !== -1 && [JV, JT].indexOf(next) !== -1) || - ([JT, H3].indexOf(current) !== -1 && next === JT)) { - return BREAK_NOT_ALLOWED$1; - } - // LB27 Treat a Korean Syllable Block the same as ID. - if ((KOREAN_SYLLABLE_BLOCK.indexOf(current) !== -1 && [IN, PO].indexOf(next) !== -1) || - (KOREAN_SYLLABLE_BLOCK.indexOf(next) !== -1 && current === PR)) { - return BREAK_NOT_ALLOWED$1; - } - // LB28 Do not break between alphabetics (“at”). - if (ALPHABETICS.indexOf(current) !== -1 && ALPHABETICS.indexOf(next) !== -1) { - return BREAK_NOT_ALLOWED$1; - } - // LB29 Do not break between numeric punctuation and alphabetics (“e.g.”). - if (current === IS && ALPHABETICS.indexOf(next) !== -1) { - return BREAK_NOT_ALLOWED$1; - } - // LB30 Do not break between letters, numbers, or ordinary symbols and opening or closing parentheses. - if ((ALPHABETICS.concat(NU).indexOf(current) !== -1 && - next === OP && - ea_OP.indexOf(codePoints[afterIndex]) === -1) || - (ALPHABETICS.concat(NU).indexOf(next) !== -1 && current === CP)) { - return BREAK_NOT_ALLOWED$1; - } - // LB30a Break between two regional indicator symbols if and only if there are an even number of regional - // indicators preceding the position of the break. - if (current === RI$1 && next === RI$1) { - var i = indicies[currentIndex]; - var count = 1; - while (i > 0) { - i--; - if (classTypes[i] === RI$1) { - count++; - } - else { - break; - } - } - if (count % 2 !== 0) { - return BREAK_NOT_ALLOWED$1; - } - } - // LB30b Do not break between an emoji base and an emoji modifier. - if (current === EB && next === EM) { - return BREAK_NOT_ALLOWED$1; - } - return BREAK_ALLOWED$1; - }; - var cssFormattedClasses = function (codePoints, options) { - if (!options) { - options = { lineBreak: 'normal', wordBreak: 'normal' }; - } - var _a = codePointsToCharacterClasses(codePoints, options.lineBreak), indicies = _a[0], classTypes = _a[1], isLetterNumber = _a[2]; - if (options.wordBreak === 'break-all' || options.wordBreak === 'break-word') { - classTypes = classTypes.map(function (type) { return ([NU, AL, SA].indexOf(type) !== -1 ? ID : type); }); - } - var forbiddenBreakpoints = options.wordBreak === 'keep-all' - ? isLetterNumber.map(function (letterNumber, i) { - return letterNumber && codePoints[i] >= 0x4e00 && codePoints[i] <= 0x9fff; - }) - : undefined; - return [indicies, classTypes, forbiddenBreakpoints]; - }; - var Break = /** @class */ (function () { - function Break(codePoints, lineBreak, start, end) { - this.codePoints = codePoints; - this.required = lineBreak === BREAK_MANDATORY; - this.start = start; - this.end = end; - } - Break.prototype.slice = function () { - return fromCodePoint$1.apply(void 0, this.codePoints.slice(this.start, this.end)); - }; - return Break; - }()); - var LineBreaker = function (str, options) { - var codePoints = toCodePoints$1(str); - var _a = cssFormattedClasses(codePoints, options), indicies = _a[0], classTypes = _a[1], forbiddenBreakpoints = _a[2]; - var length = codePoints.length; - var lastEnd = 0; - var nextIndex = 0; - return { - next: function () { - if (nextIndex >= length) { - return { done: true, value: null }; - } - var lineBreak = BREAK_NOT_ALLOWED$1; - while (nextIndex < length && - (lineBreak = _lineBreakAtIndex(codePoints, classTypes, indicies, ++nextIndex, forbiddenBreakpoints)) === - BREAK_NOT_ALLOWED$1) { } - if (lineBreak !== BREAK_NOT_ALLOWED$1 || nextIndex === length) { - var value = new Break(codePoints, lineBreak, lastEnd, nextIndex); - lastEnd = nextIndex; - return { value: value, done: false }; - } - return { done: true, value: null }; - }, - }; - }; - - // https://www.w3.org/TR/css-syntax-3 - var FLAG_UNRESTRICTED = 1 << 0; - var FLAG_ID = 1 << 1; - var FLAG_INTEGER = 1 << 2; - var FLAG_NUMBER = 1 << 3; - var LINE_FEED = 0x000a; - var SOLIDUS = 0x002f; - var REVERSE_SOLIDUS = 0x005c; - var CHARACTER_TABULATION = 0x0009; - var SPACE = 0x0020; - var QUOTATION_MARK = 0x0022; - var EQUALS_SIGN = 0x003d; - var NUMBER_SIGN = 0x0023; - var DOLLAR_SIGN = 0x0024; - var PERCENTAGE_SIGN = 0x0025; - var APOSTROPHE = 0x0027; - var LEFT_PARENTHESIS = 0x0028; - var RIGHT_PARENTHESIS = 0x0029; - var LOW_LINE = 0x005f; - var HYPHEN_MINUS = 0x002d; - var EXCLAMATION_MARK = 0x0021; - var LESS_THAN_SIGN = 0x003c; - var GREATER_THAN_SIGN = 0x003e; - var COMMERCIAL_AT = 0x0040; - var LEFT_SQUARE_BRACKET = 0x005b; - var RIGHT_SQUARE_BRACKET = 0x005d; - var CIRCUMFLEX_ACCENT = 0x003d; - var LEFT_CURLY_BRACKET = 0x007b; - var QUESTION_MARK = 0x003f; - var RIGHT_CURLY_BRACKET = 0x007d; - var VERTICAL_LINE = 0x007c; - var TILDE = 0x007e; - var CONTROL = 0x0080; - var REPLACEMENT_CHARACTER = 0xfffd; - var ASTERISK = 0x002a; - var PLUS_SIGN = 0x002b; - var COMMA = 0x002c; - var COLON = 0x003a; - var SEMICOLON = 0x003b; - var FULL_STOP = 0x002e; - var NULL = 0x0000; - var BACKSPACE = 0x0008; - var LINE_TABULATION = 0x000b; - var SHIFT_OUT = 0x000e; - var INFORMATION_SEPARATOR_ONE = 0x001f; - var DELETE = 0x007f; - var EOF = -1; - var ZERO = 0x0030; - var a = 0x0061; - var e = 0x0065; - var f = 0x0066; - var u = 0x0075; - var z = 0x007a; - var A = 0x0041; - var E = 0x0045; - var F = 0x0046; - var U = 0x0055; - var Z = 0x005a; - var isDigit = function (codePoint) { return codePoint >= ZERO && codePoint <= 0x0039; }; - var isSurrogateCodePoint = function (codePoint) { return codePoint >= 0xd800 && codePoint <= 0xdfff; }; - var isHex = function (codePoint) { - return isDigit(codePoint) || (codePoint >= A && codePoint <= F) || (codePoint >= a && codePoint <= f); - }; - var isLowerCaseLetter = function (codePoint) { return codePoint >= a && codePoint <= z; }; - var isUpperCaseLetter = function (codePoint) { return codePoint >= A && codePoint <= Z; }; - var isLetter = function (codePoint) { return isLowerCaseLetter(codePoint) || isUpperCaseLetter(codePoint); }; - var isNonASCIICodePoint = function (codePoint) { return codePoint >= CONTROL; }; - var isWhiteSpace = function (codePoint) { - return codePoint === LINE_FEED || codePoint === CHARACTER_TABULATION || codePoint === SPACE; - }; - var isNameStartCodePoint = function (codePoint) { - return isLetter(codePoint) || isNonASCIICodePoint(codePoint) || codePoint === LOW_LINE; - }; - var isNameCodePoint = function (codePoint) { - return isNameStartCodePoint(codePoint) || isDigit(codePoint) || codePoint === HYPHEN_MINUS; - }; - var isNonPrintableCodePoint = function (codePoint) { - return ((codePoint >= NULL && codePoint <= BACKSPACE) || - codePoint === LINE_TABULATION || - (codePoint >= SHIFT_OUT && codePoint <= INFORMATION_SEPARATOR_ONE) || - codePoint === DELETE); - }; - var isValidEscape = function (c1, c2) { - if (c1 !== REVERSE_SOLIDUS) { - return false; - } - return c2 !== LINE_FEED; - }; - var isIdentifierStart = function (c1, c2, c3) { - if (c1 === HYPHEN_MINUS) { - return isNameStartCodePoint(c2) || isValidEscape(c2, c3); - } - else if (isNameStartCodePoint(c1)) { - return true; - } - else if (c1 === REVERSE_SOLIDUS && isValidEscape(c1, c2)) { - return true; - } - return false; - }; - var isNumberStart = function (c1, c2, c3) { - if (c1 === PLUS_SIGN || c1 === HYPHEN_MINUS) { - if (isDigit(c2)) { - return true; - } - return c2 === FULL_STOP && isDigit(c3); - } - if (c1 === FULL_STOP) { - return isDigit(c2); - } - return isDigit(c1); - }; - var stringToNumber = function (codePoints) { - var c = 0; - var sign = 1; - if (codePoints[c] === PLUS_SIGN || codePoints[c] === HYPHEN_MINUS) { - if (codePoints[c] === HYPHEN_MINUS) { - sign = -1; - } - c++; - } - var integers = []; - while (isDigit(codePoints[c])) { - integers.push(codePoints[c++]); - } - var int = integers.length ? parseInt(fromCodePoint$1.apply(void 0, integers), 10) : 0; - if (codePoints[c] === FULL_STOP) { - c++; - } - var fraction = []; - while (isDigit(codePoints[c])) { - fraction.push(codePoints[c++]); - } - var fracd = fraction.length; - var frac = fracd ? parseInt(fromCodePoint$1.apply(void 0, fraction), 10) : 0; - if (codePoints[c] === E || codePoints[c] === e) { - c++; - } - var expsign = 1; - if (codePoints[c] === PLUS_SIGN || codePoints[c] === HYPHEN_MINUS) { - if (codePoints[c] === HYPHEN_MINUS) { - expsign = -1; - } - c++; - } - var exponent = []; - while (isDigit(codePoints[c])) { - exponent.push(codePoints[c++]); - } - var exp = exponent.length ? parseInt(fromCodePoint$1.apply(void 0, exponent), 10) : 0; - return sign * (int + frac * Math.pow(10, -fracd)) * Math.pow(10, expsign * exp); - }; - var LEFT_PARENTHESIS_TOKEN = { - type: 2 /* LEFT_PARENTHESIS_TOKEN */ - }; - var RIGHT_PARENTHESIS_TOKEN = { - type: 3 /* RIGHT_PARENTHESIS_TOKEN */ - }; - var COMMA_TOKEN = { type: 4 /* COMMA_TOKEN */ }; - var SUFFIX_MATCH_TOKEN = { type: 13 /* SUFFIX_MATCH_TOKEN */ }; - var PREFIX_MATCH_TOKEN = { type: 8 /* PREFIX_MATCH_TOKEN */ }; - var COLUMN_TOKEN = { type: 21 /* COLUMN_TOKEN */ }; - var DASH_MATCH_TOKEN = { type: 9 /* DASH_MATCH_TOKEN */ }; - var INCLUDE_MATCH_TOKEN = { type: 10 /* INCLUDE_MATCH_TOKEN */ }; - var LEFT_CURLY_BRACKET_TOKEN = { - type: 11 /* LEFT_CURLY_BRACKET_TOKEN */ - }; - var RIGHT_CURLY_BRACKET_TOKEN = { - type: 12 /* RIGHT_CURLY_BRACKET_TOKEN */ - }; - var SUBSTRING_MATCH_TOKEN = { type: 14 /* SUBSTRING_MATCH_TOKEN */ }; - var BAD_URL_TOKEN = { type: 23 /* BAD_URL_TOKEN */ }; - var BAD_STRING_TOKEN = { type: 1 /* BAD_STRING_TOKEN */ }; - var CDO_TOKEN = { type: 25 /* CDO_TOKEN */ }; - var CDC_TOKEN = { type: 24 /* CDC_TOKEN */ }; - var COLON_TOKEN = { type: 26 /* COLON_TOKEN */ }; - var SEMICOLON_TOKEN = { type: 27 /* SEMICOLON_TOKEN */ }; - var LEFT_SQUARE_BRACKET_TOKEN = { - type: 28 /* LEFT_SQUARE_BRACKET_TOKEN */ - }; - var RIGHT_SQUARE_BRACKET_TOKEN = { - type: 29 /* RIGHT_SQUARE_BRACKET_TOKEN */ - }; - var WHITESPACE_TOKEN = { type: 31 /* WHITESPACE_TOKEN */ }; - var EOF_TOKEN = { type: 32 /* EOF_TOKEN */ }; - var Tokenizer = /** @class */ (function () { - function Tokenizer() { - this._value = []; - } - Tokenizer.prototype.write = function (chunk) { - this._value = this._value.concat(toCodePoints$1(chunk)); - }; - Tokenizer.prototype.read = function () { - var tokens = []; - var token = this.consumeToken(); - while (token !== EOF_TOKEN) { - tokens.push(token); - token = this.consumeToken(); - } - return tokens; - }; - Tokenizer.prototype.consumeToken = function () { - var codePoint = this.consumeCodePoint(); - switch (codePoint) { - case QUOTATION_MARK: - return this.consumeStringToken(QUOTATION_MARK); - case NUMBER_SIGN: - var c1 = this.peekCodePoint(0); - var c2 = this.peekCodePoint(1); - var c3 = this.peekCodePoint(2); - if (isNameCodePoint(c1) || isValidEscape(c2, c3)) { - var flags = isIdentifierStart(c1, c2, c3) ? FLAG_ID : FLAG_UNRESTRICTED; - var value = this.consumeName(); - return { type: 5 /* HASH_TOKEN */, value: value, flags: flags }; - } - break; - case DOLLAR_SIGN: - if (this.peekCodePoint(0) === EQUALS_SIGN) { - this.consumeCodePoint(); - return SUFFIX_MATCH_TOKEN; - } - break; - case APOSTROPHE: - return this.consumeStringToken(APOSTROPHE); - case LEFT_PARENTHESIS: - return LEFT_PARENTHESIS_TOKEN; - case RIGHT_PARENTHESIS: - return RIGHT_PARENTHESIS_TOKEN; - case ASTERISK: - if (this.peekCodePoint(0) === EQUALS_SIGN) { - this.consumeCodePoint(); - return SUBSTRING_MATCH_TOKEN; - } - break; - case PLUS_SIGN: - if (isNumberStart(codePoint, this.peekCodePoint(0), this.peekCodePoint(1))) { - this.reconsumeCodePoint(codePoint); - return this.consumeNumericToken(); - } - break; - case COMMA: - return COMMA_TOKEN; - case HYPHEN_MINUS: - var e1 = codePoint; - var e2 = this.peekCodePoint(0); - var e3 = this.peekCodePoint(1); - if (isNumberStart(e1, e2, e3)) { - this.reconsumeCodePoint(codePoint); - return this.consumeNumericToken(); - } - if (isIdentifierStart(e1, e2, e3)) { - this.reconsumeCodePoint(codePoint); - return this.consumeIdentLikeToken(); - } - if (e2 === HYPHEN_MINUS && e3 === GREATER_THAN_SIGN) { - this.consumeCodePoint(); - this.consumeCodePoint(); - return CDC_TOKEN; - } - break; - case FULL_STOP: - if (isNumberStart(codePoint, this.peekCodePoint(0), this.peekCodePoint(1))) { - this.reconsumeCodePoint(codePoint); - return this.consumeNumericToken(); - } - break; - case SOLIDUS: - if (this.peekCodePoint(0) === ASTERISK) { - this.consumeCodePoint(); - while (true) { - var c = this.consumeCodePoint(); - if (c === ASTERISK) { - c = this.consumeCodePoint(); - if (c === SOLIDUS) { - return this.consumeToken(); - } - } - if (c === EOF) { - return this.consumeToken(); - } - } - } - break; - case COLON: - return COLON_TOKEN; - case SEMICOLON: - return SEMICOLON_TOKEN; - case LESS_THAN_SIGN: - if (this.peekCodePoint(0) === EXCLAMATION_MARK && - this.peekCodePoint(1) === HYPHEN_MINUS && - this.peekCodePoint(2) === HYPHEN_MINUS) { - this.consumeCodePoint(); - this.consumeCodePoint(); - return CDO_TOKEN; - } - break; - case COMMERCIAL_AT: - var a1 = this.peekCodePoint(0); - var a2 = this.peekCodePoint(1); - var a3 = this.peekCodePoint(2); - if (isIdentifierStart(a1, a2, a3)) { - var value = this.consumeName(); - return { type: 7 /* AT_KEYWORD_TOKEN */, value: value }; - } - break; - case LEFT_SQUARE_BRACKET: - return LEFT_SQUARE_BRACKET_TOKEN; - case REVERSE_SOLIDUS: - if (isValidEscape(codePoint, this.peekCodePoint(0))) { - this.reconsumeCodePoint(codePoint); - return this.consumeIdentLikeToken(); - } - break; - case RIGHT_SQUARE_BRACKET: - return RIGHT_SQUARE_BRACKET_TOKEN; - case CIRCUMFLEX_ACCENT: - if (this.peekCodePoint(0) === EQUALS_SIGN) { - this.consumeCodePoint(); - return PREFIX_MATCH_TOKEN; - } - break; - case LEFT_CURLY_BRACKET: - return LEFT_CURLY_BRACKET_TOKEN; - case RIGHT_CURLY_BRACKET: - return RIGHT_CURLY_BRACKET_TOKEN; - case u: - case U: - var u1 = this.peekCodePoint(0); - var u2 = this.peekCodePoint(1); - if (u1 === PLUS_SIGN && (isHex(u2) || u2 === QUESTION_MARK)) { - this.consumeCodePoint(); - this.consumeUnicodeRangeToken(); - } - this.reconsumeCodePoint(codePoint); - return this.consumeIdentLikeToken(); - case VERTICAL_LINE: - if (this.peekCodePoint(0) === EQUALS_SIGN) { - this.consumeCodePoint(); - return DASH_MATCH_TOKEN; - } - if (this.peekCodePoint(0) === VERTICAL_LINE) { - this.consumeCodePoint(); - return COLUMN_TOKEN; - } - break; - case TILDE: - if (this.peekCodePoint(0) === EQUALS_SIGN) { - this.consumeCodePoint(); - return INCLUDE_MATCH_TOKEN; - } - break; - case EOF: - return EOF_TOKEN; - } - if (isWhiteSpace(codePoint)) { - this.consumeWhiteSpace(); - return WHITESPACE_TOKEN; - } - if (isDigit(codePoint)) { - this.reconsumeCodePoint(codePoint); - return this.consumeNumericToken(); - } - if (isNameStartCodePoint(codePoint)) { - this.reconsumeCodePoint(codePoint); - return this.consumeIdentLikeToken(); - } - return { type: 6 /* DELIM_TOKEN */, value: fromCodePoint$1(codePoint) }; - }; - Tokenizer.prototype.consumeCodePoint = function () { - var value = this._value.shift(); - return typeof value === 'undefined' ? -1 : value; - }; - Tokenizer.prototype.reconsumeCodePoint = function (codePoint) { - this._value.unshift(codePoint); - }; - Tokenizer.prototype.peekCodePoint = function (delta) { - if (delta >= this._value.length) { - return -1; - } - return this._value[delta]; - }; - Tokenizer.prototype.consumeUnicodeRangeToken = function () { - var digits = []; - var codePoint = this.consumeCodePoint(); - while (isHex(codePoint) && digits.length < 6) { - digits.push(codePoint); - codePoint = this.consumeCodePoint(); - } - var questionMarks = false; - while (codePoint === QUESTION_MARK && digits.length < 6) { - digits.push(codePoint); - codePoint = this.consumeCodePoint(); - questionMarks = true; - } - if (questionMarks) { - var start_1 = parseInt(fromCodePoint$1.apply(void 0, digits.map(function (digit) { return (digit === QUESTION_MARK ? ZERO : digit); })), 16); - var end = parseInt(fromCodePoint$1.apply(void 0, digits.map(function (digit) { return (digit === QUESTION_MARK ? F : digit); })), 16); - return { type: 30 /* UNICODE_RANGE_TOKEN */, start: start_1, end: end }; - } - var start = parseInt(fromCodePoint$1.apply(void 0, digits), 16); - if (this.peekCodePoint(0) === HYPHEN_MINUS && isHex(this.peekCodePoint(1))) { - this.consumeCodePoint(); - codePoint = this.consumeCodePoint(); - var endDigits = []; - while (isHex(codePoint) && endDigits.length < 6) { - endDigits.push(codePoint); - codePoint = this.consumeCodePoint(); - } - var end = parseInt(fromCodePoint$1.apply(void 0, endDigits), 16); - return { type: 30 /* UNICODE_RANGE_TOKEN */, start: start, end: end }; - } - else { - return { type: 30 /* UNICODE_RANGE_TOKEN */, start: start, end: start }; - } - }; - Tokenizer.prototype.consumeIdentLikeToken = function () { - var value = this.consumeName(); - if (value.toLowerCase() === 'url' && this.peekCodePoint(0) === LEFT_PARENTHESIS) { - this.consumeCodePoint(); - return this.consumeUrlToken(); - } - else if (this.peekCodePoint(0) === LEFT_PARENTHESIS) { - this.consumeCodePoint(); - return { type: 19 /* FUNCTION_TOKEN */, value: value }; - } - return { type: 20 /* IDENT_TOKEN */, value: value }; - }; - Tokenizer.prototype.consumeUrlToken = function () { - var value = []; - this.consumeWhiteSpace(); - if (this.peekCodePoint(0) === EOF) { - return { type: 22 /* URL_TOKEN */, value: '' }; - } - var next = this.peekCodePoint(0); - if (next === APOSTROPHE || next === QUOTATION_MARK) { - var stringToken = this.consumeStringToken(this.consumeCodePoint()); - if (stringToken.type === 0 /* STRING_TOKEN */) { - this.consumeWhiteSpace(); - if (this.peekCodePoint(0) === EOF || this.peekCodePoint(0) === RIGHT_PARENTHESIS) { - this.consumeCodePoint(); - return { type: 22 /* URL_TOKEN */, value: stringToken.value }; - } - } - this.consumeBadUrlRemnants(); - return BAD_URL_TOKEN; - } - while (true) { - var codePoint = this.consumeCodePoint(); - if (codePoint === EOF || codePoint === RIGHT_PARENTHESIS) { - return { type: 22 /* URL_TOKEN */, value: fromCodePoint$1.apply(void 0, value) }; - } - else if (isWhiteSpace(codePoint)) { - this.consumeWhiteSpace(); - if (this.peekCodePoint(0) === EOF || this.peekCodePoint(0) === RIGHT_PARENTHESIS) { - this.consumeCodePoint(); - return { type: 22 /* URL_TOKEN */, value: fromCodePoint$1.apply(void 0, value) }; - } - this.consumeBadUrlRemnants(); - return BAD_URL_TOKEN; - } - else if (codePoint === QUOTATION_MARK || - codePoint === APOSTROPHE || - codePoint === LEFT_PARENTHESIS || - isNonPrintableCodePoint(codePoint)) { - this.consumeBadUrlRemnants(); - return BAD_URL_TOKEN; - } - else if (codePoint === REVERSE_SOLIDUS) { - if (isValidEscape(codePoint, this.peekCodePoint(0))) { - value.push(this.consumeEscapedCodePoint()); - } - else { - this.consumeBadUrlRemnants(); - return BAD_URL_TOKEN; - } - } - else { - value.push(codePoint); - } - } - }; - Tokenizer.prototype.consumeWhiteSpace = function () { - while (isWhiteSpace(this.peekCodePoint(0))) { - this.consumeCodePoint(); - } - }; - Tokenizer.prototype.consumeBadUrlRemnants = function () { - while (true) { - var codePoint = this.consumeCodePoint(); - if (codePoint === RIGHT_PARENTHESIS || codePoint === EOF) { - return; - } - if (isValidEscape(codePoint, this.peekCodePoint(0))) { - this.consumeEscapedCodePoint(); - } - } - }; - Tokenizer.prototype.consumeStringSlice = function (count) { - var SLICE_STACK_SIZE = 50000; - var value = ''; - while (count > 0) { - var amount = Math.min(SLICE_STACK_SIZE, count); - value += fromCodePoint$1.apply(void 0, this._value.splice(0, amount)); - count -= amount; - } - this._value.shift(); - return value; - }; - Tokenizer.prototype.consumeStringToken = function (endingCodePoint) { - var value = ''; - var i = 0; - do { - var codePoint = this._value[i]; - if (codePoint === EOF || codePoint === undefined || codePoint === endingCodePoint) { - value += this.consumeStringSlice(i); - return { type: 0 /* STRING_TOKEN */, value: value }; - } - if (codePoint === LINE_FEED) { - this._value.splice(0, i); - return BAD_STRING_TOKEN; - } - if (codePoint === REVERSE_SOLIDUS) { - var next = this._value[i + 1]; - if (next !== EOF && next !== undefined) { - if (next === LINE_FEED) { - value += this.consumeStringSlice(i); - i = -1; - this._value.shift(); - } - else if (isValidEscape(codePoint, next)) { - value += this.consumeStringSlice(i); - value += fromCodePoint$1(this.consumeEscapedCodePoint()); - i = -1; - } - } - } - i++; - } while (true); - }; - Tokenizer.prototype.consumeNumber = function () { - var repr = []; - var type = FLAG_INTEGER; - var c1 = this.peekCodePoint(0); - if (c1 === PLUS_SIGN || c1 === HYPHEN_MINUS) { - repr.push(this.consumeCodePoint()); - } - while (isDigit(this.peekCodePoint(0))) { - repr.push(this.consumeCodePoint()); - } - c1 = this.peekCodePoint(0); - var c2 = this.peekCodePoint(1); - if (c1 === FULL_STOP && isDigit(c2)) { - repr.push(this.consumeCodePoint(), this.consumeCodePoint()); - type = FLAG_NUMBER; - while (isDigit(this.peekCodePoint(0))) { - repr.push(this.consumeCodePoint()); - } - } - c1 = this.peekCodePoint(0); - c2 = this.peekCodePoint(1); - var c3 = this.peekCodePoint(2); - if ((c1 === E || c1 === e) && (((c2 === PLUS_SIGN || c2 === HYPHEN_MINUS) && isDigit(c3)) || isDigit(c2))) { - repr.push(this.consumeCodePoint(), this.consumeCodePoint()); - type = FLAG_NUMBER; - while (isDigit(this.peekCodePoint(0))) { - repr.push(this.consumeCodePoint()); - } - } - return [stringToNumber(repr), type]; - }; - Tokenizer.prototype.consumeNumericToken = function () { - var _a = this.consumeNumber(), number = _a[0], flags = _a[1]; - var c1 = this.peekCodePoint(0); - var c2 = this.peekCodePoint(1); - var c3 = this.peekCodePoint(2); - if (isIdentifierStart(c1, c2, c3)) { - var unit = this.consumeName(); - return { type: 15 /* DIMENSION_TOKEN */, number: number, flags: flags, unit: unit }; - } - if (c1 === PERCENTAGE_SIGN) { - this.consumeCodePoint(); - return { type: 16 /* PERCENTAGE_TOKEN */, number: number, flags: flags }; - } - return { type: 17 /* NUMBER_TOKEN */, number: number, flags: flags }; - }; - Tokenizer.prototype.consumeEscapedCodePoint = function () { - var codePoint = this.consumeCodePoint(); - if (isHex(codePoint)) { - var hex = fromCodePoint$1(codePoint); - while (isHex(this.peekCodePoint(0)) && hex.length < 6) { - hex += fromCodePoint$1(this.consumeCodePoint()); - } - if (isWhiteSpace(this.peekCodePoint(0))) { - this.consumeCodePoint(); - } - var hexCodePoint = parseInt(hex, 16); - if (hexCodePoint === 0 || isSurrogateCodePoint(hexCodePoint) || hexCodePoint > 0x10ffff) { - return REPLACEMENT_CHARACTER; - } - return hexCodePoint; - } - if (codePoint === EOF) { - return REPLACEMENT_CHARACTER; - } - return codePoint; - }; - Tokenizer.prototype.consumeName = function () { - var result = ''; - while (true) { - var codePoint = this.consumeCodePoint(); - if (isNameCodePoint(codePoint)) { - result += fromCodePoint$1(codePoint); - } - else if (isValidEscape(codePoint, this.peekCodePoint(0))) { - result += fromCodePoint$1(this.consumeEscapedCodePoint()); - } - else { - this.reconsumeCodePoint(codePoint); - return result; - } - } - }; - return Tokenizer; - }()); - - var Parser = /** @class */ (function () { - function Parser(tokens) { - this._tokens = tokens; - } - Parser.create = function (value) { - var tokenizer = new Tokenizer(); - tokenizer.write(value); - return new Parser(tokenizer.read()); - }; - Parser.parseValue = function (value) { - return Parser.create(value).parseComponentValue(); - }; - Parser.parseValues = function (value) { - return Parser.create(value).parseComponentValues(); - }; - Parser.prototype.parseComponentValue = function () { - var token = this.consumeToken(); - while (token.type === 31 /* WHITESPACE_TOKEN */) { - token = this.consumeToken(); - } - if (token.type === 32 /* EOF_TOKEN */) { - throw new SyntaxError("Error parsing CSS component value, unexpected EOF"); - } - this.reconsumeToken(token); - var value = this.consumeComponentValue(); - do { - token = this.consumeToken(); - } while (token.type === 31 /* WHITESPACE_TOKEN */); - if (token.type === 32 /* EOF_TOKEN */) { - return value; - } - throw new SyntaxError("Error parsing CSS component value, multiple values found when expecting only one"); - }; - Parser.prototype.parseComponentValues = function () { - var values = []; - while (true) { - var value = this.consumeComponentValue(); - if (value.type === 32 /* EOF_TOKEN */) { - return values; - } - values.push(value); - values.push(); - } - }; - Parser.prototype.consumeComponentValue = function () { - var token = this.consumeToken(); - switch (token.type) { - case 11 /* LEFT_CURLY_BRACKET_TOKEN */: - case 28 /* LEFT_SQUARE_BRACKET_TOKEN */: - case 2 /* LEFT_PARENTHESIS_TOKEN */: - return this.consumeSimpleBlock(token.type); - case 19 /* FUNCTION_TOKEN */: - return this.consumeFunction(token); - } - return token; - }; - Parser.prototype.consumeSimpleBlock = function (type) { - var block = { type: type, values: [] }; - var token = this.consumeToken(); - while (true) { - if (token.type === 32 /* EOF_TOKEN */ || isEndingTokenFor(token, type)) { - return block; - } - this.reconsumeToken(token); - block.values.push(this.consumeComponentValue()); - token = this.consumeToken(); - } - }; - Parser.prototype.consumeFunction = function (functionToken) { - var cssFunction = { - name: functionToken.value, - values: [], - type: 18 /* FUNCTION */ - }; - while (true) { - var token = this.consumeToken(); - if (token.type === 32 /* EOF_TOKEN */ || token.type === 3 /* RIGHT_PARENTHESIS_TOKEN */) { - return cssFunction; - } - this.reconsumeToken(token); - cssFunction.values.push(this.consumeComponentValue()); - } - }; - Parser.prototype.consumeToken = function () { - var token = this._tokens.shift(); - return typeof token === 'undefined' ? EOF_TOKEN : token; - }; - Parser.prototype.reconsumeToken = function (token) { - this._tokens.unshift(token); - }; - return Parser; - }()); - var isDimensionToken = function (token) { return token.type === 15 /* DIMENSION_TOKEN */; }; - var isNumberToken = function (token) { return token.type === 17 /* NUMBER_TOKEN */; }; - var isIdentToken = function (token) { return token.type === 20 /* IDENT_TOKEN */; }; - var isStringToken = function (token) { return token.type === 0 /* STRING_TOKEN */; }; - var isIdentWithValue = function (token, value) { - return isIdentToken(token) && token.value === value; - }; - var nonWhiteSpace = function (token) { return token.type !== 31 /* WHITESPACE_TOKEN */; }; - var nonFunctionArgSeparator = function (token) { - return token.type !== 31 /* WHITESPACE_TOKEN */ && token.type !== 4 /* COMMA_TOKEN */; - }; - var parseFunctionArgs = function (tokens) { - var args = []; - var arg = []; - tokens.forEach(function (token) { - if (token.type === 4 /* COMMA_TOKEN */) { - if (arg.length === 0) { - throw new Error("Error parsing function args, zero tokens for arg"); - } - args.push(arg); - arg = []; - return; - } - if (token.type !== 31 /* WHITESPACE_TOKEN */) { - arg.push(token); - } - }); - if (arg.length) { - args.push(arg); - } - return args; - }; - var isEndingTokenFor = function (token, type) { - if (type === 11 /* LEFT_CURLY_BRACKET_TOKEN */ && token.type === 12 /* RIGHT_CURLY_BRACKET_TOKEN */) { - return true; - } - if (type === 28 /* LEFT_SQUARE_BRACKET_TOKEN */ && token.type === 29 /* RIGHT_SQUARE_BRACKET_TOKEN */) { - return true; - } - return type === 2 /* LEFT_PARENTHESIS_TOKEN */ && token.type === 3 /* RIGHT_PARENTHESIS_TOKEN */; - }; - - var isLength = function (token) { - return token.type === 17 /* NUMBER_TOKEN */ || token.type === 15 /* DIMENSION_TOKEN */; - }; - - var isLengthPercentage = function (token) { - return token.type === 16 /* PERCENTAGE_TOKEN */ || isLength(token); - }; - var parseLengthPercentageTuple = function (tokens) { - return tokens.length > 1 ? [tokens[0], tokens[1]] : [tokens[0]]; - }; - var ZERO_LENGTH = { - type: 17 /* NUMBER_TOKEN */, - number: 0, - flags: FLAG_INTEGER - }; - var FIFTY_PERCENT = { - type: 16 /* PERCENTAGE_TOKEN */, - number: 50, - flags: FLAG_INTEGER - }; - var HUNDRED_PERCENT = { - type: 16 /* PERCENTAGE_TOKEN */, - number: 100, - flags: FLAG_INTEGER - }; - var getAbsoluteValueForTuple = function (tuple, width, height) { - var x = tuple[0], y = tuple[1]; - return [getAbsoluteValue(x, width), getAbsoluteValue(typeof y !== 'undefined' ? y : x, height)]; - }; - var getAbsoluteValue = function (token, parent) { - if (token.type === 16 /* PERCENTAGE_TOKEN */) { - return (token.number / 100) * parent; - } - if (isDimensionToken(token)) { - switch (token.unit) { - case 'rem': - case 'em': - return 16 * token.number; // TODO use correct font-size - case 'px': - default: - return token.number; - } - } - return token.number; - }; - - var DEG = 'deg'; - var GRAD = 'grad'; - var RAD = 'rad'; - var TURN = 'turn'; - var angle = { - name: 'angle', - parse: function (_context, value) { - if (value.type === 15 /* DIMENSION_TOKEN */) { - switch (value.unit) { - case DEG: - return (Math.PI * value.number) / 180; - case GRAD: - return (Math.PI / 200) * value.number; - case RAD: - return value.number; - case TURN: - return Math.PI * 2 * value.number; - } - } - throw new Error("Unsupported angle type"); - } - }; - var isAngle = function (value) { - if (value.type === 15 /* DIMENSION_TOKEN */) { - if (value.unit === DEG || value.unit === GRAD || value.unit === RAD || value.unit === TURN) { - return true; - } - } - return false; - }; - var parseNamedSide = function (tokens) { - var sideOrCorner = tokens - .filter(isIdentToken) - .map(function (ident) { return ident.value; }) - .join(' '); - switch (sideOrCorner) { - case 'to bottom right': - case 'to right bottom': - case 'left top': - case 'top left': - return [ZERO_LENGTH, ZERO_LENGTH]; - case 'to top': - case 'bottom': - return deg(0); - case 'to bottom left': - case 'to left bottom': - case 'right top': - case 'top right': - return [ZERO_LENGTH, HUNDRED_PERCENT]; - case 'to right': - case 'left': - return deg(90); - case 'to top left': - case 'to left top': - case 'right bottom': - case 'bottom right': - return [HUNDRED_PERCENT, HUNDRED_PERCENT]; - case 'to bottom': - case 'top': - return deg(180); - case 'to top right': - case 'to right top': - case 'left bottom': - case 'bottom left': - return [HUNDRED_PERCENT, ZERO_LENGTH]; - case 'to left': - case 'right': - return deg(270); - } - return 0; - }; - var deg = function (deg) { return (Math.PI * deg) / 180; }; - - var color$1 = { - name: 'color', - parse: function (context, value) { - if (value.type === 18 /* FUNCTION */) { - var colorFunction = SUPPORTED_COLOR_FUNCTIONS[value.name]; - if (typeof colorFunction === 'undefined') { - throw new Error("Attempting to parse an unsupported color function \"" + value.name + "\""); - } - return colorFunction(context, value.values); - } - if (value.type === 5 /* HASH_TOKEN */) { - if (value.value.length === 3) { - var r = value.value.substring(0, 1); - var g = value.value.substring(1, 2); - var b = value.value.substring(2, 3); - return pack(parseInt(r + r, 16), parseInt(g + g, 16), parseInt(b + b, 16), 1); - } - if (value.value.length === 4) { - var r = value.value.substring(0, 1); - var g = value.value.substring(1, 2); - var b = value.value.substring(2, 3); - var a = value.value.substring(3, 4); - return pack(parseInt(r + r, 16), parseInt(g + g, 16), parseInt(b + b, 16), parseInt(a + a, 16) / 255); - } - if (value.value.length === 6) { - var r = value.value.substring(0, 2); - var g = value.value.substring(2, 4); - var b = value.value.substring(4, 6); - return pack(parseInt(r, 16), parseInt(g, 16), parseInt(b, 16), 1); - } - if (value.value.length === 8) { - var r = value.value.substring(0, 2); - var g = value.value.substring(2, 4); - var b = value.value.substring(4, 6); - var a = value.value.substring(6, 8); - return pack(parseInt(r, 16), parseInt(g, 16), parseInt(b, 16), parseInt(a, 16) / 255); - } - } - if (value.type === 20 /* IDENT_TOKEN */) { - var namedColor = COLORS[value.value.toUpperCase()]; - if (typeof namedColor !== 'undefined') { - return namedColor; - } - } - return COLORS.TRANSPARENT; - } - }; - var isTransparent = function (color) { return (0xff & color) === 0; }; - var asString = function (color) { - var alpha = 0xff & color; - var blue = 0xff & (color >> 8); - var green = 0xff & (color >> 16); - var red = 0xff & (color >> 24); - return alpha < 255 ? "rgba(" + red + "," + green + "," + blue + "," + alpha / 255 + ")" : "rgb(" + red + "," + green + "," + blue + ")"; - }; - var pack = function (r, g, b, a) { - return ((r << 24) | (g << 16) | (b << 8) | (Math.round(a * 255) << 0)) >>> 0; - }; - var getTokenColorValue = function (token, i) { - if (token.type === 17 /* NUMBER_TOKEN */) { - return token.number; - } - if (token.type === 16 /* PERCENTAGE_TOKEN */) { - var max = i === 3 ? 1 : 255; - return i === 3 ? (token.number / 100) * max : Math.round((token.number / 100) * max); - } - return 0; - }; - var rgb = function (_context, args) { - var tokens = args.filter(nonFunctionArgSeparator); - if (tokens.length === 3) { - var _a = tokens.map(getTokenColorValue), r = _a[0], g = _a[1], b = _a[2]; - return pack(r, g, b, 1); - } - if (tokens.length === 4) { - var _b = tokens.map(getTokenColorValue), r = _b[0], g = _b[1], b = _b[2], a = _b[3]; - return pack(r, g, b, a); - } - return 0; - }; - function hue2rgb(t1, t2, hue) { - if (hue < 0) { - hue += 1; - } - if (hue >= 1) { - hue -= 1; - } - if (hue < 1 / 6) { - return (t2 - t1) * hue * 6 + t1; - } - else if (hue < 1 / 2) { - return t2; - } - else if (hue < 2 / 3) { - return (t2 - t1) * 6 * (2 / 3 - hue) + t1; - } - else { - return t1; - } - } - var hsl = function (context, args) { - var tokens = args.filter(nonFunctionArgSeparator); - var hue = tokens[0], saturation = tokens[1], lightness = tokens[2], alpha = tokens[3]; - var h = (hue.type === 17 /* NUMBER_TOKEN */ ? deg(hue.number) : angle.parse(context, hue)) / (Math.PI * 2); - var s = isLengthPercentage(saturation) ? saturation.number / 100 : 0; - var l = isLengthPercentage(lightness) ? lightness.number / 100 : 0; - var a = typeof alpha !== 'undefined' && isLengthPercentage(alpha) ? getAbsoluteValue(alpha, 1) : 1; - if (s === 0) { - return pack(l * 255, l * 255, l * 255, 1); - } - var t2 = l <= 0.5 ? l * (s + 1) : l + s - l * s; - var t1 = l * 2 - t2; - var r = hue2rgb(t1, t2, h + 1 / 3); - var g = hue2rgb(t1, t2, h); - var b = hue2rgb(t1, t2, h - 1 / 3); - return pack(r * 255, g * 255, b * 255, a); - }; - var SUPPORTED_COLOR_FUNCTIONS = { - hsl: hsl, - hsla: hsl, - rgb: rgb, - rgba: rgb - }; - var parseColor = function (context, value) { - return color$1.parse(context, Parser.create(value).parseComponentValue()); - }; - var COLORS = { - ALICEBLUE: 0xf0f8ffff, - ANTIQUEWHITE: 0xfaebd7ff, - AQUA: 0x00ffffff, - AQUAMARINE: 0x7fffd4ff, - AZURE: 0xf0ffffff, - BEIGE: 0xf5f5dcff, - BISQUE: 0xffe4c4ff, - BLACK: 0x000000ff, - BLANCHEDALMOND: 0xffebcdff, - BLUE: 0x0000ffff, - BLUEVIOLET: 0x8a2be2ff, - BROWN: 0xa52a2aff, - BURLYWOOD: 0xdeb887ff, - CADETBLUE: 0x5f9ea0ff, - CHARTREUSE: 0x7fff00ff, - CHOCOLATE: 0xd2691eff, - CORAL: 0xff7f50ff, - CORNFLOWERBLUE: 0x6495edff, - CORNSILK: 0xfff8dcff, - CRIMSON: 0xdc143cff, - CYAN: 0x00ffffff, - DARKBLUE: 0x00008bff, - DARKCYAN: 0x008b8bff, - DARKGOLDENROD: 0xb886bbff, - DARKGRAY: 0xa9a9a9ff, - DARKGREEN: 0x006400ff, - DARKGREY: 0xa9a9a9ff, - DARKKHAKI: 0xbdb76bff, - DARKMAGENTA: 0x8b008bff, - DARKOLIVEGREEN: 0x556b2fff, - DARKORANGE: 0xff8c00ff, - DARKORCHID: 0x9932ccff, - DARKRED: 0x8b0000ff, - DARKSALMON: 0xe9967aff, - DARKSEAGREEN: 0x8fbc8fff, - DARKSLATEBLUE: 0x483d8bff, - DARKSLATEGRAY: 0x2f4f4fff, - DARKSLATEGREY: 0x2f4f4fff, - DARKTURQUOISE: 0x00ced1ff, - DARKVIOLET: 0x9400d3ff, - DEEPPINK: 0xff1493ff, - DEEPSKYBLUE: 0x00bfffff, - DIMGRAY: 0x696969ff, - DIMGREY: 0x696969ff, - DODGERBLUE: 0x1e90ffff, - FIREBRICK: 0xb22222ff, - FLORALWHITE: 0xfffaf0ff, - FORESTGREEN: 0x228b22ff, - FUCHSIA: 0xff00ffff, - GAINSBORO: 0xdcdcdcff, - GHOSTWHITE: 0xf8f8ffff, - GOLD: 0xffd700ff, - GOLDENROD: 0xdaa520ff, - GRAY: 0x808080ff, - GREEN: 0x008000ff, - GREENYELLOW: 0xadff2fff, - GREY: 0x808080ff, - HONEYDEW: 0xf0fff0ff, - HOTPINK: 0xff69b4ff, - INDIANRED: 0xcd5c5cff, - INDIGO: 0x4b0082ff, - IVORY: 0xfffff0ff, - KHAKI: 0xf0e68cff, - LAVENDER: 0xe6e6faff, - LAVENDERBLUSH: 0xfff0f5ff, - LAWNGREEN: 0x7cfc00ff, - LEMONCHIFFON: 0xfffacdff, - LIGHTBLUE: 0xadd8e6ff, - LIGHTCORAL: 0xf08080ff, - LIGHTCYAN: 0xe0ffffff, - LIGHTGOLDENRODYELLOW: 0xfafad2ff, - LIGHTGRAY: 0xd3d3d3ff, - LIGHTGREEN: 0x90ee90ff, - LIGHTGREY: 0xd3d3d3ff, - LIGHTPINK: 0xffb6c1ff, - LIGHTSALMON: 0xffa07aff, - LIGHTSEAGREEN: 0x20b2aaff, - LIGHTSKYBLUE: 0x87cefaff, - LIGHTSLATEGRAY: 0x778899ff, - LIGHTSLATEGREY: 0x778899ff, - LIGHTSTEELBLUE: 0xb0c4deff, - LIGHTYELLOW: 0xffffe0ff, - LIME: 0x00ff00ff, - LIMEGREEN: 0x32cd32ff, - LINEN: 0xfaf0e6ff, - MAGENTA: 0xff00ffff, - MAROON: 0x800000ff, - MEDIUMAQUAMARINE: 0x66cdaaff, - MEDIUMBLUE: 0x0000cdff, - MEDIUMORCHID: 0xba55d3ff, - MEDIUMPURPLE: 0x9370dbff, - MEDIUMSEAGREEN: 0x3cb371ff, - MEDIUMSLATEBLUE: 0x7b68eeff, - MEDIUMSPRINGGREEN: 0x00fa9aff, - MEDIUMTURQUOISE: 0x48d1ccff, - MEDIUMVIOLETRED: 0xc71585ff, - MIDNIGHTBLUE: 0x191970ff, - MINTCREAM: 0xf5fffaff, - MISTYROSE: 0xffe4e1ff, - MOCCASIN: 0xffe4b5ff, - NAVAJOWHITE: 0xffdeadff, - NAVY: 0x000080ff, - OLDLACE: 0xfdf5e6ff, - OLIVE: 0x808000ff, - OLIVEDRAB: 0x6b8e23ff, - ORANGE: 0xffa500ff, - ORANGERED: 0xff4500ff, - ORCHID: 0xda70d6ff, - PALEGOLDENROD: 0xeee8aaff, - PALEGREEN: 0x98fb98ff, - PALETURQUOISE: 0xafeeeeff, - PALEVIOLETRED: 0xdb7093ff, - PAPAYAWHIP: 0xffefd5ff, - PEACHPUFF: 0xffdab9ff, - PERU: 0xcd853fff, - PINK: 0xffc0cbff, - PLUM: 0xdda0ddff, - POWDERBLUE: 0xb0e0e6ff, - PURPLE: 0x800080ff, - REBECCAPURPLE: 0x663399ff, - RED: 0xff0000ff, - ROSYBROWN: 0xbc8f8fff, - ROYALBLUE: 0x4169e1ff, - SADDLEBROWN: 0x8b4513ff, - SALMON: 0xfa8072ff, - SANDYBROWN: 0xf4a460ff, - SEAGREEN: 0x2e8b57ff, - SEASHELL: 0xfff5eeff, - SIENNA: 0xa0522dff, - SILVER: 0xc0c0c0ff, - SKYBLUE: 0x87ceebff, - SLATEBLUE: 0x6a5acdff, - SLATEGRAY: 0x708090ff, - SLATEGREY: 0x708090ff, - SNOW: 0xfffafaff, - SPRINGGREEN: 0x00ff7fff, - STEELBLUE: 0x4682b4ff, - TAN: 0xd2b48cff, - TEAL: 0x008080ff, - THISTLE: 0xd8bfd8ff, - TOMATO: 0xff6347ff, - TRANSPARENT: 0x00000000, - TURQUOISE: 0x40e0d0ff, - VIOLET: 0xee82eeff, - WHEAT: 0xf5deb3ff, - WHITE: 0xffffffff, - WHITESMOKE: 0xf5f5f5ff, - YELLOW: 0xffff00ff, - YELLOWGREEN: 0x9acd32ff - }; - - var backgroundClip = { - name: 'background-clip', - initialValue: 'border-box', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return tokens.map(function (token) { - if (isIdentToken(token)) { - switch (token.value) { - case 'padding-box': - return 1 /* PADDING_BOX */; - case 'content-box': - return 2 /* CONTENT_BOX */; - } - } - return 0 /* BORDER_BOX */; - }); - } - }; - - var backgroundColor = { - name: "background-color", - initialValue: 'transparent', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'color' - }; - - var parseColorStop = function (context, args) { - var color = color$1.parse(context, args[0]); - var stop = args[1]; - return stop && isLengthPercentage(stop) ? { color: color, stop: stop } : { color: color, stop: null }; - }; - var processColorStops = function (stops, lineLength) { - var first = stops[0]; - var last = stops[stops.length - 1]; - if (first.stop === null) { - first.stop = ZERO_LENGTH; - } - if (last.stop === null) { - last.stop = HUNDRED_PERCENT; - } - var processStops = []; - var previous = 0; - for (var i = 0; i < stops.length; i++) { - var stop_1 = stops[i].stop; - if (stop_1 !== null) { - var absoluteValue = getAbsoluteValue(stop_1, lineLength); - if (absoluteValue > previous) { - processStops.push(absoluteValue); - } - else { - processStops.push(previous); - } - previous = absoluteValue; - } - else { - processStops.push(null); - } - } - var gapBegin = null; - for (var i = 0; i < processStops.length; i++) { - var stop_2 = processStops[i]; - if (stop_2 === null) { - if (gapBegin === null) { - gapBegin = i; - } - } - else if (gapBegin !== null) { - var gapLength = i - gapBegin; - var beforeGap = processStops[gapBegin - 1]; - var gapValue = (stop_2 - beforeGap) / (gapLength + 1); - for (var g = 1; g <= gapLength; g++) { - processStops[gapBegin + g - 1] = gapValue * g; - } - gapBegin = null; - } - } - return stops.map(function (_a, i) { - var color = _a.color; - return { color: color, stop: Math.max(Math.min(1, processStops[i] / lineLength), 0) }; - }); - }; - var getAngleFromCorner = function (corner, width, height) { - var centerX = width / 2; - var centerY = height / 2; - var x = getAbsoluteValue(corner[0], width) - centerX; - var y = centerY - getAbsoluteValue(corner[1], height); - return (Math.atan2(y, x) + Math.PI * 2) % (Math.PI * 2); - }; - var calculateGradientDirection = function (angle, width, height) { - var radian = typeof angle === 'number' ? angle : getAngleFromCorner(angle, width, height); - var lineLength = Math.abs(width * Math.sin(radian)) + Math.abs(height * Math.cos(radian)); - var halfWidth = width / 2; - var halfHeight = height / 2; - var halfLineLength = lineLength / 2; - var yDiff = Math.sin(radian - Math.PI / 2) * halfLineLength; - var xDiff = Math.cos(radian - Math.PI / 2) * halfLineLength; - return [lineLength, halfWidth - xDiff, halfWidth + xDiff, halfHeight - yDiff, halfHeight + yDiff]; - }; - var distance = function (a, b) { return Math.sqrt(a * a + b * b); }; - var findCorner = function (width, height, x, y, closest) { - var corners = [ - [0, 0], - [0, height], - [width, 0], - [width, height] - ]; - return corners.reduce(function (stat, corner) { - var cx = corner[0], cy = corner[1]; - var d = distance(x - cx, y - cy); - if (closest ? d < stat.optimumDistance : d > stat.optimumDistance) { - return { - optimumCorner: corner, - optimumDistance: d - }; - } - return stat; - }, { - optimumDistance: closest ? Infinity : -Infinity, - optimumCorner: null - }).optimumCorner; - }; - var calculateRadius = function (gradient, x, y, width, height) { - var rx = 0; - var ry = 0; - switch (gradient.size) { - case 0 /* CLOSEST_SIDE */: - // The ending shape is sized so that that it exactly meets the side of the gradient box closest to the gradient’s center. - // If the shape is an ellipse, it exactly meets the closest side in each dimension. - if (gradient.shape === 0 /* CIRCLE */) { - rx = ry = Math.min(Math.abs(x), Math.abs(x - width), Math.abs(y), Math.abs(y - height)); - } - else if (gradient.shape === 1 /* ELLIPSE */) { - rx = Math.min(Math.abs(x), Math.abs(x - width)); - ry = Math.min(Math.abs(y), Math.abs(y - height)); - } - break; - case 2 /* CLOSEST_CORNER */: - // The ending shape is sized so that that it passes through the corner of the gradient box closest to the gradient’s center. - // If the shape is an ellipse, the ending shape is given the same aspect-ratio it would have if closest-side were specified. - if (gradient.shape === 0 /* CIRCLE */) { - rx = ry = Math.min(distance(x, y), distance(x, y - height), distance(x - width, y), distance(x - width, y - height)); - } - else if (gradient.shape === 1 /* ELLIPSE */) { - // Compute the ratio ry/rx (which is to be the same as for "closest-side") - var c = Math.min(Math.abs(y), Math.abs(y - height)) / Math.min(Math.abs(x), Math.abs(x - width)); - var _a = findCorner(width, height, x, y, true), cx = _a[0], cy = _a[1]; - rx = distance(cx - x, (cy - y) / c); - ry = c * rx; - } - break; - case 1 /* FARTHEST_SIDE */: - // Same as closest-side, except the ending shape is sized based on the farthest side(s) - if (gradient.shape === 0 /* CIRCLE */) { - rx = ry = Math.max(Math.abs(x), Math.abs(x - width), Math.abs(y), Math.abs(y - height)); - } - else if (gradient.shape === 1 /* ELLIPSE */) { - rx = Math.max(Math.abs(x), Math.abs(x - width)); - ry = Math.max(Math.abs(y), Math.abs(y - height)); - } - break; - case 3 /* FARTHEST_CORNER */: - // Same as closest-corner, except the ending shape is sized based on the farthest corner. - // If the shape is an ellipse, the ending shape is given the same aspect ratio it would have if farthest-side were specified. - if (gradient.shape === 0 /* CIRCLE */) { - rx = ry = Math.max(distance(x, y), distance(x, y - height), distance(x - width, y), distance(x - width, y - height)); - } - else if (gradient.shape === 1 /* ELLIPSE */) { - // Compute the ratio ry/rx (which is to be the same as for "farthest-side") - var c = Math.max(Math.abs(y), Math.abs(y - height)) / Math.max(Math.abs(x), Math.abs(x - width)); - var _b = findCorner(width, height, x, y, false), cx = _b[0], cy = _b[1]; - rx = distance(cx - x, (cy - y) / c); - ry = c * rx; - } - break; - } - if (Array.isArray(gradient.size)) { - rx = getAbsoluteValue(gradient.size[0], width); - ry = gradient.size.length === 2 ? getAbsoluteValue(gradient.size[1], height) : rx; - } - return [rx, ry]; - }; - - var linearGradient = function (context, tokens) { - var angle$1 = deg(180); - var stops = []; - parseFunctionArgs(tokens).forEach(function (arg, i) { - if (i === 0) { - var firstToken = arg[0]; - if (firstToken.type === 20 /* IDENT_TOKEN */ && firstToken.value === 'to') { - angle$1 = parseNamedSide(arg); - return; - } - else if (isAngle(firstToken)) { - angle$1 = angle.parse(context, firstToken); - return; - } - } - var colorStop = parseColorStop(context, arg); - stops.push(colorStop); - }); - return { angle: angle$1, stops: stops, type: 1 /* LINEAR_GRADIENT */ }; - }; - - var prefixLinearGradient = function (context, tokens) { - var angle$1 = deg(180); - var stops = []; - parseFunctionArgs(tokens).forEach(function (arg, i) { - if (i === 0) { - var firstToken = arg[0]; - if (firstToken.type === 20 /* IDENT_TOKEN */ && - ['top', 'left', 'right', 'bottom'].indexOf(firstToken.value) !== -1) { - angle$1 = parseNamedSide(arg); - return; - } - else if (isAngle(firstToken)) { - angle$1 = (angle.parse(context, firstToken) + deg(270)) % deg(360); - return; - } - } - var colorStop = parseColorStop(context, arg); - stops.push(colorStop); - }); - return { - angle: angle$1, - stops: stops, - type: 1 /* LINEAR_GRADIENT */ - }; - }; - - var webkitGradient = function (context, tokens) { - var angle = deg(180); - var stops = []; - var type = 1 /* LINEAR_GRADIENT */; - var shape = 0 /* CIRCLE */; - var size = 3 /* FARTHEST_CORNER */; - var position = []; - parseFunctionArgs(tokens).forEach(function (arg, i) { - var firstToken = arg[0]; - if (i === 0) { - if (isIdentToken(firstToken) && firstToken.value === 'linear') { - type = 1 /* LINEAR_GRADIENT */; - return; - } - else if (isIdentToken(firstToken) && firstToken.value === 'radial') { - type = 2 /* RADIAL_GRADIENT */; - return; - } - } - if (firstToken.type === 18 /* FUNCTION */) { - if (firstToken.name === 'from') { - var color = color$1.parse(context, firstToken.values[0]); - stops.push({ stop: ZERO_LENGTH, color: color }); - } - else if (firstToken.name === 'to') { - var color = color$1.parse(context, firstToken.values[0]); - stops.push({ stop: HUNDRED_PERCENT, color: color }); - } - else if (firstToken.name === 'color-stop') { - var values = firstToken.values.filter(nonFunctionArgSeparator); - if (values.length === 2) { - var color = color$1.parse(context, values[1]); - var stop_1 = values[0]; - if (isNumberToken(stop_1)) { - stops.push({ - stop: { type: 16 /* PERCENTAGE_TOKEN */, number: stop_1.number * 100, flags: stop_1.flags }, - color: color - }); - } - } - } - } - }); - return type === 1 /* LINEAR_GRADIENT */ - ? { - angle: (angle + deg(180)) % deg(360), - stops: stops, - type: type - } - : { size: size, shape: shape, stops: stops, position: position, type: type }; - }; - - var CLOSEST_SIDE = 'closest-side'; - var FARTHEST_SIDE = 'farthest-side'; - var CLOSEST_CORNER = 'closest-corner'; - var FARTHEST_CORNER = 'farthest-corner'; - var CIRCLE = 'circle'; - var ELLIPSE = 'ellipse'; - var COVER = 'cover'; - var CONTAIN = 'contain'; - var radialGradient = function (context, tokens) { - var shape = 0 /* CIRCLE */; - var size = 3 /* FARTHEST_CORNER */; - var stops = []; - var position = []; - parseFunctionArgs(tokens).forEach(function (arg, i) { - var isColorStop = true; - if (i === 0) { - var isAtPosition_1 = false; - isColorStop = arg.reduce(function (acc, token) { - if (isAtPosition_1) { - if (isIdentToken(token)) { - switch (token.value) { - case 'center': - position.push(FIFTY_PERCENT); - return acc; - case 'top': - case 'left': - position.push(ZERO_LENGTH); - return acc; - case 'right': - case 'bottom': - position.push(HUNDRED_PERCENT); - return acc; - } - } - else if (isLengthPercentage(token) || isLength(token)) { - position.push(token); - } - } - else if (isIdentToken(token)) { - switch (token.value) { - case CIRCLE: - shape = 0 /* CIRCLE */; - return false; - case ELLIPSE: - shape = 1 /* ELLIPSE */; - return false; - case 'at': - isAtPosition_1 = true; - return false; - case CLOSEST_SIDE: - size = 0 /* CLOSEST_SIDE */; - return false; - case COVER: - case FARTHEST_SIDE: - size = 1 /* FARTHEST_SIDE */; - return false; - case CONTAIN: - case CLOSEST_CORNER: - size = 2 /* CLOSEST_CORNER */; - return false; - case FARTHEST_CORNER: - size = 3 /* FARTHEST_CORNER */; - return false; - } - } - else if (isLength(token) || isLengthPercentage(token)) { - if (!Array.isArray(size)) { - size = []; - } - size.push(token); - return false; - } - return acc; - }, isColorStop); - } - if (isColorStop) { - var colorStop = parseColorStop(context, arg); - stops.push(colorStop); - } - }); - return { size: size, shape: shape, stops: stops, position: position, type: 2 /* RADIAL_GRADIENT */ }; - }; - - var prefixRadialGradient = function (context, tokens) { - var shape = 0 /* CIRCLE */; - var size = 3 /* FARTHEST_CORNER */; - var stops = []; - var position = []; - parseFunctionArgs(tokens).forEach(function (arg, i) { - var isColorStop = true; - if (i === 0) { - isColorStop = arg.reduce(function (acc, token) { - if (isIdentToken(token)) { - switch (token.value) { - case 'center': - position.push(FIFTY_PERCENT); - return false; - case 'top': - case 'left': - position.push(ZERO_LENGTH); - return false; - case 'right': - case 'bottom': - position.push(HUNDRED_PERCENT); - return false; - } - } - else if (isLengthPercentage(token) || isLength(token)) { - position.push(token); - return false; - } - return acc; - }, isColorStop); - } - else if (i === 1) { - isColorStop = arg.reduce(function (acc, token) { - if (isIdentToken(token)) { - switch (token.value) { - case CIRCLE: - shape = 0 /* CIRCLE */; - return false; - case ELLIPSE: - shape = 1 /* ELLIPSE */; - return false; - case CONTAIN: - case CLOSEST_SIDE: - size = 0 /* CLOSEST_SIDE */; - return false; - case FARTHEST_SIDE: - size = 1 /* FARTHEST_SIDE */; - return false; - case CLOSEST_CORNER: - size = 2 /* CLOSEST_CORNER */; - return false; - case COVER: - case FARTHEST_CORNER: - size = 3 /* FARTHEST_CORNER */; - return false; - } - } - else if (isLength(token) || isLengthPercentage(token)) { - if (!Array.isArray(size)) { - size = []; - } - size.push(token); - return false; - } - return acc; - }, isColorStop); - } - if (isColorStop) { - var colorStop = parseColorStop(context, arg); - stops.push(colorStop); - } - }); - return { size: size, shape: shape, stops: stops, position: position, type: 2 /* RADIAL_GRADIENT */ }; - }; - - var isLinearGradient = function (background) { - return background.type === 1 /* LINEAR_GRADIENT */; - }; - var isRadialGradient = function (background) { - return background.type === 2 /* RADIAL_GRADIENT */; - }; - var image = { - name: 'image', - parse: function (context, value) { - if (value.type === 22 /* URL_TOKEN */) { - var image_1 = { url: value.value, type: 0 /* URL */ }; - context.cache.addImage(value.value); - return image_1; - } - if (value.type === 18 /* FUNCTION */) { - var imageFunction = SUPPORTED_IMAGE_FUNCTIONS[value.name]; - if (typeof imageFunction === 'undefined') { - throw new Error("Attempting to parse an unsupported image function \"" + value.name + "\""); - } - return imageFunction(context, value.values); - } - throw new Error("Unsupported image type " + value.type); - } - }; - function isSupportedImage(value) { - return (!(value.type === 20 /* IDENT_TOKEN */ && value.value === 'none') && - (value.type !