diff --git a/spaces/17TheWord/vits-models/text/symbols.py b/spaces/17TheWord/vits-models/text/symbols.py deleted file mode 100644 index edfbd24247be8c757275ce80b9ec27a0ffa808f3..0000000000000000000000000000000000000000 --- a/spaces/17TheWord/vits-models/text/symbols.py +++ /dev/null @@ -1,39 +0,0 @@ -''' -Defines the set of symbols used in text input to the model. -''' - -'''# japanese_cleaners -_pad = '_' -_punctuation = ',.!?-' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ ' -''' - -'''# japanese_cleaners2 -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ ' -''' - -'''# korean_cleaners -_pad = '_' -_punctuation = ',.!?…~' -_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ ' -''' - -'''# chinese_cleaners -_pad = '_' -_punctuation = '，。！？—…' -_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ ' -''' - -# zh_ja_mixture_cleaners -_pad = '_' -_punctuation = ',.!?-~…' -_letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ ' - - -# Export all symbols: -symbols = [_pad] + list(_punctuation) + list(_letters) - -# Special symbol ids -SPACE_ID = symbols.index(" ") \ No newline at end of file diff --git a/spaces/1gistliPinn/ChatGPT4/Examples/Bahubali - The Beginning Movie Download In Torrent LINK.md b/spaces/1gistliPinn/ChatGPT4/Examples/Bahubali - The Beginning Movie Download In Torrent LINK.md deleted file mode 100644 index 03f0a1abc2eb38d4e3c002f87cd59960ae4eff53..0000000000000000000000000000000000000000 --- a/spaces/1gistliPinn/ChatGPT4/Examples/Bahubali - The Beginning Movie Download In Torrent LINK.md +++ /dev/null @@ -1,6 +0,0 @@ -

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Cryptoassets are a new asset class that offer investors unprecedented opportunities to diversify their portfolios, hedge against uncertainties, and access new markets and innovations. However, they also involve high volatility, complexity, regulation, and taxation. Therefore, investors need to educate themselves on the fundamentals of cryptoassets, the best platforms and strategies to use, and the benefits and risks involved. One of the best resources to learn about cryptoassets is the book Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond by Chris Burniske and Jack Tatar. This book provides a comprehensive and authoritative guide on the topic of crypto investing. It covers everything from the history and evolution of cryptoassets, to the valuation and analysis of different types of cryptoassets, to the portfolio management and risk mitigation strategies for crypto investors. You can download the PDF version of the book for free from this link: [Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond PDF Download]. You can also buy the paperback or Kindle version of the book from Amazon or other online retailers. We hope that this article has given you some useful information and insights on cryptoassets and how to invest in them wisely. If you have any questions or comments, please feel free to leave them below. Thank you for reading and happy investing!

FAQs

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What are cryptoassets?

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Cryptoassets are digital assets that use cryptography and blockchain technology to enable secure, decentralized, and peer-to-peer transactions. They include cryptocurrencies like Bitcoin and Ethereum, as well as tokens that represent various rights, utilities, or assets on a blockchain.

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Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond is a book by Chris Burniske and Jack Tatar that provides a comprehensive and authoritative guide on the topic of crypto investing. It covers everything from the history and evolution of cryptoassets, to the valuation and analysis of different types of cryptoassets, to the portfolio management and risk mitigation strategies for crypto investors.

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\ No newline at end of file diff --git a/spaces/1phancelerku/anime-remove-background/Create Your Own City and Explore Other Islands with City Island 5 MOD APK Terbaru.md b/spaces/1phancelerku/anime-remove-background/Create Your Own City and Explore Other Islands with City Island 5 MOD APK Terbaru.md deleted file mode 100644 index d5ec67720068d537999e21dea65842d32eb43b19..0000000000000000000000000000000000000000 --- a/spaces/1phancelerku/anime-remove-background/Create Your Own City and Explore Other Islands with City Island 5 MOD APK Terbaru.md +++ /dev/null @@ -1,67 +0,0 @@ -
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\ No newline at end of file diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/vis/densepose_outputs_vertex.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/vis/densepose_outputs_vertex.py deleted file mode 100644 index 71e5323c2bd3a29bc90e66d7d59d524033c120bf..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/DensePose/densepose/vis/densepose_outputs_vertex.py +++ /dev/null @@ -1,229 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -import json -import numpy as np -from functools import lru_cache -from typing import Dict, List, Optional, Tuple -import cv2 -import torch - -from detectron2.utils.file_io import PathManager - -from densepose.modeling import build_densepose_embedder -from densepose.modeling.cse.utils import get_closest_vertices_mask_from_ES - -from ..data.utils import get_class_to_mesh_name_mapping -from ..structures import DensePoseEmbeddingPredictorOutput -from ..structures.mesh import create_mesh -from .base import Boxes, Image, MatrixVisualizer -from .densepose_results_textures import get_texture_atlas - - -@lru_cache() -def get_xyz_vertex_embedding(mesh_name: str, device: torch.device): - if mesh_name == "smpl_27554": - embed_path = PathManager.get_local_path( - "https://dl.fbaipublicfiles.com/densepose/data/cse/mds_d=256.npy" - ) - embed_map, _ = np.load(embed_path, allow_pickle=True) - embed_map = torch.tensor(embed_map).float()[:, 0] - embed_map -= embed_map.min() - embed_map /= embed_map.max() - else: - mesh = create_mesh(mesh_name, device) - embed_map = mesh.vertices.sum(dim=1) - embed_map -= embed_map.min() - embed_map /= embed_map.max() - embed_map = embed_map**2 - return embed_map - - -class DensePoseOutputsVertexVisualizer(object): - def __init__( - self, - cfg, - inplace=True, - cmap=cv2.COLORMAP_JET, - alpha=0.7, - device="cuda", - default_class=0, - **kwargs, - ): - self.mask_visualizer = MatrixVisualizer( - inplace=inplace, cmap=cmap, val_scale=1.0, alpha=alpha - ) - self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) - self.embedder = build_densepose_embedder(cfg) - self.device = torch.device(device) - self.default_class = default_class - - self.mesh_vertex_embeddings = { - mesh_name: self.embedder(mesh_name).to(self.device) - for mesh_name in self.class_to_mesh_name.values() - if self.embedder.has_embeddings(mesh_name) - } - - def visualize( - self, - image_bgr: Image, - outputs_boxes_xywh_classes: Tuple[ - Optional[DensePoseEmbeddingPredictorOutput], Optional[Boxes], Optional[List[int]] - ], - ) -> Image: - if outputs_boxes_xywh_classes[0] is None: - return image_bgr - - S, E, N, bboxes_xywh, pred_classes = self.extract_and_check_outputs_and_boxes( - outputs_boxes_xywh_classes - ) - - for n in range(N): - x, y, w, h = bboxes_xywh[n].int().tolist() - mesh_name = self.class_to_mesh_name[pred_classes[n]] - closest_vertices, mask = get_closest_vertices_mask_from_ES( - E[[n]], - S[[n]], - h, - w, - self.mesh_vertex_embeddings[mesh_name], - self.device, - ) - embed_map = get_xyz_vertex_embedding(mesh_name, self.device) - vis = (embed_map[closest_vertices].clip(0, 1) * 255.0).cpu().numpy() - mask_numpy = mask.cpu().numpy().astype(dtype=np.uint8) - image_bgr = self.mask_visualizer.visualize(image_bgr, mask_numpy, vis, [x, y, w, h]) - - return image_bgr - - def extract_and_check_outputs_and_boxes(self, outputs_boxes_xywh_classes): - - densepose_output, bboxes_xywh, pred_classes = outputs_boxes_xywh_classes - - if pred_classes is None: - pred_classes = [self.default_class] * len(bboxes_xywh) - - assert isinstance( - densepose_output, DensePoseEmbeddingPredictorOutput - ), "DensePoseEmbeddingPredictorOutput expected, {} encountered".format( - type(densepose_output) - ) - - S = densepose_output.coarse_segm - E = densepose_output.embedding - N = S.size(0) - assert N == E.size( - 0 - ), "CSE coarse_segm {} and embeddings {}" " should have equal first dim size".format( - S.size(), E.size() - ) - assert N == len( - bboxes_xywh - ), "number of bounding boxes {}" " should be equal to first dim size of outputs {}".format( - len(bboxes_xywh), N - ) - assert N == len(pred_classes), ( - "number of predicted classes {}" - " should be equal to first dim size of outputs {}".format(len(bboxes_xywh), N) - ) - - return S, E, N, bboxes_xywh, pred_classes - - -def get_texture_atlases(json_str: Optional[str]) -> Optional[Dict[str, Optional[np.ndarray]]]: - """ - json_str is a JSON string representing a mesh_name -> texture_atlas_path dictionary - """ - if json_str is None: - return None - - paths = json.loads(json_str) - return {mesh_name: get_texture_atlas(path) for mesh_name, path in paths.items()} - - -class DensePoseOutputsTextureVisualizer(DensePoseOutputsVertexVisualizer): - def __init__( - self, - cfg, - texture_atlases_dict, - device="cuda", - default_class=0, - **kwargs, - ): - self.embedder = build_densepose_embedder(cfg) - - self.texture_image_dict = {} - self.alpha_dict = {} - - for mesh_name in texture_atlases_dict.keys(): - if texture_atlases_dict[mesh_name].shape[-1] == 4: # Image with alpha channel - self.alpha_dict[mesh_name] = texture_atlases_dict[mesh_name][:, :, -1] / 255.0 - self.texture_image_dict[mesh_name] = texture_atlases_dict[mesh_name][:, :, :3] - else: - self.alpha_dict[mesh_name] = texture_atlases_dict[mesh_name].sum(axis=-1) > 0 - self.texture_image_dict[mesh_name] = texture_atlases_dict[mesh_name] - - self.device = torch.device(device) - self.class_to_mesh_name = get_class_to_mesh_name_mapping(cfg) - self.default_class = default_class - - self.mesh_vertex_embeddings = { - mesh_name: self.embedder(mesh_name).to(self.device) - for mesh_name in self.class_to_mesh_name.values() - } - - def visualize( - self, - image_bgr: Image, - outputs_boxes_xywh_classes: Tuple[ - Optional[DensePoseEmbeddingPredictorOutput], Optional[Boxes], Optional[List[int]] - ], - ) -> Image: - image_target_bgr = image_bgr.copy() - if outputs_boxes_xywh_classes[0] is None: - return image_target_bgr - - S, E, N, bboxes_xywh, pred_classes = self.extract_and_check_outputs_and_boxes( - outputs_boxes_xywh_classes - ) - - meshes = { - p: create_mesh(self.class_to_mesh_name[p], self.device) for p in np.unique(pred_classes) - } - - for n in range(N): - x, y, w, h = bboxes_xywh[n].int().cpu().numpy() - mesh_name = self.class_to_mesh_name[pred_classes[n]] - closest_vertices, mask = get_closest_vertices_mask_from_ES( - E[[n]], - S[[n]], - h, - w, - self.mesh_vertex_embeddings[mesh_name], - self.device, - ) - uv_array = meshes[pred_classes[n]].texcoords[closest_vertices].permute((2, 0, 1)) - uv_array = uv_array.cpu().numpy().clip(0, 1) - textured_image = self.generate_image_with_texture( - image_target_bgr[y : y + h, x : x + w], - uv_array, - mask.cpu().numpy(), - self.class_to_mesh_name[pred_classes[n]], - ) - if textured_image is None: - continue - image_target_bgr[y : y + h, x : x + w] = textured_image - - return image_target_bgr - - def generate_image_with_texture(self, bbox_image_bgr, uv_array, mask, mesh_name): - alpha = self.alpha_dict.get(mesh_name) - texture_image = self.texture_image_dict.get(mesh_name) - if alpha is None or texture_image is None: - return None - U, V = uv_array - x_index = (U * texture_image.shape[1]).astype(int) - y_index = (V * texture_image.shape[0]).astype(int) - local_texture = texture_image[y_index, x_index][mask] - local_alpha = np.expand_dims(alpha[y_index, x_index][mask], -1) - output_image = bbox_image_bgr.copy() - output_image[mask] = output_image[mask] * (1 - local_alpha) + local_texture * local_alpha - return output_image.astype(np.uint8) diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointSup/point_sup/detection_utils.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointSup/point_sup/detection_utils.py deleted file mode 100644 index 3f95d9449277fc55e93121582f6c6a6396dc833d..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/PointSup/point_sup/detection_utils.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved - -import numpy as np -import torch - -# fmt: off -from detectron2.data.detection_utils import \ - annotations_to_instances as base_annotations_to_instances -from detectron2.data.detection_utils import \ - transform_instance_annotations as base_transform_instance_annotations - -# fmt: on - - -def annotations_to_instances(annos, image_size, sample_points=0): - """ - Create an :class:`Instances` object used by the models, - from instance annotations in the dataset dict. - - Args: - annos (list[dict]): a list of instance annotations in one image, each - element for one instance. - image_size (tuple): height, width - sample_points (int): subsample points at each iteration - - Returns: - Instances: - It will contain fields "gt_boxes", "gt_classes", - "gt_point_coords", "gt_point_labels", if they can be obtained from `annos`. - This is the format that builtin models with point supervision expect. - """ - target = base_annotations_to_instances(annos, image_size) - - assert ("point_coords" in annos[0]) == ("point_labels" in annos[0]) - - if len(annos) and "point_labels" in annos[0]: - point_coords = [] - point_labels = [] - for i, _ in enumerate(annos): - # Already in the image coordinate system - point_coords_wrt_image = np.array(annos[i]["point_coords"]) - point_labels_wrt_image = np.array(annos[i]["point_labels"]) - - if sample_points > 0: - random_indices = np.random.choice( - point_coords_wrt_image.shape[0], - sample_points, - replace=point_coords_wrt_image.shape[0] < sample_points, - ).astype(int) - point_coords_wrt_image = point_coords_wrt_image[random_indices] - point_labels_wrt_image = point_labels_wrt_image[random_indices] - assert point_coords_wrt_image.shape[0] == point_labels_wrt_image.size - - point_coords.append(point_coords_wrt_image) - point_labels.append(point_labels_wrt_image) - - point_coords = torch.stack([torch.from_numpy(x) for x in point_coords]) - point_labels = torch.stack([torch.from_numpy(x) for x in point_labels]) - target.gt_point_coords = point_coords - target.gt_point_labels = point_labels - - return target - - -def transform_instance_annotations( - annotation, transforms, image_size, *, keypoint_hflip_indices=None -): - """ - Apply transforms to box, and point annotations of a single instance. - It will use `transforms.apply_box` for the box, and - `transforms.apply_coords` for points. - Args: - annotation (dict): dict of instance annotations for a single instance. - It will be modified in-place. - transforms (TransformList or list[Transform]): - image_size (tuple): the height, width of the transformed image - keypoint_hflip_indices (ndarray[int]): see `create_keypoint_hflip_indices`. - Returns: - dict: - the same input dict with fields "bbox", "point_coords", "point_labels" - transformed according to `transforms`. - The "bbox_mode" field will be set to XYXY_ABS. - """ - annotation = base_transform_instance_annotations( - annotation, transforms, image_size, keypoint_hflip_indices - ) - - assert ("point_coords" in annotation) == ("point_labels" in annotation) - if "point_coords" in annotation and "point_labels" in annotation: - point_coords = annotation["point_coords"] - point_labels = np.array(annotation["point_labels"]).astype(np.float) - point_coords = transforms.apply_coords(point_coords) - - # Set all out-of-boundary points to "unlabeled" - inside = (point_coords >= np.array([0, 0])) & (point_coords <= np.array(image_size[::-1])) - inside = inside.all(axis=1) - point_labels[~inside] = -1 - - annotation["point_coords"] = point_coords - annotation["point_labels"] = point_labels - - return annotation diff --git a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/mask_rcnn_vitdet_h_75ep.py b/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/mask_rcnn_vitdet_h_75ep.py deleted file mode 100644 index 7de96f0a6c760ac41152726ac1e4faeb1fb9a818..0000000000000000000000000000000000000000 --- a/spaces/brjathu/HMR2.0/vendor/detectron2/projects/ViTDet/configs/COCO/mask_rcnn_vitdet_h_75ep.py +++ /dev/null @@ -1,33 +0,0 @@ -from functools import partial - -from .mask_rcnn_vitdet_b_100ep import ( - dataloader, - lr_multiplier, - model, - train, - optimizer, - get_vit_lr_decay_rate, -) - -train.init_checkpoint = ( - "detectron2://ImageNetPretrained/MAE/mae_pretrain_vit_huge_p14to16.pth?matching_heuristics=True" -) - -model.backbone.net.embed_dim = 1280 -model.backbone.net.depth = 32 -model.backbone.net.num_heads = 16 -model.backbone.net.drop_path_rate = 0.5 -# 7, 15, 23, 31 for global attention -model.backbone.net.window_block_indexes = ( - list(range(0, 7)) + list(range(8, 15)) + list(range(16, 23)) + list(range(24, 31)) -) - -optimizer.params.lr_factor_func = partial(get_vit_lr_decay_rate, lr_decay_rate=0.9, num_layers=32) -optimizer.params.overrides = {} -optimizer.params.weight_decay_norm = None - -train.max_iter = train.max_iter * 3 // 4 # 100ep -> 75ep -lr_multiplier.scheduler.milestones = [ - milestone * 3 // 4 for milestone in lr_multiplier.scheduler.milestones -] -lr_multiplier.scheduler.num_updates = train.max_iter diff --git a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_sounddevice.py b/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_sounddevice.py deleted file mode 100644 index b1e9f8c3443b040a2c75c09969bcad88cb527e02..0000000000000000000000000000000000000000 --- a/spaces/camilosegura/traductor-multilenguaje/Lib/site-packages/_sounddevice.py +++ /dev/null @@ -1,11 +0,0 @@ -# auto-generated file -import _cffi_backend - -ffi = _cffi_backend.FFI('_sounddevice', - _version = 0x2601, - _types = 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- _globals = (b'\x00\x00\x11\x23PaMacCore_GetChannelName',0,b'\x00\x00\x5F\x23PaMacCore_SetupChannelMap',0,b'\x00\x00\x64\x23PaMacCore_SetupStreamInfo',0,b'\x00\x00\x23\x23PaWasapi_IsLoopback',0,b'\x00\x00\x41\x23Pa_AbortStream',0,b'\x00\x00\x41\x23Pa_CloseStream',0,b'\x00\x00\x5A\x23Pa_GetDefaultHostApi',0,b'\x00\x00\x5A\x23Pa_GetDefaultInputDevice',0,b'\x00\x00\x5A\x23Pa_GetDefaultOutputDevice',0,b'\x00\x00\x5A\x23Pa_GetDeviceCount',0,b'\x00\x00\x00\x23Pa_GetDeviceInfo',0,b'\x00\x00\x0E\x23Pa_GetErrorText',0,b'\x00\x00\x5A\x23Pa_GetHostApiCount',0,b'\x00\x00\x03\x23Pa_GetHostApiInfo',0,b'\x00\x00\x09\x23Pa_GetLastHostErrorInfo',0,b'\x00\x00\x2A\x23Pa_GetSampleSize',0,b'\x00\x00\x18\x23Pa_GetStreamCpuLoad',0,b'\x00\x00\x06\x23Pa_GetStreamHostApiType',0,b'\x00\x00\x0B\x23Pa_GetStreamInfo',0,b'\x00\x00\x5C\x23Pa_GetStreamReadAvailable',0,b'\x00\x00\x18\x23Pa_GetStreamTime',0,b'\x00\x00\x5C\x23Pa_GetStreamWriteAvailable',0,b'\x00\x00\x5A\x23Pa_GetVersion',0,b'\x00\x00\x16\x23Pa_GetVersionText',0,b'\x00\x00\x26\x23Pa_HostApiDeviceIndexToDeviceIndex',0,b'\x00\x00\x1B\x23Pa_HostApiTypeIdToHostApiIndex',0,b'\x00\x00\x5A\x23Pa_Initialize',0,b'\x00\x00\x1E\x23Pa_IsFormatSupported',0,b'\x00\x00\x41\x23Pa_IsStreamActive',0,b'\x00\x00\x41\x23Pa_IsStreamStopped',0,b'\x00\x00\x37\x23Pa_OpenDefaultStream',0,b'\x00\x00\x2D\x23Pa_OpenStream',0,b'\x00\x00\x44\x23Pa_ReadStream',0,b'\x00\x00\x4E\x23Pa_SetStreamFinishedCallback',0,b'\x00\x00\x68\x23Pa_Sleep',0,b'\x00\x00\x41\x23Pa_StartStream',0,b'\x00\x00\x41\x23Pa_StopStream',0,b'\x00\x00\x5A\x23Pa_Terminate',0,b'\x00\x00\x49\x23Pa_WriteStream',0,b'\xFF\xFF\xFF\x0BeAudioCategoryAlerts',4,b'\xFF\xFF\xFF\x0BeAudioCategoryCommunications',3,b'\xFF\xFF\xFF\x0BeAudioCategoryGameChat',8,b'\xFF\xFF\xFF\x0BeAudioCategoryGameEffects',6,b'\xFF\xFF\xFF\x0BeAudioCategoryGameMedia',7,b'\xFF\xFF\xFF\x0BeAudioCategoryMedia',11,b'\xFF\xFF\xFF\x0BeAudioCategoryMovie',10,b'\xFF\xFF\xFF\x0BeAudioCategoryOther',0,b'\xFF\xFF\xFF\x0BeAudioCategorySoundEffects',5,b'\xFF\xFF\xFF\x0BeAudioCategorySpeech',9,b'\xFF\xFF\xFF\x0BeStreamOptionMatchFormat',2,b'\xFF\xFF\xFF\x0BeStreamOptionNone',0,b'\xFF\xFF\xFF\x0BeStreamOptionRaw',1,b'\xFF\xFF\xFF\x0BeThreadPriorityAudio',1,b'\xFF\xFF\xFF\x0BeThreadPriorityCapture',2,b'\xFF\xFF\xFF\x0BeThreadPriorityDistribution',3,b'\xFF\xFF\xFF\x0BeThreadPriorityGames',4,b'\xFF\xFF\xFF\x0BeThreadPriorityNone',0,b'\xFF\xFF\xFF\x0BeThreadPriorityPlayback',5,b'\xFF\xFF\xFF\x0BeThreadPriorityProAudio',6,b'\xFF\xFF\xFF\x0BeThreadPriorityWindowManager',7,b'\xFF\xFF\xFF\x0BpaAL',9,b'\xFF\xFF\xFF\x0BpaALSA',8,b'\xFF\xFF\xFF\x0BpaASIO',3,b'\xFF\xFF\xFF\x0BpaAbort',2,b'\xFF\xFF\xFF\x1FpaAsioUseChannelSelectors',1,b'\xFF\xFF\xFF\x0BpaAudioScienceHPI',14,b'\xFF\xFF\xFF\x0BpaBadBufferPtr',-9972,b'\xFF\xFF\xFF\x0BpaBadIODeviceCombination',-9993,b'\xFF\xFF\xFF\x0BpaBadStreamPtr',-9988,b'\xFF\xFF\xFF\x0BpaBeOS',10,b'\xFF\xFF\xFF\x0BpaBufferTooBig',-9991,b'\xFF\xFF\xFF\x0BpaBufferTooSmall',-9990,b'\xFF\xFF\xFF\x0BpaCanNotReadFromACallbackStream',-9977,b'\xFF\xFF\xFF\x0BpaCanNotReadFromAnOutputOnlyStream',-9975,b'\xFF\xFF\xFF\x0BpaCanNotWriteToACallbackStream',-9976,b'\xFF\xFF\xFF\x0BpaCanNotWriteToAnInputOnlyStream',-9974,b'\xFF\xFF\xFF\x1FpaClipOff',1,b'\xFF\xFF\xFF\x0BpaComplete',1,b'\xFF\xFF\xFF\x0BpaContinue',0,b'\xFF\xFF\xFF\x0BpaCoreAudio',5,b'\xFF\xFF\xFF\x1FpaCustomFormat',65536,b'\xFF\xFF\xFF\x0BpaDeviceUnavailable',-9985,b'\xFF\xFF\xFF\x0BpaDirectSound',1,b'\xFF\xFF\xFF\x1FpaDitherOff',2,b'\xFF\xFF\xFF\x1FpaFloat32',1,b'\xFF\xFF\xFF\x1FpaFormatIsSupported',0,b'\xFF\xFF\xFF\x1FpaFramesPerBufferUnspecified',0,b'\xFF\xFF\xFF\x0BpaHostApiNotFound',-9979,b'\xFF\xFF\xFF\x0BpaInDevelopment',0,b'\xFF\xFF\xFF\x0BpaIncompatibleHostApiSpecificStreamInfo',-9984,b'\xFF\xFF\xFF\x0BpaIncompatibleStreamHostApi',-9973,b'\xFF\xFF\xFF\x1FpaInputOverflow',2,b'\xFF\xFF\xFF\x0BpaInputOverflowed',-9981,b'\xFF\xFF\xFF\x1FpaInputUnderflow',1,b'\xFF\xFF\xFF\x0BpaInsufficientMemory',-9992,b'\xFF\xFF\xFF\x1FpaInt16',8,b'\xFF\xFF\xFF\x1FpaInt24',4,b'\xFF\xFF\xFF\x1FpaInt32',2,b'\xFF\xFF\xFF\x1FpaInt8',16,b'\xFF\xFF\xFF\x0BpaInternalError',-9986,b'\xFF\xFF\xFF\x0BpaInvalidChannelCount',-9998,b'\xFF\xFF\xFF\x0BpaInvalidDevice',-9996,b'\xFF\xFF\xFF\x0BpaInvalidFlag',-9995,b'\xFF\xFF\xFF\x0BpaInvalidHostApi',-9978,b'\xFF\xFF\xFF\x0BpaInvalidSampleRate',-9997,b'\xFF\xFF\xFF\x0BpaJACK',12,b'\xFF\xFF\xFF\x0BpaMME',2,b'\xFF\xFF\xFF\x1FpaMacCoreChangeDeviceParameters',1,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityHigh',1024,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityLow',768,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMax',0,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMedium',512,b'\xFF\xFF\xFF\x1FpaMacCoreConversionQualityMin',256,b'\xFF\xFF\xFF\x1FpaMacCoreFailIfConversionRequired',2,b'\xFF\xFF\xFF\x1FpaMacCoreMinimizeCPU',257,b'\xFF\xFF\xFF\x1FpaMacCoreMinimizeCPUButPlayNice',256,b'\xFF\xFF\xFF\x1FpaMacCorePlayNice',0,b'\xFF\xFF\xFF\x1FpaMacCorePro',1,b'\xFF\xFF\xFF\x1FpaNeverDropInput',4,b'\xFF\xFF\xFF\x1FpaNoDevice',-1,b'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- _struct_unions = 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(b'\x00\x00\x00\x78\x00\x00\x00\x15PaErrorCode\x00paNoError,paNotInitialized,paUnanticipatedHostError,paInvalidChannelCount,paInvalidSampleRate,paInvalidDevice,paInvalidFlag,paSampleFormatNotSupported,paBadIODeviceCombination,paInsufficientMemory,paBufferTooBig,paBufferTooSmall,paNullCallback,paBadStreamPtr,paTimedOut,paInternalError,paDeviceUnavailable,paIncompatibleHostApiSpecificStreamInfo,paStreamIsStopped,paStreamIsNotStopped,paInputOverflowed,paOutputUnderflowed,paHostApiNotFound,paInvalidHostApi,paCanNotReadFromACallbackStream,paCanNotWriteToACallbackStream,paCanNotReadFromAnOutputOnlyStream,paCanNotWriteToAnInputOnlyStream,paIncompatibleStreamHostApi,paBadBufferPtr',b'\x00\x00\x00\x1C\x00\x00\x00\x16PaHostApiTypeId\x00paInDevelopment,paDirectSound,paMME,paASIO,paSoundManager,paCoreAudio,paOSS,paALSA,paAL,paBeOS,paWDMKS,paJACK,paWASAPI,paAudioScienceHPI',b'\x00\x00\x00\x7E\x00\x00\x00\x16PaStreamCallbackResult\x00paContinue,paComplete,paAbort',b'\x00\x00\x00\x83\x00\x00\x00\x16PaWasapiFlags\x00paWinWasapiExclusive,paWinWasapiRedirectHostProcessor,paWinWasapiUseChannelMask,paWinWasapiPolling,paWinWasapiThreadPriority',b'\x00\x00\x00\x84\x00\x00\x00\x16PaWasapiStreamCategory\x00eAudioCategoryOther,eAudioCategoryCommunications,eAudioCategoryAlerts,eAudioCategorySoundEffects,eAudioCategoryGameEffects,eAudioCategoryGameMedia,eAudioCategoryGameChat,eAudioCategorySpeech,eAudioCategoryMovie,eAudioCategoryMedia',b'\x00\x00\x00\x86\x00\x00\x00\x16PaWasapiStreamOption\x00eStreamOptionNone,eStreamOptionRaw,eStreamOptionMatchFormat',b'\x00\x00\x00\x87\x00\x00\x00\x16PaWasapiThreadPriority\x00eThreadPriorityNone,eThreadPriorityAudio,eThreadPriorityCapture,eThreadPriorityDistribution,eThreadPriorityGames,eThreadPriorityPlayback,eThreadPriorityProAudio,eThreadPriorityWindowManager'), - _typenames = (b'\x00\x00\x00\x75PaAsioStreamInfo',b'\x00\x00\x00\x01PaDeviceIndex',b'\x00\x00\x00\x77PaDeviceInfo',b'\x00\x00\x00\x01PaError',b'\x00\x00\x00\x78PaErrorCode',b'\x00\x00\x00\x01PaHostApiIndex',b'\x00\x00\x00\x7APaHostApiInfo',b'\x00\x00\x00\x1CPaHostApiTypeId',b'\x00\x00\x00\x7CPaHostErrorInfo',b'\x00\x00\x00\x7DPaMacCoreStreamInfo',b'\x00\x00\x00\x2BPaSampleFormat',b'\x00\x00\x00\x8DPaStream',b'\x00\x00\x00\x52PaStreamCallback',b'\x00\x00\x00\x2BPaStreamCallbackFlags',b'\x00\x00\x00\x7EPaStreamCallbackResult',b'\x00\x00\x00\x7FPaStreamCallbackTimeInfo',b'\x00\x00\x00\x6BPaStreamFinishedCallback',b'\x00\x00\x00\x2BPaStreamFlags',b'\x00\x00\x00\x81PaStreamInfo',b'\x00\x00\x00\x82PaStreamParameters',b'\x00\x00\x00\x21PaTime',b'\x00\x00\x00\x83PaWasapiFlags',b'\x00\x00\x00\x8CPaWasapiHostProcessorCallback',b'\x00\x00\x00\x84PaWasapiStreamCategory',b'\x00\x00\x00\x85PaWasapiStreamInfo',b'\x00\x00\x00\x86PaWasapiStreamOption',b'\x00\x00\x00\x87PaWasapiThreadPriority',b'\x00\x00\x00\x2BPaWinWaveFormatChannelMask',b'\x00\x00\x00\x8BSInt32'), -) diff --git a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/structures/test_boxes.py b/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/structures/test_boxes.py deleted file mode 100644 index 101191818c511cf90c3c8f2cbc55aa49295697fa..0000000000000000000000000000000000000000 --- a/spaces/carlosalonso/Detection-video/carpeta_deteccion/tests/structures/test_boxes.py +++ /dev/null @@ -1,223 +0,0 @@ -# Copyright (c) Facebook, Inc. and its affiliates. -import json -import math -import numpy as np -import unittest -import torch - -from detectron2.structures import Boxes, BoxMode, pairwise_ioa, pairwise_iou -from detectron2.utils.testing import reload_script_model - - -class TestBoxMode(unittest.TestCase): - def _convert_xy_to_wh(self, x): - return BoxMode.convert(x, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS) - - def _convert_xywha_to_xyxy(self, x): - return BoxMode.convert(x, BoxMode.XYWHA_ABS, BoxMode.XYXY_ABS) - - def _convert_xywh_to_xywha(self, x): - return BoxMode.convert(x, BoxMode.XYWH_ABS, BoxMode.XYWHA_ABS) - - def test_convert_int_mode(self): - BoxMode.convert([1, 2, 3, 4], 0, 1) - - def test_box_convert_list(self): - for tp in [list, tuple]: - box = tp([5.0, 5.0, 10.0, 10.0]) - output = self._convert_xy_to_wh(box) - self.assertIsInstance(output, tp) - self.assertIsInstance(output[0], float) - self.assertEqual(output, tp([5.0, 5.0, 5.0, 5.0])) - - with self.assertRaises(Exception): - self._convert_xy_to_wh([box]) - - def test_box_convert_array(self): - box = np.asarray([[5, 5, 10, 10], [1, 1, 2, 3]]) - output = self._convert_xy_to_wh(box) - self.assertEqual(output.dtype, box.dtype) - self.assertEqual(output.shape, box.shape) - self.assertTrue((output[0] == [5, 5, 5, 5]).all()) - self.assertTrue((output[1] == [1, 1, 1, 2]).all()) - - def test_box_convert_cpu_tensor(self): - box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]) - output = self._convert_xy_to_wh(box) - self.assertEqual(output.dtype, box.dtype) - self.assertEqual(output.shape, box.shape) - output = output.numpy() - self.assertTrue((output[0] == [5, 5, 5, 5]).all()) - self.assertTrue((output[1] == [1, 1, 1, 2]).all()) - - @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") - def test_box_convert_cuda_tensor(self): - box = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]]).cuda() - output = self._convert_xy_to_wh(box) - self.assertEqual(output.dtype, box.dtype) - self.assertEqual(output.shape, box.shape) - self.assertEqual(output.device, box.device) - output = output.cpu().numpy() - self.assertTrue((output[0] == [5, 5, 5, 5]).all()) - self.assertTrue((output[1] == [1, 1, 1, 2]).all()) - - def test_box_convert_xywha_to_xyxy_list(self): - for tp in [list, tuple]: - box = tp([50, 50, 30, 20, 0]) - output = self._convert_xywha_to_xyxy(box) - self.assertIsInstance(output, tp) - self.assertEqual(output, tp([35, 40, 65, 60])) - - with self.assertRaises(Exception): - self._convert_xywha_to_xyxy([box]) - - def test_box_convert_xywha_to_xyxy_array(self): - for dtype in [np.float64, np.float32]: - box = np.asarray( - [ - [50, 50, 30, 20, 0], - [50, 50, 30, 20, 90], - [1, 1, math.sqrt(2), math.sqrt(2), -45], - ], - dtype=dtype, - ) - output = self._convert_xywha_to_xyxy(box) - self.assertEqual(output.dtype, box.dtype) - expected = np.asarray([[35, 40, 65, 60], [40, 35, 60, 65], [0, 0, 2, 2]], dtype=dtype) - self.assertTrue(np.allclose(output, expected, atol=1e-6), "output={}".format(output)) - - def test_box_convert_xywha_to_xyxy_tensor(self): - for dtype in [torch.float32, torch.float64]: - box = torch.tensor( - [ - [50, 50, 30, 20, 0], - [50, 50, 30, 20, 90], - [1, 1, math.sqrt(2), math.sqrt(2), -45], - ], - dtype=dtype, - ) - output = self._convert_xywha_to_xyxy(box) - self.assertEqual(output.dtype, box.dtype) - expected = torch.tensor([[35, 40, 65, 60], [40, 35, 60, 65], [0, 0, 2, 2]], dtype=dtype) - - self.assertTrue(torch.allclose(output, expected, atol=1e-6), "output={}".format(output)) - - def test_box_convert_xywh_to_xywha_list(self): - for tp in [list, tuple]: - box = tp([50, 50, 30, 20]) - output = self._convert_xywh_to_xywha(box) - self.assertIsInstance(output, tp) - self.assertEqual(output, tp([65, 60, 30, 20, 0])) - - with self.assertRaises(Exception): - self._convert_xywh_to_xywha([box]) - - def test_box_convert_xywh_to_xywha_array(self): - for dtype in [np.float64, np.float32]: - box = np.asarray([[30, 40, 70, 60], [30, 40, 60, 70], [-1, -1, 2, 2]], dtype=dtype) - output = self._convert_xywh_to_xywha(box) - self.assertEqual(output.dtype, box.dtype) - expected = np.asarray( - [[65, 70, 70, 60, 0], [60, 75, 60, 70, 0], [0, 0, 2, 2, 0]], dtype=dtype - ) - self.assertTrue(np.allclose(output, expected, atol=1e-6), "output={}".format(output)) - - def test_box_convert_xywh_to_xywha_tensor(self): - for dtype in [torch.float32, torch.float64]: - box = torch.tensor([[30, 40, 70, 60], [30, 40, 60, 70], [-1, -1, 2, 2]], dtype=dtype) - output = self._convert_xywh_to_xywha(box) - self.assertEqual(output.dtype, box.dtype) - expected = torch.tensor( - [[65, 70, 70, 60, 0], [60, 75, 60, 70, 0], [0, 0, 2, 2, 0]], dtype=dtype - ) - - self.assertTrue(torch.allclose(output, expected, atol=1e-6), "output={}".format(output)) - - def test_json_serializable(self): - payload = {"box_mode": BoxMode.XYWH_REL} - try: - json.dumps(payload) - except Exception: - self.fail("JSON serialization failed") - - def test_json_deserializable(self): - payload = '{"box_mode": 2}' - obj = json.loads(payload) - try: - obj["box_mode"] = BoxMode(obj["box_mode"]) - except Exception: - self.fail("JSON deserialization failed") - - -class TestBoxIOU(unittest.TestCase): - def create_boxes(self): - boxes1 = torch.tensor([[0.0, 0.0, 1.0, 1.0], [0.0, 0.0, 1.0, 1.0]]) - - boxes2 = torch.tensor( - [ - [0.0, 0.0, 1.0, 1.0], - [0.0, 0.0, 0.5, 1.0], - [0.0, 0.0, 1.0, 0.5], - [0.0, 0.0, 0.5, 0.5], - [0.5, 0.5, 1.0, 1.0], - [0.5, 0.5, 1.5, 1.5], - ] - ) - return boxes1, boxes2 - - def test_pairwise_iou(self): - boxes1, boxes2 = self.create_boxes() - expected_ious = torch.tensor( - [ - [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], - [1.0, 0.5, 0.5, 0.25, 0.25, 0.25 / (2 - 0.25)], - ] - ) - - ious = pairwise_iou(Boxes(boxes1), Boxes(boxes2)) - self.assertTrue(torch.allclose(ious, expected_ious)) - - def test_pairwise_ioa(self): - boxes1, boxes2 = self.create_boxes() - expected_ioas = torch.tensor( - [[1.0, 1.0, 1.0, 1.0, 1.0, 0.25], [1.0, 1.0, 1.0, 1.0, 1.0, 0.25]] - ) - ioas = pairwise_ioa(Boxes(boxes1), Boxes(boxes2)) - self.assertTrue(torch.allclose(ioas, expected_ioas)) - - -class TestBoxes(unittest.TestCase): - def test_empty_cat(self): - x = Boxes.cat([]) - self.assertTrue(x.tensor.shape, (0, 4)) - - def test_to(self): - x = Boxes(torch.rand(3, 4)) - self.assertEqual(x.to(device="cpu").tensor.device.type, "cpu") - - def test_scriptability(self): - def func(x): - boxes = Boxes(x) - test = boxes.to(torch.device("cpu")).tensor - return boxes.area(), test - - f = torch.jit.script(func) - f = reload_script_model(f) - f(torch.rand((3, 4))) - - data = torch.rand((3, 4)) - - def func_cat(x: torch.Tensor): - boxes1 = Boxes(x) - boxes2 = Boxes(x) - # boxes3 = Boxes.cat([boxes1, boxes2]) # this is not supported by torchsript for now. - boxes3 = boxes1.cat([boxes1, boxes2]) - return boxes3 - - f = torch.jit.script(func_cat) - script_box = f(data) - self.assertTrue(torch.equal(torch.cat([data, data]), script_box.tensor)) - - -if __name__ == "__main__": - unittest.main() diff --git a/spaces/chendl/compositional_test/multimodal/tools/instruct_tuning_data/pisc.py b/spaces/chendl/compositional_test/multimodal/tools/instruct_tuning_data/pisc.py deleted file mode 100644 index 562cec805725481fad3277543f4c8d5947473e6c..0000000000000000000000000000000000000000 --- a/spaces/chendl/compositional_test/multimodal/tools/instruct_tuning_data/pisc.py +++ /dev/null @@ -1,51 +0,0 @@ -import json -import os -from tqdm import tqdm -import webdataset as wds -from utils import MAXCOUNT, NAMING, check_sample -import numpy as np -PISC_ROOT = "/gpfs/u/home/LMCG/LMCGljnn/scratch/datasets/raw/PISC" -OUT_DIR = "/gpfs/u/home/LMCG/LMCGljnn/scratch-shared/junyan/raw/instruct/eval/pisc" - -rel_id_to_type = ["friends", "family", "couple", "professional", "commercial", "no relation"] - -if __name__ == "__main__": - os.makedirs(OUT_DIR, exist_ok=True) - annotation_image_info = json.load(open(os.path.join(PISC_ROOT, "annotation_image_info.json"))) - relationships = json.load(open(os.path.join(PISC_ROOT, "relationship.json"))) - relationship_trainidx = json.load(open(os.path.join(PISC_ROOT, "relationship_split", "relation_trainidx.json"))) - relationship_testidx = json.load(open(os.path.join(PISC_ROOT, "relationship_split", "relation_testidx.json"))) - data = {} - uuid = 0 - with wds.ShardWriter(os.path.join(OUT_DIR, NAMING), maxcount=MAXCOUNT**3) as sink: - for annotation in tqdm(annotation_image_info): - imgH = annotation["imgH"] - imgW = annotation["imgW"] - id = annotation["id"] - bbox = annotation["bbox"] # xyxy - if str(id) not in relationships: - tqdm.write(f"skip {id} due to not in relationships") - continue - if str(id) not in relationship_testidx: - tqdm.write(f"skip {id} due to not in train set") - continue - relationship = relationships[str(id)] - for rel in relationship: - type = rel_id_to_type[relationship[rel] - 1] - A_id, B_id = list(map(int, rel.split(" "))) - A_box = np.array(bbox[A_id - 1]).astype(float) / np.array([imgW, imgH, imgW, imgH]).astype(float) - B_box = np.array(bbox[B_id - 1]).astype(float) / np.array([imgW, imgH, imgW, imgH]).astype(float) - data = [A_box, B_box, type] - image_path = os.path.join(PISC_ROOT, "image", str(id).zfill(5)+".jpg") - dataset = "pisc_relation_split" - key = f"{dataset}_{id}_{uuid}" - uuid += 1 - assert os.path.exists(image_path) - sample = { - "__key__": key, - "image_path.txt": image_path, - "dataset.txt": dataset, - "data.pyd": data, - } - check_sample(sample) - sink.write(sample) diff --git a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/utils.py b/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/utils.py deleted file mode 100644 index f88b9cc049ae8c30c62f189821878b5f2587cbc6..0000000000000000000000000000000000000000 --- a/spaces/chikoto/Umamusume-DeBERTa-VITS2-TTS-JP/utils.py +++ /dev/null @@ -1,356 +0,0 @@ -import os -import glob -import argparse -import logging -import json -import subprocess -import numpy as np -from scipy.io.wavfile import read -import torch - -MATPLOTLIB_FLAG = False - -logger = logging.getLogger(__name__) - - -def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False): - assert os.path.isfile(checkpoint_path) - checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") - iteration = checkpoint_dict["iteration"] - learning_rate = checkpoint_dict["learning_rate"] - if ( - optimizer is not None - and not skip_optimizer - and checkpoint_dict["optimizer"] is not None - ): - optimizer.load_state_dict(checkpoint_dict["optimizer"]) - elif optimizer is None and not skip_optimizer: - # else: Disable this line if Infer and resume checkpoint,then enable the line upper - new_opt_dict = optimizer.state_dict() - new_opt_dict_params = new_opt_dict["param_groups"][0]["params"] - new_opt_dict["param_groups"] = checkpoint_dict["optimizer"]["param_groups"] - new_opt_dict["param_groups"][0]["params"] = new_opt_dict_params - optimizer.load_state_dict(new_opt_dict) - - saved_state_dict = checkpoint_dict["model"] - if hasattr(model, "module"): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - - new_state_dict = {} - for k, v in state_dict.items(): - try: - # assert "emb_g" not in k - new_state_dict[k] = saved_state_dict[k] - assert saved_state_dict[k].shape == v.shape, ( - saved_state_dict[k].shape, - v.shape, - ) - except: - # For upgrading from the old version - if "ja_bert_proj" in k: - v = torch.zeros_like(v) - logger.warn( - f"Seems you are using the old version of the model, the {k} is automatically set to zero for backward compatibility" - ) - else: - logger.error(f"{k} is not in the checkpoint") - - new_state_dict[k] = v - - if hasattr(model, "module"): - model.module.load_state_dict(new_state_dict, strict=False) - else: - model.load_state_dict(new_state_dict, strict=False) - - logger.info( - "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration) - ) - - return model, optimizer, learning_rate, iteration - - -def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): - logger.info( - "Saving model and optimizer state at iteration {} to {}".format( - iteration, checkpoint_path - ) - ) - if hasattr(model, "module"): - state_dict = model.module.state_dict() - else: - state_dict = model.state_dict() - torch.save( - { - "model": state_dict, - "iteration": iteration, - "optimizer": optimizer.state_dict(), - "learning_rate": learning_rate, - }, - checkpoint_path, - ) - - -def summarize( - writer, - global_step, - scalars={}, - histograms={}, - images={}, - audios={}, - audio_sampling_rate=22050, -): - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats="HWC") - for k, v in audios.items(): - writer.add_audio(k, v, global_step, audio_sampling_rate) - - -def latest_checkpoint_path(dir_path, regex="G_*.pth"): - f_list = glob.glob(os.path.join(dir_path, regex)) - f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) - x = f_list[-1] - return x - - -def plot_spectrogram_to_numpy(spectrogram): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger("matplotlib") - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(10, 2)) - im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") - plt.colorbar(im, ax=ax) - plt.xlabel("Frames") - plt.ylabel("Channels") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def plot_alignment_to_numpy(alignment, info=None): - global MATPLOTLIB_FLAG - if not MATPLOTLIB_FLAG: - import matplotlib - - matplotlib.use("Agg") - MATPLOTLIB_FLAG = True - mpl_logger = logging.getLogger("matplotlib") - mpl_logger.setLevel(logging.WARNING) - import matplotlib.pylab as plt - import numpy as np - - fig, ax = plt.subplots(figsize=(6, 4)) - im = ax.imshow( - alignment.transpose(), aspect="auto", origin="lower", interpolation="none" - ) - fig.colorbar(im, ax=ax) - xlabel = "Decoder timestep" - if info is not None: - xlabel += "\n\n" + info - plt.xlabel(xlabel) - plt.ylabel("Encoder timestep") - plt.tight_layout() - - fig.canvas.draw() - data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") - data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) - plt.close() - return data - - -def load_wav_to_torch(full_path): - sampling_rate, data = read(full_path) - return torch.FloatTensor(data.astype(np.float32)), sampling_rate - - -def load_filepaths_and_text(filename, split="|"): - with open(filename, encoding="utf-8") as f: - filepaths_and_text = [line.strip().split(split) for line in f] - return filepaths_and_text - - -def get_hparams(init=True): - parser = argparse.ArgumentParser() - parser.add_argument( - "-c", - "--config", - type=str, - default="./configs/base.json", - help="JSON file for configuration", - ) - parser.add_argument("-m", "--model", type=str, required=True, help="Model name") - - args = parser.parse_args() - model_dir = args.model - - if not os.path.exists(model_dir): - os.makedirs(model_dir) - - config_path = args.config - config_save_path = os.path.join(model_dir, "config.json") - if init: - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - with open(config_save_path, "w", encoding="utf-8") as f: - f.write(data) - else: - with open(config_save_path, "r", vencoding="utf-8") as f: - data = f.read() - config = json.loads(data) - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def clean_checkpoints(path_to_models="logs/44k/", n_ckpts_to_keep=2, sort_by_time=True): - """Freeing up space by deleting saved ckpts - - Arguments: - path_to_models -- Path to the model directory - n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth - sort_by_time -- True -> chronologically delete ckpts - False -> lexicographically delete ckpts - """ - import re - - ckpts_files = [ - f - for f in os.listdir(path_to_models) - if os.path.isfile(os.path.join(path_to_models, f)) - ] - - def name_key(_f): - return int(re.compile("._(\\d+)\\.pth").match(_f).group(1)) - - def time_key(_f): - return os.path.getmtime(os.path.join(path_to_models, _f)) - - sort_key = time_key if sort_by_time else name_key - - def x_sorted(_x): - return sorted( - [f for f in ckpts_files if f.startswith(_x) and not f.endswith("_0.pth")], - key=sort_key, - ) - - to_del = [ - os.path.join(path_to_models, fn) - for fn in (x_sorted("G")[:-n_ckpts_to_keep] + x_sorted("D")[:-n_ckpts_to_keep]) - ] - - def del_info(fn): - return logger.info(f".. Free up space by deleting ckpt {fn}") - - def del_routine(x): - return [os.remove(x), del_info(x)] - - [del_routine(fn) for fn in to_del] - - -def get_hparams_from_dir(model_dir): - config_save_path = os.path.join(model_dir, "config.json") - with open(config_save_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - hparams.model_dir = model_dir - return hparams - - -def get_hparams_from_file(config_path): - with open(config_path, "r", encoding="utf-8") as f: - data = f.read() - config = json.loads(data) - - hparams = HParams(**config) - return hparams - - -def check_git_hash(model_dir): - source_dir = os.path.dirname(os.path.realpath(__file__)) - if not os.path.exists(os.path.join(source_dir, ".git")): - logger.warn( - "{} is not a git repository, therefore hash value comparison will be ignored.".format( - source_dir - ) - ) - return - - cur_hash = subprocess.getoutput("git rev-parse HEAD") - - path = os.path.join(model_dir, "githash") - if os.path.exists(path): - saved_hash = open(path).read() - if saved_hash != cur_hash: - logger.warn( - "git hash values are different. {}(saved) != {}(current)".format( - saved_hash[:8], cur_hash[:8] - ) - ) - else: - open(path, "w").write(cur_hash) - - -def get_logger(model_dir, filename="train.log"): - global logger - logger = logging.getLogger(os.path.basename(model_dir)) - logger.setLevel(logging.DEBUG) - - formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") - if not os.path.exists(model_dir): - os.makedirs(model_dir) - h = logging.FileHandler(os.path.join(model_dir, filename)) - h.setLevel(logging.DEBUG) - h.setFormatter(formatter) - logger.addHandler(h) - return logger - - -class HParams: - def __init__(self, **kwargs): - for k, v in kwargs.items(): - if type(v) == dict: - v = HParams(**v) - self[k] = v - - def keys(self): - return self.__dict__.keys() - - def items(self): - return self.__dict__.items() - - def values(self): - return self.__dict__.values() - - def __len__(self): - return len(self.__dict__) - - def __getitem__(self, key): - return getattr(self, key) - - def __setitem__(self, key, value): - return setattr(self, key, value) - - def __contains__(self, key): - return key in self.__dict__ - - def __repr__(self): - return self.__dict__.__repr__() diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/lock.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/lock.py deleted file mode 100644 index db91b7158c4ee9aa653462fe38e79ed1b553db87..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/cffi/lock.py +++ /dev/null @@ -1,30 +0,0 @@ -import sys - -if sys.version_info < (3,): - try: - from thread import allocate_lock - except ImportError: - from dummy_thread import allocate_lock -else: - try: - from _thread import allocate_lock - except ImportError: - from _dummy_thread import allocate_lock - - -##import sys -##l1 = allocate_lock - -##class allocate_lock(object): -## def __init__(self): -## self._real = l1() -## def __enter__(self): -## for i in range(4, 0, -1): -## print sys._getframe(i).f_code -## print -## return self._real.__enter__() -## def __exit__(self, *args): -## return self._real.__exit__(*args) -## def acquire(self, f): -## assert f is False -## return self._real.acquire(f) diff --git a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/container.py b/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/container.py deleted file mode 100644 index 516a57a596330c58481bbe46fd706e601b2b84f2..0000000000000000000000000000000000000000 --- a/spaces/chuan-hd/law-assistant-chatbot/.venv/lib/python3.11/site-packages/clickhouse_connect/datatypes/container.py +++ /dev/null @@ -1,307 +0,0 @@ -import array -import logging -from typing import Sequence, Collection - -from clickhouse_connect.driver.insert import InsertContext -from clickhouse_connect.driver.query import QueryContext -from clickhouse_connect.driver.types import ByteSource -from clickhouse_connect.json_impl import any_to_json -from clickhouse_connect.datatypes.base import ClickHouseType, TypeDef -from clickhouse_connect.driver.common import must_swap -from clickhouse_connect.datatypes.registry import get_from_name - -logger = logging.getLogger(__name__) - - -class Array(ClickHouseType): - __slots__ = ('element_type',) - python_type = list - - def __init__(self, type_def: TypeDef): - super().__init__(type_def) - self.element_type = get_from_name(type_def.values[0]) - self._name_suffix = f'({self.element_type.name})' - - def read_column_prefix(self, source: ByteSource): - return self.element_type.read_column_prefix(source) - - def _data_size(self, sample: Sequence) -> int: - if len(sample) == 0: - return 8 - total = 0 - for x in sample: - total += self.element_type.data_size(x) - return total // len(sample) + 8 - - # pylint: disable=too-many-locals - def read_column_data(self, source: ByteSource, num_rows: int, ctx: QueryContext): - final_type = self.element_type - depth = 1 - while isinstance(final_type, Array): - depth += 1 - final_type = final_type.element_type - level_size = num_rows - offset_sizes = [] - for _ in range(depth): - level_offsets = source.read_array('Q', level_size) - offset_sizes.append(level_offsets) - level_size = level_offsets[-1] if level_offsets else 0 - if level_size: - all_values = final_type.read_column_data(source, level_size, ctx) - else: - all_values = [] - column = all_values if isinstance(all_values, list) else list(all_values) - for offset_range in reversed(offset_sizes): - data = [] - last = 0 - for x in offset_range: - data.append(column[last: x]) - last = x - column = data - return column - - def write_column_prefix(self, dest: bytearray): - self.element_type.write_column_prefix(dest) - - def write_column_data(self, column: Sequence, dest: bytearray, ctx: InsertContext): - final_type = self.element_type - depth = 1 - while isinstance(final_type, Array): - depth += 1 - final_type = final_type.element_type - for _ in range(depth): - total = 0 - data = [] - offsets = array.array('Q') - for x in column: - total += len(x) - offsets.append(total) - data.extend(x) - if must_swap: - offsets.byteswap() - dest += offsets.tobytes() - column = data - final_type.write_column_data(column, dest, ctx) - - -class Tuple(ClickHouseType): - _slots = 'element_names', 'element_types' - python_type = tuple - valid_formats = 'tuple', 'dict', 'json', 'native' # native is 'tuple' for unnamed tuples, and dict for named tuples - - def __init__(self, type_def: TypeDef): - super().__init__(type_def) - self.element_names = type_def.keys - self.element_types = [get_from_name(name) for name in type_def.values] - if self.element_names: - self._name_suffix = f"({', '.join(k + ' ' + str(v) for k, v in zip(type_def.keys, type_def.values))})" - else: - self._name_suffix = type_def.arg_str - - def _data_size(self, sample: Collection) -> int: - if len(sample) == 0: - return 0 - elem_size = 0 - is_dict = self.element_names and isinstance(self._first_value(list(sample)), dict) - for ix, e_type in enumerate(self.element_types): - if e_type.byte_size > 0: - elem_size += e_type.byte_size - elif is_dict: - elem_size += e_type.data_size([x.get(self.element_names[ix], None) for x in sample]) - else: - elem_size += e_type.data_size([x[ix] for x in sample]) - return elem_size - - def read_column_prefix(self, source: ByteSource): - for e_type in self.element_types: - e_type.read_column_prefix(source) - - def read_column_data(self, source: ByteSource, num_rows: int, ctx: QueryContext): - columns = [] - e_names = self.element_names - for e_type in self.element_types: - column = e_type.read_column_data(source, num_rows, ctx) - columns.append(column) - if e_names and self.read_format(ctx) != 'tuple': - dicts = [{} for _ in range(num_rows)] - for ix, x in enumerate(dicts): - for y, key in enumerate(e_names): - x[key] = columns[y][ix] - if self.read_format(ctx) == 'json': - to_json = any_to_json - return [to_json(x) for x in dicts] - return dicts - return tuple(zip(*columns)) - - def write_column_prefix(self, dest: bytearray): - for e_type in self.element_types: - e_type.write_column_prefix(dest) - - def write_column_data(self, column: Sequence, dest: bytearray, ctx: InsertContext): - if self.element_names and isinstance(self._first_value(column), dict): - columns = self.convert_dict_insert(column) - else: - columns = list(zip(*column)) - for e_type, elem_column in zip(self.element_types, columns): - e_type.write_column_data(elem_column, dest, ctx) - - def convert_dict_insert(self, column: Sequence) -> Sequence: - names = self.element_names - col = [[] for _ in names] - for x in column: - for ix, name in enumerate(names): - col[ix].append(x.get(name)) - return col - - -class Map(ClickHouseType): - _slots = 'key_type', 'value_type' - python_type = dict - - def __init__(self, type_def: TypeDef): - super().__init__(type_def) - self.key_type = get_from_name(type_def.values[0]) - self.value_type = get_from_name(type_def.values[1]) - self._name_suffix = type_def.arg_str - - def _data_size(self, sample: Collection) -> int: - total = 0 - if len(sample) == 0: - return 0 - for x in sample: - total += self.key_type.data_size(x.keys()) - total += self.value_type.data_size(x.values()) - return total // len(sample) - - def read_column_prefix(self, source: ByteSource): - self.key_type.read_column_prefix(source) - self.value_type.read_column_prefix(source) - - # pylint: disable=too-many-locals - def read_column_data(self, source: ByteSource, num_rows: int, ctx: QueryContext): - offsets = source.read_array('Q', num_rows) - total_rows = offsets[-1] - keys = self.key_type.read_column_data(source, total_rows, ctx) - values = self.value_type.read_column_data(source, total_rows, ctx) - all_pairs = tuple(zip(keys, values)) - column = [] - app = column.append - last = 0 - for offset in offsets: - app(dict(all_pairs[last: offset])) - last = offset - return column - - def write_column_prefix(self, dest: bytearray): - self.key_type.write_column_prefix(dest) - self.value_type.write_column_prefix(dest) - - def write_column_data(self, column: Sequence, dest: bytearray, ctx: InsertContext): - offsets = array.array('Q') - keys = [] - values = [] - total = 0 - for v in column: - total += len(v) - offsets.append(total) - keys.extend(v.keys()) - values.extend(v.values()) - if must_swap: - offsets.byteswap() - dest += offsets.tobytes() - self.key_type.write_column_data(keys, dest, ctx) - self.value_type.write_column_data(values, dest, ctx) - - -class Nested(ClickHouseType): - __slots__ = 'tuple_array', 'element_names', 'element_types' - python_type = Sequence[dict] - - def __init__(self, type_def): - super().__init__(type_def) - self.element_names = type_def.keys - self.tuple_array = get_from_name(f"Array(Tuple({','.join(type_def.values)}))") - self.element_types = self.tuple_array.element_type.element_types - cols = [f'{x[0]} {x[1].name}' for x in zip(type_def.keys, self.element_types)] - self._name_suffix = f"({', '.join(cols)})" - - def _data_size(self, sample: Collection) -> int: - keys = self.element_names - array_sample = [[tuple(sub_row[key] for key in keys) for sub_row in row] for row in sample] - return self.tuple_array.data_size(array_sample) - - def read_column_prefix(self, source: ByteSource): - self.tuple_array.read_column_prefix(source) - - def read_column_data(self, source: ByteSource, num_rows: int, ctx: QueryContext): - keys = self.element_names - data = self.tuple_array.read_column_data(source, num_rows, ctx) - return [[dict(zip(keys, x)) for x in row] for row in data] - - def write_column_prefix(self, dest: bytearray): - self.tuple_array.write_column_prefix(dest) - - def write_column_data(self, column: Sequence, dest: bytearray, ctx: InsertContext): - keys = self.element_names - data = [[tuple(sub_row[key] for key in keys) for sub_row in row] for row in column] - self.tuple_array.write_column_data(data, dest, ctx) - - -class JSON(ClickHouseType): - python_type = dict - # Native is a Python type (primitive, dict, array), string is an actual JSON string - valid_formats = 'string', 'native' - - def write_column_prefix(self, dest: bytearray): - dest.append(0x01) - - def _data_size(self, sample: Collection) -> int: - if len(sample) == 0: - return 0 - total = 0 - for x in sample: - if isinstance(x, str): - total += len(x) - elif x: - total += len(any_to_json(x)) - return total // len(sample) + 1 - - # pylint: disable=duplicate-code - def write_column_data(self, column: Sequence, dest: bytearray, ctx: InsertContext): - app = dest.append - first = self._first_value(column) - if isinstance(first, str) or self.write_format(ctx) == 'string': - for x in column: - v = x.encode() - sz = len(v) - while True: - b = sz & 0x7f - sz >>= 7 - if sz == 0: - app(b) - break - app(0x80 | b) - dest += v - else: - to_json = any_to_json - for x in column: - v = to_json(x) - sz = len(v) - while True: - b = sz & 0x7f - sz >>= 7 - if sz == 0: - app(b) - break - app(0x80 | b) - dest += v - - -class Object(JSON): - python_type = dict - - def __init__(self, type_def): - if type_def.values[0].lower() != "'json'": - raise NotImplementedError('Only json Object type is currently supported') - super().__init__(type_def) - self._name_suffix = type_def.arg_str diff --git a/spaces/cihyFjudo/fairness-paper-search/How to Study Constitution Of India Book By J.n. 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diff --git a/spaces/clip-italian/clip-italian-demo/text2image.py b/spaces/clip-italian/clip-italian-demo/text2image.py deleted file mode 100644 index 6e663d30c84f776ae92efb1e8a9ba6595df1300e..0000000000000000000000000000000000000000 --- a/spaces/clip-italian/clip-italian-demo/text2image.py +++ /dev/null @@ -1,211 +0,0 @@ -import io -import os -import requests -import zipfile -import natsort -import gc -from PIL import Image -from PIL import UnidentifiedImageError - -os.environ["TOKENIZERS_PARALLELISM"] = "false" -from stqdm import stqdm -import streamlit as st -from jax import numpy as jnp -import transformers -from transformers import AutoTokenizer -from torchvision.transforms import Compose, CenterCrop, Normalize, Resize, ToTensor -from torchvision.transforms.functional import InterpolationMode -from modeling_hybrid_clip import FlaxHybridCLIP - -import utils - - -@st.cache(hash_funcs={FlaxHybridCLIP: lambda _: None}) -def get_model(): - return FlaxHybridCLIP.from_pretrained("clip-italian/clip-italian") - - -@st.cache( - hash_funcs={ - transformers.models.bert.tokenization_bert_fast.BertTokenizerFast: lambda _: None - } -) -def get_tokenizer(): - return AutoTokenizer.from_pretrained( - "dbmdz/bert-base-italian-xxl-uncased", cache_dir="./", use_fast=True - ) - - -@st.cache(suppress_st_warning=True) -def download_images(): - # from sentence_transformers import SentenceTransformer, util - img_folder = "photos/" - if not os.path.exists(img_folder) or len(os.listdir(img_folder)) == 0: - os.makedirs(img_folder, exist_ok=True) - - photo_filename = "unsplash-25k-photos.zip" - if not os.path.exists(photo_filename): # Download dataset if does not exist - print(f"Downloading {photo_filename}...") - response = requests.get( - f"http://sbert.net/datasets/{photo_filename}", stream=True - ) - total_size_in_bytes = int(response.headers.get("content-length", 0)) - block_size = 1024 # 1 Kb - progress_bar = stqdm( - total=total_size_in_bytes - ) # , unit='iB', unit_scale=True - content = io.BytesIO() - for data in response.iter_content(block_size): - progress_bar.update(len(data)) - content.write(data) - progress_bar.close() - z = zipfile.ZipFile(content) - # content.close() - print("Extracting the dataset...") - z.extractall(path=img_folder) - print("Done.") - - -@st.cache() -def get_image_features(dataset_name): - if dataset_name == "Unsplash": - return jnp.load("static/features/features.npy") - else: - return jnp.load("static/features/CC_embeddings.npy") - - -@st.cache() -def load_urls(dataset_name): - if dataset_name == "CC": - with open("static/CC_urls.txt") as fp: - urls = [l.strip() for l in fp.readlines()] - return urls - else: - ValueError(f"{dataset_name} not supported here") - - -def get_image_transform(image_size): - return Compose( - [ - Resize([image_size], interpolation=InterpolationMode.BICUBIC), - CenterCrop(image_size), - ToTensor(), - Normalize( - (0.48145466, 0.4578275, 0.40821073), - (0.26862954, 0.26130258, 0.27577711), - ), - ] - ) - - -headers = { - #'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36', - "User-Agent": "Googlebot-Image/1.0", # Pretend to be googlebot - "X-Forwarded-For": "64.18.15.200", -} - - -def app(): - - st.title("From Text to Image") - st.markdown( - """ - - ### 👋 Ciao! - - Here you can search for ~150.000 images in the Conceptual Captions dataset (CC) or in the Unsplash 25.000 Photos dataset. - Even though we did not train on any of these images you will see most queries make sense. When you see errors, there might be two possibilities: - the model is answering in a wrong way or the image you are looking for is not in the dataset and the model is giving you the best answer it can get. - - - - 🤌 Italian mode on! 🤌 - - You can choose from one of the following examples: - """ - ) - - suggestions = [ - "Un gatto", - "Due gatti", - "Un fiore giallo", - "Un fiore blu", - "Una coppia in montagna", - "Una coppia al tramonto", - ] - sugg_idx = -1 - - col1, col2, col3, col4, col5, col6 = st.columns(6) - with col1: - if st.button(suggestions[0]): - sugg_idx = 0 - with col2: - if st.button(suggestions[1]): - sugg_idx = 1 - with col3: - if st.button(suggestions[2]): - sugg_idx = 2 - with col4: - if st.button(suggestions[3]): - sugg_idx = 3 - with col5: - if st.button(suggestions[4]): - sugg_idx = 4 - with col6: - if st.button(suggestions[5]): - sugg_idx = 5 - - col1, col2 = st.columns([0.75, 0.25]) - with col1: - query = st.text_input("... or insert an Italian query text") - with col2: - dataset_name = st.selectbox("IR dataset", ["CC", "Unsplash"]) - - query = suggestions[sugg_idx] if sugg_idx > -1 else query if query else "" - - if query: - with st.spinner("Computing..."): - - if dataset_name == "Unsplash": - download_images() - - image_features = get_image_features(dataset_name) - model = get_model() - tokenizer = get_tokenizer() - - if dataset_name == "Unsplash": - image_size = model.config.vision_config.image_size - dataset = utils.CustomDataSet( - "photos/", transform=get_image_transform(image_size) - ) - elif dataset_name == "CC": - dataset = load_urls(dataset_name) - else: - raise ValueError() - - N = 3 - - image_paths = utils.find_image( - query, model, dataset, tokenizer, image_features, N, dataset_name - ) - - for i, image_url in enumerate(image_paths): - try: - if dataset_name == "Unsplash": - st.image(image_url) - elif dataset_name == "CC": - image_raw = requests.get( - image_url, stream=True, allow_redirects=True, headers=headers - ).raw - image = Image.open(image_raw).convert("RGB") - st.image(image, use_column_width=True) - break - except (UnidentifiedImageError) as e: - if i == N - 1: - st.text( - f"Tried to show {N} different image URLS but none of them were reachabele.\nMaybe try a different query?" - ) - - gc.collect() - - sugg_idx = -1 diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alac_data.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alac_data.c deleted file mode 100644 index d8ed53f444208d5ab7b36c8bdb65341565e951f3..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/alac_data.c +++ /dev/null @@ -1,56 +0,0 @@ -/* - * ALAC encoder and decoder common data - * - * 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 - */ - -#include "libavutil/channel_layout.h" -#include "alac_data.h" - -const uint8_t ff_alac_channel_layout_offsets[ALAC_MAX_CHANNELS][ALAC_MAX_CHANNELS] = { - { 0 }, - { 0, 1 }, - { 2, 0, 1 }, - { 2, 0, 1, 3 }, - { 2, 0, 1, 3, 4 }, - { 2, 0, 1, 4, 5, 3 }, - { 2, 0, 1, 4, 5, 6, 3 }, - { 2, 6, 7, 0, 1, 4, 5, 3 } -}; - -const AVChannelLayout ff_alac_ch_layouts[ALAC_MAX_CHANNELS + 1] = { - AV_CHANNEL_LAYOUT_MONO, - AV_CHANNEL_LAYOUT_STEREO, - AV_CHANNEL_LAYOUT_SURROUND, - AV_CHANNEL_LAYOUT_4POINT0, - AV_CHANNEL_LAYOUT_5POINT0_BACK, - AV_CHANNEL_LAYOUT_5POINT1_BACK, - AV_CHANNEL_LAYOUT_6POINT1_BACK, - AV_CHANNEL_LAYOUT_7POINT1_WIDE_BACK, - { 0 } -}; - -const enum AlacRawDataBlockType ff_alac_channel_elements[ALAC_MAX_CHANNELS][5] = { - { TYPE_SCE, }, - { TYPE_CPE, }, - { TYPE_SCE, TYPE_CPE, }, - { TYPE_SCE, TYPE_CPE, TYPE_SCE }, - { TYPE_SCE, TYPE_CPE, TYPE_CPE, }, - { TYPE_SCE, TYPE_CPE, TYPE_CPE, TYPE_SCE, }, - { TYPE_SCE, TYPE_CPE, TYPE_CPE, TYPE_SCE, TYPE_SCE, }, - { TYPE_SCE, TYPE_CPE, TYPE_CPE, TYPE_CPE, TYPE_SCE, }, -}; diff --git a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/canopus.c b/spaces/colakin/video-generater/public/ffmpeg/libavcodec/canopus.c deleted file mode 100644 index ea6cc647d3791df5fdba67f1da6f0dc488e86b96..0000000000000000000000000000000000000000 --- a/spaces/colakin/video-generater/public/ffmpeg/libavcodec/canopus.c +++ /dev/null @@ -1,63 +0,0 @@ -/* - * Canopus common routines - * Copyright (c) 2015 Vittorio Giovara - * - * 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 - */ - -#include - -#include "libavutil/rational.h" - -#include "avcodec.h" -#include "bytestream.h" -#include "canopus.h" - -int ff_canopus_parse_info_tag(AVCodecContext *avctx, - const uint8_t *src, size_t size) -{ - GetByteContext gbc; - int par_x, par_y, field_order; - - bytestream2_init(&gbc, src, size); - - /* Parse aspect ratio. */ - bytestream2_skip(&gbc, 8); // unknown, 16 bits 1 - par_x = bytestream2_get_le32(&gbc); - par_y = bytestream2_get_le32(&gbc); - if (par_x && par_y) - av_reduce(&avctx->sample_aspect_ratio.num, - &avctx->sample_aspect_ratio.den, - par_x, par_y, 255); - - /* Short INFO tag (used in CLLC) has only AR data. */ - if (size == 0x18) - return 0; - - bytestream2_skip(&gbc, 16); // unknown RDRT tag - - /* Parse FIEL tag. */ - bytestream2_skip(&gbc, 8); // 'FIEL' and 4 bytes 0 - field_order = bytestream2_get_le32(&gbc); - switch (field_order) { - case 0: avctx->field_order = AV_FIELD_TT; break; - case 1: avctx->field_order = AV_FIELD_BB; break; - case 2: avctx->field_order = AV_FIELD_PROGRESSIVE; break; - } - - return 0; -} diff --git a/spaces/congsaPfin/Manga-OCR/logs/Crafting and Building A Free Alternative to Minecraft APK for Android.md b/spaces/congsaPfin/Manga-OCR/logs/Crafting and Building A Free Alternative to Minecraft APK for Android.md deleted file mode 100644 index 3ca8cf27f8bd89fceb76bf294d8029c190ebce74..0000000000000000000000000000000000000000 --- a/spaces/congsaPfin/Manga-OCR/logs/Crafting and Building A Free Alternative to Minecraft APK for Android.md +++ /dev/null @@ -1,140 +0,0 @@ -
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If you are a fan of sandbox games, you have probably heard of Minecraft, one of the most popular and successful games of all time. But did you know that there is a free alternative to Minecraft for Android devices? It's called Crafting and Building, and it's a game that lets you create your own world, explore, build, and survive in it. In this article, we will tell you everything you need to know about Minecraft APK download crafting and building, including what it is, how to download and install it, how to play it, and how it compares to Minecraft.