== 18 /* FUNCTION */ || !!SUPPORTED_IMAGE_FUNCTIONS[value.name])); - } - var SUPPORTED_IMAGE_FUNCTIONS = { - 'linear-gradient': linearGradient, - '-moz-linear-gradient': prefixLinearGradient, - '-ms-linear-gradient': prefixLinearGradient, - '-o-linear-gradient': prefixLinearGradient, - '-webkit-linear-gradient': prefixLinearGradient, - 'radial-gradient': radialGradient, - '-moz-radial-gradient': prefixRadialGradient, - '-ms-radial-gradient': prefixRadialGradient, - '-o-radial-gradient': prefixRadialGradient, - '-webkit-radial-gradient': prefixRadialGradient, - '-webkit-gradient': webkitGradient - }; - - var backgroundImage = { - name: 'background-image', - initialValue: 'none', - type: 1 /* LIST */, - prefix: false, - parse: function (context, tokens) { - if (tokens.length === 0) { - return []; - } - var first = tokens[0]; - if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') { - return []; - } - return tokens - .filter(function (value) { return nonFunctionArgSeparator(value) && isSupportedImage(value); }) - .map(function (value) { return image.parse(context, value); }); - } - }; - - var backgroundOrigin = { - name: 'background-origin', - initialValue: 'border-box', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return tokens.map(function (token) { - if (isIdentToken(token)) { - switch (token.value) { - case 'padding-box': - return 1 /* PADDING_BOX */; - case 'content-box': - return 2 /* CONTENT_BOX */; - } - } - return 0 /* BORDER_BOX */; - }); - } - }; - - var backgroundPosition = { - name: 'background-position', - initialValue: '0% 0%', - type: 1 /* LIST */, - prefix: false, - parse: function (_context, tokens) { - return parseFunctionArgs(tokens) - .map(function (values) { return values.filter(isLengthPercentage); }) - .map(parseLengthPercentageTuple); - } - }; - - var backgroundRepeat = { - name: 'background-repeat', - initialValue: 'repeat', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return parseFunctionArgs(tokens) - .map(function (values) { - return values - .filter(isIdentToken) - .map(function (token) { return token.value; }) - .join(' '); - }) - .map(parseBackgroundRepeat); - } - }; - var parseBackgroundRepeat = function (value) { - switch (value) { - case 'no-repeat': - return 1 /* NO_REPEAT */; - case 'repeat-x': - case 'repeat no-repeat': - return 2 /* REPEAT_X */; - case 'repeat-y': - case 'no-repeat repeat': - return 3 /* REPEAT_Y */; - case 'repeat': - default: - return 0 /* REPEAT */; - } - }; - - var BACKGROUND_SIZE; - (function (BACKGROUND_SIZE) { - BACKGROUND_SIZE["AUTO"] = "auto"; - BACKGROUND_SIZE["CONTAIN"] = "contain"; - BACKGROUND_SIZE["COVER"] = "cover"; - })(BACKGROUND_SIZE || (BACKGROUND_SIZE = {})); - var backgroundSize = { - name: 'background-size', - initialValue: '0', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return parseFunctionArgs(tokens).map(function (values) { return values.filter(isBackgroundSizeInfoToken); }); - } - }; - var isBackgroundSizeInfoToken = function (value) { - return isIdentToken(value) || isLengthPercentage(value); - }; - - var borderColorForSide = function (side) { return ({ - name: "border-" + side + "-color", - initialValue: 'transparent', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'color' - }); }; - var borderTopColor = borderColorForSide('top'); - var borderRightColor = borderColorForSide('right'); - var borderBottomColor = borderColorForSide('bottom'); - var borderLeftColor = borderColorForSide('left'); - - var borderRadiusForSide = function (side) { return ({ - name: "border-radius-" + side, - initialValue: '0 0', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return parseLengthPercentageTuple(tokens.filter(isLengthPercentage)); - } - }); }; - var borderTopLeftRadius = borderRadiusForSide('top-left'); - var borderTopRightRadius = borderRadiusForSide('top-right'); - var borderBottomRightRadius = borderRadiusForSide('bottom-right'); - var borderBottomLeftRadius = borderRadiusForSide('bottom-left'); - - var borderStyleForSide = function (side) { return ({ - name: "border-" + side + "-style", - initialValue: 'solid', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, style) { - switch (style) { - case 'none': - return 0 /* NONE */; - case 'dashed': - return 2 /* DASHED */; - case 'dotted': - return 3 /* DOTTED */; - case 'double': - return 4 /* DOUBLE */; - } - return 1 /* SOLID */; - } - }); }; - var borderTopStyle = borderStyleForSide('top'); - var borderRightStyle = borderStyleForSide('right'); - var borderBottomStyle = borderStyleForSide('bottom'); - var borderLeftStyle = borderStyleForSide('left'); - - var borderWidthForSide = function (side) { return ({ - name: "border-" + side + "-width", - initialValue: '0', - type: 0 /* VALUE */, - prefix: false, - parse: function (_context, token) { - if (isDimensionToken(token)) { - return token.number; - } - return 0; - } - }); }; - var borderTopWidth = borderWidthForSide('top'); - var borderRightWidth = borderWidthForSide('right'); - var borderBottomWidth = borderWidthForSide('bottom'); - var borderLeftWidth = borderWidthForSide('left'); - - var color = { - name: "color", - initialValue: 'transparent', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'color' - }; - - var direction = { - name: 'direction', - initialValue: 'ltr', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, direction) { - switch (direction) { - case 'rtl': - return 1 /* RTL */; - case 'ltr': - default: - return 0 /* LTR */; - } - } - }; - - var display = { - name: 'display', - initialValue: 'inline-block', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return tokens.filter(isIdentToken).reduce(function (bit, token) { - return bit | parseDisplayValue(token.value); - }, 0 /* NONE */); - } - }; - var parseDisplayValue = function (display) { - switch (display) { - case 'block': - case '-webkit-box': - return 2 /* BLOCK */; - case 'inline': - return 4 /* INLINE */; - case 'run-in': - return 8 /* RUN_IN */; - case 'flow': - return 16 /* FLOW */; - case 'flow-root': - return 32 /* FLOW_ROOT */; - case 'table': - return 64 /* TABLE */; - case 'flex': - case '-webkit-flex': - return 128 /* FLEX */; - case 'grid': - case '-ms-grid': - return 256 /* GRID */; - case 'ruby': - return 512 /* RUBY */; - case 'subgrid': - return 1024 /* SUBGRID */; - case 'list-item': - return 2048 /* LIST_ITEM */; - case 'table-row-group': - return 4096 /* TABLE_ROW_GROUP */; - case 'table-header-group': - return 8192 /* TABLE_HEADER_GROUP */; - case 'table-footer-group': - return 16384 /* TABLE_FOOTER_GROUP */; - case 'table-row': - return 32768 /* TABLE_ROW */; - case 'table-cell': - return 65536 /* TABLE_CELL */; - case 'table-column-group': - return 131072 /* TABLE_COLUMN_GROUP */; - case 'table-column': - return 262144 /* TABLE_COLUMN */; - case 'table-caption': - return 524288 /* TABLE_CAPTION */; - case 'ruby-base': - return 1048576 /* RUBY_BASE */; - case 'ruby-text': - return 2097152 /* RUBY_TEXT */; - case 'ruby-base-container': - return 4194304 /* RUBY_BASE_CONTAINER */; - case 'ruby-text-container': - return 8388608 /* RUBY_TEXT_CONTAINER */; - case 'contents': - return 16777216 /* CONTENTS */; - case 'inline-block': - return 33554432 /* INLINE_BLOCK */; - case 'inline-list-item': - return 67108864 /* INLINE_LIST_ITEM */; - case 'inline-table': - return 134217728 /* INLINE_TABLE */; - case 'inline-flex': - return 268435456 /* INLINE_FLEX */; - case 'inline-grid': - return 536870912 /* INLINE_GRID */; - } - return 0 /* NONE */; - }; - - var float = { - name: 'float', - initialValue: 'none', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, float) { - switch (float) { - case 'left': - return 1 /* LEFT */; - case 'right': - return 2 /* RIGHT */; - case 'inline-start': - return 3 /* INLINE_START */; - case 'inline-end': - return 4 /* INLINE_END */; - } - return 0 /* NONE */; - } - }; - - var letterSpacing = { - name: 'letter-spacing', - initialValue: '0', - prefix: false, - type: 0 /* VALUE */, - parse: function (_context, token) { - if (token.type === 20 /* IDENT_TOKEN */ && token.value === 'normal') { - return 0; - } - if (token.type === 17 /* NUMBER_TOKEN */) { - return token.number; - } - if (token.type === 15 /* DIMENSION_TOKEN */) { - return token.number; - } - return 0; - } - }; - - var LINE_BREAK; - (function (LINE_BREAK) { - LINE_BREAK["NORMAL"] = "normal"; - LINE_BREAK["STRICT"] = "strict"; - })(LINE_BREAK || (LINE_BREAK = {})); - var lineBreak = { - name: 'line-break', - initialValue: 'normal', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, lineBreak) { - switch (lineBreak) { - case 'strict': - return LINE_BREAK.STRICT; - case 'normal': - default: - return LINE_BREAK.NORMAL; - } - } - }; - - var lineHeight = { - name: 'line-height', - initialValue: 'normal', - prefix: false, - type: 4 /* TOKEN_VALUE */ - }; - var computeLineHeight = function (token, fontSize) { - if (isIdentToken(token) && token.value === 'normal') { - return 1.2 * fontSize; - } - else if (token.type === 17 /* NUMBER_TOKEN */) { - return fontSize * token.number; - } - else if (isLengthPercentage(token)) { - return getAbsoluteValue(token, fontSize); - } - return fontSize; - }; - - var listStyleImage = { - name: 'list-style-image', - initialValue: 'none', - type: 0 /* VALUE */, - prefix: false, - parse: function (context, token) { - if (token.type === 20 /* IDENT_TOKEN */ && token.value === 'none') { - return null; - } - return image.parse(context, token); - } - }; - - var listStylePosition = { - name: 'list-style-position', - initialValue: 'outside', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, position) { - switch (position) { - case 'inside': - return 0 /* INSIDE */; - case 'outside': - default: - return 1 /* OUTSIDE */; - } - } - }; - - var listStyleType = { - name: 'list-style-type', - initialValue: 'none', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, type) { - switch (type) { - case 'disc': - return 0 /* DISC */; - case 'circle': - return 1 /* CIRCLE */; - case 'square': - return 2 /* SQUARE */; - case 'decimal': - return 3 /* DECIMAL */; - case 'cjk-decimal': - return 4 /* CJK_DECIMAL */; - case 'decimal-leading-zero': - return 5 /* DECIMAL_LEADING_ZERO */; - case 'lower-roman': - return 6 /* LOWER_ROMAN */; - case 'upper-roman': - return 7 /* UPPER_ROMAN */; - case 'lower-greek': - return 8 /* LOWER_GREEK */; - case 'lower-alpha': - return 9 /* LOWER_ALPHA */; - case 'upper-alpha': - return 10 /* UPPER_ALPHA */; - case 'arabic-indic': - return 11 /* ARABIC_INDIC */; - case 'armenian': - return 12 /* ARMENIAN */; - case 'bengali': - return 13 /* BENGALI */; - case 'cambodian': - return 14 /* CAMBODIAN */; - case 'cjk-earthly-branch': - return 15 /* CJK_EARTHLY_BRANCH */; - case 'cjk-heavenly-stem': - return 16 /* CJK_HEAVENLY_STEM */; - case 'cjk-ideographic': - return 17 /* CJK_IDEOGRAPHIC */; - case 'devanagari': - return 18 /* DEVANAGARI */; - case 'ethiopic-numeric': - return 19 /* ETHIOPIC_NUMERIC */; - case 'georgian': - return 20 /* GEORGIAN */; - case 'gujarati': - return 21 /* GUJARATI */; - case 'gurmukhi': - return 22 /* GURMUKHI */; - case 'hebrew': - return 22 /* HEBREW */; - case 'hiragana': - return 23 /* HIRAGANA */; - case 'hiragana-iroha': - return 24 /* HIRAGANA_IROHA */; - case 'japanese-formal': - return 25 /* JAPANESE_FORMAL */; - case 'japanese-informal': - return 26 /* JAPANESE_INFORMAL */; - case 'kannada': - return 27 /* KANNADA */; - case 'katakana': - return 28 /* KATAKANA */; - case 'katakana-iroha': - return 29 /* KATAKANA_IROHA */; - case 'khmer': - return 30 /* KHMER */; - case 'korean-hangul-formal': - return 31 /* KOREAN_HANGUL_FORMAL */; - case 'korean-hanja-formal': - return 32 /* KOREAN_HANJA_FORMAL */; - case 'korean-hanja-informal': - return 33 /* KOREAN_HANJA_INFORMAL */; - case 'lao': - return 34 /* LAO */; - case 'lower-armenian': - return 35 /* LOWER_ARMENIAN */; - case 'malayalam': - return 36 /* MALAYALAM */; - case 'mongolian': - return 37 /* MONGOLIAN */; - case 'myanmar': - return 38 /* MYANMAR */; - case 'oriya': - return 39 /* ORIYA */; - case 'persian': - return 40 /* PERSIAN */; - case 'simp-chinese-formal': - return 41 /* SIMP_CHINESE_FORMAL */; - case 'simp-chinese-informal': - return 42 /* SIMP_CHINESE_INFORMAL */; - case 'tamil': - return 43 /* TAMIL */; - case 'telugu': - return 44 /* TELUGU */; - case 'thai': - return 45 /* THAI */; - case 'tibetan': - return 46 /* TIBETAN */; - case 'trad-chinese-formal': - return 47 /* TRAD_CHINESE_FORMAL */; - case 'trad-chinese-informal': - return 48 /* TRAD_CHINESE_INFORMAL */; - case 'upper-armenian': - return 49 /* UPPER_ARMENIAN */; - case 'disclosure-open': - return 50 /* DISCLOSURE_OPEN */; - case 'disclosure-closed': - return 51 /* DISCLOSURE_CLOSED */; - case 'none': - default: - return -1 /* NONE */; - } - } - }; - - var marginForSide = function (side) { return ({ - name: "margin-" + side, - initialValue: '0', - prefix: false, - type: 4 /* TOKEN_VALUE */ - }); }; - var marginTop = marginForSide('top'); - var marginRight = marginForSide('right'); - var marginBottom = marginForSide('bottom'); - var marginLeft = marginForSide('left'); - - var overflow = { - name: 'overflow', - initialValue: 'visible', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return tokens.filter(isIdentToken).map(function (overflow) { - switch (overflow.value) { - case 'hidden': - return 1 /* HIDDEN */; - case 'scroll': - return 2 /* SCROLL */; - case 'clip': - return 3 /* CLIP */; - case 'auto': - return 4 /* AUTO */; - case 'visible': - default: - return 0 /* VISIBLE */; - } - }); - } - }; - - var overflowWrap = { - name: 'overflow-wrap', - initialValue: 'normal', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, overflow) { - switch (overflow) { - case 'break-word': - return "break-word" /* BREAK_WORD */; - case 'normal': - default: - return "normal" /* NORMAL */; - } - } - }; - - var paddingForSide = function (side) { return ({ - name: "padding-" + side, - initialValue: '0', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'length-percentage' - }); }; - var paddingTop = paddingForSide('top'); - var paddingRight = paddingForSide('right'); - var paddingBottom = paddingForSide('bottom'); - var paddingLeft = paddingForSide('left'); - - var textAlign = { - name: 'text-align', - initialValue: 'left', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, textAlign) { - switch (textAlign) { - case 'right': - return 2 /* RIGHT */; - case 'center': - case 'justify': - return 1 /* CENTER */; - case 'left': - default: - return 0 /* LEFT */; - } - } - }; - - var position = { - name: 'position', - initialValue: 'static', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, position) { - switch (position) { - case 'relative': - return 1 /* RELATIVE */; - case 'absolute': - return 2 /* ABSOLUTE */; - case 'fixed': - return 3 /* FIXED */; - case 'sticky': - return 4 /* STICKY */; - } - return 0 /* STATIC */; - } - }; - - var textShadow = { - name: 'text-shadow', - initialValue: 'none', - type: 1 /* LIST */, - prefix: false, - parse: function (context, tokens) { - if (tokens.length === 1 && isIdentWithValue(tokens[0], 'none')) { - return []; - } - return parseFunctionArgs(tokens).map(function (values) { - var shadow = { - color: COLORS.TRANSPARENT, - offsetX: ZERO_LENGTH, - offsetY: ZERO_LENGTH, - blur: ZERO_LENGTH - }; - var c = 0; - for (var i = 0; i < values.length; i++) { - var token = values[i]; - if (isLength(token)) { - if (c === 0) { - shadow.offsetX = token; - } - else if (c === 1) { - shadow.offsetY = token; - } - else { - shadow.blur = token; - } - c++; - } - else { - shadow.color = color$1.parse(context, token); - } - } - return shadow; - }); - } - }; - - var textTransform = { - name: 'text-transform', - initialValue: 'none', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, textTransform) { - switch (textTransform) { - case 'uppercase': - return 2 /* UPPERCASE */; - case 'lowercase': - return 1 /* LOWERCASE */; - case 'capitalize': - return 3 /* CAPITALIZE */; - } - return 0 /* NONE */; - } - }; - - var transform$1 = { - name: 'transform', - initialValue: 'none', - prefix: true, - type: 0 /* VALUE */, - parse: function (_context, token) { - if (token.type === 20 /* IDENT_TOKEN */ && token.value === 'none') { - return null; - } - if (token.type === 18 /* FUNCTION */) { - var transformFunction = SUPPORTED_TRANSFORM_FUNCTIONS[token.name]; - if (typeof transformFunction === 'undefined') { - throw new Error("Attempting to parse an unsupported transform function \"" + token.name + "\""); - } - return transformFunction(token.values); - } - return null; - } - }; - var matrix = function (args) { - var values = args.filter(function (arg) { return arg.type === 17 /* NUMBER_TOKEN */; }).map(function (arg) { return arg.number; }); - return values.length === 6 ? values : null; - }; - // doesn't support 3D transforms at the moment - var matrix3d = function (args) { - var values = args.filter(function (arg) { return arg.type === 17 /* NUMBER_TOKEN */; }).map(function (arg) { return arg.number; }); - var a1 = values[0], b1 = values[1]; values[2]; values[3]; var a2 = values[4], b2 = values[5]; values[6]; values[7]; values[8]; values[9]; values[10]; values[11]; var a4 = values[12], b4 = values[13]; values[14]; values[15]; - return values.length === 16 ? [a1, b1, a2, b2, a4, b4] : null; - }; - var SUPPORTED_TRANSFORM_FUNCTIONS = { - matrix: matrix, - matrix3d: matrix3d - }; - - var DEFAULT_VALUE = { - type: 16 /* PERCENTAGE_TOKEN */, - number: 50, - flags: FLAG_INTEGER - }; - var DEFAULT = [DEFAULT_VALUE, DEFAULT_VALUE]; - var transformOrigin = { - name: 'transform-origin', - initialValue: '50% 50%', - prefix: true, - type: 1 /* LIST */, - parse: function (_context, tokens) { - var origins = tokens.filter(isLengthPercentage); - if (origins.length !== 2) { - return DEFAULT; - } - return [origins[0], origins[1]]; - } - }; - - var visibility = { - name: 'visible', - initialValue: 'none', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, visibility) { - switch (visibility) { - case 'hidden': - return 1 /* HIDDEN */; - case 'collapse': - return 2 /* COLLAPSE */; - case 'visible': - default: - return 0 /* VISIBLE */; - } - } - }; - - var WORD_BREAK; - (function (WORD_BREAK) { - WORD_BREAK["NORMAL"] = "normal"; - WORD_BREAK["BREAK_ALL"] = "break-all"; - WORD_BREAK["KEEP_ALL"] = "keep-all"; - })(WORD_BREAK || (WORD_BREAK = {})); - var wordBreak = { - name: 'word-break', - initialValue: 'normal', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, wordBreak) { - switch (wordBreak) { - case 'break-all': - return WORD_BREAK.BREAK_ALL; - case 'keep-all': - return WORD_BREAK.KEEP_ALL; - case 'normal': - default: - return WORD_BREAK.NORMAL; - } - } - }; - - var zIndex = { - name: 'z-index', - initialValue: 'auto', - prefix: false, - type: 0 /* VALUE */, - parse: function (_context, token) { - if (token.type === 20 /* IDENT_TOKEN */) { - return { auto: true, order: 0 }; - } - if (isNumberToken(token)) { - return { auto: false, order: token.number }; - } - throw new Error("Invalid z-index number parsed"); - } - }; - - var time = { - name: 'time', - parse: function (_context, value) { - if (value.type === 15 /* DIMENSION_TOKEN */) { - switch (value.unit.toLowerCase()) { - case 's': - return 1000 * value.number; - case 'ms': - return value.number; - } - } - throw new Error("Unsupported time type"); - } - }; - - var opacity = { - name: 'opacity', - initialValue: '1', - type: 0 /* VALUE */, - prefix: false, - parse: function (_context, token) { - if (isNumberToken(token)) { - return token.number; - } - return 1; - } - }; - - var textDecorationColor = { - name: "text-decoration-color", - initialValue: 'transparent', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'color' - }; - - var textDecorationLine = { - name: 'text-decoration-line', - initialValue: 'none', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - return tokens - .filter(isIdentToken) - .map(function (token) { - switch (token.value) { - case 'underline': - return 1 /* UNDERLINE */; - case 'overline': - return 2 /* OVERLINE */; - case 'line-through': - return 3 /* LINE_THROUGH */; - case 'none': - return 4 /* BLINK */; - } - return 0 /* NONE */; - }) - .filter(function (line) { return line !== 0 /* NONE */; }); - } - }; - - var fontFamily = { - name: "font-family", - initialValue: '', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - var accumulator = []; - var results = []; - tokens.forEach(function (token) { - switch (token.type) { - case 20 /* IDENT_TOKEN */: - case 0 /* STRING_TOKEN */: - accumulator.push(token.value); - break; - case 17 /* NUMBER_TOKEN */: - accumulator.push(token.number.toString()); - break; - case 4 /* COMMA_TOKEN */: - results.push(accumulator.join(' ')); - accumulator.length = 0; - break; - } - }); - if (accumulator.length) { - results.push(accumulator.join(' ')); - } - return results.map(function (result) { return (result.indexOf(' ') === -1 ? result : "'" + result + "'"); }); - } - }; - - var fontSize = { - name: "font-size", - initialValue: '0', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'length' - }; - - var fontWeight = { - name: 'font-weight', - initialValue: 'normal', - type: 0 /* VALUE */, - prefix: false, - parse: function (_context, token) { - if (isNumberToken(token)) { - return token.number; - } - if (isIdentToken(token)) { - switch (token.value) { - case 'bold': - return 700; - case 'normal': - default: - return 400; - } - } - return 400; - } - }; - - var fontVariant = { - name: 'font-variant', - initialValue: 'none', - type: 1 /* LIST */, - prefix: false, - parse: function (_context, tokens) { - return tokens.filter(isIdentToken).map(function (token) { return token.value; }); - } - }; - - var fontStyle = { - name: 'font-style', - initialValue: 'normal', - prefix: false, - type: 2 /* IDENT_VALUE */, - parse: function (_context, overflow) { - switch (overflow) { - case 'oblique': - return "oblique" /* OBLIQUE */; - case 'italic': - return "italic" /* ITALIC */; - case 'normal': - default: - return "normal" /* NORMAL */; - } - } - }; - - var contains = function (bit, value) { return (bit & value) !== 0; }; - - var content = { - name: 'content', - initialValue: 'none', - type: 1 /* LIST */, - prefix: false, - parse: function (_context, tokens) { - if (tokens.length === 0) { - return []; - } - var first = tokens[0]; - if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') { - return []; - } - return tokens; - } - }; - - var counterIncrement = { - name: 'counter-increment', - initialValue: 'none', - prefix: true, - type: 1 /* LIST */, - parse: function (_context, tokens) { - if (tokens.length === 0) { - return null; - } - var first = tokens[0]; - if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') { - return null; - } - var increments = []; - var filtered = tokens.filter(nonWhiteSpace); - for (var i = 0; i < filtered.length; i++) { - var counter = filtered[i]; - var next = filtered[i + 1]; - if (counter.type === 20 /* IDENT_TOKEN */) { - var increment = next && isNumberToken(next) ? next.number : 1; - increments.push({ counter: counter.value, increment: increment }); - } - } - return increments; - } - }; - - var counterReset = { - name: 'counter-reset', - initialValue: 'none', - prefix: true, - type: 1 /* LIST */, - parse: function (_context, tokens) { - if (tokens.length === 0) { - return []; - } - var resets = []; - var filtered = tokens.filter(nonWhiteSpace); - for (var i = 0; i < filtered.length; i++) { - var counter = filtered[i]; - var next = filtered[i + 1]; - if (isIdentToken(counter) && counter.value !== 'none') { - var reset = next && isNumberToken(next) ? next.number : 0; - resets.push({ counter: counter.value, reset: reset }); - } - } - return resets; - } - }; - - var duration = { - name: 'duration', - initialValue: '0s', - prefix: false, - type: 1 /* LIST */, - parse: function (context, tokens) { - return tokens.filter(isDimensionToken).map(function (token) { return time.parse(context, token); }); - } - }; - - var quotes = { - name: 'quotes', - initialValue: 'none', - prefix: true, - type: 1 /* LIST */, - parse: function (_context, tokens) { - if (tokens.length === 0) { - return null; - } - var first = tokens[0]; - if (first.type === 20 /* IDENT_TOKEN */ && first.value === 'none') { - return null; - } - var quotes = []; - var filtered = tokens.filter(isStringToken); - if (filtered.length % 2 !== 0) { - return null; - } - for (var i = 0; i < filtered.length; i += 2) { - var open_1 = filtered[i].value; - var close_1 = filtered[i + 1].value; - quotes.push({ open: open_1, close: close_1 }); - } - return quotes; - } - }; - var getQuote = function (quotes, depth, open) { - if (!quotes) { - return ''; - } - var quote = quotes[Math.min(depth, quotes.length - 1)]; - if (!quote) { - return ''; - } - return open ? quote.open : quote.close; - }; - - var paintOrder = { - name: 'paint-order', - initialValue: 'normal', - prefix: false, - type: 1 /* LIST */, - parse: function (_context, tokens) { - var DEFAULT_VALUE = [0 /* FILL */, 1 /* STROKE */, 2 /* MARKERS */]; - var layers = []; - tokens.filter(isIdentToken).forEach(function (token) { - switch (token.value) { - case 'stroke': - layers.push(1 /* STROKE */); - break; - case 'fill': - layers.push(0 /* FILL */); - break; - case 'markers': - layers.push(2 /* MARKERS */); - break; - } - }); - DEFAULT_VALUE.forEach(function (value) { - if (layers.indexOf(value) === -1) { - layers.push(value); - } - }); - return layers; - } - }; - - var webkitTextStrokeColor = { - name: "-webkit-text-stroke-color", - initialValue: 'currentcolor', - prefix: false, - type: 3 /* TYPE_VALUE */, - format: 'color' - }; - - var webkitTextStrokeWidth = { - name: "-webkit-text-stroke-width", - initialValue: '0', - type: 0 /* VALUE */, - prefix: false, - parse: function (_context, token) { - if (isDimensionToken(token)) { - return token.number; - } - return 0; - } - }; - - var CSSParsedDeclaration = /** @class */ (function () { - function CSSParsedDeclaration(context, declaration) { - var _a, _b; - this.animationDuration = parse(context, duration, declaration.animationDuration); - this.backgroundClip = parse(context, backgroundClip, declaration.backgroundClip); - this.backgroundColor = parse(context, backgroundColor, declaration.backgroundColor); - this.backgroundImage = parse(context, backgroundImage, declaration.backgroundImage); - this.backgroundOrigin = parse(context, backgroundOrigin, declaration.backgroundOrigin); - this.backgroundPosition = parse(context, backgroundPosition, declaration.backgroundPosition); - this.backgroundRepeat = parse(context, backgroundRepeat, declaration.backgroundRepeat); - this.backgroundSize = parse(context, backgroundSize, declaration.backgroundSize); - this.borderTopColor = parse(context, borderTopColor, declaration.borderTopColor); - this.borderRightColor = parse(context, borderRightColor, declaration.borderRightColor); - this.borderBottomColor = parse(context, borderBottomColor, declaration.borderBottomColor); - this.borderLeftColor = parse(context, borderLeftColor, declaration.borderLeftColor); - this.borderTopLeftRadius = parse(context, borderTopLeftRadius, declaration.borderTopLeftRadius); - this.borderTopRightRadius = parse(context, borderTopRightRadius, declaration.borderTopRightRadius); - this.borderBottomRightRadius = parse(context, borderBottomRightRadius, declaration.borderBottomRightRadius); - this.borderBottomLeftRadius = parse(context, borderBottomLeftRadius, declaration.borderBottomLeftRadius); - this.borderTopStyle = parse(context, borderTopStyle, declaration.borderTopStyle); - this.borderRightStyle = parse(context, borderRightStyle, declaration.borderRightStyle); - this.borderBottomStyle = parse(context, borderBottomStyle, declaration.borderBottomStyle); - this.borderLeftStyle = parse(context, borderLeftStyle, declaration.borderLeftStyle); - this.borderTopWidth = parse(context, borderTopWidth, declaration.borderTopWidth); - this.borderRightWidth = parse(context, borderRightWidth, declaration.borderRightWidth); - this.borderBottomWidth = parse(context, borderBottomWidth, declaration.borderBottomWidth); - this.borderLeftWidth = parse(context, borderLeftWidth, declaration.borderLeftWidth); - this.color = parse(context, color, declaration.color); - this.direction = parse(context, direction, declaration.direction); - this.display = parse(context, display, declaration.display); - this.float = parse(context, float, declaration.cssFloat); - this.fontFamily = parse(context, fontFamily, declaration.fontFamily); - this.fontSize = parse(context, fontSize, declaration.fontSize); - this.fontStyle = parse(context, fontStyle, declaration.fontStyle); - this.fontVariant = parse(context, fontVariant, declaration.fontVariant); - this.fontWeight = parse(context, fontWeight, declaration.fontWeight); - this.letterSpacing = parse(context, letterSpacing, declaration.letterSpacing); - this.lineBreak = parse(context, lineBreak, declaration.lineBreak); - this.lineHeight = parse(context, lineHeight, declaration.lineHeight); - this.listStyleImage = parse(context, listStyleImage, declaration.listStyleImage); - this.listStylePosition = parse(context, listStylePosition, declaration.listStylePosition); - this.listStyleType = parse(context, listStyleType, declaration.listStyleType); - this.marginTop = parse(context, marginTop, declaration.marginTop); - this.marginRight = parse(context, marginRight, declaration.marginRight); - this.marginBottom = parse(context, marginBottom, declaration.marginBottom); - this.marginLeft = parse(context, marginLeft, declaration.marginLeft); - this.opacity = parse(context, opacity, declaration.opacity); - var overflowTuple = parse(context, overflow, declaration.overflow); - this.overflowX = overflowTuple[0]; - this.overflowY = overflowTuple[overflowTuple.length > 1 ? 1 : 0]; - this.overflowWrap = parse(context, overflowWrap, declaration.overflowWrap); - this.paddingTop = parse(context, paddingTop, declaration.paddingTop); - this.paddingRight = parse(context, paddingRight, declaration.paddingRight); - this.paddingBottom = parse(context, paddingBottom, declaration.paddingBottom); - this.paddingLeft = parse(context, paddingLeft, declaration.paddingLeft); - this.paintOrder = parse(context, paintOrder, declaration.paintOrder); - this.position = parse(context, position, declaration.position); - this.textAlign = parse(context, textAlign, declaration.textAlign); - this.textDecorationColor = parse(context, textDecorationColor, (_a = declaration.textDecorationColor) !== null && _a !== void 0 ? _a : declaration.color); - this.textDecorationLine = parse(context, textDecorationLine, (_b = declaration.textDecorationLine) !== null && _b !== void 0 ? _b : declaration.textDecoration); - this.textShadow = parse(context, textShadow, declaration.textShadow); - this.textTransform = parse(context, textTransform, declaration.textTransform); - this.transform = parse(context, transform$1, declaration.transform); - this.transformOrigin = parse(context, transformOrigin, declaration.transformOrigin); - this.visibility = parse(context, visibility, declaration.visibility); - this.webkitTextStrokeColor = parse(context, webkitTextStrokeColor, declaration.webkitTextStrokeColor); - this.webkitTextStrokeWidth = parse(context, webkitTextStrokeWidth, declaration.webkitTextStrokeWidth); - this.wordBreak = parse(context, wordBreak, declaration.wordBreak); - this.zIndex = parse(context, zIndex, declaration.zIndex); - } - CSSParsedDeclaration.prototype.isVisible = function () { - return this.display > 0 && this.opacity > 0 && this.visibility === 0 /* VISIBLE */; - }; - CSSParsedDeclaration.prototype.isTransparent = function () { - return isTransparent(this.backgroundColor); - }; - CSSParsedDeclaration.prototype.isTransformed = function () { - return this.transform !== null; - }; - CSSParsedDeclaration.prototype.isPositioned = function () { - return this.position !== 0 /* STATIC */; - }; - CSSParsedDeclaration.prototype.isPositionedWithZIndex = function () { - return this.isPositioned() && !this.zIndex.auto; - }; - CSSParsedDeclaration.prototype.isFloating = function () { - return this.float !== 0 /* NONE */; - }; - CSSParsedDeclaration.prototype.isInlineLevel = function () { - return (contains(this.display, 4 /* INLINE */) || - contains(this.display, 33554432 /* INLINE_BLOCK */) || - contains(this.display, 268435456 /* INLINE_FLEX */) || - contains(this.display, 536870912 /* INLINE_GRID */) || - contains(this.display, 67108864 /* INLINE_LIST_ITEM */) || - contains(this.display, 134217728 /* INLINE_TABLE */)); - }; - return CSSParsedDeclaration; - }()); - var CSSParsedPseudoDeclaration = /** @class */ (function () { - function CSSParsedPseudoDeclaration(context, declaration) { - this.content = parse(context, content, declaration.content); - this.quotes = parse(context, quotes, declaration.quotes); - } - return CSSParsedPseudoDeclaration; - }()); - var CSSParsedCounterDeclaration = /** @class */ (function () { - function CSSParsedCounterDeclaration(context, declaration) { - this.counterIncrement = parse(context, counterIncrement, declaration.counterIncrement); - this.counterReset = parse(context, counterReset, declaration.counterReset); - } - return CSSParsedCounterDeclaration; - }()); - // eslint-disable-next-line @typescript-eslint/no-explicit-any - var parse = function (context, descriptor, style) { - var tokenizer = new Tokenizer(); - var value = style !== null && typeof style !== 'undefined' ? style.toString() : descriptor.initialValue; - tokenizer.write(value); - var parser = new Parser(tokenizer.read()); - switch (descriptor.type) { - case 2 /* IDENT_VALUE */: - var token = parser.parseComponentValue(); - return descriptor.parse(context, isIdentToken(token) ? token.value : descriptor.initialValue); - case 0 /* VALUE */: - return descriptor.parse(context, parser.parseComponentValue()); - case 1 /* LIST */: - return descriptor.parse(context, parser.parseComponentValues()); - case 4 /* TOKEN_VALUE */: - return parser.parseComponentValue(); - case 3 /* TYPE_VALUE */: - switch (descriptor.format) { - case 'angle': - return angle.parse(context, parser.parseComponentValue()); - case 'color': - return color$1.parse(context, parser.parseComponentValue()); - case 'image': - return image.parse(context, parser.parseComponentValue()); - case 'length': - var length_1 = parser.parseComponentValue(); - return isLength(length_1) ? length_1 : ZERO_LENGTH; - case 'length-percentage': - var value_1 = parser.parseComponentValue(); - return isLengthPercentage(value_1) ? value_1 : ZERO_LENGTH; - case 'time': - return time.parse(context, parser.parseComponentValue()); - } - break; - } - }; - - var elementDebuggerAttribute = 'data-html2canvas-debug'; - var getElementDebugType = function (element) { - var attribute = element.getAttribute(elementDebuggerAttribute); - switch (attribute) { - case 'all': - return 1 /* ALL */; - case 'clone': - return 2 /* CLONE */; - case 'parse': - return 3 /* PARSE */; - case 'render': - return 4 /* RENDER */; - default: - return 0 /* NONE */; - } - }; - var isDebugging = function (element, type) { - var elementType = getElementDebugType(element); - return elementType === 1 /* ALL */ || type === elementType; - }; - - var ElementContainer = /** @class */ (function () { - function ElementContainer(context, element) { - this.context = context; - this.textNodes = []; - this.elements = []; - this.flags = 0; - if (isDebugging(element, 3 /* PARSE */)) { - debugger; - } - this.styles = new CSSParsedDeclaration(context, window.getComputedStyle(element, null)); - if (isHTMLElementNode(element)) { - if (this.styles.animationDuration.some(function (duration) { return duration > 0; })) { - element.style.animationDuration = '0s'; - } - if (this.styles.transform !== null) { - // getBoundingClientRect takes transforms into account - element.style.transform = 'none'; - } - } - this.bounds = parseBounds(this.context, element); - if (isDebugging(element, 4 /* RENDER */)) { - this.flags |= 16 /* DEBUG_RENDER */; - } - } - return ElementContainer; - }()); - - /* - * text-segmentation 1.0.3 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ - var base64 = 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- - /* - * utrie 1.0.2 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ - var chars$1 = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'; - // Use a lookup table to find the index. - var lookup$1 = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256); - for (var i$1 = 0; i$1 < chars$1.length; i$1++) { - lookup$1[chars$1.charCodeAt(i$1)] = i$1; - } - var decode = function (base64) { - var bufferLength = base64.length * 0.75, len = base64.length, i, p = 0, encoded1, encoded2, encoded3, encoded4; - if (base64[base64.length - 1] === '=') { - bufferLength--; - if (base64[base64.length - 2] === '=') { - bufferLength--; - } - } - var buffer = typeof ArrayBuffer !== 'undefined' && - typeof Uint8Array !== 'undefined' && - typeof Uint8Array.prototype.slice !== 'undefined' - ? new ArrayBuffer(bufferLength) - : new Array(bufferLength); - var bytes = Array.isArray(buffer) ? buffer : new Uint8Array(buffer); - for (i = 0; i < len; i += 4) { - encoded1 = lookup$1[base64.charCodeAt(i)]; - encoded2 = lookup$1[base64.charCodeAt(i + 1)]; - encoded3 = lookup$1[base64.charCodeAt(i + 2)]; - encoded4 = lookup$1[base64.charCodeAt(i + 3)]; - bytes[p++] = (encoded1 << 2) | (encoded2 >> 4); - bytes[p++] = ((encoded2 & 15) << 4) | (encoded3 >> 2); - bytes[p++] = ((encoded3 & 3) << 6) | (encoded4 & 63); - } - return buffer; - }; - var polyUint16Array = function (buffer) { - var length = buffer.length; - var bytes = []; - for (var i = 0; i < length; i += 2) { - bytes.push((buffer[i + 1] << 8) | buffer[i]); - } - return bytes; - }; - var polyUint32Array = function (buffer) { - var length = buffer.length; - var bytes = []; - for (var i = 0; i < length; i += 4) { - bytes.push((buffer[i + 3] << 24) | (buffer[i + 2] << 16) | (buffer[i + 1] << 8) | buffer[i]); - } - return bytes; - }; - - /** Shift size for getting the index-2 table offset. */ - var UTRIE2_SHIFT_2 = 5; - /** Shift size for getting the index-1 table offset. */ - var UTRIE2_SHIFT_1 = 6 + 5; - /** - * Shift size for shifting left the index array values. - * Increases possible data size with 16-bit index values at the cost - * of compactability. - * This requires data blocks to be aligned by UTRIE2_DATA_GRANULARITY. - */ - var UTRIE2_INDEX_SHIFT = 2; - /** - * Difference between the two shift sizes, - * for getting an index-1 offset from an index-2 offset. 6=11-5 - */ - var UTRIE2_SHIFT_1_2 = UTRIE2_SHIFT_1 - UTRIE2_SHIFT_2; - /** - * The part of the index-2 table for U+D800..U+DBFF stores values for - * lead surrogate code _units_ not code _points_. - * Values for lead surrogate code _points_ are indexed with this portion of the table. - * Length=32=0x20=0x400>>UTRIE2_SHIFT_2. (There are 1024=0x400 lead surrogates.) - */ - var UTRIE2_LSCP_INDEX_2_OFFSET = 0x10000 >> UTRIE2_SHIFT_2; - /** Number of entries in a data block. 32=0x20 */ - var UTRIE2_DATA_BLOCK_LENGTH = 1 << UTRIE2_SHIFT_2; - /** Mask for getting the lower bits for the in-data-block offset. */ - var UTRIE2_DATA_MASK = UTRIE2_DATA_BLOCK_LENGTH - 1; - var UTRIE2_LSCP_INDEX_2_LENGTH = 0x400 >> UTRIE2_SHIFT_2; - /** Count the lengths of both BMP pieces. 2080=0x820 */ - var UTRIE2_INDEX_2_BMP_LENGTH = UTRIE2_LSCP_INDEX_2_OFFSET + UTRIE2_LSCP_INDEX_2_LENGTH; - /** - * The 2-byte UTF-8 version of the index-2 table follows at offset 2080=0x820. - * Length 32=0x20 for lead bytes C0..DF, regardless of UTRIE2_SHIFT_2. - */ - var UTRIE2_UTF8_2B_INDEX_2_OFFSET = UTRIE2_INDEX_2_BMP_LENGTH; - var UTRIE2_UTF8_2B_INDEX_2_LENGTH = 0x800 >> 6; /* U+0800 is the first code point after 2-byte UTF-8 */ - /** - * The index-1 table, only used for supplementary code points, at offset 2112=0x840. - * Variable length, for code points up to highStart, where the last single-value range starts. - * Maximum length 512=0x200=0x100000>>UTRIE2_SHIFT_1. - * (For 0x100000 supplementary code points U+10000..U+10ffff.) - * - * The part of the index-2 table for supplementary code points starts - * after this index-1 table. - * - * Both the index-1 table and the following part of the index-2 table - * are omitted completely if there is only BMP data. - */ - var UTRIE2_INDEX_1_OFFSET = UTRIE2_UTF8_2B_INDEX_2_OFFSET + UTRIE2_UTF8_2B_INDEX_2_LENGTH; - /** - * Number of index-1 entries for the BMP. 32=0x20 - * This part of the index-1 table is omitted from the serialized form. - */ - var UTRIE2_OMITTED_BMP_INDEX_1_LENGTH = 0x10000 >> UTRIE2_SHIFT_1; - /** Number of entries in an index-2 block. 64=0x40 */ - var UTRIE2_INDEX_2_BLOCK_LENGTH = 1 << UTRIE2_SHIFT_1_2; - /** Mask for getting the lower bits for the in-index-2-block offset. */ - var UTRIE2_INDEX_2_MASK = UTRIE2_INDEX_2_BLOCK_LENGTH - 1; - var slice16 = function (view, start, end) { - if (view.slice) { - return view.slice(start, end); - } - return new Uint16Array(Array.prototype.slice.call(view, start, end)); - }; - var slice32 = function (view, start, end) { - if (view.slice) { - return view.slice(start, end); - } - return new Uint32Array(Array.prototype.slice.call(view, start, end)); - }; - var createTrieFromBase64 = function (base64, _byteLength) { - var buffer = decode(base64); - var view32 = Array.isArray(buffer) ? polyUint32Array(buffer) : new Uint32Array(buffer); - var view16 = Array.isArray(buffer) ? polyUint16Array(buffer) : new Uint16Array(buffer); - var headerLength = 24; - var index = slice16(view16, headerLength / 2, view32[4] / 2); - var data = view32[5] === 2 - ? slice16(view16, (headerLength + view32[4]) / 2) - : slice32(view32, Math.ceil((headerLength + view32[4]) / 4)); - return new Trie(view32[0], view32[1], view32[2], view32[3], index, data); - }; - var Trie = /** @class */ (function () { - function Trie(initialValue, errorValue, highStart, highValueIndex, index, data) { - this.initialValue = initialValue; - this.errorValue = errorValue; - this.highStart = highStart; - this.highValueIndex = highValueIndex; - this.index = index; - this.data = data; - } - /** - * Get the value for a code point as stored in the Trie. - * - * @param codePoint the code point - * @return the value - */ - Trie.prototype.get = function (codePoint) { - var ix; - if (codePoint >= 0) { - if (codePoint < 0x0d800 || (codePoint > 0x0dbff && codePoint <= 0x0ffff)) { - // Ordinary BMP code point, excluding leading surrogates. - // BMP uses a single level lookup. BMP index starts at offset 0 in the Trie2 index. - // 16 bit data is stored in the index array itself. - ix = this.index[codePoint >> UTRIE2_SHIFT_2]; - ix = (ix << UTRIE2_INDEX_SHIFT) + (codePoint & UTRIE2_DATA_MASK); - return this.data[ix]; - } - if (codePoint <= 0xffff) { - // Lead Surrogate Code Point. A Separate index section is stored for - // lead surrogate code units and code points. - // The main index has the code unit data. - // For this function, we need the code point data. - // Note: this expression could be refactored for slightly improved efficiency, but - // surrogate code points will be so rare in practice that it's not worth it. - ix = this.index[UTRIE2_LSCP_INDEX_2_OFFSET + ((codePoint - 0xd800) >> UTRIE2_SHIFT_2)]; - ix = (ix << UTRIE2_INDEX_SHIFT) + (codePoint & UTRIE2_DATA_MASK); - return this.data[ix]; - } - if (codePoint < this.highStart) { - // Supplemental code point, use two-level lookup. - ix = UTRIE2_INDEX_1_OFFSET - UTRIE2_OMITTED_BMP_INDEX_1_LENGTH + (codePoint >> UTRIE2_SHIFT_1); - ix = this.index[ix]; - ix += (codePoint >> UTRIE2_SHIFT_2) & UTRIE2_INDEX_2_MASK; - ix = this.index[ix]; - ix = (ix << UTRIE2_INDEX_SHIFT) + (codePoint & UTRIE2_DATA_MASK); - return this.data[ix]; - } - if (codePoint <= 0x10ffff) { - return this.data[this.highValueIndex]; - } - } - // Fall through. The code point is outside of the legal range of 0..0x10ffff. - return this.errorValue; - }; - return Trie; - }()); - - /* - * base64-arraybuffer 1.0.2 - * Copyright (c) 2022 Niklas von Hertzen - * Released under MIT License - */ - var chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'; - // Use a lookup table to find the index. - var lookup = typeof Uint8Array === 'undefined' ? [] : new Uint8Array(256); - for (var i = 0; i < chars.length; i++) { - lookup[chars.charCodeAt(i)] = i; - } - - var Prepend = 1; - var CR = 2; - var LF = 3; - var Control = 4; - var Extend = 5; - var SpacingMark = 7; - var L = 8; - var V = 9; - var T = 10; - var LV = 11; - var LVT = 12; - var ZWJ = 13; - var Extended_Pictographic = 14; - var RI = 15; - var toCodePoints = function (str) { - var codePoints = []; - var i = 0; - var length = str.length; - while (i < length) { - var value = str.charCodeAt(i++); - if (value >= 0xd800 && value <= 0xdbff && i < length) { - var extra = str.charCodeAt(i++); - if ((extra & 0xfc00) === 0xdc00) { - codePoints.push(((value & 0x3ff) << 10) + (extra & 0x3ff) + 0x10000); - } - else { - codePoints.push(value); - i--; - } - } - else { - codePoints.push(value); - } - } - return codePoints; - }; - var fromCodePoint = function () { - var codePoints = []; - for (var _i = 0; _i < arguments.length; _i++) { - codePoints[_i] = arguments[_i]; - } - if (String.fromCodePoint) { - return String.fromCodePoint.apply(String, codePoints); - } - var length = codePoints.length; - if (!length) { - return ''; - } - var codeUnits = []; - var index = -1; - var result = ''; - while (++index < length) { - var codePoint = codePoints[index]; - if (codePoint <= 0xffff) { - codeUnits.push(codePoint); - } - else { - codePoint -= 0x10000; - codeUnits.push((codePoint >> 10) + 0xd800, (codePoint % 0x400) + 0xdc00); - } - if (index + 1 === length || codeUnits.length > 0x4000) { - result += String.fromCharCode.apply(String, codeUnits); - codeUnits.length = 0; - } - } - return result; - }; - var UnicodeTrie = createTrieFromBase64(base64); - var BREAK_NOT_ALLOWED = '×'; - var BREAK_ALLOWED = '÷'; - var codePointToClass = function (codePoint) { return UnicodeTrie.get(codePoint); }; - var _graphemeBreakAtIndex = function (_codePoints, classTypes, index) { - var prevIndex = index - 2; - var prev = classTypes[prevIndex]; - var current = classTypes[index - 1]; - var next = classTypes[index]; - // GB3 Do not break between a CR and LF - if (current === CR && next === LF) { - return BREAK_NOT_ALLOWED; - } - // GB4 Otherwise, break before and after controls. - if (current === CR || current === LF || current === Control) { - return BREAK_ALLOWED; - } - // GB5 - if (next === CR || next === LF || next === Control) { - return BREAK_ALLOWED; - } - // Do not break Hangul syllable sequences. - // GB6 - if (current === L && [L, V, LV, LVT].indexOf(next) !== -1) { - return BREAK_NOT_ALLOWED; - } - // GB7 - if ((current === LV || current === V) && (next === V || next === T)) { - return BREAK_NOT_ALLOWED; - } - // GB8 - if ((current === LVT || current === T) && next === T) { - return BREAK_NOT_ALLOWED; - } - // GB9 Do not break before extending characters or ZWJ. - if (next === ZWJ || next === Extend) { - return BREAK_NOT_ALLOWED; - } - // Do not break before SpacingMarks, or after Prepend characters. - // GB9a - if (next === SpacingMark) { - return BREAK_NOT_ALLOWED; - } - // GB9a - if (current === Prepend) { - return BREAK_NOT_ALLOWED; - } - // GB11 Do not break within emoji modifier sequences or emoji zwj sequences. - if (current === ZWJ && next === Extended_Pictographic) { - while (prev === Extend) { - prev = classTypes[--prevIndex]; - } - if (prev === Extended_Pictographic) { - return BREAK_NOT_ALLOWED; - } - } - // GB12 Do not break within emoji flag sequences. - // That is, do not break between regional indicator (RI) symbols - // if there is an odd number of RI characters before the break point. - if (current === RI && next === RI) { - var countRI = 0; - while (prev === RI) { - countRI++; - prev = classTypes[--prevIndex]; - } - if (countRI % 2 === 0) { - return BREAK_NOT_ALLOWED; - } - } - return BREAK_ALLOWED; - }; - var GraphemeBreaker = function (str) { - var codePoints = toCodePoints(str); - var length = codePoints.length; - var index = 0; - var lastEnd = 0; - var classTypes = codePoints.map(codePointToClass); - return { - next: function () { - if (index >= length) { - return { done: true, value: null }; - } - var graphemeBreak = BREAK_NOT_ALLOWED; - while (index < length && - (graphemeBreak = _graphemeBreakAtIndex(codePoints, classTypes, ++index)) === BREAK_NOT_ALLOWED) { } - if (graphemeBreak !== BREAK_NOT_ALLOWED || index === length) { - var value = fromCodePoint.apply(null, codePoints.slice(lastEnd, index)); - lastEnd = index; - return { value: value, done: false }; - } - return { done: true, value: null }; - }, - }; - }; - var splitGraphemes = function (str) { - var breaker = GraphemeBreaker(str); - var graphemes = []; - var bk; - while (!(bk = breaker.next()).done) { - if (bk.value) { - graphemes.push(bk.value.slice()); - } - } - return graphemes; - }; - - var testRangeBounds = function (document) { - var TEST_HEIGHT = 123; - if (document.createRange) { - var range = document.createRange(); - if (range.getBoundingClientRect) { - var testElement = document.createElement('boundtest'); - testElement.style.height = TEST_HEIGHT + "px"; - testElement.style.display = 'block'; - document.body.appendChild(testElement); - range.selectNode(testElement); - var rangeBounds = range.getBoundingClientRect(); - var rangeHeight = Math.round(rangeBounds.height); - document.body.removeChild(testElement); - if (rangeHeight === TEST_HEIGHT) { - return true; - } - } - } - return false; - }; - var testIOSLineBreak = function (document) { - var testElement = document.createElement('boundtest'); - testElement.style.width = '50px'; - testElement.style.display = 'block'; - testElement.style.fontSize = '12px'; - testElement.style.letterSpacing = '0px'; - testElement.style.wordSpacing = '0px'; - document.body.appendChild(testElement); - var range = document.createRange(); - testElement.innerHTML = typeof ''.repeat === 'function' ? '👨'.repeat(10) : ''; - var node = testElement.firstChild; - var textList = toCodePoints$1(node.data).map(function (i) { return fromCodePoint$1(i); }); - var offset = 0; - var prev = {}; - // ios 13 does not handle range getBoundingClientRect line changes correctly #2177 - var supports = textList.every(function (text, i) { - range.setStart(node, offset); - range.setEnd(node, offset + text.length); - var rect = range.getBoundingClientRect(); - offset += text.length; - var boundAhead = rect.x > prev.x || rect.y > prev.y; - prev = rect; - if (i === 0) { - return true; - } - return boundAhead; - }); - document.body.removeChild(testElement); - return supports; - }; - var testCORS = function () { return typeof new Image().crossOrigin !== 'undefined'; }; - var testResponseType = function () { return typeof new XMLHttpRequest().responseType === 'string'; }; - var testSVG = function (document) { - var img = new Image(); - var canvas = document.createElement('canvas'); - var ctx = canvas.getContext('2d'); - if (!ctx) { - return false; - } - img.src = "data:image/svg+xml,"; - try { - ctx.drawImage(img, 0, 0); - canvas.toDataURL(); - } - catch (e) { - return false; - } - return true; - }; - var isGreenPixel = function (data) { - return data[0] === 0 && data[1] === 255 && data[2] === 0 && data[3] === 255; - }; - var testForeignObject = function (document) { - var canvas = document.createElement('canvas'); - var size = 100; - canvas.width = size; - canvas.height = size; - var ctx = canvas.getContext('2d'); - if (!ctx) { - return Promise.reject(false); - } - ctx.fillStyle = 'rgb(0, 255, 0)'; - ctx.fillRect(0, 0, size, size); - var img = new Image(); - var greenImageSrc = canvas.toDataURL(); - img.src = greenImageSrc; - var svg = createForeignObjectSVG(size, size, 0, 0, img); - ctx.fillStyle = 'red'; - ctx.fillRect(0, 0, size, size); - return loadSerializedSVG$1(svg) - .then(function (img) { - ctx.drawImage(img, 0, 0); - var data = ctx.getImageData(0, 0, size, size).data; - ctx.fillStyle = 'red'; - ctx.fillRect(0, 0, size, size); - var node = document.createElement('div'); - node.style.backgroundImage = "url(" + greenImageSrc + ")"; - node.style.height = size + "px"; - // Firefox 55 does not render inline tags - return isGreenPixel(data) - ? loadSerializedSVG$1(createForeignObjectSVG(size, size, 0, 0, node)) - : Promise.reject(false); - }) - .then(function (img) { - ctx.drawImage(img, 0, 0); - // Edge does not render background-images - return isGreenPixel(ctx.getImageData(0, 0, size, size).data); - }) - .catch(function () { return false; }); - }; - var createForeignObjectSVG = function (width, height, x, y, node) { - var xmlns = 'http://www.w3.org/2000/svg'; - var svg = document.createElementNS(xmlns, 'svg'); - var foreignObject = document.createElementNS(xmlns, 'foreignObject'); - svg.setAttributeNS(null, 'width', width.toString()); - svg.setAttributeNS(null, 'height', height.toString()); - foreignObject.setAttributeNS(null, 'width', '100%'); - foreignObject.setAttributeNS(null, 'height', '100%'); - foreignObject.setAttributeNS(null, 'x', x.toString()); - foreignObject.setAttributeNS(null, 'y', y.toString()); - foreignObject.setAttributeNS(null, 'externalResourcesRequired', 'true'); - svg.appendChild(foreignObject); - foreignObject.appendChild(node); - return svg; - }; - var loadSerializedSVG$1 = function (svg) { - return new Promise(function (resolve, reject) { - var img = new Image(); - img.onload = function () { return resolve(img); }; - img.onerror = reject; - img.src = "data:image/svg+xml;charset=utf-8," + encodeURIComponent(new XMLSerializer().serializeToString(svg)); - }); - }; - var FEATURES = { - get SUPPORT_RANGE_BOUNDS() { - var value = testRangeBounds(document); - Object.defineProperty(FEATURES, 'SUPPORT_RANGE_BOUNDS', { value: value }); - return value; - }, - get SUPPORT_WORD_BREAKING() { - var value = FEATURES.SUPPORT_RANGE_BOUNDS && testIOSLineBreak(document); - Object.defineProperty(FEATURES, 'SUPPORT_WORD_BREAKING', { value: value }); - return value; - }, - get SUPPORT_SVG_DRAWING() { - var value = testSVG(document); - Object.defineProperty(FEATURES, 'SUPPORT_SVG_DRAWING', { value: value }); - return value; - }, - get SUPPORT_FOREIGNOBJECT_DRAWING() { - var value = typeof Array.from === 'function' && typeof window.fetch === 'function' - ? testForeignObject(document) - : Promise.resolve(false); - Object.defineProperty(FEATURES, 'SUPPORT_FOREIGNOBJECT_DRAWING', { value: value }); - return value; - }, - get SUPPORT_CORS_IMAGES() { - var value = testCORS(); - Object.defineProperty(FEATURES, 'SUPPORT_CORS_IMAGES', { value: value }); - return value; - }, - get SUPPORT_RESPONSE_TYPE() { - var value = testResponseType(); - Object.defineProperty(FEATURES, 'SUPPORT_RESPONSE_TYPE', { value: value }); - return value; - }, - get SUPPORT_CORS_XHR() { - var value = 'withCredentials' in new XMLHttpRequest(); - Object.defineProperty(FEATURES, 'SUPPORT_CORS_XHR', { value: value }); - return value; - }, - get SUPPORT_NATIVE_TEXT_SEGMENTATION() { - // eslint-disable-next-line @typescript-eslint/no-explicit-any - var value = !!(typeof Intl !== 'undefined' && Intl.Segmenter); - Object.defineProperty(FEATURES, 'SUPPORT_NATIVE_TEXT_SEGMENTATION', { value: value }); - return value; - } - }; - - var TextBounds = /** @class */ (function () { - function TextBounds(text, bounds) { - this.text = text; - this.bounds = bounds; - } - return TextBounds; - }()); - var parseTextBounds = function (context, value, styles, node) { - var textList = breakText(value, styles); - var textBounds = []; - var offset = 0; - textList.forEach(function (text) { - if (styles.textDecorationLine.length || text.trim().length > 0) { - if (FEATURES.SUPPORT_RANGE_BOUNDS) { - var clientRects = createRange(node, offset, text.length).getClientRects(); - if (clientRects.length > 1) { - var subSegments = segmentGraphemes(text); - var subOffset_1 = 0; - subSegments.forEach(function (subSegment) { - textBounds.push(new TextBounds(subSegment, Bounds.fromDOMRectList(context, createRange(node, subOffset_1 + offset, subSegment.length).getClientRects()))); - subOffset_1 += subSegment.length; - }); - } - else { - textBounds.push(new TextBounds(text, Bounds.fromDOMRectList(context, clientRects))); - } - } - else { - var replacementNode = node.splitText(text.length); - textBounds.push(new TextBounds(text, getWrapperBounds(context, node))); - node = replacementNode; - } - } - else if (!FEATURES.SUPPORT_RANGE_BOUNDS) { - node = node.splitText(text.length); - } - offset += text.length; - }); - return textBounds; - }; - var getWrapperBounds = function (context, node) { - var ownerDocument = node.ownerDocument; - if (ownerDocument) { - var wrapper = ownerDocument.createElement('html2canvaswrapper'); - wrapper.appendChild(node.cloneNode(true)); - var parentNode = node.parentNode; - if (parentNode) { - parentNode.replaceChild(wrapper, node); - var bounds = parseBounds(context, wrapper); - if (wrapper.firstChild) { - parentNode.replaceChild(wrapper.firstChild, wrapper); - } - return bounds; - } - } - return Bounds.EMPTY; - }; - var createRange = function (node, offset, length) { - var ownerDocument = node.ownerDocument; - if (!ownerDocument) { - throw new Error('Node has no owner document'); - } - var range = ownerDocument.createRange(); - range.setStart(node, offset); - range.setEnd(node, offset + length); - return range; - }; - var segmentGraphemes = function (value) { - if (FEATURES.SUPPORT_NATIVE_TEXT_SEGMENTATION) { - // eslint-disable-next-line @typescript-eslint/no-explicit-any - var segmenter = new Intl.Segmenter(void 0, { granularity: 'grapheme' }); - // eslint-disable-next-line @typescript-eslint/no-explicit-any - return Array.from(segmenter.segment(value)).map(function (segment) { return segment.segment; }); - } - return splitGraphemes(value); - }; - var segmentWords = function (value, styles) { - if (FEATURES.SUPPORT_NATIVE_TEXT_SEGMENTATION) { - // eslint-disable-next-line @typescript-eslint/no-explicit-any - var segmenter = new Intl.Segmenter(void 0, { - granularity: 'word' - }); - // eslint-disable-next-line @typescript-eslint/no-explicit-any - return Array.from(segmenter.segment(value)).map(function (segment) { return segment.segment; }); - } - return breakWords(value, styles); - }; - var breakText = function (value, styles) { - return styles.letterSpacing !== 0 ? segmentGraphemes(value) : segmentWords(value, styles); - }; - // https://drafts.csswg.org/css-text/#word-separator - var wordSeparators = [0x0020, 0x00a0, 0x1361, 0x10100, 0x10101, 0x1039, 0x1091]; - var breakWords = function (str, styles) { - var breaker = LineBreaker(str, { - lineBreak: styles.lineBreak, - wordBreak: styles.overflowWrap === "break-word" /* BREAK_WORD */ ? 'break-word' : styles.wordBreak - }); - var words = []; - var bk; - var _loop_1 = function () { - if (bk.value) { - var value = bk.value.slice(); - var codePoints = toCodePoints$1(value); - var word_1 = ''; - codePoints.forEach(function (codePoint) { - if (wordSeparators.indexOf(codePoint) === -1) { - word_1 += fromCodePoint$1(codePoint); - } - else { - if (word_1.length) { - words.push(word_1); - } - words.push(fromCodePoint$1(codePoint)); - word_1 = ''; - } - }); - if (word_1.length) { - words.push(word_1); - } - } - }; - while (!(bk = breaker.next()).done) { - _loop_1(); - } - return words; - }; - - var TextContainer = /** @class */ (function () { - function TextContainer(context, node, styles) { - this.text = transform(node.data, styles.textTransform); - this.textBounds = parseTextBounds(context, this.text, styles, node); - } - return TextContainer; - }()); - var transform = function (text, transform) { - switch (transform) { - case 1 /* LOWERCASE */: - return text.toLowerCase(); - case 3 /* CAPITALIZE */: - return text.replace(CAPITALIZE, capitalize); - case 2 /* UPPERCASE */: - return text.toUpperCase(); - default: - return text; - } - }; - var CAPITALIZE = /(^|\s|:|-|\(|\))([a-z])/g; - var capitalize = function (m, p1, p2) { - if (m.length > 0) { - return p1 + p2.toUpperCase(); - } - return m; - }; - - var ImageElementContainer = /** @class */ (function (_super) { - __extends(ImageElementContainer, _super); - function ImageElementContainer(context, img) { - var _this = _super.call(this, context, img) || this; - _this.src = img.currentSrc || img.src; - _this.intrinsicWidth = img.naturalWidth; - _this.intrinsicHeight = img.naturalHeight; - _this.context.cache.addImage(_this.src); - return _this; - } - return ImageElementContainer; - }(ElementContainer)); - - var CanvasElementContainer = /** @class */ (function (_super) { - __extends(CanvasElementContainer, _super); - function CanvasElementContainer(context, canvas) { - var _this = _super.call(this, context, canvas) || this; - _this.canvas = canvas; - _this.intrinsicWidth = canvas.width; - _this.intrinsicHeight = canvas.height; - return _this; - } - return CanvasElementContainer; - }(ElementContainer)); - - var SVGElementContainer = /** @class */ (function (_super) { - __extends(SVGElementContainer, _super); - function SVGElementContainer(context, img) { - var _this = _super.call(this, context, img) || this; - var s = new XMLSerializer(); - var bounds = parseBounds(context, img); - img.setAttribute('width', bounds.width + "px"); - img.setAttribute('height', bounds.height + "px"); - _this.svg = "data:image/svg+xml," + encodeURIComponent(s.serializeToString(img)); - _this.intrinsicWidth = img.width.baseVal.value; - _this.intrinsicHeight = img.height.baseVal.value; - _this.context.cache.addImage(_this.svg); - return _this; - } - return SVGElementContainer; - }(ElementContainer)); - - var LIElementContainer = /** @class */ (function (_super) { - __extends(LIElementContainer, _super); - function LIElementContainer(context, element) { - var _this = _super.call(this, context, element) || this; - _this.value = element.value; - return _this; - } - return LIElementContainer; - }(ElementContainer)); - - var OLElementContainer = /** @class */ (function (_super) { - __extends(OLElementContainer, _super); - function OLElementContainer(context, element) { - var _this = _super.call(this, context, element) || this; - _this.start = element.start; - _this.reversed = typeof element.reversed === 'boolean' && element.reversed === true; - return _this; - } - return OLElementContainer; - }(ElementContainer)); - - var CHECKBOX_BORDER_RADIUS = [ - { - type: 15 /* DIMENSION_TOKEN */, - flags: 0, - unit: 'px', - number: 3 - } - ]; - var RADIO_BORDER_RADIUS = [ - { - type: 16 /* PERCENTAGE_TOKEN */, - flags: 0, - number: 50 - } - ]; - var reformatInputBounds = function (bounds) { - if (bounds.width > bounds.height) { - return new Bounds(bounds.left + (bounds.width - bounds.height) / 2, bounds.top, bounds.height, bounds.height); - } - else if (bounds.width < bounds.height) { - return new Bounds(bounds.left, bounds.top + (bounds.height - bounds.width) / 2, bounds.width, bounds.width); - } - return bounds; - }; - var getInputValue = function (node) { - var value = node.type === PASSWORD ? new Array(node.value.length + 1).join('\u2022') : node.value; - return value.length === 0 ? node.placeholder || '' : value; - }; - var CHECKBOX = 'checkbox'; - var RADIO = 'radio'; - var PASSWORD = 'password'; - var INPUT_COLOR = 0x2a2a2aff; - var InputElementContainer = /** @class */ (function (_super) { - __extends(InputElementContainer, _super); - function InputElementContainer(context, input) { - var _this = _super.call(this, context, input) || this; - _this.type = input.type.toLowerCase(); - _this.checked = input.checked; - _this.value = getInputValue(input); - if (_this.type === CHECKBOX || _this.type === RADIO) { - _this.styles.backgroundColor = 0xdededeff; - _this.styles.borderTopColor = - _this.styles.borderRightColor = - _this.styles.borderBottomColor = - _this.styles.borderLeftColor = - 0xa5a5a5ff; - _this.styles.borderTopWidth = - _this.styles.borderRightWidth = - _this.styles.borderBottomWidth = - _this.styles.borderLeftWidth = - 1; - _this.styles.borderTopStyle = - _this.styles.borderRightStyle = - _this.styles.borderBottomStyle = - _this.styles.borderLeftStyle = - 1 /* SOLID */; - _this.styles.backgroundClip = [0 /* BORDER_BOX */]; - _this.styles.backgroundOrigin = [0 /* BORDER_BOX */]; - _this.bounds = reformatInputBounds(_this.bounds); - } - switch (_this.type) { - case CHECKBOX: - _this.styles.borderTopRightRadius = - _this.styles.borderTopLeftRadius = - _this.styles.borderBottomRightRadius = - _this.styles.borderBottomLeftRadius = - CHECKBOX_BORDER_RADIUS; - break; - case RADIO: - _this.styles.borderTopRightRadius = - _this.styles.borderTopLeftRadius = - _this.styles.borderBottomRightRadius = - _this.styles.borderBottomLeftRadius = - RADIO_BORDER_RADIUS; - break; - } - return _this; - } - return InputElementContainer; - }(ElementContainer)); - - var SelectElementContainer = /** @class */ (function (_super) { - __extends(SelectElementContainer, _super); - function SelectElementContainer(context, element) { - var _this = _super.call(this, context, element) || this; - var option = element.options[element.selectedIndex || 0]; - _this.value = option ? option.text || '' : ''; - return _this; - } - return SelectElementContainer; - }(ElementContainer)); - - var TextareaElementContainer = /** @class */ (function (_super) { - __extends(TextareaElementContainer, _super); - function TextareaElementContainer(context, element) { - var _this = _super.call(this, context, element) || this; - _this.value = element.value; - return _this; - } - return TextareaElementContainer; - }(ElementContainer)); - - var IFrameElementContainer = /** @class */ (function (_super) { - __extends(IFrameElementContainer, _super); - function IFrameElementContainer(context, iframe) { - var _this = _super.call(this, context, iframe) || this; - _this.src = iframe.src; - _this.width = parseInt(iframe.width, 10) || 0; - _this.height = parseInt(iframe.height, 10) || 0; - _this.backgroundColor = _this.styles.backgroundColor; - try { - if (iframe.contentWindow && - iframe.contentWindow.document && - iframe.contentWindow.document.documentElement) { - _this.tree = parseTree(context, iframe.contentWindow.document.documentElement); - // http://www.w3.org/TR/css3-background/#special-backgrounds - var documentBackgroundColor = iframe.contentWindow.document.documentElement - ? parseColor(context, getComputedStyle(iframe.contentWindow.document.documentElement).backgroundColor) - : COLORS.TRANSPARENT; - var bodyBackgroundColor = iframe.contentWindow.document.body - ? parseColor(context, getComputedStyle(iframe.contentWindow.document.body).backgroundColor) - : COLORS.TRANSPARENT; - _this.backgroundColor = isTransparent(documentBackgroundColor) - ? isTransparent(bodyBackgroundColor) - ? _this.styles.backgroundColor - : bodyBackgroundColor - : documentBackgroundColor; - } - } - catch (e) { } - return _this; - } - return IFrameElementContainer; - }(ElementContainer)); - - var LIST_OWNERS = ['OL', 'UL', 'MENU']; - var parseNodeTree = function (context, node, parent, root) { - for (var childNode = node.firstChild, nextNode = void 0; childNode; childNode = nextNode) { - nextNode = childNode.nextSibling; - if (isTextNode(childNode) && childNode.data.trim().length > 0) { - parent.textNodes.push(new TextContainer(context, childNode, parent.styles)); - } - else if (isElementNode(childNode)) { - if (isSlotElement(childNode) && childNode.assignedNodes) { - childNode.assignedNodes().forEach(function (childNode) { return parseNodeTree(context, childNode, parent, root); }); - } - else { - var container = createContainer(context, childNode); - if (container.styles.isVisible()) { - if (createsRealStackingContext(childNode, container, root)) { - container.flags |= 4 /* CREATES_REAL_STACKING_CONTEXT */; - } - else if (createsStackingContext(container.styles)) { - container.flags |= 2 /* CREATES_STACKING_CONTEXT */; - } - if (LIST_OWNERS.indexOf(childNode.tagName) !== -1) { - container.flags |= 8 /* IS_LIST_OWNER */; - } - parent.elements.push(container); - childNode.slot; - if (childNode.shadowRoot) { - parseNodeTree(context, childNode.shadowRoot, container, root); - } - else if (!isTextareaElement(childNode) && - !isSVGElement(childNode) && - !isSelectElement(childNode)) { - parseNodeTree(context, childNode, container, root); - } - } - } - } - } - }; - var createContainer = function (context, element) { - if (isImageElement(element)) { - return new ImageElementContainer(context, element); - } - if (isCanvasElement(element)) { - return new CanvasElementContainer(context, element); - } - if (isSVGElement(element)) { - return new SVGElementContainer(context, element); - } - if (isLIElement(element)) { - return new LIElementContainer(context, element); - } - if (isOLElement(element)) { - return new OLElementContainer(context, element); - } - if (isInputElement(element)) { - return new InputElementContainer(context, element); - } - if (isSelectElement(element)) { - return new SelectElementContainer(context, element); - } - if (isTextareaElement(element)) { - return new TextareaElementContainer(context, element); - } - if (isIFrameElement(element)) { - return new IFrameElementContainer(context, element); - } - return new ElementContainer(context, element); - }; - var parseTree = function (context, element) { - var container = createContainer(context, element); - container.flags |= 4 /* CREATES_REAL_STACKING_CONTEXT */; - parseNodeTree(context, element, container, container); - return container; - }; - var createsRealStackingContext = function (node, container, root) { - return (container.styles.isPositionedWithZIndex() || - container.styles.opacity < 1 || - container.styles.isTransformed() || - (isBodyElement(node) && root.styles.isTransparent())); - }; - var createsStackingContext = function (styles) { return styles.isPositioned() || styles.isFloating(); }; - var isTextNode = function (node) { return node.nodeType === Node.TEXT_NODE; }; - var isElementNode = function (node) { return node.nodeType === Node.ELEMENT_NODE; }; - var isHTMLElementNode = function (node) { - return isElementNode(node) && typeof node.style !== 'undefined' && !isSVGElementNode(node); - }; - var isSVGElementNode = function (element) { - return typeof element.className === 'object'; - }; - var isLIElement = function (node) { return node.tagName === 'LI'; }; - var isOLElement = function (node) { return node.tagName === 'OL'; }; - var isInputElement = function (node) { return node.tagName === 'INPUT'; }; - var isHTMLElement = function (node) { return node.tagName === 'HTML'; }; - var isSVGElement = function (node) { return node.tagName === 'svg'; }; - var isBodyElement = function (node) { return node.tagName === 'BODY'; }; - var isCanvasElement = function (node) { return node.tagName === 'CANVAS'; }; - var isVideoElement = function (node) { return node.tagName === 'VIDEO'; }; - var isImageElement = function (node) { return node.tagName === 'IMG'; }; - var isIFrameElement = function (node) { return node.tagName === 'IFRAME'; }; - var isStyleElement = function (node) { return node.tagName === 'STYLE'; }; - var isScriptElement = function (node) { return node.tagName === 'SCRIPT'; }; - var isTextareaElement = function (node) { return node.tagName === 'TEXTAREA'; }; - var isSelectElement = function (node) { return node.tagName === 'SELECT'; }; - var isSlotElement = function (node) { return node.tagName === 'SLOT'; }; - // https://html.spec.whatwg.org/multipage/custom-elements.html#valid-custom-element-name - var isCustomElement = function (node) { return node.tagName.indexOf('-') > 0; }; - - var CounterState = /** @class */ (function () { - function CounterState() { - this.counters = {}; - } - CounterState.prototype.getCounterValue = function (name) { - var counter = this.counters[name]; - if (counter && counter.length) { - return counter[counter.length - 1]; - } - return 1; - }; - CounterState.prototype.getCounterValues = function (name) { - var counter = this.counters[name]; - return counter ? counter : []; - }; - CounterState.prototype.pop = function (counters) { - var _this = this; - counters.forEach(function (counter) { return _this.counters[counter].pop(); }); - }; - CounterState.prototype.parse = function (style) { - var _this = this; - var counterIncrement = style.counterIncrement; - var counterReset = style.counterReset; - var canReset = true; - if (counterIncrement !== null) { - counterIncrement.forEach(function (entry) { - var counter = _this.counters[entry.counter]; - if (counter && entry.increment !== 0) { - canReset = false; - if (!counter.length) { - counter.push(1); - } - counter[Math.max(0, counter.length - 1)] += entry.increment; - } - }); - } - var counterNames = []; - if (canReset) { - counterReset.forEach(function (entry) { - var counter = _this.counters[entry.counter]; - counterNames.push(entry.counter); - if (!counter) { - counter = _this.counters[entry.counter] = []; - } - counter.push(entry.reset); - }); - } - return counterNames; - }; - return CounterState; - }()); - var ROMAN_UPPER = { - integers: [1000, 900, 500, 400, 100, 90, 50, 40, 10, 9, 5, 4, 1], - values: ['M', 'CM', 'D', 'CD', 'C', 'XC', 'L', 'XL', 'X', 'IX', 'V', 'IV', 'I'] - }; - var ARMENIAN = { - integers: [ - 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, - 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 - ], - values: [ - 'Ք', - 'Փ', - 'Ւ', - 'Ց', - 'Ր', - 'Տ', - 'Վ', - 'Ս', - 'Ռ', - 'Ջ', - 'Պ', - 'Չ', - 'Ո', - 'Շ', - 'Ն', - 'Յ', - 'Մ', - 'Ճ', - 'Ղ', - 'Ձ', - 'Հ', - 'Կ', - 'Ծ', - 'Խ', - 'Լ', - 'Ի', - 'Ժ', - 'Թ', - 'Ը', - 'Է', - 'Զ', - 'Ե', - 'Դ', - 'Գ', - 'Բ', - 'Ա' - ] - }; - var HEBREW = { - integers: [ - 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, - 19, 18, 17, 16, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 - ], - values: [ - 'י׳', - 'ט׳', - 'ח׳', - 'ז׳', - 'ו׳', - 'ה׳', - 'ד׳', - 'ג׳', - 'ב׳', - 'א׳', - 'ת', - 'ש', - 'ר', - 'ק', - 'צ', - 'פ', - 'ע', - 'ס', - 'נ', - 'מ', - 'ל', - 'כ', - 'יט', - 'יח', - 'יז', - 'טז', - 'טו', - 'י', - 'ט', - 'ח', - 'ז', - 'ו', - 'ה', - 'ד', - 'ג', - 'ב', - 'א' - ] - }; - var GEORGIAN = { - integers: [ - 10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, - 80, 70, 60, 50, 40, 30, 20, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 - ], - values: [ - 'ჵ', - 'ჰ', - 'ჯ', - 'ჴ', - 'ხ', - 'ჭ', - 'წ', - 'ძ', - 'ც', - 'ჩ', - 'შ', - 'ყ', - 'ღ', - 'ქ', - 'ფ', - 'ჳ', - 'ტ', - 'ს', - 'რ', - 'ჟ', - 'პ', - 'ო', - 'ჲ', - 'ნ', - 'მ', - 'ლ', - 'კ', - 'ი', - 'თ', - 'ჱ', - 'ზ', - 'ვ', - 'ე', - 'დ', - 'გ', - 'ბ', - 'ა' - ] - }; - var createAdditiveCounter = function (value, min, max, symbols, fallback, suffix) { - if (value < min || value > max) { - return createCounterText(value, fallback, suffix.length > 0); - } - return (symbols.integers.reduce(function (string, integer, index) { - while (value >= integer) { - value -= integer; - string += symbols.values[index]; - } - return string; - }, '') + suffix); - }; - var createCounterStyleWithSymbolResolver = function (value, codePointRangeLength, isNumeric, resolver) { - var string = ''; - do { - if (!isNumeric) { - value--; - } - string = resolver(value) + string; - value /= codePointRangeLength; - } while (value * codePointRangeLength >= codePointRangeLength); - return string; - }; - var createCounterStyleFromRange = function (value, codePointRangeStart, codePointRangeEnd, isNumeric, suffix) { - var codePointRangeLength = codePointRangeEnd - codePointRangeStart + 1; - return ((value < 0 ? '-' : '') + - (createCounterStyleWithSymbolResolver(Math.abs(value), codePointRangeLength, isNumeric, function (codePoint) { - return fromCodePoint$1(Math.floor(codePoint % codePointRangeLength) + codePointRangeStart); - }) + - suffix)); - }; - var createCounterStyleFromSymbols = function (value, symbols, suffix) { - if (suffix === void 0) { suffix = '. '; } - var codePointRangeLength = symbols.length; - return (createCounterStyleWithSymbolResolver(Math.abs(value), codePointRangeLength, false, function (codePoint) { return symbols[Math.floor(codePoint % codePointRangeLength)]; }) + suffix); - }; - var CJK_ZEROS = 1 << 0; - var CJK_TEN_COEFFICIENTS = 1 << 1; - var CJK_TEN_HIGH_COEFFICIENTS = 1 << 2; - var CJK_HUNDRED_COEFFICIENTS = 1 << 3; - var createCJKCounter = function (value, numbers, multipliers, negativeSign, suffix, flags) { - if (value < -9999 || value > 9999) { - return createCounterText(value, 4 /* CJK_DECIMAL */, suffix.length > 0); - } - var tmp = Math.abs(value); - var string = suffix; - if (tmp === 0) { - return numbers[0] + string; - } - for (var digit = 0; tmp > 0 && digit <= 4; digit++) { - var coefficient = tmp % 10; - if (coefficient === 0 && contains(flags, CJK_ZEROS) && string !== '') { - string = numbers[coefficient] + string; - } - else if (coefficient > 1 || - (coefficient === 1 && digit === 0) || - (coefficient === 1 && digit === 1 && contains(flags, CJK_TEN_COEFFICIENTS)) || - (coefficient === 1 && digit === 1 && contains(flags, CJK_TEN_HIGH_COEFFICIENTS) && value > 100) || - (coefficient === 1 && digit > 1 && contains(flags, CJK_HUNDRED_COEFFICIENTS))) { - string = numbers[coefficient] + (digit > 0 ? multipliers[digit - 1] : '') + string; - } - else if (coefficient === 1 && digit > 0) { - string = multipliers[digit - 1] + string; - } - tmp = Math.floor(tmp / 10); - } - return (value < 0 ? negativeSign : '') + string; - }; - var CHINESE_INFORMAL_MULTIPLIERS = '十百千萬'; - var CHINESE_FORMAL_MULTIPLIERS = '拾佰仟萬'; - var JAPANESE_NEGATIVE = 'マイナス'; - var KOREAN_NEGATIVE = '마이너스'; - var createCounterText = function (value, type, appendSuffix) { - var defaultSuffix = appendSuffix ? '. ' : ''; - var cjkSuffix = appendSuffix ? '、' : ''; - var koreanSuffix = appendSuffix ? ', ' : ''; - var spaceSuffix = appendSuffix ? ' ' : ''; - switch (type) { - case 0 /* DISC */: - return '•' + spaceSuffix; - case 1 /* CIRCLE */: - return '◦' + spaceSuffix; - case 2 /* SQUARE */: - return '◾' + spaceSuffix; - case 5 /* DECIMAL_LEADING_ZERO */: - var string = createCounterStyleFromRange(value, 48, 57, true, defaultSuffix); - return string.length < 4 ? "0" + string : string; - case 4 /* CJK_DECIMAL */: - return createCounterStyleFromSymbols(value, '〇一二三四五六七八九', cjkSuffix); - case 6 /* LOWER_ROMAN */: - return createAdditiveCounter(value, 1, 3999, ROMAN_UPPER, 3 /* DECIMAL */, defaultSuffix).toLowerCase(); - case 7 /* UPPER_ROMAN */: - return createAdditiveCounter(value, 1, 3999, ROMAN_UPPER, 3 /* DECIMAL */, defaultSuffix); - case 8 /* LOWER_GREEK */: - return createCounterStyleFromRange(value, 945, 969, false, defaultSuffix); - case 9 /* LOWER_ALPHA */: - return createCounterStyleFromRange(value, 97, 122, false, defaultSuffix); - case 10 /* UPPER_ALPHA */: - return createCounterStyleFromRange(value, 65, 90, false, defaultSuffix); - case 11 /* ARABIC_INDIC */: - return createCounterStyleFromRange(value, 1632, 1641, true, defaultSuffix); - case 12 /* ARMENIAN */: - case 49 /* UPPER_ARMENIAN */: - return createAdditiveCounter(value, 1, 9999, ARMENIAN, 3 /* DECIMAL */, defaultSuffix); - case 35 /* LOWER_ARMENIAN */: - return createAdditiveCounter(value, 1, 9999, ARMENIAN, 3 /* DECIMAL */, defaultSuffix).toLowerCase(); - case 13 /* BENGALI */: - return createCounterStyleFromRange(value, 2534, 2543, true, defaultSuffix); - case 14 /* CAMBODIAN */: - case 30 /* KHMER */: - return createCounterStyleFromRange(value, 6112, 6121, true, defaultSuffix); - case 15 /* CJK_EARTHLY_BRANCH */: - return createCounterStyleFromSymbols(value, '子丑寅卯辰巳午未申酉戌亥', cjkSuffix); - case 16 /* CJK_HEAVENLY_STEM */: - return createCounterStyleFromSymbols(value, '甲乙丙丁戊己庚辛壬癸', cjkSuffix); - case 17 /* CJK_IDEOGRAPHIC */: - case 48 /* TRAD_CHINESE_INFORMAL */: - return createCJKCounter(value, '零一二三四五六七八九', CHINESE_INFORMAL_MULTIPLIERS, '負', cjkSuffix, CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS); - case 47 /* TRAD_CHINESE_FORMAL */: - return createCJKCounter(value, '零壹貳參肆伍陸柒捌玖', CHINESE_FORMAL_MULTIPLIERS, '負', cjkSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS); - case 42 /* SIMP_CHINESE_INFORMAL */: - return createCJKCounter(value, '零一二三四五六七八九', CHINESE_INFORMAL_MULTIPLIERS, '负', cjkSuffix, CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS); - case 41 /* SIMP_CHINESE_FORMAL */: - return createCJKCounter(value, '零壹贰叁肆伍陆柒捌玖', CHINESE_FORMAL_MULTIPLIERS, '负', cjkSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS | CJK_HUNDRED_COEFFICIENTS); - case 26 /* JAPANESE_INFORMAL */: - return createCJKCounter(value, '〇一二三四五六七八九', '十百千万', JAPANESE_NEGATIVE, cjkSuffix, 0); - case 25 /* JAPANESE_FORMAL */: - return createCJKCounter(value, '零壱弐参四伍六七八九', '拾百千万', JAPANESE_NEGATIVE, cjkSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS); - case 31 /* KOREAN_HANGUL_FORMAL */: - return createCJKCounter(value, '영일이삼사오육칠팔구', '십백천만', KOREAN_NEGATIVE, koreanSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS); - case 33 /* KOREAN_HANJA_INFORMAL */: - return createCJKCounter(value, '零一二三四五六七八九', '十百千萬', KOREAN_NEGATIVE, koreanSuffix, 0); - case 32 /* KOREAN_HANJA_FORMAL */: - return createCJKCounter(value, '零壹貳參四五六七八九', '拾百千', KOREAN_NEGATIVE, koreanSuffix, CJK_ZEROS | CJK_TEN_COEFFICIENTS | CJK_TEN_HIGH_COEFFICIENTS); - case 18 /* DEVANAGARI */: - return createCounterStyleFromRange(value, 0x966, 0x96f, true, defaultSuffix); - case 20 /* GEORGIAN */: - return createAdditiveCounter(value, 1, 19999, GEORGIAN, 3 /* DECIMAL */, defaultSuffix); - case 21 /* GUJARATI */: - return createCounterStyleFromRange(value, 0xae6, 0xaef, true, defaultSuffix); - case 22 /* GURMUKHI */: - return createCounterStyleFromRange(value, 0xa66, 0xa6f, true, defaultSuffix); - case 22 /* HEBREW */: - return createAdditiveCounter(value, 1, 10999, HEBREW, 3 /* DECIMAL */, defaultSuffix); - case 23 /* HIRAGANA */: - return createCounterStyleFromSymbols(value, 'あいうえおかきくけこさしすせそたちつてとなにぬねのはひふへほまみむめもやゆよらりるれろわゐゑをん'); - case 24 /* HIRAGANA_IROHA */: - return createCounterStyleFromSymbols(value, 'いろはにほへとちりぬるをわかよたれそつねならむうゐのおくやまけふこえてあさきゆめみしゑひもせす'); - case 27 /* KANNADA */: - return createCounterStyleFromRange(value, 0xce6, 0xcef, true, defaultSuffix); - case 28 /* KATAKANA */: - return createCounterStyleFromSymbols(value, 'アイウエオカキクケコサシスセソタチツテトナニヌネノハヒフヘホマミムメモヤユヨラリルレロワヰヱヲン', cjkSuffix); - case 29 /* KATAKANA_IROHA */: - return createCounterStyleFromSymbols(value, 'イロハニホヘトチリヌルヲワカヨタレソツネナラムウヰノオクヤマケフコエテアサキユメミシヱヒモセス', cjkSuffix); - case 34 /* LAO */: - return createCounterStyleFromRange(value, 0xed0, 0xed9, true, defaultSuffix); - case 37 /* MONGOLIAN */: - return createCounterStyleFromRange(value, 0x1810, 0x1819, true, defaultSuffix); - case 38 /* MYANMAR */: - return createCounterStyleFromRange(value, 0x1040, 0x1049, true, defaultSuffix); - case 39 /* ORIYA */: - return createCounterStyleFromRange(value, 0xb66, 0xb6f, true, defaultSuffix); - case 40 /* PERSIAN */: - return createCounterStyleFromRange(value, 0x6f0, 0x6f9, true, defaultSuffix); - case 43 /* TAMIL */: - return createCounterStyleFromRange(value, 0xbe6, 0xbef, true, defaultSuffix); - case 44 /* TELUGU */: - return createCounterStyleFromRange(value, 0xc66, 0xc6f, true, defaultSuffix); - case 45 /* THAI */: - return createCounterStyleFromRange(value, 0xe50, 0xe59, true, defaultSuffix); - case 46 /* TIBETAN */: - return createCounterStyleFromRange(value, 0xf20, 0xf29, true, defaultSuffix); - case 3 /* DECIMAL */: - default: - return createCounterStyleFromRange(value, 48, 57, true, defaultSuffix); - } - }; - - var IGNORE_ATTRIBUTE = 'data-html2canvas-ignore'; - var DocumentCloner = /** @class */ (function () { - function DocumentCloner(context, element, options) { - this.context = context; - this.options = options; - this.scrolledElements = []; - this.referenceElement = element; - this.counters = new CounterState(); - this.quoteDepth = 0; - if (!element.ownerDocument) { - throw new Error('Cloned element does not have an owner document'); - } - this.documentElement = this.cloneNode(element.ownerDocument.documentElement, false); - } - DocumentCloner.prototype.toIFrame = function (ownerDocument, windowSize) { - var _this = this; - var iframe = createIFrameContainer(ownerDocument, windowSize); - if (!iframe.contentWindow) { - return Promise.reject("Unable to find iframe window"); - } - var scrollX = ownerDocument.defaultView.pageXOffset; - var scrollY = ownerDocument.defaultView.pageYOffset; - var cloneWindow = iframe.contentWindow; - var documentClone = cloneWindow.document; - /* Chrome doesn't detect relative background-images assigned in inline """, unsafe_allow_html=True) - - st.title("Text Disection : Analyze Your Text") - # st.caption("Simple NLP tool to analyze your text eg. political speech analysis ") - st.sidebar.header("Adjust Plot Dimensions") - width = st.sidebar.slider("Plot Width", 1, 25, 10) - height = st.sidebar.slider("Plot Height", 1, 25, 5) - - - # Input - st.header(f"Your Text please..") - - text = get_input() - tokenized_sent=sent_tokenize(text) - tokenized_word=word_tokenize(text) - st.markdown(f"###### Total Sentences in the text = {len(tokenized_sent)}") - st.markdown(f"###### Total words in the text = {len(tokenized_word)}") - - - st.sidebar.title("Analysis Type") - analysis = st.sidebar.radio("Select Analysis", - options = ['Wordcloud', 'N-Gram Analysis', 'Sentiment Analysis', 'Named Entity Recognition Analysis', - 'Part Of Speech Analysis', 'Text Complexity Analysis','Keep Calm!'], index=6) - - - # if st.button("Complete Analysis"): - # create_wordcloud(text) - # create_ngram(text) - # create_sentiment(text, tokenized_sent) - # create_ner(text) - # create_pos(text) - # create_complexity(text, tokenized_sent) - # analysis = 'Keep Calm!' - if text =='': - st.text('First Input Text And Then Select Analysis From Sidebar') - st.image('nlp_meme.jpg') - else: - try: - if analysis == 'Wordcloud': - create_wordcloud(text) - if analysis == 'N-Gram Analysis': - create_ngram(text) - if analysis == 'Sentiment Analysis': - create_sentiment(text, tokenized_sent) - if analysis == 'Named Entity Recognition Analysis': - create_ner(text) - if analysis == 'Part Of Speech Analysis': - create_pos(text) - if analysis == 'Text Complexity Analysis': - create_complexity(text, tokenized_sent) - if analysis == 'Keep Calm!': - st.image('nlp_meme.jpg') - except: - st.text('First Input Text And Then Select Analysis From Sidebar') - st.image('nlp_meme.jpg') - - - - - - diff --git a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/layers/shape_spec.py b/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/layers/shape_spec.py deleted file mode 100644 index 8dac3c59b96576710656abebe9b5eac25868abbb..0000000000000000000000000000000000000000 --- a/spaces/nikitaPDL2023/assignment4/detectron2/detectron2/layers/shape_spec.py +++ /dev/null @@ -1,18 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. -from dataclasses import dataclass -from typing import Optional - - -@dataclass -class ShapeSpec: - """ - A simple structure that contains basic shape specification about a tensor. - It is often used as the auxiliary inputs/outputs of models, - to complement the lack of shape inference ability among pytorch modules. - """ - - channels: Optional[int] = None - height: Optional[int] = None - width: Optional[int] = None - stride: Optional[int] = None diff --git a/spaces/njgroene/age-gender-profilepic/app.py b/spaces/njgroene/age-gender-profilepic/app.py deleted file mode 100644 index c93959c900367a72d1c8165c2409eec4065bec04..0000000000000000000000000000000000000000 --- a/spaces/njgroene/age-gender-profilepic/app.py +++ /dev/null @@ -1,114 +0,0 @@ -import gradio as gr - -from PIL import Image -import pandas as pd -import numpy as np - -import torch -import torch.nn as nn -import torchvision -from torchvision import datasets, models, transforms - -from torch_mtcnn import detect_faces -from torch_mtcnn import show_bboxes - -def pipeline(img): - bounding_boxes, landmarks = detect_faces(img) - if len(bounding_boxes) == 0: - raise Exception("Didn't find face any faces, try another image!") - if len(bounding_boxes) > 1: - raise Exception("Found more than one face, try a profile picture with only one person in it!") - - bb = [bounding_boxes[0,0], bounding_boxes[0,1], bounding_boxes[0,2], bounding_boxes[0,3]] - - img_cropped = img.crop(bb) - - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - model_fair_7 = torchvision.models.resnet34(pretrained=True) - model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18) - model_fair_7.load_state_dict(torch.load('res34_fair_align_multi_7_20190809.pt', map_location=torch.device('cpu'))) - model_fair_7 = model_fair_7.to(device) - model_fair_7.eval() - - trans = transforms.Compose([ - transforms.Resize((224, 224)), - transforms.ToTensor(), - transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) - ]) - - face_names = [] - gender_scores_fair = [] - age_scores_fair = [] - gender_preds_fair = [] - age_preds_fair = [] - - image = trans(img_cropped) - image = image.view(1, 3, 224, 224) # reshape image to match model dimensions (1 batch size) - image = image.to(device) - - # fair 7 class - outputs = model_fair_7(image) - outputs = outputs.cpu().detach().numpy() - outputs = np.squeeze(outputs) - - gender_outputs = outputs[7:9] - age_outputs = outputs[9:18] - - gender_score = np.exp(gender_outputs) / np.sum(np.exp(gender_outputs)) - age_score = np.exp(age_outputs) / np.sum(np.exp(age_outputs)) - - gender_pred = np.argmax(gender_score) - age_pred = np.argmax(age_score) - - gender_scores_fair.append(gender_score) - age_scores_fair.append(age_score) - - gender_preds_fair.append(gender_pred) - age_preds_fair.append(age_pred) - - result = pd.DataFrame([gender_preds_fair, - age_preds_fair]).T - - result.columns = ['gender_preds_fair', - 'age_preds_fair'] - # gender - result.loc[result['gender_preds_fair'] == 0, 'gender'] = 'Male' - result.loc[result['gender_preds_fair'] == 1, 'gender'] = 'Female' - - # age - result.loc[result['age_preds_fair'] == 0, 'age'] = '0-2' - result.loc[result['age_preds_fair'] == 1, 'age'] = '3-9' - result.loc[result['age_preds_fair'] == 2, 'age'] = '10-19' - result.loc[result['age_preds_fair'] == 3, 'age'] = '20-29' - result.loc[result['age_preds_fair'] == 4, 'age'] = '30-39' - result.loc[result['age_preds_fair'] == 5, 'age'] = '40-49' - result.loc[result['age_preds_fair'] == 6, 'age'] = '50-59' - result.loc[result['age_preds_fair'] == 7, 'age'] = '60-69' - result.loc[result['age_preds_fair'] == 8, 'age'] = '70+' - - return "A " + result['gender'][0] + " in the age range of " + result['age'][0] - -def predict(image): - try : - predictions = pipeline(image) - except Exception as e: - predictions = e - return predictions - -title = "Estimate age and gender from profile picture." -description = """ -This demo is based on the following work: -*Karkkainen, K., & Joo, J. (2021).* -FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation. -In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 1548-1558). -See: https://github.com/joojs/fairface -""" - -gr.Interface( - predict, - inputs=gr.inputs.Image(label="Upload a profile picture of a single person", type="pil"), - outputs=("text"), - title= title, - description = description, - examples=["ex0.jpg","ex4.jpg", "ex1.jpg","ex2.jpg","ex3.jpg","ex5.jpg","ex6.jpg"] -).launch() \ No newline at end of file diff --git a/spaces/nomic-ai/WizardLM_WizardLM_evol_instruct_70k/style.css b/spaces/nomic-ai/WizardLM_WizardLM_evol_instruct_70k/style.css deleted file mode 100644 index 114adf441e9032febb46bc056b2a8bb651075f0d..0000000000000000000000000000000000000000 --- a/spaces/nomic-ai/WizardLM_WizardLM_evol_instruct_70k/style.css +++ /dev/null @@ -1,28 +0,0 @@ -body { - padding: 2rem; - font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif; -} - -h1 { - font-size: 16px; - margin-top: 0; -} - -p { - color: rgb(107, 114, 128); - font-size: 15px; - margin-bottom: 10px; - margin-top: 5px; -} - -.card { - max-width: 620px; - margin: 0 auto; - padding: 16px; - border: 1px solid lightgray; - border-radius: 16px; -} - -.card p:last-child { - margin-bottom: 0; -} diff --git a/spaces/odettecantswim/rvc-mlbb-v2/lib/infer_pack/modules/F0Predictor/F0Predictor.py b/spaces/odettecantswim/rvc-mlbb-v2/lib/infer_pack/modules/F0Predictor/F0Predictor.py deleted file mode 100644 index f56e49e7f0e6eab3babf0711cae2933371b9f9cc..0000000000000000000000000000000000000000 --- a/spaces/odettecantswim/rvc-mlbb-v2/lib/infer_pack/modules/F0Predictor/F0Predictor.py +++ /dev/null @@ -1,16 +0,0 @@ -class F0Predictor(object): - def compute_f0(self, wav, p_len): - """ - input: wav:[signal_length] - p_len:int - output: f0:[signal_length//hop_length] - """ - pass - - def compute_f0_uv(self, wav, p_len): - """ - input: wav:[signal_length] - p_len:int - output: f0:[signal_length//hop_length],uv:[signal_length//hop_length] - """ - pass diff --git a/spaces/ondrejbiza/isa/invariant_slot_attention/configs/multishapenet_easy/equiv_transl_scale.py b/spaces/ondrejbiza/isa/invariant_slot_attention/configs/multishapenet_easy/equiv_transl_scale.py deleted file mode 100644 index 18cf7c72a79c8685bee8a6074198468ebfc02dfc..0000000000000000000000000000000000000000 --- a/spaces/ondrejbiza/isa/invariant_slot_attention/configs/multishapenet_easy/equiv_transl_scale.py +++ /dev/null @@ -1,205 +0,0 @@ -# coding=utf-8 -# Copyright 2023 The Google Research Authors. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -r"""Config for unsupervised training on MultiShapeNet-Easy.""" - -import ml_collections - - -def get_config(): - """Get the default hyperparameter configuration.""" - config = ml_collections.ConfigDict() - - config.seed = 42 - config.seed_data = True - - config.batch_size = 64 - config.num_train_steps = 500000 # from the original Slot Attention - config.init_checkpoint = ml_collections.ConfigDict() - config.init_checkpoint.xid = 0 # Disabled by default. - config.init_checkpoint.wid = 1 - - config.optimizer_configs = ml_collections.ConfigDict() - config.optimizer_configs.optimizer = "adam" - - config.optimizer_configs.grad_clip = ml_collections.ConfigDict() - config.optimizer_configs.grad_clip.clip_method = "clip_by_global_norm" - config.optimizer_configs.grad_clip.clip_value = 0.05 - - config.lr_configs = ml_collections.ConfigDict() - config.lr_configs.learning_rate_schedule = "compound" - config.lr_configs.factors = "constant * cosine_decay * linear_warmup" - config.lr_configs.warmup_steps = 10000 # from the original Slot Attention - config.lr_configs.steps_per_cycle = config.get_ref("num_train_steps") - # from the original Slot Attention - config.lr_configs.base_learning_rate = 4e-4 - - config.eval_pad_last_batch = False # True - config.log_loss_every_steps = 50 - config.eval_every_steps = 5000 - config.checkpoint_every_steps = 5000 - - config.train_metrics_spec = { - "loss": "loss", - "ari": "ari", - "ari_nobg": "ari_nobg", - } - config.eval_metrics_spec = { - "eval_loss": "loss", - "eval_ari": "ari", - "eval_ari_nobg": "ari_nobg", - } - - config.data = ml_collections.ConfigDict({ - "dataset_name": "multishapenet_easy", - "shuffle_buffer_size": config.batch_size * 8, - "resolution": (128, 128) - }) - - config.max_instances = 11 - config.num_slots = config.max_instances # Only used for metrics. - config.logging_min_n_colors = config.max_instances - - config.preproc_train = [ - "sunds_to_tfds_video", - "video_from_tfds", - "subtract_one_from_segmentations", - "central_crop(height=240, width=240)", - "resize_small({size})".format(size=min(*config.data.resolution)) - ] - - config.preproc_eval = [ - "sunds_to_tfds_video", - "video_from_tfds", - "subtract_one_from_segmentations", - "central_crop(height=240, width=240)", - "resize_small({size})".format(size=min(*config.data.resolution)) - ] - - config.eval_slice_size = 1 - config.eval_slice_keys = ["video", "segmentations_video"] - - # Dictionary of targets and corresponding channels. Losses need to match. - targets = {"video": 3} - config.losses = {"recon": {"targets": list(targets)}} - config.losses = ml_collections.ConfigDict({ - f"recon_{target}": {"loss_type": "recon", "key": target} - for target in targets}) - - config.model = ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.SAVi", - - # Encoder. - "encoder": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.FrameEncoder", - "reduction": "spatial_flatten", - "backbone": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.SimpleCNN", - "features": [64, 64, 64, 64], - "kernel_size": [(5, 5), (5, 5), (5, 5), (5, 5)], - "strides": [(2, 2), (2, 2), (2, 2), (1, 1)] - }), - "pos_emb": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.PositionEmbedding", - "embedding_type": "linear", - "update_type": "concat" - }), - }), - - # Corrector. - "corrector": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.SlotAttentionTranslScaleEquiv", # pylint: disable=line-too-long - "num_iterations": 3, - "qkv_size": 64, - "mlp_size": 128, - "grid_encoder": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.MLP", - "hidden_size": 128, - "layernorm": "pre" - }), - "add_rel_pos_to_values": True, # V3 - "zero_position_init": False, # Random positions. - "init_with_fixed_scale": None, # Random scales. - "scales_factor": 5.0, - }), - - # Predictor. - # Removed since we are running a single frame. - "predictor": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.Identity" - }), - - # Initializer. - "initializer": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.ParamStateInitRandomPositionsScales", # pylint: disable=line-too-long - "shape": (11, 64), # (num_slots, slot_size) - }), - - # Decoder. - "decoder": ml_collections.ConfigDict({ - "module": - "invariant_slot_attention.modules.SiameseSpatialBroadcastDecoder", - "resolution": (16, 16), # Update if data resolution or strides change - "backbone": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.CNN", - "features": [64, 64, 64, 64, 64], - "kernel_size": [(5, 5), (5, 5), (5, 5), (5, 5), (5, 5)], - "strides": [(2, 2), (2, 2), (2, 2), (1, 1), (1, 1)], - "max_pool_strides": [(1, 1), (1, 1), (1, 1), (1, 1), (1, 1)], - "layer_transpose": [True, True, True, False, False] - }), - "target_readout": ml_collections.ConfigDict({ - "module": "invariant_slot_attention.modules.Readout", - "keys": list(targets), - "readout_modules": [ml_collections.ConfigDict({ # pylint: disable=g-complex-comprehension - "module": "invariant_slot_attention.modules.MLP", - "num_hidden_layers": 0, - "hidden_size": 0, - "output_size": targets[k]}) for k in targets], - }), - "relative_positions_and_scales": True, - "pos_emb": ml_collections.ConfigDict({ - "module": - "invariant_slot_attention.modules.RelativePositionEmbedding", - "embedding_type": - "linear", - "update_type": - "project_add", - "scales_factor": - 5.0, - }), - }), - "decode_corrected": True, - "decode_predicted": False, - }) - - # Which video-shaped variables to visualize. - config.debug_var_video_paths = { - "recon_masks": "decoder/alphas_softmaxed/__call__/0", # pylint: disable=line-too-long - } - - # Define which attention matrices to log/visualize. - config.debug_var_attn_paths = { - "corrector_attn": "corrector/InvertedDotProductAttentionKeyPerQuery_0/attn" # pylint: disable=line-too-long - } - - # Widths of attention matrices (for reshaping to image grid). - config.debug_var_attn_widths = { - "corrector_attn": 16, - } - - return config - - diff --git a/spaces/onereal/rvc-models-convertvoice/infer_pack/models_onnx.py b/spaces/onereal/rvc-models-convertvoice/infer_pack/models_onnx.py deleted file mode 100644 index 3c5be53a572151820de7d82dfce84f2e2979ed56..0000000000000000000000000000000000000000 --- a/spaces/onereal/rvc-models-convertvoice/infer_pack/models_onnx.py +++ /dev/null @@ -1,760 +0,0 @@ -import math, pdb, os -from time import time as ttime -import torch -from torch import nn -from torch.nn import functional as F -from infer_pack import modules -from infer_pack import attentions -from infer_pack import commons -from infer_pack.commons import init_weights, get_padding -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm -from infer_pack.commons import init_weights -import numpy as np -from infer_pack import commons - - -class TextEncoder256(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - - m, logs = torch.split(stats, self.out_channels, dim=1) - return m, logs, x_mask - - -class TextEncoder256Sim(nn.Module): - def __init__( - self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - f0=True, - ): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.emb_phone = nn.Linear(256, hidden_channels) - self.lrelu = nn.LeakyReLU(0.1, inplace=True) - if f0 == True: - self.emb_pitch = nn.Embedding(256, hidden_channels) # pitch 256 - self.encoder = attentions.Encoder( - hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout - ) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - - def forward(self, phone, pitch, lengths): - if pitch == None: - x = self.emb_phone(phone) - else: - x = self.emb_phone(phone) + self.emb_pitch(pitch) - x = x * math.sqrt(self.hidden_channels) # [b, t, h] - x = self.lrelu(x) - x = torch.transpose(x, 1, -1) # [b, h, t] - x_mask = torch.unsqueeze(commons.sequence_mask(lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.encoder(x * x_mask, x_mask) - x = self.proj(x) * x_mask - return x, x_mask - - -class ResidualCouplingBlock(nn.Module): - def __init__( - self, - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - n_flows=4, - gin_channels=0, - ): - super().__init__() - self.channels = channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.n_flows = n_flows - self.gin_channels = gin_channels - - self.flows = nn.ModuleList() - for i in range(n_flows): - self.flows.append( - modules.ResidualCouplingLayer( - channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - mean_only=True, - ) - ) - self.flows.append(modules.Flip()) - - def forward(self, x, x_mask, g=None, reverse=False): - if not reverse: - for flow in self.flows: - x, _ = flow(x, x_mask, g=g, reverse=reverse) - else: - for flow in reversed(self.flows): - x = flow(x, x_mask, g=g, reverse=reverse) - return x - - def remove_weight_norm(self): - for i in range(self.n_flows): - self.flows[i * 2].remove_weight_norm() - - -class PosteriorEncoder(nn.Module): - def __init__( - self, - in_channels, - out_channels, - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=0, - ): - super().__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.dilation_rate = dilation_rate - self.n_layers = n_layers - self.gin_channels = gin_channels - - self.pre = nn.Conv1d(in_channels, hidden_channels, 1) - self.enc = modules.WN( - hidden_channels, - kernel_size, - dilation_rate, - n_layers, - gin_channels=gin_channels, - ) - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - - def forward(self, x, x_lengths, g=None): - x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to( - x.dtype - ) - x = self.pre(x) * x_mask - x = self.enc(x, x_mask, g=g) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - def remove_weight_norm(self): - self.enc.remove_weight_norm() - - -class Generator(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=0, - ): - super(Generator, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - def forward(self, x, g=None): - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -class SineGen(torch.nn.Module): - """Definition of sine generator - SineGen(samp_rate, harmonic_num = 0, - sine_amp = 0.1, noise_std = 0.003, - voiced_threshold = 0, - flag_for_pulse=False) - samp_rate: sampling rate in Hz - harmonic_num: number of harmonic overtones (default 0) - sine_amp: amplitude of sine-wavefrom (default 0.1) - noise_std: std of Gaussian noise (default 0.003) - voiced_thoreshold: F0 threshold for U/V classification (default 0) - flag_for_pulse: this SinGen is used inside PulseGen (default False) - Note: when flag_for_pulse is True, the first time step of a voiced - segment is always sin(np.pi) or cos(0) - """ - - def __init__( - self, - samp_rate, - harmonic_num=0, - sine_amp=0.1, - noise_std=0.003, - voiced_threshold=0, - flag_for_pulse=False, - ): - super(SineGen, self).__init__() - self.sine_amp = sine_amp - self.noise_std = noise_std - self.harmonic_num = harmonic_num - self.dim = self.harmonic_num + 1 - self.sampling_rate = samp_rate - self.voiced_threshold = voiced_threshold - - def _f02uv(self, f0): - # generate uv signal - uv = torch.ones_like(f0) - uv = uv * (f0 > self.voiced_threshold) - return uv - - def forward(self, f0, upp): - """sine_tensor, uv = forward(f0) - input F0: tensor(batchsize=1, length, dim=1) - f0 for unvoiced steps should be 0 - output sine_tensor: tensor(batchsize=1, length, dim) - output uv: tensor(batchsize=1, length, 1) - """ - with torch.no_grad(): - f0 = f0[:, None].transpose(1, 2) - f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device) - # fundamental component - f0_buf[:, :, 0] = f0[:, :, 0] - for idx in np.arange(self.harmonic_num): - f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * ( - idx + 2 - ) # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic - rad_values = (f0_buf / self.sampling_rate) % 1 ###%1意味着n_har的乘积无法后处理优化 - rand_ini = torch.rand( - f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device - ) - rand_ini[:, 0] = 0 - rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini - tmp_over_one = torch.cumsum(rad_values, 1) # % 1 #####%1意味着后面的cumsum无法再优化 - tmp_over_one *= upp - tmp_over_one = F.interpolate( - tmp_over_one.transpose(2, 1), - scale_factor=upp, - mode="linear", - align_corners=True, - ).transpose(2, 1) - rad_values = F.interpolate( - rad_values.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose( - 2, 1 - ) ####### - tmp_over_one %= 1 - tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0 - cumsum_shift = torch.zeros_like(rad_values) - cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0 - sine_waves = torch.sin( - torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi - ) - sine_waves = sine_waves * self.sine_amp - uv = self._f02uv(f0) - uv = F.interpolate( - uv.transpose(2, 1), scale_factor=upp, mode="nearest" - ).transpose(2, 1) - noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3 - noise = noise_amp * torch.randn_like(sine_waves) - sine_waves = sine_waves * uv + noise - return sine_waves, uv, noise - - -class SourceModuleHnNSF(torch.nn.Module): - """SourceModule for hn-nsf - SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1, - add_noise_std=0.003, voiced_threshod=0) - sampling_rate: sampling_rate in Hz - harmonic_num: number of harmonic above F0 (default: 0) - sine_amp: amplitude of sine source signal (default: 0.1) - add_noise_std: std of additive Gaussian noise (default: 0.003) - note that amplitude of noise in unvoiced is decided - by sine_amp - voiced_threshold: threhold to set U/V given F0 (default: 0) - Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) - F0_sampled (batchsize, length, 1) - Sine_source (batchsize, length, 1) - noise_source (batchsize, length 1) - uv (batchsize, length, 1) - """ - - def __init__( - self, - sampling_rate, - harmonic_num=0, - sine_amp=0.1, - add_noise_std=0.003, - voiced_threshod=0, - is_half=True, - ): - super(SourceModuleHnNSF, self).__init__() - - self.sine_amp = sine_amp - self.noise_std = add_noise_std - self.is_half = is_half - # to produce sine waveforms - self.l_sin_gen = SineGen( - sampling_rate, harmonic_num, sine_amp, add_noise_std, voiced_threshod - ) - - # to merge source harmonics into a single excitation - self.l_linear = torch.nn.Linear(harmonic_num + 1, 1) - self.l_tanh = torch.nn.Tanh() - - def forward(self, x, upp=None): - sine_wavs, uv, _ = self.l_sin_gen(x, upp) - if self.is_half: - sine_wavs = sine_wavs.half() - sine_merge = self.l_tanh(self.l_linear(sine_wavs)) - return sine_merge, None, None # noise, uv - - -class GeneratorNSF(torch.nn.Module): - def __init__( - self, - initial_channel, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels, - sr, - is_half=False, - ): - super(GeneratorNSF, self).__init__() - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - - self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(upsample_rates)) - self.m_source = SourceModuleHnNSF( - sampling_rate=sr, harmonic_num=0, is_half=is_half - ) - self.noise_convs = nn.ModuleList() - self.conv_pre = Conv1d( - initial_channel, upsample_initial_channel, 7, 1, padding=3 - ) - resblock = modules.ResBlock1 if resblock == "1" else modules.ResBlock2 - - self.ups = nn.ModuleList() - for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - c_cur = upsample_initial_channel // (2 ** (i + 1)) - self.ups.append( - weight_norm( - ConvTranspose1d( - upsample_initial_channel // (2**i), - upsample_initial_channel // (2 ** (i + 1)), - k, - u, - padding=(k - u) // 2, - ) - ) - ) - if i + 1 < len(upsample_rates): - stride_f0 = np.prod(upsample_rates[i + 1 :]) - self.noise_convs.append( - Conv1d( - 1, - c_cur, - kernel_size=stride_f0 * 2, - stride=stride_f0, - padding=stride_f0 // 2, - ) - ) - else: - self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1)) - - self.resblocks = nn.ModuleList() - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for j, (k, d) in enumerate( - zip(resblock_kernel_sizes, resblock_dilation_sizes) - ): - self.resblocks.append(resblock(ch, k, d)) - - self.conv_post = Conv1d(ch, 1, 7, 1, padding=3, bias=False) - self.ups.apply(init_weights) - - if gin_channels != 0: - self.cond = nn.Conv1d(gin_channels, upsample_initial_channel, 1) - - self.upp = np.prod(upsample_rates) - - def forward(self, x, f0, g=None): - har_source, noi_source, uv = self.m_source(f0, self.upp) - har_source = har_source.transpose(1, 2) - x = self.conv_pre(x) - if g is not None: - x = x + self.cond(g) - - for i in range(self.num_upsamples): - x = F.leaky_relu(x, modules.LRELU_SLOPE) - x = self.ups[i](x) - x_source = self.noise_convs[i](har_source) - x = x + x_source - xs = None - for j in range(self.num_kernels): - if xs is None: - xs = self.resblocks[i * self.num_kernels + j](x) - else: - xs += self.resblocks[i * self.num_kernels + j](x) - x = xs / self.num_kernels - x = F.leaky_relu(x) - x = self.conv_post(x) - x = torch.tanh(x) - return x - - def remove_weight_norm(self): - for l in self.ups: - remove_weight_norm(l) - for l in self.resblocks: - l.remove_weight_norm() - - -sr2sr = { - "32k": 32000, - "40k": 40000, - "48k": 48000, -} - - -class SynthesizerTrnMs256NSFsidO(nn.Module): - def __init__( - self, - spec_channels, - segment_size, - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - spk_embed_dim, - gin_channels, - sr, - **kwargs - ): - super().__init__() - if type(sr) == type("strr"): - sr = sr2sr[sr] - self.spec_channels = spec_channels - self.inter_channels = inter_channels - self.hidden_channels = hidden_channels - self.filter_channels = filter_channels - self.n_heads = n_heads - self.n_layers = n_layers - self.kernel_size = kernel_size - self.p_dropout = p_dropout - self.resblock = resblock - self.resblock_kernel_sizes = resblock_kernel_sizes - self.resblock_dilation_sizes = resblock_dilation_sizes - self.upsample_rates = upsample_rates - self.upsample_initial_channel = upsample_initial_channel - self.upsample_kernel_sizes = upsample_kernel_sizes - self.segment_size = segment_size - self.gin_channels = gin_channels - # self.hop_length = hop_length# - self.spk_embed_dim = spk_embed_dim - self.enc_p = TextEncoder256( - inter_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - ) - self.dec = GeneratorNSF( - inter_channels, - resblock, - resblock_kernel_sizes, - resblock_dilation_sizes, - upsample_rates, - upsample_initial_channel, - upsample_kernel_sizes, - gin_channels=gin_channels, - sr=sr, - is_half=kwargs["is_half"], - ) - self.enc_q = PosteriorEncoder( - spec_channels, - inter_channels, - hidden_channels, - 5, - 1, - 16, - gin_channels=gin_channels, - ) - self.flow = ResidualCouplingBlock( - inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels - ) - self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels) - print("gin_channels:", gin_channels, "self.spk_embed_dim:", self.spk_embed_dim) - - def remove_weight_norm(self): - self.dec.remove_weight_norm() - self.flow.remove_weight_norm() - self.enc_q.remove_weight_norm() - - def forward(self, phone, phone_lengths, pitch, nsff0, sid, max_len=None): - g = self.emb_g(sid).unsqueeze(-1) - m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths) - z_p = (m_p + torch.exp(logs_p) * torch.randn_like(m_p) * 0.66666) * x_mask - z = self.flow(z_p, x_mask, g=g, reverse=True) - o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g) - return o - - -class MultiPeriodDiscriminator(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(MultiPeriodDiscriminator, self).__init__() - periods = [2, 3, 5, 7, 11, 17] - # periods = [3, 5, 7, 11, 17, 23, 37] - - discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)] - discs = discs + [ - DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods - ] - self.discriminators = nn.ModuleList(discs) - - def forward(self, y, y_hat): - y_d_rs = [] # - y_d_gs = [] - fmap_rs = [] - fmap_gs = [] - for i, d in enumerate(self.discriminators): - y_d_r, fmap_r = d(y) - y_d_g, fmap_g = d(y_hat) - # for j in range(len(fmap_r)): - # print(i,j,y.shape,y_hat.shape,fmap_r[j].shape,fmap_g[j].shape) - y_d_rs.append(y_d_r) - y_d_gs.append(y_d_g) - fmap_rs.append(fmap_r) - fmap_gs.append(fmap_g) - - return y_d_rs, y_d_gs, fmap_rs, fmap_gs - - -class DiscriminatorS(torch.nn.Module): - def __init__(self, use_spectral_norm=False): - super(DiscriminatorS, self).__init__() - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f(Conv1d(1, 16, 15, 1, padding=7)), - norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)), - norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)), - norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)), - norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)), - norm_f(Conv1d(1024, 1024, 5, 1, padding=2)), - ] - ) - self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1)) - - def forward(self, x): - fmap = [] - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap - - -class DiscriminatorP(torch.nn.Module): - def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False): - super(DiscriminatorP, self).__init__() - self.period = period - self.use_spectral_norm = use_spectral_norm - norm_f = weight_norm if use_spectral_norm == False else spectral_norm - self.convs = nn.ModuleList( - [ - norm_f( - Conv2d( - 1, - 32, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 32, - 128, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 128, - 512, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 512, - 1024, - (kernel_size, 1), - (stride, 1), - padding=(get_padding(kernel_size, 1), 0), - ) - ), - norm_f( - Conv2d( - 1024, - 1024, - (kernel_size, 1), - 1, - padding=(get_padding(kernel_size, 1), 0), - ) - ), - ] - ) - self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0))) - - def forward(self, x): - fmap = [] - - # 1d to 2d - b, c, t = x.shape - if t % self.period != 0: # pad first - n_pad = self.period - (t % self.period) - x = F.pad(x, (0, n_pad), "reflect") - t = t + n_pad - x = x.view(b, c, t // self.period, self.period) - - for l in self.convs: - x = l(x) - x = F.leaky_relu(x, modules.LRELU_SLOPE) - fmap.append(x) - x = self.conv_post(x) - fmap.append(x) - x = torch.flatten(x, 1, -1) - - return x, fmap diff --git a/spaces/oskarvanderwal/MT-bias-demo/simple_translation.md b/spaces/oskarvanderwal/MT-bias-demo/simple_translation.md deleted file mode 100644 index 6a12e4d9d13f8c3662bc82475f635aa9f9f5a997..0000000000000000000000000000000000000000 --- a/spaces/oskarvanderwal/MT-bias-demo/simple_translation.md +++ /dev/null @@ -1,5 +0,0 @@ -Select an occupation (or the word “woman”/“man”) from the dropdown menu and press `Translate & Attribute` to translate a sentence like “He/She is a nurse” from Hungarian: - -> Ő nővér. - -Which pronouns (“she”/“he”) do the MT models go for? Does it change depending on the occupation term you choose? And can we find a difference between the target languages (you can change it in the other dropdown menu on the right)? \ No newline at end of file diff --git a/spaces/ozgur34/qb-Engine2/app.py b/spaces/ozgur34/qb-Engine2/app.py deleted file mode 100644 index d690654f0b59dca232bd6e7ab601506371dd5fde..0000000000000000000000000000000000000000 --- a/spaces/ozgur34/qb-Engine2/app.py +++ /dev/null @@ -1,273 +0,0 @@ -import os - -import gradio as gr -from huggingface_hub import Repository -from text_generation import Client - -# from dialogues import DialogueTemplate -from share_btn import (community_icon_html, loading_icon_html, share_btn_css, - share_js) - -HF_TOKEN = os.environ.get("HF_TOKEN", None) -API_TOKEN = os.environ.get("API_TOKEN", None) -API_URL = os.environ.get("API_URL", None) -API_URL = "https://api-inference.huggingface.co/models/timdettmers/guanaco-33b-merged" - -client = Client( - API_URL, - headers={"Authorization": f"Bearer {API_TOKEN}"}, -) - -repo = None - - -def get_total_inputs(inputs, chatbot, preprompt, user_name, assistant_name, sep): - past = [] - for data in chatbot: - user_data, model_data = data - - if not user_data.startswith(user_name): - user_data = user_name + user_data - if not model_data.startswith(sep + assistant_name): - model_data = sep + assistant_name + model_data - - past.append(user_data + model_data.rstrip() + sep) - - if not inputs.startswith(user_name): - inputs = user_name + inputs - - total_inputs = preprompt + "".join(past) + inputs + sep + assistant_name.rstrip() - - return total_inputs - - -def has_no_history(chatbot, history): - return not chatbot and not history - - -header = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions." -prompt_template = "### Human: {query}\n### Assistant:{response}" - -def generate( - user_message, - chatbot, - history, - temperature, - top_p, - max_new_tokens, - repetition_penalty, -): - # Don't return meaningless message when the input is empty - if not user_message: - print("Empty input") - - history.append(user_message) - - past_messages = [] - for data in chatbot: - user_data, model_data = data - - past_messages.extend( - [{"role": "user", "content": user_data}, {"role": "assistant", "content": model_data.rstrip()}] - ) - - if len(past_messages) < 1: - prompt = header + prompt_template.format(query=user_message, response="") - else: - prompt = header - for i in range(0, len(past_messages), 2): - intermediate_prompt = prompt_template.format(query=past_messages[i]["content"], response=past_messages[i+1]["content"]) - print("intermediate: ", intermediate_prompt) - prompt = prompt + '\n' + intermediate_prompt - - prompt = prompt + prompt_template.format(query=user_message, response="") - - - generate_kwargs = { - "temperature": temperature, - "top_p": top_p, - "max_new_tokens": max_new_tokens, - } - - temperature = float(temperature) - if temperature < 1e-2: - temperature = 1e-2 - top_p = float(top_p) - - generate_kwargs = dict( - temperature=temperature, - max_new_tokens=max_new_tokens, - top_p=top_p, - repetition_penalty=repetition_penalty, - do_sample=True, - truncate=999, - seed=42, - ) - - stream = client.generate_stream( - prompt, - **generate_kwargs, - ) - - output = "" - for idx, response in enumerate(stream): - if response.token.text == '': - break - - if response.token.special: - continue - output += response.token.text - if idx == 0: - history.append(" " + output) - else: - history[-1] = output - - chat = [(history[i].strip(), history[i + 1].strip()) for i in range(0, len(history) - 1, 2)] - - yield chat, history, user_message, "" - - return chat, history, user_message, "" - - -examples = [ - "Create 5 multiple choice questions on ." -] - - -def clear_chat(): - return [], [] - - -def process_example(args): - for [x, y] in generate(args): - pass - return [x, y] - - -title = """