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Minecraft is a sandbox game that was created by Mojang Studios in 2009. It is available for various platforms, including Windows, Mac, Linux, iOS, Android, Xbox, PlayStation, Nintendo Switch, and more. It has sold over 200 million copies worldwide and has over 126 million monthly active users as of 2020.

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A sandbox game with infinite possibilities

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Minecraft is a game that gives you complete freedom to create your own world and do whatever you want in it. You can mine resources, craft items, build structures, fight enemies, explore biomes, tame animals, farm crops, brew potions, enchant weapons, trade with villagers, discover secrets, and more. The game has two main modes: creative mode and survival mode. In creative mode, you have unlimited resources and can build anything you can imagine. In survival mode, you have to gather resources, craft tools and weapons, find food and shelter, and defend yourself from hostile mobs.

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Minecraft is a game that can be played on different devices and platforms. You can play it on your PC, laptop, tablet, smartphone, console, or even on a virtual reality headset. You can also play it with your friends online or on a local network. You can join servers that host different types of games, such as mini-games, adventure maps, role-playing games, survival games, etc. You can also create your own server or realm and invite your friends to join you.

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What is Crafting and Building?

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Crafting and Building is a free alternative to Minecraft for Android devices. It was developed by Mmarcel in 2017. It has over 10 million downloads on Google Play Store as of 2021.

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A free alternative to Minecraft for Android devices

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Crafting and Building is a game that lets you play Minecraft-like gameplay on your Android device without paying anything. You don't need to buy the game or have a premium account to play it. You just need to download the APK file from the official website or a trusted source and install it on your device.

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A game with similar features and gameplay to Minecraft

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Crafting and Building is a game that has many features and gameplay elements that are similar to Minecraft. You can choose between creative mode and survival mode. You can explore different worlds with different biomes. You can mine resources, craft items, build structures, fight enemies, etc. You can also customize your character with different skins and clothes.

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How to Download and Install Crafting and Building on Android?

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If you If you want to download and install Crafting and Building on your Android device, you can follow these simple steps:

Step 1: Go to the official website or a trusted source

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The first thing you need to do is to find a reliable source to download the APK file of Crafting and Building. You can go to the official website of the game, which is craftingandbuilding.net, or you can search for other websites that offer the APK file. Make sure that the website is safe and secure, and that the APK file is not corrupted or infected with malware.

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Step 2: Download the APK file and allow unknown sources

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Once you have found the source, you can download the APK file to your device. The file size is about 60 MB, so it should not take too long to download. After downloading the file, you need to allow your device to install apps from unknown sources. To do this, go to your device settings, then security, then enable the option to allow unknown sources. This will let you install apps that are not from the Google Play Store.

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Step 3: Install the APK file and launch the game

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After allowing unknown sources, you can install the APK file by tapping on it and following the instructions. The installation process should be quick and easy. Once the installation is done, you can launch the game by tapping on its icon on your home screen or app drawer. You are now ready to play Crafting and Building on your Android device.

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How to Play Crafting and Building on Android?

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Playing Crafting and Building on Android is very similar to playing Minecraft on any platform. You can choose between two game modes: creative mode and survival mode. You can also choose between different worlds with different themes and biomes. Here are some tips on how to play Crafting and Building on Android:

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Choose a game mode and a world

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When you launch the game, you will see a menu with different options. You can tap on "Play" to start a new game or continue an existing one. You can also tap on "Multiplayer" to join or create a server with other players online. You can also tap on "Options" to change the game settings, such as sound, graphics, controls, etc.

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If you choose to play a new game, you will have to choose a game mode: creative mode or survival mode. In creative mode, you have unlimited resources and can build anything you want without any restrictions. In survival mode, you have to gather resources, craft tools and weapons, find food and shelter, and survive against enemies and dangers.

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After choosing a game mode, you will have to choose a world. You can either create a new world or load an existing one. You can also customize your world by choosing its name, seed, size, theme, biome, etc. You can choose from different themes such as city, village, castle, island, etc., and different biomes such as forest, desert, snow, etc.

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Explore, build, and survive in the world

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Once you have chosen a world, you will spawn in it with your character. You can move around using the virtual joystick on the left side of the screen. You can look around using your finger on the right side of the screen. You can also use buttons on the right side of the screen to jump, fly, sneak, sprint, etc.

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You can interact with the world using your finger on the center of the screen. You can tap on blocks to break them or place them. You can also tap on items or entities to use them or interact with them. You can access your inventory by tapping on the backpack icon on the top right corner of the screen. You can drag items from your inventory to your hotbar or vice versa.

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In creative mode, you can build anything you want using any blocks or items you have in your inventory. You can also use commands by tapping on the chat icon on the top left corner of the screen. You can use commands to change the time of day, weather, game mode, etc.

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In survival mode, you have to gather resources by mining blocks or killing mobs. You have to craft tools and weapons by using a crafting table or your inventory. You have to find food by hunting animals or farming crops. You have to build shelter by placing blocks or using items such as beds or doors. You have to survive against enemies such as zombies, skeletons, spiders, etc., by fighting them or avoiding them.

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Interact with other players and customize your character

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Crafting and Building is a game that supports multiplayer mode. You can play with other players online by joining or creating a server. You can also play with your friends on a local network by using the same Wi-Fi connection. You can chat with other players by using the chat icon on the top left corner of the screen. You can also send emojis, stickers, or voice messages to express yourself. Crafting and Building is a game that lets you customize your character with different skins and clothes. You can access the character menu by tapping on the hanger icon on the top right corner of the screen. You can choose from different categories such as animals, superheroes, celebrities, etc. You can also create your own skin by using the editor or importing an image. You can also change your clothes by using the wardrobe or buying new ones from the shop.

Comparison Table of Minecraft and Crafting and Building

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Here is a table that compares some of the features and aspects of Minecraft and Crafting and Building:

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    How to Download 1080p Hindi Video Songs from 2016

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    If you are a fan of Bollywood music, you might have heard of 1080p Hindi video songs. These are high-definition videos that feature your favorite Hindi songs with stunning visuals and sound quality. They are perfect for watching on your big screen TV, laptop, or smartphone.

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    Downloading 1080p Hindi video songs can be a great way to enjoy them offline, without buffering or ads. You can also create your own playlists, share them with your friends, or burn them to DVDs. However, finding and downloading 1080p Hindi video songs can be tricky, especially if you are looking for songs from a specific year like 2016.

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    Best Sources for 1080p Hindi Video Songs from 2016

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    There are many websites that offer 1080p Hindi video songs from 2016, but not all of them are reliable, safe, or legal. Some may have low-quality videos, broken links, malware, or copyright issues. To avoid these problems, we recommend using these three sources:

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    YouTube

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    YouTube is one of the most popular and widely used platforms for watching and downloading videos online. You can find thousands of 1080p Hindi video songs from 2016 on YouTube, from official channels, fan-made videos, playlists, compilations, and more.

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    To search for 1080p Hindi video songs on YouTube, you can use the following steps:

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    1. Go to [YouTube](^2^) and type in your keywords in the search box. For example, you can type "new hindi songs 2016" or "best bollywood songs 2016".
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    2. Click on the "Filter" button below the search box and select "Video quality" > "HD". This will show you only the videos that have high-definition quality.
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    3. Browse through the results and look for the videos that have "1080p" in their titles or descriptions. You can also check the quality by clicking on the "Settings" icon at the bottom right corner of the video player and selecting "Quality" > "1080p".
      4. -
    4. Once you find the video you want, click on it to watch it.
      5. -
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    To download 1080p Hindi video songs from YouTube, you will need a video downloader software that can save YouTube videos to your device. There are many free and paid options available online, but some of the most popular ones are [4K Video Downloader], [WinX YouTube Downloader], and [Freemake Video Downloader]. To use these software, you can follow these general steps:

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    1. Download and install the video downloader software of your choice from its official website.
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    2. Copy the URL of the YouTube video you want to download and paste it into the software.
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    3. Select the output format and quality you want. For 1080p Hindi video songs, you should choose MP4 as the format and 1080p as the quality.
      4. -
    4. Click on the "Download" button and wait for the software to finish downloading the video.
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    5. Find the downloaded video in your device's folder and enjoy it offline.
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    Internet Archive

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    Internet Archive is a non-profit digital library that archives and preserves various types of media, including web pages, books, audio, video, and software. You can find many 1080p Hindi video songs from 2016 on Internet Archive, uploaded by users or organizations.

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    To browse and download 1080p Hindi video songs from Internet Archive, you can use the following steps:

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    1. Go to [Internet Archive] and click on the "Video" tab at the top of the page.
      2. -
    2. Type in your keywords in the search box and press enter. For example, you can type "hindi songs 2016" or "bollywood songs 2016".
      3. -
    3. Use the filters and categories on the left side of the page to narrow down your results. For example, you can select "Movies" as the media type, "Hindi" as the language, and "2016" as the year.
      4. -
    4. Look for the videos that have "1080p" in their titles or descriptions. You can also check the quality by clicking on the "Show All" button below the video player and selecting "Original" as the file format.
      5. -
    5. Once you find the video you want, click on it to watch it.
      6. -
    6. To download it, click on the "Download Options" button on the right side of the page and select "MP4" as the file format. Then, right-click on the download link and choose "Save link as" or "Save target as".
      7. -
    7. Choose a location on your device to save the video and wait for it to finish downloading.
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    Other Websites

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    Tips and Tricks for Downloading 1080p Hindi Video Songs from 2016

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    Downloading 1080p Hindi video songs from 2016 can be fun and easy if you use the sources we have mentioned above. However, you may still encounter some issues or difficulties along the way. To overcome them, you can use these tips and tricks:

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    Use a VPN or Proxy

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    A VPN or proxy is a service that allows you to access websites or videos that may be blocked or restricted in your region or country. This can be useful if you want to download 1080p Hindi video songs from 2016 that are not available in your area. For example, some YouTube videos may be geo-restricted or censored by your government or ISP.

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    To use a VPN or proxy, you will need to sign up for a VPN or proxy provider that suits your needs and budget. There are many free and paid options available online, but some of the most popular ones are [NordVPN], [ExpressVPN], and [ProtonVPN] for VPNs, and [Hide.me], [HMA], and [KProxy] for proxies. To use these services, you can follow these general steps:

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    1. Download and install the VPN or proxy software or app from its official website or app store.
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    2. Launch the software or app and sign in with your account or create a new one if you don't have one.
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    3. Select a server or location that you want to connect to. For example, if you want to access a YouTube video that is only available in India, you can choose an Indian server or proxy.
      4. -
    4. Once you are connected, you can open your browser or video downloader software and access the website or video you want to download.
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    Use a Download Manager

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    A download manager is a software that can help you speed up and resume your downloads, especially if they are large or slow. This can be useful if you want to download 1080p Hindi video songs from 2016 that may take a long time or encounter interruptions. For example, some websites may have low bandwidth or unstable servers.

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    To use a download manager, you will need to download and install a download manager software that works with your browser or video downloader software. There are many free and paid options available online, but some of the most popular ones are [Internet Download Manager], [Free Download Manager], and [EagleGet]. To use these software, you can follow these general steps:

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    1. Download and install the download manager software from its official website.
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    2. Launch the software and configure its settings according to your preferences. For example, you can set the number of simultaneous downloads, the download speed limit, the download folder, etc.
      3. -
    3. Copy the URL of the website or video you want to download and paste it into the software. Alternatively, you can use the browser extension or plugin that comes with the software to automatically capture the download links from your browser.
      4. -
    4. Click on the "Start" or "Download" button and wait for the software to finish downloading the file.
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    5. Find the downloaded file in your device's folder and enjoy it offline.
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    Convert and Compress Your Downloads

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    Converting and compressing your downloads can help you save space and bandwidth on your device, especially if you have limited storage or data plan. This can be useful if you want to download 1080p Hindi video songs from 2016 that may take up a lot of space or consume a lot of data. For example, some 1080p videos may be over 100 MB in size.

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    To convert and compress your downloads, you will need to use a video converter and compressor software that can change the format and size of your files. There are many free and paid options available online, but some of the most popular ones are [HandBrake], [VLC Media Player], and [Any Video Converter]. To use these software, you can follow these general steps:

    -
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    1. Download and install the video converter and compressor software from its official website.
      2. -
    2. Launch the software and add the file you want to convert and compress. You can drag and drop it into the software or use the "Add File" button.
      3. -
    3. Select the output format and quality you want. For 1080p Hindi video songs, you can choose MP4 as the format and lower the bitrate or resolution to reduce the size.
      4. -
    4. Click on the "Start" or "Convert" button and wait for the software to finish converting and compressing the file.
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    5. Find the converted and compressed file in your device's folder and enjoy it offline.
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    Conclusion

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    In this article, we have shown you how to download 1080p Hindi video songs from 2016 from various sources. We have also given you some tips and tricks to make your downloading experience easier and faster. We hope you have found this article helpful and informative.

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    If you want to learn more about 1080p Hindi video songs from 2016, you can check out these resources:

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    FAQs

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    Here are some frequently asked questions and their answers related to the topic of this article:

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    Q: What is the difference between 1080 p and 720p video quality?

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    A: 1080p and 720p are two common resolutions for video quality, measured by the number of pixels in the vertical dimension. 1080p has 1080 pixels, while 720p has 720 pixels. The more pixels, the higher the clarity and detail of the video. Therefore, 1080p is better than 720p in terms of video quality.

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    Q: How can I play 1080p Hindi video songs on my device?

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    A: To play 1080p Hindi video songs on your device, you will need a media player that can support the MP4 format and the 1080p resolution. Some of the most popular media players that can do this are [VLC Media Player], [KMPlayer], and [MX Player]. To use these media players, you can follow these general steps:

    -
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    1. Download and install the media player of your choice from its official website or app store.
      2. -
    2. Launch the media player and open the file you want to play. You can drag and drop it into the media player or use the "Open File" button.
      3. -
    3. Adjust the settings and preferences of the media player according to your needs. For example, you can change the volume, brightness, subtitles, etc.
      4. -
    4. Enjoy watching your 1080p Hindi video song on your device.
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    Q: How can I share 1080p Hindi video songs with my friends?

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    A: To share 1080p Hindi video songs with your friends, you have several options. You can use a cloud storage service like [Google Drive], [Dropbox], or [OneDrive] to upload your files and share them with your friends via a link or an email. You can also use a file-sharing service like [WeTransfer], [Send Anywhere], or [Filemail] to send your files directly to your friends via a link or an email. Alternatively, you can use a social media platform like [Facebook], [Instagram], or [WhatsApp] to upload your files and share them with your friends via a post or a message.

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    Q: How can I edit 1080p Hindi video songs?

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    A: To edit 1080p Hindi video songs, you will need a video editor software that can handle the MP4 format and the 1080p resolution. There are many free and paid options available online, but some of the most popular ones are [Filmora], [Movavi], and [Adobe Premiere Pro]. To use these software, you can follow these general steps:

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    1. Download and install the video editor software of your choice from its official website.
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    2. Launch the software and import the file you want to edit. You can drag and drop it into the software or use the "Import" button.
      3. -
    3. Use the tools and features of the software to edit your file according to your preferences. For example, you can trim, crop, rotate, add effects, transitions, text, etc.
      4. -
    4. Export your edited file in the format and quality you want. For 1080p Hindi video songs, you should choose MP4 as the format and 1080p as the quality.
      5. -
    5. Find the edited file in your device's folder and enjoy it offline or share it online.
      6. -
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    Q: How can I make my own 1080p Hindi video song?

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    A: To make your own 1080p Hindi video song, you will need a camera that can record in 1080p resolution, a microphone that can capture clear audio, a computer that can edit and produce high-quality videos, and a creative mind that can come up with an original idea and script. Here are some basic steps to make your own 1080p Hindi video song:

    -
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    1. Choose a song that you want to make a video for. It can be an existing song or a song that you have composed yourself.
      2. -
    2. Write a script or storyboard for your video. It should include the scenes, locations, characters, dialogues, actions, etc. that you want to show in your video.
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    3. Gather the equipment and materials that you need for your video. This may include a camera, a microphone, a tripod, a lighting kit, props, costumes, etc.
      4. -
    4. Find a suitable location and time for your video. It should match the mood and theme of your song and script.
      5. -
    5. Record your video according to your script or storyboard. You may need to do multiple takes or angles to get the best shots.
      6. -
    6. Transfer your video files to your computer and import them into your video editor software.
      7. Edit your video according to your preferences. You can use the tools and features of the software to trim, crop, rotate, add effects, transitions, text, etc. -
    7. Add your song to your video as the background music. You can use the tools and features of the software to sync, adjust, or mix the audio with the video.
      8. -
    8. Export your video in the format and quality you want. For 1080p Hindi video songs, you should choose MP4 as the format and 1080p as the quality.
      9. -
    9. Find the video in your device's folder and enjoy it offline or share it online.
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    Angry Birds Classic is a game that can be enjoyed by anyone, regardless of age or skill level. It has simple gameplay mechanics, but also requires logic, skill, and force to solve each level. It has hundreds of levels to play, each with different challenges and surprises. It also has a competitive mode called the Mighty League, where you can compete against other players in the leaderboards.

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    How to download and install Angry Birds Classic 4.0 0 APK on your Android device

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    1. Go to [1](https://apkcombo.com/angry-birds/com.rovio.angrybirds/) on your Android device's browser.
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    2. Tap on Download APK (99 MB) button.
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    3. Wait for the download to finish.
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    4. Go to your device's settings and enable Unknown Sources under Security or Applications.
      5. -
    5. Go to your device's file manager and locate the downloaded APK file.
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    6. Tap on it and follow the instructions to install it.
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    7. Launch Angry Birds Classic from your app drawer or home screen.
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    Congratulations! You have successfully installed Angry Birds Classic 4.0 0 APK on your Android device. Now you can enjoy playing this classic game with all its features.

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    How to play Angry Birds Classic: The basics and the tips

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    Angry Birds Classic is a game that is easy to learn but hard to master. The goal of each level is to fling birds from a slingshot to defeat the pigs on the level. You have a limited number of birds to use in each level, so you have to use them wisely. You also have to consider the physics of the game, such as gravity, trajectory, speed, and impact.

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    To help you play better, here are some basic tips that you should know:

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    The birds and their abilities

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    The golden eggs and the bonus levels

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    Golden eggs are special items that can be found in various places in the game. They unlock bonus levels that have different gameplay modes, such as drawing with Red, popping balloons with Matilda, or playing a tribute to Super Mario Bros. There are 30 golden eggs in total, and some of them are very well hidden. To find them, you have to look for clues in the menus, the backgrounds, the level selection screens, and even the credits. You can also get golden eggs by completing certain achievements, such as getting three stars in all levels of an episode, or using a certain number of birds in a level.

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    To see how many golden eggs you have collected, and to access the bonus levels, tap on the golden egg icon on the episode selection screen. You can also see a list of hints on how to find the golden eggs by tapping on the question mark icon on the same screen.

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    The hidden features and the Easter eggs

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    Angry Birds Classic also has some hidden features and Easter eggs that can enhance your gameplay experience. For example, did you know that you can make the birds sing by tapping on them repeatedly on the slingshot? Or that you can make the pigs laugh by tapping on them on the level? Or that you can make the Mighty Eagle appear by shaking your device on the level selection screen?

    -

    There are also some Easter eggs that reference other games or media, such as Pac-Man, Space Invaders, Star Wars, or Indiana Jones. For example, in level 13-12 of Danger Above, you can see a golden idol from Indiana Jones in the background. If you hit it with a bird, it will trigger a boulder that will roll down and crush the pigs. Or in level 18-15 of Surf and Turf, you can see a Star Wars logo made of wood in the background. If you hit it with a bird, it will play the Star Wars theme song.

    -

    The best strategies and the optimal angles

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    Of course, to master Angry Birds Classic, you also need to know how to use the best strategies and the optimal angles for each level. There is no definitive answer for this, as different players may have different preferences and styles. However, there are some general tips that can help you improve your skills:

    - -

    By following these tips, you will be able to master Angry Birds Classic and become a true bird flinger.

    How to enjoy Angry Birds Classic: The fun and the nostalgia

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    Angry Birds Classic is not only a game of skill and discovery, but also a game of fun and nostalgia. It is a game that can make you laugh, smile, and feel good. It is also a game that can remind you of the good old days, when Angry Birds was the most popular game in the world. Here are some ways to enjoy Angry Birds Classic:

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    The graphics and the sounds

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    Angry Birds Classic has colorful and cartoonish graphics that create a cheerful and lively atmosphere. The birds and the pigs have expressive faces and animations that show their emotions and personalities. The backgrounds and the objects have rich details and textures that make them look realistic and appealing. The game also has catchy and upbeat music that matches the mood of each episode. The sound effects are also humorous and satisfying, such as the birds' squawks, the pigs' grunts, the explosions, and the crashes.

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    Angry Birds Classic has graphics and sounds that can make you happy and entertained. You can also adjust the settings to change the quality of the graphics and the volume of the sounds according to your preference.

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    The characters and the stories

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    Angry Birds Classic has characters and stories that can make you care and curious. The birds and the pigs have distinct personalities and traits that make them unique and memorable. For example, Red is the leader and the protector of the flock, Chuck is the fast and impatient yellow bird, Bomb is the explosive black bird, Matilda is the motherly white bird, Hal is the boomerang green bird, Terence is the big and silent red bird, Bubbles is the inflatable orange bird, Stella is the bubbly pink bird, Mighty Eagle is the legendary savior of the birds, King Pig is the greedy ruler of the pigs, Foreman Pig is the builder of the piggy structures, Corporal Pig is the helmet-wearing soldier of the pigs, Chef Pig is the cook of the pigs, Professor Pig is the inventor of the piggy gadgets, and Chronicler Pig is the recorder of the piggy history . The game also has stories that explain the motivation and the background of the characters. The main story is about the birds' quest to retrieve their eggs from the pigs, who are constantly trying to steal them for various reasons. The game also has sub-stories that explore the origins and the adventures of the characters, such as how Red met Chuck and Bomb, how Matilda became a yoga instructor, how Hal got his boomerang, how Bubbles got his candy addiction, how Stella met her friends, how Mighty Eagle became a legend, how King Pig rose to power, how Foreman Pig built his empire, how Corporal Pig earned his helmet, how Chef Pig cooked his dishes, how Professor Pig invented his gadgets, and how Chronicler Pig wrote his books . Angry Birds Classic has characters and stories that can make you invested and interested. You can also learn more about them by watching the animated series and movies based on the game, such as Angry Birds Toons, Angry Birds Stella, Angry Birds Blues, Angry Birds MakerSpace, Angry Birds Movie, and Angry Birds Movie 2 .

    The updates and the expansions

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    Angry Birds Classic is a game that is constantly updated and expanded with new content and features. The game has received many updates since its launch in 2009, adding new episodes, levels, birds, pigs, power-ups, spells, themes, graphics, sounds, and more. The game also has several expansions that offer different gameplay modes and experiences, such as Angry Birds Seasons, Angry Birds Rio, Angry Birds Space, Angry Birds Star Wars, Angry Birds Star Wars II, Angry Birds Go!, Angry Birds Epic, Angry Birds Transformers, Angry Birds Fight!, Angry Birds 2, Angry Birds Blast!, Angry Birds Evolution, Angry Birds Match, Angry Birds Dream Blast, and Angry Birds POP! .

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    Angry Birds Classic is a game that is always fresh and exciting. You can always find something new and different to play and enjoy. You can also download and install the expansions from the Google Play Store or from their respective APK files.

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    Conclusion: Why Angry Birds Classic is still worth playing in 2023

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    Angry Birds Classic is a game that has stood the test of time. It is a game that has millions of fans around the world. It is a game that has inspired many spin-offs, adaptations, merchandise, and even a theme park. It is a game that has become a cultural phenomenon and a part of pop culture.

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    But more importantly, it is a game that is fun, addictive, challenging, and enjoyable. It is a game that can make you happy and nostalgic. It is a game that can challenge your skills and creativity. It is a game that can offer you endless hours of entertainment and satisfaction.

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    Angry Birds Classic is a game that is still worth playing in 2023. Whether you are a beginner or a fan, you will find something to love and appreciate in this game. So what are you waiting for? Download and install Angry Birds Classic 4.0 0 APK on your Android device today, and join the flock of bird flingers.

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    FAQs

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    Here are some frequently asked questions about Angry Birds Classic:

    -
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    1. Q: How do I get the Mighty Eagle in Angry Birds Classic? A: The Mighty Eagle is a special bird that can be used to clear any level with one shot. However, you have to pay for it with real money or watch an ad to use it once. To get the Mighty Eagle, tap on the eagle icon on the top left corner of the level selection screen, and follow the instructions to purchase or watch an ad.
      2. -
    2. Q: How do I get the power-ups and the spells in Angry Birds Classic? A: The power-ups and the spells are special items that can enhance your gameplay and help you beat the levels. However, you have to pay for them with coins or gems, which can be earned by playing the game, watching ads, or buying them with real money. To get the power-ups and the spells, tap on the shop icon on the top right corner of the level selection screen, and choose the items you want to buy.
      3. -
    3. Q: How do I play the Mighty League in Angry Birds Classic? A: The Mighty League is a competitive mode where you can play against other players in the leaderboards. You have to use tickets to enter each level, which can be earned by playing the game, watching ads, or buying them with real money. To play the Mighty League, tap on the trophy icon on the bottom right corner of the episode selection screen, and choose the level you want to play.
      4. -
    4. Q: How do I sync my progress in Angry Birds Classic across different devices? A: You can sync your progress in Angry Birds Classic by connecting your game to your Rovio Account, which can be created for free with your email address or Facebook account. To sync your progress, tap on the settings icon on the bottom left corner of the episode selection screen, and choose Rovio Account. Then, follow the instructions to create or log in to your account.
      5. -
    5. Q: How do I contact the support team of Angry Birds Classic if I have any issues or feedback? A: You can contact the support team of Angry Birds Classic by tapping on the settings icon on the bottom left corner of the episode selection screen, and choosing Help & Support. Then, follow the instructions to submit your query or feedback.
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    I hope this article has helped you learn more about Angry Birds Classic and how to play it. If you have any other questions or comments, please let me know. I'm happy to help.

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    Feature/AspectMinecraftCrafting and Building
    Price$6.99 for Android versionFree
    DeveloperMojang StudiosMmarcel
    Release Date2009 for PC version, 2011 for Android version2017
    DownloadsOver 200 million copies sold worldwideOver 10 million downloads on Google Play Store
    Game ModesCreative mode, survival mode, adventure mode, hardcore mode, spectator modeCreative mode, survival mode
    Multiplayer SupportYes, online and localYes, online and local
    World Themes and BiomesOver 60 different biomes, such as forest, desert, ocean, jungle, etc.Over 20 different themes, such as city, village, castle, island, etc.
    Blocks and ItemsOver 700 different blocks and items, such as wood, stone, iron, diamond, etc.Over 300 different blocks and items, such as wood, stone, iron, diamond, etc.
    Mobs and EntitiesOver 100 different mobs and entities, such as animals, villagers, zombies, skeletons, etc.Over 50 different mobs and entities, such as animals, villagers, zombies, skeletons, etc.
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    Some of the characters are:

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    EarthrealmOutworld
    Liu KangShao Kahn
    Kung LaoKotal Kahn
    RaidenKitana
    Johnny CageJade
    Sonya BladeMileena
    Jax BriggsBaraka
    Cassie CageSkarlet
    Jacqui BriggsErron Black
    ScorpionD'Vorah
    Sub-ZeroKollector
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    Why do you need APK data to play Mortal Kombat 8 on Android?

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    Mortal Kombat 8 is not officially released for Android devices, so you cannot find it on the Google Play Store or any other official app store. However, you can still play it on your Android device by downloading APK data from third-party sources. APK data are files that contain the application package and the data needed to run it on your device.

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    APK data usually consist of two types of files: APK and OBB. APK stands for Android Package Kit, which is the file format used to distribute and install applications on Android devices. OBB stands for Opaque Binary Blob, which is a file format used to store large amounts of data, such as graphics, audio, and video. OBB files are usually associated with APK files, and they are stored in a separate folder on your device's internal or external storage.

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    The benefits and risks of downloading APK data from third-party sources

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    Downloading APK data from third-party sources can have some benefits and risks. Some of the benefits are:

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    Some of the risks are:

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    The requirements and compatibility of Mortal Kombat 8 APK data for Android devices

    -

    Mortal Kombat 8 APK data are not officially supported by NetherRealm Studios or Warner Bros. Interactive Entertainment, so there is no guarantee that they will work on every Android device or version. However, based on some user reports and reviews, here are some of the general requirements and compatibility factors for Mortal Kombat 8 APK data for Android devices:

    -

    How to download and install Mortal Kombat 8 APK data on your Android device?

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    Now that you know what Mortal Kombat 8 APK data are and why you need them, you may be wondering how to download and install them on your Android device. Here are the steps you need to follow:

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    The steps to download Mortal Kombat 8 APK data from a reliable source

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    The first step is to find a reliable source that offers Mortal Kombat 8 APK data for Android devices. There are many websites and blogs that claim to provide the APK data, but not all of them are trustworthy or safe. You should do some research and check the reviews and ratings of the source before downloading anything from it. You should also avoid clicking on any suspicious links or ads that may redirect you to malicious or fraudulent sites.

    -

    One of the sources that we recommend is [APKPure], which is a reputable and popular platform that provides APK files and updates for various apps and games. You can visit their website or download their app to access their library of APK files. Here is how to download Mortal Kombat 8 APK data from APKPure:

    -
      -
    1. Go to [APKPure] website or app and search for Mortal Kombat 8 in the search bar.
      2. -
    2. Select the Mortal Kombat 8 game from the search results and click on the download button.
      3. -
    3. Choose the version and the file size that you want to download. The latest version is 3.2.0 and the file size is about 1.1 GB.
      4. -
    4. Wait for the download to complete. You will get two files: an APK file and an OBB file.
      5. -
    -

    The steps to install Mortal Kombat 8 APK data on your Android device

    -

    The next step is to install the Mortal Kombat 8 APK data on your Android device. Before you do that, you need to make sure that you have enabled the option to install apps from unknown sources on your device. This option allows you to install apps that are not from the official app stores, such as the APK files from APKPure. Here is how to enable this option:

    -
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    1. Go to your device's settings and look for security or privacy options.
      2. -
    2. Find the option that says "Unknown sources" or "Install unknown apps" and toggle it on.
      3. -
    3. Confirm your choice by tapping OK or Allow.
      4. -
    -

    Now you are ready to install the Mortal Kombat 8 APK data on your device. Here is how to do it:

    -
      -
    1. Locate the downloaded APK file and OBB file on your device's storage, either in the downloads folder or in the APKPure folder.
      2. -
    2. Tap on the APK file and follow the instructions to install it on your device.
      3. -
    3. Do not open the game yet. You need to copy the OBB file to the right location first.
      4. -
    4. Use a file manager app to copy or move the OBB file to this location: /Android/obb/com.wb.goog.mkx/
      5. -
    5. If you don't have this folder, you can create it manually.
      6. -
    -

    The steps to verify and launch Mortal Kombat 8 on your Android device

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    The final step is to verify and launch Mortal Kombat 8 on your Android device. Here is how to do it:

    -
      -
    1. Make sure that you have a stable internet connection, as the game will need to verify the APK data before launching.
      2. -
    2. Open the game from your app drawer or home screen.
      3. -
    3. Wait for the game to load and verify the data. This may take a few minutes, depending on your connection speed.
      4. -
    4. If everything goes well, you will see the main menu of the game, where you can choose your mode and start playing.
      5. -
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    How to optimize your gaming experience with Mortal Kombat 8 on Android?

    -

    Mortal Kombat 8 is a high-quality and demanding game that requires a lot of resources and power from your device. Therefore, you may encounter some issues or problems while playing it, such as lag, crashes, overheating, battery drain, etc. To avoid these issues and optimize your gaming experience with Mortal Kombat 8 on Android, here are some tips and tricks that you can try:

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    The tips and tricks to improve your performance and battery life while playing Mortal Kombat 8 on Android

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    Here are some tips and tricks that can help you improve your performance and battery life while playing Mort al Kombat 8 on Android:

    - -

    The best settings and controls to customize your gameplay with Mortal Kombat 8 on Android

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    Here are some of the best settings and controls that you can customize your gameplay with Mortal Kombat 8 on Android:

    - -

    The best resources and guides to learn more about Mortal Kombat 8 on Android

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    If you want to learn more about Mortal Kombat 8 on Android, such as tips, tricks, guides, cheats, hacks, etc., you can check out some of these resources and guides:

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    Conclusion

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    Mortal Kombat 8 is one of the best fighting games ever made, and you can play it on your Android device by downloading APK data from third-party sources. However, you need to be careful and follow some steps to ensure that you download and install it safely and correctly. You also need to optimize your gaming experience by adjusting some settings and controls according to your device and preferences. Finally, you can learn more about Mortal Kombat 8 by checking out some resources and guides that will help you master the game.

    -

    We hope that this article has helped you understand how to download and play Mortal Kombat 8 APK data on your Android device. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading!

    -

    FAQs

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    Here are some frequently asked questions about Mortal Kombat 8 APK data for Android devices:

    -
      -
    1. Is Mortal Kombat 8 APK data safe to download?
      2. -

      Mortal Kombat 8 APK data are not officially supported by NetherRealm Studios or Warner Bros. Interactive Entertainment, so you need to be careful and download them from reliable sources, such as APKPure. You also need to enable the option to install apps from unknown sources on your device, which can expose you to potential risks. You should always scan the APK data with an antivirus or malware detector before installing them, and avoid clicking on any suspicious links or ads that may redirect you to malicious or fraudulent sites.

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    2. Is Mortal Kombat 8 APK data legal to download?
      3. -

      Mortal Kombat 8 APK data are not legal to download, as they violate the terms and conditions of the developers and publishers of the game. By downloading and installing them, you are infringing on their intellectual property rights and may face legal consequences. You should always respect the rights and wishes of the creators of the game and support them by purchasing the game from the official app stores or platforms.

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    3. Is Mortal Kombat 8 APK data compatible with all Android devices?
      4. -

      Mortal Kombat 8 APK data are not compatible with all Android devices, as they require a lot of resources and power from your device. Your device should have at least 4 GB of RAM and 64 GB of storage space, run on Android 9.0 (Pie) or higher, support OpenGL ES 3.1 or higher, and have a screen resolution of at least 720p (1280 x 720 pixels). You should also have a stable internet connection to download and verify the APK data, as well as to access some online features of the game.

      -
    4. How to update Mortal Kombat 8 APK data on Android devices?
      5. -

      Mortal Kombat 8 APK data are not automatically updated on your Android device, as they are not from the official app stores. You need to manually check for updates and download them from the same source that you downloaded the original APK data from. You should also backup your progress and data before updating, as you may lose them if the update is not compatible or stable with your device or version of Android.

      -
    5. How to uninstall Mortal Kombat 8 APK data from Android devices?
      6. -

      If you want to uninstall Mortal Kombat 8 APK data from your Android device, you can follow these steps:

      -
        -
      1. Go to your device's settings and look for apps or applications options.
        2. -
      2. Find and select Mortal Kombat 8 from the list of apps and tap on uninstall.
        3. -
      3. Confirm your choice by tapping OK or Delete.
        4. -
      4. Go to your device's storage and look for the folder /Android/obb/com.wb.goog.mkx/
        5. -
      5. Delete this folder and its contents.
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      If you are a fan of truck simulation games, you might have heard of Truck Simulator 1.0.8 Hile Apk, a popular Android game that lets you experience the thrill of driving a truck across different countries and cities.

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      How to Download Truck Simulator 1.0.8 Hile Apk

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      To download Truck Simulator 1.0.8 Hile Apk, you will need to find a reliable source that provides the apk file, which is a package file format used by Android devices to install applications.

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      One of the websites that offers Truck Simulator 1.0.8 Hile Apk is APKCombo, where you can download the apk file for free by clicking on the "Download APK" button.

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      Before installing any apk file, you should always verify its authenticity and security by checking its size, version, developer, permissions, reviews, and ratings.

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      How to Play Truck Simulator 1.0.8 Hile Apk

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      Truck Simulator 1.0.8 Hile Apk is a game that combines simulation and tycoon elements

      Truck Simulator 1.0.8 Hile Apk is a game that combines simulation and tycoon elements, allowing you to create your own truck company and transport various cargo across different countries and cities.

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      Tips and Tricks for Truck Simulator 1.0.8 Hile Apk

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      Truck Simulator 1.0.8 Hile Apk is a fun and challenging game that requires skill and strategy to succeed. Here are some tips and tricks that can help you improve your gameplay:

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      • To earn more money and reputation in the game, you should complete more contracts, deliver cargo on time, avoid damage to your truck or cargo, follow traffic rules, and drive safely.
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      Conclusion

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      Truck Simulator 1.0.8 Hile Apk is a game that offers you a realistic and immersive truck simulation experience. You can download it for free from APKCombo or other sources, install it on your device easily, and start playing it right away. You can create your own truck company, transport various cargo across different countries and cities, customize your trucks and upgrade them with mods, participate in multiplayer mode and compete with other players online. You can also use some tips and tricks to make the most out of this amazing game. If you are a fan of truck simulation games, you should definitely give Truck Simulator 1.0.8 Hile Apk a try!

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      FAQs

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      Here are some of the frequently asked questions about Truck Simulator 1.0.8 Hile Apk:

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      What are the minimum system requirements for Truck Simulator 1.0.8 Hile Apk?

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      The minimum system requirements for Truck Simulator 1.0.8 Hile Apk are:

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      OSAndroid 5.0 or higher
      CPUDual core 1.8 GHz or higher
      RAM2 GB or higher
      Storage500 MB or higher
      InternetRequired for multiplayer mode
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      Is Truck Simulator 1.0.8 Hile Apk free or paid?

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      Truck Simulator 1.0.8 H

      Truck Simulator 1.0.8 Hile Apk is free to download and play, but it contains some in-app purchases that can enhance your gameplay or unlock some premium features. You can buy these items with real money or earn them by completing certain tasks or achievements in the game.

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      Is Truck Simulator 1.0.8 Hile Apk safe and legal?

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      Truck Simulator 1.0.8 Hile Apk is safe and legal to use, as long as you download it from a trusted source and scan it with an antivirus software or an online scanner before installing it on your device. However, you should be aware that Truck Simulator 1.0.8 Hile Apk is a modified version of Truck Simulator : Ultimate, which means that it may not be compatible with the original game or its updates, and it may violate the terms and conditions of the original game or its developers. Therefore, you should use Truck Simulator 1.0.8 Hile Apk at your own risk and discretion, and respect the rights and interests of the original game and its developers.

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      Truck Simulator 1.0.8 Hile Apk is different from other truck simulation games in several ways, such as:

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      How can I contact the developers of Truck Simulator 1.0.8 Hile Apk?

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      If you have any questions, feedback, suggestions, or issues regarding Truck Simulator 1.0.8 Hile Apk, you can contact the developers of the game by using one of the following methods:

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        • Footwear: Wear shoes that are closed-toe and have good grip. Avoid wearing sandals, flip-flops, heels, or boots that are too bulky or heavy. Wear socks that can prevent blisters and keep your feet warm.
          • -
        • Accessories: Wear sunglasses, a hat, or a bandana that can protect your eyes and head from the sun and the wind. Wear sunscreen, insect repellent, and lip balm that can protect your skin from the elements. Wear a camera, a GoPro, or a smartphone that can capture your zipline adventure. Make sure they are securely attached to your body or your helmet.
          • -
        -

        What to expect from zipline

        -

        When you arrive at the zipline site, you will be greeted by the staff and the guides. They will check your reservation, waiver, and health condition. They will also weigh you and measure your height to ensure you meet the requirements for ziplining. You will then be given a safety briefing and instructions on how to use the equipment and how to behave on the zipline. You will also be fitted with a helmet, gloves, harness, and trolley. You will then be transported to the starting point of the zipline, where you will be hooked up to the cable and ready to go.

        -

        How to enjoy zipline

        -

        Ziplining is an exhilarating and enjoyable activity that can make you feel alive and free. To make the most of it, you should:

        -
          -
        • Relax: Don't let fear or nervousness ruin your fun. Trust the equipment, the guides, and yourself. Breathe deeply and calmly before you jump off. Smile and laugh as you zip through the air.
          • -
        • Look around: Don't just focus on the cable or the end point. Look around and appreciate the beauty of nature and the surroundings. Notice the colors, shapes, sounds, and smells of the environment. Spot any animals or plants that catch your eye.
          • -
        • Have fun: Don't be afraid to express yourself and your emotions. Scream, shout, sing, or talk as you zip along. Make jokes, compliments, or comments with your guide or your fellow riders. Try different poses or gestures as you fly.
          • -
        -

        Conclusion

        -

        Ziplining is a wonderful way to experience nature, adventure, and excitement in one activity. It can also provide many benefits for your physical and mental health. Whether you are looking for a relaxing getaway, a thrilling challenge, or a unique perspective, ziplining can offer something for everyone. If you are interested in ziplining, you should do some research on the best locations, operators, and tips for ziplining. You should also prepare yourself physically and mentally for ziplining. And most importantly, you should have fun and enjoy every moment of ziplining.

        -

        FAQs

        -

        Here are some frequently asked questions about ziplining:

        -
          -
        1. Is ziplining safe?
          Ziplining is generally safe if you follow the instructions and rules given by the guides and operators. Ziplining equipment is tested regularly and inspected before each use. Ziplining guides are trained and certified in safety procedures and emergency situations.
          2. -
        2. Who can zipline?
          Ziplining is suitable for most people who are in good health and physical condition. However , there are some restrictions and limitations for ziplining, depending on the operator and the location. Some common factors that may affect your eligibility for ziplining are age, weight, height, medical condition, and pregnancy. You should check with the operator before you book a zipline tour to make sure you meet their requirements.
          3. -
        3. How much does ziplining cost?
          The cost of ziplining varies depending on the operator, the location, the duration, and the type of zipline tour. Generally, ziplining can range from $20 to $200 per person. You may also have to pay extra for transportation, photos, videos, or souvenirs. You should compare different options and look for discounts or deals before you book a zipline tour.
          4. -
        4. What if I am afraid of heights?
          If you are afraid of heights, ziplining may seem scary or intimidating. However, ziplining can also be a great way to overcome your fear and challenge yourself. Ziplining is safe and controlled, and you will be supported by a harness and a cable at all times. You will also be accompanied by a guide who can help you relax and enjoy the ride. You may find that ziplining is not as scary as you thought, and that it is actually fun and exhilarating.
          5. -
        5. Can I zipline with my friends or family?
          Yes, you can zipline with your friends or family, as long as they meet the requirements for ziplining. Ziplining can be a fun and social activity that can strengthen your bonds and create lasting memories. Some zipline operators offer tandem or group ziplines that allow you to zip together with your partner or your group. You can also take photos or videos of each other as you zip along.
          6. -

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        \ No newline at end of file diff --git a/spaces/contluForse/HuggingGPT/assets/Contoh Soal Psikotes Polri dan Jawabannya PDF 18 Kumpulan Materi dan Tips Lulus Tes.md b/spaces/contluForse/HuggingGPT/assets/Contoh Soal Psikotes Polri dan Jawabannya PDF 18 Kumpulan Materi dan Tips Lulus Tes.md deleted file mode 100644 index c528be71b045178eb3acab2624caacfb8d43c1ab..0000000000000000000000000000000000000000 --- a/spaces/contluForse/HuggingGPT/assets/Contoh Soal Psikotes Polri dan Jawabannya PDF 18 Kumpulan Materi dan Tips Lulus Tes.md +++ /dev/null @@ -1,31 +0,0 @@ -
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        Tes IST adalah salah satu tes yang diberikan kepada kandidat satu posisi oleh HRD. Tujuannya adalah untuk melihat potensi dan kecocokan dengan posisi yang dilamar. Agar bisa mengerjakan soal potongan gambar dengan baik kamu harus rajin berlatih. Semoga artikel contoh soal dan jawaban ini bisa membantumu!

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        Ada beragam jenis psikotes yang sering digunakan perusahaan sebagai bahan pertimbangan utama untuk menerima atau menolaknya, salah satunya psikotes matematika deret angka. Berikut sejumlah soal psikotes matematika deret angka dan jawabannya:

        -

        Setelah Anda mengetahui contoh-contoh soal psikotes Polri, saatnya Anda juga perlu mengetahui tips maupun trik untuk menghadapi tes psikotes tersebut. Ini beberapa tips yang bisa Anda terapkan saat mengerjakan tes psikotes polisi RI agar lolos seleksi:

        -

        Jangan terlalu capek belajar contoh soal psikotes polri saja. Persiapkan juga kondisi tubuh Anda agar tetap sehat dan fit untuk menjalankan rangkaian tes lainnya. Jika fisik Anda sehat dan fit, maka saat mengerjakan tes psikotes pun Anda akan merasa nyaman, tenang, dan fresh.

        -

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        Itulah beberapa contoh soal psikotes Polri yang bisa Anda pelajari. Anda bisa membeli buku fisik maupun elektronik untuk mendapatkan latihan soal yang lebih banyak. Meskipun dalam mengerjakan tidak ada cara yang absolut, tetapi Anda bisa mencoba mempelajari pola pertanyaan yang ada sebagai persiapan tes. Teruslah berlatih soal psikotes dan jaga selalu kondisi fisik dan mental Anda agar prima dan sehat, karena rangkaian tes untuk menjadi Polri tentu

        -

        Hai Teman Kerja semua! Ketika kalian menjalani tes seleksi kerja, selain seleksi berkas administratif dan wawancara, kalian harus mengikuti serangkaian tes psikotes. Salah satu psikotes yang biasanya ada di setiap perusahaan adalah tes logika aritmatika. Apa saja contoh soal, kunci jawaban dan cara mengerjakan tes logika aritmatika ini, Kinar akan bagikan informasinya kepada kalian Teman Kerja terutama fresh graduate.

        -

        Pada tes logika, sebenarnya tidak hanya mengerjakan soal yang berhubungan dengan angka. Tes logika juga bisa berupa logika kalimat yang melibatkan 2 premis dan pengambilan kesimpulan, dan juga logika pola gambar. Baik deret, logika kalimat, dan pola gambar akan diberikan contoh-contohnya di bawah, simak terus artikel ini ya?

        -

        Setelah melihat sendiri contoh tes logika artimatika dan lainnya, apakah sekarang kamu sudah cukup percaya diri untuk mengikuti tes psikotes? Kalian bisa coba latihan sendiri tes logika, bisa dari internet maupun buku-buku tes yang menyediakannya.

        -

        Maksudnya kalo nemu soal pencerminan secara umum atau tentang soal di atas aja? Sebenernya tahun-tahun kemaren kan belom pernah ada, tapi ya harusnya kalo muncul soal kayak gitu, di perintahnya dibilang posisi cerminnya di mana. Tapi kalo gw dapet soal kayak di contoh atas, kalo gak disebut di mana, gw asumsiin cerminnya ada di antara kotak gambar soal sama kotak pilihan jawabannya (vertikal).

        -

        JAKARTA, FIN.CO.ID -- Petugas Pemungutan Suara atau PPS hari ini, Rabu 18 Januari 2022 melakukan tes wawancara dengan Komisi Pemilihan Umum (KPU) di tiap daerah. Agar tak tersesat saat menjalani ujian, berikut kami ulas bocoran 15 soal tes wawancara PPS pemilu 2024, lengkap dengan kunci jawabannya.

        -

        Kebanyakan pertanyaan soal ini disajikan dalam format pilihan ganda. Dalam soal seperti kedua contoh di atas, kamu harus cerdik mencari hubungan angka yang berada di kolom atau baris yang sama. Berikut contoh pemecahan soal psikotes numerik tabel:

        -

        Selanjutnya, ada soal psikotes kerja bernama Tes bahasa artifisial. Pada dasarnya, tes ini adalah penggunaan bahasa bermakna yang mengikuti pola tertentu. Tugas pengisi soal adalah memecahkan pola yang digunakan untuk mencari jawaban.

        -

        Jenis soal psikotes satu ini berfungsi untuk mengetahui seberapa percaya diri, stabilitas, dan tanggung jawab kamu dalam bekerja. Kamu akan diminta untuk menggambar seseorang dan mendeskripsikannya secara detail.

        -

        Penemu tes Wartegg adalah Ehrig Wartegg. Pada tes ini kamu akan menemukan soal psikotes dengan berbagai 8 pola kotak yang berbeda. Kamu akan diminta untuk sesuai dengan imajinasi kalian dengan pola tersebut.

        -

        Nah, itu dia 13 jenis soal psikotes yang seringkali muncul dalam rekrutmen calon karyawan. Usahakan untuk mengerjakan semua soal yang diberikan. Namun, dahulukan soal yang paling mudah untuk terlebih dahulu.

        -

        Pastinya, psikotes diberikan dengan tujuan untuk mengukur individual para calon TNI dari berbagai aspek, bisa secara verbal maupun visual. Mengingat begitu pentingnya psikotes ini, para calon TNI sebaiknya memang mempersiapkannya dengan baik, termasuk dengan mempelajari contoh soalnya.

        -

        Sama seperti namanya, soal psikotes yang satu ini adalah kumpulan dari deretan angka. Tes ini biasanya diberikan dengan tujuan untuk mengukur kemampuan analisis dari para peserta tes dalam memahami pola tertentu.

        -

        Selain itu, tes ini juga bertujuan untuk memprediksikan hal-hal lainnya dengan berdasarkan pola angka yang dimaksud. Selain biasa disebut dengan psikotes deret angka, soal ini juga biasa disebut dengan tes angka. Biasanya operasi Matematika yang berlaku dalam tes ni adalah operasi yang biasanya.

        -

        Meskipun ditujukan untuk menyeleksi calon anggota TNI, soal psikotesnya juga berhubungan dengan angka. Soal psikotes TNI satu ini akan menyajikan kolom yang di dalamnya diisi oleh angka-angka.

        -

        Lebih lanjut lagi, ada tes logika Aritmatika yang menjadi salah satu soal psikotes TNI. Dalam soal satu ini biasanya akan diperlihatkan secara gamblang bagaimana cara mengerjakannya. Contohnya seperti berikut.

        -

        Kemampuan berbahasa juga menjadi salah satu hal yang diuji dalam psikotes TNI, diantaranya ialah dengan soal sinonim dan antonim. Sebagaimana yang sudah diketahui, sinonim itu merupakan persamaan kata sedangkan antonim adalah lawan kata.

        -

        Tes ini diberikan dengan tujuan untuk mengetahui motivasi serta kebutuhan seseorang. Tes ini juga akan menunjukkan hal-hal yang mungkin disukai dan hal-hal yang mungkin tidak disukai oleh peserta tersebut. Contohnya soal psikotes TNI jenis EPPS ini seperti berikut.

        -

        Bahkan dalam soal psikotes TNI, ada juga bagian menggambar. Menggambar ini bisa menggambar manusia, pohon maupun rumah sesuai dengan perintahnya. Biasanya, keindahan bukanlah hal yang terlalu diperhatikan dalam soal satu ini, namun lebih ke detail tidaknya objek yang digambarkan

        -

        Selain berbagai soal psikotes TNI di atas, tidak menutup kemungkinan jenis psikotes lainnya juga keluar. Misalnya tes koran. Namun, apapun jenis tesnya, semuanya harus dikerjakan dengan baik dengan konsentrasi yang bagus.

        -

        Ujian seleksi CPNS di tahun 2023 akan menggunakan sistem Computer Assisted Test (CAT). Bila kamu masih bingung dengan bentuk-bentuk dari soal tes nanti, mengerjakan latihan contoh soal SKD CPNS berikut ini bisa membantu kamu lho.

        -

        Kini pendaftar yang lolos seleksi administrasi akan mengikuti tes tertulis, computer assisted test (CAT) PPS Pemilu 2024. Untuk sukses mengikuti tes ini, berikut tersedia beberapa contoh soal dan kunci jawaban untuk simulasi.

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        diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/modules/multidilated_conv.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/modules/multidilated_conv.py deleted file mode 100644 index c57d0b457d4b30aeeffcd8cba138a502ba7affc5..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/lama/saicinpainting/training/modules/multidilated_conv.py +++ /dev/null @@ -1,98 +0,0 @@ -import torch -import torch.nn as nn -import random -from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv - -class MultidilatedConv(nn.Module): - def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True, - shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs): - super().__init__() - convs = [] - self.equal_dim = equal_dim - assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode - if comb_mode in ('cat_out', 'cat_both'): - self.cat_out = True - if equal_dim: - assert out_dim % dilation_num == 0 - out_dims = [out_dim // dilation_num] * dilation_num - self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], []) - else: - out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] - out_dims.append(out_dim - sum(out_dims)) - index = [] - starts = [0] + out_dims[:-1] - lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)] - for i in range(out_dims[-1]): - for j in range(dilation_num): - index += list(range(starts[j], starts[j] + lengths[j])) - starts[j] += lengths[j] - self.index = index - assert(len(index) == out_dim) - self.out_dims = out_dims - else: - self.cat_out = False - self.out_dims = [out_dim] * dilation_num - - if comb_mode in ('cat_in', 'cat_both'): - if equal_dim: - assert in_dim % dilation_num == 0 - in_dims = [in_dim // dilation_num] * dilation_num - else: - in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)] - in_dims.append(in_dim - sum(in_dims)) - self.in_dims = in_dims - self.cat_in = True - else: - self.cat_in = False - self.in_dims = [in_dim] * dilation_num - - conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d - dilation = min_dilation - for i in range(dilation_num): - if isinstance(padding, int): - cur_padding = padding * dilation - else: - cur_padding = padding[i] - convs.append(conv_type( - self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs - )) - if i > 0 and shared_weights: - convs[-1].weight = convs[0].weight - convs[-1].bias = convs[0].bias - dilation *= 2 - self.convs = nn.ModuleList(convs) - - self.shuffle_in_channels = shuffle_in_channels - if self.shuffle_in_channels: - # shuffle list as shuffling of tensors is nondeterministic - in_channels_permute = list(range(in_dim)) - random.shuffle(in_channels_permute) - # save as buffer so it is saved and loaded with checkpoint - self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute)) - - def forward(self, x): - if self.shuffle_in_channels: - x = x[:, self.in_channels_permute] - - outs = [] - if self.cat_in: - if self.equal_dim: - x = x.chunk(len(self.convs), dim=1) - else: - new_x = [] - start = 0 - for dim in self.in_dims: - new_x.append(x[:, start:start+dim]) - start += dim - x = new_x - for i, conv in enumerate(self.convs): - if self.cat_in: - input = x[i] - else: - input = x - outs.append(conv(input)) - if self.cat_out: - out = torch.cat(outs, dim=1)[:, self.index] - else: - out = sum(outs) - return out diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/logger/tensorboard.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/logger/tensorboard.py deleted file mode 100644 index 4dd5011dc08def6c09eef86d3ce5b124c9fc5372..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/uniformer/mmcv/runner/hooks/logger/tensorboard.py +++ /dev/null @@ -1,57 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import os.path as osp - -from annotator.uniformer.mmcv.utils import TORCH_VERSION, digit_version -from ...dist_utils import master_only -from ..hook import HOOKS -from .base import LoggerHook - - -@HOOKS.register_module() -class TensorboardLoggerHook(LoggerHook): - - def __init__(self, - log_dir=None, - interval=10, - ignore_last=True, - reset_flag=False, - by_epoch=True): - super(TensorboardLoggerHook, self).__init__(interval, ignore_last, - reset_flag, by_epoch) - self.log_dir = log_dir - - @master_only - def before_run(self, runner): - super(TensorboardLoggerHook, self).before_run(runner) - if (TORCH_VERSION == 'parrots' - or digit_version(TORCH_VERSION) < digit_version('1.1')): - try: - from tensorboardX import SummaryWriter - except ImportError: - raise ImportError('Please install tensorboardX to use ' - 'TensorboardLoggerHook.') - else: - try: - from torch.utils.tensorboard import SummaryWriter - except ImportError: - raise ImportError( - 'Please run "pip install future tensorboard" to install ' - 'the dependencies to use torch.utils.tensorboard ' - '(applicable to PyTorch 1.1 or higher)') - - if self.log_dir is None: - self.log_dir = osp.join(runner.work_dir, 'tf_logs') - self.writer = SummaryWriter(self.log_dir) - - @master_only - def log(self, runner): - tags = self.get_loggable_tags(runner, allow_text=True) - for tag, val in tags.items(): - if isinstance(val, str): - self.writer.add_text(tag, val, self.get_iter(runner)) - else: - self.writer.add_scalar(tag, val, self.get_iter(runner)) - - @master_only - def after_run(self, runner): - self.writer.close() diff --git a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/__init__.py b/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/__init__.py deleted file mode 100644 index 5f2668792389157609abb2a0846fb620e7d67eb9..0000000000000000000000000000000000000000 --- a/spaces/coreml-community/ControlNet-v1-1-Annotators-cpu/annotator/zoe/zoedepth/utils/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# MIT License - -# Copyright (c) 2022 Intelligent Systems Lab Org - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -# File author: Shariq Farooq Bhat - diff --git a/spaces/cozyanduofen/bingo/src/components/user-menu.tsx b/spaces/cozyanduofen/bingo/src/components/user-menu.tsx deleted file mode 100644 index 9bd1edc9cf9f39b63629b021f0c1186b1a7c1341..0000000000000000000000000000000000000000 --- a/spaces/cozyanduofen/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/crashedice/signify/SOURCE/yolo_files/utils/__init__.py b/spaces/crashedice/signify/SOURCE/yolo_files/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/crazybber/docker-demo-t5-translation/README.md b/spaces/crazybber/docker-demo-t5-translation/README.md deleted file mode 100644 index 0c0a402c2f07997157292292cdcddb5f381dddea..0000000000000000000000000000000000000000 --- a/spaces/crazybber/docker-demo-t5-translation/README.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -title: Docker Demo - Translation by T5 -emoji: 💻 -colorFrom: red -colorTo: pink -sdk: docker -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/csuer/vits/app.py b/spaces/csuer/vits/app.py deleted file mode 100644 index e9928712bfe4acb61de1e8f9d0c924f48aeba012..0000000000000000000000000000000000000000 --- a/spaces/csuer/vits/app.py +++ /dev/null @@ -1,137 +0,0 @@ -import os -import numpy as np -import torch -from torch import no_grad, LongTensor -import argparse -import commons -from mel_processing import spectrogram_torch -import utils -from models_infer import SynthesizerTrn -import gradio as gr -import librosa - -from text import text_to_sequence, _clean_text -device = "cuda:0" if torch.cuda.is_available() else "cpu" -language_marks = { - "Japanese": "", - "日本語": "[JA]", - "简体中文": "[ZH]", - "English": "[EN]", - "Mix": "", -} -lang = ['日本語', '简体中文', 'English', 'Mix'] -def get_text(text, hps, is_symbol): - text_norm = text_to_sequence(text, hps.symbols, [] if is_symbol else hps.data.text_cleaners) - if hps.data.add_blank: - text_norm = commons.intersperse(text_norm, 0) - text_norm = LongTensor(text_norm) - return text_norm - -def create_tts_fn(model, hps, speaker_ids): - def tts_fn(text, speaker, language, speed): - if language is not None: - text = language_marks[language] + text + language_marks[language] - speaker_id = speaker_ids[speaker] - stn_tst = get_text(text, hps, False) - with no_grad(): - x_tst = stn_tst.unsqueeze(0).to(device) - x_tst_lengths = LongTensor([stn_tst.size(0)]).to(device) - sid = LongTensor([speaker_id]).to(device) - audio = model.infer(x_tst, x_tst_lengths, sid=sid, noise_scale=.667, noise_scale_w=0.8, - length_scale=1.0 / speed)[0][0, 0].data.cpu().float().numpy() - del stn_tst, x_tst, x_tst_lengths, sid - return "Success", (hps.data.sampling_rate, audio) - - return tts_fn - -def create_vc_fn(model, hps, speaker_ids): - def vc_fn(original_speaker, target_speaker, record_audio, upload_audio): - input_audio = record_audio if record_audio is not None else upload_audio - if input_audio is None: - return "You need to record or upload an audio", None - sampling_rate, audio = input_audio - original_speaker_id = speaker_ids[original_speaker] - target_speaker_id = speaker_ids[target_speaker] - - 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 != hps.data.sampling_rate: - audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=hps.data.sampling_rate) - with no_grad(): - y = torch.FloatTensor(audio) - y = y / max(-y.min(), y.max()) / 0.99 - y = y.to(device) - y = y.unsqueeze(0) - spec = spectrogram_torch(y, hps.data.filter_length, - hps.data.sampling_rate, hps.data.hop_length, hps.data.win_length, - center=False).to(device) - spec_lengths = LongTensor([spec.size(-1)]).to(device) - sid_src = LongTensor([original_speaker_id]).to(device) - sid_tgt = LongTensor([target_speaker_id]).to(device) - audio = model.voice_conversion(spec, spec_lengths, sid_src=sid_src, sid_tgt=sid_tgt)[0][ - 0, 0].data.cpu().float().numpy() - del y, spec, spec_lengths, sid_src, sid_tgt - return "Success", (hps.data.sampling_rate, audio) - - return vc_fn -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--model_dir", default="./G.pth", help="directory to your fine-tuned model") - parser.add_argument("--config_dir", default="./finetune_speaker.json", help="directory to your model config file") - parser.add_argument("--share", default=False, help="make link public (used in colab)") - - args = parser.parse_args() - hps = utils.get_hparams_from_file(args.config_dir) - - - net_g = SynthesizerTrn( - len(hps.symbols), - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - n_speakers=hps.data.n_speakers, - **hps.model).to(device) - _ = net_g.eval() - - _ = utils.load_checkpoint(args.model_dir, net_g, None) - speaker_ids = hps.speakers - speakers = list(hps.speakers.keys()) - tts_fn = create_tts_fn(net_g, hps, speaker_ids) - vc_fn = create_vc_fn(net_g, hps, speaker_ids) - app = gr.Blocks() - with app: - with gr.Tab("Text-to-Speech"): - with gr.Row(): - with gr.Column(): - textbox = gr.TextArea(label="Text", - placeholder="Type your sentence here", - value="こんにちわ。", elem_id=f"tts-input") - # select character - char_dropdown = gr.Dropdown(choices=speakers, value=speakers[0], label='character') - language_dropdown = gr.Dropdown(choices=lang, value=lang[0], label='language') - duration_slider = gr.Slider(minimum=0.1, maximum=5, value=1, step=0.1, - label='速度 Speed') - with gr.Column(): - text_output = gr.Textbox(label="Message") - audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio") - btn = gr.Button("Generate!") - btn.click(tts_fn, - inputs=[textbox, char_dropdown, language_dropdown, duration_slider,], api_name='gen', - outputs=[text_output, audio_output]) - with gr.Tab("Voice Conversion"): - gr.Markdown(""" - 录制或上传声音，并选择要转换的音色。User代表的音色是你自己。 - """) - with gr.Column(): - record_audio = gr.Audio(label="record your voice", source="microphone") - upload_audio = gr.Audio(label="or upload audio here", source="upload") - source_speaker = gr.Dropdown(choices=speakers, value="User", label="source speaker") - target_speaker = gr.Dropdown(choices=speakers, value=speakers[0], label="target speaker") - with gr.Column(): - message_box = gr.Textbox(label="Message") - converted_audio = gr.Audio(label='converted audio') - btn = gr.Button("Convert!") - btn.click(vc_fn, inputs=[source_speaker, target_speaker, record_audio, upload_audio], - outputs=[message_box, converted_audio]) - app.launch(share=args.share, show_api=True) - diff --git a/spaces/cymic/Waifu_Diffusion_Webui/modules/textual_inversion/dataset.py b/spaces/cymic/Waifu_Diffusion_Webui/modules/textual_inversion/dataset.py deleted file mode 100644 index 2e69d39629e966e819beb728444052673084a9a4..0000000000000000000000000000000000000000 --- a/spaces/cymic/Waifu_Diffusion_Webui/modules/textual_inversion/dataset.py +++ /dev/null @@ -1,81 +0,0 @@ -import os -import numpy as np -import PIL -import torch -from PIL import Image -from torch.utils.data import Dataset -from torchvision import transforms - -import random -import tqdm -from modules import devices -import re - -re_tag = re.compile(r"[a-zA-Z][_\w\d()]+") - - -class PersonalizedBase(Dataset): - def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None): - - self.placeholder_token = placeholder_token - - self.size = size - self.width = width - self.height = height - self.flip = transforms.RandomHorizontalFlip(p=flip_p) - - self.dataset = [] - - with open(template_file, "r") as file: - lines = [x.strip() for x in file.readlines()] - - self.lines = lines - - assert data_root, 'dataset directory not specified' - - self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)] - print("Preparing dataset...") - for path in tqdm.tqdm(self.image_paths): - image = Image.open(path) - image = image.convert('RGB') - image = image.resize((self.width, self.height), PIL.Image.BICUBIC) - - filename = os.path.basename(path) - filename_tokens = os.path.splitext(filename)[0] - filename_tokens = re_tag.findall(filename_tokens) - - npimage = np.array(image).astype(np.uint8) - npimage = (npimage / 127.5 - 1.0).astype(np.float32) - - torchdata = torch.from_numpy(npimage).to(device=device, dtype=torch.float32) - torchdata = torch.moveaxis(torchdata, 2, 0) - - init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze() - init_latent = init_latent.to(devices.cpu) - - self.dataset.append((init_latent, filename_tokens)) - - self.length = len(self.dataset) * repeats - - self.initial_indexes = np.arange(self.length) % len(self.dataset) - self.indexes = None - self.shuffle() - - def shuffle(self): - self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])] - - def __len__(self): - return self.length - - def __getitem__(self, i): - if i % len(self.dataset) == 0: - self.shuffle() - - index = self.indexes[i % len(self.indexes)] - x, filename_tokens = self.dataset[index] - - text = random.choice(self.lines) - text = text.replace("[name]", self.placeholder_token) - text = text.replace("[filewords]", ' '.join(filename_tokens)) - - return x, text diff --git a/spaces/cymic/Waifu_Diffusion_Webui/webui.sh b/spaces/cymic/Waifu_Diffusion_Webui/webui.sh deleted file mode 100644 index 05ca497d2e181c9c11409451794b1fe920f7895c..0000000000000000000000000000000000000000 --- a/spaces/cymic/Waifu_Diffusion_Webui/webui.sh +++ /dev/null @@ -1,141 +0,0 @@ -#!/bin/bash -################################################# -# Please do not make any changes to this file, # -# change the variables in webui-user.sh instead # -################################################# -# Read variables from webui-user.sh -# shellcheck source=/dev/null -if [[ -f webui-user.sh ]] -then - source ./webui-user.sh -fi - -# Set defaults -# Install directory without trailing slash -if [[ -z "${install_dir}" ]] -then - install_dir="/home/$(whoami)" -fi - -# Name of the subdirectory (defaults to stable-diffusion-webui) -if [[ -z "${clone_dir}" ]] -then - clone_dir="stable-diffusion-webui" -fi - -# python3 executable -if [[ -z "${python_cmd}" ]] -then - python_cmd="python3" -fi - -# git executable -if [[ -z "${GIT}" ]] -then - export GIT="git" -fi - -# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv) -if [[ -z "${venv_dir}" ]] -then - venv_dir="venv" -fi - -if [[ -z "${LAUNCH_SCRIPT}" ]] -then - LAUNCH_SCRIPT="launch.py" -fi - -# Disable sentry logging -export ERROR_REPORTING=FALSE - -# Do not reinstall existing pip packages on Debian/Ubuntu -export PIP_IGNORE_INSTALLED=0 - -# Pretty print -delimiter="################################################################" - -printf "\n%s\n" "${delimiter}" -printf "\e[1m\e[32mInstall script for stable-diffusion + Web UI\n" -printf "\e[1m\e[34mTested on Debian 11 (Bullseye)\e[0m" -printf "\n%s\n" "${delimiter}" - -# Do not run as root -if [[ $(id -u) -eq 0 ]] -then - printf "\n%s\n" "${delimiter}" - printf "\e[1m\e[31mERROR: This script must not be launched as root, aborting...\e[0m" - printf "\n%s\n" "${delimiter}" - exit 1 -else - printf "\n%s\n" "${delimiter}" - printf "Running on \e[1m\e[32m%s\e[0m user" "$(whoami)" - printf "\n%s\n" "${delimiter}" -fi - -if [[ -d .git ]] -then - printf "\n%s\n" "${delimiter}" - printf "Repo already cloned, using it as install directory" - printf "\n%s\n" "${delimiter}" - install_dir="${PWD}/../" - clone_dir="${PWD##*/}" -fi - -# Check prequisites -for preq in git python3 -do - if ! hash "${preq}" &>/dev/null - then - printf "\n%s\n" "${delimiter}" - printf "\e[1m\e[31mERROR: %s is not installed, aborting...\e[0m" "${preq}" - printf "\n%s\n" "${delimiter}" - exit 1 - fi -done - -if ! "${python_cmd}" -c "import venv" &>/dev/null -then - printf "\n%s\n" "${delimiter}" - printf "\e[1m\e[31mERROR: python3-venv is not installed, aborting...\e[0m" - printf "\n%s\n" "${delimiter}" - exit 1 -fi - -printf "\n%s\n" "${delimiter}" -printf "Clone or update stable-diffusion-webui" -printf "\n%s\n" "${delimiter}" -cd "${install_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/, aborting...\e[0m" "${install_dir}"; exit 1; } -if [[ -d "${clone_dir}" ]] -then - cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; } - "${GIT}" pull -else - "${GIT}" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git "${clone_dir}" - cd "${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; } -fi - -printf "\n%s\n" "${delimiter}" -printf "Create and activate python venv" -printf "\n%s\n" "${delimiter}" -cd "${install_dir}"/"${clone_dir}"/ || { printf "\e[1m\e[31mERROR: Can't cd to %s/%s/, aborting...\e[0m" "${install_dir}" "${clone_dir}"; exit 1; } -if [[ ! -d "${venv_dir}" ]] -then - "${python_cmd}" -m venv "${venv_dir}" - first_launch=1 -fi -# shellcheck source=/dev/null -if [[ -f "${venv_dir}"/bin/activate ]] -then - source "${venv_dir}"/bin/activate -else - printf "\n%s\n" "${delimiter}" - printf "\e[1m\e[31mERROR: Cannot activate python venv, aborting...\e[0m" - printf "\n%s\n" "${delimiter}" - exit 1 -fi - -printf "\n%s\n" "${delimiter}" -printf "Launching launch.py..." -printf "\n%s\n" "${delimiter}" -"${python_cmd}" "${LAUNCH_SCRIPT}" diff --git a/spaces/d0r1h/youtube_summarization/app.py b/spaces/d0r1h/youtube_summarization/app.py deleted file mode 100644 index 0b5ca0325b46b1ecd7347c1c6fe2fac50467a054..0000000000000000000000000000000000000000 --- a/spaces/d0r1h/youtube_summarization/app.py +++ /dev/null @@ -1,17 +0,0 @@ -import gradio as gr -from summarize import Summarizer - -interface = gr.Interface(fn = Summarizer, - inputs = [gr.inputs.Textbox(lines=2, - placeholder="Enter your link...", - label='YouTube Video Link'), - gr.inputs.Radio(["mT5", "BART", "Pegasus"], type="value", label='Model')], - outputs = [gr.outputs.Textbox( - label="Summary")], - - title = "Youtube Summarizer", - examples = [['https://www.youtube.com/watch?v=A4OmtyaBHFE', 'mT5'], - ['https://www.youtube.com/watch?v=cU6xVZfkcgo', 'mT5']], - enable_queue=True) - -interface.launch(debug=True) \ No newline at end of file diff --git a/spaces/danieldux/isco-gpt/app.py b/spaces/danieldux/isco-gpt/app.py deleted file mode 100644 index 386e59aa760b0b39d8f5742cd7a3923ffbd5c0a2..0000000000000000000000000000000000000000 --- a/spaces/danieldux/isco-gpt/app.py +++ /dev/null @@ -1,128 +0,0 @@ -import streamlit as st -from streamlit_chat import message -import os -from utils import ( - parse_docx, - parse_pdf, - parse_txt, - parse_csv, - parse_pptx, - search_docs, - embed_docs, - text_to_docs, - get_answer, - parse_any, - get_sources, - wrap_text_in_html, -) -from openai.error import OpenAIError - -def clear_submit(): - st.session_state["submit"] = False - -def set_openai_api_key(api_key: str): - st.session_state["OPENAI_API_KEY"] = api_key - -st.markdown('