        Quizbot Playground 💬

        """ -custom_css = """ -#banner-image { - display: block; - margin-left: auto; - margin-right: auto; -} -#chat-message { - font-size: 14px; - min-height: 300px; -} -""" - -with gr.Blocks(analytics_enabled=False, css=custom_css) as demo: - gr.HTML(title) - - with gr.Row(): - with gr.Column(): - gr.Markdown( - """ - 💻 This demo showcases 33B model - """ - ) - - with gr.Row(): - with gr.Box(): - output = gr.Markdown() - chatbot = gr.Chatbot(elem_id="chat-message", label="Chat") - - with gr.Row(): - with gr.Column(scale=3): - user_message = gr.Textbox(placeholder="Enter your message here", show_label=False, elem_id="q-input") - with gr.Row(): - send_button = gr.Button("Send", elem_id="send-btn", visible=True, api_name="quizbot") - - clear_chat_button = gr.Button("Clear chat", elem_id="clear-btn", visible=True) - - with gr.Accordion(label="Parameters", open=False, elem_id="parameters-accordion"): - temperature = gr.Slider( - label="Temperature", - value=0.7, - minimum=0.0, - maximum=1.0, - step=0.1, - interactive=True, - info="Higher values produce more diverse outputs", - ) - top_p = gr.Slider( - label="Top-p (nucleus sampling)", - value=0.9, - minimum=0.0, - maximum=1, - step=0.05, - interactive=True, - info="Higher values sample more low-probability tokens", - ) - max_new_tokens = gr.Slider( - label="Max new tokens", - value=1024, - minimum=0, - maximum=2048, - step=4, - interactive=True, - info="The maximum numbers of new tokens", - ) - repetition_penalty = gr.Slider( - label="Repetition Penalty", - value=1.2, - minimum=0.0, - maximum=10, - step=0.1, - interactive=True, - info="The parameter for repetition penalty. 1.0 means no penalty.", - ) - with gr.Row(): - gr.Examples( - examples=examples, - inputs=[user_message], - cache_examples=False, - fn=process_example, - outputs=[output], - ) - - with gr.Row(): - gr.Markdown( - "Disclaimer: The model can produce factually incorrect output, and should not be relied on to produce " - "factually accurate information. The model was trained on various public datasets; while great efforts " - "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " - "biased, or otherwise offensive outputs.", - elem_classes=["disclaimer"], - ) - - - history = gr.State([]) - last_user_message = gr.State("") - - user_message.submit( - generate, - inputs=[ - user_message, - chatbot, - history, - temperature, - top_p, - max_new_tokens, - repetition_penalty, - ], - outputs=[chatbot, history, last_user_message, user_message], - ) - - send_button.click( - generate, - inputs=[ - user_message, - chatbot, - history, - temperature, - top_p, - max_new_tokens, - repetition_penalty, - ], - outputs=[chatbot, history, last_user_message, user_message], - ) - - clear_chat_button.click(clear_chat, outputs=[chatbot, history]) - -demo.queue(concurrency_count=16).launch(debug=True) \ No newline at end of file diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_tensorrt_txt2img.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_tensorrt_txt2img.py deleted file mode 100644 index b51f3176b958263c174e9cbb16d28e1575c8d1fb..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/examples/community/stable_diffusion_tensorrt_txt2img.py +++ /dev/null @@ -1,928 +0,0 @@ -# -# Copyright 2023 The HuggingFace Inc. team. -# SPDX-FileCopyrightText: Copyright (c) 1993-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import gc -import os -from collections import OrderedDict -from copy import copy -from typing import List, Optional, Union - -import numpy as np -import onnx -import onnx_graphsurgeon as gs -import tensorrt as trt -import torch -from huggingface_hub import snapshot_download -from onnx import shape_inference -from polygraphy import cuda -from polygraphy.backend.common import bytes_from_path -from polygraphy.backend.onnx.loader import fold_constants -from polygraphy.backend.trt import ( - CreateConfig, - Profile, - engine_from_bytes, - engine_from_network, - network_from_onnx_path, - save_engine, -) -from polygraphy.backend.trt import util as trt_util -from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer - -from diffusers.models import AutoencoderKL, UNet2DConditionModel -from diffusers.pipelines.stable_diffusion import ( - StableDiffusionPipeline, - StableDiffusionPipelineOutput, - StableDiffusionSafetyChecker, -) -from diffusers.schedulers import DDIMScheduler -from diffusers.utils import DIFFUSERS_CACHE, logging - - -""" -Installation instructions -python3 -m pip install --upgrade transformers diffusers>=0.16.0 -python3 -m pip install --upgrade tensorrt>=8.6.1 -python3 -m pip install --upgrade polygraphy>=0.47.0 onnx-graphsurgeon --extra-index-url https://pypi.ngc.nvidia.com -python3 -m pip install onnxruntime -""" - -TRT_LOGGER = trt.Logger(trt.Logger.ERROR) -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -# Map of numpy dtype -> torch dtype -numpy_to_torch_dtype_dict = { - np.uint8: torch.uint8, - np.int8: torch.int8, - np.int16: torch.int16, - np.int32: torch.int32, - np.int64: torch.int64, - np.float16: torch.float16, - np.float32: torch.float32, - np.float64: torch.float64, - np.complex64: torch.complex64, - np.complex128: torch.complex128, -} -if np.version.full_version >= "1.24.0": - numpy_to_torch_dtype_dict[np.bool_] = torch.bool -else: - numpy_to_torch_dtype_dict[np.bool] = torch.bool - -# Map of torch dtype -> numpy dtype -torch_to_numpy_dtype_dict = {value: key for (key, value) in numpy_to_torch_dtype_dict.items()} - - -def device_view(t): - return cuda.DeviceView(ptr=t.data_ptr(), shape=t.shape, dtype=torch_to_numpy_dtype_dict[t.dtype]) - - -class Engine: - def __init__(self, engine_path): - self.engine_path = engine_path - self.engine = None - self.context = None - self.buffers = OrderedDict() - self.tensors = OrderedDict() - - def __del__(self): - [buf.free() for buf in self.buffers.values() if isinstance(buf, cuda.DeviceArray)] - del self.engine - del self.context - del self.buffers - del self.tensors - - def build( - self, - onnx_path, - fp16, - input_profile=None, - enable_preview=False, - enable_all_tactics=False, - timing_cache=None, - workspace_size=0, - ): - logger.warning(f"Building TensorRT engine for {onnx_path}: {self.engine_path}") - p = Profile() - if input_profile: - for name, dims in input_profile.items(): - assert len(dims) == 3 - p.add(name, min=dims[0], opt=dims[1], max=dims[2]) - - config_kwargs = {} - - config_kwargs["preview_features"] = [trt.PreviewFeature.DISABLE_EXTERNAL_TACTIC_SOURCES_FOR_CORE_0805] - if enable_preview: - # Faster dynamic shapes made optional since it increases engine build time. - config_kwargs["preview_features"].append(trt.PreviewFeature.FASTER_DYNAMIC_SHAPES_0805) - if workspace_size > 0: - config_kwargs["memory_pool_limits"] = {trt.MemoryPoolType.WORKSPACE: workspace_size} - if not enable_all_tactics: - config_kwargs["tactic_sources"] = [] - - engine = engine_from_network( - network_from_onnx_path(onnx_path, flags=[trt.OnnxParserFlag.NATIVE_INSTANCENORM]), - config=CreateConfig(fp16=fp16, profiles=[p], load_timing_cache=timing_cache, **config_kwargs), - save_timing_cache=timing_cache, - ) - save_engine(engine, path=self.engine_path) - - def load(self): - logger.warning(f"Loading TensorRT engine: {self.engine_path}") - self.engine = engine_from_bytes(bytes_from_path(self.engine_path)) - - def activate(self): - self.context = self.engine.create_execution_context() - - def allocate_buffers(self, shape_dict=None, device="cuda"): - for idx in range(trt_util.get_bindings_per_profile(self.engine)): - binding = self.engine[idx] - if shape_dict and binding in shape_dict: - shape = shape_dict[binding] - else: - shape = self.engine.get_binding_shape(binding) - dtype = trt.nptype(self.engine.get_binding_dtype(binding)) - if self.engine.binding_is_input(binding): - self.context.set_binding_shape(idx, shape) - tensor = torch.empty(tuple(shape), dtype=numpy_to_torch_dtype_dict[dtype]).to(device=device) - self.tensors[binding] = tensor - self.buffers[binding] = cuda.DeviceView(ptr=tensor.data_ptr(), shape=shape, dtype=dtype) - - def infer(self, feed_dict, stream): - start_binding, end_binding = trt_util.get_active_profile_bindings(self.context) - # shallow copy of ordered dict - device_buffers = copy(self.buffers) - for name, buf in feed_dict.items(): - assert isinstance(buf, cuda.DeviceView) - device_buffers[name] = buf - bindings = [0] * start_binding + [buf.ptr for buf in device_buffers.values()] - noerror = self.context.execute_async_v2(bindings=bindings, stream_handle=stream.ptr) - if not noerror: - raise ValueError("ERROR: inference failed.") - - return self.tensors - - -class Optimizer: - def __init__(self, onnx_graph): - self.graph = gs.import_onnx(onnx_graph) - - def cleanup(self, return_onnx=False): - self.graph.cleanup().toposort() - if return_onnx: - return gs.export_onnx(self.graph) - - def select_outputs(self, keep, names=None): - self.graph.outputs = [self.graph.outputs[o] for o in keep] - if names: - for i, name in enumerate(names): - self.graph.outputs[i].name = name - - def fold_constants(self, return_onnx=False): - onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True) - self.graph = gs.import_onnx(onnx_graph) - if return_onnx: - return onnx_graph - - def infer_shapes(self, return_onnx=False): - onnx_graph = gs.export_onnx(self.graph) - if onnx_graph.ByteSize() > 2147483648: - raise TypeError("ERROR: model size exceeds supported 2GB limit") - else: - onnx_graph = shape_inference.infer_shapes(onnx_graph) - - self.graph = gs.import_onnx(onnx_graph) - if return_onnx: - return onnx_graph - - -class BaseModel: - def __init__(self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77): - self.model = model - self.name = "SD Model" - self.fp16 = fp16 - self.device = device - - self.min_batch = 1 - self.max_batch = max_batch_size - self.min_image_shape = 256 # min image resolution: 256x256 - self.max_image_shape = 1024 # max image resolution: 1024x1024 - self.min_latent_shape = self.min_image_shape // 8 - self.max_latent_shape = self.max_image_shape // 8 - - self.embedding_dim = embedding_dim - self.text_maxlen = text_maxlen - - def get_model(self): - return self.model - - def get_input_names(self): - pass - - def get_output_names(self): - pass - - def get_dynamic_axes(self): - return None - - def get_sample_input(self, batch_size, image_height, image_width): - pass - - def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape): - return None - - def get_shape_dict(self, batch_size, image_height, image_width): - return None - - def optimize(self, onnx_graph): - opt = Optimizer(onnx_graph) - opt.cleanup() - opt.fold_constants() - opt.infer_shapes() - onnx_opt_graph = opt.cleanup(return_onnx=True) - return onnx_opt_graph - - def check_dims(self, batch_size, image_height, image_width): - assert batch_size >= self.min_batch and batch_size <= self.max_batch - assert image_height % 8 == 0 or image_width % 8 == 0 - latent_height = image_height // 8 - latent_width = image_width // 8 - assert latent_height >= self.min_latent_shape and latent_height <= self.max_latent_shape - assert latent_width >= self.min_latent_shape and latent_width <= self.max_latent_shape - return (latent_height, latent_width) - - def get_minmax_dims(self, batch_size, image_height, image_width, static_batch, static_shape): - min_batch = batch_size if static_batch else self.min_batch - max_batch = batch_size if static_batch else self.max_batch - latent_height = image_height // 8 - latent_width = image_width // 8 - min_image_height = image_height if static_shape else self.min_image_shape - max_image_height = image_height if static_shape else self.max_image_shape - min_image_width = image_width if static_shape else self.min_image_shape - max_image_width = image_width if static_shape else self.max_image_shape - min_latent_height = latent_height if static_shape else self.min_latent_shape - max_latent_height = latent_height if static_shape else self.max_latent_shape - min_latent_width = latent_width if static_shape else self.min_latent_shape - max_latent_width = latent_width if static_shape else self.max_latent_shape - return ( - min_batch, - max_batch, - min_image_height, - max_image_height, - min_image_width, - max_image_width, - min_latent_height, - max_latent_height, - min_latent_width, - max_latent_width, - ) - - -def getOnnxPath(model_name, onnx_dir, opt=True): - return os.path.join(onnx_dir, model_name + (".opt" if opt else "") + ".onnx") - - -def getEnginePath(model_name, engine_dir): - return os.path.join(engine_dir, model_name + ".plan") - - -def build_engines( - models: dict, - engine_dir, - onnx_dir, - onnx_opset, - opt_image_height, - opt_image_width, - opt_batch_size=1, - force_engine_rebuild=False, - static_batch=False, - static_shape=True, - enable_preview=False, - enable_all_tactics=False, - timing_cache=None, - max_workspace_size=0, -): - built_engines = {} - if not os.path.isdir(onnx_dir): - os.makedirs(onnx_dir) - if not os.path.isdir(engine_dir): - os.makedirs(engine_dir) - - # Export models to ONNX - for model_name, model_obj in models.items(): - engine_path = getEnginePath(model_name, engine_dir) - if force_engine_rebuild or not os.path.exists(engine_path): - logger.warning("Building Engines...") - logger.warning("Engine build can take a while to complete") - onnx_path = getOnnxPath(model_name, onnx_dir, opt=False) - onnx_opt_path = getOnnxPath(model_name, onnx_dir) - if force_engine_rebuild or not os.path.exists(onnx_opt_path): - if force_engine_rebuild or not os.path.exists(onnx_path): - logger.warning(f"Exporting model: {onnx_path}") - model = model_obj.get_model() - with torch.inference_mode(), torch.autocast("cuda"): - inputs = model_obj.get_sample_input(opt_batch_size, opt_image_height, opt_image_width) - torch.onnx.export( - model, - inputs, - onnx_path, - export_params=True, - opset_version=onnx_opset, - do_constant_folding=True, - input_names=model_obj.get_input_names(), - output_names=model_obj.get_output_names(), - dynamic_axes=model_obj.get_dynamic_axes(), - ) - del model - torch.cuda.empty_cache() - gc.collect() - else: - logger.warning(f"Found cached model: {onnx_path}") - - # Optimize onnx - if force_engine_rebuild or not os.path.exists(onnx_opt_path): - logger.warning(f"Generating optimizing model: {onnx_opt_path}") - onnx_opt_graph = model_obj.optimize(onnx.load(onnx_path)) - onnx.save(onnx_opt_graph, onnx_opt_path) - else: - logger.warning(f"Found cached optimized model: {onnx_opt_path} ") - - # Build TensorRT engines - for model_name, model_obj in models.items(): - engine_path = getEnginePath(model_name, engine_dir) - engine = Engine(engine_path) - onnx_path = getOnnxPath(model_name, onnx_dir, opt=False) - onnx_opt_path = getOnnxPath(model_name, onnx_dir) - - if force_engine_rebuild or not os.path.exists(engine.engine_path): - engine.build( - onnx_opt_path, - fp16=True, - input_profile=model_obj.get_input_profile( - opt_batch_size, - opt_image_height, - opt_image_width, - static_batch=static_batch, - static_shape=static_shape, - ), - enable_preview=enable_preview, - timing_cache=timing_cache, - workspace_size=max_workspace_size, - ) - built_engines[model_name] = engine - - # Load and activate TensorRT engines - for model_name, model_obj in models.items(): - engine = built_engines[model_name] - engine.load() - engine.activate() - - return built_engines - - -def runEngine(engine, feed_dict, stream): - return engine.infer(feed_dict, stream) - - -class CLIP(BaseModel): - def __init__(self, model, device, max_batch_size, embedding_dim): - super(CLIP, self).__init__( - model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim - ) - self.name = "CLIP" - - def get_input_names(self): - return ["input_ids"] - - def get_output_names(self): - return ["text_embeddings", "pooler_output"] - - def get_dynamic_axes(self): - return {"input_ids": {0: "B"}, "text_embeddings": {0: "B"}} - - def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape): - self.check_dims(batch_size, image_height, image_width) - min_batch, max_batch, _, _, _, _, _, _, _, _ = self.get_minmax_dims( - batch_size, image_height, image_width, static_batch, static_shape - ) - return { - "input_ids": [(min_batch, self.text_maxlen), (batch_size, self.text_maxlen), (max_batch, self.text_maxlen)] - } - - def get_shape_dict(self, batch_size, image_height, image_width): - self.check_dims(batch_size, image_height, image_width) - return { - "input_ids": (batch_size, self.text_maxlen), - "text_embeddings": (batch_size, self.text_maxlen, self.embedding_dim), - } - - def get_sample_input(self, batch_size, image_height, image_width): - self.check_dims(batch_size, image_height, image_width) - return torch.zeros(batch_size, self.text_maxlen, dtype=torch.int32, device=self.device) - - def optimize(self, onnx_graph): - opt = Optimizer(onnx_graph) - opt.select_outputs([0]) # delete graph output#1 - opt.cleanup() - opt.fold_constants() - opt.infer_shapes() - opt.select_outputs([0], names=["text_embeddings"]) # rename network output - opt_onnx_graph = opt.cleanup(return_onnx=True) - return opt_onnx_graph - - -def make_CLIP(model, device, max_batch_size, embedding_dim, inpaint=False): - return CLIP(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim) - - -class UNet(BaseModel): - def __init__( - self, model, fp16=False, device="cuda", max_batch_size=16, embedding_dim=768, text_maxlen=77, unet_dim=4 - ): - super(UNet, self).__init__( - model=model, - fp16=fp16, - device=device, - max_batch_size=max_batch_size, - embedding_dim=embedding_dim, - text_maxlen=text_maxlen, - ) - self.unet_dim = unet_dim - self.name = "UNet" - - def get_input_names(self): - return ["sample", "timestep", "encoder_hidden_states"] - - def get_output_names(self): - return ["latent"] - - def get_dynamic_axes(self): - return { - "sample": {0: "2B", 2: "H", 3: "W"}, - "encoder_hidden_states": {0: "2B"}, - "latent": {0: "2B", 2: "H", 3: "W"}, - } - - def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape): - latent_height, latent_width = self.check_dims(batch_size, image_height, image_width) - ( - min_batch, - max_batch, - _, - _, - _, - _, - min_latent_height, - max_latent_height, - min_latent_width, - max_latent_width, - ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape) - return { - "sample": [ - (2 * min_batch, self.unet_dim, min_latent_height, min_latent_width), - (2 * batch_size, self.unet_dim, latent_height, latent_width), - (2 * max_batch, self.unet_dim, max_latent_height, max_latent_width), - ], - "encoder_hidden_states": [ - (2 * min_batch, self.text_maxlen, self.embedding_dim), - (2 * batch_size, self.text_maxlen, self.embedding_dim), - (2 * max_batch, self.text_maxlen, self.embedding_dim), - ], - } - - def get_shape_dict(self, batch_size, image_height, image_width): - latent_height, latent_width = self.check_dims(batch_size, image_height, image_width) - return { - "sample": (2 * batch_size, self.unet_dim, latent_height, latent_width), - "encoder_hidden_states": (2 * batch_size, self.text_maxlen, self.embedding_dim), - "latent": (2 * batch_size, 4, latent_height, latent_width), - } - - def get_sample_input(self, batch_size, image_height, image_width): - latent_height, latent_width = self.check_dims(batch_size, image_height, image_width) - dtype = torch.float16 if self.fp16 else torch.float32 - return ( - torch.randn( - 2 * batch_size, self.unet_dim, latent_height, latent_width, dtype=torch.float32, device=self.device - ), - torch.tensor([1.0], dtype=torch.float32, device=self.device), - torch.randn(2 * batch_size, self.text_maxlen, self.embedding_dim, dtype=dtype, device=self.device), - ) - - -def make_UNet(model, device, max_batch_size, embedding_dim, inpaint=False): - return UNet( - model, - fp16=True, - device=device, - max_batch_size=max_batch_size, - embedding_dim=embedding_dim, - unet_dim=(9 if inpaint else 4), - ) - - -class VAE(BaseModel): - def __init__(self, model, device, max_batch_size, embedding_dim): - super(VAE, self).__init__( - model=model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim - ) - self.name = "VAE decoder" - - def get_input_names(self): - return ["latent"] - - def get_output_names(self): - return ["images"] - - def get_dynamic_axes(self): - return {"latent": {0: "B", 2: "H", 3: "W"}, "images": {0: "B", 2: "8H", 3: "8W"}} - - def get_input_profile(self, batch_size, image_height, image_width, static_batch, static_shape): - latent_height, latent_width = self.check_dims(batch_size, image_height, image_width) - ( - min_batch, - max_batch, - _, - _, - _, - _, - min_latent_height, - max_latent_height, - min_latent_width, - max_latent_width, - ) = self.get_minmax_dims(batch_size, image_height, image_width, static_batch, static_shape) - return { - "latent": [ - (min_batch, 4, min_latent_height, min_latent_width), - (batch_size, 4, latent_height, latent_width), - (max_batch, 4, max_latent_height, max_latent_width), - ] - } - - def get_shape_dict(self, batch_size, image_height, image_width): - latent_height, latent_width = self.check_dims(batch_size, image_height, image_width) - return { - "latent": (batch_size, 4, latent_height, latent_width), - "images": (batch_size, 3, image_height, image_width), - } - - def get_sample_input(self, batch_size, image_height, image_width): - latent_height, latent_width = self.check_dims(batch_size, image_height, image_width) - return torch.randn(batch_size, 4, latent_height, latent_width, dtype=torch.float32, device=self.device) - - -def make_VAE(model, device, max_batch_size, embedding_dim, inpaint=False): - return VAE(model, device=device, max_batch_size=max_batch_size, embedding_dim=embedding_dim) - - -class TensorRTStableDiffusionPipeline(StableDiffusionPipeline): - r""" - Pipeline for text-to-image generation using TensorRT accelerated Stable Diffusion. - - This model inherits from [`StableDiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`CLIPTextModel`]): - Frozen text-encoder. Stable Diffusion uses the text portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically - the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - safety_checker ([`StableDiffusionSafetyChecker`]): - Classification module that estimates whether generated images could be considered offensive or harmful. - Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details. - feature_extractor ([`CLIPFeatureExtractor`]): - Model that extracts features from generated images to be used as inputs for the `safety_checker`. - """ - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - tokenizer: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: DDIMScheduler, - safety_checker: StableDiffusionSafetyChecker, - feature_extractor: CLIPFeatureExtractor, - requires_safety_checker: bool = True, - stages=["clip", "unet", "vae"], - image_height: int = 768, - image_width: int = 768, - max_batch_size: int = 16, - # ONNX export parameters - onnx_opset: int = 17, - onnx_dir: str = "onnx", - # TensorRT engine build parameters - engine_dir: str = "engine", - build_preview_features: bool = True, - force_engine_rebuild: bool = False, - timing_cache: str = "timing_cache", - ): - super().__init__( - vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker - ) - - self.vae.forward = self.vae.decode - - self.stages = stages - self.image_height, self.image_width = image_height, image_width - self.inpaint = False - self.onnx_opset = onnx_opset - self.onnx_dir = onnx_dir - self.engine_dir = engine_dir - self.force_engine_rebuild = force_engine_rebuild - self.timing_cache = timing_cache - self.build_static_batch = False - self.build_dynamic_shape = False - self.build_preview_features = build_preview_features - - self.max_batch_size = max_batch_size - # TODO: Restrict batch size to 4 for larger image dimensions as a WAR for TensorRT limitation. - if self.build_dynamic_shape or self.image_height > 512 or self.image_width > 512: - self.max_batch_size = 4 - - self.stream = None # loaded in loadResources() - self.models = {} # loaded in __loadModels() - self.engine = {} # loaded in build_engines() - - def __loadModels(self): - # Load pipeline models - self.embedding_dim = self.text_encoder.config.hidden_size - models_args = { - "device": self.torch_device, - "max_batch_size": self.max_batch_size, - "embedding_dim": self.embedding_dim, - "inpaint": self.inpaint, - } - if "clip" in self.stages: - self.models["clip"] = make_CLIP(self.text_encoder, **models_args) - if "unet" in self.stages: - self.models["unet"] = make_UNet(self.unet, **models_args) - if "vae" in self.stages: - self.models["vae"] = make_VAE(self.vae, **models_args) - - @classmethod - def set_cached_folder(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], **kwargs): - cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE) - resume_download = kwargs.pop("resume_download", False) - proxies = kwargs.pop("proxies", None) - local_files_only = kwargs.pop("local_files_only", False) - use_auth_token = kwargs.pop("use_auth_token", None) - revision = kwargs.pop("revision", None) - - cls.cached_folder = ( - pretrained_model_name_or_path - if os.path.isdir(pretrained_model_name_or_path) - else snapshot_download( - pretrained_model_name_or_path, - cache_dir=cache_dir, - resume_download=resume_download, - proxies=proxies, - local_files_only=local_files_only, - use_auth_token=use_auth_token, - revision=revision, - ) - ) - - def to(self, torch_device: Optional[Union[str, torch.device]] = None, silence_dtype_warnings: bool = False): - super().to(torch_device, silence_dtype_warnings=silence_dtype_warnings) - - self.onnx_dir = os.path.join(self.cached_folder, self.onnx_dir) - self.engine_dir = os.path.join(self.cached_folder, self.engine_dir) - self.timing_cache = os.path.join(self.cached_folder, self.timing_cache) - - # set device - self.torch_device = self._execution_device - logger.warning(f"Running inference on device: {self.torch_device}") - - # load models - self.__loadModels() - - # build engines - self.engine = build_engines( - self.models, - self.engine_dir, - self.onnx_dir, - self.onnx_opset, - opt_image_height=self.image_height, - opt_image_width=self.image_width, - force_engine_rebuild=self.force_engine_rebuild, - static_batch=self.build_static_batch, - static_shape=not self.build_dynamic_shape, - enable_preview=self.build_preview_features, - timing_cache=self.timing_cache, - ) - - return self - - def __encode_prompt(self, prompt, negative_prompt): - r""" - Encodes the prompt into text encoder hidden states. - - Args: - prompt (`str` or `List[str]`, *optional*): - prompt to be encoded - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. - Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - """ - # Tokenize prompt - text_input_ids = ( - self.tokenizer( - prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - .input_ids.type(torch.int32) - .to(self.torch_device) - ) - - text_input_ids_inp = device_view(text_input_ids) - # NOTE: output tensor for CLIP must be cloned because it will be overwritten when called again for negative prompt - text_embeddings = runEngine(self.engine["clip"], {"input_ids": text_input_ids_inp}, self.stream)[ - "text_embeddings" - ].clone() - - # Tokenize negative prompt - uncond_input_ids = ( - self.tokenizer( - negative_prompt, - padding="max_length", - max_length=self.tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - .input_ids.type(torch.int32) - .to(self.torch_device) - ) - uncond_input_ids_inp = device_view(uncond_input_ids) - uncond_embeddings = runEngine(self.engine["clip"], {"input_ids": uncond_input_ids_inp}, self.stream)[ - "text_embeddings" - ] - - # Concatenate the unconditional and text embeddings into a single batch to avoid doing two forward passes for classifier free guidance - text_embeddings = torch.cat([uncond_embeddings, text_embeddings]).to(dtype=torch.float16) - - return text_embeddings - - def __denoise_latent( - self, latents, text_embeddings, timesteps=None, step_offset=0, mask=None, masked_image_latents=None - ): - if not isinstance(timesteps, torch.Tensor): - timesteps = self.scheduler.timesteps - for step_index, timestep in enumerate(timesteps): - # Expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat([latents] * 2) - latent_model_input = self.scheduler.scale_model_input(latent_model_input, timestep) - if isinstance(mask, torch.Tensor): - latent_model_input = torch.cat([latent_model_input, mask, masked_image_latents], dim=1) - - # Predict the noise residual - timestep_float = timestep.float() if timestep.dtype != torch.float32 else timestep - - sample_inp = device_view(latent_model_input) - timestep_inp = device_view(timestep_float) - embeddings_inp = device_view(text_embeddings) - noise_pred = runEngine( - self.engine["unet"], - {"sample": sample_inp, "timestep": timestep_inp, "encoder_hidden_states": embeddings_inp}, - self.stream, - )["latent"] - - # Perform guidance - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) - - latents = self.scheduler.step(noise_pred, timestep, latents).prev_sample - - latents = 1.0 / 0.18215 * latents - return latents - - def __decode_latent(self, latents): - images = runEngine(self.engine["vae"], {"latent": device_view(latents)}, self.stream)["images"] - images = (images / 2 + 0.5).clamp(0, 1) - return images.cpu().permute(0, 2, 3, 1).float().numpy() - - def __loadResources(self, image_height, image_width, batch_size): - self.stream = cuda.Stream() - - # Allocate buffers for TensorRT engine bindings - for model_name, obj in self.models.items(): - self.engine[model_name].allocate_buffers( - shape_dict=obj.get_shape_dict(batch_size, image_height, image_width), device=self.torch_device - ) - - @torch.no_grad() - def __call__( - self, - prompt: Union[str, List[str]] = None, - num_inference_steps: int = 50, - guidance_scale: float = 7.5, - negative_prompt: Optional[Union[str, List[str]]] = None, - generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, - ): - r""" - Function invoked when calling the pipeline for generation. - - Args: - prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. - instead. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - guidance_scale (`float`, *optional*, defaults to 7.5): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds`. instead. If not defined, one has to pass `negative_prompt_embeds`. instead. - Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - - """ - self.generator = generator - self.denoising_steps = num_inference_steps - self.guidance_scale = guidance_scale - - # Pre-compute latent input scales and linear multistep coefficients - self.scheduler.set_timesteps(self.denoising_steps, device=self.torch_device) - - # Define call parameters - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - prompt = [prompt] - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - raise ValueError(f"Expected prompt to be of type list or str but got {type(prompt)}") - - if negative_prompt is None: - negative_prompt = [""] * batch_size - - if negative_prompt is not None and isinstance(negative_prompt, str): - negative_prompt = [negative_prompt] - - assert len(prompt) == len(negative_prompt) - - if batch_size > self.max_batch_size: - raise ValueError( - f"Batch size {len(prompt)} is larger than allowed {self.max_batch_size}. If dynamic shape is used, then maximum batch size is 4" - ) - - # load resources - self.__loadResources(self.image_height, self.image_width, batch_size) - - with torch.inference_mode(), torch.autocast("cuda"), trt.Runtime(TRT_LOGGER): - # CLIP text encoder - text_embeddings = self.__encode_prompt(prompt, negative_prompt) - - # Pre-initialize latents - num_channels_latents = self.unet.in_channels - latents = self.prepare_latents( - batch_size, - num_channels_latents, - self.image_height, - self.image_width, - torch.float32, - self.torch_device, - generator, - ) - - # UNet denoiser - latents = self.__denoise_latent(latents, text_embeddings) - - # VAE decode latent - images = self.__decode_latent(latents) - - images, has_nsfw_concept = self.run_safety_checker(images, self.torch_device, text_embeddings.dtype) - images = self.numpy_to_pil(images) - return StableDiffusionPipelineOutput(images=images, nsfw_content_detected=has_nsfw_concept) diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_gligen_to_diffusers.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_gligen_to_diffusers.py deleted file mode 100644 index 816e4c112e6fc342db40a66722641a07412ddc22..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/scripts/convert_gligen_to_diffusers.py +++ /dev/null @@ -1,587 +0,0 @@ -import argparse -import re - -import torch -from transformers import ( - CLIPProcessor, - CLIPTextModel, - CLIPTokenizer, - CLIPVisionModelWithProjection, -) - -from diffusers import ( - AutoencoderKL, - DDIMScheduler, - StableDiffusionGLIGENPipeline, - StableDiffusionGLIGENTextImagePipeline, - UNet2DConditionModel, -) -from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( - assign_to_checkpoint, - conv_attn_to_linear, - protected, - renew_attention_paths, - renew_resnet_paths, - renew_vae_attention_paths, - renew_vae_resnet_paths, - shave_segments, - textenc_conversion_map, - textenc_pattern, -) -from diffusers.utils import is_omegaconf_available -from diffusers.utils.import_utils import BACKENDS_MAPPING - - -def convert_open_clip_checkpoint(checkpoint): - checkpoint = checkpoint["text_encoder"] - text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14") - - keys = list(checkpoint.keys()) - - text_model_dict = {} - - if "cond_stage_model.model.text_projection" in checkpoint: - d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0]) - else: - d_model = 1024 - - for key in keys: - if "resblocks.23" in key: # Diffusers drops the final layer and only uses the penultimate layer - continue - if key in textenc_conversion_map: - text_model_dict[textenc_conversion_map[key]] = checkpoint[key] - # if key.startswith("cond_stage_model.model.transformer."): - new_key = key[len("transformer.") :] - if new_key.endswith(".in_proj_weight"): - new_key = new_key[: -len(".in_proj_weight")] - new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) - text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :] - text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :] - text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :] - elif new_key.endswith(".in_proj_bias"): - new_key = new_key[: -len(".in_proj_bias")] - new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) - text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model] - text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2] - text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :] - else: - if key != "transformer.text_model.embeddings.position_ids": - new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key) - - text_model_dict[new_key] = checkpoint[key] - - if key == "transformer.text_model.embeddings.token_embedding.weight": - text_model_dict["text_model.embeddings.token_embedding.weight"] = checkpoint[key] - - text_model_dict.pop("text_model.embeddings.transformer.text_model.embeddings.token_embedding.weight") - - text_model.load_state_dict(text_model_dict) - - return text_model - - -def convert_gligen_vae_checkpoint(checkpoint, config): - checkpoint = checkpoint["autoencoder"] - vae_state_dict = {} - vae_key = "first_stage_model." - keys = list(checkpoint.keys()) - for key in keys: - vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key) - - new_checkpoint = {} - - new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"] - new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"] - new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"] - new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"] - new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"] - new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"] - - new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"] - new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"] - new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"] - new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"] - new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"] - new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"] - - new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"] - new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"] - new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"] - new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"] - - # Retrieves the keys for the encoder down blocks only - num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer}) - down_blocks = { - layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks) - } - - # Retrieves the keys for the decoder up blocks only - num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer}) - up_blocks = { - layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks) - } - - for i in range(num_down_blocks): - resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key] - - if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict: - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.weight" - ) - new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop( - f"encoder.down.{i}.downsample.conv.bias" - ) - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for i in range(num_up_blocks): - block_id = num_up_blocks - 1 - i - resnets = [ - key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key - ] - - if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict: - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.weight" - ] - new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[ - f"decoder.up.{block_id}.upsample.conv.bias" - ] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key] - num_mid_res_blocks = 2 - for i in range(1, num_mid_res_blocks + 1): - resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key] - - paths = renew_vae_resnet_paths(resnets) - meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - - mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key] - paths = renew_vae_attention_paths(mid_attentions) - meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"} - assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config) - conv_attn_to_linear(new_checkpoint) - - for key in new_checkpoint.keys(): - if "encoder.mid_block.attentions.0" in key or "decoder.mid_block.attentions.0" in key: - if "query" in key: - new_checkpoint[key.replace("query", "to_q")] = new_checkpoint.pop(key) - if "value" in key: - new_checkpoint[key.replace("value", "to_v")] = new_checkpoint.pop(key) - if "key" in key: - new_checkpoint[key.replace("key", "to_k")] = new_checkpoint.pop(key) - if "proj_attn" in key: - new_checkpoint[key.replace("proj_attn", "to_out.0")] = new_checkpoint.pop(key) - - return new_checkpoint - - -def convert_gligen_unet_checkpoint(checkpoint, config, path=None, extract_ema=False): - unet_state_dict = {} - checkpoint = checkpoint["model"] - keys = list(checkpoint.keys()) - - unet_key = "model.diffusion_model." - - if sum(k.startswith("model_ema") for k in keys) > 100 and extract_ema: - print(f"Checkpoint {path} has bot EMA and non-EMA weights.") - print( - "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA" - " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag." - ) - for key in keys: - if key.startswith("model.diffusion_model"): - flat_ema_key = "model_ema." + "".join(key.split(".")[1:]) - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key) - else: - if sum(k.startswith("model_ema") for k in keys) > 100: - print( - "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA" - " weights (usually better for inference), please make sure to add the `--extract_ema` flag." - ) - for key in keys: - unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key) - - new_checkpoint = {} - - new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"] - new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"] - new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"] - new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"] - - new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"] - new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"] - - new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"] - new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"] - new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"] - new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"] - - # Retrieves the keys for the input blocks only - num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer}) - input_blocks = { - layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key] - for layer_id in range(num_input_blocks) - } - - # Retrieves the keys for the middle blocks only - num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer}) - middle_blocks = { - layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key] - for layer_id in range(num_middle_blocks) - } - - # Retrieves the keys for the output blocks only - num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer}) - output_blocks = { - layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key] - for layer_id in range(num_output_blocks) - } - - for i in range(1, num_input_blocks): - block_id = (i - 1) // (config["layers_per_block"] + 1) - layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1) - - resnets = [ - key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key - ] - attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key] - - if f"input_blocks.{i}.0.op.weight" in unet_state_dict: - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.weight" - ) - new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop( - f"input_blocks.{i}.0.op.bias" - ) - - paths = renew_resnet_paths(resnets) - meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - resnet_0 = middle_blocks[0] - attentions = middle_blocks[1] - resnet_1 = middle_blocks[2] - - resnet_0_paths = renew_resnet_paths(resnet_0) - assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config) - - resnet_1_paths = renew_resnet_paths(resnet_1) - assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config) - - attentions_paths = renew_attention_paths(attentions) - meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"} - assign_to_checkpoint( - attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - for i in range(num_output_blocks): - block_id = i // (config["layers_per_block"] + 1) - layer_in_block_id = i % (config["layers_per_block"] + 1) - output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]] - output_block_list = {} - - for layer in output_block_layers: - layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1) - if layer_id in output_block_list: - output_block_list[layer_id].append(layer_name) - else: - output_block_list[layer_id] = [layer_name] - - if len(output_block_list) > 1: - resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key] - attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key] - - resnet_0_paths = renew_resnet_paths(resnets) - paths = renew_resnet_paths(resnets) - - meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"} - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - - output_block_list = {k: sorted(v) for k, v in output_block_list.items()} - if ["conv.bias", "conv.weight"] in output_block_list.values(): - index = list(output_block_list.values()).index(["conv.bias", "conv.weight"]) - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.weight" - ] - new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[ - f"output_blocks.{i}.{index}.conv.bias" - ] - - # Clear attentions as they have been attributed above. - if len(attentions) == 2: - attentions = [] - - if len(attentions): - paths = renew_attention_paths(attentions) - meta_path = { - "old": f"output_blocks.{i}.1", - "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}", - } - assign_to_checkpoint( - paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config - ) - else: - resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1) - for path in resnet_0_paths: - old_path = ".".join(["output_blocks", str(i), path["old"]]) - new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]]) - - new_checkpoint[new_path] = unet_state_dict[old_path] - - for key in keys: - if "position_net" in key: - new_checkpoint[key] = unet_state_dict[key] - - return new_checkpoint - - -def create_vae_config(original_config, image_size: int): - vae_params = original_config.autoencoder.params.ddconfig - _ = original_config.autoencoder.params.embed_dim - - block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult] - down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels) - up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels) - - config = { - "sample_size": image_size, - "in_channels": vae_params.in_channels, - "out_channels": vae_params.out_ch, - "down_block_types": tuple(down_block_types), - "up_block_types": tuple(up_block_types), - "block_out_channels": tuple(block_out_channels), - "latent_channels": vae_params.z_channels, - "layers_per_block": vae_params.num_res_blocks, - } - - return config - - -def create_unet_config(original_config, image_size: int, attention_type): - unet_params = original_config.model.params - vae_params = original_config.autoencoder.params.ddconfig - - block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult] - - down_block_types = [] - resolution = 1 - for i in range(len(block_out_channels)): - block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D" - down_block_types.append(block_type) - if i != len(block_out_channels) - 1: - resolution *= 2 - - up_block_types = [] - for i in range(len(block_out_channels)): - block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D" - up_block_types.append(block_type) - resolution //= 2 - - vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1) - - head_dim = unet_params.num_heads if "num_heads" in unet_params else None - use_linear_projection = ( - unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False - ) - if use_linear_projection: - if head_dim is None: - head_dim = [5, 10, 20, 20] - - config = { - "sample_size": image_size // vae_scale_factor, - "in_channels": unet_params.in_channels, - "down_block_types": tuple(down_block_types), - "block_out_channels": tuple(block_out_channels), - "layers_per_block": unet_params.num_res_blocks, - "cross_attention_dim": unet_params.context_dim, - "attention_head_dim": head_dim, - "use_linear_projection": use_linear_projection, - "attention_type": attention_type, - } - - return config - - -def convert_gligen_to_diffusers( - checkpoint_path: str, - original_config_file: str, - attention_type: str, - image_size: int = 512, - extract_ema: bool = False, - num_in_channels: int = None, - device: str = None, -): - if not is_omegaconf_available(): - raise ValueError(BACKENDS_MAPPING["omegaconf"][1]) - - from omegaconf import OmegaConf - - if device is None: - device = "cuda" if torch.cuda.is_available() else "cpu" - checkpoint = torch.load(checkpoint_path, map_location=device) - else: - checkpoint = torch.load(checkpoint_path, map_location=device) - - if "global_step" in checkpoint: - checkpoint["global_step"] - else: - print("global_step key not found in model") - - original_config = OmegaConf.load(original_config_file) - - if num_in_channels is not None: - original_config["model"]["params"]["in_channels"] = num_in_channels - - num_train_timesteps = original_config.diffusion.params.timesteps - beta_start = original_config.diffusion.params.linear_start - beta_end = original_config.diffusion.params.linear_end - - scheduler = DDIMScheduler( - beta_end=beta_end, - beta_schedule="scaled_linear", - beta_start=beta_start, - num_train_timesteps=num_train_timesteps, - steps_offset=1, - clip_sample=False, - set_alpha_to_one=False, - prediction_type="epsilon", - ) - - # Convert the UNet2DConditionalModel model - unet_config = create_unet_config(original_config, image_size, attention_type) - unet = UNet2DConditionModel(**unet_config) - - converted_unet_checkpoint = convert_gligen_unet_checkpoint( - checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema - ) - - unet.load_state_dict(converted_unet_checkpoint) - - # Convert the VAE model - vae_config = create_vae_config(original_config, image_size) - converted_vae_checkpoint = convert_gligen_vae_checkpoint(checkpoint, vae_config) - - vae = AutoencoderKL(**vae_config) - vae.load_state_dict(converted_vae_checkpoint) - - # Convert the text model - text_encoder = convert_open_clip_checkpoint(checkpoint) - tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14") - - if attention_type == "gated-text-image": - image_encoder = CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14") - processor = CLIPProcessor.from_pretrained("openai/clip-vit-large-patch14") - - pipe = StableDiffusionGLIGENTextImagePipeline( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - image_encoder=image_encoder, - processor=processor, - unet=unet, - scheduler=scheduler, - safety_checker=None, - feature_extractor=None, - ) - elif attention_type == "gated": - pipe = StableDiffusionGLIGENPipeline( - vae=vae, - text_encoder=text_encoder, - tokenizer=tokenizer, - unet=unet, - scheduler=scheduler, - safety_checker=None, - feature_extractor=None, - ) - - return pipe - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - - parser.add_argument( - "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert." - ) - parser.add_argument( - "--original_config_file", - default=None, - type=str, - required=True, - help="The YAML config file corresponding to the gligen architecture.", - ) - parser.add_argument( - "--num_in_channels", - default=None, - type=int, - help="The number of input channels. If `None` number of input channels will be automatically inferred.", - ) - parser.add_argument( - "--extract_ema", - action="store_true", - help=( - "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights" - " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield" - " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning." - ), - ) - parser.add_argument( - "--attention_type", - default=None, - type=str, - required=True, - help="Type of attention ex: gated or gated-text-image", - ) - parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.") - parser.add_argument("--device", type=str, help="Device to use.") - parser.add_argument("--half", action="store_true", help="Save weights in half precision.") - - args = parser.parse_args() - - pipe = convert_gligen_to_diffusers( - checkpoint_path=args.checkpoint_path, - original_config_file=args.original_config_file, - attention_type=args.attention_type, - extract_ema=args.extract_ema, - num_in_channels=args.num_in_channels, - device=args.device, - ) - - if args.half: - pipe.to(torch_dtype=torch.float16) - - pipe.save_pretrained(args.dump_path) diff --git a/spaces/phiyodr/dacl-challenge/README.md b/spaces/phiyodr/dacl-challenge/README.md deleted file mode 100644 index 774c53d65c08f6fbcea9f2b70f6f08d84447bdbd..0000000000000000000000000000000000000000 --- a/spaces/phiyodr/dacl-challenge/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: Dacl Challenge -emoji: 👁 -colorFrom: gray -colorTo: pink -sdk: gradio -sdk_version: 3.44.3 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/pkiage/time_series_decomposition_demo/src/data/utils.py b/spaces/pkiage/time_series_decomposition_demo/src/data/utils.py deleted file mode 100644 index 554cfb1889de4f871ddda02c61791f0a7b0baa7e..0000000000000000000000000000000000000000 --- a/spaces/pkiage/time_series_decomposition_demo/src/data/utils.py +++ /dev/null @@ -1,19 +0,0 @@ -import pandas as pd -data_set_options = [ - 'Airline', - 'Milk production' -] - - -def import_sample_data(sample_data_selected, data_set_options): - if sample_data_selected == data_set_options[0]: - data = pd.read_csv('data/processed/airline.csv', - parse_dates=['Month'], index_col='Month') - - if sample_data_selected == data_set_options[1]: - data = pd.read_csv('data/processed/milk_production.csv', - parse_dates=['date'], index_col='date') - - graph_data = data.reset_index() - graph_data.columns.values[0] = 'Date' - return data, graph_data diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distlib/util.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distlib/util.py deleted file mode 100644 index dd01849d997e5ae9dc9809295e29ceb871b14216..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/distlib/util.py +++ /dev/null @@ -1,1932 +0,0 @@ -# -# Copyright (C) 2012-2021 The Python Software Foundation. -# See LICENSE.txt and CONTRIBUTORS.txt. -# -import codecs -from collections import deque -import contextlib -import csv -from glob import iglob as std_iglob -import io -import json -import logging -import os -import py_compile -import re -import socket -try: - import ssl -except ImportError: # pragma: no cover - ssl = None -import subprocess -import sys -import tarfile -import tempfile -import textwrap - -try: - import threading -except ImportError: # pragma: no cover - import dummy_threading as threading -import time - -from . import DistlibException -from .compat import (string_types, text_type, shutil, raw_input, StringIO, - cache_from_source, urlopen, urljoin, httplib, xmlrpclib, - splittype, HTTPHandler, BaseConfigurator, valid_ident, - Container, configparser, URLError, ZipFile, fsdecode, - unquote, urlparse) - -logger = logging.getLogger(__name__) - -# -# Requirement parsing code as per PEP 508 -# - -IDENTIFIER = re.compile(r'^([\w\.-]+)\s*') -VERSION_IDENTIFIER = re.compile(r'^([\w\.*+-]+)\s*') -COMPARE_OP = re.compile(r'^(<=?|>=?|={2,3}|[~!]=)\s*') -MARKER_OP = re.compile(r'^((<=?)|(>=?)|={2,3}|[~!]=|in|not\s+in)\s*') -OR = re.compile(r'^or\b\s*') -AND = re.compile(r'^and\b\s*') -NON_SPACE = re.compile(r'(\S+)\s*') -STRING_CHUNK = re.compile(r'([\s\w\.