        File GPT 🤖 by Code GPT

        ', unsafe_allow_html=True) - -# Sidebar -index = None -doc = None -with st.sidebar: - user_secret = st.text_input( - "OpenAI API Key", - type="password", - placeholder="Paste your OpenAI API key here (sk-...)", - help="You can get your API key from https://platform.openai.com/account/api-keys.", - value=st.session_state.get("OPENAI_API_KEY", ""), - ) - if user_secret: - set_openai_api_key(user_secret) - - uploaded_file = st.file_uploader( - "Upload a pdf, docx, or txt file", - type=["pdf", "docx", "txt", "csv", "pptx", "js", "py", "json", "html", "css", "md"], - help="Scanned documents are not supported yet!", - on_change=clear_submit, - ) - - if uploaded_file is not None: - if uploaded_file.name.endswith(".pdf"): - doc = parse_pdf(uploaded_file) - elif uploaded_file.name.endswith(".docx"): - doc = parse_docx(uploaded_file) - elif uploaded_file.name.endswith(".csv"): - doc = parse_csv(uploaded_file) - elif uploaded_file.name.endswith(".txt"): - doc = parse_txt(uploaded_file) - elif uploaded_file.name.endswith(".pptx"): - doc = parse_pptx(uploaded_file) - else: - doc = parse_any(uploaded_file) - #st.error("File type not supported") - #doc = None - text = text_to_docs(doc) - st.write(text) - try: - with st.spinner("Indexing document... This may take a while⏳"): - index = embed_docs(text) - st.session_state["api_key_configured"] = True - except OpenAIError as e: - st.error(e._message) - -tab1, tab2 = st.tabs(["Intro", "Chat with the File"]) -with tab1: - st.markdown("### How does it work?") - st.write("File GPT is a tool that allows you to ask questions about a document and get answers from the document. The tool uses the OpenAI API to embed the document and then uses the Embedding API to find the most similar documents to the question. The tool then uses LangChain to obtain the answer from the most similar documents.") - st.write("The tool is currently in beta and is not perfect. It is recommended to use it with short documents.") - st.write("""---""") - st.markdown("### How to use it?") - st.write("To use the tool you must first add your OpenAI API Key and then upload a document. The tool currently supports the following file types: pdf, docx, txt, csv, pptx. Once the document is uploaded, the tool will index the document and embed it. This may take a while depending on the size of the document. Once the document is indexed, you can ask questions about the document. The tool will return the answer to the question and the source of the answer.") - st.markdown('

        Read the article to know more details: Medium Article (Spanish)

        ', unsafe_allow_html=True) - st.write("## File GPT was written with the following tools:") - st.markdown("#### Code GPT") - st.write('All code was written with the help of Code GPT. Visit https://codegpt.co to get the extension.') - st.markdown("#### Streamlit") - st.write('The design was written with Streamlit.', unsafe_allow_html=True) - st.markdown("#### LangChain") - st.write('Question answering with source Langchain QA.', unsafe_allow_html=True) - st.markdown("#### Embedding") - st.write('Embedding is done via the OpenAI API with "text-embedding-ada-002"', unsafe_allow_html=True) - st.write("Please note that you must have credits in your OpenAI account to use this tool. Each file uploaded to the platform consumes credits for embedding and each query consumes credits to obtain the response.") - st.markdown("""---""") - st.write('Author: Daniel Avila', unsafe_allow_html=True) - st.write('Repo: Github', unsafe_allow_html=True) - st.write("This software was developed with Code GPT, for more information visit: https://codegpt.co", unsafe_allow_html=True) - -with tab2: - st.write('To obtain an API Key you must create an OpenAI account at the following link: https://openai.com/api/') - if 'generated' not in st.session_state: - st.session_state['generated'] = [] - - if 'past' not in st.session_state: - st.session_state['past'] = [] - - def get_text(): - if user_secret: - st.header("Ask me something about the document:") - input_text = st.text_area("You:", on_change=clear_submit) - return input_text - user_input = get_text() - - button = st.button("Submit") - if button or st.session_state.get("submit"): - if not user_input: - st.error("Please enter a question!") - else: - st.session_state["submit"] = True - sources = search_docs(index, user_input) - try: - answer = get_answer(sources, user_input) - st.session_state.past.append(user_input) - st.session_state.generated.append(answer["output_text"].split("SOURCES: ")[0]) - except OpenAIError as e: - st.error(e._message) - if st.session_state['generated']: - for i in range(len(st.session_state['generated'])-1, -1, -1): - message(st.session_state["generated"][i], key=str(i)) - message(st.session_state['past'][i], is_user=True, key=str(i) + '_user') \ No newline at end of file diff --git a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/retinaface/retinaface_net.py b/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/retinaface/retinaface_net.py deleted file mode 100644 index ab6aa82d3e9055a838f1f9076b12f05fdfc154d0..0000000000000000000000000000000000000000 --- a/spaces/dawood17/SayBot_Enchancer/CodeFormer/facelib/detection/retinaface/retinaface_net.py +++ /dev/null @@ -1,196 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - - -def conv_bn(inp, oup, stride=1, leaky=0): - return nn.Sequential( - nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), - nn.LeakyReLU(negative_slope=leaky, inplace=True)) - - -def conv_bn_no_relu(inp, oup, stride): - return nn.Sequential( - nn.Conv2d(inp, oup, 3, stride, 1, bias=False), - nn.BatchNorm2d(oup), - ) - - -def conv_bn1X1(inp, oup, stride, leaky=0): - return nn.Sequential( - nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup), - nn.LeakyReLU(negative_slope=leaky, inplace=True)) - - -def conv_dw(inp, oup, stride, leaky=0.1): - return nn.Sequential( - nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), - nn.BatchNorm2d(inp), - nn.LeakyReLU(negative_slope=leaky, inplace=True), - nn.Conv2d(inp, oup, 1, 1, 0, bias=False), - nn.BatchNorm2d(oup), - nn.LeakyReLU(negative_slope=leaky, inplace=True), - ) - - -class SSH(nn.Module): - - def __init__(self, in_channel, out_channel): - super(SSH, self).__init__() - assert out_channel % 4 == 0 - leaky = 0 - if (out_channel <= 64): - leaky = 0.1 - self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1) - - self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky) - self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) - - self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky) - self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) - - def forward(self, input): - conv3X3 = self.conv3X3(input) - - conv5X5_1 = self.conv5X5_1(input) - conv5X5 = self.conv5X5_2(conv5X5_1) - - conv7X7_2 = self.conv7X7_2(conv5X5_1) - conv7X7 = self.conv7x7_3(conv7X7_2) - - out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1) - out = F.relu(out) - return out - - -class FPN(nn.Module): - - def __init__(self, in_channels_list, out_channels): - super(FPN, self).__init__() - leaky = 0 - if (out_channels <= 64): - leaky = 0.1 - self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky) - self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky) - self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky) - - self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky) - self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky) - - def forward(self, input): - # names = list(input.keys()) - # input = list(input.values()) - - output1 = self.output1(input[0]) - output2 = self.output2(input[1]) - output3 = self.output3(input[2]) - - up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest') - output2 = output2 + up3 - output2 = self.merge2(output2) - - up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest') - output1 = output1 + up2 - output1 = self.merge1(output1) - - out = [output1, output2, output3] - return out - - -class MobileNetV1(nn.Module): - - def __init__(self): - super(MobileNetV1, self).__init__() - self.stage1 = nn.Sequential( - conv_bn(3, 8, 2, leaky=0.1), # 3 - conv_dw(8, 16, 1), # 7 - conv_dw(16, 32, 2), # 11 - conv_dw(32, 32, 1), # 19 - conv_dw(32, 64, 2), # 27 - conv_dw(64, 64, 1), # 43 - ) - self.stage2 = nn.Sequential( - conv_dw(64, 128, 2), # 43 + 16 = 59 - conv_dw(128, 128, 1), # 59 + 32 = 91 - conv_dw(128, 128, 1), # 91 + 32 = 123 - conv_dw(128, 128, 1), # 123 + 32 = 155 - conv_dw(128, 128, 1), # 155 + 32 = 187 - conv_dw(128, 128, 1), # 187 + 32 = 219 - ) - self.stage3 = nn.Sequential( - conv_dw(128, 256, 2), # 219 +3 2 = 241 - conv_dw(256, 256, 1), # 241 + 64 = 301 - ) - self.avg = nn.AdaptiveAvgPool2d((1, 1)) - self.fc = nn.Linear(256, 1000) - - def forward(self, x): - x = self.stage1(x) - x = self.stage2(x) - x = self.stage3(x) - x = self.avg(x) - # x = self.model(x) - x = x.view(-1, 256) - x = self.fc(x) - return x - - -class ClassHead(nn.Module): - - def __init__(self, inchannels=512, num_anchors=3): - super(ClassHead, self).__init__() - self.num_anchors = num_anchors - self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0) - - def forward(self, x): - out = self.conv1x1(x) - out = out.permute(0, 2, 3, 1).contiguous() - - return out.view(out.shape[0], -1, 2) - - -class BboxHead(nn.Module): - - def __init__(self, inchannels=512, num_anchors=3): - super(BboxHead, self).__init__() - self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0) - - def forward(self, x): - out = self.conv1x1(x) - out = out.permute(0, 2, 3, 1).contiguous() - - return out.view(out.shape[0], -1, 4) - - -class LandmarkHead(nn.Module): - - def __init__(self, inchannels=512, num_anchors=3): - super(LandmarkHead, self).__init__() - self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0) - - def forward(self, x): - out = self.conv1x1(x) - out = out.permute(0, 2, 3, 1).contiguous() - - return out.view(out.shape[0], -1, 10) - - -def make_class_head(fpn_num=3, inchannels=64, anchor_num=2): - classhead = nn.ModuleList() - for i in range(fpn_num): - classhead.append(ClassHead(inchannels, anchor_num)) - return classhead - - -def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2): - bboxhead = nn.ModuleList() - for i in range(fpn_num): - bboxhead.append(BboxHead(inchannels, anchor_num)) - return bboxhead - - -def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2): - landmarkhead = nn.ModuleList() - for i in range(fpn_num): - landmarkhead.append(LandmarkHead(inchannels, anchor_num)) - return landmarkhead diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/utils.py b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/utils.py deleted file mode 100644 index dd2d245a0bebcd5fc37ac20526aabbd5358dab0e..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/dateutil/utils.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -""" -This module offers general convenience and utility functions for dealing with -datetimes. - -.. versionadded:: 2.7.0 -""" -from __future__ import unicode_literals - -from datetime import datetime, time - - -def today(tzinfo=None): - """ - Returns a :py:class:`datetime` representing the current day at midnight - - :param tzinfo: - The time zone to attach (also used to determine the current day). - - :return: - A :py:class:`datetime.datetime` object representing the current day - at midnight. - """ - - dt = datetime.now(tzinfo) - return datetime.combine(dt.date(), time(0, tzinfo=tzinfo)) - - -def default_tzinfo(dt, tzinfo): - """ - Sets the ``tzinfo`` parameter on naive datetimes only - - This is useful for example when you are provided a datetime that may have - either an implicit or explicit time zone, such as when parsing a time zone - string. - - .. doctest:: - - >>> from dateutil.tz import tzoffset - >>> from dateutil.parser import parse - >>> from dateutil.utils import default_tzinfo - >>> dflt_tz = tzoffset("EST", -18000) - >>> print(default_tzinfo(parse('2014-01-01 12:30 UTC'), dflt_tz)) - 2014-01-01 12:30:00+00:00 - >>> print(default_tzinfo(parse('2014-01-01 12:30'), dflt_tz)) - 2014-01-01 12:30:00-05:00 - - :param dt: - The datetime on which to replace the time zone - - :param tzinfo: - The :py:class:`datetime.tzinfo` subclass instance to assign to - ``dt`` if (and only if) it is naive. - - :return: - Returns an aware :py:class:`datetime.datetime`. - """ - if dt.tzinfo is not None: - return dt - else: - return dt.replace(tzinfo=tzinfo) - - -def within_delta(dt1, dt2, delta): - """ - Useful for comparing two datetimes that may have a negligible difference - to be considered equal. - """ - delta = abs(delta) - difference = dt1 - dt2 - return -delta <= difference <= delta diff --git a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-b496c98d.css b/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-b496c98d.css deleted file mode 100644 index cc80c0f759677ec3700fb346864183b8bd529dbb..0000000000000000000000000000000000000000 --- a/spaces/dcarpintero/nlp-summarizer-pegasus/.venv/lib/python3.9/site-packages/gradio/templates/frontend/assets/Image-b496c98d.css +++ /dev/null @@ -1 +0,0 @@ -.wrap.svelte-425ent{position:relative;width:var(--size-full);height:var(--size-full);min-height:var(--size-60)}video.svelte-425ent{width:var(--size-full);height:var(--size-full)}button.svelte-425ent{display:flex;position:absolute;right:0;bottom:var(--size-2);left:0;justify-content:center;align-items:center;margin:auto;box-shadow:var(--shadow-drop-lg);border-radius:var(--radius-xl);background-color:#000000e6;width:var(--size-10);height:var(--size-10)}@media (min-width: 768px){button.svelte-425ent{bottom:var(--size-4)}}@media (min-width: 1280px){button.svelte-425ent{bottom:var(--size-8)}}.icon.svelte-425ent{opacity:.8;width:50%;height:50%;color:#fff}.flip.svelte-425ent{transform:scaleX(-1)}canvas.svelte-yigbas{display:block;position:absolute;inset:0;margin:auto}.lr.svelte-yigbas{border-right:1px solid var(--border-color-primary);border-left:1px solid var(--border-color-primary)}.tb.svelte-yigbas{border-top:1px solid var(--border-color-primary);border-bottom:1px solid var(--border-color-primary)}canvas.svelte-yigbas:hover{cursor:none}.wrap.svelte-yigbas{position:relative;width:var(--size-full);height:var(--size-full);touch-action:none}.start-prompt.svelte-yigbas{display:flex;position:absolute;inset:0;justify-content:center;align-items:center;z-index:var(--layer-4);touch-action:none;pointer-events:none;color:var(--body-text-color-subdued)}div.svelte-s6ybro{display:flex;position:absolute;top:var(--size-2);right:var(--size-2);justify-content:flex-end;gap:var(--spacing-sm);z-index:var(--layer-5)}.wrap.svelte-p4aq0j.svelte-p4aq0j{display:flex;position:absolute;top:var(--size-10);right:var(--size-2);flex-direction:column;justify-content:flex-end;gap:var(--spacing-sm);z-index:var(--layer-5)}.brush.svelte-p4aq0j.svelte-p4aq0j{top:0;right:0}.brush.svelte-p4aq0j input.svelte-p4aq0j{position:absolute;top:3px;right:calc(100% + 5px)}.col.svelte-p4aq0j input.svelte-p4aq0j{position:absolute;right:calc(100% + 5px);bottom:-4px}.image-container.svelte-p3y7hu,img.svelte-p3y7hu{width:var(--size-full);height:var(--size-full)}img.svelte-p3y7hu{object-fit:contain}.selectable.svelte-p3y7hu{cursor:crosshair}.absolute-img.svelte-p3y7hu{position:absolute;opacity:0}.webcam.svelte-p3y7hu{transform:scaleX(-1)} diff --git a/spaces/declare-lab/tango/diffusers/tests/pipelines/dit/test_dit.py b/spaces/declare-lab/tango/diffusers/tests/pipelines/dit/test_dit.py deleted file mode 100644 index c514c3c7fa1d7b7a83307a04c37ca63dece289e5..0000000000000000000000000000000000000000 --- a/spaces/declare-lab/tango/diffusers/tests/pipelines/dit/test_dit.py +++ /dev/null @@ -1,152 +0,0 @@ -# coding=utf-8 -# Copyright 2023 HuggingFace Inc. -# -# 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 unittest - -import numpy as np -import torch - -from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, Transformer2DModel -from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device -from diffusers.utils.testing_utils import require_torch_gpu - -from ...pipeline_params import ( - CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS, - CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS, -) -from ...test_pipelines_common import PipelineTesterMixin - - -torch.backends.cuda.matmul.allow_tf32 = False - - -class DiTPipelineFastTests(PipelineTesterMixin, unittest.TestCase): - pipeline_class = DiTPipeline - params = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS - required_optional_params = PipelineTesterMixin.required_optional_params - { - "latents", - "num_images_per_prompt", - "callback", - "callback_steps", - } - batch_params = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS - test_cpu_offload = False - - def get_dummy_components(self): - torch.manual_seed(0) - transformer = Transformer2DModel( - sample_size=16, - num_layers=2, - patch_size=4, - attention_head_dim=8, - num_attention_heads=2, - in_channels=4, - out_channels=8, - attention_bias=True, - activation_fn="gelu-approximate", - num_embeds_ada_norm=1000, - norm_type="ada_norm_zero", - norm_elementwise_affine=False, - ) - vae = AutoencoderKL() - scheduler = DDIMScheduler() - components = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler} - return components - - def get_dummy_inputs(self, device, seed=0): - if str(device).startswith("mps"): - generator = torch.manual_seed(seed) - else: - generator = torch.Generator(device=device).manual_seed(seed) - inputs = { - "class_labels": [1], - "generator": generator, - "num_inference_steps": 2, - "output_type": "numpy", - } - return inputs - - def test_inference(self): - device = "cpu" - - components = self.get_dummy_components() - pipe = self.pipeline_class(**components) - pipe.to(device) - pipe.set_progress_bar_config(disable=None) - - inputs = self.get_dummy_inputs(device) - image = pipe(**inputs).images - image_slice = image[0, -3:, -3:, -1] - - self.assertEqual(image.shape, (1, 16, 16, 3)) - expected_slice = np.array([0.4380, 0.4141, 0.5159, 0.0000, 0.4282, 0.6680, 0.5485, 0.2545, 0.6719]) - max_diff = np.abs(image_slice.flatten() - expected_slice).max() - self.assertLessEqual(max_diff, 1e-3) - - def test_inference_batch_single_identical(self): - self._test_inference_batch_single_identical(relax_max_difference=True, expected_max_diff=1e-3) - - @unittest.skipIf( - torch_device != "cuda" or not is_xformers_available(), - reason="XFormers attention is only available with CUDA and `xformers` installed", - ) - def test_xformers_attention_forwardGenerator_pass(self): - self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3) - - -@require_torch_gpu -@slow -class DiTPipelineIntegrationTests(unittest.TestCase): - def tearDown(self): - super().tearDown() - gc.collect() - torch.cuda.empty_cache() - - def test_dit_256(self): - generator = torch.manual_seed(0) - - pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256") - pipe.to("cuda") - - words = ["vase", "umbrella", "white shark", "white wolf"] - ids = pipe.get_label_ids(words) - - images = pipe(ids, generator=generator, num_inference_steps=40, output_type="np").images - - for word, image in zip(words, images): - expected_image = load_numpy( - f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" - ) - assert np.abs((expected_image - image).max()) < 1e-2 - - def test_dit_512(self): - pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512") - pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) - pipe.to("cuda") - - words = ["vase", "umbrella"] - ids = pipe.get_label_ids(words) - - generator = torch.manual_seed(0) - images = pipe(ids, generator=generator, num_inference_steps=25, output_type="np").images - - for word, image in zip(words, images): - expected_image = load_numpy( - "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" - f"/dit/{word}_512.npy" - ) - - assert np.abs((expected_image - image).max()) < 1e-1 diff --git a/spaces/diacanFperku/AutoGPT/Icc Immo Pin Code Calculator V 154 [TOP].md b/spaces/diacanFperku/AutoGPT/Icc Immo Pin Code Calculator V 154 [TOP].md deleted file mode 100644 index 6d8fbf88dff335b21c69a9bd55e10d433cb72424..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Icc Immo Pin Code Calculator V 154 [TOP].md +++ /dev/null @@ -1,6 +0,0 @@ -

        Icc Immo Pin Code Calculator V 154


        Download File ===> https://gohhs.com/2uFUX8



        -
        -.160 is a security and usability improvement for the device In addition, it has a larger display than other applications. jpg' : 'Scriptacao:', // Title // siSupport.avvacari_ativos) $("tbody td:nth-child(" + value.id + ")").css('background-color', '#CCCCCC'); else if (value.ativo == 3) $("#popupNewLogins").show(); // $("#popupNewLogins.tpNewLogin").css('color','#D87A25'); else if (value.ativo == 4) $("#popupNewLogins").hide(); // $("#popupNewLogins.tpNewLogin").css('color','#292929'); else $("#popupNewLogins").hide(); }); $('#' + value.id +'a').on("click", function() $('#' + value.id + '.t-icon').toggleClass("t-icon-custom-vibrate"); ); }); 4fefd39f24
        -
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        diff --git a/spaces/diacanFperku/AutoGPT/Mutant Reverb V1.0.1 WiN.MAC RETAiL.md b/spaces/diacanFperku/AutoGPT/Mutant Reverb V1.0.1 WiN.MAC RETAiL.md deleted file mode 100644 index 6e4bafe80baeb13545565909380a0e4a5702df5e..0000000000000000000000000000000000000000 --- a/spaces/diacanFperku/AutoGPT/Mutant Reverb V1.0.1 WiN.MAC RETAiL.md +++ /dev/null @@ -1,9 +0,0 @@ -
        -

        the other problem is that the core audio framework is loaded by augraph. mutant reverb is designed to use augraph, so it is included when building it. this means that if the core audio framework is missing then mutant reverb will not work. to avoid this it is recommended that you use the -ccoreaudio or -ccoreauo switches to build mutant reverb.

        -

        Mutant Reverb v1.0.1 WiN.MAC RETAiL


        Download ✯✯✯ https://gohhs.com/2uFSZ4



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        anyways.. here's a list of the new features.

        • unicode support
        • new gui.
        • 16', 24', and 32' (and other) reverb types
        • mixed reverb types (can be m/s or m/s/e)
        • parameter editing
        • ui changes
        • gui improvements
        • more reverb types
        -

        special features:

        • new, expanded gui with new buttons and sliders for real-time control of reverb gain.
        • new icons.
        • multi-core support.
        • added a gui for midi cc numbers.
        • added a gui for midi patch number.
        • a midi mapping feature for easy midi cc assignment.
        • added an option for using the same aux channel as the master channel.
        • added an option to use either the computer speaker or the aux channel as the master output.
        • added a ‘failed’ button to the main window when loading a file fails.
        • added a ‘save’ button to the main window for saving preset files.
        • added a button for choosing a folder for storing preset files.
        -

        installation:

        -

        1. download mutant reverb v1.0.1 win.mac retail from the link below. a file named ‘mutant reverb.exe’ will be downloaded to your desktop. please run this file to install mutant reverb. this will take a few minutes to complete.
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        -
        -
        \ No newline at end of file diff --git a/spaces/dilums/sentence-similarity/components/ui/input.tsx b/spaces/dilums/sentence-similarity/components/ui/input.tsx deleted file mode 100644 index a92b8e0e58e275294b4cbd8c0b154fd11b016c7a..0000000000000000000000000000000000000000 --- a/spaces/dilums/sentence-similarity/components/ui/input.tsx +++ /dev/null @@ -1,25 +0,0 @@ -import * as React from "react" - -import { cn } from "@/lib/utils" - -export interface InputProps - extends React.InputHTMLAttributes {} - -const Input = React.forwardRef( - ({ className, type, ...props }, ref) => { - return ( - - ) - } -) -Input.displayName = "Input" - -export { Input } diff --git a/spaces/doevent/ArcaneGAN/README.md b/spaces/doevent/ArcaneGAN/README.md deleted file mode 100644 index a01e41791e165f91e8ae905b52db646f46bcb16c..0000000000000000000000000000000000000000 --- a/spaces/doevent/ArcaneGAN/README.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -title: ArcaneGANv2 On Photo -emoji: ⚡ -colorFrom: yellow -colorTo: blue -sdk: gradio -sdk_version: 3.0.5 -app_file: app.py -pinned: false ---- - -# 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. - -`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. \ No newline at end of file diff --git a/spaces/dorkai/singpt/extensions/google_translate/script.py b/spaces/dorkai/singpt/extensions/google_translate/script.py deleted file mode 100644 index 68bc54b293086bed1a070a310d276060ee939d44..0000000000000000000000000000000000000000 --- a/spaces/dorkai/singpt/extensions/google_translate/script.py +++ /dev/null @@ -1,42 +0,0 @@ -import gradio as gr -from deep_translator import GoogleTranslator - -params = { - "language string": "ja", -} - -language_codes = {'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chinese (Simplified)': 'zh-CN', 'Chinese (Traditional)': 'zh-TW', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Nyanja (Chichewa)': 'ny', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese (Portugal, Brazil)': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala (Sinhalese)': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog (Filipino)': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu'} - -def input_modifier(string): - """ - This function is applied to your text inputs before - they are fed into the model. - """ - - return GoogleTranslator(source=params['language string'], target='en').translate(string) - -def output_modifier(string): - """ - This function is applied to the model outputs. - """ - - return GoogleTranslator(source='en', target=params['language string']).translate(string) - -def bot_prefix_modifier(string): - """ - This function is only applied in chat mode. It modifies - the prefix text for the Bot and can be used to bias its - behavior. - """ - - return string - -def ui(): - # Finding the language name from the language code to use as the default value - language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])] - - # Gradio elements - language = gr.Dropdown(value=language_name, choices=[k for k in language_codes], label='Language') - - # Event functions to update the parameters in the backend - language.change(lambda x: params.update({"language string": language_codes[x]}), language, None) diff --git a/spaces/dragonSwing/isr/upsample.py b/spaces/dragonSwing/isr/upsample.py deleted file mode 100644 index 5a247c578e0b7b1574935c5422c6b2b0149aca0a..0000000000000000000000000000000000000000 --- a/spaces/dragonSwing/isr/upsample.py +++ /dev/null @@ -1,144 +0,0 @@ -import argparse -import cv2 -import os - -from imutils import paths -from tqdm import tqdm -from config import * -from utils import get_face_enhancer, get_upsampler - - -def process(image_path, upsampler_name, face_enhancer_name=None, scale=2, device="cpu"): - if scale > 4: - scale = 4 # avoid too large scale value - try: - img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED) - - h, w = img.shape[0:2] - if h > 3500 or w > 3500: - output = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - return output - - if (h < 300 and w < 300) and upsampler_name != "srcnn": - img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4) - return img - - upsampler = get_upsampler(upsampler_name, device=device) - - if face_enhancer_name: - face_enhancer = get_face_enhancer( - face_enhancer_name, scale, upsampler, device=device - ) - else: - face_enhancer = None - - try: - if face_enhancer is not None: - _, _, output = face_enhancer.enhance( - img, has_aligned=False, only_center_face=False, paste_back=True - ) - else: - output, _ = upsampler.enhance(img, outscale=scale) - except RuntimeError as error: - print(f"Runtime error: {error}") - - return output - except Exception as error: - print(f"global exception: {error}") - - -def main(args: argparse.Namespace) -> None: - device = args.device - scale = args.scale - - upsampler_name = args.upsampler - face_enhancer_name = args.face_enhancer - - if face_enhancer_name and ("srcnn" in upsampler_name or "anime" in upsampler_name): - print( - "Warnings: SRCNN and Anime model aren't compatible with face enhance. We will turn it off for you" - ) - face_enhancer_name = None - - os.makedirs(args.output, exist_ok=True) - 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) - - with tqdm(image_paths) as pbar: - for image_path in pbar: - filename = os.path.basename(image_path) - pbar.set_postfix_str(f"Processing {image_path}") - upsampled_image = process( - image_path=image_path, - upsampler_name=upsampler_name, - face_enhancer_name=face_enhancer_name, - scale=scale, - device=device, - ) - if upsampled_image is not None: - save_path = os.path.join(args.output, filename) - cv2.imwrite(save_path, upsampled_image) - - -if __name__ == "__main__": - 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 output directory.", - ) - - parser.add_argument( - "--upsampler", - type=str, - default="realesr-general-x4v3", - choices=[ - "srcnn", - "RealESRGAN_x2plus", - "RealESRGAN_x4plus", - "RealESRNet_x4plus", - "realesr-general-x4v3", - "RealESRGAN_x4plus_anime_6B", - "realesr-animevideov3", - ], - help="The type of upsampler model to load", - ) - - parser.add_argument( - "--face-enhancer", - type=str, - choices=["GFPGANv1.3", "GFPGANv1.4", "RestoreFormer"], - help="The type of face enhancer model to load", - ) - - parser.add_argument( - "--scale", - type=float, - default=2, - choices=[1.5, 2, 2.5, 3, 3.5, 4], - help="scaling factor", - ) - parser.add_argument( - "--device", type=str, default="cuda", help="The device to run upsampling on." - ) - args = parser.parse_args() - main(args) diff --git a/spaces/dragonSwing/wav2vec2-vi-asr/README.md b/spaces/dragonSwing/wav2vec2-vi-asr/README.md deleted file mode 100644 index 7ebde6d8ea29997ca3dd0a70e6e22289fb4b38b5..0000000000000000000000000000000000000000 --- a/spaces/dragonSwing/wav2vec2-vi-asr/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Wav2vec2 Vi Asr -emoji: 🏢 -colorFrom: purple -colorTo: pink -sdk: gradio -app_file: app.py -pinned: false ---- - -# 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. - -`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/duycse1603/math2tex/ScanSSD/gtdb/remove_rect.py b/spaces/duycse1603/math2tex/ScanSSD/gtdb/remove_rect.py deleted file mode 100644 index 6be67c8c8afeea0505f3392964712e00ba6259da..0000000000000000000000000000000000000000 --- a/spaces/duycse1603/math2tex/ScanSSD/gtdb/remove_rect.py +++ /dev/null @@ -1,96 +0,0 @@ -# Author: Parag Mali -# This file contains functions to remove rectangles -# that are inside other rectangles - -import sys -sys.path.extend(['/home/psm2208/code', '/home/psm2208/code']) -import cv2 -import os -import csv -import numpy as np -import utils.visualize as visualize -from multiprocessing import Pool -from cv2.dnn import NMSBoxes -from scipy.ndimage.measurements import label -import scipy.ndimage as ndimage -import copy -import shutil -from gtdb import box_utils - -def remove(args): - - try: - output_dir, pdf_name, page_num, page_math = args - - valid = [True] * page_math.shape[0] - - for i, m1 in enumerate(page_math): - for j, m2 in enumerate(page_math): - if i!=j and box_utils.check_inside(m1, m2): - valid[i] = False - break - - final_math = page_math[valid] - - math_file = open(os.path.join(output_dir, pdf_name + ".csv"), 'a') - writer = csv.writer(math_file, delimiter=",") - - for math_region in final_math: - math_region = math_region.tolist() - math_region.insert(0, page_num) - writer.writerow(math_region) - - print("Saved ", os.path.join(output_dir, pdf_name + ".csv"), " > ", page_num) - print('Before ', len(page_math), '--> after ', len(final_math)) - - except: - print("Exception while processing ", pdf_name, " ", page_num, " ", sys.exc_info()[0]) - - -def remove_rect(filename, math_dir, output_dir): - - if os.path.exists(output_dir): - shutil.rmtree(output_dir) - - if not os.path.exists(output_dir): - os.mkdir(output_dir) - - pages_list = [] - pdf_names = open(filename, 'r') - - for pdf_name in pdf_names: - print('Processing-1', pdf_name) - pdf_name = pdf_name.strip() - - if pdf_name != '': - math_file = os.path.join(math_dir, pdf_name + ".csv") - math_regions = np.genfromtxt(math_file, delimiter=',', dtype=float) - - pages = np.unique(math_regions[:, 0]) - - for page_num in pages: - - page_math = math_regions[np.where(math_regions[:,0]==page_num)] - page_math = page_math[:,1:] - pages_list.append([output_dir, pdf_name, page_num, page_math]) - - pdf_names.close() - - pool = Pool(processes=4) - pool.map(remove, pages_list) - pool.close() - pool.join() - - -if __name__ == "__main__": - home_data = "/home/psm2208/data/GTDB/" - home_eval = "/home/psm2208/code/eval/" - home_images = "/home/psm2208/data/GTDB/images/" - home_anno = "/home/psm2208/data/GTDB/annotations/" - - math_dir = "/home/psm2208/code/eval/tt_samsung" #"/home/psm2208/data/GTDB/relations_train_adjust_csv" - output_dir = "/home/psm2208/code/eval/tt_samsung_removed" #"/home/psm2208/data/GTDB/relations_train_adjust_csv_removed" - - type = sys.argv[1] - - remove_rect(home_data + type, math_dir, output_dir) diff --git a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/cleaners.py b/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/cleaners.py deleted file mode 100644 index c80e113b2b81a66134800dbdaa29c7d96a0152a7..0000000000000000000000000000000000000000 --- a/spaces/eIysia/VITS-Umamusume-voice-synthesizer/text/cleaners.py +++ /dev/null @@ -1,146 +0,0 @@ -import re - - -def japanese_cleaners(text): - from text.japanese import japanese_to_romaji_with_accent - text = japanese_to_romaji_with_accent(text) - text = re.sub(r'([A-Za-z])$', r'\1.', text) - return text - - -def japanese_cleaners2(text): - return japanese_cleaners(text).replace('ts', 'ʦ').replace('...', '…') - - -def korean_cleaners(text): - '''Pipeline for Korean text''' - from text.korean import latin_to_hangul, number_to_hangul, divide_hangul - text = latin_to_hangul(text) - text = number_to_hangul(text) - text = divide_hangul(text) - text = re.sub(r'([\u3131-\u3163])$', r'\1.', text) - return text - - -def chinese_cleaners(text): - '''Pipeline for Chinese text''' - from text.mandarin import number_to_chinese, chinese_to_bopomofo, latin_to_bopomofo - text = number_to_chinese(text) - text = chinese_to_bopomofo(text) - text = latin_to_bopomofo(text) - text = re.sub(r'([ˉˊˇˋ˙])$', r'\1。', text) - return text - - -def zh_ja_mixture_cleaners(text): - from text.mandarin import chinese_to_romaji - from text.japanese import japanese_to_romaji_with_accent - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_romaji(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_romaji_with_accent( - x.group(1)).replace('ts', 'ʦ').replace('u', 'ɯ').replace('...', '…')+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def sanskrit_cleaners(text): - text = text.replace('॥', '।').replace('ॐ', 'ओम्') - if text[-1] != '।': - text += ' ।' - return text - - -def cjks_cleaners(text): - from text.mandarin import chinese_to_lazy_ipa - from text.japanese import japanese_to_ipa - from text.korean import korean_to_lazy_ipa - from text.sanskrit import devanagari_to_ipa - from text.english import english_to_lazy_ipa - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_lazy_ipa(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', - lambda x: japanese_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[KO\](.*?)\[KO\]', - lambda x: korean_to_lazy_ipa(x.group(1))+' ', text) - text = re.sub(r'\[SA\](.*?)\[SA\]', - lambda x: devanagari_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', - lambda x: english_to_lazy_ipa(x.group(1))+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def cjke_cleaners(text): - from text.mandarin import chinese_to_lazy_ipa - from text.japanese import japanese_to_ipa - from text.korean import korean_to_ipa - from text.english import english_to_ipa2 - text = re.sub(r'\[ZH\](.*?)\[ZH\]', lambda x: chinese_to_lazy_ipa(x.group(1)).replace( - 'ʧ', 'tʃ').replace('ʦ', 'ts').replace('ɥan', 'ɥæn')+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', lambda x: japanese_to_ipa(x.group(1)).replace('ʧ', 'tʃ').replace( - 'ʦ', 'ts').replace('ɥan', 'ɥæn').replace('ʥ', 'dz')+' ', text) - text = re.sub(r'\[KO\](.*?)\[KO\]', - lambda x: korean_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', lambda x: english_to_ipa2(x.group(1)).replace('ɑ', 'a').replace( - 'ɔ', 'o').replace('ɛ', 'e').replace('ɪ', 'i').replace('ʊ', 'u')+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def cjke_cleaners2(text): - from text.mandarin import chinese_to_ipa - from text.japanese import japanese_to_ipa2 - from text.korean import korean_to_ipa - from text.english import english_to_ipa2 - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', - lambda x: japanese_to_ipa2(x.group(1))+' ', text) - text = re.sub(r'\[KO\](.*?)\[KO\]', - lambda x: korean_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', - lambda x: english_to_ipa2(x.group(1))+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def thai_cleaners(text): - from text.thai import num_to_thai, latin_to_thai - text = num_to_thai(text) - text = latin_to_thai(text) - return text - - -def shanghainese_cleaners(text): - from text.shanghainese import shanghainese_to_ipa - text = shanghainese_to_ipa(text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text - - -def chinese_dialect_cleaners(text): - from text.mandarin import chinese_to_ipa2 - from text.japanese import japanese_to_ipa3 - from text.shanghainese import shanghainese_to_ipa - from text.cantonese import cantonese_to_ipa - from text.english import english_to_lazy_ipa2 - from text.ngu_dialect import ngu_dialect_to_ipa - text = re.sub(r'\[ZH\](.*?)\[ZH\]', - lambda x: chinese_to_ipa2(x.group(1))+' ', text) - text = re.sub(r'\[JA\](.*?)\[JA\]', - lambda x: japanese_to_ipa3(x.group(1)).replace('Q', 'ʔ')+' ', text) - text = re.sub(r'\[SH\](.*?)\[SH\]', lambda x: shanghainese_to_ipa(x.group(1)).replace('1', '˥˧').replace('5', - '˧˧˦').replace('6', '˩˩˧').replace('7', '˥').replace('8', '˩˨').replace('ᴀ', 'ɐ').replace('ᴇ', 'e')+' ', text) - text = re.sub(r'\[GD\](.*?)\[GD\]', - lambda x: cantonese_to_ipa(x.group(1))+' ', text) - text = re.sub(r'\[EN\](.*?)\[EN\]', - lambda x: english_to_lazy_ipa2(x.group(1))+' ', text) - text = re.sub(r'\[([A-Z]{2})\](.*?)\[\1\]', lambda x: ngu_dialect_to_ipa(x.group(2), x.group( - 1)).replace('ʣ', 'dz').replace('ʥ', 'dʑ').replace('ʦ', 'ts').replace('ʨ', 'tɕ')+' ', text) - text = re.sub(r'\s+$', '', text) - text = re.sub(r'([^\.,!\?\-…~])$', r'\1.', text) - return text diff --git a/spaces/egan/clothing-attribute-recognition/README.md b/spaces/egan/clothing-attribute-recognition/README.md deleted file mode 100644 index 943345a56d2c05ca9b4d626276206db9f4aa21d9..0000000000000000000000000000000000000000 --- a/spaces/egan/clothing-attribute-recognition/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Clothing Attribute Recognition -emoji: 🐨 -colorFrom: blue -colorTo: green -sdk: gradio -sdk_version: 3.4 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/erc/entity-referring-classifier/README.md b/spaces/erc/entity-referring-classifier/README.md deleted file mode 100644 index b51237d182cb5e7adc1ee1110fd2fc062da87cbe..0000000000000000000000000000000000000000 --- a/spaces/erc/entity-referring-classifier/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Entity Referring Classifier -emoji: 🚀 -colorFrom: blue -colorTo: yellow -sdk: streamlit -app_file: app.py -pinned: true ---- - -# 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. - -`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/facebook/MusicGen/audiocraft/modules/seanet.py b/spaces/facebook/MusicGen/audiocraft/modules/seanet.py deleted file mode 100644 index 3e5998e9153afb6e68ea410d565e00ea835db248..0000000000000000000000000000000000000000 --- a/spaces/facebook/MusicGen/audiocraft/modules/seanet.py +++ /dev/null @@ -1,258 +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. - -import typing as tp - -import numpy as np -import torch.nn as nn - -from .conv import StreamableConv1d, StreamableConvTranspose1d -from .lstm import StreamableLSTM - - -class SEANetResnetBlock(nn.Module): - """Residual block from SEANet model. - - Args: - dim (int): Dimension of the input/output. - kernel_sizes (list): List of kernel sizes for the convolutions. - dilations (list): List of dilations for the convolutions. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function. - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - true_skip (bool): Whether to use true skip connection or a simple - (streamable) convolution as the skip connection. - """ - def __init__(self, dim: int, kernel_sizes: tp.List[int] = [3, 1], dilations: tp.List[int] = [1, 1], - activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, causal: bool = False, - pad_mode: str = 'reflect', compress: int = 2, true_skip: bool = True): - super().__init__() - assert len(kernel_sizes) == len(dilations), 'Number of kernel sizes should match number of dilations' - act = getattr(nn, activation) - hidden = dim // compress - block = [] - for i, (kernel_size, dilation) in enumerate(zip(kernel_sizes, dilations)): - in_chs = dim if i == 0 else hidden - out_chs = dim if i == len(kernel_sizes) - 1 else hidden - block += [ - act(**activation_params), - StreamableConv1d(in_chs, out_chs, kernel_size=kernel_size, dilation=dilation, - norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode), - ] - self.block = nn.Sequential(*block) - self.shortcut: nn.Module - if true_skip: - self.shortcut = nn.Identity() - else: - self.shortcut = StreamableConv1d(dim, dim, kernel_size=1, norm=norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode) - - def forward(self, x): - return self.shortcut(x) + self.block(x) - - -class SEANetEncoder(nn.Module): - """SEANet encoder. - - Args: - channels (int): Audio channels. - dimension (int): Intermediate representation dimension. - n_filters (int): Base width for the model. - n_residual_layers (int): nb of residual layers. - ratios (Sequence[int]): kernel size and stride ratios. The encoder uses downsampling ratios instead of - upsampling ratios, hence it will use the ratios in the reverse order to the ones specified here - that must match the decoder order. We use the decoder order as some models may only employ the decoder. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function. - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - kernel_size (int): Kernel size for the initial convolution. - last_kernel_size (int): Kernel size for the initial convolution. - residual_kernel_size (int): Kernel size for the residual layers. - dilation_base (int): How much to increase the dilation with each layer. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - true_skip (bool): Whether to use true skip connection or a simple - (streamable) convolution as the skip connection in the residual network blocks. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - lstm (int): Number of LSTM layers at the end of the encoder. - disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm. - For the encoder, it corresponds to the N first blocks. - """ - def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3, - ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, - last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, - pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0, - disable_norm_outer_blocks: int = 0): - super().__init__() - self.channels = channels - self.dimension = dimension - self.n_filters = n_filters - self.ratios = list(reversed(ratios)) - del ratios - self.n_residual_layers = n_residual_layers - self.hop_length = np.prod(self.ratios) - self.n_blocks = len(self.ratios) + 2 # first and last conv + residual blocks - self.disable_norm_outer_blocks = disable_norm_outer_blocks - assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \ - "Number of blocks for which to disable norm is invalid." \ - "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0." - - act = getattr(nn, activation) - mult = 1 - model: tp.List[nn.Module] = [ - StreamableConv1d(channels, mult * n_filters, kernel_size, - norm='none' if self.disable_norm_outer_blocks >= 1 else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - # Downsample to raw audio scale - for i, ratio in enumerate(self.ratios): - block_norm = 'none' if self.disable_norm_outer_blocks >= i + 2 else norm - # Add residual layers - for j in range(n_residual_layers): - model += [ - SEANetResnetBlock(mult * n_filters, kernel_sizes=[residual_kernel_size, 1], - dilations=[dilation_base ** j, 1], - norm=block_norm, norm_params=norm_params, - activation=activation, activation_params=activation_params, - causal=causal, pad_mode=pad_mode, compress=compress, true_skip=true_skip)] - - # Add downsampling layers - model += [ - act(**activation_params), - StreamableConv1d(mult * n_filters, mult * n_filters * 2, - kernel_size=ratio * 2, stride=ratio, - norm=block_norm, norm_kwargs=norm_params, - causal=causal, pad_mode=pad_mode), - ] - mult *= 2 - - if lstm: - model += [StreamableLSTM(mult * n_filters, num_layers=lstm)] - - model += [ - act(**activation_params), - StreamableConv1d(mult * n_filters, dimension, last_kernel_size, - norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - - self.model = nn.Sequential(*model) - - def forward(self, x): - return self.model(x) - - -class SEANetDecoder(nn.Module): - """SEANet decoder. - - Args: - channels (int): Audio channels. - dimension (int): Intermediate representation dimension. - n_filters (int): Base width for the model. - n_residual_layers (int): nb of residual layers. - ratios (Sequence[int]): kernel size and stride ratios. - activation (str): Activation function. - activation_params (dict): Parameters to provide to the activation function. - final_activation (str): Final activation function after all convolutions. - final_activation_params (dict): Parameters to provide to the activation function. - norm (str): Normalization method. - norm_params (dict): Parameters to provide to the underlying normalization used along with the convolution. - kernel_size (int): Kernel size for the initial convolution. - last_kernel_size (int): Kernel size for the initial convolution. - residual_kernel_size (int): Kernel size for the residual layers. - dilation_base (int): How much to increase the dilation with each layer. - causal (bool): Whether to use fully causal convolution. - pad_mode (str): Padding mode for the convolutions. - true_skip (bool): Whether to use true skip connection or a simple. - (streamable) convolution as the skip connection in the residual network blocks. - compress (int): Reduced dimensionality in residual branches (from Demucs v3). - lstm (int): Number of LSTM layers at the end of the encoder. - disable_norm_outer_blocks (int): Number of blocks for which we don't apply norm. - For the decoder, it corresponds to the N last blocks. - trim_right_ratio (float): Ratio for trimming at the right of the transposed convolution under the causal setup. - If equal to 1.0, it means that all the trimming is done at the right. - """ - def __init__(self, channels: int = 1, dimension: int = 128, n_filters: int = 32, n_residual_layers: int = 3, - ratios: tp.List[int] = [8, 5, 4, 2], activation: str = 'ELU', activation_params: dict = {'alpha': 1.0}, - final_activation: tp.Optional[str] = None, final_activation_params: tp.Optional[dict] = None, - norm: str = 'none', norm_params: tp.Dict[str, tp.Any] = {}, kernel_size: int = 7, - last_kernel_size: int = 7, residual_kernel_size: int = 3, dilation_base: int = 2, causal: bool = False, - pad_mode: str = 'reflect', true_skip: bool = True, compress: int = 2, lstm: int = 0, - disable_norm_outer_blocks: int = 0, trim_right_ratio: float = 1.0): - super().__init__() - self.dimension = dimension - self.channels = channels - self.n_filters = n_filters - self.ratios = ratios - del ratios - self.n_residual_layers = n_residual_layers - self.hop_length = np.prod(self.ratios) - self.n_blocks = len(self.ratios) + 2 # first and last conv + residual blocks - self.disable_norm_outer_blocks = disable_norm_outer_blocks - assert self.disable_norm_outer_blocks >= 0 and self.disable_norm_outer_blocks <= self.n_blocks, \ - "Number of blocks for which to disable norm is invalid." \ - "It should be lower or equal to the actual number of blocks in the network and greater or equal to 0." - - act = getattr(nn, activation) - mult = int(2 ** len(self.ratios)) - model: tp.List[nn.Module] = [ - StreamableConv1d(dimension, mult * n_filters, kernel_size, - norm='none' if self.disable_norm_outer_blocks == self.n_blocks else norm, - norm_kwargs=norm_params, causal=causal, pad_mode=pad_mode) - ] - - if lstm: - model += [StreamableLSTM(mult * n_filters, num_layers=lstm)] - - # Upsample to raw audio scale - for i, ratio in enumerate(self.ratios): - block_norm = 'none' if self.disable_norm_outer_blocks >= self.n_blocks - (i + 1) else norm - # Add upsampling layers - model += [ - act(**activation_params), - StreamableConvTranspose1d(mult * n_filters, mult * n_filters // 2, - kernel_size=ratio * 2, stride=ratio, - norm=block_norm, norm_kwargs=norm_params, - causal=causal, trim_right_ratio=trim_right_ratio), - ] - # Add residual layers - for j in range(n_residual_layers): - model += [ - SEANetResnetBlock(mult * n_filters // 2, kernel_sizes=[residual_kernel_size, 1], - dilations=[dilation_base ** j, 1], - activation=activation, activation_params=activation_params, - norm=block_norm, norm_params=norm_params, causal=causal, - pad_mode=pad_mode, compress=compress, true_skip=true_skip)] - - mult //= 2 - - # Add final layers - model += [ - act(**activation_params), - StreamableConv1d(n_filters, channels, last_kernel_size, - 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        After choosing your platform, you will see a link that says "Download". Click on it and you will start downloading a ZIP file that contains the game. The file size is about 1 GB, so make sure you have enough space and a stable internet connection.