{}()*+#:;,/?!~`@$%^&=|<>\[\]-]+)') - - -def parse_marker(marker_string): - """ - Parse a marker string and return a dictionary containing a marker expression. - - The dictionary will contain keys "op", "lhs" and "rhs" for non-terminals in - the expression grammar, or strings. A string contained in quotes is to be - interpreted as a literal string, and a string not contained in quotes is a - variable (such as os_name). - """ - def marker_var(remaining): - # either identifier, or literal string - m = IDENTIFIER.match(remaining) - if m: - result = m.groups()[0] - remaining = remaining[m.end():] - elif not remaining: - raise SyntaxError('unexpected end of input') - else: - q = remaining[0] - if q not in '\'"': - raise SyntaxError('invalid expression: %s' % remaining) - oq = '\'"'.replace(q, '') - remaining = remaining[1:] - parts = [q] - while remaining: - # either a string chunk, or oq, or q to terminate - if remaining[0] == q: - break - elif remaining[0] == oq: - parts.append(oq) - remaining = remaining[1:] - else: - m = STRING_CHUNK.match(remaining) - if not m: - raise SyntaxError('error in string literal: %s' % remaining) - parts.append(m.groups()[0]) - remaining = remaining[m.end():] - else: - s = ''.join(parts) - raise SyntaxError('unterminated string: %s' % s) - parts.append(q) - result = ''.join(parts) - remaining = remaining[1:].lstrip() # skip past closing quote - return result, remaining - - def marker_expr(remaining): - if remaining and remaining[0] == '(': - result, remaining = marker(remaining[1:].lstrip()) - if remaining[0] != ')': - raise SyntaxError('unterminated parenthesis: %s' % remaining) - remaining = remaining[1:].lstrip() - else: - lhs, remaining = marker_var(remaining) - while remaining: - m = MARKER_OP.match(remaining) - if not m: - break - op = m.groups()[0] - remaining = remaining[m.end():] - rhs, remaining = marker_var(remaining) - lhs = {'op': op, 'lhs': lhs, 'rhs': rhs} - result = lhs - return result, remaining - - def marker_and(remaining): - lhs, remaining = marker_expr(remaining) - while remaining: - m = AND.match(remaining) - if not m: - break - remaining = remaining[m.end():] - rhs, remaining = marker_expr(remaining) - lhs = {'op': 'and', 'lhs': lhs, 'rhs': rhs} - return lhs, remaining - - def marker(remaining): - lhs, remaining = marker_and(remaining) - while remaining: - m = OR.match(remaining) - if not m: - break - remaining = remaining[m.end():] - rhs, remaining = marker_and(remaining) - lhs = {'op': 'or', 'lhs': lhs, 'rhs': rhs} - return lhs, remaining - - return marker(marker_string) - - -def parse_requirement(req): - """ - Parse a requirement passed in as a string. Return a Container - whose attributes contain the various parts of the requirement. - """ - remaining = req.strip() - if not remaining or remaining.startswith('#'): - return None - m = IDENTIFIER.match(remaining) - if not m: - raise SyntaxError('name expected: %s' % remaining) - distname = m.groups()[0] - remaining = remaining[m.end():] - extras = mark_expr = versions = uri = None - if remaining and remaining[0] == '[': - i = remaining.find(']', 1) - if i < 0: - raise SyntaxError('unterminated extra: %s' % remaining) - s = remaining[1:i] - remaining = remaining[i + 1:].lstrip() - extras = [] - while s: - m = IDENTIFIER.match(s) - if not m: - raise SyntaxError('malformed extra: %s' % s) - extras.append(m.groups()[0]) - s = s[m.end():] - if not s: - break - if s[0] != ',': - raise SyntaxError('comma expected in extras: %s' % s) - s = s[1:].lstrip() - if not extras: - extras = None - if remaining: - if remaining[0] == '@': - # it's a URI - remaining = remaining[1:].lstrip() - m = NON_SPACE.match(remaining) - if not m: - raise SyntaxError('invalid URI: %s' % remaining) - uri = m.groups()[0] - t = urlparse(uri) - # there are issues with Python and URL parsing, so this test - # is a bit crude. See bpo-20271, bpo-23505. Python doesn't - # always parse invalid URLs correctly - it should raise - # exceptions for malformed URLs - if not (t.scheme and t.netloc): - raise SyntaxError('Invalid URL: %s' % uri) - remaining = remaining[m.end():].lstrip() - else: - - def get_versions(ver_remaining): - """ - Return a list of operator, version tuples if any are - specified, else None. - """ - m = COMPARE_OP.match(ver_remaining) - versions = None - if m: - versions = [] - while True: - op = m.groups()[0] - ver_remaining = ver_remaining[m.end():] - m = VERSION_IDENTIFIER.match(ver_remaining) - if not m: - raise SyntaxError('invalid version: %s' % ver_remaining) - v = m.groups()[0] - versions.append((op, v)) - ver_remaining = ver_remaining[m.end():] - if not ver_remaining or ver_remaining[0] != ',': - break - ver_remaining = ver_remaining[1:].lstrip() - # Some packages have a trailing comma which would break things - # See issue #148 - if not ver_remaining: - break - m = COMPARE_OP.match(ver_remaining) - if not m: - raise SyntaxError('invalid constraint: %s' % ver_remaining) - if not versions: - versions = None - return versions, ver_remaining - - if remaining[0] != '(': - versions, remaining = get_versions(remaining) - else: - i = remaining.find(')', 1) - if i < 0: - raise SyntaxError('unterminated parenthesis: %s' % remaining) - s = remaining[1:i] - remaining = remaining[i + 1:].lstrip() - # As a special diversion from PEP 508, allow a version number - # a.b.c in parentheses as a synonym for ~= a.b.c (because this - # is allowed in earlier PEPs) - if COMPARE_OP.match(s): - versions, _ = get_versions(s) - else: - m = VERSION_IDENTIFIER.match(s) - if not m: - raise SyntaxError('invalid constraint: %s' % s) - v = m.groups()[0] - s = s[m.end():].lstrip() - if s: - raise SyntaxError('invalid constraint: %s' % s) - versions = [('~=', v)] - - if remaining: - if remaining[0] != ';': - raise SyntaxError('invalid requirement: %s' % remaining) - remaining = remaining[1:].lstrip() - - mark_expr, remaining = parse_marker(remaining) - - if remaining and remaining[0] != '#': - raise SyntaxError('unexpected trailing data: %s' % remaining) - - if not versions: - rs = distname - else: - rs = '%s %s' % (distname, ', '.join(['%s %s' % con for con in versions])) - return Container(name=distname, extras=extras, constraints=versions, - marker=mark_expr, url=uri, requirement=rs) - - -def get_resources_dests(resources_root, rules): - """Find destinations for resources files""" - - def get_rel_path(root, path): - # normalizes and returns a lstripped-/-separated path - root = root.replace(os.path.sep, '/') - path = path.replace(os.path.sep, '/') - assert path.startswith(root) - return path[len(root):].lstrip('/') - - destinations = {} - for base, suffix, dest in rules: - prefix = os.path.join(resources_root, base) - for abs_base in iglob(prefix): - abs_glob = os.path.join(abs_base, suffix) - for abs_path in iglob(abs_glob): - resource_file = get_rel_path(resources_root, abs_path) - if dest is None: # remove the entry if it was here - destinations.pop(resource_file, None) - else: - rel_path = get_rel_path(abs_base, abs_path) - rel_dest = dest.replace(os.path.sep, '/').rstrip('/') - destinations[resource_file] = rel_dest + '/' + rel_path - return destinations - - -def in_venv(): - if hasattr(sys, 'real_prefix'): - # virtualenv venvs - result = True - else: - # PEP 405 venvs - result = sys.prefix != getattr(sys, 'base_prefix', sys.prefix) - return result - - -def get_executable(): -# The __PYVENV_LAUNCHER__ dance is apparently no longer needed, as -# changes to the stub launcher mean that sys.executable always points -# to the stub on OS X -# if sys.platform == 'darwin' and ('__PYVENV_LAUNCHER__' -# in os.environ): -# result = os.environ['__PYVENV_LAUNCHER__'] -# else: -# result = sys.executable -# return result - # Avoid normcasing: see issue #143 - # result = os.path.normcase(sys.executable) - result = sys.executable - if not isinstance(result, text_type): - result = fsdecode(result) - return result - - -def proceed(prompt, allowed_chars, error_prompt=None, default=None): - p = prompt - while True: - s = raw_input(p) - p = prompt - if not s and default: - s = default - if s: - c = s[0].lower() - if c in allowed_chars: - break - if error_prompt: - p = '%c: %s\n%s' % (c, error_prompt, prompt) - return c - - -def extract_by_key(d, keys): - if isinstance(keys, string_types): - keys = keys.split() - result = {} - for key in keys: - if key in d: - result[key] = d[key] - return result - -def read_exports(stream): - if sys.version_info[0] >= 3: - # needs to be a text stream - stream = codecs.getreader('utf-8')(stream) - # Try to load as JSON, falling back on legacy format - data = stream.read() - stream = StringIO(data) - try: - jdata = json.load(stream) - result = jdata['extensions']['python.exports']['exports'] - for group, entries in result.items(): - for k, v in entries.items(): - s = '%s = %s' % (k, v) - entry = get_export_entry(s) - assert entry is not None - entries[k] = entry - return result - except Exception: - stream.seek(0, 0) - - def read_stream(cp, stream): - if hasattr(cp, 'read_file'): - cp.read_file(stream) - else: - cp.readfp(stream) - - cp = configparser.ConfigParser() - try: - read_stream(cp, stream) - except configparser.MissingSectionHeaderError: - stream.close() - data = textwrap.dedent(data) - stream = StringIO(data) - read_stream(cp, stream) - - result = {} - for key in cp.sections(): - result[key] = entries = {} - for name, value in cp.items(key): - s = '%s = %s' % (name, value) - entry = get_export_entry(s) - assert entry is not None - #entry.dist = self - entries[name] = entry - return result - - -def write_exports(exports, stream): - if sys.version_info[0] >= 3: - # needs to be a text stream - stream = codecs.getwriter('utf-8')(stream) - cp = configparser.ConfigParser() - for k, v in exports.items(): - # TODO check k, v for valid values - cp.add_section(k) - for entry in v.values(): - if entry.suffix is None: - s = entry.prefix - else: - s = '%s:%s' % (entry.prefix, entry.suffix) - if entry.flags: - s = '%s [%s]' % (s, ', '.join(entry.flags)) - cp.set(k, entry.name, s) - cp.write(stream) - - -@contextlib.contextmanager -def tempdir(): - td = tempfile.mkdtemp() - try: - yield td - finally: - shutil.rmtree(td) - -@contextlib.contextmanager -def chdir(d): - cwd = os.getcwd() - try: - os.chdir(d) - yield - finally: - os.chdir(cwd) - - -@contextlib.contextmanager -def socket_timeout(seconds=15): - cto = socket.getdefaulttimeout() - try: - socket.setdefaulttimeout(seconds) - yield - finally: - socket.setdefaulttimeout(cto) - - -class cached_property(object): - def __init__(self, func): - self.func = func - #for attr in ('__name__', '__module__', '__doc__'): - # setattr(self, attr, getattr(func, attr, None)) - - def __get__(self, obj, cls=None): - if obj is None: - return self - value = self.func(obj) - object.__setattr__(obj, self.func.__name__, value) - #obj.__dict__[self.func.__name__] = value = self.func(obj) - return value - -def convert_path(pathname): - """Return 'pathname' as a name that will work on the native filesystem. - - The path is split on '/' and put back together again using the current - directory separator. Needed because filenames in the setup script are - always supplied in Unix style, and have to be converted to the local - convention before we can actually use them in the filesystem. Raises - ValueError on non-Unix-ish systems if 'pathname' either starts or - ends with a slash. - """ - if os.sep == '/': - return pathname - if not pathname: - return pathname - if pathname[0] == '/': - raise ValueError("path '%s' cannot be absolute" % pathname) - if pathname[-1] == '/': - raise ValueError("path '%s' cannot end with '/'" % pathname) - - paths = pathname.split('/') - while os.curdir in paths: - paths.remove(os.curdir) - if not paths: - return os.curdir - return os.path.join(*paths) - - -class FileOperator(object): - def __init__(self, dry_run=False): - self.dry_run = dry_run - self.ensured = set() - self._init_record() - - def _init_record(self): - self.record = False - self.files_written = set() - self.dirs_created = set() - - def record_as_written(self, path): - if self.record: - self.files_written.add(path) - - def newer(self, source, target): - """Tell if the target is newer than the source. - - Returns true if 'source' exists and is more recently modified than - 'target', or if 'source' exists and 'target' doesn't. - - Returns false if both exist and 'target' is the same age or younger - than 'source'. Raise PackagingFileError if 'source' does not exist. - - Note that this test is not very accurate: files created in the same - second will have the same "age". - """ - if not os.path.exists(source): - raise DistlibException("file '%r' does not exist" % - os.path.abspath(source)) - if not os.path.exists(target): - return True - - return os.stat(source).st_mtime > os.stat(target).st_mtime - - def copy_file(self, infile, outfile, check=True): - """Copy a file respecting dry-run and force flags. - """ - self.ensure_dir(os.path.dirname(outfile)) - logger.info('Copying %s to %s', infile, outfile) - if not self.dry_run: - msg = None - if check: - if os.path.islink(outfile): - msg = '%s is a symlink' % outfile - elif os.path.exists(outfile) and not os.path.isfile(outfile): - msg = '%s is a non-regular file' % outfile - if msg: - raise ValueError(msg + ' which would be overwritten') - shutil.copyfile(infile, outfile) - self.record_as_written(outfile) - - def copy_stream(self, instream, outfile, encoding=None): - assert not os.path.isdir(outfile) - self.ensure_dir(os.path.dirname(outfile)) - logger.info('Copying stream %s to %s', instream, outfile) - if not self.dry_run: - if encoding is None: - outstream = open(outfile, 'wb') - else: - outstream = codecs.open(outfile, 'w', encoding=encoding) - try: - shutil.copyfileobj(instream, outstream) - finally: - outstream.close() - self.record_as_written(outfile) - - def write_binary_file(self, path, data): - self.ensure_dir(os.path.dirname(path)) - if not self.dry_run: - if os.path.exists(path): - os.remove(path) - with open(path, 'wb') as f: - f.write(data) - self.record_as_written(path) - - def write_text_file(self, path, data, encoding): - self.write_binary_file(path, data.encode(encoding)) - - def set_mode(self, bits, mask, files): - if os.name == 'posix' or (os.name == 'java' and os._name == 'posix'): - # Set the executable bits (owner, group, and world) on - # all the files specified. - for f in files: - if self.dry_run: - logger.info("changing mode of %s", f) - else: - mode = (os.stat(f).st_mode | bits) & mask - logger.info("changing mode of %s to %o", f, mode) - os.chmod(f, mode) - - set_executable_mode = lambda s, f: s.set_mode(0o555, 0o7777, f) - - def ensure_dir(self, path): - path = os.path.abspath(path) - if path not in self.ensured and not os.path.exists(path): - self.ensured.add(path) - d, f = os.path.split(path) - self.ensure_dir(d) - logger.info('Creating %s' % path) - if not self.dry_run: - os.mkdir(path) - if self.record: - self.dirs_created.add(path) - - def byte_compile(self, path, optimize=False, force=False, prefix=None, hashed_invalidation=False): - dpath = cache_from_source(path, not optimize) - logger.info('Byte-compiling %s to %s', path, dpath) - if not self.dry_run: - if force or self.newer(path, dpath): - if not prefix: - diagpath = None - else: - assert path.startswith(prefix) - diagpath = path[len(prefix):] - compile_kwargs = {} - if hashed_invalidation and hasattr(py_compile, 'PycInvalidationMode'): - compile_kwargs['invalidation_mode'] = py_compile.PycInvalidationMode.CHECKED_HASH - py_compile.compile(path, dpath, diagpath, True, **compile_kwargs) # raise error - self.record_as_written(dpath) - return dpath - - def ensure_removed(self, path): - if os.path.exists(path): - if os.path.isdir(path) and not os.path.islink(path): - logger.debug('Removing directory tree at %s', path) - if not self.dry_run: - shutil.rmtree(path) - if self.record: - if path in self.dirs_created: - self.dirs_created.remove(path) - else: - if os.path.islink(path): - s = 'link' - else: - s = 'file' - logger.debug('Removing %s %s', s, path) - if not self.dry_run: - os.remove(path) - if self.record: - if path in self.files_written: - self.files_written.remove(path) - - def is_writable(self, path): - result = False - while not result: - if os.path.exists(path): - result = os.access(path, os.W_OK) - break - parent = os.path.dirname(path) - if parent == path: - break - path = parent - return result - - def commit(self): - """ - Commit recorded changes, turn off recording, return - changes. - """ - assert self.record - result = self.files_written, self.dirs_created - self._init_record() - return result - - def rollback(self): - if not self.dry_run: - for f in list(self.files_written): - if os.path.exists(f): - os.remove(f) - # dirs should all be empty now, except perhaps for - # __pycache__ subdirs - # reverse so that subdirs appear before their parents - dirs = sorted(self.dirs_created, reverse=True) - for d in dirs: - flist = os.listdir(d) - if flist: - assert flist == ['__pycache__'] - sd = os.path.join(d, flist[0]) - os.rmdir(sd) - os.rmdir(d) # should fail if non-empty - self._init_record() - -def resolve(module_name, dotted_path): - if module_name in sys.modules: - mod = sys.modules[module_name] - else: - mod = __import__(module_name) - if dotted_path is None: - result = mod - else: - parts = dotted_path.split('.') - result = getattr(mod, parts.pop(0)) - for p in parts: - result = getattr(result, p) - return result - - -class ExportEntry(object): - def __init__(self, name, prefix, suffix, flags): - self.name = name - self.prefix = prefix - self.suffix = suffix - self.flags = flags - - @cached_property - def value(self): - return resolve(self.prefix, self.suffix) - - def __repr__(self): # pragma: no cover - return '' % (self.name, self.prefix, - self.suffix, self.flags) - - def __eq__(self, other): - if not isinstance(other, ExportEntry): - result = False - else: - result = (self.name == other.name and - self.prefix == other.prefix and - self.suffix == other.suffix and - self.flags == other.flags) - return result - - __hash__ = object.__hash__ - - -ENTRY_RE = re.compile(r'''(?P(\w|[-.+])+) - \s*=\s*(?P(\w+)([:\.]\w+)*) - \s*(\[\s*(?P[\w-]+(=\w+)?(,\s*\w+(=\w+)?)*)\s*\])? - ''', re.VERBOSE) - -def get_export_entry(specification): - m = ENTRY_RE.search(specification) - if not m: - result = None - if '[' in specification or ']' in specification: - raise DistlibException("Invalid specification " - "'%s'" % specification) - else: - d = m.groupdict() - name = d['name'] - path = d['callable'] - colons = path.count(':') - if colons == 0: - prefix, suffix = path, None - else: - if colons != 1: - raise DistlibException("Invalid specification " - "'%s'" % specification) - prefix, suffix = path.split(':') - flags = d['flags'] - if flags is None: - if '[' in specification or ']' in specification: - raise DistlibException("Invalid specification " - "'%s'" % specification) - flags = [] - else: - flags = [f.strip() for f in flags.split(',')] - result = ExportEntry(name, prefix, suffix, flags) - return result - - -def get_cache_base(suffix=None): - """ - Return the default base location for distlib caches. If the directory does - not exist, it is created. Use the suffix provided for the base directory, - and default to '.distlib' if it isn't provided. - - On Windows, if LOCALAPPDATA is defined in the environment, then it is - assumed to be a directory, and will be the parent directory of the result. - On POSIX, and on Windows if LOCALAPPDATA is not defined, the user's home - directory - using os.expanduser('~') - will be the parent directory of - the result. - - The result is just the directory '.distlib' in the parent directory as - determined above, or with the name specified with ``suffix``. - """ - if suffix is None: - suffix = '.distlib' - if os.name == 'nt' and 'LOCALAPPDATA' in os.environ: - result = os.path.expandvars('$localappdata') - else: - # Assume posix, or old Windows - result = os.path.expanduser('~') - # we use 'isdir' instead of 'exists', because we want to - # fail if there's a file with that name - if os.path.isdir(result): - usable = os.access(result, os.W_OK) - if not usable: - logger.warning('Directory exists but is not writable: %s', result) - else: - try: - os.makedirs(result) - usable = True - except OSError: - logger.warning('Unable to create %s', result, exc_info=True) - usable = False - if not usable: - result = tempfile.mkdtemp() - logger.warning('Default location unusable, using %s', result) - return os.path.join(result, suffix) - - -def path_to_cache_dir(path): - """ - Convert an absolute path to a directory name for use in a cache. - - The algorithm used is: - - #. On Windows, any ``':'`` in the drive is replaced with ``'---'``. - #. Any occurrence of ``os.sep`` is replaced with ``'--'``. - #. ``'.cache'`` is appended. - """ - d, p = os.path.splitdrive(os.path.abspath(path)) - if d: - d = d.replace(':', '---') - p = p.replace(os.sep, '--') - return d + p + '.cache' - - -def ensure_slash(s): - if not s.endswith('/'): - return s + '/' - return s - - -def parse_credentials(netloc): - username = password = None - if '@' in netloc: - prefix, netloc = netloc.rsplit('@', 1) - if ':' not in prefix: - username = prefix - else: - username, password = prefix.split(':', 1) - if username: - username = unquote(username) - if password: - password = unquote(password) - return username, password, netloc - - -def get_process_umask(): - result = os.umask(0o22) - os.umask(result) - return result - -def is_string_sequence(seq): - result = True - i = None - for i, s in enumerate(seq): - if not isinstance(s, string_types): - result = False - break - assert i is not None - return result - -PROJECT_NAME_AND_VERSION = re.compile('([a-z0-9_]+([.-][a-z_][a-z0-9_]*)*)-' - '([a-z0-9_.+-]+)', re.I) -PYTHON_VERSION = re.compile(r'-py(\d\.?\d?)') - - -def split_filename(filename, project_name=None): - """ - Extract name, version, python version from a filename (no extension) - - Return name, version, pyver or None - """ - result = None - pyver = None - filename = unquote(filename).replace(' ', '-') - m = PYTHON_VERSION.search(filename) - if m: - pyver = m.group(1) - filename = filename[:m.start()] - if project_name and len(filename) > len(project_name) + 1: - m = re.match(re.escape(project_name) + r'\b', filename) - if m: - n = m.end() - result = filename[:n], filename[n + 1:], pyver - if result is None: - m = PROJECT_NAME_AND_VERSION.match(filename) - if m: - result = m.group(1), m.group(3), pyver - return result - -# Allow spaces in name because of legacy dists like "Twisted Core" -NAME_VERSION_RE = re.compile(r'(?P[\w .-]+)\s*' - r'\(\s*(?P[^\s)]+)\)$') - -def parse_name_and_version(p): - """ - A utility method used to get name and version from a string. - - From e.g. a Provides-Dist value. - - :param p: A value in a form 'foo (1.0)' - :return: The name and version as a tuple. - """ - m = NAME_VERSION_RE.match(p) - if not m: - raise DistlibException('Ill-formed name/version string: \'%s\'' % p) - d = m.groupdict() - return d['name'].strip().lower(), d['ver'] - -def get_extras(requested, available): - result = set() - requested = set(requested or []) - available = set(available or []) - if '*' in requested: - requested.remove('*') - result |= available - for r in requested: - if r == '-': - result.add(r) - elif r.startswith('-'): - unwanted = r[1:] - if unwanted not in available: - logger.warning('undeclared extra: %s' % unwanted) - if unwanted in result: - result.remove(unwanted) - else: - if r not in available: - logger.warning('undeclared extra: %s' % r) - result.add(r) - return result -# -# Extended metadata functionality -# - -def _get_external_data(url): - result = {} - try: - # urlopen might fail if it runs into redirections, - # because of Python issue #13696. Fixed in locators - # using a custom redirect handler. - resp = urlopen(url) - headers = resp.info() - ct = headers.get('Content-Type') - if not ct.startswith('application/json'): - logger.debug('Unexpected response for JSON request: %s', ct) - else: - reader = codecs.getreader('utf-8')(resp) - #data = reader.read().decode('utf-8') - #result = json.loads(data) - result = json.load(reader) - except Exception as e: - logger.exception('Failed to get external data for %s: %s', url, e) - return result - -_external_data_base_url = 'https://www.red-dove.com/pypi/projects/' - -def get_project_data(name): - url = '%s/%s/project.json' % (name[0].upper(), name) - url = urljoin(_external_data_base_url, url) - result = _get_external_data(url) - return result - -def get_package_data(name, version): - url = '%s/%s/package-%s.json' % (name[0].upper(), name, version) - url = urljoin(_external_data_base_url, url) - return _get_external_data(url) - - -class Cache(object): - """ - A class implementing a cache for resources that need to live in the file system - e.g. shared libraries. This class was moved from resources to here because it - could be used by other modules, e.g. the wheel module. - """ - - def __init__(self, base): - """ - Initialise an instance. - - :param base: The base directory where the cache should be located. - """ - # we use 'isdir' instead of 'exists', because we want to - # fail if there's a file with that name - if not os.path.isdir(base): # pragma: no cover - os.makedirs(base) - if (os.stat(base).st_mode & 0o77) != 0: - logger.warning('Directory \'%s\' is not private', base) - self.base = os.path.abspath(os.path.normpath(base)) - - def prefix_to_dir(self, prefix): - """ - Converts a resource prefix to a directory name in the cache. - """ - return path_to_cache_dir(prefix) - - def clear(self): - """ - Clear the cache. - """ - not_removed = [] - for fn in os.listdir(self.base): - fn = os.path.join(self.base, fn) - try: - if os.path.islink(fn) or os.path.isfile(fn): - os.remove(fn) - elif os.path.isdir(fn): - shutil.rmtree(fn) - except Exception: - not_removed.append(fn) - return not_removed - - -class EventMixin(object): - """ - A very simple publish/subscribe system. - """ - def __init__(self): - self._subscribers = {} - - def add(self, event, subscriber, append=True): - """ - Add a subscriber for an event. - - :param event: The name of an event. - :param subscriber: The subscriber to be added (and called when the - event is published). - :param append: Whether to append or prepend the subscriber to an - existing subscriber list for the event. - """ - subs = self._subscribers - if event not in subs: - subs[event] = deque([subscriber]) - else: - sq = subs[event] - if append: - sq.append(subscriber) - else: - sq.appendleft(subscriber) - - def remove(self, event, subscriber): - """ - Remove a subscriber for an event. - - :param event: The name of an event. - :param subscriber: The subscriber to be removed. - """ - subs = self._subscribers - if event not in subs: - raise ValueError('No subscribers: %r' % event) - subs[event].remove(subscriber) - - def get_subscribers(self, event): - """ - Return an iterator for the subscribers for an event. - :param event: The event to return subscribers for. - """ - return iter(self._subscribers.get(event, ())) - - def publish(self, event, *args, **kwargs): - """ - Publish a event and return a list of values returned by its - subscribers. - - :param event: The event to publish. - :param args: The positional arguments to pass to the event's - subscribers. - :param kwargs: The keyword arguments to pass to the event's - subscribers. - """ - result = [] - for subscriber in self.get_subscribers(event): - try: - value = subscriber(event, *args, **kwargs) - except Exception: - logger.exception('Exception during event publication') - value = None - result.append(value) - logger.debug('publish %s: args = %s, kwargs = %s, result = %s', - event, args, kwargs, result) - return result - -# -# Simple sequencing -# -class Sequencer(object): - def __init__(self): - self._preds = {} - self._succs = {} - self._nodes = set() # nodes with no preds/succs - - def add_node(self, node): - self._nodes.add(node) - - def remove_node(self, node, edges=False): - if node in self._nodes: - self._nodes.remove(node) - if edges: - for p in set(self._preds.get(node, ())): - self.remove(p, node) - for s in set(self._succs.get(node, ())): - self.remove(node, s) - # Remove empties - for k, v in list(self._preds.items()): - if not v: - del self._preds[k] - for k, v in list(self._succs.items()): - if not v: - del self._succs[k] - - def add(self, pred, succ): - assert pred != succ - self._preds.setdefault(succ, set()).add(pred) - self._succs.setdefault(pred, set()).add(succ) - - def remove(self, pred, succ): - assert pred != succ - try: - preds = self._preds[succ] - succs = self._succs[pred] - except KeyError: # pragma: no cover - raise ValueError('%r not a successor of anything' % succ) - try: - preds.remove(pred) - succs.remove(succ) - except KeyError: # pragma: no cover - raise ValueError('%r not a successor of %r' % (succ, pred)) - - def is_step(self, step): - return (step in self._preds or step in self._succs or - step in self._nodes) - - def get_steps(self, final): - if not self.is_step(final): - raise ValueError('Unknown: %r' % final) - result = [] - todo = [] - seen = set() - todo.append(final) - while todo: - step = todo.pop(0) - if step in seen: - # if a step was already seen, - # move it to the end (so it will appear earlier - # when reversed on return) ... but not for the - # final step, as that would be confusing for - # users - if step != final: - result.remove(step) - result.append(step) - else: - seen.add(step) - result.append(step) - preds = self._preds.get(step, ()) - todo.extend(preds) - return reversed(result) - - @property - def strong_connections(self): - #http://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithm - index_counter = [0] - stack = [] - lowlinks = {} - index = {} - result = [] - - graph = self._succs - - def strongconnect(node): - # set the depth index for this node to the smallest unused index - index[node] = index_counter[0] - lowlinks[node] = index_counter[0] - index_counter[0] += 1 - stack.append(node) - - # Consider successors - try: - successors = graph[node] - except Exception: - successors = [] - for successor in successors: - if successor not in lowlinks: - # Successor has not yet been visited - strongconnect(successor) - lowlinks[node] = min(lowlinks[node],lowlinks[successor]) - elif successor in stack: - # the successor is in the stack and hence in the current - # strongly connected component (SCC) - lowlinks[node] = min(lowlinks[node],index[successor]) - - # If `node` is a root node, pop the stack and generate an SCC - if lowlinks[node] == index[node]: - connected_component = [] - - while True: - successor = stack.pop() - connected_component.append(successor) - if successor == node: break - component = tuple(connected_component) - # storing the result - result.append(component) - - for node in graph: - if node not in lowlinks: - strongconnect(node) - - return result - - @property - def dot(self): - result = ['digraph G {'] - for succ in self._preds: - preds = self._preds[succ] - for pred in preds: - result.append(' %s -> %s;' % (pred, succ)) - for node in self._nodes: - result.append(' %s;' % node) - result.append('}') - return '\n'.join(result) - -# -# Unarchiving functionality for zip, tar, tgz, tbz, whl -# - -ARCHIVE_EXTENSIONS = ('.tar.gz', '.tar.bz2', '.tar', '.zip', - '.tgz', '.tbz', '.whl') - -def unarchive(archive_filename, dest_dir, format=None, check=True): - - def check_path(path): - if not isinstance(path, text_type): - path = path.decode('utf-8') - p = os.path.abspath(os.path.join(dest_dir, path)) - if not p.startswith(dest_dir) or p[plen] != os.sep: - raise ValueError('path outside destination: %r' % p) - - dest_dir = os.path.abspath(dest_dir) - plen = len(dest_dir) - archive = None - if format is None: - if archive_filename.endswith(('.zip', '.whl')): - format = 'zip' - elif archive_filename.endswith(('.tar.gz', '.tgz')): - format = 'tgz' - mode = 'r:gz' - elif archive_filename.endswith(('.tar.bz2', '.tbz')): - format = 'tbz' - mode = 'r:bz2' - elif archive_filename.endswith('.tar'): - format = 'tar' - mode = 'r' - else: # pragma: no cover - raise ValueError('Unknown format for %r' % archive_filename) - try: - if format == 'zip': - archive = ZipFile(archive_filename, 'r') - if check: - names = archive.namelist() - for name in names: - check_path(name) - else: - archive = tarfile.open(archive_filename, mode) - if check: - names = archive.getnames() - for name in names: - check_path(name) - if format != 'zip' and sys.version_info[0] < 3: - # See Python issue 17153. If the dest path contains Unicode, - # tarfile extraction fails on Python 2.x if a member path name - # contains non-ASCII characters - it leads to an implicit - # bytes -> unicode conversion using ASCII to decode. - for tarinfo in archive.getmembers(): - if not isinstance(tarinfo.name, text_type): - tarinfo.name = tarinfo.name.decode('utf-8') - archive.extractall(dest_dir) - - finally: - if archive: - archive.close() - - -def zip_dir(directory): - """zip a directory tree into a BytesIO object""" - result = io.BytesIO() - dlen = len(directory) - with ZipFile(result, "w") as zf: - for root, dirs, files in os.walk(directory): - for name in files: - full = os.path.join(root, name) - rel = root[dlen:] - dest = os.path.join(rel, name) - zf.write(full, dest) - return result - -# -# Simple progress bar -# - -UNITS = ('', 'K', 'M', 'G','T','P') - - -class Progress(object): - unknown = 'UNKNOWN' - - def __init__(self, minval=0, maxval=100): - assert maxval is None or maxval >= minval - self.min = self.cur = minval - self.max = maxval - self.started = None - self.elapsed = 0 - self.done = False - - def update(self, curval): - assert self.min <= curval - assert self.max is None or curval <= self.max - self.cur = curval - now = time.time() - if self.started is None: - self.started = now - else: - self.elapsed = now - self.started - - def increment(self, incr): - assert incr >= 0 - self.update(self.cur + incr) - - def start(self): - self.update(self.min) - return self - - def stop(self): - if self.max is not None: - self.update(self.max) - self.done = True - - @property - def maximum(self): - return self.unknown if self.max is None else self.max - - @property - def percentage(self): - if self.done: - result = '100 %' - elif self.max is None: - result = ' ?? %' - else: - v = 100.0 * (self.cur - self.min) / (self.max - self.min) - result = '%3d %%' % v - return result - - def format_duration(self, duration): - if (duration <= 0) and self.max is None or self.cur == self.min: - result = '??:??:??' - #elif duration < 1: - # result = '--:--:--' - else: - result = time.strftime('%H:%M:%S', time.gmtime(duration)) - return result - - @property - def ETA(self): - if self.done: - prefix = 'Done' - t = self.elapsed - #import pdb; pdb.set_trace() - else: - prefix = 'ETA ' - if self.max is None: - t = -1 - elif self.elapsed == 0 or (self.cur == self.min): - t = 0 - else: - #import pdb; pdb.set_trace() - t = float(self.max - self.min) - t /= self.cur - self.min - t = (t - 1) * self.elapsed - return '%s: %s' % (prefix, self.format_duration(t)) - - @property - def speed(self): - if self.elapsed == 0: - result = 0.0 - else: - result = (self.cur - self.min) / self.elapsed - for unit in UNITS: - if result < 1000: - break - result /= 1000.0 - return '%d %sB/s' % (result, unit) - -# -# Glob functionality -# - -RICH_GLOB = re.compile(r'\{([^}]*)\}') -_CHECK_RECURSIVE_GLOB = re.compile(r'[^/\\,{]\*\*|\*\*[^/\\,}]') -_CHECK_MISMATCH_SET = re.compile(r'^[^{]*\}|\{[^}]*$') - - -def iglob(path_glob): - """Extended globbing function that supports ** and {opt1,opt2,opt3}.""" - if _CHECK_RECURSIVE_GLOB.search(path_glob): - msg = """invalid glob %r: recursive glob "**" must be used alone""" - raise ValueError(msg % path_glob) - if _CHECK_MISMATCH_SET.search(path_glob): - msg = """invalid glob %r: mismatching set marker '{' or '}'""" - raise ValueError(msg % path_glob) - return _iglob(path_glob) - - -def _iglob(path_glob): - rich_path_glob = RICH_GLOB.split(path_glob, 1) - if len(rich_path_glob) > 1: - assert len(rich_path_glob) == 3, rich_path_glob - prefix, set, suffix = rich_path_glob - for item in set.split(','): - for path in _iglob(''.join((prefix, item, suffix))): - yield path - else: - if '**' not in path_glob: - for item in std_iglob(path_glob): - yield item - else: - prefix, radical = path_glob.split('**', 1) - if prefix == '': - prefix = '.' - if radical == '': - radical = '*' - else: - # we support both - radical = radical.lstrip('/') - radical = radical.lstrip('\\') - for path, dir, files in os.walk(prefix): - path = os.path.normpath(path) - for fn in _iglob(os.path.join(path, radical)): - yield fn - -if ssl: - from .compat import (HTTPSHandler as BaseHTTPSHandler, match_hostname, - CertificateError) - - -# -# HTTPSConnection which verifies certificates/matches domains -# - - class HTTPSConnection(httplib.HTTPSConnection): - ca_certs = None # set this to the path to the certs file (.pem) - check_domain = True # only used if ca_certs is not None - - # noinspection PyPropertyAccess - def connect(self): - sock = socket.create_connection((self.host, self.port), self.timeout) - if getattr(self, '_tunnel_host', False): - self.sock = sock - self._tunnel() - - context = ssl.SSLContext(ssl.PROTOCOL_SSLv23) - if hasattr(ssl, 'OP_NO_SSLv2'): - context.options |= ssl.OP_NO_SSLv2 - if self.cert_file: - context.load_cert_chain(self.cert_file, self.key_file) - kwargs = {} - if self.ca_certs: - context.verify_mode = ssl.CERT_REQUIRED - context.load_verify_locations(cafile=self.ca_certs) - if getattr(ssl, 'HAS_SNI', False): - kwargs['server_hostname'] = self.host - - self.sock = context.wrap_socket(sock, **kwargs) - if self.ca_certs and self.check_domain: - try: - match_hostname(self.sock.getpeercert(), self.host) - logger.debug('Host verified: %s', self.host) - except CertificateError: # pragma: no cover - self.sock.shutdown(socket.SHUT_RDWR) - self.sock.close() - raise - - class HTTPSHandler(BaseHTTPSHandler): - def __init__(self, ca_certs, check_domain=True): - BaseHTTPSHandler.__init__(self) - self.ca_certs = ca_certs - self.check_domain = check_domain - - def _conn_maker(self, *args, **kwargs): - """ - This is called to create a connection instance. Normally you'd - pass a connection class to do_open, but it doesn't actually check for - a class, and just expects a callable. As long as we behave just as a - constructor would have, we should be OK. If it ever changes so that - we *must* pass a class, we'll create an UnsafeHTTPSConnection class - which just sets check_domain to False in the class definition, and - choose which one to pass to do_open. - """ - result = HTTPSConnection(*args, **kwargs) - if self.ca_certs: - result.ca_certs = self.ca_certs - result.check_domain = self.check_domain - return result - - def https_open(self, req): - try: - return self.do_open(self._conn_maker, req) - except URLError as e: - if 'certificate verify failed' in str(e.reason): - raise CertificateError('Unable to verify server certificate ' - 'for %s' % req.host) - else: - raise - - # - # To prevent against mixing HTTP traffic with HTTPS (examples: A Man-In-The- - # Middle proxy using HTTP listens on port 443, or an index mistakenly serves - # HTML containing a http://xyz link when it should be https://xyz), - # you can use the following handler class, which does not allow HTTP traffic. - # - # It works by inheriting from HTTPHandler - so build_opener won't add a - # handler for HTTP itself. - # - class HTTPSOnlyHandler(HTTPSHandler, HTTPHandler): - def http_open(self, req): - raise URLError('Unexpected HTTP request on what should be a secure ' - 'connection: %s' % req) - -# -# XML-RPC with timeouts -# -class Transport(xmlrpclib.Transport): - def __init__(self, timeout, use_datetime=0): - self.timeout = timeout - xmlrpclib.Transport.__init__(self, use_datetime) - - def make_connection(self, host): - h, eh, x509 = self.get_host_info(host) - if not self._connection or host != self._connection[0]: - self._extra_headers = eh - self._connection = host, httplib.HTTPConnection(h) - return self._connection[1] - -if ssl: - class SafeTransport(xmlrpclib.SafeTransport): - def __init__(self, timeout, use_datetime=0): - self.timeout = timeout - xmlrpclib.SafeTransport.__init__(self, use_datetime) - - def make_connection(self, host): - h, eh, kwargs = self.get_host_info(host) - if not kwargs: - kwargs = {} - kwargs['timeout'] = self.timeout - if not self._connection or host != self._connection[0]: - self._extra_headers = eh - self._connection = host, httplib.HTTPSConnection(h, None, - **kwargs) - return self._connection[1] - - -class ServerProxy(xmlrpclib.ServerProxy): - def __init__(self, uri, **kwargs): - self.timeout = timeout = kwargs.pop('timeout', None) - # The above classes only come into play if a timeout - # is specified - if timeout is not None: - # scheme = splittype(uri) # deprecated as of Python 3.8 - scheme = urlparse(uri)[0] - use_datetime = kwargs.get('use_datetime', 0) - if scheme == 'https': - tcls = SafeTransport - else: - tcls = Transport - kwargs['transport'] = t = tcls(timeout, use_datetime=use_datetime) - self.transport = t - xmlrpclib.ServerProxy.__init__(self, uri, **kwargs) - -# -# CSV functionality. This is provided because on 2.x, the csv module can't -# handle Unicode. However, we need to deal with Unicode in e.g. RECORD files. -# - -def _csv_open(fn, mode, **kwargs): - if sys.version_info[0] < 3: - mode += 'b' - else: - kwargs['newline'] = '' - # Python 3 determines encoding from locale. Force 'utf-8' - # file encoding to match other forced utf-8 encoding - kwargs['encoding'] = 'utf-8' - return open(fn, mode, **kwargs) - - -class CSVBase(object): - defaults = { - 'delimiter': str(','), # The strs are used because we need native - 'quotechar': str('"'), # str in the csv API (2.x won't take - 'lineterminator': str('\n') # Unicode) - } - - def __enter__(self): - return self - - def __exit__(self, *exc_info): - self.stream.close() - - -class CSVReader(CSVBase): - def __init__(self, **kwargs): - if 'stream' in kwargs: - stream = kwargs['stream'] - if sys.version_info[0] >= 3: - # needs to be a text stream - stream = codecs.getreader('utf-8')(stream) - self.stream = stream - else: - self.stream = _csv_open(kwargs['path'], 'r') - self.reader = csv.reader(self.stream, **self.defaults) - - def __iter__(self): - return self - - def next(self): - result = next(self.reader) - if sys.version_info[0] < 3: - for i, item in enumerate(result): - if not isinstance(item, text_type): - result[i] = item.decode('utf-8') - return result - - __next__ = next - -class CSVWriter(CSVBase): - def __init__(self, fn, **kwargs): - self.stream = _csv_open(fn, 'w') - self.writer = csv.writer(self.stream, **self.defaults) - - def writerow(self, row): - if sys.version_info[0] < 3: - r = [] - for item in row: - if isinstance(item, text_type): - item = item.encode('utf-8') - r.append(item) - row = r - self.writer.writerow(row) - -# -# Configurator functionality -# - -class Configurator(BaseConfigurator): - - value_converters = dict(BaseConfigurator.value_converters) - value_converters['inc'] = 'inc_convert' - - def __init__(self, config, base=None): - super(Configurator, self).__init__(config) - self.base = base or os.getcwd() - - def configure_custom(self, config): - def convert(o): - if isinstance(o, (list, tuple)): - result = type(o)([convert(i) for i in o]) - elif isinstance(o, dict): - if '()' in o: - result = self.configure_custom(o) - else: - result = {} - for k in o: - result[k] = convert(o[k]) - else: - result = self.convert(o) - return result - - c = config.pop('()') - if not callable(c): - c = self.resolve(c) - props = config.pop('.', None) - # Check for valid identifiers - args = config.pop('[]', ()) - if args: - args = tuple([convert(o) for o in args]) - items = [(k, convert(config[k])) for k in config if valid_ident(k)] - kwargs = dict(items) - result = c(*args, **kwargs) - if props: - for n, v in props.items(): - setattr(result, n, convert(v)) - return result - - def __getitem__(self, key): - result = self.config[key] - if isinstance(result, dict) and '()' in result: - self.config[key] = result = self.configure_custom(result) - return result - - def inc_convert(self, value): - """Default converter for the inc:// protocol.""" - if not os.path.isabs(value): - value = os.path.join(self.base, value) - with codecs.open(value, 'r', encoding='utf-8') as f: - result = json.load(f) - return result - - -class SubprocessMixin(object): - """ - Mixin for running subprocesses and capturing their output - """ - def __init__(self, verbose=False, progress=None): - self.verbose = verbose - self.progress = progress - - def reader(self, stream, context): - """ - Read lines from a subprocess' output stream and either pass to a progress - callable (if specified) or write progress information to sys.stderr. - """ - progress = self.progress - verbose = self.verbose - while True: - s = stream.readline() - if not s: - break - if progress is not None: - progress(s, context) - else: - if not verbose: - sys.stderr.write('.') - else: - sys.stderr.write(s.decode('utf-8')) - sys.stderr.flush() - stream.close() - - def run_command(self, cmd, **kwargs): - p = subprocess.Popen(cmd, stdout=subprocess.PIPE, - stderr=subprocess.PIPE, **kwargs) - t1 = threading.Thread(target=self.reader, args=(p.stdout, 'stdout')) - t1.start() - t2 = threading.Thread(target=self.reader, args=(p.stderr, 'stderr')) - t2.start() - p.wait() - t1.join() - t2.join() - if self.progress is not None: - self.progress('done.', 'main') - elif self.verbose: - sys.stderr.write('done.\n') - return p - - -def normalize_name(name): - """Normalize a python package name a la PEP 503""" - # https://www.python.org/dev/peps/pep-0503/#normalized-names - return re.sub('[-_.]+', '-', name).lower() - -# def _get_pypirc_command(): - # """ - # Get the distutils command for interacting with PyPI configurations. - # :return: the command. - # """ - # from distutils.core import Distribution - # from distutils.config import PyPIRCCommand - # d = Distribution() - # return PyPIRCCommand(d) - -class PyPIRCFile(object): - - DEFAULT_REPOSITORY = 'https://upload.pypi.org/legacy/' - DEFAULT_REALM = 'pypi' - - def __init__(self, fn=None, url=None): - if fn is None: - fn = os.path.join(os.path.expanduser('~'), '.pypirc') - self.filename = fn - self.url = url - - def read(self): - result = {} - - if os.path.exists(self.filename): - repository = self.url or self.DEFAULT_REPOSITORY - - config = configparser.RawConfigParser() - config.read(self.filename) - sections = config.sections() - if 'distutils' in sections: - # let's get the list of servers - index_servers = config.get('distutils', 'index-servers') - _servers = [server.strip() for server in - index_servers.split('\n') - if server.strip() != ''] - if _servers == []: - # nothing set, let's try to get the default pypi - if 'pypi' in sections: - _servers = ['pypi'] - else: - for server in _servers: - result = {'server': server} - result['username'] = config.get(server, 'username') - - # optional params - for key, default in (('repository', self.DEFAULT_REPOSITORY), - ('realm', self.DEFAULT_REALM), - ('password', None)): - if config.has_option(server, key): - result[key] = config.get(server, key) - else: - result[key] = default - - # work around people having "repository" for the "pypi" - # section of their config set to the HTTP (rather than - # HTTPS) URL - if (server == 'pypi' and - repository in (self.DEFAULT_REPOSITORY, 'pypi')): - result['repository'] = self.DEFAULT_REPOSITORY - elif (result['server'] != repository and - result['repository'] != repository): - result = {} - elif 'server-login' in sections: - # old format - server = 'server-login' - if config.has_option(server, 'repository'): - repository = config.get(server, 'repository') - else: - repository = self.DEFAULT_REPOSITORY - result = { - 'username': config.get(server, 'username'), - 'password': config.get(server, 'password'), - 'repository': repository, - 'server': server, - 'realm': self.DEFAULT_REALM - } - return result - - def update(self, username, password): - # import pdb; pdb.set_trace() - config = configparser.RawConfigParser() - fn = self.filename - config.read(fn) - if not config.has_section('pypi'): - config.add_section('pypi') - config.set('pypi', 'username', username) - config.set('pypi', 'password', password) - with open(fn, 'w') as f: - config.write(f) - -def _load_pypirc(index): - """ - Read the PyPI access configuration as supported by distutils. - """ - return PyPIRCFile(url=index.url).read() - -def _store_pypirc(index): - PyPIRCFile().update(index.username, index.password) - -# -# get_platform()/get_host_platform() copied from Python 3.10.a0 source, with some minor -# tweaks -# - -def get_host_platform(): - """Return a string that identifies the current platform. This is used mainly to - distinguish platform-specific build directories and platform-specific built - distributions. Typically includes the OS name and version and the - architecture (as supplied by 'os.uname()'), although the exact information - included depends on the OS; eg. on Linux, the kernel version isn't - particularly important. - - Examples of returned values: - linux-i586 - linux-alpha (?) - solaris-2.6-sun4u - - Windows will return one of: - win-amd64 (64bit Windows on AMD64 (aka x86_64, Intel64, EM64T, etc) - win32 (all others - specifically, sys.platform is returned) - - For other non-POSIX platforms, currently just returns 'sys.platform'. - - """ - if os.name == 'nt': - if 'amd64' in sys.version.lower(): - return 'win-amd64' - if '(arm)' in sys.version.lower(): - return 'win-arm32' - if '(arm64)' in sys.version.lower(): - return 'win-arm64' - return sys.platform - - # Set for cross builds explicitly - if "_PYTHON_HOST_PLATFORM" in os.environ: - return os.environ["_PYTHON_HOST_PLATFORM"] - - if os.name != 'posix' or not hasattr(os, 'uname'): - # XXX what about the architecture? NT is Intel or Alpha, - # Mac OS is M68k or PPC, etc. - return sys.platform - - # Try to distinguish various flavours of Unix - - (osname, host, release, version, machine) = os.uname() - - # Convert the OS name to lowercase, remove '/' characters, and translate - # spaces (for "Power Macintosh") - osname = osname.lower().replace('/', '') - machine = machine.replace(' ', '_').replace('/', '-') - - if osname[:5] == 'linux': - # At least on Linux/Intel, 'machine' is the processor -- - # i386, etc. - # XXX what about Alpha, SPARC, etc? - return "%s-%s" % (osname, machine) - - elif osname[:5] == 'sunos': - if release[0] >= '5': # SunOS 5 == Solaris 2 - osname = 'solaris' - release = '%d.%s' % (int(release[0]) - 3, release[2:]) - # We can't use 'platform.architecture()[0]' because a - # bootstrap problem. We use a dict to get an error - # if some suspicious happens. - bitness = {2147483647:'32bit', 9223372036854775807:'64bit'} - machine += '.%s' % bitness[sys.maxsize] - # fall through to standard osname-release-machine representation - elif osname[:3] == 'aix': - from _aix_support import aix_platform - return aix_platform() - elif osname[:6] == 'cygwin': - osname = 'cygwin' - rel_re = re.compile (r'[\d.]+', re.ASCII) - m = rel_re.match(release) - if m: - release = m.group() - elif osname[:6] == 'darwin': - import _osx_support, distutils.sysconfig - osname, release, machine = _osx_support.get_platform_osx( - distutils.sysconfig.get_config_vars(), - osname, release, machine) - - return '%s-%s-%s' % (osname, release, machine) - - -_TARGET_TO_PLAT = { - 'x86' : 'win32', - 'x64' : 'win-amd64', - 'arm' : 'win-arm32', -} - - -def get_platform(): - if os.name != 'nt': - return get_host_platform() - cross_compilation_target = os.environ.get('VSCMD_ARG_TGT_ARCH') - if cross_compilation_target not in _TARGET_TO_PLAT: - return get_host_platform() - return _TARGET_TO_PLAT[cross_compilation_target] diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/platformdirs/__init__.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/platformdirs/__init__.py deleted file mode 100644 index 5ebf5957b46598f5d6a922edcf1c0bc162af4bab..