        -

        Step 4: Extract the game file

        -

        Once the download is complete, you will need to extract the ZIP -

        The last step is to install and run the game APK file that you downloaded. To do this, locate the file in your device's file manager and tap on it. You may see a prompt asking you to confirm the installation. Tap on "Install" and wait for the process to finish. Once the installation is done, you can tap on "Open" to launch the game. You can also find the game icon on your home screen or app drawer.

        -

        Conclusion

        -

        Summertime Saga is a game that will keep you entertained and engaged for hours. It has a lot of content and features that will appeal to different tastes and preferences. You can download Summertime Saga for Windows and Android devices by following the steps we have outlined in this article. We hope you enjoy playing the game and discovering its secrets.

        -

        FAQs

        -

        Q: Is Summertime Saga free to play?

        -

        A: Yes, Summertime Saga is free to play. You can download and play the game without paying anything. However, you can support the developers by donating or becoming a patron on their website.

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        Q: Is Summertime Saga safe to download?

        -

        A: Yes, Summertime Saga is safe to download. The game does not contain any viruses or malware. However, you should always download the game from the official website or trusted sources.

        -

        Q: Is Summertime Saga suitable for all ages?

        -

        A: No, Summertime Saga is not suitable for all ages. The game contains mature themes and content, such as nudity, sex, violence, and drugs. The game is intended for adults only and you should play it at your own discretion.

        -

        Q: How often is Summertime Saga updated?

        -

        A: Summertime Saga is updated regularly by the developers. They usually release a new version every few months, adding new content and features to the game. You can check their website or social media for the latest news and updates.

        -

        Q: How can I contact the developers of Summertime Saga?

        -

        A: You can contact the developers of Summertime Saga by visiting their website or social media pages. You can also join their Discord server or Reddit community to interact with them and other players.

        401be4b1e0
        -
        -
        \ No newline at end of file diff --git a/spaces/fb700/chatglm-fitness-RLHF/docs/waifu_plugin/waifu.css b/spaces/fb700/chatglm-fitness-RLHF/docs/waifu_plugin/waifu.css deleted file mode 100644 index 42639df0794e46fc58f66e2c772e2bf9ba605eed..0000000000000000000000000000000000000000 --- a/spaces/fb700/chatglm-fitness-RLHF/docs/waifu_plugin/waifu.css +++ /dev/null @@ -1,290 +0,0 @@ -.waifu { - position: fixed; - bottom: 0; - z-index: 1; - font-size: 0; - -webkit-transform: translateY(3px); - transform: translateY(3px); -} -.waifu:hover { - -webkit-transform: translateY(0); - transform: translateY(0); -} -.waifu-tips { - opacity: 0; - margin: -20px 20px; - padding: 5px 10px; - border: 1px solid rgba(224, 186, 140, 0.62); - border-radius: 12px; - background-color: rgba(236, 217, 188, 0.5); - box-shadow: 0 3px 15px 2px rgba(191, 158, 118, 0.2); - text-overflow: ellipsis; - overflow: hidden; - position: absolute; - animation-delay: 5s; - animation-duration: 50s; - animation-iteration-count: infinite; - animation-name: shake; - animation-timing-function: ease-in-out; -} -.waifu-tool { - display: none; - color: #aaa; - top: 50px; - right: 10px; - position: absolute; -} -.waifu:hover .waifu-tool { - display: block; -} -.waifu-tool span { - display: block; - cursor: pointer; - color: #5b6c7d; - transition: 0.2s; -} -.waifu-tool span:hover { - color: #34495e; -} -.waifu #live2d{ - position: relative; -} - -@keyframes shake { - 2% { - transform: translate(0.5px, -1.5px) rotate(-0.5deg); - } - - 4% { - transform: translate(0.5px, 1.5px) rotate(1.5deg); - } - - 6% { - transform: translate(1.5px, 1.5px) rotate(1.5deg); - } - - 8% { - transform: translate(2.5px, 1.5px) rotate(0.5deg); - } - - 10% { - transform: translate(0.5px, 2.5px) rotate(0.5deg); - } - - 12% { - transform: translate(1.5px, 1.5px) rotate(0.5deg); - } - - 14% { - transform: translate(0.5px, 0.5px) rotate(0.5deg); - } - - 16% { - transform: translate(-1.5px, -0.5px) rotate(1.5deg); - } - - 18% { - transform: translate(0.5px, 0.5px) rotate(1.5deg); 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-} -[class^="fui-"], -[class*="fui-"] { - font-family: 'Flat-UI-Icons'; - speak: none; - font-style: normal; - font-weight: normal; - font-variant: normal; - text-transform: none; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; -} -.fui-cross:before { - content: "\e609"; -} -.fui-info-circle:before { - content: "\e60f"; -} -.fui-photo:before { - content: "\e62a"; -} -.fui-eye:before { - content: "\e62c"; -} -.fui-chat:before { - content: "\e62d"; -} -.fui-home:before { - content: "\e62e"; -} -.fui-user:before { - content: "\e631"; -} \ No newline at end of file diff --git a/spaces/fclong/summary/fengshen/data/bert_dataloader/auto_split.sh b/spaces/fclong/summary/fengshen/data/bert_dataloader/auto_split.sh deleted file mode 100644 index 0a0f66d01df8f1728e44d9deb1d37e0396c5143a..0000000000000000000000000000000000000000 --- a/spaces/fclong/summary/fengshen/data/bert_dataloader/auto_split.sh +++ /dev/null @@ -1,10 +0,0 @@ -files=`find $1 -type f -size +1024M` - -for p in $files -do -echo "processing $p" -name=`basename $p .json` -file=`dirname $p` -split -a 2 -C 300M $p $file/$name- && ls|grep -E "(-[a-zA-Z]{2})" |xargs -n1 -i{} mv {} {}.json -rm -f $p -done \ No newline at end of file diff --git a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Do You Believe In Love by Huey Lewis and The News - MP3 Song with Lyrics.md b/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Do You Believe In Love by Huey Lewis and The News - MP3 Song with Lyrics.md deleted file mode 100644 index 377eac684a1ad7f1f9bcd0a76c2eeba02c2713c0..0000000000000000000000000000000000000000 --- a/spaces/feregVcuzo/sanity-test-midi/checkpoint/Download Do You Believe In Love by Huey Lewis and The News - MP3 Song with Lyrics.md +++ /dev/null @@ -1,130 +0,0 @@ - -

        How to Download Do You Believe in Love MP3

        -

        Do you believe in love? If you do, then you might want to download the MP3 version of the song Do You Believe in Love by Huey Lewis and The News. This song is a classic hit from the 1980s that has been remastered in HD and is still popular today. In this article, we will show you how to download Do You Believe in Love MP3 from different sources, such as YouTube, Archive.org, and other websites and apps. We will also explain what this song is about and why you might want to download it.

        -

        download do you believe in love mp3


        Download Zip 🗹 https://gohhs.com/2uPtcn



        -

        Introduction

        -

        Do You Believe in Love is a song by American rock band Huey Lewis and The News, released in 1982 as the first single from their second album Picture This. The song was written by Robert John "Mutt" Lange, who later became famous for producing albums for artists like AC/DC, Def Leppard, Shania Twain, and Bryan Adams. The song was a success, reaching number seven on the Billboard Hot 100 chart and becoming the band's first top ten hit. The song also has a catchy chorus that asks "Do you believe in love? Do you believe it's true?"

        -

        What is Do You Believe in Love?

        -

        Do You Believe in Love is a song that expresses the feelings of a man who is falling in love with a woman and wants to know if she feels the same way. He sings about how he has been looking for love for a long time and how he finally found it with her. He also sings about how he wants to make her happy and how he hopes she will stay with him forever. He asks her repeatedly if she believes in love and if she believes it's true, implying that he wants her to trust him and his feelings.

        -

        Why download Do You Believe in Love MP3?

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        There are many reasons why you might want to download Do You Believe in Love MP3. Here are some of them:

        -
          -
        • You love the song and want to listen to it anytime, anywhere.
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        • You want to add the song to your playlist or music library.
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        • You want to use the song as your ringtone or alarm tone.
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        • You want to share the song with your friends or family.
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        • You want to enjoy the high-quality sound of the remastered version.
          • -
        -

        No matter what your reason is, downloading Do You Believe in Love MP3 is easy and fast. All you need is a device with an internet connection and some storage space. In the next sections, we will show you how to download Do You Believe in Love MP3 from different sources.

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        How to download Do You Believe in Love MP3 from YouTube

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        One of the most popular sources for downloading Do You Believe in Love MP3 is YouTube. YouTube is a video-sharing platform that hosts millions of videos, including music videos, trailers, documentaries, tutorials, and more. YouTube also allows users to upload their own videos and share them with others. One of the videos that you can find on YouTube is the official music video of Do You Believe in Love by Huey Lewis and The News. This video has over 25 million views and was uploaded by the band's official YouTube channel in 2013. The video shows the band performing the song in a studio, while various scenes of couples in love are shown on a screen behind them. The video also has subtitles in different languages, including English, Spanish, French, German, and Italian. To download Do You Believe in Love MP3 from YouTube, you need to follow these steps:

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        Step 1: Find the official music video on YouTube

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        The first step is to find the official music video of Do You Believe in Love on YouTube. You can do this by typing "Do You Believe in Love Huey Lewis" in the search bar and clicking on the first result. Alternatively, you can use this link: https://www.youtube.com/watch?v=BzIbyDbmsyg

        -

        Step 2: Copy the video URL

        -

        The next step is to copy the video URL from the address bar of your browser. The URL is the web address that starts with "https://" and ends with a series of letters and numbers. For example, the URL of the official music video of Do You Believe in Love is https://www.youtube.com/watch?v=BzIbyDbmsyg. To copy the URL, you can either right-click on it and select "Copy" or highlight it and press Ctrl+C on your keyboard.

        -

        Step 3: Paste the URL into a YouTube to MP3 converter

        -

        The third step is to paste the URL into a YouTube to MP3 converter. A YouTube to MP3 converter is a website or an app that allows you to convert YouTube videos into MP3 files that you can download and save on your device. There are many YouTube to MP3 converters available online, but some of them may not be safe or reliable. Therefore, we recommend using one of these trusted and tested converters:

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        • YTMP3.cc: This is a simple and fast converter that supports both video and audio formats. It also has no ads or pop-ups.
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        • 4K Video Downloader: This is a powerful and versatile converter that allows you to download videos and playlists in high quality. It also has a smart mode that automatically applies your preferred settings.
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        • MP3FY.com: This is an advanced and efficient converter that can handle long videos and large files. It also has a search function that lets you find videos directly from the website.
          • -
        -

        To paste the URL into a YouTube to MP3 converter, you need to go to the website or open the app of your choice and look for a box that says "Paste your video link here" or something similar. Then, you need to right-click on the box and select "Paste" or press Ctrl+V on your keyboard.

        -

        Step 4: Choose the output format and quality

        -

        The fourth step is to choose the output format and quality of your MP3 file. The output format is the type of file that you want to download, such as MP3, MP4, WAV, etc. The output quality is the level of sound clarity and detail that you want to have, such as 128 kbps, 192 kbps, 320 kbps, etc. Generally, the higher the quality, the larger the file size and the longer the download time.

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        To choose the output format and quality of your MP3 file, you need to look for a drop-down menu or a button that says "Format", "Quality", "Settings", or something similar. Then, you need to click on it and select your preferred options. For example, if you want to download Do You Believe in Love MP3 in high quality, you can choose MP3 as the format and 320 kbps as the quality.

        -

        Step 5: Download and save the MP3 file

        -

        The final step is to download and save the MP3 file on your device. To do this, you need to look for a button that says "Download", "Convert", "Start", or something similar. Then, you need to click on it and wait for the conversion process to finish. Once it is done, you will see a link or a button that says "Download your converted file", "Download MP3", "Save file", or something similar. Then, you need to click on it and choose where you want to save your file on your device. For example, if you want to save Do You Believe in Love MP3 in your music folder, you can select that folder and click on "Save".

        -

        Congratulations! You have successfully downloaded Do You Believe in Love MP3 from YouTube. You can now enjoy listening to this song anytime, anywhere.

        -

        How to download Do You Believe in Love MP3 from Archive.org

        -

        Another source for downloading Do You Believe in Love MP3 is Archive.org. Archive.org is a website that provides free access to millions of digital items, such as books, movies, music, software, and more. Archive.org also preserves and archives historical and cultural artifacts for future generations. One of the items that you can find on Archive.org is the audio file of Do You Believe in Love by Huey Lewis and The News. This file was uploaded by a user named "jimmyjames" in 2011 and has over 10,000 views. The file is part of a collection called "Community Audio" that contains user-generated audio content. To download Do You Believe in Love MP3 from Archive.org, you need to follow these steps:

        -

        Step 1: Go to the Archive.org website

        -

        The first step is to go to the Archive.org website. You can do this by typing "archive.org" in the address bar of your browser and pressing Enter. Alternatively, you can use this link: https://archive.org/

        -

        Step 2: Search for Huey Lewis And The News Do You Believe In Love

        -

        The next step is to search for Huey Lewis And The News Do You Believe In Love on the Archive.org website. You can do this by typing "Huey Lewis And The News Do You Believe In Love" in the search bar at the top of the page and clicking on the magnifying glass icon. Alternatively, you can use this link: https://archive.org/search.php?query=Huey+Lewis+And+The+News+Do+You+Believe+In+Love

        -

        Step 3: Click on the download options

        -

        The third step is to click on the download options for the audio file of Do You Believe in Love by Huey Lewis and The News. You can do this by scrolling down the page and looking for a result that says "Huey Lewis And The News - Do You Believe In Love". Then, you need to click on the downward arrow icon next to the result. This will open a drop-down menu that shows different download options, such as VBR MP3, OGG VORBIS, TORRENT, etc.

        -

        Step 4: Choose the MP3 format and download the file

        -

        The fourth step is to choose the MP3 format and download the file from the drop-down menu. The MP3 format is the most common and compatible audio format that works on most devices and players. To choose the MP3 format and download the file, you need to click on the option that says "VBR MP3". This will start the download process and save the file on your device.

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        Congratulations! You have successfully downloaded Do You Believe in Love MP3 from Archive.org. You can now enjoy listening to this song anytime, anywhere.

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        How to download Do You Believe in Love MP3 from other sources

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        Besides YouTube and Archive.org, there are other sources for downloading Do You Believe in Love MP3. Some of these sources are:

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        Ali Gatie, Marshmello & Ty Dolla $ign – Do You Believe

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        If you are looking for a modern version of Do You Believe in Love, you might want to check out Do You Believe by Ali Gatie, Marshmello & Ty Dolla $ign. This is a song that was released in 2021 as part of Ali Gatie's album The Idea of Her. The song features vocals from Canadian singer Ali Gatie, American DJ Marshmello, and American rapper Ty Dolla $ign. The song is a romantic ballad that asks "Do you believe in love? And all the things that we dream of?" The song also has a music video that shows the three artists singing in different locations, such as a beach, a rooftop, and a studio.

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        To download Do You Believe by Ali Gatie, Marshmello & Ty Dolla $ign, you can use one of these links:

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        Other websites and apps that offer MP3 downloads

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        There are also other websites and apps that offer MP3 downloads of various songs, including Do You Believe in Love by Huey Lewis and The News. However, some of these websites and apps may not be legal, safe, or reliable. Therefore, we advise you to be careful and use them at your own risk. Some of the websites and apps that offer MP3 downloads are:

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        Conclusion

        -

        In conclusion, Do You Believe in Love is a song by Huey Lewis and The News that was released in 1982 and became a hit. The song is about a man who is falling in love with a woman and wants to know if she believes in love too. The song has a catchy chorus and a remastered version in HD. You can download Do You Believe in Love MP3 from different sources, such as YouTube, Archive.org, and other websites and apps. However, you need to be careful and use only trusted and tested sources. We hope this article has helped you learn how to download Do You Believe in Love MP3 and enjoy this song anytime, anywhere.

        -

        FAQs

        -

        Here are some frequently asked questions about Do You Believe in Love MP3:

        -

        Q: Who wrote Do You Believe in Love?

        -

        A: Do You Believe in Love was written by Robert John "Mutt" Lange, who later became famous for producing albums for artists like AC/DC, Def Leppard, Shania Twain, and Bryan Adams.

        -

        Q: Who sang Do You Believe in Love?

        -

        A: Do You Believe in Love was sung by Huey Lewis and The News, an American rock band that was formed in 1979. The band consists of Huey Lewis (lead vocals and harmonica), Johnny Colla (saxophone and guitar), Bill Gibson (drums), Sean Hopper (keyboards), Stef Burns (guitar), and John Pierce (bass).

        -

        Q: When was Do You Believe in Love released?

        -

        A: Do You Believe in Love was released in 1982 as the first single from the band's second album Picture This. The song was a success, reaching number seven on the Billboard Hot 100 chart and becoming the band's first top ten hit.

        -

        Q: What is the meaning of Do You Believe in Love?

        -

        A: Do You Believe in Love is a song that expresses the feelings of a man who is falling in love with a woman and wants to know if she believes in love too. He sings about how he has been looking for love for a long time and how he finally found it with her. He also sings about how he wants to make her happy and how he hopes she will stay with him forever. He asks her repeatedly if she believes in love and if she believes it's true, implying that he wants her to trust him and his feelings.

        -

        Q: How can I download Do You Believe in Love MP3?

        -

        A: You can download Do You Believe in Love MP3 from different sources, such as YouTube, Archive.org , and other websites and apps. However, you need to be careful and use only trusted and tested sources. To download Do You Believe in Love MP3 from YouTube, you need to find the official music video on YouTube, copy the video URL, paste the URL into a YouTube to MP3 converter, choose the output format and quality, and download and save the MP3 file. To download Do You Believe in Love MP3 from Archive.org, you need to go to the Archive.org website, search for Huey Lewis And The News Do You Believe In Love, click on the download options, choose the MP3 format, and download the file. To download Do You Believe in Love MP3 from other sources, you need to find a website or an app that offers MP3 downloads of various songs, such as Ali Gatie, Marshmello & Ty Dolla $ign – Do You Believe, MP3Juices.cc, FreeMP3Cloud.com, MP3Skull.com, MP3Quack.com, Music Downloader, MP3 Music Downloader & Free Music Download, or Free Music Downloader & Mp3 Music Download.

        401be4b1e0
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        \ No newline at end of file diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ee-first/index.js b/spaces/fffiloni/controlnet-animation-doodle/node_modules/ee-first/index.js deleted file mode 100644 index 501287cd3b7024435d85a872bb1ba0b234db8e7f..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/ee-first/index.js +++ /dev/null @@ -1,95 +0,0 @@ -/*! - * ee-first - * Copyright(c) 2014 Jonathan Ong - * MIT Licensed - */ - -'use strict' - -/** - * Module exports. - * @public - */ - -module.exports = first - -/** - * Get the first event in a set of event emitters and event pairs. - * - * @param {array} stuff - * @param {function} done - * @public - */ - -function first(stuff, done) { - if (!Array.isArray(stuff)) - throw new TypeError('arg must be an array of [ee, events...] arrays') - - var cleanups = [] - - for (var i = 0; i < stuff.length; i++) { - var arr = stuff[i] - - if (!Array.isArray(arr) || arr.length < 2) - throw new TypeError('each array member must be [ee, events...]') - - var ee = arr[0] - - for (var j = 1; j < arr.length; j++) { - var event = arr[j] - var fn = listener(event, callback) - - // listen to the event - ee.on(event, fn) - // push this listener to the list of cleanups - cleanups.push({ - ee: ee, - event: event, - fn: fn, - }) - } - } - - function callback() { - cleanup() - done.apply(null, arguments) - } - - function cleanup() { - var x - for (var i = 0; i < cleanups.length; i++) { - x = cleanups[i] - x.ee.removeListener(x.event, x.fn) - } - } - - function thunk(fn) { - done = fn - } - - thunk.cancel = cleanup - - return thunk -} - -/** - * Create the event listener. - * @private - */ - -function listener(event, done) { - return function onevent(arg1) { - var args = new Array(arguments.length) - var ee = this - var err = event === 'error' - ? arg1 - : null - - // copy args to prevent arguments escaping scope - for (var i = 0; i < args.length; i++) { - args[i] = arguments[i] - } - - done(err, ee, event, args) - } -} diff --git a/spaces/fffiloni/controlnet-animation-doodle/node_modules/methods/HISTORY.md b/spaces/fffiloni/controlnet-animation-doodle/node_modules/methods/HISTORY.md deleted file mode 100644 index c0ecf072db3f9809c46c83f5641b5df99c686bbf..0000000000000000000000000000000000000000 --- a/spaces/fffiloni/controlnet-animation-doodle/node_modules/methods/HISTORY.md +++ /dev/null @@ -1,29 +0,0 @@ -1.1.2 / 2016-01-17 -================== - - * perf: enable strict mode - -1.1.1 / 2014-12-30 -================== - - * Improve `browserify` support - -1.1.0 / 2014-07-05 -================== - - * Add `CONNECT` method - -1.0.1 / 2014-06-02 -================== - - * Fix module to work with harmony transform - -1.0.0 / 2014-05-08 -================== - - * Add `PURGE` method - -0.1.0 / 2013-10-28 -================== - - * Add `http.METHODS` support diff --git a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_51.py b/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_51.py deleted file mode 100644 index 0caf6e6a6ac350cf62bcda6ac3688ee644e29f40..0000000000000000000000000000000000000000 --- a/spaces/fgenie/scamtext_PAL_self_consistency/funcs/f_51.py +++ /dev/null @@ -1,19 +0,0 @@ - -import re - -def is_spam(message: str) -> bool: - # Patterns to detect spam - url_pattern = re.compile(r'https?://\S+|www\.\S+') # URLs - num_pattern = re.compile(r'\d{4,}') # Large numbers (4 or more digits) - special_char_pattern = re.compile(r'[!"#$%&\'()*+,-./[\\\]^_`{|}~]') # Special characters - - # Filters to identify spam - has_url = bool(url_pattern.search(message)) - has_long_num = bool(num_pattern.search(message)) - has_special_chars = bool(special_char_pattern.search(message)) - - # If the message contains URLs, large numbers or special chars, classify it as spam - if has_url or has_long_num or has_special_chars: - return True - else: - return False diff --git a/spaces/firzaelbuho/rvc-models/infer_pack/transforms.py b/spaces/firzaelbuho/rvc-models/infer_pack/transforms.py deleted file mode 100644 index a11f799e023864ff7082c1f49c0cc18351a13b47..0000000000000000000000000000000000000000 --- a/spaces/firzaelbuho/rvc-models/infer_pack/transforms.py +++ /dev/null @@ -1,209 +0,0 @@ -import torch -from torch.nn import functional as F - -import numpy as np - - -DEFAULT_MIN_BIN_WIDTH = 1e-3 -DEFAULT_MIN_BIN_HEIGHT = 1e-3 -DEFAULT_MIN_DERIVATIVE = 1e-3 - - -def piecewise_rational_quadratic_transform( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails=None, - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if tails is None: - spline_fn = rational_quadratic_spline - spline_kwargs = {} - else: - spline_fn = unconstrained_rational_quadratic_spline - spline_kwargs = {"tails": tails, "tail_bound": tail_bound} - - outputs, logabsdet = spline_fn( - inputs=inputs, - unnormalized_widths=unnormalized_widths, - unnormalized_heights=unnormalized_heights, - unnormalized_derivatives=unnormalized_derivatives, - inverse=inverse, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - **spline_kwargs - ) - return outputs, logabsdet - - -def searchsorted(bin_locations, inputs, eps=1e-6): - bin_locations[..., -1] += eps - return torch.sum(inputs[..., None] >= bin_locations, dim=-1) - 1 - - -def unconstrained_rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - tails="linear", - tail_bound=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - inside_interval_mask = (inputs >= -tail_bound) & (inputs <= tail_bound) - outside_interval_mask = ~inside_interval_mask - - outputs = torch.zeros_like(inputs) - logabsdet = torch.zeros_like(inputs) - - if tails == "linear": - unnormalized_derivatives = F.pad(unnormalized_derivatives, pad=(1, 1)) - constant = np.log(np.exp(1 - min_derivative) - 1) - unnormalized_derivatives[..., 0] = constant - unnormalized_derivatives[..., -1] = constant - - outputs[outside_interval_mask] = inputs[outside_interval_mask] - logabsdet[outside_interval_mask] = 0 - else: - raise RuntimeError("{} tails are not implemented.".format(tails)) - - ( - outputs[inside_interval_mask], - logabsdet[inside_interval_mask], - ) = rational_quadratic_spline( - inputs=inputs[inside_interval_mask], - unnormalized_widths=unnormalized_widths[inside_interval_mask, :], - unnormalized_heights=unnormalized_heights[inside_interval_mask, :], - unnormalized_derivatives=unnormalized_derivatives[inside_interval_mask, :], - inverse=inverse, - left=-tail_bound, - right=tail_bound, - bottom=-tail_bound, - top=tail_bound, - min_bin_width=min_bin_width, - min_bin_height=min_bin_height, - min_derivative=min_derivative, - ) - - return outputs, logabsdet - - -def rational_quadratic_spline( - inputs, - unnormalized_widths, - unnormalized_heights, - unnormalized_derivatives, - inverse=False, - left=0.0, - right=1.0, - bottom=0.0, - top=1.0, - min_bin_width=DEFAULT_MIN_BIN_WIDTH, - min_bin_height=DEFAULT_MIN_BIN_HEIGHT, - min_derivative=DEFAULT_MIN_DERIVATIVE, -): - if torch.min(inputs) < left or torch.max(inputs) > right: - raise ValueError("Input to a transform is not within its domain") - - num_bins = unnormalized_widths.shape[-1] - - if min_bin_width * num_bins > 1.0: - raise ValueError("Minimal bin width too large for the number of bins") - if min_bin_height * num_bins > 1.0: - raise ValueError("Minimal bin height too large for the number of bins") - - widths = F.softmax(unnormalized_widths, dim=-1) - widths = min_bin_width + (1 - min_bin_width * num_bins) * widths - cumwidths = torch.cumsum(widths, dim=-1) - cumwidths = F.pad(cumwidths, pad=(1, 0), mode="constant", value=0.0) - cumwidths = (right - left) * cumwidths + left - cumwidths[..., 0] = left - cumwidths[..., -1] = right - widths = cumwidths[..., 1:] - cumwidths[..., :-1] - - derivatives = min_derivative + F.softplus(unnormalized_derivatives) - - heights = F.softmax(unnormalized_heights, dim=-1) - heights = min_bin_height + (1 - min_bin_height * num_bins) * heights - cumheights = torch.cumsum(heights, dim=-1) - cumheights = F.pad(cumheights, pad=(1, 0), mode="constant", value=0.0) - cumheights = (top - bottom) * cumheights + bottom - cumheights[..., 0] = bottom - cumheights[..., -1] = top - heights = cumheights[..., 1:] - cumheights[..., :-1] - - if inverse: - bin_idx = searchsorted(cumheights, inputs)[..., None] - else: - bin_idx = searchsorted(cumwidths, inputs)[..., None] - - input_cumwidths = cumwidths.gather(-1, bin_idx)[..., 0] - input_bin_widths = widths.gather(-1, bin_idx)[..., 0] - - input_cumheights = cumheights.gather(-1, bin_idx)[..., 0] - delta = heights / widths - input_delta = delta.gather(-1, bin_idx)[..., 0] - - input_derivatives = derivatives.gather(-1, bin_idx)[..., 0] - input_derivatives_plus_one = derivatives[..., 1:].gather(-1, bin_idx)[..., 0] - - input_heights = heights.gather(-1, bin_idx)[..., 0] - - if inverse: - a = (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) + input_heights * (input_delta - input_derivatives) - b = input_heights * input_derivatives - (inputs - input_cumheights) * ( - input_derivatives + input_derivatives_plus_one - 2 * input_delta - ) - c = -input_delta * (inputs - input_cumheights) - - discriminant = b.pow(2) - 4 * a * c - assert (discriminant >= 0).all() - - root = (2 * c) / (-b - torch.sqrt(discriminant)) - outputs = root * input_bin_widths + input_cumwidths - - theta_one_minus_theta = root * (1 - root) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * root.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - root).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, -logabsdet - else: - theta = (inputs - input_cumwidths) / input_bin_widths - theta_one_minus_theta = theta * (1 - theta) - - numerator = input_heights * ( - input_delta * theta.pow(2) + input_derivatives * theta_one_minus_theta - ) - denominator = input_delta + ( - (input_derivatives + input_derivatives_plus_one - 2 * input_delta) - * theta_one_minus_theta - ) - outputs = input_cumheights + numerator / denominator - - derivative_numerator = input_delta.pow(2) * ( - input_derivatives_plus_one * theta.pow(2) - + 2 * input_delta * theta_one_minus_theta - + input_derivatives * (1 - theta).pow(2) - ) - logabsdet = torch.log(derivative_numerator) - 2 * torch.log(denominator) - - return outputs, logabsdet diff --git a/spaces/florim/MedGPT/autogpt/spinner.py b/spaces/florim/MedGPT/autogpt/spinner.py deleted file mode 100644 index 4e33d74213881352546f334ccb1eb4772b8b7b70..0000000000000000000000000000000000000000 --- a/spaces/florim/MedGPT/autogpt/spinner.py +++ /dev/null @@ -1,65 +0,0 @@ -"""A simple spinner module""" -import itertools -import sys -import threading -import time - - -class Spinner: - """A simple spinner class""" - - def __init__(self, message: str = "Loading...", delay: float = 0.1) -> None: - """Initialize the spinner class - - Args: - message (str): The message to display. - delay (float): The delay between each spinner update. - """ - self.spinner = itertools.cycle(["-", "/", "|", "\\"]) - self.delay = delay - self.message = message - self.running = False - self.spinner_thread = None - - def spin(self) -> None: - """Spin the spinner""" - while self.running: - sys.stdout.write(f"{next(self.spinner)} {self.message}\r") - sys.stdout.flush() - time.sleep(self.delay) - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - - def __enter__(self): - """Start the spinner""" - self.running = True - self.spinner_thread = threading.Thread(target=self.spin) - self.spinner_thread.start() - - return self - - def __exit__(self, exc_type, exc_value, exc_traceback) -> None: - """Stop the spinner - - Args: - exc_type (Exception): The exception type. - exc_value (Exception): The exception value. - exc_traceback (Exception): The exception traceback. - """ - self.running = False - if self.spinner_thread is not None: - self.spinner_thread.join() - sys.stdout.write(f"\r{' ' * (len(self.message) + 2)}\r") - sys.stdout.flush() - - def update_message(self, new_message, delay=0.1): - """Update the spinner message - Args: - new_message (str): New message to display - delay: Delay in seconds before updating the message - """ - time.sleep(delay) - sys.stdout.write( - f"\r{' ' * (len(self.message) + 2)}\r" - ) # Clear the current message - sys.stdout.flush() - self.message = new_message diff --git a/spaces/gerhug/dalle-mini/html2canvas.js b/spaces/gerhug/dalle-mini/html2canvas.js deleted file mode 100644 index 96e2dc5707b1a584ff7b3b583aea7c6c18d4ea76..0000000000000000000000000000000000000000 --- a/spaces/gerhug/dalle-mini/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 - - - -
        - -
        - - - \ No newline at end of file diff --git a/spaces/omdenatopekachapter/left_ejection_fraction/home_page.py b/spaces/omdenatopekachapter/left_ejection_fraction/home_page.py deleted file mode 100644 index aeb776068d720b70dcd86621211438ad0820c143..0000000000000000000000000000000000000000 --- a/spaces/omdenatopekachapter/left_ejection_fraction/home_page.py +++ /dev/null @@ -1,79 +0,0 @@ -import streamlit as st - -def home_page(): - - #st.set_page_config(page_title="Home Page", page_icon=":heart:") - - - st.markdown( - f""" - - """, - unsafe_allow_html=True, - ) - - st.write(""" -
        -

        Welcome to LVEF Assessment App

        -

        This app is designed to help medical professionals assess left ventricular ejection fraction (LVEF), which is a measure of how well the heart is functioning. The app takes in data on a patient's echocardiogram and uses it to calculate LVEF. This can help doctors make more accurate diagnoses and better treatment plans.

        -
        - """, - unsafe_allow_html=True) - - # Add the image below the text - #st.image("https://www.usbiotek.com/hubfs/heart%20health.jpg", width=400, use_column_width=True) - - # Add a continue button to navigate to Page 2 - #st.markdown( - # """ - #
        - # - #
        - #""", - #unsafe_allow_html=True, - #) - - st.markdown( - """ -
        - - -
        - """, - unsafe_allow_html=True, - ) diff --git a/spaces/omlab/vlchecklist_demo/models/vilt/datasets/vqav2_dataset.py b/spaces/omlab/vlchecklist_demo/models/vilt/datasets/vqav2_dataset.py deleted file mode 100644 index f802deadb3e274d2255aaa71b323476585f3c7bc..0000000000000000000000000000000000000000 --- a/spaces/omlab/vlchecklist_demo/models/vilt/datasets/vqav2_dataset.py +++ /dev/null @@ -1,47 +0,0 @@ -from .base_dataset import BaseDataset - - -class VQAv2Dataset(BaseDataset): - def __init__(self, *args, split="", **kwargs): - assert split in ["train", "val", "test"] - self.split = split - - if split == "train": - names = ["vqav2_train", "vqav2_trainable_val"] - elif split == "val": - names = ["vqav2_rest_val"] - elif split == "test": - names = ["vqav2_test"] # vqav2_test-dev for test-dev - - super().__init__( - *args, - **kwargs, - names=names, - text_column_name="questions", - remove_duplicate=False, - ) - - def __getitem__(self, index): - image_tensor = self.get_image(index)["image"] - text = self.get_text(index)["text"] - - index, question_index = self.index_mapper[index] - qid = self.table["question_id"][index][question_index].as_py() - - if self.split != "test": - answers = self.table["answers"][index][question_index].as_py() - labels = self.table["answer_labels"][index][question_index].as_py() - scores = self.table["answer_scores"][index][question_index].as_py() - else: - answers = list() - labels = list() - scores = list() - - return { - "image": image_tensor, - "text": text, - "vqa_answer": answers, - "vqa_labels": labels, - "vqa_scores": scores, - "qid": qid, - } diff --git a/spaces/onereal/rvc-models-convertvoice/config.py b/spaces/onereal/rvc-models-convertvoice/config.py deleted file mode 100644 index 26e5c63e0ea863a89453424ab8fc3190151b79a7..0000000000000000000000000000000000000000 --- a/spaces/onereal/rvc-models-convertvoice/config.py +++ /dev/null @@ -1,120 +0,0 @@ -import argparse -import torch -from multiprocessing import cpu_count - - -class Config: - def __init__(self): - self.device = "cuda:0" - self.is_half = True - self.n_cpu = 0 - self.gpu_name = None - self.gpu_mem = None - ( - self.python_cmd, - self.listen_port, - self.colab, - self.noparallel, - self.noautoopen, - self.api - ) = self.arg_parse() - self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config() - - @staticmethod - def arg_parse() -> tuple: - parser = argparse.ArgumentParser() - parser.add_argument("--port", type=int, default=7865, help="Listen port") - parser.add_argument( - "--pycmd", type=str, default="python", help="Python command" - ) - parser.add_argument("--colab", action="store_true", help="Launch in colab") - parser.add_argument( - "--noparallel", action="store_true", help="Disable parallel processing" - ) - parser.add_argument( - "--noautoopen", - action="store_true", - help="Do not open in browser automatically", - ) - parser.add_argument('--api', action="store_true", default=False) - cmd_opts = parser.parse_args() - - cmd_opts.port = cmd_opts.port if 0 <= cmd_opts.port <= 65535 else 7865 - - return ( - cmd_opts.pycmd, - cmd_opts.port, - cmd_opts.colab, - cmd_opts.noparallel, - cmd_opts.noautoopen, - cmd_opts.api, - ) - - def device_config(self) -> tuple: - if torch.cuda.is_available(): - i_device = int(self.device.split(":")[-1]) - self.gpu_name = torch.cuda.get_device_name(i_device) - if ( - ("16" in self.gpu_name and "V100" not in self.gpu_name.upper()) - or "P40" in self.gpu_name.upper() - or "1060" in self.gpu_name - or "1070" in self.gpu_name - or "1080" in self.gpu_name - ): - print("16系/10系显卡和P40强制单精度") - self.is_half = False - for config_file in ["32k.json", "40k.json", "48k.json"]: - with open(f"configs/{config_file}", "r") as f: - strr = f.read().replace("true", "false") - with open(f"configs/{config_file}", "w") as f: - f.write(strr) - with open("trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open("trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - else: - self.gpu_name = None - self.gpu_mem = int( - torch.cuda.get_device_properties(i_device).total_memory - / 1024 - / 1024 - / 1024 - + 0.4 - ) - if self.gpu_mem <= 4: - with open("trainset_preprocess_pipeline_print.py", "r") as f: - strr = f.read().replace("3.7", "3.0") - with open("trainset_preprocess_pipeline_print.py", "w") as f: - f.write(strr) - elif torch.backends.mps.is_available(): - print("没有发现支持的N卡, 使用MPS进行推理") - self.device = "mps" - self.is_half = False - else: - print("没有发现支持的N卡, 使用CPU进行推理") - self.device = "cpu" - self.is_half = False - - if self.n_cpu == 0: - self.n_cpu = cpu_count() - - if self.is_half: - # 6G显存配置 - x_pad = 3 - x_query = 10 - x_center = 60 - x_max = 65 - else: - # 5G显存配置 - x_pad = 1 - x_query = 6 - x_center = 38 - x_max = 41 - - if self.gpu_mem != None and self.gpu_mem <= 4: - x_pad = 1 - x_query = 5 - x_center = 30 - x_max = 32 - - return x_pad, x_query, x_center, x_max \ No newline at end of file diff --git a/spaces/openMUSE/MUSE-vs-SD.1.5/README.md b/spaces/openMUSE/MUSE-vs-SD.1.5/README.md deleted file mode 100644 index 1cbce0776b8a9e07b181f3a150267bd45a209c2f..0000000000000000000000000000000000000000 --- a/spaces/openMUSE/MUSE-vs-SD.1.5/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: MUSE Compare -emoji: 📊 -colorFrom: gray -colorTo: red -sdk: gradio -sdk_version: 3.44.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/osanseviero/hugging-pic/README.md b/spaces/osanseviero/hugging-pic/README.md deleted file mode 100644 index 009b1a90c5056e0694ae9331c10c812bd8dbecc2..0000000000000000000000000000000000000000 --- a/spaces/osanseviero/hugging-pic/README.md +++ /dev/null @@ -1,37 +0,0 @@ ---- -title: Hugging Pic -emoji: 📊 -colorFrom: blue -colorTo: gray -sdk: gradio -app_file: app.py -pinned: false ---- - -# 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. - -`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/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/models/autoencoder_tiny.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/models/autoencoder_tiny.md deleted file mode 100644 index 9b97b6e8e999d2a0c932a06390e48e07dbff65d2..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/en/api/models/autoencoder_tiny.md +++ /dev/null @@ -1,45 +0,0 @@ -# Tiny AutoEncoder - -Tiny AutoEncoder for Stable Diffusion (TAESD) was introduced in [madebyollin/taesd](https://github.com/madebyollin/taesd) by Ollin Boer Bohan. It is a tiny distilled version of Stable Diffusion's VAE that can quickly decode the latents in a [`StableDiffusionPipeline`] or [`StableDiffusionXLPipeline`] almost instantly. - -To use with Stable Diffusion v-2.1: - -```python -import torch -from diffusers import DiffusionPipeline, AutoencoderTiny - -pipe = DiffusionPipeline.from_pretrained( - "stabilityai/stable-diffusion-2-1-base", torch_dtype=torch.float16 -) -pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesd", torch_dtype=torch.float16) -pipe = pipe.to("cuda") - -prompt = "slice of delicious New York-style berry cheesecake" -image = pipe(prompt, num_inference_steps=25).images[0] -image.save("cheesecake.png") -``` - -To use with Stable Diffusion XL 1.0 - -```python -import torch -from diffusers import DiffusionPipeline, AutoencoderTiny - -pipe = DiffusionPipeline.from_pretrained( - "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 -) -pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taesdxl", torch_dtype=torch.float16) -pipe = pipe.to("cuda") - -prompt = "slice of delicious New York-style berry cheesecake" -image = pipe(prompt, num_inference_steps=25).images[0] -image.save("cheesecake_sdxl.png") -``` - -## AutoencoderTiny - -[[autodoc]] AutoencoderTiny - -## AutoencoderTinyOutput - -[[autodoc]] models.autoencoder_tiny.AutoencoderTinyOutput \ No newline at end of file diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/training/unconditional_training.md b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/training/unconditional_training.md deleted file mode 100644 index 62c846311114a08d15b05994a6694ad44d16542e..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/docs/source/ko/training/unconditional_training.md +++ /dev/null @@ -1,144 +0,0 @@ - - -# Unconditional 이미지 생성 - -unconditional 이미지 생성은 text-to-image 또는 image-to-image 모델과 달리 텍스트나 이미지에 대한 조건이 없이 학습 데이터 분포와 유사한 이미지만을 생성합니다. - - - - -이 가이드에서는 기존에 존재하던 데이터셋과 자신만의 커스텀 데이터셋에 대해 unconditional image generation 모델을 훈련하는 방법을 설명합니다. 훈련 세부 사항에 대해 더 자세히 알고 싶다면 unconditional image generation을 위한 모든 학습 스크립트를 [여기](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)에서 확인할 수 있습니다. - -스크립트를 실행하기 전, 먼저 의존성 라이브러리들을 설치해야 합니다. - -```bash -pip install diffusers[training] accelerate datasets -``` - -그 다음 🤗 [Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화합니다. - -```bash -accelerate config -``` - -별도의 설정 없이 기본 설정으로 🤗 [Accelerate](https://github.com/huggingface/accelerate/) 환경을 초기화해봅시다. - -```bash -accelerate config default -``` - -노트북과 같은 대화형 쉘을 지원하지 않는 환경의 경우, 다음과 같이 사용해볼 수도 있습니다. - -```py -from accelerate.utils import write_basic_config - -write_basic_config() -``` - -## 모델을 허브에 업로드하기 - -학습 스크립트에 다음 인자를 추가하여 허브에 모델을 업로드할 수 있습니다. - -```bash ---push_to_hub -``` - -## 체크포인트 저장하고 불러오기 - -훈련 중 문제가 발생할 경우를 대비하여 체크포인트를 정기적으로 저장하는 것이 좋습니다. 체크포인트를 저장하려면 학습 스크립트에 다음 인자를 전달합니다: - -```bash ---checkpointing_steps=500 -``` - -전체 훈련 상태는 500스텝마다 `output_dir`의 하위 폴더에 저장되며, 학습 스크립트에 `--resume_from_checkpoint` 인자를 전달함으로써 체크포인트를 불러오고 훈련을 재개할 수 있습니다. - -```bash ---resume_from_checkpoint="checkpoint-1500" -``` - -## 파인튜닝 - -이제 학습 스크립트를 시작할 준비가 되었습니다! `--dataset_name` 인자에 파인튜닝할 데이터셋 이름을 지정한 다음, `--output_dir` 인자에 지정된 경로로 저장합니다. 본인만의 데이터셋를 사용하려면, [학습용 데이터셋 만들기](create_dataset) 가이드를 참조하세요. - -학습 스크립트는 `diffusion_pytorch_model.bin` 파일을 생성하고, 그것을 당신의 리포지토리에 저장합니다. - - - -💡 전체 학습은 V100 GPU 4개를 사용할 경우, 2시간이 소요됩니다. - - - -예를 들어, [Oxford Flowers](https://huggingface.co/datasets/huggan/flowers-102-categories) 데이터셋을 사용해 파인튜닝할 경우: - -```bash -accelerate launch train_unconditional.py \ - --dataset_name="huggan/flowers-102-categories" \ - --resolution=64 \ - --output_dir="ddpm-ema-flowers-64" \ - --train_batch_size=16 \ - --num_epochs=100 \ - --gradient_accumulation_steps=1 \ - --learning_rate=1e-4 \ - --lr_warmup_steps=500 \ - --mixed_precision=no \ - --push_to_hub -``` - -
        - -
        -[Pokemon](https://huggingface.co/datasets/huggan/pokemon) 데이터셋을 사용할 경우: - -```bash -accelerate launch train_unconditional.py \ - --dataset_name="huggan/pokemon" \ - --resolution=64 \ - --output_dir="ddpm-ema-pokemon-64" \ - --train_batch_size=16 \ - --num_epochs=100 \ - --gradient_accumulation_steps=1 \ - --learning_rate=1e-4 \ - --lr_warmup_steps=500 \ - --mixed_precision=no \ - --push_to_hub -``` - -
        - -
        - -### 여러개의 GPU로 훈련하기 - -`accelerate`을 사용하면 원활한 다중 GPU 훈련이 가능합니다. `accelerate`을 사용하여 분산 훈련을 실행하려면 [여기](https://huggingface.co/docs/accelerate/basic_tutorials/launch) 지침을 따르세요. 다음은 명령어 예제입니다. - -```bash -accelerate launch --mixed_precision="fp16" --multi_gpu train_unconditional.py \ - --dataset_name="huggan/pokemon" \ - --resolution=64 --center_crop --random_flip \ - --output_dir="ddpm-ema-pokemon-64" \ - --train_batch_size=16 \ - --num_epochs=100 \ - --gradient_accumulation_steps=1 \ - --use_ema \ - --learning_rate=1e-4 \ - --lr_warmup_steps=500 \ - --mixed_precision="fp16" \ - --logger="wandb" \ - --push_to_hub -``` diff --git a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/midas/vit.py b/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/midas/vit.py deleted file mode 100644 index ea46b1be88b261b0dec04f3da0256f5f66f88a74..0000000000000000000000000000000000000000 --- a/spaces/pablodawson/ldm3d-inpainting/diffuserslocal/midas/midas/vit.py +++ /dev/null @@ -1,491 +0,0 @@ -import torch -import torch.nn as nn -import timm -import types -import math -import torch.nn.functional as F - - -class Slice(nn.Module): - def __init__(self, start_index=1): - super(Slice, self).__init__() - self.start_index = start_index - - def forward(self, x): - return x[:, self.start_index :] - - -class AddReadout(nn.Module): - def __init__(self, start_index=1): - super(AddReadout, self).__init__() - self.start_index = start_index - - def forward(self, x): - if self.start_index == 2: - readout = (x[:, 0] + x[:, 1]) / 2 - else: - readout = x[:, 0] - return x[:, self.start_index :] + readout.unsqueeze(1) - - -class ProjectReadout(nn.Module): - def __init__(self, in_features, start_index=1): - super(ProjectReadout, self).__init__() - self.start_index = start_index - - self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU()) - - def forward(self, x): - readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :]) - features = torch.cat((x[:, self.start_index :], readout), -1) - - return self.project(features) - - -class Transpose(nn.Module): - def __init__(self, dim0, dim1): - super(Transpose, self).__init__() - self.dim0 = dim0 - self.dim1 = dim1 - - def forward(self, x): - x = x.transpose(self.dim0, self.dim1) - return x - - -def forward_vit(pretrained, x): - b, c, h, w = x.shape - - glob = pretrained.model.forward_flex(x) - - layer_1 = pretrained.activations["1"] - layer_2 = pretrained.activations["2"] - layer_3 = pretrained.activations["3"] - layer_4 = pretrained.activations["4"] - - layer_1 = pretrained.act_postprocess1[0:2](layer_1) - layer_2 = pretrained.act_postprocess2[0:2](layer_2) - layer_3 = pretrained.act_postprocess3[0:2](layer_3) - layer_4 = pretrained.act_postprocess4[0:2](layer_4) - - unflatten = nn.Sequential( - nn.Unflatten( - 2, - torch.Size( - [ - h // pretrained.model.patch_size[1], - w // pretrained.model.patch_size[0], - ] - ), - ) - ) - - if layer_1.ndim == 3: - layer_1 = unflatten(layer_1) - if layer_2.ndim == 3: - layer_2 = unflatten(layer_2) - if layer_3.ndim == 3: - layer_3 = unflatten(layer_3) - if layer_4.ndim == 3: - layer_4 = unflatten(layer_4) - - layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1) - layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2) - layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3) - layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4) - - return layer_1, layer_2, layer_3, layer_4 - - -def _resize_pos_embed(self, posemb, gs_h, gs_w): - posemb_tok, posemb_grid = ( - posemb[:, : self.start_index], - posemb[0, self.start_index :], - ) - - gs_old = int(math.sqrt(len(posemb_grid))) - - posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) - posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear") - posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1) - - posemb = torch.cat([posemb_tok, posemb_grid], dim=1) - - return posemb - - -def forward_flex(self, x): - b, c, h, w = x.shape - - pos_embed = self._resize_pos_embed( - self.pos_embed, h // self.patch_size[1], w // self.patch_size[0] - ) - - B = x.shape[0] - - if hasattr(self.patch_embed, "backbone"): - x = self.patch_embed.backbone(x) - if isinstance(x, (list, tuple)): - x = x[-1] # last feature if backbone outputs list/tuple of features - - x = self.patch_embed.proj(x).flatten(2).transpose(1, 2) - - if getattr(self, "dist_token", None) is not None: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - dist_token = self.dist_token.expand(B, -1, -1) - x = torch.cat((cls_tokens, dist_token, x), dim=1) - else: - cls_tokens = self.cls_token.expand( - B, -1, -1 - ) # stole cls_tokens impl from Phil Wang, thanks - x = torch.cat((cls_tokens, x), dim=1) - - x = x + pos_embed - x = self.pos_drop(x) - - for blk in self.blocks: - x = blk(x) - - x = self.norm(x) - - return x - - -activations = {} - - -def get_activation(name): - def hook(model, input, output): - activations[name] = output - - return hook - - -def get_readout_oper(vit_features, features, use_readout, start_index=1): - if use_readout == "ignore": - readout_oper = [Slice(start_index)] * len(features) - elif use_readout == "add": - readout_oper = [AddReadout(start_index)] * len(features) - elif use_readout == "project": - readout_oper = [ - ProjectReadout(vit_features, start_index) for out_feat in features - ] - else: - assert ( - False - ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'" - - return readout_oper - - -def _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - size=[384, 384], - hooks=[2, 5, 8, 11], - vit_features=768, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - # 32, 48, 136, 384 - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_large_patch16_384", pretrained=pretrained) - - hooks = [5, 11, 17, 23] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[256, 512, 1024, 1024], - hooks=hooks, - vit_features=1024, - use_readout=use_readout, - ) - - -def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout - ) - - -def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None): - model = timm.create_model( - "vit_deit_base_distilled_patch16_384", pretrained=pretrained - ) - - hooks = [2, 5, 8, 11] if hooks == None else hooks - return _make_vit_b16_backbone( - model, - features=[96, 192, 384, 768], - hooks=hooks, - use_readout=use_readout, - start_index=2, - ) - - -def _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=[0, 1, 8, 11], - vit_features=768, - use_vit_only=False, - use_readout="ignore", - start_index=1, -): - pretrained = nn.Module() - - pretrained.model = model - - if use_vit_only == True: - pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1")) - pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2")) - else: - pretrained.model.patch_embed.backbone.stages[0].register_forward_hook( - get_activation("1") - ) - pretrained.model.patch_embed.backbone.stages[1].register_forward_hook( - get_activation("2") - ) - - pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3")) - pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4")) - - pretrained.activations = activations - - readout_oper = get_readout_oper(vit_features, features, use_readout, start_index) - - if use_vit_only == True: - pretrained.act_postprocess1 = nn.Sequential( - readout_oper[0], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[0], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[0], - out_channels=features[0], - kernel_size=4, - stride=4, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - - pretrained.act_postprocess2 = nn.Sequential( - readout_oper[1], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[1], - kernel_size=1, - stride=1, - padding=0, - ), - nn.ConvTranspose2d( - in_channels=features[1], - out_channels=features[1], - kernel_size=2, - stride=2, - padding=0, - bias=True, - dilation=1, - groups=1, - ), - ) - else: - pretrained.act_postprocess1 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - pretrained.act_postprocess2 = nn.Sequential( - nn.Identity(), nn.Identity(), nn.Identity() - ) - - pretrained.act_postprocess3 = nn.Sequential( - readout_oper[2], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[2], - kernel_size=1, - stride=1, - padding=0, - ), - ) - - pretrained.act_postprocess4 = nn.Sequential( - readout_oper[3], - Transpose(1, 2), - nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])), - nn.Conv2d( - in_channels=vit_features, - out_channels=features[3], - kernel_size=1, - stride=1, - padding=0, - ), - nn.Conv2d( - in_channels=features[3], - out_channels=features[3], - kernel_size=3, - stride=2, - padding=1, - ), - ) - - pretrained.model.start_index = start_index - pretrained.model.patch_size = [16, 16] - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model) - - # We inject this function into the VisionTransformer instances so that - # we can use it with interpolated position embeddings without modifying the library source. - pretrained.model._resize_pos_embed = types.MethodType( - _resize_pos_embed, pretrained.model - ) - - return pretrained - - -def _make_pretrained_vitb_rn50_384( - pretrained, use_readout="ignore", hooks=None, use_vit_only=False -): - model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained) - - hooks = [0, 1, 8, 11] if hooks == None else hooks - return _make_vit_b_rn50_backbone( - model, - features=[256, 512, 768, 768], - size=[384, 384], - hooks=hooks, - use_vit_only=use_vit_only, - use_readout=use_readout, - ) diff --git a/spaces/pablovela5620/grounding-sam/app.py b/spaces/pablovela5620/grounding-sam/app.py deleted file mode 100644 index e70912be60c132d78424628accfffafcd96a037e..0000000000000000000000000000000000000000 --- a/spaces/pablovela5620/grounding-sam/app.py +++ /dev/null @@ -1,5 +0,0 @@ -from os import system - -system("git submodule update --init --recursive") -system("cd GroundingDINO && pip install -e .") -system("python gradio_demo.py") diff --git a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/msvc.py b/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/msvc.py deleted file mode 100644 index 4a08dffe36eea0f1fbb9bad2fa9a106a6e13adc9..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/mynewshinyroop/Lib/site-packages/setuptools/msvc.py +++ /dev/null @@ -1,1690 +0,0 @@ -""" -Improved support for Microsoft Visual C++ compilers. - -Known supported compilers: --------------------------- -Microsoft Visual C++ 14.X: - Microsoft Visual C++ Build Tools 2015 (x86, x64, arm) - Microsoft Visual Studio Build Tools 2017 (x86, x64, arm, arm64) - Microsoft Visual Studio Build Tools 2019 (x86, x64, arm, arm64) - -This may also support compilers shipped with compatible Visual Studio versions. -""" - -import json -from io import open -from os import listdir, pathsep -from os.path import join, isfile, isdir, dirname -from subprocess import CalledProcessError -import contextlib -import platform -import itertools -import subprocess -import distutils.errors -from setuptools.extern.more_itertools import unique_everseen - -if platform.system() == 'Windows': - import winreg - from os import environ -else: - # Mock winreg and environ so the module can be imported on this platform. - - class winreg: - HKEY_USERS = None - HKEY_CURRENT_USER = None - HKEY_LOCAL_MACHINE = None - HKEY_CLASSES_ROOT = None - - environ = dict() - - -def _msvc14_find_vc2015(): - """Python 3.8 "distutils/_msvccompiler.py" backport""" - try: - key = winreg.OpenKey( - winreg.HKEY_LOCAL_MACHINE, - r"Software\Microsoft\VisualStudio\SxS\VC7", - 0, - winreg.KEY_READ | winreg.KEY_WOW64_32KEY - ) - except OSError: - return None, None - - best_version = 0 - best_dir = None - with key: - for i in itertools.count(): - try: - v, vc_dir, vt = winreg.EnumValue(key, i) - except OSError: - break - if v and vt == winreg.REG_SZ and isdir(vc_dir): - try: - version = int(float(v)) - except (ValueError, TypeError): - continue - if version >= 14 and version > best_version: - best_version, best_dir = version, vc_dir - return best_version, best_dir - - -def _msvc14_find_vc2017(): - """Python 3.8 "distutils/_msvccompiler.py" backport - - Returns "15, path" based on the result of invoking vswhere.exe - If no install is found, returns "None, None" - - The version is returned to avoid unnecessarily changing the function - result. It may be ignored when the path is not None. - - If vswhere.exe is not available, by definition, VS 2017 is not - installed. - """ - root = environ.get("ProgramFiles(x86)") or environ.get("ProgramFiles") - if not root: - return None, None - - suitable_components = ( - "Microsoft.VisualStudio.Component.VC.Tools.x86.x64", - "Microsoft.VisualStudio.Workload.WDExpress", - ) - - for component in suitable_components: - # Workaround for `-requiresAny` (only available on VS 2017 > 15.6) - with contextlib.suppress(CalledProcessError, OSError, UnicodeDecodeError): - path = subprocess.check_output([ - join(root, "Microsoft Visual Studio", "Installer", "vswhere.exe"), - "-latest", - "-prerelease", - "-requires", component, - "-property", "installationPath", - "-products", "*", - ]).decode(encoding="mbcs", errors="strict").strip() - - path = join(path, "VC", "Auxiliary", "Build") - if isdir(path): - return 15, path - - return None, None # no suitable component found - - -PLAT_SPEC_TO_RUNTIME = { - 'x86': 'x86', - 'x86_amd64': 'x64', - 'x86_arm': 'arm', - 'x86_arm64': 'arm64' -} - - -def _msvc14_find_vcvarsall(plat_spec): - """Python 3.8 "distutils/_msvccompiler.py" backport""" - _, best_dir = _msvc14_find_vc2017() - vcruntime = None - - if plat_spec in PLAT_SPEC_TO_RUNTIME: - vcruntime_plat = PLAT_SPEC_TO_RUNTIME[plat_spec] - else: - vcruntime_plat = 'x64' if 'amd64' in plat_spec else 'x86' - - if best_dir: - vcredist = join(best_dir, "..", "..", "redist", "MSVC", "**", - vcruntime_plat, "Microsoft.VC14*.CRT", - "vcruntime140.dll") - try: - import glob - vcruntime = glob.glob(vcredist, recursive=True)[-1] - except (ImportError, OSError, LookupError): - vcruntime = None - - if not best_dir: - best_version, best_dir = _msvc14_find_vc2015() - if best_version: - vcruntime = join(best_dir, 'redist', vcruntime_plat, - "Microsoft.VC140.CRT", "vcruntime140.dll") - - if not best_dir: - return None, None - - vcvarsall = join(best_dir, "vcvarsall.bat") - if not isfile(vcvarsall): - return None, None - - if not vcruntime or not isfile(vcruntime): - vcruntime = None - - return vcvarsall, vcruntime - - -def _msvc14_get_vc_env(plat_spec): - """Python 3.8 "distutils/_msvccompiler.py" backport""" - if "DISTUTILS_USE_SDK" in environ: - return { - key.lower(): value - for key, value in environ.items() - } - - vcvarsall, vcruntime = _msvc14_find_vcvarsall(plat_spec) - if not vcvarsall: - raise distutils.errors.DistutilsPlatformError( - "Unable to find vcvarsall.bat" - ) - - try: - out = subprocess.check_output( - 'cmd /u /c "{}" {} && set'.format(vcvarsall, plat_spec), - stderr=subprocess.STDOUT, - ).decode('utf-16le', errors='replace') - except subprocess.CalledProcessError as exc: - raise distutils.errors.DistutilsPlatformError( - "Error executing {}".format(exc.cmd) - ) from exc - - env = { - key.lower(): value - for key, _, value in - (line.partition('=') for line in out.splitlines()) - if key and value - } - - if vcruntime: - env['py_vcruntime_redist'] = vcruntime - return env - - -def msvc14_get_vc_env(plat_spec): - """ - Patched "distutils._msvccompiler._get_vc_env" for support extra - Microsoft Visual C++ 14.X compilers. - - Set environment without use of "vcvarsall.bat". - - Parameters - ---------- - plat_spec: str - Target architecture. - - Return - ------ - dict - environment - """ - - # Always use backport from CPython 3.8 - try: - return _msvc14_get_vc_env(plat_spec) - except distutils.errors.DistutilsPlatformError as exc: - _augment_exception(exc, 14.0) - raise - - -def _augment_exception(exc, version, arch=''): - """ - Add details to the exception message to help guide the user - as to what action will resolve it. - """ - # Error if MSVC++ directory not found or environment not set - message = exc.args[0] - - if "vcvarsall" in message.lower() or "visual c" in message.lower(): - # Special error message if MSVC++ not installed - tmpl = 'Microsoft Visual C++ {version:0.1f} or greater is required.' - message = tmpl.format(**locals()) - msdownload = 'www.microsoft.com/download/details.aspx?id=%d' - if version == 9.0: - if arch.lower().find('ia64') > -1: - # For VC++ 9.0, if IA64 support is needed, redirect user - # to Windows SDK 7.0. - # Note: No download link available from Microsoft. - message += ' Get it with "Microsoft Windows SDK 7.0"' - else: - # For VC++ 9.0 redirect user to Vc++ for Python 2.7 : - # This redirection link is maintained by Microsoft. - # Contact vspython@microsoft.com if it needs updating. - message += ' Get it from http://aka.ms/vcpython27' - elif version == 10.0: - # For VC++ 10.0 Redirect user to Windows SDK 7.1 - message += ' Get it with "Microsoft Windows SDK 7.1": ' - message += msdownload % 8279 - elif version >= 14.0: - # For VC++ 14.X Redirect user to latest Visual C++ Build Tools - message += (' Get it with "Microsoft C++ Build Tools": ' - r'https://visualstudio.microsoft.com' - r'/visual-cpp-build-tools/') - - exc.args = (message, ) - - -class PlatformInfo: - """ - Current and Target Architectures information. - - Parameters - ---------- - arch: str - Target architecture. - """ - current_cpu = environ.get('processor_architecture', '').lower() - - def __init__(self, arch): - self.arch = arch.lower().replace('x64', 'amd64') - - @property - def target_cpu(self): - """ - Return Target CPU architecture. - - Return - ------ - str - Target CPU - """ - return self.arch[self.arch.find('_') + 1:] - - def target_is_x86(self): - """ - Return True if target CPU is x86 32 bits.. - - Return - ------ - bool - CPU is x86 32 bits - """ - return self.target_cpu == 'x86' - - def current_is_x86(self): - """ - Return True if current CPU is x86 32 bits.. - - Return - ------ - bool - CPU is x86 32 bits - """ - return self.current_cpu == 'x86' - - def current_dir(self, hidex86=False, x64=False): - """ - Current platform specific subfolder. - - Parameters - ---------- - hidex86: bool - return '' and not '\x86' if architecture is x86. - x64: bool - return '\x64' and not '\amd64' if architecture is amd64. - - Return - ------ - str - subfolder: '\target', or '' (see hidex86 parameter) - """ - return ( - '' if (self.current_cpu == 'x86' and hidex86) else - r'\x64' if (self.current_cpu == 'amd64' and x64) else - r'\%s' % self.current_cpu - ) - - def target_dir(self, hidex86=False, x64=False): - r""" - Target platform specific subfolder. - - Parameters - ---------- - hidex86: bool - return '' and not '\x86' if architecture is x86. - x64: bool - return '\x64' and not '\amd64' if architecture is amd64. - - Return - ------ - str - subfolder: '\current', or '' (see hidex86 parameter) - """ - return ( - '' if (self.target_cpu == 'x86' and hidex86) else - r'\x64' if (self.target_cpu == 'amd64' and x64) else - r'\%s' % self.target_cpu - ) - - def cross_dir(self, forcex86=False): - r""" - Cross platform specific subfolder. - - Parameters - ---------- - forcex86: bool - Use 'x86' as current architecture even if current architecture is - not x86. - - Return - ------ - str - subfolder: '' if target architecture is current architecture, - '\current_target' if not. - """ - current = 'x86' if forcex86 else self.current_cpu - return ( - '' if self.target_cpu == current else - self.target_dir().replace('\\', '\\%s_' % current) - ) - - -class RegistryInfo: - """ - Microsoft Visual Studio related registry information. - - Parameters - ---------- - platform_info: PlatformInfo - "PlatformInfo" instance. - """ - HKEYS = (winreg.HKEY_USERS, - winreg.HKEY_CURRENT_USER, - winreg.HKEY_LOCAL_MACHINE, - winreg.HKEY_CLASSES_ROOT) - - def __init__(self, platform_info): - self.pi = platform_info - - @property - def visualstudio(self): - """ - Microsoft Visual Studio root registry key. - - Return - ------ - str - Registry key - """ - return 'VisualStudio' - - @property - def sxs(self): - """ - Microsoft Visual Studio SxS registry key. - - Return - ------ - str - Registry key - """ - return join(self.visualstudio, 'SxS') - - @property - def vc(self): - """ - Microsoft Visual C++ VC7 registry key. - - Return - ------ - str - Registry key - """ - return join(self.sxs, 'VC7') - - @property - def vs(self): - """ - Microsoft Visual Studio VS7 registry key. - - Return - ------ - str - Registry key - """ - return join(self.sxs, 'VS7') - - @property - def vc_for_python(self): - """ - Microsoft Visual C++ for Python registry key. - - Return - ------ - str - Registry key - """ - return r'DevDiv\VCForPython' - - @property - def microsoft_sdk(self): - """ - Microsoft SDK registry key. - - Return - ------ - str - Registry key - """ - return 'Microsoft SDKs' - - @property - def windows_sdk(self): - """ - Microsoft Windows/Platform SDK registry key. - - Return - ------ - str - Registry key - """ - return join(self.microsoft_sdk, 'Windows') - - @property - def netfx_sdk(self): - """ - Microsoft .NET Framework SDK registry key. - - Return - ------ - str - Registry key - """ - return join(self.microsoft_sdk, 'NETFXSDK') - - @property - def windows_kits_roots(self): - """ - Microsoft Windows Kits Roots registry key. - - Return - ------ - str - Registry key - """ - return r'Windows Kits\Installed Roots' - - def microsoft(self, key, x86=False): - """ - Return key in Microsoft software registry. - - Parameters - ---------- - key: str - Registry key path where look. - x86: str - Force x86 software registry. - - Return - ------ - str - Registry key - """ - node64 = '' if self.pi.current_is_x86() or x86 else 'Wow6432Node' - return join('Software', node64, 'Microsoft', key) - - def lookup(self, key, name): - """ - Look for values in registry in Microsoft software registry. - - Parameters - ---------- - key: str - Registry key path where look. - name: str - Value name to find. - - Return - ------ - str - value - """ - key_read = winreg.KEY_READ - openkey = winreg.OpenKey - closekey = winreg.CloseKey - ms = self.microsoft - for hkey in self.HKEYS: - bkey = None - try: - bkey = openkey(hkey, ms(key), 0, key_read) - except (OSError, IOError): - if not self.pi.current_is_x86(): - try: - bkey = openkey(hkey, ms(key, True), 0, key_read) - except (OSError, IOError): - continue - else: - continue - try: - return winreg.QueryValueEx(bkey, name)[0] - except (OSError, IOError): - pass - finally: - if bkey: - closekey(bkey) - - -class SystemInfo: - """ - Microsoft Windows and Visual Studio related system information. - - Parameters - ---------- - registry_info: RegistryInfo - "RegistryInfo" instance. - vc_ver: float - Required Microsoft Visual C++ version. - """ - - # Variables and properties in this class use originals CamelCase variables - # names from Microsoft source files for more easy comparison. - WinDir = environ.get('WinDir', '') - ProgramFiles = environ.get('ProgramFiles', '') - ProgramFilesx86 = environ.get('ProgramFiles(x86)', ProgramFiles) - - def __init__(self, registry_info, vc_ver=None): - self.ri = registry_info - self.pi = self.ri.pi - - self.known_vs_paths = self.find_programdata_vs_vers() - - # Except for VS15+, VC version is aligned with VS version - self.vs_ver = self.vc_ver = ( - vc_ver or self._find_latest_available_vs_ver()) - - def _find_latest_available_vs_ver(self): - """ - Find the latest VC version - - Return - ------ - float - version - """ - reg_vc_vers = self.find_reg_vs_vers() - - if not (reg_vc_vers or self.known_vs_paths): - raise distutils.errors.DistutilsPlatformError( - 'No Microsoft Visual C++ version found') - - vc_vers = set(reg_vc_vers) - vc_vers.update(self.known_vs_paths) - return sorted(vc_vers)[-1] - - def find_reg_vs_vers(self): - """ - Find Microsoft Visual Studio versions available in registry. - - Return - ------ - list of float - Versions - """ - ms = self.ri.microsoft - vckeys = (self.ri.vc, self.ri.vc_for_python, self.ri.vs) - vs_vers = [] - for hkey, key in itertools.product(self.ri.HKEYS, vckeys): - try: - bkey = winreg.OpenKey(hkey, ms(key), 0, winreg.KEY_READ) - except (OSError, IOError): - continue - with bkey: - subkeys, values, _ = winreg.QueryInfoKey(bkey) - for i in range(values): - with contextlib.suppress(ValueError): - ver = float(winreg.EnumValue(bkey, i)[0]) - if ver not in vs_vers: - vs_vers.append(ver) - for i in range(subkeys): - with contextlib.suppress(ValueError): - ver = float(winreg.EnumKey(bkey, i)) - if ver not in vs_vers: - vs_vers.append(ver) - return sorted(vs_vers) - - def find_programdata_vs_vers(self): - r""" - Find Visual studio 2017+ versions from information in - "C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances". - - Return - ------ - dict - float version as key, path as value. - """ - vs_versions = {} - instances_dir = \ - r'C:\ProgramData\Microsoft\VisualStudio\Packages\_Instances' - - try: - hashed_names = listdir(instances_dir) - - except (OSError, IOError): - # Directory not exists with all Visual Studio versions - return vs_versions - - for name in hashed_names: - try: - # Get VS installation path from "state.json" file - state_path = join(instances_dir, name, 'state.json') - with open(state_path, 'rt', encoding='utf-8') as state_file: - state = json.load(state_file) - vs_path = state['installationPath'] - - # Raises OSError if this VS installation does not contain VC - listdir(join(vs_path, r'VC\Tools\MSVC')) - - # Store version and path - vs_versions[self._as_float_version( - state['installationVersion'])] = vs_path - - except (OSError, IOError, KeyError): - # Skip if "state.json" file is missing or bad format - continue - - return vs_versions - - @staticmethod - def _as_float_version(version): - """ - Return a string version as a simplified float version (major.minor) - - Parameters - ---------- - version: str - Version. - - Return - ------ - float - version - """ - return float('.'.join(version.split('.')[:2])) - - @property - def VSInstallDir(self): - """ - Microsoft Visual Studio directory. - - Return - ------ - str - path - """ - # Default path - default = join(self.ProgramFilesx86, - 'Microsoft Visual Studio %0.1f' % self.vs_ver) - - # Try to get path from registry, if fail use default path - return self.ri.lookup(self.ri.vs, '%0.1f' % self.vs_ver) or default - - @property - def VCInstallDir(self): - """ - Microsoft Visual C++ directory. - - Return - ------ - str - path - """ - path = self._guess_vc() or self._guess_vc_legacy() - - if not isdir(path): - msg = 'Microsoft Visual C++ directory not found' - raise distutils.errors.DistutilsPlatformError(msg) - - return path - - def _guess_vc(self): - """ - Locate Visual C++ for VS2017+. - - Return - ------ - str - path - """ - if self.vs_ver <= 14.0: - return '' - - try: - # First search in known VS paths - vs_dir = self.known_vs_paths[self.vs_ver] - except KeyError: - # Else, search with path from registry - vs_dir = self.VSInstallDir - - guess_vc = join(vs_dir, r'VC\Tools\MSVC') - - # Subdir with VC exact version as name - try: - # Update the VC version with real one instead of VS version - vc_ver = listdir(guess_vc)[-1] - self.vc_ver = self._as_float_version(vc_ver) - return join(guess_vc, vc_ver) - except (OSError, IOError, IndexError): - return '' - - def _guess_vc_legacy(self): - """ - Locate Visual C++ for versions prior to 2017. - - Return - ------ - str - path - """ - default = join(self.ProgramFilesx86, - r'Microsoft Visual Studio %0.1f\VC' % self.vs_ver) - - # Try to get "VC++ for Python" path from registry as default path - reg_path = join(self.ri.vc_for_python, '%0.1f' % self.vs_ver) - python_vc = self.ri.lookup(reg_path, 'installdir') - default_vc = join(python_vc, 'VC') if python_vc else default - - # Try to get path from registry, if fail use default path - return self.ri.lookup(self.ri.vc, '%0.1f' % self.vs_ver) or default_vc - - @property - def WindowsSdkVersion(self): - """ - Microsoft Windows SDK versions for specified MSVC++ version. - - Return - ------ - tuple of str - versions - """ - if self.vs_ver <= 9.0: - return '7.0', '6.1', '6.0a' - elif self.vs_ver == 10.0: - return '7.1', '7.0a' - elif self.vs_ver == 11.0: - return '8.0', '8.0a' - elif self.vs_ver == 12.0: - return '8.1', '8.1a' - elif self.vs_ver >= 14.0: - return '10.0', '8.1' - - @property - def WindowsSdkLastVersion(self): - """ - Microsoft Windows SDK last version. - - Return - ------ - str - version - """ - return self._use_last_dir_name(join(self.WindowsSdkDir, 'lib')) - - @property # noqa: C901 - def WindowsSdkDir(self): # noqa: C901 # is too complex (12) # FIXME - """ - Microsoft Windows SDK directory. - - Return - ------ - str - path - """ - sdkdir = '' - for ver in self.WindowsSdkVersion: - # Try to get it from registry - loc = join(self.ri.windows_sdk, 'v%s' % ver) - sdkdir = self.ri.lookup(loc, 'installationfolder') - if sdkdir: - break - if not sdkdir or not isdir(sdkdir): - # Try to get "VC++ for Python" version from registry - path = join(self.ri.vc_for_python, '%0.1f' % self.vc_ver) - install_base = self.ri.lookup(path, 'installdir') - if install_base: - sdkdir = join(install_base, 'WinSDK') - if not sdkdir or not isdir(sdkdir): - # If fail, use default new path - for ver in self.WindowsSdkVersion: - intver = ver[:ver.rfind('.')] - path = r'Microsoft SDKs\Windows Kits\%s' % intver - d = join(self.ProgramFiles, path) - if isdir(d): - sdkdir = d - if not sdkdir or not isdir(sdkdir): - # If fail, use default old path - for ver in self.WindowsSdkVersion: - path = r'Microsoft SDKs\Windows\v%s' % ver - d = join(self.ProgramFiles, path) - if isdir(d): - sdkdir = d - if not sdkdir: - # If fail, use Platform SDK - sdkdir = join(self.VCInstallDir, 'PlatformSDK') - return sdkdir - - @property - def WindowsSDKExecutablePath(self): - """ - Microsoft Windows SDK executable directory. - - Return - ------ - str - path - """ - # Find WinSDK NetFx Tools registry dir name - if self.vs_ver <= 11.0: - netfxver = 35 - arch = '' - else: - netfxver = 40 - hidex86 = True if self.vs_ver <= 12.0 else False - arch = self.pi.current_dir(x64=True, hidex86=hidex86) - fx = 'WinSDK-NetFx%dTools%s' % (netfxver, arch.replace('\\', '-')) - - # list all possibles registry paths - regpaths = [] - if self.vs_ver >= 14.0: - for ver in self.NetFxSdkVersion: - regpaths += [join(self.ri.netfx_sdk, ver, fx)] - - for ver in self.WindowsSdkVersion: - regpaths += [join(self.ri.windows_sdk, 'v%sA' % ver, fx)] - - # Return installation folder from the more recent path - for path in regpaths: - execpath = self.ri.lookup(path, 'installationfolder') - if execpath: - return execpath - - @property - def FSharpInstallDir(self): - """ - Microsoft Visual F# directory. - - Return - ------ - str - path - """ - path = join(self.ri.visualstudio, r'%0.1f\Setup\F#' % self.vs_ver) - return self.ri.lookup(path, 'productdir') or '' - - @property - def UniversalCRTSdkDir(self): - """ - Microsoft Universal CRT SDK directory. - - Return - ------ - str - path - """ - # Set Kit Roots versions for specified MSVC++ version - vers = ('10', '81') if self.vs_ver >= 14.0 else () - - # Find path of the more recent Kit - for ver in vers: - sdkdir = self.ri.lookup(self.ri.windows_kits_roots, - 'kitsroot%s' % ver) - if sdkdir: - return sdkdir or '' - - @property - def UniversalCRTSdkLastVersion(self): - """ - Microsoft Universal C Runtime SDK last version. - - Return - ------ - str - version - """ - return self._use_last_dir_name(join(self.UniversalCRTSdkDir, 'lib')) - - @property - def NetFxSdkVersion(self): - """ - Microsoft .NET Framework SDK versions. - - Return - ------ - tuple of str - versions - """ - # Set FxSdk versions for specified VS version - return (('4.7.2', '4.7.1', '4.7', - '4.6.2', '4.6.1', '4.6', - '4.5.2', '4.5.1', '4.5') - if self.vs_ver >= 14.0 else ()) - - @property - def NetFxSdkDir(self): - """ - Microsoft .NET Framework SDK directory. - - Return - ------ - str - path - """ - sdkdir = '' - for ver in self.NetFxSdkVersion: - loc = join(self.ri.netfx_sdk, ver) - sdkdir = self.ri.lookup(loc, 'kitsinstallationfolder') - if sdkdir: - break - return sdkdir - - @property - def FrameworkDir32(self): - """ - Microsoft .NET Framework 32bit directory. - - Return - ------ - str - path - """ - # Default path - guess_fw = join(self.WinDir, r'Microsoft.NET\Framework') - - # Try to get path from registry, if fail use default path - return self.ri.lookup(self.ri.vc, 'frameworkdir32') or guess_fw - - @property - def FrameworkDir64(self): - """ - Microsoft .NET Framework 64bit directory. - - Return - ------ - str - path - """ - # Default path - guess_fw = join(self.WinDir, r'Microsoft.NET\Framework64') - - # Try to get path from registry, if fail use default path - return self.ri.lookup(self.ri.vc, 'frameworkdir64') or guess_fw - - @property - def FrameworkVersion32(self): - """ - Microsoft .NET Framework 32bit versions. - - Return - ------ - tuple of str - versions - """ - return self._find_dot_net_versions(32) - - @property - def FrameworkVersion64(self): - """ - Microsoft .NET Framework 64bit versions. - - Return - ------ - tuple of str - versions - """ - return self._find_dot_net_versions(64) - - def _find_dot_net_versions(self, bits): - """ - Find Microsoft .NET Framework versions. - - Parameters - ---------- - bits: int - Platform number of bits: 32 or 64. - - Return - ------ - tuple of str - versions - """ - # Find actual .NET version in registry - reg_ver = self.ri.lookup(self.ri.vc, 'frameworkver%d' % bits) - dot_net_dir = getattr(self, 'FrameworkDir%d' % bits) - ver = reg_ver or self._use_last_dir_name(dot_net_dir, 'v') or '' - - # Set .NET versions for specified MSVC++ version - if self.vs_ver >= 12.0: - return ver, 'v4.0' - elif self.vs_ver >= 10.0: - return 'v4.0.30319' if ver.lower()[:2] != 'v4' else ver, 'v3.5' - elif self.vs_ver == 9.0: - return 'v3.5', 'v2.0.50727' - elif self.vs_ver == 8.0: - return 'v3.0', 'v2.0.50727' - - @staticmethod - def _use_last_dir_name(path, prefix=''): - """ - Return name of the last dir in path or '' if no dir found. - - Parameters - ---------- - path: str - Use dirs in this path - prefix: str - Use only dirs starting by this prefix - - Return - ------ - str - name - """ - matching_dirs = ( - dir_name - for dir_name in reversed(listdir(path)) - if isdir(join(path, dir_name)) and - dir_name.startswith(prefix) - ) - return next(matching_dirs, None) or '' - - -class EnvironmentInfo: - """ - Return environment variables for specified Microsoft Visual C++ version - and platform : Lib, Include, Path and libpath. - - This function is compatible with Microsoft Visual C++ 9.0 to 14.X. - - Script created by analysing Microsoft environment configuration files like - "vcvars[...].bat", "SetEnv.Cmd", "vcbuildtools.bat", ... - - Parameters - ---------- - arch: str - Target architecture. - vc_ver: float - Required Microsoft Visual C++ version. If not set, autodetect the last - version. - vc_min_ver: float - Minimum Microsoft Visual C++ version. - """ - - # Variables and properties in this class use originals CamelCase variables - # names from Microsoft source files for more easy comparison. - - def __init__(self, arch, vc_ver=None, vc_min_ver=0): - self.pi = PlatformInfo(arch) - self.ri = RegistryInfo(self.pi) - self.si = SystemInfo(self.ri, vc_ver) - - if self.vc_ver < vc_min_ver: - err = 'No suitable Microsoft Visual C++ version found' - raise distutils.errors.DistutilsPlatformError(err) - - @property - def vs_ver(self): - """ - Microsoft Visual Studio. - - Return - ------ - float - version - """ - return self.si.vs_ver - - @property - def vc_ver(self): - """ - Microsoft Visual C++ version. - - Return - ------ - float - version - """ - return self.si.vc_ver - - @property - def VSTools(self): - """ - Microsoft Visual Studio Tools. - - Return - ------ - list of str - paths - """ - paths = [r'Common7\IDE', r'Common7\Tools'] - - if self.vs_ver >= 14.0: - arch_subdir = self.pi.current_dir(hidex86=True, x64=True) - paths += [r'Common7\IDE\CommonExtensions\Microsoft\TestWindow'] - paths += [r'Team Tools\Performance Tools'] - paths += [r'Team Tools\Performance Tools%s' % arch_subdir] - - return [join(self.si.VSInstallDir, path) for path in paths] - - @property - def VCIncludes(self): - """ - Microsoft Visual C++ & Microsoft Foundation Class Includes. - - Return - ------ - list of str - paths - """ - return [join(self.si.VCInstallDir, 'Include'), - join(self.si.VCInstallDir, r'ATLMFC\Include')] - - @property - def VCLibraries(self): - """ - Microsoft Visual C++ & Microsoft Foundation Class Libraries. - - Return - ------ - list of str - paths - """ - if self.vs_ver >= 15.0: - arch_subdir = self.pi.target_dir(x64=True) - else: - arch_subdir = self.pi.target_dir(hidex86=True) - paths = ['Lib%s' % arch_subdir, r'ATLMFC\Lib%s' % arch_subdir] - - if self.vs_ver >= 14.0: - paths += [r'Lib\store%s' % arch_subdir] - - return [join(self.si.VCInstallDir, path) for path in paths] - - @property - def VCStoreRefs(self): - """ - Microsoft Visual C++ store references Libraries. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 14.0: - return [] - return [join(self.si.VCInstallDir, r'Lib\store\references')] - - @property - def VCTools(self): - """ - Microsoft Visual C++ Tools. - - Return - ------ - list of str - paths - """ - si = self.si - tools = [join(si.VCInstallDir, 'VCPackages')] - - forcex86 = True if self.vs_ver <= 10.0 else False - arch_subdir = self.pi.cross_dir(forcex86) - if arch_subdir: - tools += [join(si.VCInstallDir, 'Bin%s' % arch_subdir)] - - if self.vs_ver == 14.0: - path = 'Bin%s' % self.pi.current_dir(hidex86=True) - tools += [join(si.VCInstallDir, path)] - - elif self.vs_ver >= 15.0: - host_dir = (r'bin\HostX86%s' if self.pi.current_is_x86() else - r'bin\HostX64%s') - tools += [join( - si.VCInstallDir, host_dir % self.pi.target_dir(x64=True))] - - if self.pi.current_cpu != self.pi.target_cpu: - tools += [join( - si.VCInstallDir, host_dir % self.pi.current_dir(x64=True))] - - else: - tools += [join(si.VCInstallDir, 'Bin')] - - return tools - - @property - def OSLibraries(self): - """ - Microsoft Windows SDK Libraries. - - Return - ------ - list of str - paths - """ - if self.vs_ver <= 10.0: - arch_subdir = self.pi.target_dir(hidex86=True, x64=True) - return [join(self.si.WindowsSdkDir, 'Lib%s' % arch_subdir)] - - else: - arch_subdir = self.pi.target_dir(x64=True) - lib = join(self.si.WindowsSdkDir, 'lib') - libver = self._sdk_subdir - return [join(lib, '%sum%s' % (libver, arch_subdir))] - - @property - def OSIncludes(self): - """ - Microsoft Windows SDK Include. - - Return - ------ - list of str - paths - """ - include = join(self.si.WindowsSdkDir, 'include') - - if self.vs_ver <= 10.0: - return [include, join(include, 'gl')] - - else: - if self.vs_ver >= 14.0: - sdkver = self._sdk_subdir - else: - sdkver = '' - return [join(include, '%sshared' % sdkver), - join(include, '%sum' % sdkver), - join(include, '%swinrt' % sdkver)] - - @property - def OSLibpath(self): - """ - Microsoft Windows SDK Libraries Paths. - - Return - ------ - list of str - paths - """ - ref = join(self.si.WindowsSdkDir, 'References') - libpath = [] - - if self.vs_ver <= 9.0: - libpath += self.OSLibraries - - if self.vs_ver >= 11.0: - libpath += [join(ref, r'CommonConfiguration\Neutral')] - - if self.vs_ver >= 14.0: - libpath += [ - ref, - join(self.si.WindowsSdkDir, 'UnionMetadata'), - join( - ref, 'Windows.Foundation.UniversalApiContract', '1.0.0.0'), - join(ref, 'Windows.Foundation.FoundationContract', '1.0.0.0'), - join( - ref, 'Windows.Networking.Connectivity.WwanContract', - '1.0.0.0'), - join( - self.si.WindowsSdkDir, 'ExtensionSDKs', 'Microsoft.VCLibs', - '%0.1f' % self.vs_ver, 'References', 'CommonConfiguration', - 'neutral'), - ] - return libpath - - @property - def SdkTools(self): - """ - Microsoft Windows SDK Tools. - - Return - ------ - list of str - paths - """ - return list(self._sdk_tools()) - - def _sdk_tools(self): - """ - Microsoft Windows SDK Tools paths generator. - - Return - ------ - generator of str - paths - """ - if self.vs_ver < 15.0: - bin_dir = 'Bin' if self.vs_ver <= 11.0 else r'Bin\x86' - yield join(self.si.WindowsSdkDir, bin_dir) - - if not self.pi.current_is_x86(): - arch_subdir = self.pi.current_dir(x64=True) - path = 'Bin%s' % arch_subdir - yield join(self.si.WindowsSdkDir, path) - - if self.vs_ver in (10.0, 11.0): - if self.pi.target_is_x86(): - arch_subdir = '' - else: - arch_subdir = self.pi.current_dir(hidex86=True, x64=True) - path = r'Bin\NETFX 4.0 Tools%s' % arch_subdir - yield join(self.si.WindowsSdkDir, path) - - elif self.vs_ver >= 15.0: - path = join(self.si.WindowsSdkDir, 'Bin') - arch_subdir = self.pi.current_dir(x64=True) - sdkver = self.si.WindowsSdkLastVersion - yield join(path, '%s%s' % (sdkver, arch_subdir)) - - if self.si.WindowsSDKExecutablePath: - yield self.si.WindowsSDKExecutablePath - - @property - def _sdk_subdir(self): - """ - Microsoft Windows SDK version subdir. - - Return - ------ - str - subdir - """ - ucrtver = self.si.WindowsSdkLastVersion - return ('%s\\' % ucrtver) if ucrtver else '' - - @property - def SdkSetup(self): - """ - Microsoft Windows SDK Setup. - - Return - ------ - list of str - paths - """ - if self.vs_ver > 9.0: - return [] - - return [join(self.si.WindowsSdkDir, 'Setup')] - - @property - def FxTools(self): - """ - Microsoft .NET Framework Tools. - - Return - ------ - list of str - paths - """ - pi = self.pi - si = self.si - - if self.vs_ver <= 10.0: - include32 = True - include64 = not pi.target_is_x86() and not pi.current_is_x86() - else: - include32 = pi.target_is_x86() or pi.current_is_x86() - include64 = pi.current_cpu == 'amd64' or pi.target_cpu == 'amd64' - - tools = [] - if include32: - tools += [join(si.FrameworkDir32, ver) - for ver in si.FrameworkVersion32] - if include64: - tools += [join(si.FrameworkDir64, ver) - for ver in si.FrameworkVersion64] - return tools - - @property - def NetFxSDKLibraries(self): - """ - Microsoft .Net Framework SDK Libraries. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 14.0 or not self.si.NetFxSdkDir: - return [] - - arch_subdir = self.pi.target_dir(x64=True) - return [join(self.si.NetFxSdkDir, r'lib\um%s' % arch_subdir)] - - @property - def NetFxSDKIncludes(self): - """ - Microsoft .Net Framework SDK Includes. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 14.0 or not self.si.NetFxSdkDir: - return [] - - return [join(self.si.NetFxSdkDir, r'include\um')] - - @property - def VsTDb(self): - """ - Microsoft Visual Studio Team System Database. - - Return - ------ - list of str - paths - """ - return [join(self.si.VSInstallDir, r'VSTSDB\Deploy')] - - @property - def MSBuild(self): - """ - Microsoft Build Engine. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 12.0: - return [] - elif self.vs_ver < 15.0: - base_path = self.si.ProgramFilesx86 - arch_subdir = self.pi.current_dir(hidex86=True) - else: - base_path = self.si.VSInstallDir - arch_subdir = '' - - path = r'MSBuild\%0.1f\bin%s' % (self.vs_ver, arch_subdir) - build = [join(base_path, path)] - - if self.vs_ver >= 15.0: - # Add Roslyn C# & Visual Basic Compiler - build += [join(base_path, path, 'Roslyn')] - - return build - - @property - def HTMLHelpWorkshop(self): - """ - Microsoft HTML Help Workshop. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 11.0: - return [] - - return [join(self.si.ProgramFilesx86, 'HTML Help Workshop')] - - @property - def UCRTLibraries(self): - """ - Microsoft Universal C Runtime SDK Libraries. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 14.0: - return [] - - arch_subdir = self.pi.target_dir(x64=True) - lib = join(self.si.UniversalCRTSdkDir, 'lib') - ucrtver = self._ucrt_subdir - return [join(lib, '%sucrt%s' % (ucrtver, arch_subdir))] - - @property - def UCRTIncludes(self): - """ - Microsoft Universal C Runtime SDK Include. - - Return - ------ - list of str - paths - """ - if self.vs_ver < 14.0: - return [] - - include = join(self.si.UniversalCRTSdkDir, 'include') - return [join(include, '%sucrt' % self._ucrt_subdir)] - - @property - def _ucrt_subdir(self): - """ - Microsoft Universal C Runtime SDK version subdir. - - Return - ------ - str - subdir - """ - ucrtver = self.si.UniversalCRTSdkLastVersion - return ('%s\\' % ucrtver) if ucrtver else '' - - @property - def FSharp(self): - """ - Microsoft Visual F#. - - Return - ------ - list of str - paths - """ - if 11.0 > self.vs_ver > 12.0: - return [] - - return [self.si.FSharpInstallDir] - - @property - def VCRuntimeRedist(self): - """ - Microsoft Visual C++ runtime redistributable dll. - - Return - ------ - str - path - """ - vcruntime = 'vcruntime%d0.dll' % self.vc_ver - arch_subdir = self.pi.target_dir(x64=True).strip('\\') - - # Installation prefixes candidates - prefixes = [] - tools_path = self.si.VCInstallDir - redist_path = dirname(tools_path.replace(r'\Tools', r'\Redist')) - if isdir(redist_path): - # Redist version may not be exactly the same as tools - redist_path = join(redist_path, listdir(redist_path)[-1]) - prefixes += [redist_path, join(redist_path, 'onecore')] - - prefixes += [join(tools_path, 'redist')] # VS14 legacy path - - # CRT directory - crt_dirs = ('Microsoft.VC%d.CRT' % (self.vc_ver * 10), - # Sometime store in directory with VS version instead of VC - 'Microsoft.VC%d.CRT' % (int(self.vs_ver) * 10)) - - # vcruntime path - for prefix, crt_dir in itertools.product(prefixes, crt_dirs): - path = join(prefix, arch_subdir, crt_dir, vcruntime) - if isfile(path): - return path - - def return_env(self, exists=True): - """ - Return environment dict. - - Parameters - ---------- - exists: bool - It True, only return existing paths. - - Return - ------ - dict - environment - """ - env = dict( - include=self._build_paths('include', - [self.VCIncludes, - self.OSIncludes, - self.UCRTIncludes, - self.NetFxSDKIncludes], - exists), - lib=self._build_paths('lib', - [self.VCLibraries, - self.OSLibraries, - self.FxTools, - self.UCRTLibraries, - self.NetFxSDKLibraries], - exists), - libpath=self._build_paths('libpath', - [self.VCLibraries, - self.FxTools, - self.VCStoreRefs, - self.OSLibpath], - exists), - path=self._build_paths('path', - [self.VCTools, - self.VSTools, - self.VsTDb, - self.SdkTools, - self.SdkSetup, - self.FxTools, - self.MSBuild, - self.HTMLHelpWorkshop, - self.FSharp], - exists), - ) - if self.vs_ver >= 14 and isfile(self.VCRuntimeRedist): - env['py_vcruntime_redist'] = self.VCRuntimeRedist - return env - - def _build_paths(self, name, spec_path_lists, exists): - """ - Given an environment variable name and specified paths, - return a pathsep-separated string of paths containing - unique, extant, directories from those paths and from - the environment variable. Raise an error if no paths - are resolved. - - Parameters - ---------- - name: str - Environment variable name - spec_path_lists: list of str - Paths - exists: bool - It True, only return existing paths. - - Return - ------ - str - Pathsep-separated paths - """ - # flatten spec_path_lists - spec_paths = itertools.chain.from_iterable(spec_path_lists) - env_paths = environ.get(name, '').split(pathsep) - paths = itertools.chain(spec_paths, env_paths) - extant_paths = list(filter(isdir, paths)) if exists else paths - if not extant_paths: - msg = "%s environment variable is empty" % name.upper() - raise distutils.errors.DistutilsPlatformError(msg) - unique_paths = unique_everseen(extant_paths) - return pathsep.join(unique_paths) diff --git a/spaces/pknez/face-swap-docker/run.py b/spaces/pknez/face-swap-docker/run.py deleted file mode 100644 index b52e5cc4a8ea9ce5cadd4e7111fb15531f380314..0000000000000000000000000000000000000000 --- a/spaces/pknez/face-swap-docker/run.py +++ /dev/null @@ -1,6 +0,0 @@ -#!/usr/bin/env python3 - -from roop import core - -if __name__ == '__main__': - core.run() diff --git a/spaces/plzdontcry/dakubettergpt/src/components/Chat/ChatContent/Message/View/Button/DownButton.tsx b/spaces/plzdontcry/dakubettergpt/src/components/Chat/ChatContent/Message/View/Button/DownButton.tsx deleted file mode 100644 index b42d836d425a70e63112fe9f7826397be5d6065e..0000000000000000000000000000000000000000 --- a/spaces/plzdontcry/dakubettergpt/src/components/Chat/ChatContent/Message/View/Button/DownButton.tsx +++ /dev/null @@ -1,21 +0,0 @@ -import React from 'react'; - -import DownChevronArrow from '@icon/DownChevronArrow'; - -import BaseButton from './BaseButton'; - -const DownButton = ({ - onClick, -}: { - onClick: React.MouseEventHandler; -}) => { - return ( - } - buttonProps={{ 'aria-label': 'shift message down' }} - onClick={onClick} - /> - ); -}; - -export default DownButton; diff --git a/spaces/plzdontcry/dakubettergpt/src/components/ImportExportChat/ImportChatOpenAI.tsx b/spaces/plzdontcry/dakubettergpt/src/components/ImportExportChat/ImportChatOpenAI.tsx deleted file mode 100644 index b3022dcd4d6e553b49fd9aca777f820b11f01260..0000000000000000000000000000000000000000 --- a/spaces/plzdontcry/dakubettergpt/src/components/ImportExportChat/ImportChatOpenAI.tsx +++ /dev/null @@ -1,79 +0,0 @@ -import React, { useRef } from 'react'; -import { useTranslation } from 'react-i18next'; - -import useStore from '@store/store'; - -import { importOpenAIChatExport } from '@utils/import'; - -import { ChatInterface } from '@type/chat'; - -const ImportChatOpenAI = ({ - setIsModalOpen, -}: { - setIsModalOpen: React.Dispatch>; -}) => { - const { t } = useTranslation(); - - const inputRef = useRef(null); - - const setToastStatus = useStore((state) => state.setToastStatus); - const setToastMessage = useStore((state) => state.setToastMessage); - const setToastShow = useStore((state) => state.setToastShow); - const setChats = useStore.getState().setChats; - - const handleFileUpload = () => { - if (!inputRef || !inputRef.current) return; - const file = inputRef.current.files?.[0]; - if (!file) return; - - const reader = new FileReader(); - - reader.onload = (event) => { - const data = event.target?.result as string; - - try { - const parsedData = JSON.parse(data); - const chats = importOpenAIChatExport(parsedData); - const prevChats: ChatInterface[] = JSON.parse( - JSON.stringify(useStore.getState().chats) - ); - setChats(chats.concat(prevChats)); - - setToastStatus('success'); - setToastMessage('Imported successfully!'); - setIsModalOpen(false); - } catch (error: unknown) { - setToastStatus('error'); - setToastMessage(`Invalid format! ${(error as Error).message}`); - } - setToastShow(true); - }; - - reader.readAsText(file); - }; - - return ( - <> -
        - {t('import')} OpenAI ChatGPT {t('export')} -
        - - - - - ); -}; - -export default ImportChatOpenAI; diff --git a/spaces/power2/JoJoGan-powerhow2/e4e/criteria/id_loss.py b/spaces/power2/JoJoGan-powerhow2/e4e/criteria/id_loss.py deleted file mode 100644 index bab806172eff18c0630536ae96817508c3197b8b..0000000000000000000000000000000000000000 --- a/spaces/power2/JoJoGan-powerhow2/e4e/criteria/id_loss.py +++ /dev/null @@ -1,47 +0,0 @@ -import torch -from torch import nn -from configs.paths_config import model_paths -from models.encoders.model_irse import Backbone - - -class IDLoss(nn.Module): - def __init__(self): - super(IDLoss, self).__init__() - print('Loading ResNet ArcFace') - self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se') - self.facenet.load_state_dict(torch.load(model_paths['ir_se50'])) - self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112)) - self.facenet.eval() - for module in [self.facenet, self.face_pool]: - for param in module.parameters(): - param.requires_grad = False - - def extract_feats(self, x): - x = x[:, :, 35:223, 32:220] # Crop interesting region - x = self.face_pool(x) - x_feats = self.facenet(x) - return x_feats - - def forward(self, y_hat, y, x): - n_samples = x.shape[0] - x_feats = self.extract_feats(x) - y_feats = self.extract_feats(y) # Otherwise use the feature from there - y_hat_feats = self.extract_feats(y_hat) - y_feats = y_feats.detach() - loss = 0 - sim_improvement = 0 - id_logs = [] - count = 0 - for i in range(n_samples): - diff_target = y_hat_feats[i].dot(y_feats[i]) - diff_input = y_hat_feats[i].dot(x_feats[i]) - diff_views = y_feats[i].dot(x_feats[i]) - id_logs.append({'diff_target': float(diff_target), - 'diff_input': float(diff_input), - 'diff_views': float(diff_views)}) - loss += 1 - diff_target - id_diff = float(diff_target) - float(diff_views) - sim_improvement += id_diff - count += 1 - - return loss / count, sim_improvement / count, id_logs diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/SpiderImagePlugin.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/SpiderImagePlugin.py deleted file mode 100644 index 408b982b515e7a120faa173295c628e0d7ba0265..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/PIL/SpiderImagePlugin.py +++ /dev/null @@ -1,318 +0,0 @@ -# -# The Python Imaging Library. -# -# SPIDER image file handling -# -# History: -# 2004-08-02 Created BB -# 2006-03-02 added save method -# 2006-03-13 added support for stack images -# -# Copyright (c) 2004 by Health Research Inc. (HRI) RENSSELAER, NY 12144. -# Copyright (c) 2004 by William Baxter. -# Copyright (c) 2004 by Secret Labs AB. -# Copyright (c) 2004 by Fredrik Lundh. -# - -## -# Image plugin for the Spider image format. This format is used -# by the SPIDER software, in processing image data from electron -# microscopy and tomography. -## - -# -# SpiderImagePlugin.py -# -# The Spider image format is used by SPIDER software, in processing -# image data from electron microscopy and tomography. -# -# Spider home page: -# https://spider.wadsworth.org/spider_doc/spider/docs/spider.html -# -# Details about the Spider image format: -# https://spider.wadsworth.org/spider_doc/spider/docs/image_doc.html -# -import os -import struct -import sys - -from . import Image, ImageFile - - -def isInt(f): - try: - i = int(f) - if f - i == 0: - return 1 - else: - return 0 - except (ValueError, OverflowError): - return 0 - - -iforms = [1, 3, -11, -12, -21, -22] - - -# There is no magic number to identify Spider files, so just check a -# series of header locations to see if they have reasonable values. -# Returns no. of bytes in the header, if it is a valid Spider header, -# otherwise returns 0 - - -def isSpiderHeader(t): - h = (99,) + t # add 1 value so can use spider header index start=1 - # header values 1,2,5,12,13,22,23 should be integers - for i in [1, 2, 5, 12, 13, 22, 23]: - if not isInt(h[i]): - return 0 - # check iform - iform = int(h[5]) - if iform not in iforms: - return 0 - # check other header values - labrec = int(h[13]) # no. records in file header - labbyt = int(h[22]) # total no. of bytes in header - lenbyt = int(h[23]) # record length in bytes - if labbyt != (labrec * lenbyt): - return 0 - # looks like a valid header - return labbyt - - -def isSpiderImage(filename): - with open(filename, "rb") as fp: - f = fp.read(92) # read 23 * 4 bytes - t = struct.unpack(">23f", f) # try big-endian first - hdrlen = isSpiderHeader(t) - if hdrlen == 0: - t = struct.unpack("<23f", f) # little-endian - hdrlen = isSpiderHeader(t) - return hdrlen - - -class SpiderImageFile(ImageFile.ImageFile): - format = "SPIDER" - format_description = "Spider 2D image" - _close_exclusive_fp_after_loading = False - - def _open(self): - # check header - n = 27 * 4 # read 27 float values - f = self.fp.read(n) - - try: - self.bigendian = 1 - t = struct.unpack(">27f", f) # try big-endian first - hdrlen = isSpiderHeader(t) - if hdrlen == 0: - self.bigendian = 0 - t = struct.unpack("<27f", f) # little-endian - hdrlen = isSpiderHeader(t) - if hdrlen == 0: - msg = "not a valid Spider file" - raise SyntaxError(msg) - except struct.error as e: - msg = "not a valid Spider file" - raise SyntaxError(msg) from e - - h = (99,) + t # add 1 value : spider header index starts at 1 - iform = int(h[5]) - if iform != 1: - msg = "not a Spider 2D image" - raise SyntaxError(msg) - - self._size = int(h[12]), int(h[2]) # size in pixels (width, height) - self.istack = int(h[24]) - self.imgnumber = int(h[27]) - - if self.istack == 0 and self.imgnumber == 0: - # stk=0, img=0: a regular 2D image - offset = hdrlen - self._nimages = 1 - elif self.istack > 0 and self.imgnumber == 0: - # stk>0, img=0: Opening the stack for the first time - self.imgbytes = int(h[12]) * int(h[2]) * 4 - self.hdrlen = hdrlen - self._nimages = int(h[26]) - # Point to the first image in the stack - offset = hdrlen * 2 - self.imgnumber = 1 - elif self.istack == 0 and self.imgnumber > 0: - # stk=0, img>0: an image within the stack - offset = hdrlen + self.stkoffset - self.istack = 2 # So Image knows it's still a stack - else: - msg = "inconsistent stack header values" - raise SyntaxError(msg) - - if self.bigendian: - self.rawmode = "F;32BF" - else: - self.rawmode = "F;32F" - self._mode = "F" - - self.tile = [("raw", (0, 0) + self.size, offset, (self.rawmode, 0, 1))] - self._fp = self.fp # FIXME: hack - - @property - def n_frames(self): - return self._nimages - - @property - def is_animated(self): - return self._nimages > 1 - - # 1st image index is zero (although SPIDER imgnumber starts at 1) - def tell(self): - if self.imgnumber < 1: - return 0 - else: - return self.imgnumber - 1 - - def seek(self, frame): - if self.istack == 0: - msg = "attempt to seek in a non-stack file" - raise EOFError(msg) - if not self._seek_check(frame): - return - self.stkoffset = self.hdrlen + frame * (self.hdrlen + self.imgbytes) - self.fp = self._fp - self.fp.seek(self.stkoffset) - self._open() - - # returns a byte image after rescaling to 0..255 - def convert2byte(self, depth=255): - (minimum, maximum) = self.getextrema() - m = 1 - if maximum != minimum: - m = depth / (maximum - minimum) - b = -m * minimum - return self.point(lambda i, m=m, b=b: i * m + b).convert("L") - - # returns a ImageTk.PhotoImage object, after rescaling to 0..255 - def tkPhotoImage(self): - from . import ImageTk - - return ImageTk.PhotoImage(self.convert2byte(), palette=256) - - -# -------------------------------------------------------------------- -# Image series - - -# given a list of filenames, return a list of images -def loadImageSeries(filelist=None): - """create a list of :py:class:`~PIL.Image.Image` objects for use in a montage""" - if filelist is None or len(filelist) < 1: - return - - imglist = [] - for img in filelist: - if not os.path.exists(img): - print(f"unable to find {img}") - continue - try: - with Image.open(img) as im: - im = im.convert2byte() - except Exception: - if not isSpiderImage(img): - print(img + " is not a Spider image file") - continue - im.info["filename"] = img - imglist.append(im) - return imglist - - -# -------------------------------------------------------------------- -# For saving images in Spider format - - -def makeSpiderHeader(im): - nsam, nrow = im.size - lenbyt = nsam * 4 # There are labrec records in the header - labrec = int(1024 / lenbyt) - if 1024 % lenbyt != 0: - labrec += 1 - labbyt = labrec * lenbyt - nvalues = int(labbyt / 4) - if nvalues < 23: - return [] - - hdr = [] - for i in range(nvalues): - hdr.append(0.0) - - # NB these are Fortran indices - hdr[1] = 1.0 # nslice (=1 for an image) - hdr[2] = float(nrow) # number of rows per slice - hdr[3] = float(nrow) # number of records in the image - hdr[5] = 1.0 # iform for 2D image - hdr[12] = float(nsam) # number of pixels per line - hdr[13] = float(labrec) # number of records in file header - hdr[22] = float(labbyt) # total number of bytes in header - hdr[23] = float(lenbyt) # record length in bytes - - # adjust for Fortran indexing - hdr = hdr[1:] - hdr.append(0.0) - # pack binary data into a string - return [struct.pack("f", v) for v in hdr] - - -def _save(im, fp, filename): - if im.mode[0] != "F": - im = im.convert("F") - - hdr = makeSpiderHeader(im) - if len(hdr) < 256: - msg = "Error creating Spider header" - raise OSError(msg) - - # write the SPIDER header - fp.writelines(hdr) - - rawmode = "F;32NF" # 32-bit native floating point - ImageFile._save(im, fp, [("raw", (0, 0) + im.size, 0, (rawmode, 0, 1))]) - - -def _save_spider(im, fp, filename): - # get the filename extension and register it with Image - ext = os.path.splitext(filename)[1] - Image.register_extension(SpiderImageFile.format, ext) - _save(im, fp, filename) - - -# -------------------------------------------------------------------- - - -Image.register_open(SpiderImageFile.format, SpiderImageFile) -Image.register_save(SpiderImageFile.format, _save_spider) - -if __name__ == "__main__": - if len(sys.argv) < 2: - print("Syntax: python3 SpiderImagePlugin.py [infile] [outfile]") - sys.exit() - - filename = sys.argv[1] - if not isSpiderImage(filename): - print("input image must be in Spider format") - sys.exit() - - with Image.open(filename) as im: - print("image: " + str(im)) - print("format: " + str(im.format)) - print("size: " + str(im.size)) - print("mode: " + str(im.mode)) - print("max, min: ", end=" ") - print(im.getextrema()) - - if len(sys.argv) > 2: - outfile = sys.argv[2] - - # perform some image operation - im = im.transpose(Image.Transpose.FLIP_LEFT_RIGHT) - print( - f"saving a flipped version of {os.path.basename(filename)} " - f"as {outfile} " - ) - im.save(outfile, SpiderImageFile.format) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/_common.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/_common.py deleted file mode 100644 index e6ac11831522b266114d5b68ee1da298e3aeb14a..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/_common.py +++ /dev/null @@ -1,419 +0,0 @@ -from six import PY2 - -from functools import wraps - -from datetime import datetime, timedelta, tzinfo - - -ZERO = timedelta(0) - -__all__ = ['tzname_in_python2', 'enfold'] - - -def tzname_in_python2(namefunc): - """Change unicode output into bytestrings in Python 2 - - tzname() API changed in Python 3. It used to return bytes, but was changed - to unicode strings - """ - if PY2: - @wraps(namefunc) - def adjust_encoding(*args, **kwargs): - name = namefunc(*args, **kwargs) - if name is not None: - name = name.encode() - - return name - - return adjust_encoding - else: - return namefunc - - -# The following is adapted from Alexander Belopolsky's tz library -# https://github.com/abalkin/tz -if hasattr(datetime, 'fold'): - # This is the pre-python 3.6 fold situation - def enfold(dt, fold=1): - """ - Provides a unified interface for assigning the ``fold`` attribute to - datetimes both before and after the implementation of PEP-495. - - :param fold: - The value for the ``fold`` attribute in the returned datetime. This - should be either 0 or 1. - - :return: - Returns an object for which ``getattr(dt, 'fold', 0)`` returns - ``fold`` for all versions of Python. In versions prior to - Python 3.6, this is a ``_DatetimeWithFold`` object, which is a - subclass of :py:class:`datetime.datetime` with the ``fold`` - attribute added, if ``fold`` is 1. - - .. versionadded:: 2.6.0 - """ - return dt.replace(fold=fold) - -else: - class _DatetimeWithFold(datetime): - """ - This is a class designed to provide a PEP 495-compliant interface for - Python versions before 3.6. It is used only for dates in a fold, so - the ``fold`` attribute is fixed at ``1``. - - .. versionadded:: 2.6.0 - """ - __slots__ = () - - def replace(self, *args, **kwargs): - """ - Return a datetime with the same attributes, except for those - attributes given new values by whichever keyword arguments are - specified. Note that tzinfo=None can be specified to create a naive - datetime from an aware datetime with no conversion of date and time - data. - - This is reimplemented in ``_DatetimeWithFold`` because pypy3 will - return a ``datetime.datetime`` even if ``fold`` is unchanged. - """ - argnames = ( - 'year', 'month', 'day', 'hour', 'minute', 'second', - 'microsecond', 'tzinfo' - ) - - for arg, argname in zip(args, argnames): - if argname in kwargs: - raise TypeError('Duplicate argument: {}'.format(argname)) - - kwargs[argname] = arg - - for argname in argnames: - if argname not in kwargs: - kwargs[argname] = getattr(self, argname) - - dt_class = self.__class__ if kwargs.get('fold', 1) else datetime - - return dt_class(**kwargs) - - @property - def fold(self): - return 1 - - def enfold(dt, fold=1): - """ - Provides a unified interface for assigning the ``fold`` attribute to - datetimes both before and after the implementation of PEP-495. - - :param fold: - The value for the ``fold`` attribute in the returned datetime. This - should be either 0 or 1. - - :return: - Returns an object for which ``getattr(dt, 'fold', 0)`` returns - ``fold`` for all versions of Python. In versions prior to - Python 3.6, this is a ``_DatetimeWithFold`` object, which is a - subclass of :py:class:`datetime.datetime` with the ``fold`` - attribute added, if ``fold`` is 1. - - .. versionadded:: 2.6.0 - """ - if getattr(dt, 'fold', 0) == fold: - return dt - - args = dt.timetuple()[:6] - args += (dt.microsecond, dt.tzinfo) - - if fold: - return _DatetimeWithFold(*args) - else: - return datetime(*args) - - -def _validate_fromutc_inputs(f): - """ - The CPython version of ``fromutc`` checks that the input is a ``datetime`` - object and that ``self`` is attached as its ``tzinfo``. - """ - @wraps(f) - def fromutc(self, dt): - if not isinstance(dt, datetime): - raise TypeError("fromutc() requires a datetime argument") - if dt.tzinfo is not self: - raise ValueError("dt.tzinfo is not self") - - return f(self, dt) - - return fromutc - - -class _tzinfo(tzinfo): - """ - Base class for all ``dateutil`` ``tzinfo`` objects. - """ - - def is_ambiguous(self, dt): - """ - Whether or not the "wall time" of a given datetime is ambiguous in this - zone. - - :param dt: - A :py:class:`datetime.datetime`, naive or time zone aware. - - - :return: - Returns ``True`` if ambiguous, ``False`` otherwise. - - .. versionadded:: 2.6.0 - """ - - dt = dt.replace(tzinfo=self) - - wall_0 = enfold(dt, fold=0) - wall_1 = enfold(dt, fold=1) - - same_offset = wall_0.utcoffset() == wall_1.utcoffset() - same_dt = wall_0.replace(tzinfo=None) == wall_1.replace(tzinfo=None) - - return same_dt and not same_offset - - def _fold_status(self, dt_utc, dt_wall): - """ - Determine the fold status of a "wall" datetime, given a representation - of the same datetime as a (naive) UTC datetime. This is calculated based - on the assumption that ``dt.utcoffset() - dt.dst()`` is constant for all - datetimes, and that this offset is the actual number of hours separating - ``dt_utc`` and ``dt_wall``. - - :param dt_utc: - Representation of the datetime as UTC - - :param dt_wall: - Representation of the datetime as "wall time". This parameter must - either have a `fold` attribute or have a fold-naive - :class:`datetime.tzinfo` attached, otherwise the calculation may - fail. - """ - if self.is_ambiguous(dt_wall): - delta_wall = dt_wall - dt_utc - _fold = int(delta_wall == (dt_utc.utcoffset() - dt_utc.dst())) - else: - _fold = 0 - - return _fold - - def _fold(self, dt): - return getattr(dt, 'fold', 0) - - def _fromutc(self, dt): - """ - Given a timezone-aware datetime in a given timezone, calculates a - timezone-aware datetime in a new timezone. - - Since this is the one time that we *know* we have an unambiguous - datetime object, we take this opportunity to determine whether the - datetime is ambiguous and in a "fold" state (e.g. if it's the first - occurrence, chronologically, of the ambiguous datetime). - - :param dt: - A timezone-aware :class:`datetime.datetime` object. - """ - - # Re-implement the algorithm from Python's datetime.py - dtoff = dt.utcoffset() - if dtoff is None: - raise ValueError("fromutc() requires a non-None utcoffset() " - "result") - - # The original datetime.py code assumes that `dst()` defaults to - # zero during ambiguous times. PEP 495 inverts this presumption, so - # for pre-PEP 495 versions of python, we need to tweak the algorithm. - dtdst = dt.dst() - if dtdst is None: - raise ValueError("fromutc() requires a non-None dst() result") - delta = dtoff - dtdst - - dt += delta - # Set fold=1 so we can default to being in the fold for - # ambiguous dates. - dtdst = enfold(dt, fold=1).dst() - if dtdst is None: - raise ValueError("fromutc(): dt.dst gave inconsistent " - "results; cannot convert") - return dt + dtdst - - @_validate_fromutc_inputs - def fromutc(self, dt): - """ - Given a timezone-aware datetime in a given timezone, calculates a - timezone-aware datetime in a new timezone. - - Since this is the one time that we *know* we have an unambiguous - datetime object, we take this opportunity to determine whether the - datetime is ambiguous and in a "fold" state (e.g. if it's the first - occurrence, chronologically, of the ambiguous datetime). - - :param dt: - A timezone-aware :class:`datetime.datetime` object. - """ - dt_wall = self._fromutc(dt) - - # Calculate the fold status given the two datetimes. - _fold = self._fold_status(dt, dt_wall) - - # Set the default fold value for ambiguous dates - return enfold(dt_wall, fold=_fold) - - -class tzrangebase(_tzinfo): - """ - This is an abstract base class for time zones represented by an annual - transition into and out of DST. Child classes should implement the following - methods: - - * ``__init__(self, *args, **kwargs)`` - * ``transitions(self, year)`` - this is expected to return a tuple of - datetimes representing the DST on and off transitions in standard - time. - - A fully initialized ``tzrangebase`` subclass should also provide the - following attributes: - * ``hasdst``: Boolean whether or not the zone uses DST. - * ``_dst_offset`` / ``_std_offset``: :class:`datetime.timedelta` objects - representing the respective UTC offsets. - * ``_dst_abbr`` / ``_std_abbr``: Strings representing the timezone short - abbreviations in DST and STD, respectively. - * ``_hasdst``: Whether or not the zone has DST. - - .. versionadded:: 2.6.0 - """ - def __init__(self): - raise NotImplementedError('tzrangebase is an abstract base class') - - def utcoffset(self, dt): - isdst = self._isdst(dt) - - if isdst is None: - return None - elif isdst: - return self._dst_offset - else: - return self._std_offset - - def dst(self, dt): - isdst = self._isdst(dt) - - if isdst is None: - return None - elif isdst: - return self._dst_base_offset - else: - return ZERO - - @tzname_in_python2 - def tzname(self, dt): - if self._isdst(dt): - return self._dst_abbr - else: - return self._std_abbr - - def fromutc(self, dt): - """ Given a datetime in UTC, return local time """ - if not isinstance(dt, datetime): - raise TypeError("fromutc() requires a datetime argument") - - if dt.tzinfo is not self: - raise ValueError("dt.tzinfo is not self") - - # Get transitions - if there are none, fixed offset - transitions = self.transitions(dt.year) - if transitions is None: - return dt + self.utcoffset(dt) - - # Get the transition times in UTC - dston, dstoff = transitions - - dston -= self._std_offset - dstoff -= self._std_offset - - utc_transitions = (dston, dstoff) - dt_utc = dt.replace(tzinfo=None) - - isdst = self._naive_isdst(dt_utc, utc_transitions) - - if isdst: - dt_wall = dt + self._dst_offset - else: - dt_wall = dt + self._std_offset - - _fold = int(not isdst and self.is_ambiguous(dt_wall)) - - return enfold(dt_wall, fold=_fold) - - def is_ambiguous(self, dt): - """ - Whether or not the "wall time" of a given datetime is ambiguous in this - zone. - - :param dt: - A :py:class:`datetime.datetime`, naive or time zone aware. - - - :return: - Returns ``True`` if ambiguous, ``False`` otherwise. - - .. versionadded:: 2.6.0 - """ - if not self.hasdst: - return False - - start, end = self.transitions(dt.year) - - dt = dt.replace(tzinfo=None) - return (end <= dt < end + self._dst_base_offset) - - def _isdst(self, dt): - if not self.hasdst: - return False - elif dt is None: - return None - - transitions = self.transitions(dt.year) - - if transitions is None: - return False - - dt = dt.replace(tzinfo=None) - - isdst = self._naive_isdst(dt, transitions) - - # Handle ambiguous dates - if not isdst and self.is_ambiguous(dt): - return not self._fold(dt) - else: - return isdst - - def _naive_isdst(self, dt, transitions): - dston, dstoff = transitions - - dt = dt.replace(tzinfo=None) - - if dston < dstoff: - isdst = dston <= dt < dstoff - else: - isdst = not dstoff <= dt < dston - - return isdst - - @property - def _dst_base_offset(self): - return self._dst_offset - self._std_offset - - __hash__ = None - - def __ne__(self, other): - return not (self == other) - - def __repr__(self): - return "%s(...)" % self.__class__.__name__ - - __reduce__ = object.__reduce__ diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/tz.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/tz.py deleted file mode 100644 index c67f56d4659f17aab4540dfd42511bb850871a77..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/dateutil/tz/tz.py +++ /dev/null @@ -1,1849 +0,0 @@ -# -*- coding: utf-8 -*- -""" -This module offers timezone implementations subclassing the abstract -:py:class:`datetime.tzinfo` type. There are classes to handle tzfile format -files (usually are in :file:`/etc/localtime`, :file:`/usr/share/zoneinfo`, -etc), TZ environment string (in all known formats), given ranges (with help -from relative deltas), local machine timezone, fixed offset timezone, and UTC -timezone. -""" -import datetime -import struct -import time -import sys -import os -import bisect -import weakref -from collections import OrderedDict - -import six -from six import string_types -from six.moves import _thread -from ._common import tzname_in_python2, _tzinfo -from ._common import tzrangebase, enfold -from ._common import _validate_fromutc_inputs - -from ._factories import _TzSingleton, _TzOffsetFactory -from ._factories import _TzStrFactory -try: - from .win import tzwin, tzwinlocal -except ImportError: - tzwin = tzwinlocal = None - -# For warning about rounding tzinfo -from warnings import warn - -ZERO = datetime.timedelta(0) -EPOCH = datetime.datetime.utcfromtimestamp(0) -EPOCHORDINAL = EPOCH.toordinal() - - -@six.add_metaclass(_TzSingleton) -class tzutc(datetime.tzinfo): - """ - This is a tzinfo object that represents the UTC time zone. - - **Examples:** - - .. doctest:: - - >>> from datetime import * - >>> from dateutil.tz import * - - >>> datetime.now() - datetime.datetime(2003, 9, 27, 9, 40, 1, 521290) - - >>> datetime.now(tzutc()) - datetime.datetime(2003, 9, 27, 12, 40, 12, 156379, tzinfo=tzutc()) - - >>> datetime.now(tzutc()).tzname() - 'UTC' - - .. versionchanged:: 2.7.0 - ``tzutc()`` is now a singleton, so the result of ``tzutc()`` will - always return the same object. - - .. doctest:: - - >>> from dateutil.tz import tzutc, UTC - >>> tzutc() is tzutc() - True - >>> tzutc() is UTC - True - """ - def utcoffset(self, dt): - return ZERO - - def dst(self, dt): - return ZERO - - @tzname_in_python2 - def tzname(self, dt): - return "UTC" - - def is_ambiguous(self, dt): - """ - Whether or not the "wall time" of a given datetime is ambiguous in this - zone. - - :param dt: - A :py:class:`datetime.datetime`, naive or time zone aware. - - - :return: - Returns ``True`` if ambiguous, ``False`` otherwise. - - .. versionadded:: 2.6.0 - """ - return False - - @_validate_fromutc_inputs - def fromutc(self, dt): - """ - Fast track version of fromutc() returns the original ``dt`` object for - any valid :py:class:`datetime.datetime` object. - """ - return dt - - def __eq__(self, other): - if not isinstance(other, (tzutc, tzoffset)): - return NotImplemented - - return (isinstance(other, tzutc) or - (isinstance(other, tzoffset) and other._offset == ZERO)) - - __hash__ = None - - def __ne__(self, other): - return not (self == other) - - def __repr__(self): - return "%s()" % self.__class__.__name__ - - __reduce__ = object.__reduce__ - - -#: Convenience constant providing a :class:`tzutc()` instance -#: -#: .. versionadded:: 2.7.0 -UTC = tzutc() - - -@six.add_metaclass(_TzOffsetFactory) -class tzoffset(datetime.tzinfo): - """ - A simple class for representing a fixed offset from UTC. - - :param name: - The timezone name, to be returned when ``tzname()`` is called. - :param offset: - The time zone offset in seconds, or (since version 2.6.0, represented - as a :py:class:`datetime.timedelta` object). - """ - def __init__(self, name, offset): - self._name = name - - try: - # Allow a timedelta - offset = offset.total_seconds() - except (TypeError, AttributeError): - pass - - self._offset = datetime.timedelta(seconds=_get_supported_offset(offset)) - - def utcoffset(self, dt): - return self._offset - - def dst(self, dt): - return ZERO - - @tzname_in_python2 - def tzname(self, dt): - return self._name - - @_validate_fromutc_inputs - def fromutc(self, dt): - return dt + self._offset - - def is_ambiguous(self, dt): - """ - Whether or not the "wall time" of a given datetime is ambiguous in this - zone. - - :param dt: - A :py:class:`datetime.datetime`, naive or time zone aware. - :return: - Returns ``True`` if ambiguous, ``False`` otherwise. - - .. versionadded:: 2.6.0 - """ - return False - - def __eq__(self, other): - if not isinstance(other, tzoffset): - return NotImplemented - - return self._offset == other._offset - - __hash__ = None - - def __ne__(self, other): - return not (self == other) - - def __repr__(self): - return "%s(%s, %s)" % (self.__class__.__name__, - repr(self._name), - int(self._offset.total_seconds())) - - __reduce__ = object.__reduce__ - - -class tzlocal(_tzinfo): - """ - A :class:`tzinfo` subclass built around the ``time`` timezone functions. - """ - def __init__(self): - super(tzlocal, self).__init__() - - self._std_offset = datetime.timedelta(seconds=-time.timezone) - if time.daylight: - self._dst_offset = datetime.timedelta(seconds=-time.altzone) - else: - self._dst_offset = self._std_offset - - self._dst_saved = self._dst_offset - self._std_offset - self._hasdst = bool(self._dst_saved) - self._tznames = tuple(time.tzname) - - def utcoffset(self, dt): - if dt is None and self._hasdst: - return None - - if self._isdst(dt): - return self._dst_offset - else: - return self._std_offset - - def dst(self, dt): - if dt is None and self._hasdst: - return None - - if self._isdst(dt): - return self._dst_offset - self._std_offset - else: - return ZERO - - @tzname_in_python2 - def tzname(self, dt): - return self._tznames[self._isdst(dt)] - - def is_ambiguous(self, dt): - """ - Whether or not the "wall time" of a given datetime is ambiguous in this - zone. - - :param dt: - A :py:class:`datetime.datetime`, naive or time zone aware. - - - :return: - Returns ``True`` if ambiguous, ``False`` otherwise. - - .. versionadded:: 2.6.0 - """ - naive_dst = self._naive_is_dst(dt) - return (not naive_dst and - (naive_dst != self._naive_is_dst(dt - self._dst_saved))) - - def _naive_is_dst(self, dt): - timestamp = _datetime_to_timestamp(dt) - return time.localtime(timestamp + time.timezone).tm_isdst - - def _isdst(self, dt, fold_naive=True): - # We can't use mktime here. It is unstable when deciding if - # the hour near to a change is DST or not. - # - # timestamp = time.mktime((dt.year, dt.month, dt.day, dt.hour, - # dt.minute, dt.second, dt.weekday(), 0, -1)) - # return time.localtime(timestamp).tm_isdst - # - # The code above yields the following result: - # - # >>> import tz, datetime - # >>> t = tz.tzlocal() - # >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname() - # 'BRDT' - # >>> datetime.datetime(2003,2,16,0,tzinfo=t).tzname() - # 'BRST' - # >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname() - # 'BRST' - # >>> datetime.datetime(2003,2,15,22,tzinfo=t).tzname() - # 'BRDT' - # >>> datetime.datetime(2003,2,15,23,tzinfo=t).tzname() - # 'BRDT' - # - # Here is a more stable implementation: - # - if not self._hasdst: - return False - - # Check for ambiguous times: - dstval = self._naive_is_dst(dt) - fold = getattr(dt, 'fold', None) - - if self.is_ambiguous(dt): - if fold is not None: - return not self._fold(dt) - else: - return True - - return dstval - - def __eq__(self, other): - if isinstance(other, tzlocal): - return (self._std_offset == other._std_offset and - self._dst_offset == other._dst_offset) - elif isinstance(other, tzutc): - return (not self._hasdst and - self._tznames[0] in {'UTC', 'GMT'} and - self._std_offset == ZERO) - elif isinstance(other, tzoffset): - return (not self._hasdst and - self._tznames[0] == other._name and - self._std_offset == other._offset) - else: - return NotImplemented - - __hash__ = None - - def __ne__(self, other): - return not (self == other) - - def __repr__(self): - return "%s()" % self.__class__.__name__ - - __reduce__ = object.__reduce__ - - -class _ttinfo(object): - __slots__ = ["offset", "delta", "isdst", "abbr", - "isstd", "isgmt", "dstoffset"] - - def __init__(self): - for attr in self.__slots__: - setattr(self, attr, None) - - def __repr__(self): - l = [] - for attr in self.__slots__: - value = getattr(self, attr) - if value is not None: - l.append("%s=%s" % (attr, repr(value))) - return "%s(%s)" % (self.__class__.__name__, ", ".join(l)) - - def __eq__(self, other): - if not isinstance(other, _ttinfo): - return NotImplemented - - return (self.offset == other.offset and - self.delta == other.delta and - self.isdst == other.isdst and - self.abbr == other.abbr and - self.isstd == other.isstd and - self.isgmt == other.isgmt and - self.dstoffset == other.dstoffset) - - __hash__ = None - - def __ne__(self, other): - return not (self == other) - - def __getstate__(self): - state = {} - for name in self.__slots__: - state[name] = getattr(self, name, None) - return state - - def __setstate__(self, state): - for name in self.__slots__: - if name in state: - setattr(self, name, state[name]) - - -class _tzfile(object): - """ - Lightweight class for holding the relevant transition and time zone - information read from binary tzfiles. - """ - attrs = ['trans_list', 'trans_list_utc', 'trans_idx', 'ttinfo_list', - 'ttinfo_std', 'ttinfo_dst', 'ttinfo_before', 'ttinfo_first'] - - def __init__(self, **kwargs): - for attr in self.attrs: - setattr(self, attr, kwargs.get(attr, None)) - - -class tzfile(_tzinfo): - """ - This is a ``tzinfo`` subclass that allows one to use the ``tzfile(5)`` - format timezone files to extract current and historical zone information. - - :param fileobj: - This can be an opened file stream or a file name that the time zone - information can be read from. - - :param filename: - This is an optional parameter specifying the source of the time zone - information in the event that ``fileobj`` is a file object. If omitted - and ``fileobj`` is a file stream, this parameter will be set either to - ``fileobj``'s ``name`` attribute or to ``repr(fileobj)``. - - See `Sources for Time Zone and Daylight Saving Time Data - `_ for more information. - Time zone files can be compiled from the `IANA Time Zone database files - `_ with the `zic time zone compiler - `_ - - .. note:: - - Only construct a ``tzfile`` directly if you have a specific timezone - file on disk that you want to read into a Python ``tzinfo`` object. - If you want to get a ``tzfile`` representing a specific IANA zone, - (e.g. ``'America/New_York'``), you should call - :func:`dateutil.tz.gettz` with the zone identifier. - - - **Examples:** - - Using the US Eastern time zone as an example, we can see that a ``tzfile`` - provides time zone information for the standard Daylight Saving offsets: - - .. testsetup:: tzfile - - from dateutil.tz import gettz - from datetime import datetime - - .. doctest:: tzfile - - >>> NYC = gettz('America/New_York') - >>> NYC - tzfile('/usr/share/zoneinfo/America/New_York') - - >>> print(datetime(2016, 1, 3, tzinfo=NYC)) # EST - 2016-01-03 00:00:00-05:00 - - >>> print(datetime(2016, 7, 7, tzinfo=NYC)) # EDT - 2016-07-07 00:00:00-04:00 - - - The ``tzfile`` structure contains a fully history of the time zone, - so historical dates will also have the right offsets. For example, before - the adoption of the UTC standards, New York used local solar mean time: - - .. doctest:: tzfile - - >>> print(datetime(1901, 4, 12, tzinfo=NYC)) # LMT - 1901-04-12 00:00:00-04:56 - - And during World War II, New York was on "Eastern War Time", which was a - state of permanent daylight saving time: - - .. doctest:: tzfile - - >>> print(datetime(1944, 2, 7, tzinfo=NYC)) # EWT - 1944-02-07 00:00:00-04:00 - - """ - - def __init__(self, fileobj, filename=None): - super(tzfile, self).