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/pip/_vendor/platformdirs/__init__.py +++ /dev/null @@ -1,566 +0,0 @@ -""" -Utilities for determining application-specific dirs. See for details and -usage. -""" -from __future__ import annotations - -import os -import sys -from typing import TYPE_CHECKING - -from .api import PlatformDirsABC -from .version import __version__ -from .version import __version_tuple__ as __version_info__ - -if TYPE_CHECKING: - from pathlib import Path - - if sys.version_info >= (3, 8): # pragma: no cover (py38+) - from typing import Literal - else: # pragma: no cover (py38+) - from pip._vendor.typing_extensions import Literal - - -def _set_platform_dir_class() -> type[PlatformDirsABC]: - if sys.platform == "win32": - from pip._vendor.platformdirs.windows import Windows as Result - elif sys.platform == "darwin": - from pip._vendor.platformdirs.macos import MacOS as Result - else: - from pip._vendor.platformdirs.unix import Unix as Result - - if os.getenv("ANDROID_DATA") == "/data" and os.getenv("ANDROID_ROOT") == "/system": - if os.getenv("SHELL") or os.getenv("PREFIX"): - return Result - - from pip._vendor.platformdirs.android import _android_folder - - if _android_folder() is not None: - from pip._vendor.platformdirs.android import Android - - return Android # return to avoid redefinition of result - - return Result - - -PlatformDirs = _set_platform_dir_class() #: Currently active platform -AppDirs = PlatformDirs #: Backwards compatibility with appdirs - - -def user_data_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - roaming: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param roaming: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: data directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - roaming=roaming, - ensure_exists=ensure_exists, - ).user_data_dir - - -def site_data_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - multipath: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param multipath: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: data directory shared by users - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - multipath=multipath, - ensure_exists=ensure_exists, - ).site_data_dir - - -def user_config_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - roaming: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param roaming: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: config directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - roaming=roaming, - ensure_exists=ensure_exists, - ).user_config_dir - - -def site_config_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - multipath: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param multipath: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: config directory shared by the users - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - multipath=multipath, - ensure_exists=ensure_exists, - ).site_config_dir - - -def user_cache_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: cache directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).user_cache_dir - - -def site_cache_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `opinion `. - :param ensure_exists: See `ensure_exists `. - :returns: cache directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).site_cache_dir - - -def user_state_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - roaming: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param roaming: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: state directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - roaming=roaming, - ensure_exists=ensure_exists, - ).user_state_dir - - -def user_log_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: log directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).user_log_dir - - -def user_documents_dir() -> str: - """:returns: documents directory tied to the user""" - return PlatformDirs().user_documents_dir - - -def user_downloads_dir() -> str: - """:returns: downloads directory tied to the user""" - return PlatformDirs().user_downloads_dir - - -def user_pictures_dir() -> str: - """:returns: pictures directory tied to the user""" - return PlatformDirs().user_pictures_dir - - -def user_videos_dir() -> str: - """:returns: videos directory tied to the user""" - return PlatformDirs().user_videos_dir - - -def user_music_dir() -> str: - """:returns: music directory tied to the user""" - return PlatformDirs().user_music_dir - - -def user_runtime_dir( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> str: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `opinion `. - :param ensure_exists: See `ensure_exists `. - :returns: runtime directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).user_runtime_dir - - -def user_data_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - roaming: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param roaming: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: data path tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - roaming=roaming, - ensure_exists=ensure_exists, - ).user_data_path - - -def site_data_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - multipath: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param multipath: See `multipath `. - :param ensure_exists: See `ensure_exists `. - :returns: data path shared by users - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - multipath=multipath, - ensure_exists=ensure_exists, - ).site_data_path - - -def user_config_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - roaming: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param roaming: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: config path tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - roaming=roaming, - ensure_exists=ensure_exists, - ).user_config_path - - -def site_config_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - multipath: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param multipath: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: config path shared by the users - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - multipath=multipath, - ensure_exists=ensure_exists, - ).site_config_path - - -def site_cache_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `opinion `. - :param ensure_exists: See `ensure_exists `. - :returns: cache directory tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).site_cache_path - - -def user_cache_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: cache path tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).user_cache_path - - -def user_state_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - roaming: bool = False, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param roaming: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: state path tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - roaming=roaming, - ensure_exists=ensure_exists, - ).user_state_path - - -def user_log_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `roaming `. - :param ensure_exists: See `ensure_exists `. - :returns: log path tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).user_log_path - - -def user_documents_path() -> Path: - """:returns: documents path tied to the user""" - return PlatformDirs().user_documents_path - - -def user_downloads_path() -> Path: - """:returns: downloads path tied to the user""" - return PlatformDirs().user_downloads_path - - -def user_pictures_path() -> Path: - """:returns: pictures path tied to the user""" - return PlatformDirs().user_pictures_path - - -def user_videos_path() -> Path: - """:returns: videos path tied to the user""" - return PlatformDirs().user_videos_path - - -def user_music_path() -> Path: - """:returns: music path tied to the user""" - return PlatformDirs().user_music_path - - -def user_runtime_path( - appname: str | None = None, - appauthor: str | None | Literal[False] = None, - version: str | None = None, - opinion: bool = True, # noqa: FBT001, FBT002 - ensure_exists: bool = False, # noqa: FBT001, FBT002 -) -> Path: - """ - :param appname: See `appname `. - :param appauthor: See `appauthor `. - :param version: See `version `. - :param opinion: See `opinion `. - :param ensure_exists: See `ensure_exists `. - :returns: runtime path tied to the user - """ - return PlatformDirs( - appname=appname, - appauthor=appauthor, - version=version, - opinion=opinion, - ensure_exists=ensure_exists, - ).user_runtime_path - - -__all__ = [ - "__version__", - "__version_info__", - "PlatformDirs", - "AppDirs", - "PlatformDirsABC", - "user_data_dir", - "user_config_dir", - "user_cache_dir", - "user_state_dir", - "user_log_dir", - "user_documents_dir", - "user_downloads_dir", - "user_pictures_dir", - "user_videos_dir", - "user_music_dir", - "user_runtime_dir", - "site_data_dir", - "site_config_dir", - "site_cache_dir", - "user_data_path", - "user_config_path", - "user_cache_path", - "user_state_path", - "user_log_path", - "user_documents_path", - "user_downloads_path", - "user_pictures_path", - "user_videos_path", - "user_music_path", - "user_runtime_path", - "site_data_path", - "site_config_path", - "site_cache_path", -] diff --git a/spaces/pknez/face-swap-docker/roop/core.py b/spaces/pknez/face-swap-docker/roop/core.py deleted file mode 100644 index 68d1808117837f1d903f7ba90f954dfd3c0f9554..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/roop/core.py +++ /dev/null @@ -1,323 +0,0 @@ -#!/usr/bin/env python3 - -import os -import sys -import shutil -# single thread doubles cuda performance - needs to be set before torch import -if any(arg.startswith('--execution-provider') for arg in sys.argv): - os.environ['OMP_NUM_THREADS'] = '1' - -import warnings -from typing import List -import platform -import signal -import argparse -import torch -import onnxruntime - -import roop.globals -import roop.metadata -import roop.utilities as util -import roop.ui as ui -from settings import Settings -from roop.face_helper import extract_face_images -from chain_img_processor import ChainImgProcessor, ChainVideoProcessor, ChainBatchImageProcessor - -clip_text = None - - -if 'ROCMExecutionProvider' in roop.globals.execution_providers: - del torch - -warnings.filterwarnings('ignore', category=FutureWarning, module='insightface') -warnings.filterwarnings('ignore', category=UserWarning, module='torchvision') - - -def parse_args() -> None: - signal.signal(signal.SIGINT, lambda signal_number, frame: destroy()) - program = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=100)) - program.add_argument('-s', '--source', help='select a source image', dest='source_path') - program.add_argument('-t', '--target', help='select a target image or video', dest='target_path') - program.add_argument('-o', '--output', help='select output file or directory', dest='output_path') - program.add_argument('-f', '--folder', help='select a target folder with images or videos to batch process', dest='target_folder_path') - program.add_argument('--frame-processor', help='frame processors (choices: face_swapper, face_enhancer, ...)', dest='frame_processor', default=['face_swapper'], nargs='+') - program.add_argument('--keep-fps', help='keep target fps', dest='keep_fps', action='store_true') - program.add_argument('--keep-frames', help='keep temporary frames', dest='keep_frames', action='store_true') - program.add_argument('--skip-audio', help='skip target audio', dest='skip_audio', action='store_true') - program.add_argument('--many-faces', help='process every face', dest='many_faces', action='store_true') - program.add_argument('--source-face_index', help='index position of source face in image', dest='source_face_index', type=int, default=0) - program.add_argument('--target-face_index', help='index position of target face in image', dest='target_face_index', type=int, default=0) - program.add_argument('--video-encoder', help='adjust output video encoder', dest='video_encoder', default='libx264', choices=['libx264', 'libx265', 'libvpx-vp9']) - program.add_argument('--video-quality', help='adjust output video quality', dest='video_quality', type=int, default=18, choices=range(52), metavar='[0-51]') - program.add_argument('--max-memory', help='maximum amount of RAM in GB', dest='max_memory', type=int, default=suggest_max_memory()) - program.add_argument('--execution-provider', help='available execution provider (choices: cpu, ...)', dest='execution_provider', default=['cpu'], choices=suggest_execution_providers(), nargs='+') - program.add_argument('--execution-threads', help='number of execution threads', dest='execution_threads', type=int, default=suggest_execution_threads()) - program.add_argument('-v', '--version', action='version', version=f'{roop.metadata.name} {roop.metadata.version}') - - args = program.parse_args() - - roop.globals.source_path = args.source_path - roop.globals.target_path = args.target_path - roop.globals.output_path = util.normalize_output_path(roop.globals.source_path, roop.globals.target_path, args.output_path) - roop.globals.target_folder_path = args.target_folder_path - roop.globals.headless = args.source_path or args.target_path or args.output_path - # Always enable all processors when using GUI - if not roop.globals.headless: - roop.globals.frame_processors = ['face_swapper', 'face_enhancer'] - else: - roop.globals.frame_processors = args.frame_processor - - roop.globals.keep_fps = args.keep_fps - roop.globals.keep_frames = args.keep_frames - roop.globals.skip_audio = args.skip_audio - roop.globals.many_faces = args.many_faces - roop.globals.source_face_index = args.source_face_index - roop.globals.target_face_index = args.target_face_index - roop.globals.video_encoder = args.video_encoder - roop.globals.video_quality = args.video_quality - roop.globals.max_memory = args.max_memory - roop.globals.execution_providers = decode_execution_providers(args.execution_provider) - roop.globals.execution_threads = args.execution_threads - - -def encode_execution_providers(execution_providers: List[str]) -> List[str]: - return [execution_provider.replace('ExecutionProvider', '').lower() for execution_provider in execution_providers] - - -def decode_execution_providers(execution_providers: List[str]) -> List[str]: - return [provider for provider, encoded_execution_provider in zip(onnxruntime.get_available_providers(), encode_execution_providers(onnxruntime.get_available_providers())) - if any(execution_provider in encoded_execution_provider for execution_provider in execution_providers)] - - -def suggest_max_memory() -> int: - if platform.system().lower() == 'darwin': - return 4 - return 16 - - -def suggest_execution_providers() -> List[str]: - return encode_execution_providers(onnxruntime.get_available_providers()) - - -def suggest_execution_threads() -> int: - if 'DmlExecutionProvider' in roop.globals.execution_providers: - return 1 - if 'ROCMExecutionProvider' in roop.globals.execution_providers: - return 1 - return 8 - - -def limit_resources() -> None: - # prevent tensorflow memory leak - # gpus = tensorflow.config.experimental.list_physical_devices('GPU') - # for gpu in gpus: - # tensorflow.config.experimental.set_virtual_device_configuration(gpu, [ - # tensorflow.config.experimental.VirtualDeviceConfiguration(memory_limit=1024) - # ]) - # limit memory usage - if roop.globals.max_memory: - memory = roop.globals.max_memory * 1024 ** 3 - if platform.system().lower() == 'darwin': - memory = roop.globals.max_memory * 1024 ** 6 - if platform.system().lower() == 'windows': - import ctypes - kernel32 = ctypes.windll.kernel32 - kernel32.SetProcessWorkingSetSize(-1, ctypes.c_size_t(memory), ctypes.c_size_t(memory)) - else: - import resource - resource.setrlimit(resource.RLIMIT_DATA, (memory, memory)) - - -def release_resources() -> None: - if 'CUDAExecutionProvider' in roop.globals.execution_providers: - torch.cuda.empty_cache() - - -def pre_check() -> bool: - if sys.version_info < (3, 9): - update_status('Python version is not supported - please upgrade to 3.9 or higher.') - return False - - download_directory_path = util.resolve_relative_path('../models') - util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/inswapper_128.onnx']) - util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/GFPGANv1.4.pth']) - util.conditional_download(download_directory_path, ['https://github.com/csxmli2016/DMDNet/releases/download/v1/DMDNet.pth']) - download_directory_path = util.resolve_relative_path('../models/CLIP') - util.conditional_download(download_directory_path, ['https://huggingface.co/countfloyd/deepfake/resolve/main/rd64-uni-refined.pth']) - download_directory_path = util.resolve_relative_path('../models/CodeFormer') - util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth']) - download_directory_path = util.resolve_relative_path('../models/CodeFormer/facelib') - util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/detection_Resnet50_Final.pth']) - util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/parsing_parsenet.pth']) - download_directory_path = util.resolve_relative_path('../models/CodeFormer/realesrgan') - util.conditional_download(download_directory_path, ['https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/RealESRGAN_x2plus.pth']) - - if not shutil.which('ffmpeg'): - update_status('ffmpeg is not installed.') - return True - - -def update_status(message: str, scope: str = 'ROOP.CORE') -> None: - print(f'[{scope}] {message}') - # if not roop.globals.headless: - # ui.update_status(message) - - - -def start() -> None: - if roop.globals.headless: - faces = extract_face_images(roop.globals.source_path, (False, 0)) - roop.globals.SELECTED_FACE_DATA_INPUT = faces[roop.globals.source_face_index] - faces = extract_face_images(roop.globals.target_path, (False, util.has_image_extension(roop.globals.target_path))) - roop.globals.SELECTED_FACE_DATA_OUTPUT = faces[roop.globals.target_face_index] - if 'face_enhancer' in roop.globals.frame_processors: - roop.globals.selected_enhancer = 'GFPGAN' - - batch_process(None, False, None) - - -def InitPlugins(): - if not roop.globals.IMAGE_CHAIN_PROCESSOR: - roop.globals.IMAGE_CHAIN_PROCESSOR = ChainImgProcessor() - roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR = ChainBatchImageProcessor() - roop.globals.VIDEO_CHAIN_PROCESSOR = ChainVideoProcessor() - roop.globals.IMAGE_CHAIN_PROCESSOR.init_with_plugins() - roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.init_with_plugins() - roop.globals.VIDEO_CHAIN_PROCESSOR.init_with_plugins() - - -def get_processing_plugins(use_clip): - processors = "faceswap" - if use_clip: - processors += ",txt2clip" - - if roop.globals.selected_enhancer == 'GFPGAN': - processors += ",gfpgan" - elif roop.globals.selected_enhancer == 'Codeformer': - processors += ",codeformer" - elif roop.globals.selected_enhancer == 'DMDNet': - processors += ",dmdnet" - - return processors - - -def live_swap(frame, swap_mode, use_clip, clip_text): - if frame is None: - return frame - - InitPlugins() - processors = get_processing_plugins(use_clip) - - - temp_frame, _ = roop.globals.IMAGE_CHAIN_PROCESSOR.run_chain(frame, - {"swap_mode": swap_mode, - "original_frame": frame, - "blend_ratio": roop.globals.blend_ratio, - "face_distance_threshold": roop.globals.distance_threshold, - "input_face_datas": [roop.globals.SELECTED_FACE_DATA_INPUT], "target_face_datas": [roop.globals.SELECTED_FACE_DATA_OUTPUT], - "clip_prompt": clip_text}, - processors) - return temp_frame - - - -def params_gen_func(proc, frame): - global clip_text - - return {"original_frame": frame, "blend_ratio": roop.globals.blend_ratio, - "swap_mode": roop.globals.face_swap_mode, "face_distance_threshold": roop.globals.distance_threshold, - "input_face_datas": [roop.globals.SELECTED_FACE_DATA_INPUT], "target_face_datas": [roop.globals.SELECTED_FACE_DATA_OUTPUT], - "clip_prompt": clip_text} - -def batch_process(files, use_clip, new_clip_text) -> None: - global clip_text - - InitPlugins() - processors = get_processing_plugins(use_clip) - - clip_text = new_clip_text - - imagefiles = [] - imagefinalnames = [] - videofiles = [] - videofinalnames = [] - need_join = False - - if files is None: - need_join = True - if roop.globals.target_folder_path is None: - roop.globals.target_folder_path = os.path.dirname(roop.globals.target_path) - files = [os.path.basename(roop.globals.target_path)] - roop.globals.output_path = os.path.dirname(roop.globals.output_path) - else: - files = [f for f in os.listdir(roop.globals.target_folder_path) if os.path.isfile(os.path.join(roop.globals.target_folder_path, f))] - - update_status('Sorting videos/images') - - - for f in files: - if need_join: - fullname = os.path.join(roop.globals.target_folder_path, f) - else: - fullname = f - if util.has_image_extension(fullname): - imagefiles.append(fullname) - imagefinalnames.append(util.get_destfilename_from_path(fullname, roop.globals.output_path, f'_fake.{roop.globals.CFG.output_image_format}')) - elif util.is_video(fullname) or util.has_extension(fullname, ['gif']): - videofiles.append(fullname) - videofinalnames.append(util.get_destfilename_from_path(fullname, roop.globals.output_path, f'_fake.{roop.globals.CFG.output_video_format}')) - - - if(len(imagefiles) > 0): - update_status('Processing image(s)') - roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.run_batch_chain(imagefiles, imagefinalnames, roop.globals.execution_threads, processors, params_gen_func) - if(len(videofiles) > 0): - for index,v in enumerate(videofiles): - update_status(f'Processing video {v}') - fps = util.detect_fps(v) - if roop.globals.keep_frames: - update_status('Creating temp resources...') - util.create_temp(v) - update_status('Extracting frames...') - util.extract_frames(v) - temp_frame_paths = util.get_temp_frame_paths(v) - roop.globals.BATCH_IMAGE_CHAIN_PROCESSOR.run_batch_chain(temp_frame_paths, temp_frame_paths, roop.globals.execution_threads, processors, params_gen_func) - update_status(f'Creating video with {fps} FPS...') - util.create_video(v, videofinalnames[index], fps) - else: - update_status(f'Creating video with {fps} FPS...') - roop.globals.VIDEO_CHAIN_PROCESSOR.run_video_chain(v,videofinalnames[index], fps, roop.globals.execution_threads, processors, params_gen_func, roop.globals.target_path) - if os.path.isfile(videofinalnames[index]): - if util.has_extension(v, ['gif']): - gifname = roop.utilities.get_destfilename_from_path(v, './output', '_fake.gif') - update_status('Creating final GIF') - util.create_gif_from_video(videofinalnames[index], gifname) - elif not roop.globals.skip_audio: - finalname = roop.utilities.get_destfilename_from_path(videofinalnames[index], roop.globals.output_path, f'_final.{roop.globals.CFG.output_video_format}') - util.restore_audio(videofinalnames[index], v, finalname) - if os.path.isfile(videofinalnames[index]): - os.remove(videofinalnames[index]) - else: - update_status('Failed!') - - - update_status('Finished') - roop.globals.target_folder_path = None - - -def destroy() -> None: - if roop.globals.target_path: - util.clean_temp(roop.globals.target_path) - sys.exit() - - -def run() -> None: - parse_args() - if not pre_check(): - return - limit_resources() - roop.globals.CFG = Settings('config.yaml') - if roop.globals.headless: - start() - else: - ui.run() diff --git a/spaces/politweet-sh/politweet/__init__.py b/spaces/politweet-sh/politweet/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/web_ws.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/web_ws.py deleted file mode 100644 index 0d32a218b52b87ec04f36a6f95bfb303984b2e43..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/aiohttp/web_ws.py +++ /dev/null @@ -1,487 +0,0 @@ -import asyncio -import base64 -import binascii -import hashlib -import json -from typing import Any, Iterable, Optional, Tuple, cast - -import async_timeout -import attr -from multidict import CIMultiDict - -from . import hdrs -from .abc import AbstractStreamWriter -from .helpers import call_later, set_result -from .http import ( - WS_CLOSED_MESSAGE, - WS_CLOSING_MESSAGE, - WS_KEY, - WebSocketError, - WebSocketReader, - WebSocketWriter, - WSCloseCode, - WSMessage, - WSMsgType as WSMsgType, - ws_ext_gen, - ws_ext_parse, -) -from .log import ws_logger -from .streams import EofStream, FlowControlDataQueue -from .typedefs import Final, JSONDecoder, JSONEncoder -from .web_exceptions import HTTPBadRequest, HTTPException -from .web_request import BaseRequest -from .web_response import StreamResponse - -__all__ = ( - "WebSocketResponse", - "WebSocketReady", - "WSMsgType", -) - -THRESHOLD_CONNLOST_ACCESS: Final[int] = 5 - - -@attr.s(auto_attribs=True, frozen=True, slots=True) -class WebSocketReady: - ok: bool - protocol: Optional[str] - - def __bool__(self) -> bool: - return self.ok - - -class WebSocketResponse(StreamResponse): - - _length_check = False - - def __init__( - self, - *, - timeout: float = 10.0, - receive_timeout: Optional[float] = None, - autoclose: bool = True, - autoping: bool = True, - heartbeat: Optional[float] = None, - protocols: Iterable[str] = (), - compress: bool = True, - max_msg_size: int = 4 * 1024 * 1024, - ) -> None: - super().__init__(status=101) - self._protocols = protocols - self._ws_protocol: Optional[str] = None - self._writer: Optional[WebSocketWriter] = None - self._reader: Optional[FlowControlDataQueue[WSMessage]] = None - self._closed = False - self._closing = False - self._conn_lost = 0 - self._close_code: Optional[int] = None - self._loop: Optional[asyncio.AbstractEventLoop] = None - self._waiting: Optional[asyncio.Future[bool]] = None - self._exception: Optional[BaseException] = None - self._timeout = timeout - self._receive_timeout = receive_timeout - self._autoclose = autoclose - self._autoping = autoping - self._heartbeat = heartbeat - self._heartbeat_cb: Optional[asyncio.TimerHandle] = None - if heartbeat is not None: - self._pong_heartbeat = heartbeat / 2.0 - self._pong_response_cb: Optional[asyncio.TimerHandle] = None - self._compress = compress - self._max_msg_size = max_msg_size - - def _cancel_heartbeat(self) -> None: - if self._pong_response_cb is not None: - self._pong_response_cb.cancel() - self._pong_response_cb = None - - if self._heartbeat_cb is not None: - self._heartbeat_cb.cancel() - self._heartbeat_cb = None - - def _reset_heartbeat(self) -> None: - self._cancel_heartbeat() - - if self._heartbeat is not None: - assert self._loop is not None - self._heartbeat_cb = call_later( - self._send_heartbeat, self._heartbeat, self._loop - ) - - def _send_heartbeat(self) -> None: - if self._heartbeat is not None and not self._closed: - assert self._loop is not None - # fire-and-forget a task is not perfect but maybe ok for - # sending ping. Otherwise we need a long-living heartbeat - # task in the class. - self._loop.create_task(self._writer.ping()) # type: ignore[union-attr] - - if self._pong_response_cb is not None: - self._pong_response_cb.cancel() - self._pong_response_cb = call_later( - self._pong_not_received, self._pong_heartbeat, self._loop - ) - - def _pong_not_received(self) -> None: - if self._req is not None and self._req.transport is not None: - self._closed = True - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - self._exception = asyncio.TimeoutError() - self._req.transport.close() - - async def prepare(self, request: BaseRequest) -> AbstractStreamWriter: - # make pre-check to don't hide it by do_handshake() exceptions - if self._payload_writer is not None: - return self._payload_writer - - protocol, writer = self._pre_start(request) - payload_writer = await super().prepare(request) - assert payload_writer is not None - self._post_start(request, protocol, writer) - await payload_writer.drain() - return payload_writer - - def _handshake( - self, request: BaseRequest - ) -> Tuple["CIMultiDict[str]", str, bool, bool]: - headers = request.headers - if "websocket" != headers.get(hdrs.UPGRADE, "").lower().strip(): - raise HTTPBadRequest( - text=( - "No WebSocket UPGRADE hdr: {}\n Can " - '"Upgrade" only to "WebSocket".' - ).format(headers.get(hdrs.UPGRADE)) - ) - - if "upgrade" not in headers.get(hdrs.CONNECTION, "").lower(): - raise HTTPBadRequest( - text="No CONNECTION upgrade hdr: {}".format( - headers.get(hdrs.CONNECTION) - ) - ) - - # find common sub-protocol between client and server - protocol = None - if hdrs.SEC_WEBSOCKET_PROTOCOL in headers: - req_protocols = [ - str(proto.strip()) - for proto in headers[hdrs.SEC_WEBSOCKET_PROTOCOL].split(",") - ] - - for proto in req_protocols: - if proto in self._protocols: - protocol = proto - break - else: - # No overlap found: Return no protocol as per spec - ws_logger.warning( - "Client protocols %r don’t overlap server-known ones %r", - req_protocols, - self._protocols, - ) - - # check supported version - version = headers.get(hdrs.SEC_WEBSOCKET_VERSION, "") - if version not in ("13", "8", "7"): - raise HTTPBadRequest(text=f"Unsupported version: {version}") - - # check client handshake for validity - key = headers.get(hdrs.SEC_WEBSOCKET_KEY) - try: - if not key or len(base64.b64decode(key)) != 16: - raise HTTPBadRequest(text=f"Handshake error: {key!r}") - except binascii.Error: - raise HTTPBadRequest(text=f"Handshake error: {key!r}") from None - - accept_val = base64.b64encode( - hashlib.sha1(key.encode() + WS_KEY).digest() - ).decode() - response_headers = CIMultiDict( - { - hdrs.UPGRADE: "websocket", - hdrs.CONNECTION: "upgrade", - hdrs.SEC_WEBSOCKET_ACCEPT: accept_val, - } - ) - - notakeover = False - compress = 0 - if self._compress: - extensions = headers.get(hdrs.SEC_WEBSOCKET_EXTENSIONS) - # Server side always get return with no exception. - # If something happened, just drop compress extension - compress, notakeover = ws_ext_parse(extensions, isserver=True) - if compress: - enabledext = ws_ext_gen( - compress=compress, isserver=True, server_notakeover=notakeover - ) - response_headers[hdrs.SEC_WEBSOCKET_EXTENSIONS] = enabledext - - if protocol: - response_headers[hdrs.SEC_WEBSOCKET_PROTOCOL] = protocol - return ( - response_headers, - protocol, - compress, - notakeover, - ) # type: ignore[return-value] - - def _pre_start(self, request: BaseRequest) -> Tuple[str, WebSocketWriter]: - self._loop = request._loop - - headers, protocol, compress, notakeover = self._handshake(request) - - self.set_status(101) - self.headers.update(headers) - self.force_close() - self._compress = compress - transport = request._protocol.transport - assert transport is not None - writer = WebSocketWriter( - request._protocol, transport, compress=compress, notakeover=notakeover - ) - - return protocol, writer - - def _post_start( - self, request: BaseRequest, protocol: str, writer: WebSocketWriter - ) -> None: - self._ws_protocol = protocol - self._writer = writer - - self._reset_heartbeat() - - loop = self._loop - assert loop is not None - self._reader = FlowControlDataQueue(request._protocol, 2**16, loop=loop) - request.protocol.set_parser( - WebSocketReader(self._reader, self._max_msg_size, compress=self._compress) - ) - # disable HTTP keepalive for WebSocket - request.protocol.keep_alive(False) - - def can_prepare(self, request: BaseRequest) -> WebSocketReady: - if self._writer is not None: - raise RuntimeError("Already started") - try: - _, protocol, _, _ = self._handshake(request) - except HTTPException: - return WebSocketReady(False, None) - else: - return WebSocketReady(True, protocol) - - @property - def closed(self) -> bool: - return self._closed - - @property - def close_code(self) -> Optional[int]: - return self._close_code - - @property - def ws_protocol(self) -> Optional[str]: - return self._ws_protocol - - @property - def compress(self) -> bool: - return self._compress - - def exception(self) -> Optional[BaseException]: - return self._exception - - async def ping(self, message: bytes = b"") -> None: - if self._writer is None: - raise RuntimeError("Call .prepare() first") - await self._writer.ping(message) - - async def pong(self, message: bytes = b"") -> None: - # unsolicited pong - if self._writer is None: - raise RuntimeError("Call .prepare() first") - await self._writer.pong(message) - - async def send_str(self, data: str, compress: Optional[bool] = None) -> None: - if self._writer is None: - raise RuntimeError("Call .prepare() first") - if not isinstance(data, str): - raise TypeError("data argument must be str (%r)" % type(data)) - await self._writer.send(data, binary=False, compress=compress) - - async def send_bytes(self, data: bytes, compress: Optional[bool] = None) -> None: - if self._writer is None: - raise RuntimeError("Call .prepare() first") - if not isinstance(data, (bytes, bytearray, memoryview)): - raise TypeError("data argument must be byte-ish (%r)" % type(data)) - await self._writer.send(data, binary=True, compress=compress) - - async def send_json( - self, - data: Any, - compress: Optional[bool] = None, - *, - dumps: JSONEncoder = json.dumps, - ) -> None: - await self.send_str(dumps(data), compress=compress) - - async def write_eof(self) -> None: # type: ignore[override] - if self._eof_sent: - return - if self._payload_writer is None: - raise RuntimeError("Response has not been started") - - await self.close() - self._eof_sent = True - - async def close(self, *, code: int = WSCloseCode.OK, message: bytes = b"") -> bool: - if self._writer is None: - raise RuntimeError("Call .prepare() first") - - self._cancel_heartbeat() - reader = self._reader - assert reader is not None - - # we need to break `receive()` cycle first, - # `close()` may be called from different task - if self._waiting is not None and not self._closed: - reader.feed_data(WS_CLOSING_MESSAGE, 0) - await self._waiting - - if not self._closed: - self._closed = True - try: - await self._writer.close(code, message) - writer = self._payload_writer - assert writer is not None - await writer.drain() - except (asyncio.CancelledError, asyncio.TimeoutError): - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - raise - except Exception as exc: - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - self._exception = exc - return True - - if self._closing: - return True - - reader = self._reader - assert reader is not None - try: - async with async_timeout.timeout(self._timeout): - msg = await reader.read() - except asyncio.CancelledError: - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - raise - except Exception as exc: - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - self._exception = exc - return True - - if msg.type == WSMsgType.CLOSE: - self._close_code = msg.data - return True - - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - self._exception = asyncio.TimeoutError() - return True - else: - return False - - async def receive(self, timeout: Optional[float] = None) -> WSMessage: - if self._reader is None: - raise RuntimeError("Call .prepare() first") - - loop = self._loop - assert loop is not None - while True: - if self._waiting is not None: - raise RuntimeError("Concurrent call to receive() is not allowed") - - if self._closed: - self._conn_lost += 1 - if self._conn_lost >= THRESHOLD_CONNLOST_ACCESS: - raise RuntimeError("WebSocket connection is closed.") - return WS_CLOSED_MESSAGE - elif self._closing: - return WS_CLOSING_MESSAGE - - try: - self._waiting = loop.create_future() - try: - async with async_timeout.timeout(timeout or self._receive_timeout): - msg = await self._reader.read() - self._reset_heartbeat() - finally: - waiter = self._waiting - set_result(waiter, True) - self._waiting = None - except (asyncio.CancelledError, asyncio.TimeoutError): - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - raise - except EofStream: - self._close_code = WSCloseCode.OK - await self.close() - return WSMessage(WSMsgType.CLOSED, None, None) - except WebSocketError as exc: - self._close_code = exc.code - await self.close(code=exc.code) - return WSMessage(WSMsgType.ERROR, exc, None) - except Exception as exc: - self._exception = exc - self._closing = True - self._close_code = WSCloseCode.ABNORMAL_CLOSURE - await self.close() - return WSMessage(WSMsgType.ERROR, exc, None) - - if msg.type == WSMsgType.CLOSE: - self._closing = True - self._close_code = msg.data - if not self._closed and self._autoclose: - await self.close() - elif msg.type == WSMsgType.CLOSING: - self._closing = True - elif msg.type == WSMsgType.PING and self._autoping: - await self.pong(msg.data) - continue - elif msg.type == WSMsgType.PONG and self._autoping: - continue - - return msg - - async def receive_str(self, *, timeout: Optional[float] = None) -> str: - msg = await self.receive(timeout) - if msg.type != WSMsgType.TEXT: - raise TypeError( - "Received message {}:{!r} is not WSMsgType.TEXT".format( - msg.type, msg.data - ) - ) - return cast(str, msg.data) - - async def receive_bytes(self, *, timeout: Optional[float] = None) -> bytes: - msg = await self.receive(timeout) - if msg.type != WSMsgType.BINARY: - raise TypeError(f"Received message {msg.type}:{msg.data!r} is not bytes") - return cast(bytes, msg.data) - - async def receive_json( - self, *, loads: JSONDecoder = json.loads, timeout: Optional[float] = None - ) -> Any: - data = await self.receive_str(timeout=timeout) - return loads(data) - - async def write(self, data: bytes) -> None: - raise RuntimeError("Cannot call .write() for websocket") - - def __aiter__(self) -> "WebSocketResponse": - return self - - async def __anext__(self) -> WSMessage: - msg = await self.receive() - if msg.type in (WSMsgType.CLOSE, WSMsgType.CLOSING, WSMsgType.CLOSED): - raise StopAsyncIteration - return msg - - def _cancel(self, exc: BaseException) -> None: - if self._reader is not None: - self._reader.set_exception(exc) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/colorLib/unbuilder.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/colorLib/unbuilder.py deleted file mode 100644 index ac243550b8908aef120b395e740b9974559d65b5..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/colorLib/unbuilder.py +++ /dev/null @@ -1,81 +0,0 @@ -from fontTools.ttLib.tables import otTables as ot -from .table_builder import TableUnbuilder - - -def unbuildColrV1(layerList, baseGlyphList): - layers = [] - if layerList: - layers = layerList.Paint - unbuilder = LayerListUnbuilder(layers) - return { - rec.BaseGlyph: unbuilder.unbuildPaint(rec.Paint) - for rec in baseGlyphList.BaseGlyphPaintRecord - } - - -def _flatten_layers(lst): - for paint in lst: - if paint["Format"] == ot.PaintFormat.PaintColrLayers: - yield from _flatten_layers(paint["Layers"]) - else: - yield paint - - -class LayerListUnbuilder: - def __init__(self, layers): - self.layers = layers - - callbacks = { - ( - ot.Paint, - ot.PaintFormat.PaintColrLayers, - ): self._unbuildPaintColrLayers, - } - self.tableUnbuilder = TableUnbuilder(callbacks) - - def unbuildPaint(self, paint): - assert isinstance(paint, ot.Paint) - return self.tableUnbuilder.unbuild(paint) - - def _unbuildPaintColrLayers(self, source): - assert source["Format"] == ot.PaintFormat.PaintColrLayers - - layers = list( - _flatten_layers( - [ - self.unbuildPaint(childPaint) - for childPaint in self.layers[ - source["FirstLayerIndex"] : source["FirstLayerIndex"] - + source["NumLayers"] - ] - ] - ) - ) - - if len(layers) == 1: - return layers[0] - - return {"Format": source["Format"], "Layers": layers} - - -if __name__ == "__main__": - from pprint import pprint - import sys - from fontTools.ttLib import TTFont - - try: - fontfile = sys.argv[1] - except IndexError: - sys.exit("usage: fonttools colorLib.unbuilder FONTFILE") - - font = TTFont(fontfile) - colr = font["COLR"] - if colr.version < 1: - sys.exit(f"error: No COLR table version=1 found in {fontfile}") - - colorGlyphs = unbuildColrV1( - colr.table.LayerList, - colr.table.BaseGlyphList, - ) - - pprint(colorGlyphs) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/command/build.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/command/build.py deleted file mode 100644 index 80830d559c61dde4b46dacc7d24e387486476349..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/distutils/command/build.py +++ /dev/null @@ -1,62 +0,0 @@ -import os -import sys -from distutils.command.build import build as old_build -from distutils.util import get_platform -from numpy.distutils.command.config_compiler import show_fortran_compilers - -class build(old_build): - - sub_commands = [('config_cc', lambda *args: True), - ('config_fc', lambda *args: True), - ('build_src', old_build.has_ext_modules), - ] + old_build.sub_commands - - user_options = old_build.user_options + [ - ('fcompiler=', None, - "specify the Fortran compiler type"), - ('warn-error', None, - "turn all warnings into errors (-Werror)"), - ('cpu-baseline=', None, - "specify a list of enabled baseline CPU optimizations"), - ('cpu-dispatch=', None, - "specify a list of dispatched CPU optimizations"), - ('disable-optimization', None, - "disable CPU optimized code(dispatch,simd,fast...)"), - ('simd-test=', None, - "specify a list of CPU optimizations to be tested against NumPy SIMD interface"), - ] - - help_options = old_build.help_options + [ - ('help-fcompiler', None, "list available Fortran compilers", - show_fortran_compilers), - ] - - def initialize_options(self): - old_build.initialize_options(self) - self.fcompiler = None - self.warn_error = False - self.cpu_baseline = "min" - self.cpu_dispatch = "max -xop -fma4" # drop AMD legacy features by default - self.disable_optimization = False - """ - the '_simd' module is a very large. Adding more dispatched features - will increase binary size and compile time. By default we minimize - the targeted features to those most commonly used by the NumPy SIMD interface(NPYV), - NOTE: any specified features will be ignored if they're: - - part of the baseline(--cpu-baseline) - - not part of dispatch-able features(--cpu-dispatch) - - not supported by compiler or platform - """ - self.simd_test = "BASELINE SSE2 SSE42 XOP FMA4 (FMA3 AVX2) AVX512F " \ - "AVX512_SKX VSX VSX2 VSX3 VSX4 NEON ASIMD VX VXE VXE2" - - def finalize_options(self): - build_scripts = self.build_scripts - old_build.finalize_options(self) - plat_specifier = ".{}-{}.{}".format(get_platform(), *sys.version_info[:2]) - if build_scripts is None: - self.build_scripts = os.path.join(self.build_base, - 'scripts' + plat_specifier) - - def run(self): - old_build.run(self) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/window/rolling.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/window/rolling.py deleted file mode 100644 index ddd6caaa7f783730aa42eaf962b54e9546412038..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/core/window/rolling.py +++ /dev/null @@ -1,2921 +0,0 @@ -""" -Provide a generic structure to support window functions, -similar to how we have a Groupby object. -""" -from __future__ import annotations - -import copy -from datetime import timedelta -from functools import partial -import inspect -from textwrap import dedent -from typing import ( - TYPE_CHECKING, - Any, - Callable, - Literal, - cast, -) - -import numpy as np - -from pandas._libs.tslibs import ( - BaseOffset, - Timedelta, - to_offset, -) -import pandas._libs.window.aggregations as window_aggregations -from pandas.compat._optional import import_optional_dependency -from pandas.errors import DataError -from pandas.util._decorators import ( - deprecate_kwarg, - doc, -) - -from pandas.core.dtypes.common import ( - ensure_float64, - is_bool, - is_integer, - is_numeric_dtype, - needs_i8_conversion, -) -from pandas.core.dtypes.generic import ( - ABCDataFrame, - ABCSeries, -) -from pandas.core.dtypes.missing import notna - -from pandas.core._numba import executor -from pandas.core.algorithms import factorize -from pandas.core.apply import ResamplerWindowApply -from pandas.core.arrays import ExtensionArray -from pandas.core.base import SelectionMixin -import pandas.core.common as com -from pandas.core.indexers.objects import ( - BaseIndexer, - FixedWindowIndexer, - GroupbyIndexer, - VariableWindowIndexer, -) -from pandas.core.indexes.api import ( - DatetimeIndex, - Index, - MultiIndex, - PeriodIndex, - TimedeltaIndex, -) -from pandas.core.reshape.concat import concat -from pandas.core.util.numba_ import ( - get_jit_arguments, - maybe_use_numba, -) -from pandas.core.window.common import ( - flex_binary_moment, - zsqrt, -) -from pandas.core.window.doc import ( - _shared_docs, - create_section_header, - kwargs_numeric_only, - kwargs_scipy, - numba_notes, - template_header, - template_returns, - template_see_also, - window_agg_numba_parameters, - window_apply_parameters, -) -from pandas.core.window.numba_ import ( - generate_manual_numpy_nan_agg_with_axis, - generate_numba_apply_func, - generate_numba_table_func, -) - -if TYPE_CHECKING: - from collections.abc import ( - Hashable, - Iterator, - Sized, - ) - - from pandas._typing import ( - ArrayLike, - Axis, - NDFrameT, - QuantileInterpolation, - WindowingRankType, - ) - - from pandas import ( - DataFrame, - Series, - ) - from pandas.core.generic import NDFrame - from pandas.core.groupby.ops import BaseGrouper - -from pandas.core.arrays.datetimelike import dtype_to_unit - - -class BaseWindow(SelectionMixin): - """Provides utilities for performing windowing operations.""" - - _attributes: list[str] = [] - exclusions: frozenset[Hashable] = frozenset() - _on: Index - - def __init__( - self, - obj: NDFrame, - window=None, - min_periods: int | None = None, - center: bool | None = False, - win_type: str | None = None, - axis: Axis = 0, - on: str | Index | None = None, - closed: str | None = None, - step: int | None = None, - method: str = "single", - *, - selection=None, - ) -> None: - self.obj = obj - self.on = on - self.closed = closed - self.step = step - self.window = window - self.min_periods = min_periods - self.center = center - self.win_type = win_type - self.axis = obj._get_axis_number(axis) if axis is not None else None - self.method = method - self._win_freq_i8: int | None = None - if self.on is None: - if self.axis == 0: - self._on = self.obj.index - else: - # i.e. self.axis == 1 - self._on = self.obj.columns - elif isinstance(self.on, Index): - self._on = self.on - elif isinstance(self.obj, ABCDataFrame) and self.on in self.obj.columns: - self._on = Index(self.obj[self.on]) - else: - raise ValueError( - f"invalid on specified as {self.on}, " - "must be a column (of DataFrame), an Index or None" - ) - - self._selection = selection - self._validate() - - def _validate(self) -> None: - if self.center is not None and not is_bool(self.center): - raise ValueError("center must be a boolean") - if self.min_periods is not None: - if not is_integer(self.min_periods): - raise ValueError("min_periods must be an integer") - if self.min_periods < 0: - raise ValueError("min_periods must be >= 0") - if is_integer(self.window) and self.min_periods > self.window: - raise ValueError( - f"min_periods {self.min_periods} must be <= window {self.window}" - ) - if self.closed is not None and self.closed not in [ - "right", - "both", - "left", - "neither", - ]: - raise ValueError("closed must be 'right', 'left', 'both' or 'neither'") - if not isinstance(self.obj, (ABCSeries, ABCDataFrame)): - raise TypeError(f"invalid type: {type(self)}") - if isinstance(self.window, BaseIndexer): - # Validate that the passed BaseIndexer subclass has - # a get_window_bounds with the correct signature. - get_window_bounds_signature = inspect.signature( - self.window.get_window_bounds - ).parameters.keys() - expected_signature = inspect.signature( - BaseIndexer().get_window_bounds - ).parameters.keys() - if get_window_bounds_signature != expected_signature: - raise ValueError( - f"{type(self.window).__name__} does not implement " - f"the correct signature for get_window_bounds" - ) - if self.method not in ["table", "single"]: - raise ValueError("method must be 'table' or 'single") - if self.step is not None: - if not is_integer(self.step): - raise ValueError("step must be an integer") - if self.step < 0: - raise ValueError("step must be >= 0") - - def _check_window_bounds( - self, start: np.ndarray, end: np.ndarray, num_vals: int - ) -> None: - if len(start) != len(end): - raise ValueError( - f"start ({len(start)}) and end ({len(end)}) bounds must be the " - f"same length" - ) - if len(start) != (num_vals + (self.step or 1) - 1) // (self.step or 1): - raise ValueError( - f"start and end bounds ({len(start)}) must be the same length " - f"as the object ({num_vals}) divided by the step ({self.step}) " - f"if given and rounded up" - ) - - def _slice_axis_for_step(self, index: Index, result: Sized | None = None) -> Index: - """ - Slices the index for a given result and the preset step. - """ - return ( - index - if result is None or len(result) == len(index) - else index[:: self.step] - ) - - def _validate_numeric_only(self, name: str, numeric_only: bool) -> None: - """ - Validate numeric_only argument, raising if invalid for the input. - - Parameters - ---------- - name : str - Name of the operator (kernel). - numeric_only : bool - Value passed by user. - """ - if ( - self._selected_obj.ndim == 1 - and numeric_only - and not is_numeric_dtype(self._selected_obj.dtype) - ): - raise NotImplementedError( - f"{type(self).__name__}.{name} does not implement numeric_only" - ) - - def _make_numeric_only(self, obj: NDFrameT) -> NDFrameT: - """Subset DataFrame to numeric columns. - - Parameters - ---------- - obj : DataFrame - - Returns - ------- - obj subset to numeric-only columns. - """ - result = obj.select_dtypes(include=["number"], exclude=["timedelta"]) - return result - - def _create_data(self, obj: NDFrameT, numeric_only: bool = False) -> NDFrameT: - """ - Split data into blocks & return conformed data. - """ - # filter out the on from the object - if self.on is not None and not isinstance(self.on, Index) and obj.ndim == 2: - obj = obj.reindex(columns=obj.columns.difference([self.on]), copy=False) - if obj.ndim > 1 and (numeric_only or self.axis == 1): - # GH: 20649 in case of mixed dtype and axis=1 we have to convert everything - # to float to calculate the complete row at once. We exclude all non-numeric - # dtypes. - obj = self._make_numeric_only(obj) - if self.axis == 1: - obj = obj.astype("float64", copy=False) - obj._mgr = obj._mgr.consolidate() - return obj - - def _gotitem(self, key, ndim, subset=None): - """ - Sub-classes to define. Return a sliced object. - - Parameters - ---------- - key : str / list of selections - ndim : {1, 2} - requested ndim of result - subset : object, default None - subset to act on - """ - # create a new object to prevent aliasing - if subset is None: - subset = self.obj - - # we need to make a shallow copy of ourselves - # with the same groupby - kwargs = {attr: getattr(self, attr) for attr in self._attributes} - - selection = self._infer_selection(key, subset) - new_win = type(self)(subset, selection=selection, **kwargs) - return new_win - - def __getattr__(self, attr: str): - if attr in self._internal_names_set: - return object.__getattribute__(self, attr) - if attr in self.obj: - return self[attr] - - raise AttributeError( - f"'{type(self).__name__}' object has no attribute '{attr}'" - ) - - def _dir_additions(self): - return self.obj._dir_additions() - - def __repr__(self) -> str: - """ - Provide a nice str repr of our rolling object. - """ - attrs_list = ( - f"{attr_name}={getattr(self, attr_name)}" - for attr_name in self._attributes - if getattr(self, attr_name, None) is not None and attr_name[0] != "_" - ) - attrs = ",".join(attrs_list) - return f"{type(self).__name__} [{attrs}]" - - def __iter__(self) -> Iterator: - obj = self._selected_obj.set_axis(self._on) - obj = self._create_data(obj) - indexer = self._get_window_indexer() - - start, end = indexer.get_window_bounds( - num_values=len(obj), - min_periods=self.min_periods, - center=self.center, - closed=self.closed, - step=self.step, - ) - self._check_window_bounds(start, end, len(obj)) - - for s, e in zip(start, end): - result = obj.iloc[slice(s, e)] - yield result - - def _prep_values(self, values: ArrayLike) -> np.ndarray: - """Convert input to numpy arrays for Cython routines""" - if needs_i8_conversion(values.dtype): - raise NotImplementedError( - f"ops for {type(self).__name__} for this " - f"dtype {values.dtype} are not implemented" - ) - # GH #12373 : rolling functions error on float32 data - # make sure the data is coerced to float64 - try: - if isinstance(values, ExtensionArray): - values = values.to_numpy(np.float64, na_value=np.nan) - else: - values = ensure_float64(values) - except (ValueError, TypeError) as err: - raise TypeError(f"cannot handle this type -> {values.dtype}") from err - - # Convert inf to nan for C funcs - inf = np.isinf(values) - if inf.any(): - values = np.where(inf, np.nan, values) - - return values - - def _insert_on_column(self, result: DataFrame, obj: DataFrame) -> None: - # if we have an 'on' column we want to put it back into - # the results in the same location - from pandas import Series - - if self.on is not None and not self._on.equals(obj.index): - name = self._on.name - extra_col = Series(self._on, index=self.obj.index, name=name, copy=False) - if name in result.columns: - # TODO: sure we want to overwrite results? - result[name] = extra_col - elif name in result.index.names: - pass - elif name in self._selected_obj.columns: - # insert in the same location as we had in _selected_obj - old_cols = self._selected_obj.columns - new_cols = result.columns - old_loc = old_cols.get_loc(name) - overlap = new_cols.intersection(old_cols[:old_loc]) - new_loc = len(overlap) - result.insert(new_loc, name, extra_col) - else: - # insert at the end - result[name] = extra_col - - @property - def _index_array(self): - # TODO: why do we get here with e.g. MultiIndex? - if needs_i8_conversion(self._on.dtype): - idx = cast("PeriodIndex | DatetimeIndex | TimedeltaIndex", self._on) - return idx.asi8 - return None - - def _resolve_output(self, out: DataFrame, obj: DataFrame) -> DataFrame: - """Validate and finalize result.""" - if out.shape[1] == 0 and obj.shape[1] > 0: - raise DataError("No numeric types to aggregate") - if out.shape[1] == 0: - return obj.astype("float64") - - self._insert_on_column(out, obj) - return out - - def _get_window_indexer(self) -> BaseIndexer: - """ - Return an indexer class that will compute the window start and end bounds - """ - if isinstance(self.window, BaseIndexer): - return self.window - if self._win_freq_i8 is not None: - return VariableWindowIndexer( - index_array=self._index_array, - window_size=self._win_freq_i8, - center=self.center, - ) - return FixedWindowIndexer(window_size=self.window) - - def _apply_series( - self, homogeneous_func: Callable[..., ArrayLike], name: str | None = None - ) -> Series: - """ - Series version of _apply_blockwise - """ - obj = self._create_data(self._selected_obj) - - if name == "count": - # GH 12541: Special case for count where we support date-like types - obj = notna(obj).astype(int) - try: - values = self._prep_values(obj._values) - except (TypeError, NotImplementedError) as err: - raise DataError("No numeric types to aggregate") from err - - result = homogeneous_func(values) - index = self._slice_axis_for_step(obj.index, result) - return obj._constructor(result, index=index, name=obj.name) - - def _apply_blockwise( - self, - homogeneous_func: Callable[..., ArrayLike], - name: str, - numeric_only: bool = False, - ) -> DataFrame | Series: - """ - Apply the given function to the DataFrame broken down into homogeneous - sub-frames. - """ - self._validate_numeric_only(name, numeric_only) - if self._selected_obj.ndim == 1: - return self._apply_series(homogeneous_func, name) - - obj = self._create_data(self._selected_obj, numeric_only) - if name == "count": - # GH 12541: Special case for count where we support date-like types - obj = notna(obj).astype(int) - obj._mgr = obj._mgr.consolidate() - - if self.axis == 1: - obj = obj.T - - taker = [] - res_values = [] - for i, arr in enumerate(obj._iter_column_arrays()): - # GH#42736 operate column-wise instead of block-wise - # As of 2.0, hfunc will raise for nuisance columns - try: - arr = self._prep_values(arr) - except (TypeError, NotImplementedError) as err: - raise DataError( - f"Cannot aggregate non-numeric type: {arr.dtype}" - ) from err - res = homogeneous_func(arr) - res_values.append(res) - taker.append(i) - - index = self._slice_axis_for_step( - obj.index, res_values[0] if len(res_values) > 0 else None - ) - df = type(obj)._from_arrays( - res_values, - index=index, - columns=obj.columns.take(taker), - verify_integrity=False, - ) - - if self.axis == 1: - df = df.T - - return self._resolve_output(df, obj) - - def _apply_tablewise( - self, - homogeneous_func: Callable[..., ArrayLike], - name: str | None = None, - numeric_only: bool = False, - ) -> DataFrame | Series: - """ - Apply the given function to the DataFrame across the entire object - """ - if self._selected_obj.ndim == 1: - raise ValueError("method='table' not applicable for Series objects.") - obj = self._create_data(self._selected_obj, numeric_only) - values = self._prep_values(obj.to_numpy()) - values = values.T if self.axis == 1 else values - result = homogeneous_func(values) - result = result.T if self.axis == 1 else result - index = self._slice_axis_for_step(obj.index, result) - columns = ( - obj.columns - if result.shape[1] == len(obj.columns) - else obj.columns[:: self.step] - ) - out = obj._constructor(result, index=index, columns=columns) - - return self._resolve_output(out, obj) - - def _apply_pairwise( - self, - target: DataFrame | Series, - other: DataFrame | Series | None, - pairwise: bool | None, - func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series], - numeric_only: bool, - ) -> DataFrame | Series: - """ - Apply the given pairwise function given 2 pandas objects (DataFrame/Series) - """ - target = self._create_data(target, numeric_only) - if other is None: - other = target - # only default unset - pairwise = True if pairwise is None else pairwise - elif not isinstance(other, (ABCDataFrame, ABCSeries)): - raise ValueError("other must be a DataFrame or Series") - elif other.ndim == 2 and numeric_only: - other = self._make_numeric_only(other) - - return flex_binary_moment(target, other, func, pairwise=bool(pairwise)) - - def _apply( - self, - func: Callable[..., Any], - name: str, - numeric_only: bool = False, - numba_args: tuple[Any, ...] = (), - **kwargs, - ): - """ - Rolling statistical measure using supplied function. - - Designed to be used with passed-in Cython array-based functions. - - Parameters - ---------- - func : callable function to apply - name : str, - numba_args : tuple - args to be passed when func is a numba func - **kwargs - additional arguments for rolling function and window function - - Returns - ------- - y : type of input - """ - window_indexer = self._get_window_indexer() - min_periods = ( - self.min_periods - if self.min_periods is not None - else window_indexer.window_size - ) - - def homogeneous_func(values: np.ndarray): - # calculation function - - if values.size == 0: - return values.copy() - - def calc(x): - start, end = window_indexer.get_window_bounds( - num_values=len(x), - min_periods=min_periods, - center=self.center, - closed=self.closed, - step=self.step, - ) - self._check_window_bounds(start, end, len(x)) - - return func(x, start, end, min_periods, *numba_args) - - with np.errstate(all="ignore"): - result = calc(values) - - return result - - if self.method == "single": - return self._apply_blockwise(homogeneous_func, name, numeric_only) - else: - return self._apply_tablewise(homogeneous_func, name, numeric_only) - - def _numba_apply( - self, - func: Callable[..., Any], - engine_kwargs: dict[str, bool] | None = None, - **func_kwargs, - ): - window_indexer = self._get_window_indexer() - min_periods = ( - self.min_periods - if self.min_periods is not None - else window_indexer.window_size - ) - obj = self._create_data(self._selected_obj) - if self.axis == 1: - obj = obj.T - values = self._prep_values(obj.to_numpy()) - if values.ndim == 1: - values = values.reshape(-1, 1) - start, end = window_indexer.get_window_bounds( - num_values=len(values), - min_periods=min_periods, - center=self.center, - closed=self.closed, - step=self.step, - ) - self._check_window_bounds(start, end, len(values)) - # For now, map everything to float to match the Cython impl - # even though it is wrong - # TODO: Could preserve correct dtypes in future - # xref #53214 - dtype_mapping = executor.float_dtype_mapping - aggregator = executor.generate_shared_aggregator( - func, - dtype_mapping, - is_grouped_kernel=False, - **get_jit_arguments(engine_kwargs), - ) - result = aggregator( - values.T, start=start, end=end, min_periods=min_periods, **func_kwargs - ).T - result = result.T if self.axis == 1 else result - index = self._slice_axis_for_step(obj.index, result) - if obj.ndim == 1: - result = result.squeeze() - out = obj._constructor(result, index=index, name=obj.name) - return out - else: - columns = self._slice_axis_for_step(obj.columns, result.T) - out = obj._constructor(result, index=index, columns=columns) - return self._resolve_output(out, obj) - - def aggregate(self, func, *args, **kwargs): - result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg() - if result is None: - return self.apply(func, raw=False, args=args, kwargs=kwargs) - return result - - agg = aggregate - - -class BaseWindowGroupby(BaseWindow): - """ - Provide the groupby windowing facilities. - """ - - _grouper: BaseGrouper - _as_index: bool - _attributes: list[str] = ["_grouper"] - - def __init__( - self, - obj: DataFrame | Series, - *args, - _grouper: BaseGrouper, - _as_index: bool = True, - **kwargs, - ) -> None: - from pandas.core.groupby.ops import BaseGrouper - - if not isinstance(_grouper, BaseGrouper): - raise ValueError("Must pass a BaseGrouper object.") - self._grouper = _grouper - self._as_index = _as_index - # GH 32262: It's convention to keep the grouping column in - # groupby., but unexpected to users in - # groupby.rolling. - obj = obj.drop(columns=self._grouper.names, errors="ignore") - # GH 15354 - if kwargs.get("step") is not None: - raise NotImplementedError("step not implemented for groupby") - super().__init__(obj, *args, **kwargs) - - def _apply( - self, - func: Callable[..., Any], - name: str, - numeric_only: bool = False, - numba_args: tuple[Any, ...] = (), - **kwargs, - ) -> DataFrame | Series: - result = super()._apply( - func, - name, - numeric_only, - numba_args, - **kwargs, - ) - # Reconstruct the resulting MultiIndex - # 1st set of levels = group by labels - # 2nd set of levels = original DataFrame/Series index - grouped_object_index = self.obj.index - grouped_index_name = [*grouped_object_index.names] - groupby_keys = copy.copy(self._grouper.names) - result_index_names = groupby_keys + grouped_index_name - - drop_columns = [ - key - for key in self._grouper.names - if key not in self.obj.index.names or key is None - ] - - if len(drop_columns) != len(groupby_keys): - # Our result will have still kept the column in the result - result = result.drop(columns=drop_columns, errors="ignore") - - codes = self._grouper.codes - levels = copy.copy(self._grouper.levels) - - group_indices = self._grouper.indices.values() - if group_indices: - indexer = np.concatenate(list(group_indices)) - else: - indexer = np.array([], dtype=np.intp) - codes = [c.take(indexer) for c in codes] - - # if the index of the original dataframe needs to be preserved, append - # this index (but reordered) to the codes/levels from the groupby - if grouped_object_index is not None: - idx = grouped_object_index.take(indexer) - if not isinstance(idx, MultiIndex): - idx = MultiIndex.from_arrays([idx]) - codes.extend(list(idx.codes)) - levels.extend(list(idx.levels)) - - result_index = MultiIndex( - levels, codes, names=result_index_names, verify_integrity=False - ) - - result.index = result_index - if not self._as_index: - result = result.reset_index(level=list(range(len(groupby_keys)))) - return result - - def _apply_pairwise( - self, - target: DataFrame | Series, - other: DataFrame | Series | None, - pairwise: bool | None, - func: Callable[[DataFrame | Series, DataFrame | Series], DataFrame | Series], - numeric_only: bool, - ) -> DataFrame | Series: - """ - Apply the given pairwise function given 2 pandas objects (DataFrame/Series) - """ - # Manually drop the grouping column first - target = target.drop(columns=self._grouper.names, errors="ignore") - result = super()._apply_pairwise(target, other, pairwise, func, numeric_only) - # 1) Determine the levels + codes of the groupby levels - if other is not None and not all( - len(group) == len(other) for group in self._grouper.indices.values() - ): - # GH 42915 - # len(other) != len(any group), so must reindex (expand) the result - # from flex_binary_moment to a "transform"-like result - # per groupby combination - old_result_len = len(result) - result = concat( - [ - result.take(gb_indices).reindex(result.index) - for gb_indices in self._grouper.indices.values() - ] - ) - - gb_pairs = ( - com.maybe_make_list(pair) for pair in self._grouper.indices.keys() - ) - groupby_codes = [] - groupby_levels = [] - # e.g. [[1, 2], [4, 5]] as [[1, 4], [2, 5]] - for gb_level_pair in map(list, zip(*gb_pairs)): - labels = np.repeat(np.array(gb_level_pair), old_result_len) - codes, levels = factorize(labels) - groupby_codes.append(codes) - groupby_levels.append(levels) - else: - # pairwise=True or len(other) == len(each group), so repeat - # the groupby labels by the number of columns in the original object - groupby_codes = self._grouper.codes - # error: Incompatible types in assignment (expression has type - # "List[Index]", variable has type "List[Union[ndarray, Index]]") - groupby_levels = self._grouper.levels # type: ignore[assignment] - - group_indices = self._grouper.indices.values() - if group_indices: - indexer = np.concatenate(list(group_indices)) - else: - indexer = np.array([], dtype=np.intp) - - if target.ndim == 1: - repeat_by = 1 - else: - repeat_by = len(target.columns) - groupby_codes = [ - np.repeat(c.take(indexer), repeat_by) for c in groupby_codes - ] - # 2) Determine the levels + codes of the result from super()._apply_pairwise - if isinstance(result.index, MultiIndex): - result_codes = list(result.index.codes) - result_levels = list(result.index.levels) - result_names = list(result.index.names) - else: - idx_codes, idx_levels = factorize(result.index) - result_codes = [idx_codes] - result_levels = [idx_levels] - result_names = [result.index.name] - - # 3) Create the resulting index by combining 1) + 2) - result_codes = groupby_codes + result_codes - result_levels = groupby_levels + result_levels - result_names = self._grouper.names + result_names - - result_index = MultiIndex( - result_levels, result_codes, names=result_names, verify_integrity=False - ) - result.index = result_index - return result - - def _create_data(self, obj: NDFrameT, numeric_only: bool = False) -> NDFrameT: - """ - Split data into blocks & return conformed data. - """ - # Ensure the object we're rolling over is monotonically sorted relative - # to the groups - # GH 36197 - if not obj.empty: - groupby_order = np.concatenate(list(self._grouper.indices.values())).astype( - np.int64 - ) - obj = obj.take(groupby_order) - return super()._create_data(obj, numeric_only) - - def _gotitem(self, key, ndim, subset=None): - # we are setting the index on the actual object - # here so our index is carried through to the selected obj - # when we do the splitting for the groupby - if self.on is not None: - # GH 43355 - subset = self.obj.set_index(self._on) - return super()._gotitem(key, ndim, subset=subset) - - -class Window(BaseWindow): - """ - Provide rolling window calculations. - - Parameters - ---------- - window : int, timedelta, str, offset, or BaseIndexer subclass - Size of the moving window. - - If an integer, the fixed number of observations used for - each window. - - If a timedelta, str, or offset, the time period of each window. Each - window will be a variable sized based on the observations included in - the time-period. This is only valid for datetimelike indexes. - To learn more about the offsets & frequency strings, please see `this link - `__. - - If a BaseIndexer subclass, the window boundaries - based on the defined ``get_window_bounds`` method. Additional rolling - keyword arguments, namely ``min_periods``, ``center``, ``closed`` and - ``step`` will be passed to ``get_window_bounds``. - - min_periods : int, default None - Minimum number of observations in window required to have a value; - otherwise, result is ``np.nan``. - - For a window that is specified by an offset, ``min_periods`` will default to 1. - - For a window that is specified by an integer, ``min_periods`` will default - to the size of the window. - - center : bool, default False - If False, set the window labels as the right edge of the window index. - - If True, set the window labels as the center of the window index. - - win_type : str, default None - If ``None``, all points are evenly weighted. - - If a string, it must be a valid `scipy.signal window function - `__. - - Certain Scipy window types require additional parameters to be passed - in the aggregation function. The additional parameters must match - the keywords specified in the Scipy window type method signature. - - on : str, optional - For a DataFrame, a column label or Index level on which - to calculate the rolling window, rather than the DataFrame's index. - - Provided integer column is ignored and excluded from result since - an integer index is not used to calculate the rolling window. - - axis : int or str, default 0 - If ``0`` or ``'index'``, roll across the rows. - - If ``1`` or ``'columns'``, roll across the columns. - - For `Series` this parameter is unused and defaults to 0. - - closed : str, default None - If ``'right'``, the first point in the window is excluded from calculations. - - If ``'left'``, the last point in the window is excluded from calculations. - - If ``'both'``, the no points in the window are excluded from calculations. - - If ``'neither'``, the first and last points in the window are excluded - from calculations. - - Default ``None`` (``'right'``). - - .. versionchanged:: 1.2.0 - - The closed parameter with fixed windows is now supported. - - step : int, default None - - .. versionadded:: 1.5.0 - - Evaluate the window at every ``step`` result, equivalent to slicing as - ``[::step]``. ``window`` must be an integer. Using a step argument other - than None or 1 will produce a result with a different shape than the input. - - method : str {'single', 'table'}, default 'single' - - .. versionadded:: 1.3.0 - - Execute the rolling operation per single column or row (``'single'``) - or over the entire object (``'table'``). - - This argument is only implemented when specifying ``engine='numba'`` - in the method call. - - Returns - ------- - pandas.api.typing.Window or pandas.api.typing.Rolling - An instance of Window is returned if ``win_type`` is passed. Otherwise, - an instance of Rolling is returned. - - See Also - -------- - expanding : Provides expanding transformations. - ewm : Provides exponential weighted functions. - - Notes - ----- - See :ref:`Windowing Operations ` for further usage details - and examples. - - Examples - -------- - >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}) - >>> df - B - 0 0.0 - 1 1.0 - 2 2.0 - 3 NaN - 4 4.0 - - **window** - - Rolling sum with a window length of 2 observations. - - >>> df.rolling(2).sum() - B - 0 NaN - 1 1.0 - 2 3.0 - 3 NaN - 4 NaN - - Rolling sum with a window span of 2 seconds. - - >>> df_time = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]}, - ... index=[pd.Timestamp('20130101 09:00:00'), - ... pd.Timestamp('20130101 09:00:02'), - ... pd.Timestamp('20130101 09:00:03'), - ... pd.Timestamp('20130101 09:00:05'), - ... pd.Timestamp('20130101 09:00:06')]) - - >>> df_time - B - 2013-01-01 09:00:00 0.0 - 2013-01-01 09:00:02 1.0 - 2013-01-01 09:00:03 2.0 - 2013-01-01 09:00:05 NaN - 2013-01-01 09:00:06 4.0 - - >>> df_time.rolling('2s').sum() - B - 2013-01-01 09:00:00 0.0 - 2013-01-01 09:00:02 1.0 - 2013-01-01 09:00:03 3.0 - 2013-01-01 09:00:05 NaN - 2013-01-01 09:00:06 4.0 - - Rolling sum with forward looking windows with 2 observations. - - >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2) - >>> df.rolling(window=indexer, min_periods=1).sum() - B - 0 1.0 - 1 3.0 - 2 2.0 - 3 4.0 - 4 4.0 - - **min_periods** - - Rolling sum with a window length of 2 observations, but only needs a minimum of 1 - observation to calculate a value. - - >>> df.rolling(2, min_periods=1).sum() - B - 0 0.0 - 1 1.0 - 2 3.0 - 3 2.0 - 4 4.0 - - **center** - - Rolling sum with the result assigned to the center of the window index. - - >>> df.rolling(3, min_periods=1, center=True).sum() - B - 0 1.0 - 1 3.0 - 2 3.0 - 3 6.0 - 4 4.0 - - >>> df.rolling(3, min_periods=1, center=False).sum() - B - 0 0.0 - 1 1.0 - 2 3.0 - 3 3.0 - 4 6.0 - - **step** - - Rolling sum with a window length of 2 observations, minimum of 1 observation to - calculate a value, and a step of 2. - - >>> df.rolling(2, min_periods=1, step=2).sum() - B - 0 0.0 - 2 3.0 - 4 4.0 - - **win_type** - - Rolling sum with a window length of 2, using the Scipy ``'gaussian'`` - window type. ``std`` is required in the aggregation function. - - >>> df.rolling(2, win_type='gaussian').sum(std=3) - B - 0 NaN - 1 0.986207 - 2 2.958621 - 3 NaN - 4 NaN - - **on** - - Rolling sum with a window length of 2 days. - - >>> df = pd.DataFrame({ - ... 'A': [pd.to_datetime('2020-01-01'), - ... pd.to_datetime('2020-01-01'), - ... pd.to_datetime('2020-01-02'),], - ... 'B': [1, 2, 3], }, - ... index=pd.date_range('2020', periods=3)) - - >>> df - A B - 2020-01-01 2020-01-01 1 - 2020-01-02 2020-01-01 2 - 2020-01-03 2020-01-02 3 - - >>> df.rolling('2D', on='A').sum() - A B - 2020-01-01 2020-01-01 1.0 - 2020-01-02 2020-01-01 3.0 - 2020-01-03 2020-01-02 6.0 - """ - - _attributes = [ - "window", - "min_periods", - "center", - "win_type", - "axis", - "on", - "closed", - "step", - "method", - ] - - def _validate(self): - super()._validate() - - if not isinstance(self.win_type, str): - raise ValueError(f"Invalid win_type {self.win_type}") - signal = import_optional_dependency( - "scipy.signal.windows", extra="Scipy is required to generate window weight." - ) - self._scipy_weight_generator = getattr(signal, self.win_type, None) - if self._scipy_weight_generator is None: - raise ValueError(f"Invalid win_type {self.win_type}") - - if isinstance(self.window, BaseIndexer): - raise NotImplementedError( - "BaseIndexer subclasses not implemented with win_types." - ) - if not is_integer(self.window) or self.window < 0: - raise ValueError("window must be an integer 0 or greater") - - if self.method != "single": - raise NotImplementedError("'single' is the only supported method type.") - - def _center_window(self, result: np.ndarray, offset: int) -> np.ndarray: - """ - Center the result in the window for weighted rolling aggregations. - """ - if offset > 0: - lead_indexer = [slice(offset, None)] - result = np.copy(result[tuple(lead_indexer)]) - return result - - def _apply( - self, - func: Callable[[np.ndarray, int, int], np.ndarray], - name: str, - numeric_only: bool = False, - numba_args: tuple[Any, ...] = (), - **kwargs, - ): - """ - Rolling with weights statistical measure using supplied function. - - Designed to be used with passed-in Cython array-based functions. - - Parameters - ---------- - func : callable function to apply - name : str, - numeric_only : bool, default False - Whether to only operate on bool, int, and float columns - numba_args : tuple - unused - **kwargs - additional arguments for scipy windows if necessary - - Returns - ------- - y : type of input - """ - # "None" not callable [misc] - window = self._scipy_weight_generator( # type: ignore[misc] - self.window, **kwargs - ) - offset = (len(window) - 1) // 2 if self.center else 0 - - def homogeneous_func(values: np.ndarray): - # calculation function - - if values.size == 0: - return values.copy() - - def calc(x): - additional_nans = np.array([np.nan] * offset) - x = np.concatenate((x, additional_nans)) - return func( - x, - window, - self.min_periods if self.min_periods is not None else len(window), - ) - - with np.errstate(all="ignore"): - # Our weighted aggregations return memoryviews - result = np.asarray(calc(values)) - - if self.center: - result = self._center_window(result, offset) - - return result - - return self._apply_blockwise(homogeneous_func, name, numeric_only)[:: self.step] - - @doc( - _shared_docs["aggregate"], - see_also=dedent( - """ - See Also - -------- - pandas.DataFrame.aggregate : Similar DataFrame method. - pandas.Series.aggregate : Similar Series method. - """ - ), - examples=dedent( - """ - Examples - -------- - >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) - >>> df - A B C - 0 1 4 7 - 1 2 5 8 - 2 3 6 9 - - >>> df.rolling(2, win_type="boxcar").agg("mean") - A B C - 0 NaN NaN NaN - 1 1.5 4.5 7.5 - 2 2.5 5.5 8.5 - """ - ), - klass="Series/DataFrame", - axis="", - ) - def aggregate(self, func, *args, **kwargs): - result = ResamplerWindowApply(self, func, args=args, kwargs=kwargs).agg() - if result is None: - # these must apply directly - result = func(self) - - return result - - agg = aggregate - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - kwargs_scipy, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([0, 1, 5, 2, 8]) - - To get an instance of :class:`~pandas.core.window.rolling.Window` we need - to pass the parameter `win_type`. - - >>> type(ser.rolling(2, win_type='gaussian')) - - - In order to use the `SciPy` Gaussian window we need to provide the parameters - `M` and `std`. The parameter `M` corresponds to 2 in our example. - We pass the second parameter `std` as a parameter of the following method - (`sum` in this case): - - >>> ser.rolling(2, win_type='gaussian').sum(std=3) - 0 NaN - 1 0.986207 - 2 5.917243 - 3 6.903450 - 4 9.862071 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="weighted window sum", - agg_method="sum", - ) - def sum(self, numeric_only: bool = False, **kwargs): - window_func = window_aggregations.roll_weighted_sum - # error: Argument 1 to "_apply" of "Window" has incompatible type - # "Callable[[ndarray, ndarray, int], ndarray]"; expected - # "Callable[[ndarray, int, int], ndarray]" - return self._apply( - window_func, # type: ignore[arg-type] - name="sum", - numeric_only=numeric_only, - **kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - kwargs_scipy, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([0, 1, 5, 2, 8]) - - To get an instance of :class:`~pandas.core.window.rolling.Window` we need - to pass the parameter `win_type`. - - >>> type(ser.rolling(2, win_type='gaussian')) - - - In order to use the `SciPy` Gaussian window we need to provide the parameters - `M` and `std`. The parameter `M` corresponds to 2 in our example. - We pass the second parameter `std` as a parameter of the following method: - - >>> ser.rolling(2, win_type='gaussian').mean(std=3) - 0 NaN - 1 0.5 - 2 3.0 - 3 3.5 - 4 5.0 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="weighted window mean", - agg_method="mean", - ) - def mean(self, numeric_only: bool = False, **kwargs): - window_func = window_aggregations.roll_weighted_mean - # error: Argument 1 to "_apply" of "Window" has incompatible type - # "Callable[[ndarray, ndarray, int], ndarray]"; expected - # "Callable[[ndarray, int, int], ndarray]" - return self._apply( - window_func, # type: ignore[arg-type] - name="mean", - numeric_only=numeric_only, - **kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - kwargs_scipy, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([0, 1, 5, 2, 8]) - - To get an instance of :class:`~pandas.core.window.rolling.Window` we need - to pass the parameter `win_type`. - - >>> type(ser.rolling(2, win_type='gaussian')) - - - In order to use the `SciPy` Gaussian window we need to provide the parameters - `M` and `std`. The parameter `M` corresponds to 2 in our example. - We pass the second parameter `std` as a parameter of the following method: - - >>> ser.rolling(2, win_type='gaussian').var(std=3) - 0 NaN - 1 0.5 - 2 8.0 - 3 4.5 - 4 18.0 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="weighted window variance", - agg_method="var", - ) - def var(self, ddof: int = 1, numeric_only: bool = False, **kwargs): - window_func = partial(window_aggregations.roll_weighted_var, ddof=ddof) - kwargs.pop("name", None) - return self._apply(window_func, name="var", numeric_only=numeric_only, **kwargs) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - kwargs_scipy, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([0, 1, 5, 2, 8]) - - To get an instance of :class:`~pandas.core.window.rolling.Window` we need - to pass the parameter `win_type`. - - >>> type(ser.rolling(2, win_type='gaussian')) - - - In order to use the `SciPy` Gaussian window we need to provide the parameters - `M` and `std`. The parameter `M` corresponds to 2 in our example. - We pass the second parameter `std` as a parameter of the following method: - - >>> ser.rolling(2, win_type='gaussian').std(std=3) - 0 NaN - 1 0.707107 - 2 2.828427 - 3 2.121320 - 4 4.242641 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="weighted window standard deviation", - agg_method="std", - ) - def std(self, ddof: int = 1, numeric_only: bool = False, **kwargs): - return zsqrt( - self.var(ddof=ddof, name="std", numeric_only=numeric_only, **kwargs) - ) - - -class RollingAndExpandingMixin(BaseWindow): - def count(self, numeric_only: bool = False): - window_func = window_aggregations.roll_sum - return self._apply(window_func, name="count", numeric_only=numeric_only) - - def apply( - self, - func: Callable[..., Any], - raw: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - args: tuple[Any, ...] | None = None, - kwargs: dict[str, Any] | None = None, - ): - if args is None: - args = () - if kwargs is None: - kwargs = {} - - if not is_bool(raw): - raise ValueError("raw parameter must be `True` or `False`") - - numba_args: tuple[Any, ...] = () - if maybe_use_numba(engine): - if raw is False: - raise ValueError("raw must be `True` when using the numba engine") - numba_args = args - if self.method == "single": - apply_func = generate_numba_apply_func( - func, **get_jit_arguments(engine_kwargs, kwargs) - ) - else: - apply_func = generate_numba_table_func( - func, **get_jit_arguments(engine_kwargs, kwargs) - ) - elif engine in ("cython", None): - if engine_kwargs is not None: - raise ValueError("cython engine does not accept engine_kwargs") - apply_func = self._generate_cython_apply_func(args, kwargs, raw, func) - else: - raise ValueError("engine must be either 'numba' or 'cython'") - - return self._apply( - apply_func, - name="apply", - numba_args=numba_args, - ) - - def _generate_cython_apply_func( - self, - args: tuple[Any, ...], - kwargs: dict[str, Any], - raw: bool | np.bool_, - function: Callable[..., Any], - ) -> Callable[[np.ndarray, np.ndarray, np.ndarray, int], np.ndarray]: - from pandas import Series - - window_func = partial( - window_aggregations.roll_apply, - args=args, - kwargs=kwargs, - raw=raw, - function=function, - ) - - def apply_func(values, begin, end, min_periods, raw=raw): - if not raw: - # GH 45912 - values = Series(values, index=self._on, copy=False) - return window_func(values, begin, end, min_periods) - - return apply_func - - def sum( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - func = generate_manual_numpy_nan_agg_with_axis(np.nansum) - return self.apply( - func, - raw=True, - engine=engine, - engine_kwargs=engine_kwargs, - ) - else: - from pandas.core._numba.kernels import sliding_sum - - return self._numba_apply(sliding_sum, engine_kwargs) - window_func = window_aggregations.roll_sum - return self._apply(window_func, name="sum", numeric_only=numeric_only) - - def max( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - func = generate_manual_numpy_nan_agg_with_axis(np.nanmax) - return self.apply( - func, - raw=True, - engine=engine, - engine_kwargs=engine_kwargs, - ) - else: - from pandas.core._numba.kernels import sliding_min_max - - return self._numba_apply(sliding_min_max, engine_kwargs, is_max=True) - window_func = window_aggregations.roll_max - return self._apply(window_func, name="max", numeric_only=numeric_only) - - def min( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - func = generate_manual_numpy_nan_agg_with_axis(np.nanmin) - return self.apply( - func, - raw=True, - engine=engine, - engine_kwargs=engine_kwargs, - ) - else: - from pandas.core._numba.kernels import sliding_min_max - - return self._numba_apply(sliding_min_max, engine_kwargs, is_max=False) - window_func = window_aggregations.roll_min - return self._apply(window_func, name="min", numeric_only=numeric_only) - - def mean( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - func = generate_manual_numpy_nan_agg_with_axis(np.nanmean) - return self.apply( - func, - raw=True, - engine=engine, - engine_kwargs=engine_kwargs, - ) - else: - from pandas.core._numba.kernels import sliding_mean - - return self._numba_apply(sliding_mean, engine_kwargs) - window_func = window_aggregations.roll_mean - return self._apply(window_func, name="mean", numeric_only=numeric_only) - - def median( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - func = generate_manual_numpy_nan_agg_with_axis(np.nanmedian) - else: - func = np.nanmedian - - return self.apply( - func, - raw=True, - engine=engine, - engine_kwargs=engine_kwargs, - ) - window_func = window_aggregations.roll_median_c - return self._apply(window_func, name="median", numeric_only=numeric_only) - - def std( - self, - ddof: int = 1, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - raise NotImplementedError("std not supported with method='table'") - from pandas.core._numba.kernels import sliding_var - - return zsqrt(self._numba_apply(sliding_var, engine_kwargs, ddof=ddof)) - window_func = window_aggregations.roll_var - - def zsqrt_func(values, begin, end, min_periods): - return zsqrt(window_func(values, begin, end, min_periods, ddof=ddof)) - - return self._apply( - zsqrt_func, - name="std", - numeric_only=numeric_only, - ) - - def var( - self, - ddof: int = 1, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - if maybe_use_numba(engine): - if self.method == "table": - raise NotImplementedError("var not supported with method='table'") - from pandas.core._numba.kernels import sliding_var - - return self._numba_apply(sliding_var, engine_kwargs, ddof=ddof) - window_func = partial(window_aggregations.roll_var, ddof=ddof) - return self._apply( - window_func, - name="var", - numeric_only=numeric_only, - ) - - def skew(self, numeric_only: bool = False): - window_func = window_aggregations.roll_skew - return self._apply( - window_func, - name="skew", - numeric_only=numeric_only, - ) - - def sem(self, ddof: int = 1, numeric_only: bool = False): - # Raise here so error message says sem instead of std - self._validate_numeric_only("sem", numeric_only) - return self.std(numeric_only=numeric_only) / ( - self.count(numeric_only=numeric_only) - ddof - ).pow(0.5) - - def kurt(self, numeric_only: bool = False): - window_func = window_aggregations.roll_kurt - return self._apply( - window_func, - name="kurt", - numeric_only=numeric_only, - ) - - def quantile( - self, - q: float, - interpolation: QuantileInterpolation = "linear", - numeric_only: bool = False, - ): - if q == 1.0: - window_func = window_aggregations.roll_max - elif q == 0.0: - window_func = window_aggregations.roll_min - else: - window_func = partial( - window_aggregations.roll_quantile, - quantile=q, - interpolation=interpolation, - ) - - return self._apply(window_func, name="quantile", numeric_only=numeric_only) - - def rank( - self, - method: WindowingRankType = "average", - ascending: bool = True, - pct: bool = False, - numeric_only: bool = False, - ): - window_func = partial( - window_aggregations.roll_rank, - method=method, - ascending=ascending, - percentile=pct, - ) - - return self._apply(window_func, name="rank", numeric_only=numeric_only) - - def cov( - self, - other: DataFrame | Series | None = None, - pairwise: bool | None = None, - ddof: int = 1, - numeric_only: bool = False, - ): - if self.step is not None: - raise NotImplementedError("step not implemented for cov") - self._validate_numeric_only("cov", numeric_only) - - from pandas import Series - - def cov_func(x, y): - x_array = self._prep_values(x) - y_array = self._prep_values(y) - window_indexer = self._get_window_indexer() - min_periods = ( - self.min_periods - if self.min_periods is not None - else window_indexer.window_size - ) - start, end = window_indexer.get_window_bounds( - num_values=len(x_array), - min_periods=min_periods, - center=self.center, - closed=self.closed, - step=self.step, - ) - self._check_window_bounds(start, end, len(x_array)) - - with np.errstate(all="ignore"): - mean_x_y = window_aggregations.roll_mean( - x_array * y_array, start, end, min_periods - ) - mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods) - mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods) - count_x_y = window_aggregations.roll_sum( - notna(x_array + y_array).astype(np.float64), start, end, 0 - ) - result = (mean_x_y - mean_x * mean_y) * (count_x_y / (count_x_y - ddof)) - return Series(result, index=x.index, name=x.name, copy=False) - - return self._apply_pairwise( - self._selected_obj, other, pairwise, cov_func, numeric_only - ) - - def corr( - self, - other: DataFrame | Series | None = None, - pairwise: bool | None = None, - ddof: int = 1, - numeric_only: bool = False, - ): - if self.step is not None: - raise NotImplementedError("step not implemented for corr") - self._validate_numeric_only("corr", numeric_only) - - from pandas import Series - - def corr_func(x, y): - x_array = self._prep_values(x) - y_array = self._prep_values(y) - window_indexer = self._get_window_indexer() - min_periods = ( - self.min_periods - if self.min_periods is not None - else window_indexer.window_size - ) - start, end = window_indexer.get_window_bounds( - num_values=len(x_array), - min_periods=min_periods, - center=self.center, - closed=self.closed, - step=self.step, - ) - self._check_window_bounds(start, end, len(x_array)) - - with np.errstate(all="ignore"): - mean_x_y = window_aggregations.roll_mean( - x_array * y_array, start, end, min_periods - ) - mean_x = window_aggregations.roll_mean(x_array, start, end, min_periods) - mean_y = window_aggregations.roll_mean(y_array, start, end, min_periods) - count_x_y = window_aggregations.roll_sum( - notna(x_array + y_array).astype(np.float64), start, end, 0 - ) - x_var = window_aggregations.roll_var( - x_array, start, end, min_periods, ddof - ) - y_var = window_aggregations.roll_var( - y_array, start, end, min_periods, ddof - ) - numerator = (mean_x_y - mean_x * mean_y) * ( - count_x_y / (count_x_y - ddof) - ) - denominator = (x_var * y_var) ** 0.5 - result = numerator / denominator - return Series(result, index=x.index, name=x.name, copy=False) - - return self._apply_pairwise( - self._selected_obj, other, pairwise, corr_func, numeric_only - ) - - -class Rolling(RollingAndExpandingMixin): - _attributes: list[str] = [ - "window", - "min_periods", - "center", - "win_type", - "axis", - "on", - "closed", - "step", - "method", - ] - - def _validate(self): - super()._validate() - - # we allow rolling on a datetimelike index - if ( - self.obj.empty - or isinstance(self._on, (DatetimeIndex, TimedeltaIndex, PeriodIndex)) - ) and isinstance(self.window, (str, BaseOffset, timedelta)): - self._validate_datetimelike_monotonic() - - # this will raise ValueError on non-fixed freqs - try: - freq = to_offset(self.window) - except (TypeError, ValueError) as err: - raise ValueError( - f"passed window {self.window} is not " - "compatible with a datetimelike index" - ) from err - if isinstance(self._on, PeriodIndex): - # error: Incompatible types in assignment (expression has type - # "float", variable has type "Optional[int]") - self._win_freq_i8 = freq.nanos / ( # type: ignore[assignment] - self._on.freq.nanos / self._on.freq.n - ) - else: - try: - unit = dtype_to_unit(self._on.dtype) # type: ignore[arg-type] - except TypeError: - # if not a datetime dtype, eg for empty dataframes - unit = "ns" - self._win_freq_i8 = Timedelta(freq.nanos).as_unit(unit)._value - - # min_periods must be an integer - if self.min_periods is None: - self.min_periods = 1 - - if self.step is not None: - raise NotImplementedError( - "step is not supported with frequency windows" - ) - - elif isinstance(self.window, BaseIndexer): - # Passed BaseIndexer subclass should handle all other rolling kwargs - pass - elif not is_integer(self.window) or self.window < 0: - raise ValueError("window must be an integer 0 or greater") - - def _validate_datetimelike_monotonic(self) -> None: - """ - Validate self._on is monotonic (increasing or decreasing) and has - no NaT values for frequency windows. - """ - if self._on.hasnans: - self._raise_monotonic_error("values must not have NaT") - if not (self._on.is_monotonic_increasing or self._on.is_monotonic_decreasing): - self._raise_monotonic_error("values must be monotonic") - - def _raise_monotonic_error(self, msg: str): - on = self.on - if on is None: - if self.axis == 0: - on = "index" - else: - on = "column" - raise ValueError(f"{on} {msg}") - - @doc( - _shared_docs["aggregate"], - see_also=dedent( - """ - See Also - -------- - pandas.Series.rolling : Calling object with Series data. - pandas.DataFrame.rolling : Calling object with DataFrame data. - """ - ), - examples=dedent( - """ - Examples - -------- - >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6], "C": [7, 8, 9]}) - >>> df - A B C - 0 1 4 7 - 1 2 5 8 - 2 3 6 9 - - >>> df.rolling(2).sum() - A B C - 0 NaN NaN NaN - 1 3.0 9.0 15.0 - 2 5.0 11.0 17.0 - - >>> df.rolling(2).agg({"A": "sum", "B": "min"}) - A B - 0 NaN NaN - 1 3.0 4.0 - 2 5.0 5.0 - """ - ), - klass="Series/Dataframe", - axis="", - ) - def aggregate(self, func, *args, **kwargs): - return super().aggregate(func, *args, **kwargs) - - agg = aggregate - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([2, 3, np.nan, 10]) - >>> s.rolling(2).count() - 0 NaN - 1 2.0 - 2 1.0 - 3 1.0 - dtype: float64 - >>> s.rolling(3).count() - 0 NaN - 1 NaN - 2 2.0 - 3 2.0 - dtype: float64 - >>> s.rolling(4).count() - 0 NaN - 1 NaN - 2 NaN - 3 3.0 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="count of non NaN observations", - agg_method="count", - ) - def count(self, numeric_only: bool = False): - return super().count(numeric_only) - - @doc( - template_header, - create_section_header("Parameters"), - window_apply_parameters, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([1, 6, 5, 4]) - >>> ser.rolling(2).apply(lambda s: s.sum() - s.min()) - 0 NaN - 1 6.0 - 2 6.0 - 3 5.0 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="custom aggregation function", - agg_method="apply", - ) - def apply( - self, - func: Callable[..., Any], - raw: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - args: tuple[Any, ...] | None = None, - kwargs: dict[str, Any] | None = None, - ): - return super().apply( - func, - raw=raw, - engine=engine, - engine_kwargs=engine_kwargs, - args=args, - kwargs=kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - window_agg_numba_parameters(), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Notes"), - numba_notes, - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([1, 2, 3, 4, 5]) - >>> s - 0 1 - 1 2 - 2 3 - 3 4 - 4 5 - dtype: int64 - - >>> s.rolling(3).sum() - 0 NaN - 1 NaN - 2 6.0 - 3 9.0 - 4 12.0 - dtype: float64 - - >>> s.rolling(3, center=True).sum() - 0 NaN - 1 6.0 - 2 9.0 - 3 12.0 - 4 NaN - dtype: float64 - - For DataFrame, each sum is computed column-wise. - - >>> df = pd.DataFrame({{"A": s, "B": s ** 2}}) - >>> df - A B - 0 1 1 - 1 2 4 - 2 3 9 - 3 4 16 - 4 5 25 - - >>> df.rolling(3).sum() - A B - 0 NaN NaN - 1 NaN NaN - 2 6.0 14.0 - 3 9.0 29.0 - 4 12.0 50.0 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="sum", - agg_method="sum", - ) - def sum( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - return super().sum( - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - window_agg_numba_parameters(), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Notes"), - numba_notes, - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([1, 2, 3, 4]) - >>> ser.rolling(2).max() - 0 NaN - 1 2.0 - 2 3.0 - 3 4.0 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="maximum", - agg_method="max", - ) - def max( - self, - numeric_only: bool = False, - *args, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - **kwargs, - ): - return super().max( - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - window_agg_numba_parameters(), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Notes"), - numba_notes, - create_section_header("Examples"), - dedent( - """ - Performing a rolling minimum with a window size of 3. - - >>> s = pd.Series([4, 3, 5, 2, 6]) - >>> s.rolling(3).min() - 0 NaN - 1 NaN - 2 3.0 - 3 2.0 - 4 2.0 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="minimum", - agg_method="min", - ) - def min( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - return super().min( - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - window_agg_numba_parameters(), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Notes"), - numba_notes, - create_section_header("Examples"), - dedent( - """ - The below examples will show rolling mean calculations with window sizes of - two and three, respectively. - - >>> s = pd.Series([1, 2, 3, 4]) - >>> s.rolling(2).mean() - 0 NaN - 1 1.5 - 2 2.5 - 3 3.5 - dtype: float64 - - >>> s.rolling(3).mean() - 0 NaN - 1 NaN - 2 2.0 - 3 3.0 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="mean", - agg_method="mean", - ) - def mean( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - return super().mean( - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - window_agg_numba_parameters(), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Notes"), - numba_notes, - create_section_header("Examples"), - dedent( - """ - Compute the rolling median of a series with a window size of 3. - - >>> s = pd.Series([0, 1, 2, 3, 4]) - >>> s.rolling(3).median() - 0 NaN - 1 NaN - 2 1.0 - 3 2.0 - 4 3.0 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="median", - agg_method="median", - ) - def median( - self, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - return super().median( - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - dedent( - """ - ddof : int, default 1 - Delta Degrees of Freedom. The divisor used in calculations - is ``N - ddof``, where ``N`` represents the number of elements. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - window_agg_numba_parameters("1.4"), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - "numpy.std : Equivalent method for NumPy array.\n", - template_see_also, - create_section_header("Notes"), - dedent( - """ - The default ``ddof`` of 1 used in :meth:`Series.std` is different - than the default ``ddof`` of 0 in :func:`numpy.std`. - - A minimum of one period is required for the rolling calculation.\n - """ - ).replace("\n", "", 1), - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) - >>> s.rolling(3).std() - 0 NaN - 1 NaN - 2 0.577350 - 3 1.000000 - 4 1.000000 - 5 1.154701 - 6 0.000000 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="standard deviation", - agg_method="std", - ) - def std( - self, - ddof: int = 1, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - return super().std( - ddof=ddof, - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - dedent( - """ - ddof : int, default 1 - Delta Degrees of Freedom. The divisor used in calculations - is ``N - ddof``, where ``N`` represents the number of elements. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - window_agg_numba_parameters("1.4"), - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - "numpy.var : Equivalent method for NumPy array.\n", - template_see_also, - create_section_header("Notes"), - dedent( - """ - The default ``ddof`` of 1 used in :meth:`Series.var` is different - than the default ``ddof`` of 0 in :func:`numpy.var`. - - A minimum of one period is required for the rolling calculation.\n - """ - ).replace("\n", "", 1), - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([5, 5, 6, 7, 5, 5, 5]) - >>> s.rolling(3).var() - 0 NaN - 1 NaN - 2 0.333333 - 3 1.000000 - 4 1.000000 - 5 1.333333 - 6 0.000000 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="variance", - agg_method="var", - ) - def var( - self, - ddof: int = 1, - numeric_only: bool = False, - engine: Literal["cython", "numba"] | None = None, - engine_kwargs: dict[str, bool] | None = None, - ): - return super().var( - ddof=ddof, - numeric_only=numeric_only, - engine=engine, - engine_kwargs=engine_kwargs, - ) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - "scipy.stats.skew : Third moment of a probability density.\n", - template_see_also, - create_section_header("Notes"), - dedent( - """ - A minimum of three periods is required for the rolling calculation.\n - """ - ), - create_section_header("Examples"), - dedent( - """\ - >>> ser = pd.Series([1, 5, 2, 7, 12, 6]) - >>> ser.rolling(3).skew().round(6) - 0 NaN - 1 NaN - 2 1.293343 - 3 -0.585583 - 4 0.000000 - 5 1.545393 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="unbiased skewness", - agg_method="skew", - ) - def skew(self, numeric_only: bool = False): - return super().skew(numeric_only=numeric_only) - - @doc( - template_header, - create_section_header("Parameters"), - dedent( - """ - ddof : int, default 1 - Delta Degrees of Freedom. The divisor used in calculations - is ``N - ddof``, where ``N`` represents the number of elements. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Notes"), - "A minimum of one period is required for the calculation.\n\n", - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([0, 1, 2, 3]) - >>> s.rolling(2, min_periods=1).sem() - 0 NaN - 1 0.707107 - 2 0.707107 - 3 0.707107 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="standard error of mean", - agg_method="sem", - ) - def sem(self, ddof: int = 1, numeric_only: bool = False): - # Raise here so error message says sem instead of std - self._validate_numeric_only("sem", numeric_only) - return self.std(numeric_only=numeric_only) / ( - self.count(numeric_only) - ddof - ).pow(0.5) - - @doc( - template_header, - create_section_header("Parameters"), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - "scipy.stats.kurtosis : Reference SciPy method.\n", - template_see_also, - create_section_header("Notes"), - "A minimum of four periods is required for the calculation.\n\n", - create_section_header("Examples"), - dedent( - """ - The example below will show a rolling calculation with a window size of - four matching the equivalent function call using `scipy.stats`. - - >>> arr = [1, 2, 3, 4, 999] - >>> import scipy.stats - >>> print(f"{{scipy.stats.kurtosis(arr[:-1], bias=False):.6f}}") - -1.200000 - >>> print(f"{{scipy.stats.kurtosis(arr[1:], bias=False):.6f}}") - 3.999946 - >>> s = pd.Series(arr) - >>> s.rolling(4).kurt() - 0 NaN - 1 NaN - 2 NaN - 3 -1.200000 - 4 3.999946 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="Fisher's definition of kurtosis without bias", - agg_method="kurt", - ) - def kurt(self, numeric_only: bool = False): - return super().kurt(numeric_only=numeric_only) - - @doc( - template_header, - create_section_header("Parameters"), - dedent( - """ - quantile : float - Quantile to compute. 0 <= quantile <= 1. - - .. deprecated:: 2.1.0 - This will be renamed to 'q' in a future version. - interpolation : {{'linear', 'lower', 'higher', 'midpoint', 'nearest'}} - This optional parameter specifies the interpolation method to use, - when the desired quantile lies between two data points `i` and `j`: - - * linear: `i + (j - i) * fraction`, where `fraction` is the - fractional part of the index surrounded by `i` and `j`. - * lower: `i`. - * higher: `j`. - * nearest: `i` or `j` whichever is nearest. - * midpoint: (`i` + `j`) / 2. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([1, 2, 3, 4]) - >>> s.rolling(2).quantile(.4, interpolation='lower') - 0 NaN - 1 1.0 - 2 2.0 - 3 3.0 - dtype: float64 - - >>> s.rolling(2).quantile(.4, interpolation='midpoint') - 0 NaN - 1 1.5 - 2 2.5 - 3 3.5 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="quantile", - agg_method="quantile", - ) - @deprecate_kwarg(old_arg_name="quantile", new_arg_name="q") - def quantile( - self, - q: float, - interpolation: QuantileInterpolation = "linear", - numeric_only: bool = False, - ): - return super().quantile( - q=q, - interpolation=interpolation, - numeric_only=numeric_only, - ) - - @doc( - template_header, - ".. versionadded:: 1.4.0 \n\n", - create_section_header("Parameters"), - dedent( - """ - method : {{'average', 'min', 'max'}}, default 'average' - How to rank the group of records that have the same value (i.e. ties): - - * average: average rank of the group - * min: lowest rank in the group - * max: highest rank in the group - - ascending : bool, default True - Whether or not the elements should be ranked in ascending order. - pct : bool, default False - Whether or not to display the returned rankings in percentile - form. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """ - >>> s = pd.Series([1, 4, 2, 3, 5, 3]) - >>> s.rolling(3).rank() - 0 NaN - 1 NaN - 2 2.0 - 3 2.0 - 4 3.0 - 5 1.5 - dtype: float64 - - >>> s.rolling(3).rank(method="max") - 0 NaN - 1 NaN - 2 2.0 - 3 2.0 - 4 3.0 - 5 2.0 - dtype: float64 - - >>> s.rolling(3).rank(method="min") - 0 NaN - 1 NaN - 2 2.0 - 3 2.0 - 4 3.0 - 5 1.0 - dtype: float64 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="rank", - agg_method="rank", - ) - def rank( - self, - method: WindowingRankType = "average", - ascending: bool = True, - pct: bool = False, - numeric_only: bool = False, - ): - return super().rank( - method=method, - ascending=ascending, - pct=pct, - numeric_only=numeric_only, - ) - - @doc( - template_header, - create_section_header("Parameters"), - dedent( - """ - other : Series or DataFrame, optional - If not supplied then will default to self and produce pairwise - output. - pairwise : bool, default None - If False then only matching columns between self and other will be - used and the output will be a DataFrame. - If True then all pairwise combinations will be calculated and the - output will be a MultiIndexed DataFrame in the case of DataFrame - inputs. In the case of missing elements, only complete pairwise - observations will be used. - ddof : int, default 1 - Delta Degrees of Freedom. The divisor used in calculations - is ``N - ddof``, where ``N`` represents the number of elements. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - template_see_also, - create_section_header("Examples"), - dedent( - """\ - >>> ser1 = pd.Series([1, 2, 3, 4]) - >>> ser2 = pd.Series([1, 4, 5, 8]) - >>> ser1.rolling(2).cov(ser2) - 0 NaN - 1 1.5 - 2 0.5 - 3 1.5 - dtype: float64 - """ - ), - window_method="rolling", - aggregation_description="sample covariance", - agg_method="cov", - ) - def cov( - self, - other: DataFrame | Series | None = None, - pairwise: bool | None = None, - ddof: int = 1, - numeric_only: bool = False, - ): - return super().cov( - other=other, - pairwise=pairwise, - ddof=ddof, - numeric_only=numeric_only, - ) - - @doc( - template_header, - create_section_header("Parameters"), - dedent( - """ - other : Series or DataFrame, optional - If not supplied then will default to self and produce pairwise - output. - pairwise : bool, default None - If False then only matching columns between self and other will be - used and the output will be a DataFrame. - If True then all pairwise combinations will be calculated and the - output will be a MultiIndexed DataFrame in the case of DataFrame - inputs. In the case of missing elements, only complete pairwise - observations will be used. - ddof : int, default 1 - Delta Degrees of Freedom. The divisor used in calculations - is ``N - ddof``, where ``N`` represents the number of elements. - """ - ).replace("\n", "", 1), - kwargs_numeric_only, - create_section_header("Returns"), - template_returns, - create_section_header("See Also"), - dedent( - """ - cov : Similar method to calculate covariance. - numpy.corrcoef : NumPy Pearson's correlation calculation. - """ - ).replace("\n", "", 1), - template_see_also, - create_section_header("Notes"), - dedent( - """ - This function uses Pearson's definition of correlation - (https://en.wikipedia.org/wiki/Pearson_correlation_coefficient). - - When `other` is not specified, the output will be self correlation (e.g. - all 1's), except for :class:`~pandas.DataFrame` inputs with `pairwise` - set to `True`. - - Function will return ``NaN`` for correlations of equal valued sequences; - this is the result of a 0/0 division error. - - When `pairwise` is set to `False`, only matching columns between `self` and - `other` will be used. - - When `pairwise` is set to `True`, the output will be a MultiIndex DataFrame - with the original index on the first level, and the `other` DataFrame - columns on the second level. - - In the case of missing elements, only complete pairwise observations - will be used.\n - """ - ).replace("\n", "", 1), - create_section_header("Examples"), - dedent( - """ - The below example shows a rolling calculation with a window size of - four matching the equivalent function call using :meth:`numpy.corrcoef`. - - >>> v1 = [3, 3, 3, 5, 8] - >>> v2 = [3, 4, 4, 4, 8] - >>> # numpy returns a 2X2 array, the correlation coefficient - >>> # is the number at entry [0][1] - >>> print(f"{{np.corrcoef(v1[:-1], v2[:-1])[0][1]:.6f}}") - 0.333333 - >>> print(f"{{np.corrcoef(v1[1:], v2[1:])[0][1]:.6f}}") - 0.916949 - >>> s1 = pd.Series(v1) - >>> s2 = pd.Series(v2) - >>> s1.rolling(4).corr(s2) - 0 NaN - 1 NaN - 2 NaN - 3 0.333333 - 4 0.916949 - dtype: float64 - - The below example shows a similar rolling calculation on a - DataFrame using the pairwise option. - - >>> matrix = np.array([[51., 35.], [49., 30.], [47., 32.],\ - [46., 31.], [50., 36.]]) - >>> print(np.corrcoef(matrix[:-1,0], matrix[:-1,1]).round(7)) - [[1. 0.6263001] - [0.6263001 1. ]] - >>> print(np.corrcoef(matrix[1:,0], matrix[1:,1]).round(7)) - [[1. 0.5553681] - [0.5553681 1. ]] - >>> df = pd.DataFrame(matrix, columns=['X','Y']) - >>> df - X Y - 0 51.0 35.0 - 1 49.0 30.0 - 2 47.0 32.0 - 3 46.0 31.0 - 4 50.0 36.0 - >>> df.rolling(4).corr(pairwise=True) - X Y - 0 X NaN NaN - Y NaN NaN - 1 X NaN NaN - Y NaN NaN - 2 X NaN NaN - Y NaN NaN - 3 X 1.000000 0.626300 - Y 0.626300 1.000000 - 4 X 1.000000 0.555368 - Y 0.555368 1.000000 - """ - ).replace("\n", "", 1), - window_method="rolling", - aggregation_description="correlation", - agg_method="corr", - ) - def corr( - self, - other: DataFrame | Series | None = None, - pairwise: bool | None = None, - ddof: int = 1, - numeric_only: bool = False, - ): - return super().corr( - other=other, - pairwise=pairwise, - ddof=ddof, - numeric_only=numeric_only, - ) - - -Rolling.__doc__ = Window.__doc__ - - -class RollingGroupby(BaseWindowGroupby, Rolling): - """ - Provide a rolling groupby implementation. - """ - - _attributes = Rolling._attributes + BaseWindowGroupby._attributes - - def _get_window_indexer(self) -> GroupbyIndexer: - """ - Return an indexer class that will compute the window start and end bounds - - Returns - ------- - GroupbyIndexer - """ - rolling_indexer: type[BaseIndexer] - indexer_kwargs: dict[str, Any] | None = None - index_array = self._index_array - if isinstance(self.window, BaseIndexer): - rolling_indexer = type(self.window) - indexer_kwargs = self.window.__dict__.copy() - assert isinstance(indexer_kwargs, dict) # for mypy - # We'll be using the index of each group later - indexer_kwargs.pop("index_array", None) - window = self.window - elif self._win_freq_i8 is not None: - rolling_indexer = VariableWindowIndexer - # error: Incompatible types in assignment (expression has type - # "int", variable has type "BaseIndexer") - window = self._win_freq_i8 # type: ignore[assignment] - else: - rolling_indexer = FixedWindowIndexer - window = self.window - window_indexer = GroupbyIndexer( - index_array=index_array, - window_size=window, - groupby_indices=self._grouper.indices, - window_indexer=rolling_indexer, - indexer_kwargs=indexer_kwargs, - ) - return window_indexer - - def _validate_datetimelike_monotonic(self): - """ - Validate that each group in self._on is monotonic - """ - # GH 46061 - if self._on.hasnans: - self._raise_monotonic_error("values must not have NaT") - for group_indices in self._grouper.indices.values(): - group_on = self._on.take(group_indices) - if not ( - group_on.is_monotonic_increasing or group_on.is_monotonic_decreasing - ): - on = "index" if self.on is None else self.on - raise ValueError( - f"Each group within {on} must be monotonic. " - f"Sort the values in {on} first." - ) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/accumulate.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/accumulate.py deleted file mode 100644 index 4648f66112e80972dd427f88b2f0c6d4fc2b2578..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/extension/base/accumulate.py +++ /dev/null @@ -1,41 +0,0 @@ -import pytest - -import pandas as pd -import pandas._testing as tm - - -class BaseAccumulateTests: - """ - Accumulation specific tests. Generally these only - make sense for numeric/boolean operations. - """ - - def _supports_accumulation(self, ser: pd.Series, op_name: str) -> bool: - # Do we expect this accumulation to be supported for this dtype? - # We default to assuming "no"; subclass authors should override here. - return False - - def check_accumulate(self, ser: pd.Series, op_name: str, skipna: bool): - alt = ser.astype("float64") - result = getattr(ser, op_name)(skipna=skipna) - - if result.dtype == pd.Float32Dtype() and op_name == "cumprod" and skipna: - # TODO: avoid special-casing here - pytest.skip( - f"Float32 precision lead to large differences with op {op_name} " - f"and skipna={skipna}" - ) - - expected = getattr(alt, op_name)(skipna=skipna) - tm.assert_series_equal(result, expected, check_dtype=False) - - @pytest.mark.parametrize("skipna", [True, False]) - def test_accumulate_series(self, data, all_numeric_accumulations, skipna): - op_name = all_numeric_accumulations - ser = pd.Series(data) - - if self._supports_accumulation(ser, op_name): - self.check_accumulate(ser, op_name, skipna) - else: - with pytest.raises(NotImplementedError): - getattr(ser, op_name)(skipna=skipna) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/reshape/concat/test_series.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/reshape/concat/test_series.py deleted file mode 100644 index 4bef86ce6a941cbc24e39c5b90bc0ff6021fc22b..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/reshape/concat/test_series.py +++ /dev/null @@ -1,169 +0,0 @@ -import numpy as np -import pytest - -from pandas import ( - DataFrame, - DatetimeIndex, - Index, - MultiIndex, - Series, - concat, - date_range, -) -import pandas._testing as tm - - -class TestSeriesConcat: - def test_concat_series(self): - ts = tm.makeTimeSeries() - ts.name = "foo" - - pieces = [ts[:5], ts[5:15], ts[15:]] - - result = concat(pieces) - tm.assert_series_equal(result, ts) - assert result.name == ts.name - - result = concat(pieces, keys=[0, 1, 2]) - expected = ts.copy() - - ts.index = DatetimeIndex(np.array(ts.index.values, dtype="M8[ns]")) - - exp_codes = [np.repeat([0, 1, 2], [len(x) for x in pieces]), np.arange(len(ts))] - exp_index = MultiIndex(levels=[[0, 1, 2], ts.index], codes=exp_codes) - expected.index = exp_index - tm.assert_series_equal(result, expected) - - def test_concat_empty_and_non_empty_series_regression(self): - # GH 18187 regression test - s1 = Series([1]) - s2 = Series([], dtype=object) - - expected = s1 - msg = "The behavior of array concatenation with empty entries is deprecated" - with tm.assert_produces_warning(FutureWarning, match=msg): - result = concat([s1, s2]) - tm.assert_series_equal(result, expected) - - def test_concat_series_axis1(self): - ts = tm.makeTimeSeries() - - pieces = [ts[:-2], ts[2:], ts[2:-2]] - - result = concat(pieces, axis=1) - expected = DataFrame(pieces).T - tm.assert_frame_equal(result, expected) - - result = concat(pieces, keys=["A", "B", "C"], axis=1) - expected = DataFrame(pieces, index=["A", "B", "C"]).T - tm.assert_frame_equal(result, expected) - - def test_concat_series_axis1_preserves_series_names(self): - # preserve series names, #2489 - s = Series(np.random.default_rng(2).standard_normal(5), name="A") - s2 = Series(np.random.default_rng(2).standard_normal(5), name="B") - - result = concat([s, s2], axis=1) - expected = DataFrame({"A": s, "B": s2}) - tm.assert_frame_equal(result, expected) - - s2.name = None - result = concat([s, s2], axis=1) - tm.assert_index_equal(result.columns, Index(["A", 0], dtype="object")) - - def test_concat_series_axis1_with_reindex(self, sort): - # must reindex, #2603 - s = Series( - np.random.default_rng(2).standard_normal(3), index=["c", "a", "b"], name="A" - ) - s2 = Series( - np.random.default_rng(2).standard_normal(4), - index=["d", "a", "b", "c"], - name="B", - ) - result = concat([s, s2], axis=1, sort=sort) - expected = DataFrame({"A": s, "B": s2}, index=["c", "a", "b", "d"]) - if sort: - expected = expected.sort_index() - tm.assert_frame_equal(result, expected) - - def test_concat_series_axis1_names_applied(self): - # ensure names argument is not ignored on axis=1, #23490 - s = Series([1, 2, 3]) - s2 = Series([4, 5, 6]) - result = concat([s, s2], axis=1, keys=["a", "b"], names=["A"]) - expected = DataFrame( - [[1, 4], [2, 5], [3, 6]], columns=Index(["a", "b"], name="A") - ) - tm.assert_frame_equal(result, expected) - - result = concat([s, s2], axis=1, keys=[("a", 1), ("b", 2)], names=["A", "B"]) - expected = DataFrame( - [[1, 4], [2, 5], [3, 6]], - columns=MultiIndex.from_tuples([("a", 1), ("b", 2)], names=["A", "B"]), - ) - tm.assert_frame_equal(result, expected) - - def test_concat_series_axis1_same_names_ignore_index(self): - dates = date_range("01-Jan-2013", "01-Jan-2014", freq="MS")[0:-1] - s1 = Series( - np.random.default_rng(2).standard_normal(len(dates)), - index=dates, - name="value", - ) - s2 = Series( - np.random.default_rng(2).standard_normal(len(dates)), - index=dates, - name="value", - ) - - result = concat([s1, s2], axis=1, ignore_index=True) - expected = Index(range(2)) - - tm.assert_index_equal(result.columns, expected, exact=True) - - @pytest.mark.parametrize( - "s1name,s2name", [(np.int64(190), (43, 0)), (190, (43, 0))] - ) - def test_concat_series_name_npscalar_tuple(self, s1name, s2name): - # GH21015 - s1 = Series({"a": 1, "b": 2}, name=s1name) - s2 = Series({"c": 5, "d": 6}, name=s2name) - result = concat([s1, s2]) - expected = Series({"a": 1, "b": 2, "c": 5, "d": 6}) - tm.assert_series_equal(result, expected) - - def test_concat_series_partial_columns_names(self): - # GH10698 - named_series = Series([1, 2], name="foo") - unnamed_series1 = Series([1, 2]) - unnamed_series2 = Series([4, 5]) - - result = concat([named_series, unnamed_series1, unnamed_series2], axis=1) - expected = DataFrame( - {"foo": [1, 2], 0: [1, 2], 1: [4, 5]}, columns=["foo", 0, 1] - ) - tm.assert_frame_equal(result, expected) - - result = concat( - [named_series, unnamed_series1, unnamed_series2], - axis=1, - keys=["red", "blue", "yellow"], - ) - expected = DataFrame( - {"red": [1, 2], "blue": [1, 2], "yellow": [4, 5]}, - columns=["red", "blue", "yellow"], - ) - tm.assert_frame_equal(result, expected) - - result = concat( - [named_series, unnamed_series1, unnamed_series2], axis=1, ignore_index=True - ) - expected = DataFrame({0: [1, 2], 1: [1, 2], 2: [4, 5]}) - tm.assert_frame_equal(result, expected) - - def test_concat_series_length_one_reversed(self, frame_or_series): - # GH39401 - obj = frame_or_series([100]) - result = concat([obj.iloc[::-1]]) - tm.assert_equal(result, obj) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/requests/_internal_utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/requests/_internal_utils.py deleted file mode 100644 index 759d9a56ba0102bb3b7b3abfcfae6731a2ecc243..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/requests/_internal_utils.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -requests._internal_utils -~~~~~~~~~~~~~~ - -Provides utility functions that are consumed internally by Requests -which depend on extremely few external helpers (such as compat) -""" - -from .compat import is_py2, builtin_str, str - - -def to_native_string(string, encoding='ascii'): - """Given a string object, regardless of type, returns a representation of - that string in the native string type, encoding and decoding where - necessary. This assumes ASCII unless told otherwise. - """ - if isinstance(string, builtin_str): - out = string - else: - if is_py2: - out = string.encode(encoding) - else: - out = string.decode(encoding) - - return out - - -def unicode_is_ascii(u_string): - """Determine if unicode string only contains ASCII characters. - - :param str u_string: unicode string to check. Must be unicode - and not Python 2 `str`. - :rtype: bool - """ - assert isinstance(u_string, str) - try: - u_string.encode('ascii') - return True - except UnicodeEncodeError: - return False diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/int_fiction.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/int_fiction.py deleted file mode 100644 index 4f4d55d6cb2626d95d1155bfcdc33bfda8793e9f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pygments/lexers/int_fiction.py +++ /dev/null @@ -1,1382 +0,0 @@ -""" - pygments.lexers.int_fiction - ~~~~~~~~~~~~~~~~~~~~~~~~~~~ - - Lexers for interactive fiction languages. - - :copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS. - :license: BSD, see LICENSE for details. -""" - -import re - -from pygments.lexer import RegexLexer, include, bygroups, using, \ - this, default, words -from pygments.token import Text, Comment, Operator, Keyword, Name, String, \ - Number, Punctuation, Error, Generic - -__all__ = ['Inform6Lexer', 'Inform6TemplateLexer', 'Inform7Lexer', - 'Tads3Lexer'] - - -class Inform6Lexer(RegexLexer): - """ - For Inform 6 source code. - - .. versionadded:: 2.0 - """ - - name = 'Inform 6' - url = 'http://inform-fiction.org/' - aliases = ['inform6', 'i6'] - filenames = ['*.inf'] - - flags = re.MULTILINE | re.DOTALL - - _name = r'[a-zA-Z_]\w*' - - # Inform 7 maps these four character classes to their ASCII - # equivalents. To support Inform 6 inclusions within Inform 7, - # Inform6Lexer maps them too. - _dash = '\\-\u2010-\u2014' - _dquote = '"\u201c\u201d' - _squote = "'\u2018\u2019" - _newline = '\\n\u0085\u2028\u2029' - - tokens = { - 'root': [ - (r'\A(!%%[^%s]*[%s])+' % (_newline, _newline), Comment.Preproc, - 'directive'), - default('directive') - ], - '_whitespace': [ - (r'\s+', Text), - (r'![^%s]*' % _newline, Comment.Single) - ], - 'default': [ - include('_whitespace'), - (r'\[', Punctuation, 'many-values'), # Array initialization - (r':|(?=;)', Punctuation, '#pop'), - (r'<', Punctuation), # Second angle bracket in an action statement - default(('expression', '_expression')) - ], - - # Expressions - '_expression': [ - include('_whitespace'), - (r'(?=sp\b)', Text, '#pop'), - (r'(?=[%s%s$0-9#a-zA-Z_])' % (_dquote, _squote), Text, - ('#pop', 'value')), - (r'\+\+|[%s]{1,2}(?!>)|~~?' % _dash, Operator), - (r'(?=[()\[%s,?@{:;])' % _dash, Text, '#pop') - ], - 'expression': [ - include('_whitespace'), - (r'\(', Punctuation, ('expression', '_expression')), - (r'\)', Punctuation, '#pop'), - (r'\[', Punctuation, ('#pop', 'statements', 'locals')), - (r'>(?=(\s+|(![^%s]*))*[>;])' % _newline, Punctuation), - (r'\+\+|[%s]{2}(?!>)' % _dash, Operator), - (r',', Punctuation, '_expression'), - (r'&&?|\|\|?|[=~><]?=|[%s]{1,2}>?|\.\.?[&#]?|::|[<>+*/%%]' % _dash, - Operator, '_expression'), - (r'(has|hasnt|in|notin|ofclass|or|provides)\b', Operator.Word, - '_expression'), - (r'sp\b', Name), - (r'\?~?', Name.Label, 'label?'), - (r'[@{]', Error), - default('#pop') - ], - '_assembly-expression': [ - (r'\(', Punctuation, ('#push', '_expression')), - (r'[\[\]]', Punctuation), - (r'[%s]>' % _dash, Punctuation, '_expression'), - (r'sp\b', Keyword.Pseudo), - (r';', Punctuation, '#pop:3'), - include('expression') - ], - '_for-expression': [ - (r'\)', Punctuation, '#pop:2'), - (r':', Punctuation, '#pop'), - include('expression') - ], - '_keyword-expression': [ - (r'(from|near|to)\b', Keyword, '_expression'), - include('expression') - ], - '_list-expression': [ - (r',', Punctuation, '#pop'), - include('expression') - ], - '_object-expression': [ - (r'has\b', Keyword.Declaration, '#pop'), - include('_list-expression') - ], - - # Values - 'value': [ - include('_whitespace'), - # Strings - (r'[%s][^@][%s]' % (_squote, _squote), String.Char, '#pop'), - (r'([%s])(@\{[0-9a-fA-F]*\})([%s])' % (_squote, _squote), - bygroups(String.Char, String.Escape, String.Char), '#pop'), - (r'([%s])(@.{2})([%s])' % (_squote, _squote), - bygroups(String.Char, String.Escape, String.Char), '#pop'), - (r'[%s]' % _squote, String.Single, ('#pop', 'dictionary-word')), - (r'[%s]' % _dquote, String.Double, ('#pop', 'string')), - # Numbers - (r'\$[<>]?[+%s][0-9]*\.?[0-9]*([eE][+%s]?[0-9]+)?' % (_dash, _dash), - Number.Float, '#pop'), - (r'\$[0-9a-fA-F]+', Number.Hex, '#pop'), - (r'\$\$[01]+', Number.Bin, '#pop'), - (r'[0-9]+', Number.Integer, '#pop'), - # Values prefixed by hashes - (r'(##|#a\$)(%s)' % _name, bygroups(Operator, Name), '#pop'), - (r'(#g\$)(%s)' % _name, - bygroups(Operator, Name.Variable.Global), '#pop'), - (r'#[nw]\$', Operator, ('#pop', 'obsolete-dictionary-word')), - (r'(#r\$)(%s)' % _name, bygroups(Operator, Name.Function), '#pop'), - (r'#', Name.Builtin, ('#pop', 'system-constant')), - # System functions - (words(( - 'child', 'children', 'elder', 'eldest', 'glk', 'indirect', 'metaclass', - 'parent', 'random', 'sibling', 'younger', 'youngest'), suffix=r'\b'), - Name.Builtin, '#pop'), - # Metaclasses - (r'(?i)(Class|Object|Routine|String)\b', Name.Builtin, '#pop'), - # Veneer routines - (words(( - 'Box__Routine', 'CA__Pr', 'CDefArt', 'CInDefArt', 'Cl__Ms', - 'Copy__Primitive', 'CP__Tab', 'DA__Pr', 'DB__Pr', 'DefArt', 'Dynam__String', - 'EnglishNumber', 'Glk__Wrap', 'IA__Pr', 'IB__Pr', 'InDefArt', 'Main__', - 'Meta__class', 'OB__Move', 'OB__Remove', 'OC__Cl', 'OP__Pr', 'Print__Addr', - 'Print__PName', 'PrintShortName', 'RA__Pr', 'RA__Sc', 'RL__Pr', 'R_Process', - 'RT__ChG', 'RT__ChGt', 'RT__ChLDB', 'RT__ChLDW', 'RT__ChPR', 'RT__ChPrintA', - 'RT__ChPrintC', 'RT__ChPrintO', 'RT__ChPrintS', 'RT__ChPS', 'RT__ChR', - 'RT__ChSTB', 'RT__ChSTW', 'RT__ChT', 'RT__Err', 'RT__TrPS', 'RV__Pr', - 'Symb__Tab', 'Unsigned__Compare', 'WV__Pr', 'Z__Region'), - prefix='(?i)', suffix=r'\b'), - Name.Builtin, '#pop'), - # Other built-in symbols - (words(( - 'call', 'copy', 'create', 'DEBUG', 'destroy', 'DICT_CHAR_SIZE', - 'DICT_ENTRY_BYTES', 'DICT_IS_UNICODE', 'DICT_WORD_SIZE', 'DOUBLE_HI_INFINITY', - 'DOUBLE_HI_NAN', 'DOUBLE_HI_NINFINITY', 'DOUBLE_LO_INFINITY', 'DOUBLE_LO_NAN', - 'DOUBLE_LO_NINFINITY', 'false', 'FLOAT_INFINITY', 'FLOAT_NAN', 'FLOAT_NINFINITY', - 'GOBJFIELD_CHAIN', 'GOBJFIELD_CHILD', 'GOBJFIELD_NAME', 'GOBJFIELD_PARENT', - 'GOBJFIELD_PROPTAB', 'GOBJFIELD_SIBLING', 'GOBJ_EXT_START', - 'GOBJ_TOTAL_LENGTH', 'Grammar__Version', 'INDIV_PROP_START', 'INFIX', - 'infix__watching', 'MODULE_MODE', 'name', 'nothing', 'NUM_ATTR_BYTES', 'print', - 'print_to_array', 'recreate', 'remaining', 'self', 'sender', 'STRICT_MODE', - 'sw__var', 'sys__glob0', 'sys__glob1', 'sys__glob2', 'sys_statusline_flag', - 'TARGET_GLULX', 'TARGET_ZCODE', 'temp__global2', 'temp__global3', - 'temp__global4', 'temp_global', 'true', 'USE_MODULES', 'WORDSIZE'), - prefix='(?i)', suffix=r'\b'), - Name.Builtin, '#pop'), - # Other values - (_name, Name, '#pop') - ], - 'value?': [ - include('value'), - default('#pop') - ], - # Strings - 'dictionary-word': [ - (r'[~^]+', String.Escape), - (r'[^~^\\@({%s]+' % _squote, String.Single), - (r'[({]', String.Single), - (r'@\{[0-9a-fA-F]*\}', String.Escape), - (r'@.{2}', String.Escape), - (r'[%s]' % _squote, String.Single, '#pop') - ], - 'string': [ - (r'[~^]+', String.Escape), - (r'[^~^\\@({%s]+' % _dquote, String.Double), - (r'[({]', String.Double), - (r'\\', String.Escape), - (r'@(\\\s*[%s]\s*)*@((\\\s*[%s]\s*)*[0-9])*' % - (_newline, _newline), String.Escape), - (r'@(\\\s*[%s]\s*)*[({]((\\\s*[%s]\s*)*[0-9a-zA-Z_])*' - r'(\\\s*[%s]\s*)*[)}]' % (_newline, _newline, _newline), - String.Escape), - (r'@(\\\s*[%s]\s*)*.(\\\s*[%s]\s*)*.' % (_newline, _newline), - String.Escape), - (r'[%s]' % _dquote, String.Double, '#pop') - ], - 'plain-string': [ - (r'[^~^\\({\[\]%s]+' % _dquote, String.Double), - (r'[~^({\[\]]', String.Double), - (r'\\', String.Escape), - (r'[%s]' % _dquote, String.Double, '#pop') - ], - # Names - '_constant': [ - include('_whitespace'), - (_name, Name.Constant, '#pop'), - include('value') - ], - 'constant*': [ - include('_whitespace'), - (r',', Punctuation), - (r'=', Punctuation, 'value?'), - (_name, Name.Constant, 'value?'), - default('#pop') - ], - '_global': [ - include('_whitespace'), - (_name, Name.Variable.Global, '#pop'), - include('value') - ], - 'label?': [ - include('_whitespace'), - (_name, Name.Label, '#pop'), - default('#pop') - ], - 'variable?': [ - include('_whitespace'), - (_name, Name.Variable, '#pop'), - default('#pop') - ], - # Values after hashes - 'obsolete-dictionary-word': [ - (r'\S\w*', String.Other, '#pop') - ], - 'system-constant': [ - include('_whitespace'), - (_name, Name.Builtin, '#pop') - ], - - # Directives - 'directive': [ - include('_whitespace'), - (r'#', Punctuation), - (r';', Punctuation, '#pop'), - (r'\[', Punctuation, - ('default', 'statements', 'locals', 'routine-name?')), - (words(( - 'abbreviate', 'endif', 'dictionary', 'ifdef', 'iffalse', 'ifndef', 'ifnot', - 'iftrue', 'ifv3', 'ifv5', 'release', 'serial', 'switches', 'system_file', - 'version'), prefix='(?i)', suffix=r'\b'), - Keyword, 'default'), - (r'(?i)(array|global)\b', Keyword, - ('default', 'directive-keyword?', '_global')), - (r'(?i)attribute\b', Keyword, ('default', 'alias?', '_constant')), - (r'(?i)class\b', Keyword, - ('object-body', 'duplicates', 'class-name')), - (r'(?i)(constant|default)\b', Keyword, - ('default', 'constant*')), - (r'(?i)(end\b)(.*)', bygroups(Keyword, Text)), - (r'(?i)(extend|verb)\b', Keyword, 'grammar'), - (r'(?i)fake_action\b', Keyword, ('default', '_constant')), - (r'(?i)import\b', Keyword, 'manifest'), - (r'(?i)(include|link|origsource)\b', Keyword, - ('default', 'before-plain-string?')), - (r'(?i)(lowstring|undef)\b', Keyword, ('default', '_constant')), - (r'(?i)message\b', Keyword, ('default', 'diagnostic')), - (r'(?i)(nearby|object)\b', Keyword, - ('object-body', '_object-head')), - (r'(?i)property\b', Keyword, - ('default', 'alias?', '_constant', 'property-keyword*')), - (r'(?i)replace\b', Keyword, - ('default', 'routine-name?', 'routine-name?')), - (r'(?i)statusline\b', Keyword, ('default', 'directive-keyword?')), - (r'(?i)stub\b', Keyword, ('default', 'routine-name?')), - (r'(?i)trace\b', Keyword, - ('default', 'trace-keyword?', 'trace-keyword?')), - (r'(?i)zcharacter\b', Keyword, - ('default', 'directive-keyword?', 'directive-keyword?')), - (_name, Name.Class, ('object-body', '_object-head')) - ], - # [, Replace, Stub - 'routine-name?': [ - include('_whitespace'), - (_name, Name.Function, '#pop'), - default('#pop') - ], - 'locals': [ - include('_whitespace'), - (r';', Punctuation, '#pop'), - (r'\*', Punctuation), - (r'"', String.Double, 'plain-string'), - (_name, Name.Variable) - ], - # Array - 'many-values': [ - include('_whitespace'), - (r';', Punctuation), - (r'\]', Punctuation, '#pop'), - (r':', Error), - default(('expression', '_expression')) - ], - # Attribute, Property - 'alias?': [ - include('_whitespace'), - (r'alias\b', Keyword, ('#pop', '_constant')), - default('#pop') - ], - # Class, Object, Nearby - 'class-name': [ - include('_whitespace'), - (r'(?=[,;]|(class|has|private|with)\b)', Text, '#pop'), - (_name, Name.Class, '#pop') - ], - 'duplicates': [ - include('_whitespace'), - (r'\(', Punctuation, ('#pop', 'expression', '_expression')), - default('#pop') - ], - '_object-head': [ - (r'[%s]>' % _dash, Punctuation), - (r'(class|has|private|with)\b', Keyword.Declaration, '#pop'), - include('_global') - ], - 'object-body': [ - include('_whitespace'), - (r';', Punctuation, '#pop:2'), - (r',', Punctuation), - (r'class\b', Keyword.Declaration, 'class-segment'), - (r'(has|private|with)\b', Keyword.Declaration), - (r':', Error), - default(('_object-expression', '_expression')) - ], - 'class-segment': [ - include('_whitespace'), - (r'(?=[,;]|(class|has|private|with)\b)', Text, '#pop'), - (_name, Name.Class), - default('value') - ], - # Extend, Verb - 'grammar': [ - include('_whitespace'), - (r'=', Punctuation, ('#pop', 'default')), - (r'\*', Punctuation, ('#pop', 'grammar-line')), - default('_directive-keyword') - ], - 'grammar-line': [ - include('_whitespace'), - (r';', Punctuation, '#pop'), - (r'[/*]', Punctuation), - (r'[%s]>' % _dash, Punctuation, 'value'), - (r'(noun|scope)\b', Keyword, '=routine'), - default('_directive-keyword') - ], - '=routine': [ - include('_whitespace'), - (r'=', Punctuation, 'routine-name?'), - default('#pop') - ], - # Import - 'manifest': [ - include('_whitespace'), - (r';', Punctuation, '#pop'), - (r',', Punctuation), - (r'(?i)global\b', Keyword, '_global'), - default('_global') - ], - # Include, Link, Message - 'diagnostic': [ - include('_whitespace'), - (r'[%s]' % _dquote, String.Double, ('#pop', 'message-string')), - default(('#pop', 'before-plain-string?', 'directive-keyword?')) - ], - 'before-plain-string?': [ - include('_whitespace'), - (r'[%s]' % _dquote, String.Double, ('#pop', 'plain-string')), - default('#pop') - ], - 'message-string': [ - (r'[~^]+', String.Escape), - include('plain-string') - ], - - # Keywords used in directives - '_directive-keyword!': [ - include('_whitespace'), - (words(( - 'additive', 'alias', 'buffer', 'class', 'creature', 'data', 'error', 'fatalerror', - 'first', 'has', 'held', 'individual', 'initial', 'initstr', 'last', 'long', 'meta', - 'multi', 'multiexcept', 'multiheld', 'multiinside', 'noun', 'number', 'only', - 'private', 'replace', 'reverse', 'scope', 'score', 'special', 'string', 'table', - 'terminating', 'time', 'topic', 'warning', 'with'), suffix=r'\b'), - Keyword, '#pop'), - (r'static\b', Keyword), - (r'[%s]{1,2}>|[+=]' % _dash, Punctuation, '#pop') - ], - '_directive-keyword': [ - include('_directive-keyword!'), - include('value') - ], - 'directive-keyword?': [ - include('_directive-keyword!'), - default('#pop') - ], - 'property-keyword*': [ - include('_whitespace'), - (words(('additive', 'individual', 'long'), - suffix=r'\b(?=(\s*|(![^%s]*[%s]))*[_a-zA-Z])' % (_newline, _newline)), - Keyword), - default('#pop') - ], - 'trace-keyword?': [ - include('_whitespace'), - (words(( - 'assembly', 'dictionary', 'expressions', 'lines', 'linker', - 'objects', 'off', 'on', 'symbols', 'tokens', 'verbs'), suffix=r'\b'), - Keyword, '#pop'), - default('#pop') - ], - - # Statements - 'statements': [ - include('_whitespace'), - (r'\]', Punctuation, '#pop'), - (r'[;{}]', Punctuation), - (words(( - 'box', 'break', 'continue', 'default', 'give', 'inversion', - 'new_line', 'quit', 'read', 'remove', 'return', 'rfalse', 'rtrue', - 'spaces', 'string', 'until'), suffix=r'\b'), - Keyword, 'default'), - (r'(do|else)\b', Keyword), - (r'(font|style)\b', Keyword, - ('default', 'miscellaneous-keyword?')), - (r'for\b', Keyword, ('for', '(?')), - (r'(if|switch|while)', Keyword, - ('expression', '_expression', '(?')), - (r'(jump|save|restore)\b', Keyword, ('default', 'label?')), - (r'objectloop\b', Keyword, - ('_keyword-expression', 'variable?', '(?')), - (r'print(_ret)?\b|(?=[%s])' % _dquote, Keyword, 'print-list'), - (r'\.', Name.Label, 'label?'), - (r'@', Keyword, 'opcode'), - (r'#(?![agrnw]\$|#)', Punctuation, 'directive'), - (r'<', Punctuation, 'default'), - (r'move\b', Keyword, - ('default', '_keyword-expression', '_expression')), - default(('default', '_keyword-expression', '_expression')) - ], - 'miscellaneous-keyword?': [ - include('_whitespace'), - (r'(bold|fixed|from|near|off|on|reverse|roman|to|underline)\b', - Keyword, '#pop'), - (r'(a|A|an|address|char|name|number|object|property|string|the|' - r'The)\b(?=(\s+|(![^%s]*))*\))' % _newline, Keyword.Pseudo, - '#pop'), - (r'%s(?=(\s+|(![^%s]*))*\))' % (_name, _newline), Name.Function, - '#pop'), - default('#pop') - ], - '(?': [ - include('_whitespace'), - (r'\(', Punctuation, '#pop'), - default('#pop') - ], - 'for': [ - include('_whitespace'), - (r';', Punctuation, ('_for-expression', '_expression')), - default(('_for-expression', '_expression')) - ], - 'print-list': [ - include('_whitespace'), - (r';', Punctuation, '#pop'), - (r':', Error), - default(('_list-expression', '_expression', '_list-expression', 'form')) - ], - 'form': [ - include('_whitespace'), - (r'\(', Punctuation, ('#pop', 'miscellaneous-keyword?')), - default('#pop') - ], - - # Assembly - 'opcode': [ - include('_whitespace'), - (r'[%s]' % _dquote, String.Double, ('operands', 'plain-string')), - (_name, Keyword, 'operands') - ], - 'operands': [ - (r':', Error), - default(('_assembly-expression', '_expression')) - ] - } - - def get_tokens_unprocessed(self, text): - # 'in' is either a keyword or an operator. - # If the token two tokens after 'in' is ')', 'in' is a keyword: - # objectloop(a in b) - # Otherwise, it is an operator: - # objectloop(a in b && true) - objectloop_queue = [] - objectloop_token_count = -1 - previous_token = None - for index, token, value in RegexLexer.get_tokens_unprocessed(self, - text): - if previous_token is Name.Variable and value == 'in': - objectloop_queue = [[index, token, value]] - objectloop_token_count = 2 - elif objectloop_token_count > 0: - if token not in Comment and token not in Text: - objectloop_token_count -= 1 - objectloop_queue.append((index, token, value)) - else: - if objectloop_token_count == 0: - if objectloop_queue[-1][2] == ')': - objectloop_queue[0][1] = Keyword - while objectloop_queue: - yield objectloop_queue.pop(0) - objectloop_token_count = -1 - yield index, token, value - if token not in Comment and token not in Text: - previous_token = token - while objectloop_queue: - yield objectloop_queue.pop(0) - - def analyse_text(text): - """We try to find a keyword which seem relatively common, unfortunately - there is a decent overlap with Smalltalk keywords otherwise here..""" - result = 0 - if re.search('\borigsource\b', text, re.IGNORECASE): - result += 0.05 - - return result - - -class Inform7Lexer(RegexLexer): - """ - For Inform 7 source code. - - .. versionadded:: 2.0 - """ - - name = 'Inform 7' - url = 'http://inform7.com/' - aliases = ['inform7', 'i7'] - filenames = ['*.ni', '*.i7x'] - - flags = re.MULTILINE | re.DOTALL - - _dash = Inform6Lexer._dash - _dquote = Inform6Lexer._dquote - _newline = Inform6Lexer._newline - _start = r'\A|(?<=[%s])' % _newline - - # There are three variants of Inform 7, differing in how to - # interpret at signs and braces in I6T. In top-level inclusions, at - # signs in the first column are inweb syntax. In phrase definitions - # and use options, tokens in braces are treated as I7. Use options - # also interpret "{N}". - tokens = {} - token_variants = ['+i6t-not-inline', '+i6t-inline', '+i6t-use-option'] - - for level in token_variants: - tokens[level] = { - '+i6-root': list(Inform6Lexer.tokens['root']), - '+i6t-root': [ # For Inform6TemplateLexer - (r'[^%s]*' % Inform6Lexer._newline, Comment.Preproc, - ('directive', '+p')) - ], - 'root': [ - (r'(\|?\s)+', Text), - (r'\[', Comment.Multiline, '+comment'), - (r'[%s]' % _dquote, Generic.Heading, - ('+main', '+titling', '+titling-string')), - default(('+main', '+heading?')) - ], - '+titling-string': [ - (r'[^%s]+' % _dquote, Generic.Heading), - (r'[%s]' % _dquote, Generic.Heading, '#pop') - ], - '+titling': [ - (r'\[', Comment.Multiline, '+comment'), - (r'[^%s.;:|%s]+' % (_dquote, _newline), Generic.Heading), - (r'[%s]' % _dquote, Generic.Heading, '+titling-string'), - (r'[%s]{2}|(?<=[\s%s])\|[\s%s]' % (_newline, _dquote, _dquote), - Text, ('#pop', '+heading?')), - (r'[.;:]|(?<=[\s%s])\|' % _dquote, Text, '#pop'), - (r'[|%s]' % _newline, Generic.Heading) - ], - '+main': [ - (r'(?i)[^%s:a\[(|%s]+' % (_dquote, _newline), Text), - (r'[%s]' % _dquote, String.Double, '+text'), - (r':', Text, '+phrase-definition'), - (r'(?i)\bas\b', Text, '+use-option'), - (r'\[', Comment.Multiline, '+comment'), - (r'(\([%s])(.*?)([%s]\))' % (_dash, _dash), - bygroups(Punctuation, - using(this, state=('+i6-root', 'directive'), - i6t='+i6t-not-inline'), Punctuation)), - (r'(%s|(?<=[\s;:.%s]))\|\s|[%s]{2,}' % - (_start, _dquote, _newline), Text, '+heading?'), - (r'(?i)[a(|%s]' % _newline, Text) - ], - '+phrase-definition': [ - (r'\s+', Text), - (r'\[', Comment.Multiline, '+comment'), - (r'(\([%s])(.*?)([%s]\))' % (_dash, _dash), - bygroups(Punctuation, - using(this, state=('+i6-root', 'directive', - 'default', 'statements'), - i6t='+i6t-inline'), Punctuation), '#pop'), - default('#pop') - ], - '+use-option': [ - (r'\s+', Text), - (r'\[', Comment.Multiline, '+comment'), - (r'(\([%s])(.*?)([%s]\))' % (_dash, _dash), - bygroups(Punctuation, - using(this, state=('+i6-root', 'directive'), - i6t='+i6t-use-option'), Punctuation), '#pop'), - default('#pop') - ], - '+comment': [ - (r'[^\[\]]+', Comment.Multiline), - (r'\[', Comment.Multiline, '#push'), - (r'\]', Comment.Multiline, '#pop') - ], - '+text': [ - (r'[^\[%s]+' % _dquote, String.Double), - (r'\[.*?\]', String.Interpol), - (r'[%s]' % _dquote, String.Double, '#pop') - ], - '+heading?': [ - (r'(\|?\s)+', Text), - (r'\[', Comment.Multiline, '+comment'), - (r'[%s]{4}\s+' % _dash, Text, '+documentation-heading'), - (r'[%s]{1,3}' % _dash, Text), - (r'(?i)(volume|book|part|chapter|section)\b[^%s]*' % _newline, - Generic.Heading, '#pop'), - default('#pop') - ], - '+documentation-heading': [ - (r'\s+', Text), - (r'\[', Comment.Multiline, '+comment'), - (r'(?i)documentation\s+', Text, '+documentation-heading2'), - default('#pop') - ], - '+documentation-heading2': [ - (r'\s+', Text), - (r'\[', Comment.Multiline, '+comment'), - (r'[%s]{4}\s' % _dash, Text, '+documentation'), - default('#pop:2') - ], - '+documentation': [ - (r'(?i)(%s)\s*(chapter|example)\s*:[^%s]*' % - (_start, _newline), Generic.Heading), - (r'(?i)(%s)\s*section\s*:[^%s]*' % (_start, _newline), - Generic.Subheading), - (r'((%s)\t.*?[%s])+' % (_start, _newline), - using(this, state='+main')), - (r'[^%s\[]+|[%s\[]' % (_newline, _newline), Text), - (r'\[', Comment.Multiline, '+comment'), - ], - '+i6t-not-inline': [ - (r'(%s)@c( .*?)?([%s]|\Z)' % (_start, _newline), - Comment.Preproc), - (r'(%s)@([%s]+|Purpose:)[^%s]*' % (_start, _dash, _newline), - Comment.Preproc), - (r'(%s)@p( .*?)?([%s]|\Z)' % (_start, _newline), - Generic.Heading, '+p') - ], - '+i6t-use-option': [ - include('+i6t-not-inline'), - (r'(\{)(N)(\})', bygroups(Punctuation, Text, Punctuation)) - ], - '+i6t-inline': [ - (r'(\{)(\S[^}]*)?(\})', - bygroups(Punctuation, using(this, state='+main'), - Punctuation)) - ], - '+i6t': [ - (r'(\{[%s])(![^}]*)(\}?)' % _dash, - bygroups(Punctuation, Comment.Single, Punctuation)), - (r'(\{[%s])(lines)(:)([^}]*)(\}?)' % _dash, - bygroups(Punctuation, Keyword, Punctuation, Text, - Punctuation), '+lines'), - (r'(\{[%s])([^:}]*)(:?)([^}]*)(\}?)' % _dash, - bygroups(Punctuation, Keyword, Punctuation, Text, - Punctuation)), - (r'(\(\+)(.*?)(\+\)|\Z)', - bygroups(Punctuation, using(this, state='+main'), - Punctuation)) - ], - '+p': [ - (r'[^@]+', Comment.Preproc), - (r'(%s)@c( .*?)?([%s]|\Z)' % (_start, _newline), - Comment.Preproc, '#pop'), - (r'(%s)@([%s]|Purpose:)' % (_start, _dash), Comment.Preproc), - (r'(%s)@p( .*?)?([%s]|\Z)' % (_start, _newline), - Generic.Heading), - (r'@', Comment.Preproc) - ], - '+lines': [ - (r'(%s)@c( .*?)?([%s]|\Z)' % (_start, _newline), - Comment.Preproc), - (r'(%s)@([%s]|Purpose:)[^%s]*' % (_start, _dash, _newline), - Comment.Preproc), - (r'(%s)@p( .*?)?([%s]|\Z)' % (_start, _newline), - Generic.Heading, '+p'), - (r'(%s)@\w*[ %s]' % (_start, _newline), Keyword), - (r'![^%s]*' % _newline, Comment.Single), - (r'(\{)([%s]endlines)(\})' % _dash, - bygroups(Punctuation, Keyword, Punctuation), '#pop'), - (r'[^@!{]+?([%s]|\Z)|.' % _newline, Text) - ] - } - # Inform 7 can include snippets of Inform 6 template language, - # so all of Inform6Lexer's states are copied here, with - # modifications to account for template syntax. Inform7Lexer's - # own states begin with '+' to avoid name conflicts. Some of - # Inform6Lexer's states begin with '_': these are not modified. - # They deal with template syntax either by including modified - # states, or by matching r'' then pushing to modified states. - for token in Inform6Lexer.tokens: - if token == 'root': - continue - tokens[level][token] = list(Inform6Lexer.tokens[token]) - if not token.startswith('_'): - tokens[level][token][:0] = [include('+i6t'), include(level)] - - def __init__(self, **options): - level = options.get('i6t', '+i6t-not-inline') - if level not in self._all_tokens: - self._tokens = self.__class__.process_tokendef(level) - else: - self._tokens = self._all_tokens[level] - RegexLexer.__init__(self, **options) - - -class Inform6TemplateLexer(Inform7Lexer): - """ - For Inform 6 template code. - - .. versionadded:: 2.0 - """ - - name = 'Inform 6 template' - aliases = ['i6t'] - filenames = ['*.i6t'] - - def get_tokens_unprocessed(self, text, stack=('+i6t-root',)): - return Inform7Lexer.get_tokens_unprocessed(self, text, stack) - - -class Tads3Lexer(RegexLexer): - """ - For TADS 3 source code. - """ - - name = 'TADS 3' - aliases = ['tads3'] - filenames = ['*.t'] - - flags = re.DOTALL | re.MULTILINE - - _comment_single = r'(?://(?:[^\\\n]|\\+[\w\W])*$)' - _comment_multiline = r'(?:/\*(?:[^*]|\*(?!/))*\*/)' - _escape = (r'(?:\\(?:[\n\\<>"\'^v bnrt]|u[\da-fA-F]{,4}|x[\da-fA-F]{,2}|' - r'[0-3]?[0-7]{1,2}))') - _name = r'(?:[_a-zA-Z]\w*)' - _no_quote = r'(?=\s|\\?>)' - _operator = (r'(?:&&|\|\||\+\+|--|\?\?|::|[.,@\[\]~]|' - r'(?:[=+\-*/%!&|^]|<>?>?)=?)') - _ws = r'(?:\\|\s|%s|%s)' % (_comment_single, _comment_multiline) - _ws_pp = r'(?:\\\n|[^\S\n]|%s|%s)' % (_comment_single, _comment_multiline) - - def _make_string_state(triple, double, verbatim=None, _escape=_escape): - if verbatim: - verbatim = ''.join(['(?:%s|%s)' % (re.escape(c.lower()), - re.escape(c.upper())) - for c in verbatim]) - char = r'"' if double else r"'" - token = String.Double if double else String.Single - escaped_quotes = r'+|%s(?!%s{2})' % (char, char) if triple else r'' - prefix = '%s%s' % ('t' if triple else '', 'd' if double else 's') - tag_state_name = '%sqt' % prefix - state = [] - if triple: - state += [ - (r'%s{3,}' % char, token, '#pop'), - (r'\\%s+' % char, String.Escape), - (char, token) - ] - else: - state.append((char, token, '#pop')) - state += [ - include('s/verbatim'), - (r'[^\\<&{}%s]+' % char, token) - ] - if verbatim: - # This regex can't use `(?i)` because escape sequences are - # case-sensitive. `<\XMP>` works; `<\xmp>` doesn't. - state.append((r'\\?<(/|\\\\|(?!%s)\\)%s(?=[\s=>])' % - (_escape, verbatim), - Name.Tag, ('#pop', '%sqs' % prefix, tag_state_name))) - else: - state += [ - (r'\\?<\\%s]|<(?!<)|\\%s%s|%s|\\.)*>?' % - (char, char, escaped_quotes, _escape), Comment.Multiline), - (r'(?i)\\?]|\\>)', Name.Tag, - ('#pop', '%sqs/listing' % prefix, tag_state_name)), - (r'(?i)\\?]|\\>)', Name.Tag, - ('#pop', '%sqs/xmp' % prefix, tag_state_name)), - (r'\\?<([^\s=><\\%s]|<(?!<)|\\%s%s|%s|\\.)*' % - (char, char, escaped_quotes, _escape), Name.Tag, - tag_state_name), - include('s/entity') - ] - state += [ - include('s/escape'), - (r'\{([^}<\\%s]|<(?!<)|\\%s%s|%s|\\.)*\}' % - (char, char, escaped_quotes, _escape), String.Interpol), - (r'[\\&{}<]', token) - ] - return state - - def _make_tag_state(triple, double, _escape=_escape): - char = r'"' if double else r"'" - quantifier = r'{3,}' if triple else r'' - state_name = '%s%sqt' % ('t' if triple else '', 'd' if double else 's') - token = String.Double if double else String.Single - escaped_quotes = r'+|%s(?!%s{2})' % (char, char) if triple else r'' - return [ - (r'%s%s' % (char, quantifier), token, '#pop:2'), - (r'(\s|\\\n)+', Text), - (r'(=)(\\?")', bygroups(Punctuation, String.Double), - 'dqs/%s' % state_name), - (r"(=)(\\?')", bygroups(Punctuation, String.Single), - 'sqs/%s' % state_name), - (r'=', Punctuation, 'uqs/%s' % state_name), - (r'\\?>', Name.Tag, '#pop'), - (r'\{([^}<\\%s]|<(?!<)|\\%s%s|%s|\\.)*\}' % - (char, char, escaped_quotes, _escape), String.Interpol), - (r'([^\s=><\\%s]|<(?!<)|\\%s%s|%s|\\.)+' % - (char, char, escaped_quotes, _escape), Name.Attribute), - include('s/escape'), - include('s/verbatim'), - include('s/entity'), - (r'[\\{}&]', Name.Attribute) - ] - - def _make_attribute_value_state(terminator, host_triple, host_double, - _escape=_escape): - token = (String.Double if terminator == r'"' else - String.Single if terminator == r"'" else String.Other) - host_char = r'"' if host_double else r"'" - host_quantifier = r'{3,}' if host_triple else r'' - host_token = String.Double if host_double else String.Single - escaped_quotes = (r'+|%s(?!%s{2})' % (host_char, host_char) - if host_triple else r'') - return [ - (r'%s%s' % (host_char, host_quantifier), host_token, '#pop:3'), - (r'%s%s' % (r'' if token is String.Other else r'\\?', terminator), - token, '#pop'), - include('s/verbatim'), - include('s/entity'), - (r'\{([^}<\\%s]|<(?!<)|\\%s%s|%s|\\.)*\}' % - (host_char, host_char, escaped_quotes, _escape), String.Interpol), - (r'([^\s"\'<%s{}\\&])+' % (r'>' if token is String.Other else r''), - token), - include('s/escape'), - (r'["\'\s&{<}\\]', token) - ] - - tokens = { - 'root': [ - ('\ufeff', Text), - (r'\{', Punctuation, 'object-body'), - (r';+', Punctuation), - (r'(?=(argcount|break|case|catch|continue|default|definingobj|' - r'delegated|do|else|for|foreach|finally|goto|if|inherited|' - r'invokee|local|nil|new|operator|replaced|return|self|switch|' - r'targetobj|targetprop|throw|true|try|while)\b)', Text, 'block'), - (r'(%s)(%s*)(\()' % (_name, _ws), - bygroups(Name.Function, using(this, state='whitespace'), - Punctuation), - ('block?/root', 'more/parameters', 'main/parameters')), - include('whitespace'), - (r'\++', Punctuation), - (r'[^\s!"%-(*->@-_a-z{-~]+', Error), # Averts an infinite loop - (r'(?!\Z)', Text, 'main/root') - ], - 'main/root': [ - include('main/basic'), - default(('#pop', 'object-body/no-braces', 'classes', 'class')) - ], - 'object-body/no-braces': [ - (r';', Punctuation, '#pop'), - (r'\{', Punctuation, ('#pop', 'object-body')), - include('object-body') - ], - 'object-body': [ - (r';', Punctuation), - (r'\{', Punctuation, '#push'), - (r'\}', Punctuation, '#pop'), - (r':', Punctuation, ('classes', 'class')), - (r'(%s?)(%s*)(\()' % (_name, _ws), - bygroups(Name.Function, using(this, state='whitespace'), - Punctuation), - ('block?', 'more/parameters', 'main/parameters')), - (r'(%s)(%s*)(\{)' % (_name, _ws), - bygroups(Name.Function, using(this, state='whitespace'), - Punctuation), 'block'), - (r'(%s)(%s*)(:)' % (_name, _ws), - bygroups(Name.Variable, using(this, state='whitespace'), - Punctuation), - ('object-body/no-braces', 'classes', 'class')), - include('whitespace'), - (r'->|%s' % _operator, Punctuation, 'main'), - default('main/object-body') - ], - 'main/object-body': [ - include('main/basic'), - (r'(%s)(%s*)(=?)' % (_name, _ws), - bygroups(Name.Variable, using(this, state='whitespace'), - Punctuation), ('#pop', 'more', 'main')), - default('#pop:2') - ], - 'block?/root': [ - (r'\{', Punctuation, ('#pop', 'block')), - include('whitespace'), - (r'(?=[\[\'"<(:])', Text, # It might be a VerbRule macro. - ('#pop', 'object-body/no-braces', 'grammar', 'grammar-rules')), - # It might be a macro like DefineAction. - default(('#pop', 'object-body/no-braces')) - ], - 'block?': [ - (r'\{', Punctuation, ('#pop', 'block')), - include('whitespace'), - default('#pop') - ], - 'block/basic': [ - (r'[;:]+', Punctuation), - (r'\{', Punctuation, '#push'), - (r'\}', Punctuation, '#pop'), - (r'default\b', Keyword.Reserved), - (r'(%s)(%s*)(:)' % (_name, _ws), - bygroups(Name.Label, using(this, state='whitespace'), - Punctuation)), - include('whitespace') - ], - 'block': [ - include('block/basic'), - (r'(?!\Z)', Text, ('more', 'main')) - ], - 'block/embed': [ - (r'>>', String.Interpol, '#pop'), - include('block/basic'), - (r'(?!\Z)', Text, ('more/embed', 'main')) - ], - 'main/basic': [ - include('whitespace'), - (r'\(', Punctuation, ('#pop', 'more', 'main')), - (r'\[', Punctuation, ('#pop', 'more/list', 'main')), - (r'\{', Punctuation, ('#pop', 'more/inner', 'main/inner', - 'more/parameters', 'main/parameters')), - (r'\*|\.{3}', Punctuation, '#pop'), - (r'(?i)0x[\da-f]+', Number.Hex, '#pop'), - (r'(\d+\.(?!\.)\d*|\.\d+)([eE][-+]?\d+)?|\d+[eE][-+]?\d+', - Number.Float, '#pop'), - (r'0[0-7]+', Number.Oct, '#pop'), - (r'\d+', Number.Integer, '#pop'), - (r'"""', String.Double, ('#pop', 'tdqs')), - (r"'''", String.Single, ('#pop', 'tsqs')), - (r'"', String.Double, ('#pop', 'dqs')), - (r"'", String.Single, ('#pop', 'sqs')), - (r'R"""', String.Regex, ('#pop', 'tdqr')), - (r"R'''", String.Regex, ('#pop', 'tsqr')), - (r'R"', String.Regex, ('#pop', 'dqr')), - (r"R'", String.Regex, ('#pop', 'sqr')), - # Two-token keywords - (r'(extern)(%s+)(object\b)' % _ws, - bygroups(Keyword.Reserved, using(this, state='whitespace'), - Keyword.Reserved)), - (r'(function|method)(%s*)(\()' % _ws, - bygroups(Keyword.Reserved, using(this, state='whitespace'), - Punctuation), - ('#pop', 'block?', 'more/parameters', 'main/parameters')), - (r'(modify)(%s+)(grammar\b)' % _ws, - bygroups(Keyword.Reserved, using(this, state='whitespace'), - Keyword.Reserved), - ('#pop', 'object-body/no-braces', ':', 'grammar')), - (r'(new)(%s+(?=(?:function|method)\b))' % _ws, - bygroups(Keyword.Reserved, using(this, state='whitespace'))), - (r'(object)(%s+)(template\b)' % _ws, - bygroups(Keyword.Reserved, using(this, state='whitespace'), - Keyword.Reserved), ('#pop', 'template')), - (r'(string)(%s+)(template\b)' % _ws, - bygroups(Keyword, using(this, state='whitespace'), - Keyword.Reserved), ('#pop', 'function-name')), - # Keywords - (r'(argcount|definingobj|invokee|replaced|targetobj|targetprop)\b', - Name.Builtin, '#pop'), - (r'(break|continue|goto)\b', Keyword.Reserved, ('#pop', 'label')), - (r'(case|extern|if|intrinsic|return|static|while)\b', - Keyword.Reserved), - (r'catch\b', Keyword.Reserved, ('#pop', 'catch')), - (r'class\b', Keyword.Reserved, - ('#pop', 'object-body/no-braces', 'class')), - (r'(default|do|else|finally|try)\b', Keyword.Reserved, '#pop'), - (r'(dictionary|property)\b', Keyword.Reserved, - ('#pop', 'constants')), - (r'enum\b', Keyword.Reserved, ('#pop', 'enum')), - (r'export\b', Keyword.Reserved, ('#pop', 'main')), - (r'(for|foreach)\b', Keyword.Reserved, - ('#pop', 'more/inner', 'main/inner')), - (r'(function|method)\b', Keyword.Reserved, - ('#pop', 'block?', 'function-name')), - (r'grammar\b', Keyword.Reserved, - ('#pop', 'object-body/no-braces', 'grammar')), - (r'inherited\b', Keyword.Reserved, ('#pop', 'inherited')), - (r'local\b', Keyword.Reserved, - ('#pop', 'more/local', 'main/local')), - (r'(modify|replace|switch|throw|transient)\b', Keyword.Reserved, - '#pop'), - (r'new\b', Keyword.Reserved, ('#pop', 'class')), - (r'(nil|true)\b', Keyword.Constant, '#pop'), - (r'object\b', Keyword.Reserved, ('#pop', 'object-body/no-braces')), - (r'operator\b', Keyword.Reserved, ('#pop', 'operator')), - (r'propertyset\b', Keyword.Reserved, - ('#pop', 'propertyset', 'main')), - (r'self\b', Name.Builtin.Pseudo, '#pop'), - (r'template\b', Keyword.Reserved, ('#pop', 'template')), - # Operators - (r'(__objref|defined)(%s*)(\()' % _ws, - bygroups(Operator.Word, using(this, state='whitespace'), - Operator), ('#pop', 'more/__objref', 'main')), - (r'delegated\b', Operator.Word), - # Compiler-defined macros and built-in properties - (r'(__DATE__|__DEBUG|__LINE__|__FILE__|' - r'__TADS_MACRO_FORMAT_VERSION|__TADS_SYS_\w*|__TADS_SYSTEM_NAME|' - r'__TADS_VERSION_MAJOR|__TADS_VERSION_MINOR|__TADS3|__TIME__|' - r'construct|finalize|grammarInfo|grammarTag|lexicalParent|' - r'miscVocab|sourceTextGroup|sourceTextGroupName|' - r'sourceTextGroupOrder|sourceTextOrder)\b', Name.Builtin, '#pop') - ], - 'main': [ - include('main/basic'), - (_name, Name, '#pop'), - default('#pop') - ], - 'more/basic': [ - (r'\(', Punctuation, ('more/list', 'main')), - (r'\[', Punctuation, ('more', 'main')), - (r'\.{3}', Punctuation), - (r'->|\.\.', Punctuation, 'main'), - (r'(?=;)|[:)\]]', Punctuation, '#pop'), - include('whitespace'), - (_operator, Operator, 'main'), - (r'\?', Operator, ('main', 'more/conditional', 'main')), - (r'(is|not)(%s+)(in\b)' % _ws, - bygroups(Operator.Word, using(this, state='whitespace'), - Operator.Word)), - (r'[^\s!"%-_a-z{-~]+', Error) # Averts an infinite loop - ], - 'more': [ - include('more/basic'), - default('#pop') - ], - # Then expression (conditional operator) - 'more/conditional': [ - (r':(?!:)', Operator, '#pop'), - include('more') - ], - # Embedded expressions - 'more/embed': [ - (r'>>', String.Interpol, '#pop:2'), - include('more') - ], - # For/foreach loop initializer or short-form anonymous function - 'main/inner': [ - (r'\(', Punctuation, ('#pop', 'more/inner', 'main/inner')), - (r'local\b', Keyword.Reserved, ('#pop', 'main/local')), - include('main') - ], - 'more/inner': [ - (r'\}', Punctuation, '#pop'), - (r',', Punctuation, 'main/inner'), - (r'(in|step)\b', Keyword, 'main/inner'), - include('more') - ], - # Local - 'main/local': [ - (_name, Name.Variable, '#pop'), - include('whitespace') - ], - 'more/local': [ - (r',', Punctuation, 'main/local'), - include('more') - ], - # List - 'more/list': [ - (r'[,:]', Punctuation, 'main'), - include('more') - ], - # Parameter list - 'main/parameters': [ - (r'(%s)(%s*)(?=:)' % (_name, _ws), - bygroups(Name.Variable, using(this, state='whitespace')), '#pop'), - (r'(%s)(%s+)(%s)' % (_name, _ws, _name), - bygroups(Name.Class, using(this, state='whitespace'), - Name.Variable), '#pop'), - (r'\[+', Punctuation), - include('main/basic'), - (_name, Name.Variable, '#pop'), - default('#pop') - ], - 'more/parameters': [ - (r'(:)(%s*(?=[?=,:)]))' % _ws, - bygroups(Punctuation, using(this, state='whitespace'))), - (r'[?\]]+', Punctuation), - (r'[:)]', Punctuation, ('#pop', 'multimethod?')), - (r',', Punctuation, 'main/parameters'), - (r'=', Punctuation, ('more/parameter', 'main')), - include('more') - ], - 'more/parameter': [ - (r'(?=[,)])', Text, '#pop'), - include('more') - ], - 'multimethod?': [ - (r'multimethod\b', Keyword, '#pop'), - include('whitespace'), - default('#pop') - ], - - # Statements and expressions - 'more/__objref': [ - (r',', Punctuation, 'mode'), - (r'\)', Operator, '#pop'), - include('more') - ], - 'mode': [ - (r'(error|warn)\b', Keyword, '#pop'), - include('whitespace') - ], - 'catch': [ - (r'\(+', Punctuation), - (_name, Name.Exception, ('#pop', 'variables')), - include('whitespace') - ], - 'enum': [ - include('whitespace'), - (r'token\b', Keyword, ('#pop', 'constants')), - default(('#pop', 'constants')) - ], - 'grammar': [ - (r'\)+', Punctuation), - (r'\(', Punctuation, 'grammar-tag'), - (r':', Punctuation, 'grammar-rules'), - (_name, Name.Class), - include('whitespace') - ], - 'grammar-tag': [ - include('whitespace'), - (r'"""([^\\"<]|""?(?!")|\\"+|\\.|<(?!<))+("{3,}|<<)|' - r'R"""([^\\"]|""?(?!")|\\"+|\\.)+"{3,}|' - r"'''([^\\'<]|''?(?!')|\\'+|\\.|<(?!<))+('{3,}|<<)|" - r"R'''([^\\']|''?(?!')|\\'+|\\.)+'{3,}|" - r'"([^\\"<]|\\.|<(?!<))+("|<<)|R"([^\\"]|\\.)+"|' - r"'([^\\'<]|\\.|<(?!<))+('|<<)|R'([^\\']|\\.)+'|" - r"([^)\s\\/]|/(?![/*]))+|\)", String.Other, '#pop') - ], - 'grammar-rules': [ - include('string'), - include('whitespace'), - (r'(\[)(%s*)(badness)' % _ws, - bygroups(Punctuation, using(this, state='whitespace'), Keyword), - 'main'), - (r'->|%s|[()]' % _operator, Punctuation), - (_name, Name.Constant), - default('#pop:2') - ], - ':': [ - (r':', Punctuation, '#pop') - ], - 'function-name': [ - (r'(<<([^>]|>>>|>(?!>))*>>)+', String.Interpol), - (r'(?=%s?%s*[({])' % (_name, _ws), Text, '#pop'), - (_name, Name.Function, '#pop'), - include('whitespace') - ], - 'inherited': [ - (r'<', Punctuation, ('#pop', 'classes', 'class')), - include('whitespace'), - (_name, Name.Class, '#pop'), - default('#pop') - ], - 'operator': [ - (r'negate\b', Operator.Word, '#pop'), - include('whitespace'), - (_operator, Operator), - default('#pop') - ], - 'propertyset': [ - (r'\(', Punctuation, ('more/parameters', 'main/parameters')), - (r'\{', Punctuation, ('#pop', 'object-body')), - include('whitespace') - ], - 'template': [ - (r'(?=;)', Text, '#pop'), - include('string'), - (r'inherited\b', Keyword.Reserved), - include('whitespace'), - (r'->|\?|%s' % _operator, Punctuation), - (_name, Name.Variable) - ], - - # Identifiers - 'class': [ - (r'\*|\.{3}', Punctuation, '#pop'), - (r'object\b', Keyword.Reserved, '#pop'), - (r'transient\b', Keyword.Reserved), - (_name, Name.Class, '#pop'), - include('whitespace'), - default('#pop') - ], - 'classes': [ - (r'[:,]', Punctuation, 'class'), - include('whitespace'), - (r'>', Punctuation, '#pop'), - default('#pop') - ], - 'constants': [ - (r',+', Punctuation), - (r';', Punctuation, '#pop'), - (r'property\b', Keyword.Reserved), - (_name, Name.Constant), - include('whitespace') - ], - 'label': [ - (_name, Name.Label, '#pop'), - include('whitespace'), - default('#pop') - ], - 'variables': [ - (r',+', Punctuation), - (r'\)', Punctuation, '#pop'), - include('whitespace'), - (_name, Name.Variable) - ], - - # Whitespace and comments - 'whitespace': [ - (r'^%s*#(%s|[^\n]|(?<=\\)\n)*\n?' % (_ws_pp, _comment_multiline), - Comment.Preproc), - (_comment_single, Comment.Single), - (_comment_multiline, Comment.Multiline), - (r'\\+\n+%s*#?|\n+|([^\S\n]|\\)+' % _ws_pp, Text) - ], - - # Strings - 'string': [ - (r'"""', String.Double, 'tdqs'), - (r"'''", String.Single, 'tsqs'), - (r'"', String.Double, 'dqs'), - (r"'", String.Single, 'sqs') - ], - 's/escape': [ - (r'\{\{|\}\}|%s' % _escape, String.Escape) - ], - 's/verbatim': [ - (r'<<\s*(as\s+decreasingly\s+likely\s+outcomes|cycling|else|end|' - r'first\s+time|one\s+of|only|or|otherwise|' - r'(sticky|(then\s+)?(purely\s+)?at)\s+random|stopping|' - r'(then\s+)?(half\s+)?shuffled|\|\|)\s*>>', String.Interpol), - (r'<<(%%(_(%s|\\?.)|[\-+ ,#]|\[\d*\]?)*\d*\.?\d*(%s|\\?.)|' - r'\s*((else|otherwise)\s+)?(if|unless)\b)?' % (_escape, _escape), - String.Interpol, ('block/embed', 'more/embed', 'main')) - ], - 's/entity': [ - (r'(?i)&(#(x[\da-f]+|\d+)|[a-z][\da-z]*);?', Name.Entity) - ], - 'tdqs': _make_string_state(True, True), - 'tsqs': _make_string_state(True, False), - 'dqs': _make_string_state(False, True), - 'sqs': _make_string_state(False, False), - 'tdqs/listing': _make_string_state(True, True, 'listing'), - 'tsqs/listing': _make_string_state(True, False, 'listing'), - 'dqs/listing': _make_string_state(False, True, 'listing'), - 'sqs/listing': _make_string_state(False, False, 'listing'), - 'tdqs/xmp': _make_string_state(True, True, 'xmp'), - 'tsqs/xmp': _make_string_state(True, False, 'xmp'), - 'dqs/xmp': _make_string_state(False, True, 'xmp'), - 'sqs/xmp': _make_string_state(False, False, 'xmp'), - - # Tags - 'tdqt': _make_tag_state(True, True), - 'tsqt': _make_tag_state(True, False), - 'dqt': _make_tag_state(False, True), - 'sqt': _make_tag_state(False, False), - 'dqs/tdqt': _make_attribute_value_state(r'"', True, True), - 'dqs/tsqt': _make_attribute_value_state(r'"', True, False), - 'dqs/dqt': _make_attribute_value_state(r'"', False, True), - 'dqs/sqt': _make_attribute_value_state(r'"', False, False), - 'sqs/tdqt': _make_attribute_value_state(r"'", True, True), - 'sqs/tsqt': _make_attribute_value_state(r"'", True, False), - 'sqs/dqt': _make_attribute_value_state(r"'", False, True), - 'sqs/sqt': _make_attribute_value_state(r"'", False, False), - 'uqs/tdqt': _make_attribute_value_state(_no_quote, True, True), - 'uqs/tsqt': _make_attribute_value_state(_no_quote, True, False), - 'uqs/dqt': _make_attribute_value_state(_no_quote, False, True), - 'uqs/sqt': _make_attribute_value_state(_no_quote, False, False), - - # Regular expressions - 'tdqr': [ - (r'[^\\"]+', String.Regex), - (r'\\"*', String.Regex), - (r'"{3,}', String.Regex, '#pop'), - (r'"', String.Regex) - ], - 'tsqr': [ - (r"[^\\']+", String.Regex), - (r"\\'*", String.Regex), - (r"'{3,}", String.Regex, '#pop'), - (r"'", String.Regex) - ], - 'dqr': [ - (r'[^\\"]+', String.Regex), - (r'\\"?', String.Regex), - (r'"', String.Regex, '#pop') - ], - 'sqr': [ - (r"[^\\']+", String.Regex), - (r"\\'?", String.Regex), - (r"'", String.Regex, '#pop') - ] - } - - def get_tokens_unprocessed(self, text, **kwargs): - pp = r'^%s*#%s*' % (self._ws_pp, self._ws_pp) - if_false_level = 0 - for index, token, value in ( - RegexLexer.get_tokens_unprocessed(self, text, **kwargs)): - if if_false_level == 0: # Not in a false #if - if (token is Comment.Preproc and - re.match(r'%sif%s+(0|nil)%s*$\n?' % - (pp, self._ws_pp, self._ws_pp), value)): - if_false_level = 1 - else: # In a false #if - if token is Comment.Preproc: - if (if_false_level == 1 and - re.match(r'%sel(if|se)\b' % pp, value)): - if_false_level = 0 - elif re.match(r'%sif' % pp, value): - if_false_level += 1 - elif re.match(r'%sendif\b' % pp, value): - if_false_level -= 1 - else: - token = Comment - yield index, token, value - - def analyse_text(text): - """This is a rather generic descriptive language without strong - identifiers. It looks like a 'GameMainDef' has to be present, - and/or a 'versionInfo' with an 'IFID' field.""" - result = 0 - if '__TADS' in text or 'GameMainDef' in text: - result += 0.2 - - # This is a fairly unique keyword which is likely used in source as well - if 'versionInfo' in text and 'IFID' in text: - result += 0.1 - - return result diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/py38compat.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/py38compat.py deleted file mode 100644 index 7dbe8cef54a692c49e5fd01ddb5d01b224ee2d6d..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/setuptools/_distutils/py38compat.py +++ /dev/null @@ -1,7 +0,0 @@ -def aix_platform(osname, version, release): - try: - import _aix_support - return _aix_support.aix_platform() - except ImportError: - pass - return "%s-%s.%s" % (osname, version, release) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/utils.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/utils.py deleted file mode 100644 index 1002c4649f8dc114f55e1169e3ad5ff23599e256..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/toolz/utils.py +++ /dev/null @@ -1,9 +0,0 @@ -def raises(err, lamda): - try: - lamda() - return False - except err: - return True - - -no_default = '__no__default__' diff --git a/spaces/pustozerov/poc_call_transcription/README.md b/spaces/pustozerov/poc_call_transcription/README.md deleted file mode 100644 index 989e0b7ce343bb1855006311c4cf56e5eed45907..0000000000000000000000000000000000000000 --- a/spaces/pustozerov/poc_call_transcription/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Poc Call Transcription -emoji: ⚡ -colorFrom: green -colorTo: green -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: afl-3.0 ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/pyInter/Liyuu_sovits4/app.py b/spaces/pyInter/Liyuu_sovits4/app.py deleted file mode 100644 index 6df9e2340b5729ae4674e0d3b65e07bdf3173a57..0000000000000000000000000000000000000000 --- a/spaces/pyInter/Liyuu_sovits4/app.py +++ /dev/null @@ -1,70 +0,0 @@ -import io -import os - -os.system("wget -P hubert/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt") -import gradio as gr -import librosa -import numpy as np -import soundfile -from inference.infer_tool import Svc -import logging - -logging.getLogger('numba').setLevel(logging.WARNING) -logging.getLogger('markdown_it').setLevel(logging.WARNING) -logging.getLogger('urllib3').setLevel(logging.WARNING) -logging.getLogger('matplotlib').setLevel(logging.WARNING) - -model = Svc("logs/44k/Liella/G_100000.pth", "logs/44k/Liella/config.json", cluster_model_path="logs/44k/kmeans_10000.pt") - - - -def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, noise_scale): - if input_audio is None: - return "You need to upload an audio", None - sampling_rate, audio = input_audio - # print(audio.shape,sampling_rate) - duration = audio.shape[0] / sampling_rate - if duration > 45: - return "请上传小于45s的音频，需要转换长音频请本地进行转换", None - audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32) - if len(audio.shape) > 1: - audio = librosa.to_mono(audio.transpose(1, 0)) - if sampling_rate != 16000: - audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000) - print(audio.shape) - out_wav_path = "temp.wav" - soundfile.write(out_wav_path, audio, 16000, format="wav") - print( cluster_ratio, auto_f0, noise_scale) - out_audio, out_sr = model.infer(sid, vc_transform, out_wav_path, - cluster_infer_ratio=cluster_ratio, - auto_predict_f0=auto_f0, - noice_scale=noise_scale - ) - return "Success", (44100, out_audio.cpu().numpy()) - - -app = gr.Blocks() -with app: - with gr.Tabs(): - with gr.TabItem("Basic"): - gr.Markdown(value=""" - Liella一期生 sovits4.0 在线demo - - 使用sif语音训练，目前未包含聚类模型（咕咕咕） - """) - spks = list(model.spk2id.keys()) - sid = gr.Dropdown(label="音色", choices=spks, value=spks[0]) - vc_input3 = gr.Audio(label="上传音频（长度小于45秒）") - vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12），使用变调请务必同时将伴奏变调相同半音数！", value=0) - cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0) - auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False) - noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4) - vc_submit = gr.Button("转换", variant="primary") - vc_output1 = gr.Textbox(label="Output Message") - vc_output2 = gr.Audio(label="Output Audio") - vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, noise_scale], [vc_output1, vc_output2]) - - app.launch() - - - diff --git a/spaces/pycoming/bingo/src/components/chat-panel.tsx b/spaces/pycoming/bingo/src/components/chat-panel.tsx deleted file mode 100644 index 1fbc3c2bf05b914e0c229661832fbb560745f488..0000000000000000000000000000000000000000 --- a/spaces/pycoming/bingo/src/components/chat-panel.tsx +++ /dev/null @@ -1,153 +0,0 @@ -'use client' - -import * as React from 'react' -import Image from 'next/image' -import Textarea from 'react-textarea-autosize' -import { useAtomValue } from 'jotai' -import { useEnterSubmit } from '@/lib/hooks/use-enter-submit' -import { cn } from '@/lib/utils' - -import BrushIcon from '@/assets/images/brush.svg' -import ChatIcon from '@/assets/images/chat.svg' -import VisualSearchIcon from '@/assets/images/visual-search.svg' -import SendIcon from '@/assets/images/send.svg' -import PinIcon from '@/assets/images/pin.svg' -import PinFillIcon from '@/assets/images/pin-fill.svg' - -import { useBing } from '@/lib/hooks/use-bing' -import { voiceListenAtom } from '@/state' -import Voice from './voice' -import { ChatImage } from './chat-image' -import { ChatAttachments } from './chat-attachments' - -export interface ChatPanelProps - extends Pick< - ReturnType, - | 'generating' - | 'input' - | 'setInput' - | 'sendMessage' - | 'resetConversation' - | 'isSpeaking' - | 'attachmentList' - | 'uploadImage' - | 'setAttachmentList' - > { - id?: string - className?: string -} - -export function ChatPanel({ - isSpeaking, - generating, - input, - setInput, - className, - sendMessage, - resetConversation, - attachmentList, - uploadImage, - setAttachmentList -}: ChatPanelProps) { - const inputRef = React.useRef(null) - const {formRef, onKeyDown} = useEnterSubmit() - const [focused, setFocused] = React.useState(false) - const [active, setActive] = React.useState(false) - const [pin, setPin] = React.useState(false) - const [tid, setTid] = React.useState() - const voiceListening = useAtomValue(voiceListenAtom) - - const setBlur = React.useCallback(() => { - clearTimeout(tid) - setActive(false) - const _tid = setTimeout(() => setFocused(false), 2000); - setTid(_tid) - }, [tid]) - - const setFocus = React.useCallback(() => { - setFocused(true) - setActive(true) - clearTimeout(tid) - inputRef.current?.focus() - }, [tid]) - - React.useEffect(() => { - if (input) { - setFocus() - } - }, [input]) - - return ( -
        { - e.preventDefault() - if (generating) { - return; - } - if (!input?.trim()) { - return - } - setInput('') - setPin(false) - await sendMessage(input) - }} - ref={formRef} - > -
        -
        -
        -
        -
        -
        -
        - -
        -
        -
        -
        - chat -