__init__() - - file_opened_here = False - if isinstance(fileobj, string_types): - self._filename = fileobj - fileobj = open(fileobj, 'rb') - file_opened_here = True - elif filename is not None: - self._filename = filename - elif hasattr(fileobj, "name"): - self._filename = fileobj.name - else: - self._filename = repr(fileobj) - - if fileobj is not None: - if not file_opened_here: - fileobj = _nullcontext(fileobj) - - with fileobj as file_stream: - tzobj = self._read_tzfile(file_stream) - - self._set_tzdata(tzobj) - - def _set_tzdata(self, tzobj): - """ Set the time zone data of this object from a _tzfile object """ - # Copy the relevant attributes over as private attributes - for attr in _tzfile.attrs: - setattr(self, '_' + attr, getattr(tzobj, attr)) - - def _read_tzfile(self, fileobj): - out = _tzfile() - - # From tzfile(5): - # - # The time zone information files used by tzset(3) - # begin with the magic characters "TZif" to identify - # them as time zone information files, followed by - # sixteen bytes reserved for future use, followed by - # six four-byte values of type long, written in a - # ``standard'' byte order (the high-order byte - # of the value is written first). - if fileobj.read(4).decode() != "TZif": - raise ValueError("magic not found") - - fileobj.read(16) - - ( - # The number of UTC/local indicators stored in the file. - ttisgmtcnt, - - # The number of standard/wall indicators stored in the file. - ttisstdcnt, - - # The number of leap seconds for which data is - # stored in the file. - leapcnt, - - # The number of "transition times" for which data - # is stored in the file. - timecnt, - - # The number of "local time types" for which data - # is stored in the file (must not be zero). - typecnt, - - # The number of characters of "time zone - # abbreviation strings" stored in the file. - charcnt, - - ) = struct.unpack(">6l", fileobj.read(24)) - - # The above header is followed by tzh_timecnt four-byte - # values of type long, sorted in ascending order. - # These values are written in ``standard'' byte order. - # Each is used as a transition time (as returned by - # time(2)) at which the rules for computing local time - # change. - - if timecnt: - out.trans_list_utc = list(struct.unpack(">%dl" % timecnt, - fileobj.read(timecnt*4))) - else: - out.trans_list_utc = [] - - # Next come tzh_timecnt one-byte values of type unsigned - # char; each one tells which of the different types of - # ``local time'' types described in the file is associated - # with the same-indexed transition time. These values - # serve as indices into an array of ttinfo structures that - # appears next in the file. - - if timecnt: - out.trans_idx = struct.unpack(">%dB" % timecnt, - fileobj.read(timecnt)) - else: - out.trans_idx = [] - - # Each ttinfo structure is written as a four-byte value - # for tt_gmtoff of type long, in a standard byte - # order, followed by a one-byte value for tt_isdst - # and a one-byte value for tt_abbrind. In each - # structure, tt_gmtoff gives the number of - # seconds to be added to UTC, tt_isdst tells whether - # tm_isdst should be set by localtime(3), and - # tt_abbrind serves as an index into the array of - # time zone abbreviation characters that follow the - # ttinfo structure(s) in the file. - - ttinfo = [] - - for i in range(typecnt): - ttinfo.append(struct.unpack(">lbb", fileobj.read(6))) - - abbr = fileobj.read(charcnt).decode() - - # Then there are tzh_leapcnt pairs of four-byte - # values, written in standard byte order; the - # first value of each pair gives the time (as - # returned by time(2)) at which a leap second - # occurs; the second gives the total number of - # leap seconds to be applied after the given time. - # The pairs of values are sorted in ascending order - # by time. - - # Not used, for now (but seek for correct file position) - if leapcnt: - fileobj.seek(leapcnt * 8, os.SEEK_CUR) - - # Then there are tzh_ttisstdcnt standard/wall - # indicators, each stored as a one-byte value; - # they tell whether the transition times associated - # with local time types were specified as standard - # time or wall clock time, and are used when - # a time zone file is used in handling POSIX-style - # time zone environment variables. - - if ttisstdcnt: - isstd = struct.unpack(">%db" % ttisstdcnt, - fileobj.read(ttisstdcnt)) - - # Finally, there are tzh_ttisgmtcnt UTC/local - # indicators, each stored as a one-byte value; - # they tell whether the transition times associated - # with local time types were specified as UTC or - # local time, and are used when a time zone file - # is used in handling POSIX-style time zone envi- - # ronment variables. - - if ttisgmtcnt: - isgmt = struct.unpack(">%db" % ttisgmtcnt, - fileobj.read(ttisgmtcnt)) - - # Build ttinfo list - out.ttinfo_list = [] - for i in range(typecnt): - gmtoff, isdst, abbrind = ttinfo[i] - gmtoff = _get_supported_offset(gmtoff) - tti = _ttinfo() - tti.offset = gmtoff - tti.dstoffset = datetime.timedelta(0) - tti.delta = datetime.timedelta(seconds=gmtoff) - tti.isdst = isdst - tti.abbr = abbr[abbrind:abbr.find('\x00', abbrind)] - tti.isstd = (ttisstdcnt > i and isstd[i] != 0) - tti.isgmt = (ttisgmtcnt > i and isgmt[i] != 0) - out.ttinfo_list.append(tti) - - # Replace ttinfo indexes for ttinfo objects. - out.trans_idx = [out.ttinfo_list[idx] for idx in out.trans_idx] - - # Set standard, dst, and before ttinfos. before will be - # used when a given time is before any transitions, - # and will be set to the first non-dst ttinfo, or to - # the first dst, if all of them are dst. - out.ttinfo_std = None - out.ttinfo_dst = None - out.ttinfo_before = None - if out.ttinfo_list: - if not out.trans_list_utc: - out.ttinfo_std = out.ttinfo_first = out.ttinfo_list[0] - else: - for i in range(timecnt-1, -1, -1): - tti = out.trans_idx[i] - if not out.ttinfo_std and not tti.isdst: - out.ttinfo_std = tti - elif not out.ttinfo_dst and tti.isdst: - out.ttinfo_dst = tti - - if out.ttinfo_std and out.ttinfo_dst: - break - else: - if out.ttinfo_dst and not out.ttinfo_std: - out.ttinfo_std = out.ttinfo_dst - - for tti in out.ttinfo_list: - if not tti.isdst: - out.ttinfo_before = tti - break - else: - out.ttinfo_before = out.ttinfo_list[0] - - # Now fix transition times to become relative to wall time. - # - # I'm not sure about this. In my tests, the tz source file - # is setup to wall time, and in the binary file isstd and - # isgmt are off, so it should be in wall time. OTOH, it's - # always in gmt time. Let me know if you have comments - # about this. - lastdst = None - lastoffset = None - lastdstoffset = None - lastbaseoffset = None - out.trans_list = [] - - for i, tti in enumerate(out.trans_idx): - offset = tti.offset - dstoffset = 0 - - if lastdst is not None: - if tti.isdst: - if not lastdst: - dstoffset = offset - lastoffset - - if not dstoffset and lastdstoffset: - dstoffset = lastdstoffset - - tti.dstoffset = datetime.timedelta(seconds=dstoffset) - lastdstoffset = dstoffset - - # If a time zone changes its base offset during a DST transition, - # then you need to adjust by the previous base offset to get the - # transition time in local time. Otherwise you use the current - # base offset. Ideally, I would have some mathematical proof of - # why this is true, but I haven't really thought about it enough. - baseoffset = offset - dstoffset - adjustment = baseoffset - if (lastbaseoffset is not None and baseoffset != lastbaseoffset - and tti.isdst != lastdst): - # The base DST has changed - adjustment = lastbaseoffset - - lastdst = tti.isdst - lastoffset = offset - lastbaseoffset = baseoffset - - out.trans_list.append(out.trans_list_utc[i] + adjustment) - - out.trans_idx = tuple(out.trans_idx) - out.trans_list = tuple(out.trans_list) - out.trans_list_utc = tuple(out.trans_list_utc) - - return out - - def _find_last_transition(self, dt, in_utc=False): - # If there's no list, there are no transitions to find - if not self._trans_list: - return None - - timestamp = _datetime_to_timestamp(dt) - - # Find where the timestamp fits in the transition list - if the - # timestamp is a transition time, it's part of the "after" period. - trans_list = self._trans_list_utc if in_utc else self._trans_list - idx = bisect.bisect_right(trans_list, timestamp) - - # We want to know when the previous transition was, so subtract off 1 - return idx - 1 - - def _get_ttinfo(self, idx): - # For no list or after the last transition, default to _ttinfo_std - if idx is None or (idx + 1) >= len(self._trans_list): - return self._ttinfo_std - - # If there is a list and the time is before it, return _ttinfo_before - if idx < 0: - return self._ttinfo_before - - return self._trans_idx[idx] - - def _find_ttinfo(self, dt): - idx = self._resolve_ambiguous_time(dt) - - return self._get_ttinfo(idx) - - def fromutc(self, dt): - """ - The ``tzfile`` implementation of :py:func:`datetime.tzinfo.fromutc`. - - :param dt: - A :py:class:`datetime.datetime` object. - - :raises TypeError: - Raised if ``dt`` is not a :py:class:`datetime.datetime` object. - - :raises ValueError: - Raised if this is called with a ``dt`` which does not have this - ``tzinfo`` attached. - - :return: - Returns a :py:class:`datetime.datetime` object representing the - wall time in ``self``'s time zone. - """ - # These isinstance checks are in datetime.tzinfo, so we'll preserve - # them, even if we don't care about duck typing. - if not isinstance(dt, datetime.datetime): - raise TypeError("fromutc() requires a datetime argument") - - if dt.tzinfo is not self: - raise ValueError("dt.tzinfo is not self") - - # First treat UTC as wall time and get the transition we're in. - idx = self._find_last_transition(dt, in_utc=True) - tti = self._get_ttinfo(idx) - - dt_out = dt + datetime.timedelta(seconds=tti.offset) - - fold = self.is_ambiguous(dt_out, idx=idx) - - return enfold(dt_out, fold=int(fold)) - - def is_ambiguous(self, dt, idx=None): - """ - Whether or not the "wall time" of a given datetime is ambiguous in this - zone. - - :param dt: - A :py:class:`datetime.datetime`, naive or time zone aware. - - - :return: - Returns ``True`` if ambiguous, ``False`` otherwise. - - .. versionadded:: 2.6.0 - """ - if idx is None: - idx = self._find_last_transition(dt) - - # Calculate the difference in offsets from current to previous - timestamp = _datetime_to_timestamp(dt) - tti = self._get_ttinfo(idx) - - if idx is None or idx <= 0: - return False - - od = self._get_ttinfo(idx - 1).offset - tti.offset - tt = self._trans_list[idx] # Transition time - - return timestamp < tt + od - - def _resolve_ambiguous_time(self, dt): - idx = self._find_last_transition(dt) - - # If we have no transitions, return the index - _fold = self._fold(dt) - if idx is None or idx == 0: - return idx - - # If it's ambiguous and we're in a fold, shift to a different index. - idx_offset = int(not _fold and self.is_ambiguous(dt, idx)) - - return idx - idx_offset - - def utcoffset(self, dt): - if dt is None: - return None - - if not self._ttinfo_std: - return ZERO - - return self._find_ttinfo(dt).delta - - def dst(self, dt): - if dt is None: - return None - - if not self._ttinfo_dst: - return ZERO - - tti = self._find_ttinfo(dt) - - if not tti.isdst: - return ZERO - - # The documentation says that utcoffset()-dst() must - # be constant for every dt. - return tti.dstoffset - - @tzname_in_python2 - def tzname(self, dt): - if not self._ttinfo_std or dt is None: - return None - return self._find_ttinfo(dt).abbr - - def __eq__(self, other): - if not isinstance(other, tzfile): - return NotImplemented - return (self._trans_list == other._trans_list and - self._trans_idx == other._trans_idx and - self._ttinfo_list == other._ttinfo_list) - - __hash__ = None - - def __ne__(self, other): - return not (self == other) - - def __repr__(self): - return "%s(%s)" % (self.__class__.__name__, repr(self._filename)) - - def __reduce__(self): - return self.__reduce_ex__(None) - - def __reduce_ex__(self, protocol): - return (self.__class__, (None, self._filename), self.__dict__) - - -class tzrange(tzrangebase): - """ - The ``tzrange`` object is a time zone specified by a set of offsets and - abbreviations, equivalent to the way the ``TZ`` variable can be specified - in POSIX-like systems, but using Python delta objects to specify DST - start, end and offsets. - - :param stdabbr: - The abbreviation for standard time (e.g. ``'EST'``). - - :param stdoffset: - An integer or :class:`datetime.timedelta` object or equivalent - specifying the base offset from UTC. - - If unspecified, +00:00 is used. - - :param dstabbr: - The abbreviation for DST / "Summer" time (e.g. ``'EDT'``). - - If specified, with no other DST information, DST is assumed to occur - and the default behavior or ``dstoffset``, ``start`` and ``end`` is - used. If unspecified and no other DST information is specified, it - is assumed that this zone has no DST. - - If this is unspecified and other DST information is *is* specified, - DST occurs in the zone but the time zone abbreviation is left - unchanged. - - :param dstoffset: - A an integer or :class:`datetime.timedelta` object or equivalent - specifying the UTC offset during DST. If unspecified and any other DST - information is specified, it is assumed to be the STD offset +1 hour. - - :param start: - A :class:`relativedelta.relativedelta` object or equivalent specifying - the time and time of year that daylight savings time starts. To - specify, for example, that DST starts at 2AM on the 2nd Sunday in - March, pass: - - ``relativedelta(hours=2, month=3, day=1, weekday=SU(+2))`` - - If unspecified and any other DST information is specified, the default - value is 2 AM on the first Sunday in April. - - :param end: - A :class:`relativedelta.relativedelta` object or equivalent - representing the time and time of year that daylight savings time - ends, with the same specification method as in ``start``. One note is - that this should point to the first time in the *standard* zone, so if - a transition occurs at 2AM in the DST zone and the clocks are set back - 1 hour to 1AM, set the ``hours`` parameter to +1. - - - **Examples:** - - .. testsetup:: tzrange - - from dateutil.tz import tzrange, tzstr - - .. doctest:: tzrange - - >>> tzstr('EST5EDT') == tzrange("EST", -18000, "EDT") - True - - >>> from dateutil.relativedelta import * - >>> range1 = tzrange("EST", -18000, "EDT") - >>> range2 = tzrange("EST", -18000, "EDT", -14400, - ... relativedelta(hours=+2, month=4, day=1, - ... weekday=SU(+1)), - ... relativedelta(hours=+1, month=10, day=31, - ... weekday=SU(-1))) - >>> tzstr('EST5EDT') == range1 == range2 - True - - """ - def __init__(self, stdabbr, stdoffset=None, - dstabbr=None, dstoffset=None, - start=None, end=None): - - global relativedelta - from dateutil import relativedelta - - self._std_abbr = stdabbr - self._dst_abbr = dstabbr - - try: - stdoffset = stdoffset.total_seconds() - except (TypeError, AttributeError): - pass - - try: - dstoffset = dstoffset.total_seconds() - except (TypeError, AttributeError): - pass - - if stdoffset is not None: - self._std_offset = datetime.timedelta(seconds=stdoffset) - else: - self._std_offset = ZERO - - if dstoffset is not None: - self._dst_offset = datetime.timedelta(seconds=dstoffset) - elif dstabbr and stdoffset is not None: - self._dst_offset = self._std_offset + datetime.timedelta(hours=+1) - else: - self._dst_offset = ZERO - - if dstabbr and start is None: - self._start_delta = relativedelta.relativedelta( - hours=+2, month=4, day=1, weekday=relativedelta.SU(+1)) - else: - self._start_delta = start - - if dstabbr and end is None: - self._end_delta = relativedelta.relativedelta( - hours=+1, month=10, day=31, weekday=relativedelta.SU(-1)) - else: - self._end_delta = end - - self._dst_base_offset_ = self._dst_offset - self._std_offset - self.hasdst = bool(self._start_delta) - - def transitions(self, year): - """ - For a given year, get the DST on and off transition times, expressed - always on the standard time side. For zones with no transitions, this - function returns ``None``. - - :param year: - The year whose transitions you would like to query. - - :return: - Returns a :class:`tuple` of :class:`datetime.datetime` objects, - ``(dston, dstoff)`` for zones with an annual DST transition, or - ``None`` for fixed offset zones. - """ - if not self.hasdst: - return None - - base_year = datetime.datetime(year, 1, 1) - - start = base_year + self._start_delta - end = base_year + self._end_delta - - return (start, end) - - def __eq__(self, other): - if not isinstance(other, tzrange): - return NotImplemented - - return (self._std_abbr == other._std_abbr and - self._dst_abbr == other._dst_abbr and - self._std_offset == other._std_offset and - self._dst_offset == other._dst_offset and - self._start_delta == other._start_delta and - self._end_delta == other._end_delta) - - @property - def _dst_base_offset(self): - return self._dst_base_offset_ - - -@six.add_metaclass(_TzStrFactory) -class tzstr(tzrange): - """ - ``tzstr`` objects are time zone objects specified by a time-zone string as - it would be passed to a ``TZ`` variable on POSIX-style systems (see - the `GNU C Library: TZ Variable`_ for more details). - - There is one notable exception, which is that POSIX-style time zones use an - inverted offset format, so normally ``GMT+3`` would be parsed as an offset - 3 hours *behind* GMT. The ``tzstr`` time zone object will parse this as an - offset 3 hours *ahead* of GMT. If you would like to maintain the POSIX - behavior, pass a ``True`` value to ``posix_offset``. - - The :class:`tzrange` object provides the same functionality, but is - specified using :class:`relativedelta.relativedelta` objects. rather than - strings. - - :param s: - A time zone string in ``TZ`` variable format. This can be a - :class:`bytes` (2.x: :class:`str`), :class:`str` (2.x: - :class:`unicode`) or a stream emitting unicode characters - (e.g. :class:`StringIO`). - - :param posix_offset: - Optional. If set to ``True``, interpret strings such as ``GMT+3`` or - ``UTC+3`` as being 3 hours *behind* UTC rather than ahead, per the - POSIX standard. - - .. caution:: - - Prior to version 2.7.0, this function also supported time zones - in the format: - - * ``EST5EDT,4,0,6,7200,10,0,26,7200,3600`` - * ``EST5EDT,4,1,0,7200,10,-1,0,7200,3600`` - - This format is non-standard and has been deprecated; this function - will raise a :class:`DeprecatedTZFormatWarning` until - support is removed in a future version. - - .. _`GNU C Library: TZ Variable`: - https://www.gnu.org/software/libc/manual/html_node/TZ-Variable.html - """ - def __init__(self, s, posix_offset=False): - global parser - from dateutil.parser import _parser as parser - - self._s = s - - res = parser._parsetz(s) - if res is None or res.any_unused_tokens: - raise ValueError("unknown string format") - - # Here we break the compatibility with the TZ variable handling. - # GMT-3 actually *means* the timezone -3. - if res.stdabbr in ("GMT", "UTC") and not posix_offset: - res.stdoffset *= -1 - - # We must initialize it first, since _delta() needs - # _std_offset and _dst_offset set. Use False in start/end - # to avoid building it two times. - tzrange.__init__(self, res.stdabbr, res.stdoffset, - res.dstabbr, res.dstoffset, - start=False, end=False) - - if not res.dstabbr: - self._start_delta = None - self._end_delta = None - else: - self._start_delta = self._delta(res.start) - if self._start_delta: - self._end_delta = self._delta(res.end, isend=1) - - self.hasdst = bool(self._start_delta) - - def _delta(self, x, isend=0): - from dateutil import relativedelta - kwargs = {} - if x.month is not None: - kwargs["month"] = x.month - if x.weekday is not None: - kwargs["weekday"] = relativedelta.weekday(x.weekday, x.week) - if x.week > 0: - kwargs["day"] = 1 - else: - kwargs["day"] = 31 - elif x.day: - kwargs["day"] = x.day - elif x.yday is not None: - kwargs["yearday"] = x.yday - elif x.jyday is not None: - kwargs["nlyearday"] = x.jyday - if not kwargs: - # Default is to start on first sunday of april, and end - # on last sunday of october. - if not isend: - kwargs["month"] = 4 - kwargs["day"] = 1 - kwargs["weekday"] = relativedelta.SU(+1) - else: - kwargs["month"] = 10 - kwargs["day"] = 31 - kwargs["weekday"] = relativedelta.SU(-1) - if x.time is not None: - kwargs["seconds"] = x.time - else: - # Default is 2AM. - kwargs["seconds"] = 7200 - if isend: - # Convert to standard time, to follow the documented way - # of working with the extra hour. See the documentation - # of the tzinfo class. - delta = self._dst_offset - self._std_offset - kwargs["seconds"] -= delta.seconds + delta.days * 86400 - return relativedelta.relativedelta(**kwargs) - - def __repr__(self): - return "%s(%s)" % (self.__class__.__name__, repr(self._s)) - - -class _tzicalvtzcomp(object): - def __init__(self, tzoffsetfrom, tzoffsetto, isdst, - tzname=None, rrule=None): - self.tzoffsetfrom = datetime.timedelta(seconds=tzoffsetfrom) - self.tzoffsetto = datetime.timedelta(seconds=tzoffsetto) - self.tzoffsetdiff = self.tzoffsetto - self.tzoffsetfrom - self.isdst = isdst - self.tzname = tzname - self.rrule = rrule - - -class _tzicalvtz(_tzinfo): - def __init__(self, tzid, comps=[]): - super(_tzicalvtz, self).__init__() - - self._tzid = tzid - self._comps = comps - self._cachedate = [] - self._cachecomp = [] - self._cache_lock = _thread.allocate_lock() - - def _find_comp(self, dt): - if len(self._comps) == 1: - return self._comps[0] - - dt = dt.replace(tzinfo=None) - - try: - with self._cache_lock: - return self._cachecomp[self._cachedate.index( - (dt, self._fold(dt)))] - except ValueError: - pass - - lastcompdt = None - lastcomp = None - - for comp in self._comps: - compdt = self._find_compdt(comp, dt) - - if compdt and (not lastcompdt or lastcompdt < compdt): - lastcompdt = compdt - lastcomp = comp - - if not lastcomp: - # RFC says nothing about what to do when a given - # time is before the first onset date. We'll look for the - # first standard component, or the first component, if - # none is found. - for comp in self._comps: - if not comp.isdst: - lastcomp = comp - break - else: - lastcomp = comp[0] - - with self._cache_lock: - self._cachedate.insert(0, (dt, self._fold(dt))) - self._cachecomp.insert(0, lastcomp) - - if len(self._cachedate) > 10: - self._cachedate.pop() - self._cachecomp.pop() - - return lastcomp - - def _find_compdt(self, comp, dt): - if comp.tzoffsetdiff < ZERO and self._fold(dt): - dt -= comp.tzoffsetdiff - - compdt = comp.rrule.before(dt, inc=True) - - return compdt - - def utcoffset(self, dt): - if dt is None: - return None - - return self._find_comp(dt).tzoffsetto - - def dst(self, dt): - comp = self._find_comp(dt) - if comp.isdst: - return comp.tzoffsetdiff - else: - return ZERO - - @tzname_in_python2 - def tzname(self, dt): - return self._find_comp(dt).tzname - - def __repr__(self): - return "" % repr(self._tzid) - - __reduce__ = object.__reduce__ - - -class tzical(object): - """ - This object is designed to parse an iCalendar-style ``VTIMEZONE`` structure - as set out in `RFC 5545`_ Section 4.6.5 into one or more `tzinfo` objects. - - :param `fileobj`: - A file or stream in iCalendar format, which should be UTF-8 encoded - with CRLF endings. - - .. _`RFC 5545`: https://tools.ietf.org/html/rfc5545 - """ - def __init__(self, fileobj): - global rrule - from dateutil import rrule - - if isinstance(fileobj, string_types): - self._s = fileobj - # ical should be encoded in UTF-8 with CRLF - fileobj = open(fileobj, 'r') - else: - self._s = getattr(fileobj, 'name', repr(fileobj)) - fileobj = _nullcontext(fileobj) - - self._vtz = {} - - with fileobj as fobj: - self._parse_rfc(fobj.read()) - - def keys(self): - """ - Retrieves the available time zones as a list. - """ - return list(self._vtz.keys()) - - def get(self, tzid=None): - """ - Retrieve a :py:class:`datetime.tzinfo` object by its ``tzid``. - - :param tzid: - If there is exactly one time zone available, omitting ``tzid`` - or passing :py:const:`None` value returns it. Otherwise a valid - key (which can be retrieved from :func:`keys`) is required. - - :raises ValueError: - Raised if ``tzid`` is not specified but there are either more - or fewer than 1 zone defined. - - :returns: - Returns either a :py:class:`datetime.tzinfo` object representing - the relevant time zone or :py:const:`None` if the ``tzid`` was - not found. - """ - if tzid is None: - if len(self._vtz) == 0: - raise ValueError("no timezones defined") - elif len(self._vtz) > 1: - raise ValueError("more than one timezone available") - tzid = next(iter(self._vtz)) - - return self._vtz.get(tzid) - - def _parse_offset(self, s): - s = s.strip() - if not s: - raise ValueError("empty offset") - if s[0] in ('+', '-'): - signal = (-1, +1)[s[0] == '+'] - s = s[1:] - else: - signal = +1 - if len(s) == 4: - return (int(s[:2]) * 3600 + int(s[2:]) * 60) * signal - elif len(s) == 6: - return (int(s[:2]) * 3600 + int(s[2:4]) * 60 + int(s[4:])) * signal - else: - raise ValueError("invalid offset: " + s) - - def _parse_rfc(self, s): - lines = s.splitlines() - if not lines: - raise ValueError("empty string") - - # Unfold - i = 0 - while i < len(lines): - line = lines[i].rstrip() - if not line: - del lines[i] - elif i > 0 and line[0] == " ": - lines[i-1] += line[1:] - del lines[i] - else: - i += 1 - - tzid = None - comps = [] - invtz = False - comptype = None - for line in lines: - if not line: - continue - name, value = line.split(':', 1) - parms = name.split(';') - if not parms: - raise ValueError("empty property name") - name = parms[0].upper() - parms = parms[1:] - if invtz: - if name == "BEGIN": - if value in ("STANDARD", "DAYLIGHT"): - # Process component - pass - else: - raise ValueError("unknown component: "+value) - comptype = value - founddtstart = False - tzoffsetfrom = None - tzoffsetto = None - rrulelines = [] - tzname = None - elif name == "END": - if value == "VTIMEZONE": - if comptype: - raise ValueError("component not closed: "+comptype) - if not tzid: - raise ValueError("mandatory TZID not found") - if not comps: - raise ValueError( - "at least one component is needed") - # Process vtimezone - self._vtz[tzid] = _tzicalvtz(tzid, comps) - invtz = False - elif value == comptype: - if not founddtstart: - raise ValueError("mandatory DTSTART not found") - if tzoffsetfrom is None: - raise ValueError( - "mandatory TZOFFSETFROM not found") - if tzoffsetto is None: - raise ValueError( - "mandatory TZOFFSETFROM not found") - # Process component - rr = None - if rrulelines: - rr = rrule.rrulestr("\n".join(rrulelines), - compatible=True, - ignoretz=True, - cache=True) - comp = _tzicalvtzcomp(tzoffsetfrom, tzoffsetto, - (comptype == "DAYLIGHT"), - tzname, rr) - comps.append(comp) - comptype = None - else: - raise ValueError("invalid component end: "+value) - elif comptype: - if name == "DTSTART": - # DTSTART in VTIMEZONE takes a subset of valid RRULE - # values under RFC 5545. - for parm in parms: - if parm != 'VALUE=DATE-TIME': - msg = ('Unsupported DTSTART param in ' + - 'VTIMEZONE: ' + parm) - raise ValueError(msg) - rrulelines.append(line) - founddtstart = True - elif name in ("RRULE", "RDATE", "EXRULE", "EXDATE"): - rrulelines.append(line) - elif name == "TZOFFSETFROM": - if parms: - raise ValueError( - "unsupported %s parm: %s " % (name, parms[0])) - tzoffsetfrom = self._parse_offset(value) - elif name == "TZOFFSETTO": - if parms: - raise ValueError( - "unsupported TZOFFSETTO parm: "+parms[0]) - tzoffsetto = self._parse_offset(value) - elif name == "TZNAME": - if parms: - raise ValueError( - "unsupported TZNAME parm: "+parms[0]) - tzname = value - elif name == "COMMENT": - pass - else: - raise ValueError("unsupported property: "+name) - else: - if name == "TZID": - if parms: - raise ValueError( - "unsupported TZID parm: "+parms[0]) - tzid = value - elif name in ("TZURL", "LAST-MODIFIED", "COMMENT"): - pass - else: - raise ValueError("unsupported property: "+name) - elif name == "BEGIN" and value == "VTIMEZONE": - tzid = None - comps = [] - invtz = True - - def __repr__(self): - return "%s(%s)" % (self.__class__.__name__, repr(self._s)) - - -if sys.platform != "win32": - TZFILES = ["/etc/localtime", "localtime"] - TZPATHS = ["/usr/share/zoneinfo", - "/usr/lib/zoneinfo", - "/usr/share/lib/zoneinfo", - "/etc/zoneinfo"] -else: - TZFILES = [] - TZPATHS = [] - - -def __get_gettz(): - tzlocal_classes = (tzlocal,) - if tzwinlocal is not None: - tzlocal_classes += (tzwinlocal,) - - class GettzFunc(object): - """ - Retrieve a time zone object from a string representation - - This function is intended to retrieve the :py:class:`tzinfo` subclass - that best represents the time zone that would be used if a POSIX - `TZ variable`_ were set to the same value. - - If no argument or an empty string is passed to ``gettz``, local time - is returned: - - .. code-block:: python3 - - >>> gettz() - tzfile('/etc/localtime') - - This function is also the preferred way to map IANA tz database keys - to :class:`tzfile` objects: - - .. code-block:: python3 - - >>> gettz('Pacific/Kiritimati') - tzfile('/usr/share/zoneinfo/Pacific/Kiritimati') - - On Windows, the standard is extended to include the Windows-specific - zone names provided by the operating system: - - .. code-block:: python3 - - >>> gettz('Egypt Standard Time') - tzwin('Egypt Standard Time') - - Passing a GNU ``TZ`` style string time zone specification returns a - :class:`tzstr` object: - - .. code-block:: python3 - - >>> gettz('AEST-10AEDT-11,M10.1.0/2,M4.1.0/3') - tzstr('AEST-10AEDT-11,M10.1.0/2,M4.1.0/3') - - :param name: - A time zone name (IANA, or, on Windows, Windows keys), location of - a ``tzfile(5)`` zoneinfo file or ``TZ`` variable style time zone - specifier. An empty string, no argument or ``None`` is interpreted - as local time. - - :return: - Returns an instance of one of ``dateutil``'s :py:class:`tzinfo` - subclasses. - - .. versionchanged:: 2.7.0 - - After version 2.7.0, any two calls to ``gettz`` using the same - input strings will return the same object: - - .. code-block:: python3 - - >>> tz.gettz('America/Chicago') is tz.gettz('America/Chicago') - True - - In addition to improving performance, this ensures that - `"same zone" semantics`_ are used for datetimes in the same zone. - - - .. _`TZ variable`: - https://www.gnu.org/software/libc/manual/html_node/TZ-Variable.html - - .. _`"same zone" semantics`: - https://blog.ganssle.io/articles/2018/02/aware-datetime-arithmetic.html - """ - def __init__(self): - - self.__instances = weakref.WeakValueDictionary() - self.__strong_cache_size = 8 - self.__strong_cache = OrderedDict() - self._cache_lock = _thread.allocate_lock() - - def __call__(self, name=None): - with self._cache_lock: - rv = self.__instances.get(name, None) - - if rv is None: - rv = self.nocache(name=name) - if not (name is None - or isinstance(rv, tzlocal_classes) - or rv is None): - # tzlocal is slightly more complicated than the other - # time zone providers because it depends on environment - # at construction time, so don't cache that. - # - # We also cannot store weak references to None, so we - # will also not store that. - self.__instances[name] = rv - else: - # No need for strong caching, return immediately - return rv - - self.__strong_cache[name] = self.__strong_cache.pop(name, rv) - - if len(self.__strong_cache) > self.__strong_cache_size: - self.__strong_cache.popitem(last=False) - - return rv - - def set_cache_size(self, size): - with self._cache_lock: - self.__strong_cache_size = size - while len(self.__strong_cache) > size: - self.__strong_cache.popitem(last=False) - - def cache_clear(self): - with self._cache_lock: - self.__instances = weakref.WeakValueDictionary() - self.__strong_cache.clear() - - @staticmethod - def nocache(name=None): - """A non-cached version of gettz""" - tz = None - if not name: - try: - name = os.environ["TZ"] - except KeyError: - pass - if name is None or name in ("", ":"): - for filepath in TZFILES: - if not os.path.isabs(filepath): - filename = filepath - for path in TZPATHS: - filepath = os.path.join(path, filename) - if os.path.isfile(filepath): - break - else: - continue - if os.path.isfile(filepath): - try: - tz = tzfile(filepath) - break - except (IOError, OSError, ValueError): - pass - else: - tz = tzlocal() - else: - try: - if name.startswith(":"): - name = name[1:] - except TypeError as e: - if isinstance(name, bytes): - new_msg = "gettz argument should be str, not bytes" - six.raise_from(TypeError(new_msg), e) - else: - raise - if os.path.isabs(name): - if os.path.isfile(name): - tz = tzfile(name) - else: - tz = None - else: - for path in TZPATHS: - filepath = os.path.join(path, name) - if not os.path.isfile(filepath): - filepath = filepath.replace(' ', '_') - if not os.path.isfile(filepath): - continue - try: - tz = tzfile(filepath) - break - except (IOError, OSError, ValueError): - pass - else: - tz = None - if tzwin is not None: - try: - tz = tzwin(name) - except (WindowsError, UnicodeEncodeError): - # UnicodeEncodeError is for Python 2.7 compat - tz = None - - if not tz: - from dateutil.zoneinfo import get_zonefile_instance - tz = get_zonefile_instance().get(name) - - if not tz: - for c in name: - # name is not a tzstr unless it has at least - # one offset. For short values of "name", an - # explicit for loop seems to be the fastest way - # To determine if a string contains a digit - if c in "0123456789": - try: - tz = tzstr(name) - except ValueError: - pass - break - else: - if name in ("GMT", "UTC"): - tz = UTC - elif name in time.tzname: - tz = tzlocal() - return tz - - return GettzFunc() - - -gettz = __get_gettz() -del __get_gettz - - -def datetime_exists(dt, tz=None): - """ - Given a datetime and a time zone, determine whether or not a given datetime - would fall in a gap. - - :param dt: - A :class:`datetime.datetime` (whose time zone will be ignored if ``tz`` - is provided.) - - :param tz: - A :class:`datetime.tzinfo` with support for the ``fold`` attribute. If - ``None`` or not provided, the datetime's own time zone will be used. - - :return: - Returns a boolean value whether or not the "wall time" exists in - ``tz``. - - .. versionadded:: 2.7.0 - """ - if tz is None: - if dt.tzinfo is None: - raise ValueError('Datetime is naive and no time zone provided.') - tz = dt.tzinfo - - dt = dt.replace(tzinfo=None) - - # This is essentially a test of whether or not the datetime can survive - # a round trip to UTC. - dt_rt = dt.replace(tzinfo=tz).astimezone(UTC).astimezone(tz) - dt_rt = dt_rt.replace(tzinfo=None) - - return dt == dt_rt - - -def datetime_ambiguous(dt, tz=None): - """ - Given a datetime and a time zone, determine whether or not a given datetime - is ambiguous (i.e if there are two times differentiated only by their DST - status). - - :param dt: - A :class:`datetime.datetime` (whose time zone will be ignored if ``tz`` - is provided.) - - :param tz: - A :class:`datetime.tzinfo` with support for the ``fold`` attribute. If - ``None`` or not provided, the datetime's own time zone will be used. - - :return: - Returns a boolean value whether or not the "wall time" is ambiguous in - ``tz``. - - .. versionadded:: 2.6.0 - """ - if tz is None: - if dt.tzinfo is None: - raise ValueError('Datetime is naive and no time zone provided.') - - tz = dt.tzinfo - - # If a time zone defines its own "is_ambiguous" function, we'll use that. - is_ambiguous_fn = getattr(tz, 'is_ambiguous', None) - if is_ambiguous_fn is not None: - try: - return tz.is_ambiguous(dt) - except Exception: - pass - - # If it doesn't come out and tell us it's ambiguous, we'll just check if - # the fold attribute has any effect on this particular date and time. - dt = dt.replace(tzinfo=tz) - wall_0 = enfold(dt, fold=0) - wall_1 = enfold(dt, fold=1) - - same_offset = wall_0.utcoffset() == wall_1.utcoffset() - same_dst = wall_0.dst() == wall_1.dst() - - return not (same_offset and same_dst) - - -def resolve_imaginary(dt): - """ - Given a datetime that may be imaginary, return an existing datetime. - - This function assumes that an imaginary datetime represents what the - wall time would be in a zone had the offset transition not occurred, so - it will always fall forward by the transition's change in offset. - - .. doctest:: - - >>> from dateutil import tz - >>> from datetime import datetime - >>> NYC = tz.gettz('America/New_York') - >>> print(tz.resolve_imaginary(datetime(2017, 3, 12, 2, 30, tzinfo=NYC))) - 2017-03-12 03:30:00-04:00 - - >>> KIR = tz.gettz('Pacific/Kiritimati') - >>> print(tz.resolve_imaginary(datetime(1995, 1, 1, 12, 30, tzinfo=KIR))) - 1995-01-02 12:30:00+14:00 - - As a note, :func:`datetime.astimezone` is guaranteed to produce a valid, - existing datetime, so a round-trip to and from UTC is sufficient to get - an extant datetime, however, this generally "falls back" to an earlier time - rather than falling forward to the STD side (though no guarantees are made - about this behavior). - - :param dt: - A :class:`datetime.datetime` which may or may not exist. - - :return: - Returns an existing :class:`datetime.datetime`. If ``dt`` was not - imaginary, the datetime returned is guaranteed to be the same object - passed to the function. - - .. versionadded:: 2.7.0 - """ - if dt.tzinfo is not None and not datetime_exists(dt): - - curr_offset = (dt + datetime.timedelta(hours=24)).utcoffset() - old_offset = (dt - datetime.timedelta(hours=24)).utcoffset() - - dt += curr_offset - old_offset - - return dt - - -def _datetime_to_timestamp(dt): - """ - Convert a :class:`datetime.datetime` object to an epoch timestamp in - seconds since January 1, 1970, ignoring the time zone. - """ - return (dt.replace(tzinfo=None) - EPOCH).total_seconds() - - -if sys.version_info >= (3, 6): - def _get_supported_offset(second_offset): - return second_offset -else: - def _get_supported_offset(second_offset): - # For python pre-3.6, round to full-minutes if that's not the case. - # Python's datetime doesn't accept sub-minute timezones. Check - # http://python.org/sf/1447945 or https://bugs.python.org/issue5288 - # for some information. - old_offset = second_offset - calculated_offset = 60 * ((second_offset + 30) // 60) - return calculated_offset - - -try: - # Python 3.7 feature - from contextlib import nullcontext as _nullcontext -except ImportError: - class _nullcontext(object): - """ - Class for wrapping contexts so that they are passed through in a - with statement. - """ - def __init__(self, context): - self.context = context - - def __enter__(self): - return self.context - - def __exit__(*args, **kwargs): - pass - -# vim:ts=4:sw=4:et diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/merge/__main__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/merge/__main__.py deleted file mode 100644 index ff632d49c54e678623a27998a9d51b7cf84df81f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/fontTools/merge/__main__.py +++ /dev/null @@ -1,6 +0,0 @@ -import sys -from fontTools.merge import main - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Example-abf8aa92.css b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Example-abf8aa92.css deleted file mode 100644 index cda34f5c27ad8cd5d0c5d4744147abaf3d7fdf78..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/gradio/templates/cdn/assets/Example-abf8aa92.css +++ /dev/null @@ -1 +0,0 @@ -@font-face{font-family:KaTeX_AMS;font-style:normal;font-weight:400;src:url(https://gradio.s3-us-west-2.amazonaws.com/4.0.2/assets/KaTeX_AMS-Regular-0cdd387c.woff2) format("woff2"),url(https://gradio.s3-us-west-2.amazonaws.com/4.0.2/assets/KaTeX_AMS-Regular-30da91e8.woff) 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diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/utils/_headers.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/utils/_headers.py deleted file mode 100644 index 846cca3f1d3c3f000de92840a89fb11e35f2083f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/huggingface_hub/utils/_headers.py +++ /dev/null @@ -1,234 +0,0 @@ -# coding=utf-8 -# Copyright 2022-present, the HuggingFace Inc. team. -# -# 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. -"""Contains utilities to handle headers to send in calls to Huggingface Hub.""" -from typing import Dict, Optional, Union - -from .. import constants -from ._hf_folder import HfFolder -from ._runtime import ( - get_fastai_version, - get_fastcore_version, - get_hf_hub_version, - get_python_version, - get_tf_version, - get_torch_version, - is_fastai_available, - is_fastcore_available, - is_tf_available, - is_torch_available, -) -from ._validators import validate_hf_hub_args - - -class LocalTokenNotFoundError(EnvironmentError): - """Raised if local token is required but not found.""" - - -@validate_hf_hub_args -def build_hf_headers( - *, - token: Optional[Union[bool, str]] = None, - is_write_action: bool = False, - library_name: Optional[str] = None, - library_version: Optional[str] = None, - user_agent: Union[Dict, str, None] = None, -) -> Dict[str, str]: - """ - Build headers dictionary to send in a HF Hub call. - - By default, authorization token is always provided either from argument (explicit - use) or retrieved from the cache (implicit use). To explicitly avoid sending the - token to the Hub, set `token=False` or set the `HF_HUB_DISABLE_IMPLICIT_TOKEN` - environment variable. - - In case of an API call that requires write access, an error is thrown if token is - `None` or token is an organization token (starting with `"api_org***"`). - - In addition to the auth header, a user-agent is added to provide information about - the installed packages (versions of python, huggingface_hub, torch, tensorflow, - fastai and fastcore). - - Args: - token (`str`, `bool`, *optional*): - The token to be sent in authorization header for the Hub call: - - if a string, it is used as the Hugging Face token - - if `True`, the token is read from the machine (cache or env variable) - - if `False`, authorization header is not set - - if `None`, the token is read from the machine only except if - `HF_HUB_DISABLE_IMPLICIT_TOKEN` env variable is set. - is_write_action (`bool`, default to `False`): - Set to True if the API call requires a write access. If `True`, the token - will be validated (cannot be `None`, cannot start by `"api_org***"`). - library_name (`str`, *optional*): - The name of the library that is making the HTTP request. Will be added to - the user-agent header. - library_version (`str`, *optional*): - The version of the library that is making the HTTP request. Will be added - to the user-agent header. - user_agent (`str`, `dict`, *optional*): - The user agent info in the form of a dictionary or a single string. It will - be completed with information about the installed packages. - - Returns: - A `Dict` of headers to pass in your API call. - - Example: - ```py - >>> build_hf_headers(token="hf_***") # explicit token - {"authorization": "Bearer hf_***", "user-agent": ""} - - >>> build_hf_headers(token=True) # explicitly use cached token - {"authorization": "Bearer hf_***",...} - - >>> build_hf_headers(token=False) # explicitly don't use cached token - {"user-agent": ...} - - >>> build_hf_headers() # implicit use of the cached token - {"authorization": "Bearer hf_***",...} - - # HF_HUB_DISABLE_IMPLICIT_TOKEN=True # to set as env variable - >>> build_hf_headers() # token is not sent - {"user-agent": ...} - - >>> build_hf_headers(token="api_org_***", is_write_action=True) - ValueError: You must use your personal account token for write-access methods. - - >>> build_hf_headers(library_name="transformers", library_version="1.2.3") - {"authorization": ..., "user-agent": "transformers/1.2.3; hf_hub/0.10.2; python/3.10.4; tensorflow/1.55"} - ``` - - Raises: - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If organization token is passed and "write" access is required. - [`ValueError`](https://docs.python.org/3/library/exceptions.html#ValueError) - If "write" access is required but token is not passed and not saved locally. - [`EnvironmentError`](https://docs.python.org/3/library/exceptions.html#EnvironmentError) - If `token=True` but token is not saved locally. - """ - # Get auth token to send - token_to_send = get_token_to_send(token) - _validate_token_to_send(token_to_send, is_write_action=is_write_action) - - # Combine headers - headers = { - "user-agent": _http_user_agent( - library_name=library_name, - library_version=library_version, - user_agent=user_agent, - ) - } - if token_to_send is not None: - headers["authorization"] = f"Bearer {token_to_send}" - return headers - - -def get_token_to_send(token: Optional[Union[bool, str]]) -> Optional[str]: - """Select the token to send from either `token` or the cache.""" - # Case token is explicitly provided - if isinstance(token, str): - return token - - # Case token is explicitly forbidden - if token is False: - return None - - # Token is not provided: we get it from local cache - cached_token = HfFolder().get_token() - - # Case token is explicitly required - if token is True: - if cached_token is None: - raise LocalTokenNotFoundError( - "Token is required (`token=True`), but no token found. You" - " need to provide a token or be logged in to Hugging Face with" - " `huggingface-cli login` or `huggingface_hub.login`. See" - " https://huggingface.co/settings/tokens." - ) - return cached_token - - # Case implicit use of the token is forbidden by env variable - if constants.HF_HUB_DISABLE_IMPLICIT_TOKEN: - return None - - # Otherwise: we use the cached token as the user has not explicitly forbidden it - return cached_token - - -def _validate_token_to_send(token: Optional[str], is_write_action: bool) -> None: - if is_write_action: - if token is None: - raise ValueError( - "Token is required (write-access action) but no token found. You need" - " to provide a token or be logged in to Hugging Face with" - " `huggingface-cli login` or `huggingface_hub.login`. See" - " https://huggingface.co/settings/tokens." - ) - if token.startswith("api_org"): - raise ValueError( - "You must use your personal account token for write-access methods. To" - " generate a write-access token, go to" - " https://huggingface.co/settings/tokens" - ) - - -def _http_user_agent( - *, - library_name: Optional[str] = None, - library_version: Optional[str] = None, - user_agent: Union[Dict, str, None] = None, -) -> str: - """Format a user-agent string containing information about the installed packages. - - Args: - library_name (`str`, *optional*): - The name of the library that is making the HTTP request. - library_version (`str`, *optional*): - The version of the library that is making the HTTP request. - user_agent (`str`, `dict`, *optional*): - The user agent info in the form of a dictionary or a single string. - - Returns: - The formatted user-agent string. - """ - if library_name is not None: - ua = f"{library_name}/{library_version}" - else: - ua = "unknown/None" - ua += f"; hf_hub/{get_hf_hub_version()}" - ua += f"; python/{get_python_version()}" - - if not constants.HF_HUB_DISABLE_TELEMETRY: - if is_torch_available(): - ua += f"; torch/{get_torch_version()}" - if is_tf_available(): - ua += f"; tensorflow/{get_tf_version()}" - if is_fastai_available(): - ua += f"; fastai/{get_fastai_version()}" - if is_fastcore_available(): - ua += f"; fastcore/{get_fastcore_version()}" - - if isinstance(user_agent, dict): - ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()) - elif isinstance(user_agent, str): - ua += "; " + user_agent - - return _deduplicate_user_agent(ua) - - -def _deduplicate_user_agent(user_agent: str) -> str: - """Deduplicate redundant information in the generated user-agent.""" - # Split around ";" > Strip whitespaces > Store as dict keys (ensure unicity) > format back as string - # Order is implicitly preserved by dictionary structure (see https://stackoverflow.com/a/53657523). - return "; ".join({key.strip(): None for key in user_agent.split(";")}.keys()) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/rules_inline/fragments_join.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/rules_inline/fragments_join.py deleted file mode 100644 index f795c1364b8ac098b7a17f34cd31d7070280cf36..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/markdown_it/rules_inline/fragments_join.py +++ /dev/null @@ -1,43 +0,0 @@ -from .state_inline import StateInline - - -def fragments_join(state: StateInline) -> None: - """ - Clean up tokens after emphasis and strikethrough postprocessing: - merge adjacent text nodes into one and re-calculate all token levels - - This is necessary because initially emphasis delimiter markers (``*, _, ~``) - are treated as their own separate text tokens. Then emphasis rule either - leaves them as text (needed to merge with adjacent text) or turns them - into opening/closing tags (which messes up levels inside). - """ - level = 0 - maximum = len(state.tokens) - - curr = last = 0 - while curr < maximum: - # re-calculate levels after emphasis/strikethrough turns some text nodes - # into opening/closing tags - if state.tokens[curr].nesting < 0: - level -= 1 # closing tag - state.tokens[curr].level = level - if state.tokens[curr].nesting > 0: - level += 1 # opening tag - - if ( - state.tokens[curr].type == "text" - and curr + 1 < maximum - and state.tokens[curr + 1].type == "text" - ): - # collapse two adjacent text nodes - state.tokens[curr + 1].content = ( - state.tokens[curr].content + state.tokens[curr + 1].content - ) - else: - if curr != last: - state.tokens[last] = state.tokens[curr] - last += 1 - curr += 1 - - if curr != last: - del state.tokens[last:] diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_simd_module.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_simd_module.py deleted file mode 100644 index 4fbaa9f3008b67e18d0bb6e3c87e057addd3ae5e..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/core/tests/test_simd_module.py +++ /dev/null @@ -1,101 +0,0 @@ -import pytest -from numpy.core._simd import targets -""" -This testing unit only for checking the sanity of common functionality, -therefore all we need is just to take one submodule that represents any -of enabled SIMD extensions to run the test on it and the second submodule -required to run only one check related to the possibility of mixing -the data types among each submodule. -""" -npyvs = [npyv_mod for npyv_mod in targets.values() if npyv_mod and npyv_mod.simd] -npyv, npyv2 = (npyvs + [None, None])[:2] - -unsigned_sfx = ["u8", "u16", "u32", "u64"] -signed_sfx = ["s8", "s16", "s32", "s64"] -fp_sfx = [] -if npyv and npyv.simd_f32: - fp_sfx.append("f32") -if npyv and npyv.simd_f64: - fp_sfx.append("f64") - -int_sfx = unsigned_sfx + signed_sfx -all_sfx = unsigned_sfx + int_sfx - -@pytest.mark.skipif(not npyv, reason="could not find any SIMD extension with NPYV support") -class Test_SIMD_MODULE: - - @pytest.mark.parametrize('sfx', all_sfx) - def test_num_lanes(self, sfx): - nlanes = getattr(npyv, "nlanes_" + sfx) - vector = getattr(npyv, "setall_" + sfx)(1) - assert len(vector) == nlanes - - @pytest.mark.parametrize('sfx', all_sfx) - def test_type_name(self, sfx): - vector = getattr(npyv, "setall_" + sfx)(1) - assert vector.__name__ == "npyv_" + sfx - - def test_raises(self): - a, b = [npyv.setall_u32(1)]*2 - for sfx in all_sfx: - vcb = lambda intrin: getattr(npyv, f"{intrin}_{sfx}") - pytest.raises(TypeError, vcb("add"), a) - pytest.raises(TypeError, vcb("add"), a, b, a) - pytest.raises(TypeError, vcb("setall")) - pytest.raises(TypeError, vcb("setall"), [1]) - pytest.raises(TypeError, vcb("load"), 1) - pytest.raises(ValueError, vcb("load"), [1]) - pytest.raises(ValueError, vcb("store"), [1], getattr(npyv, f"reinterpret_{sfx}_u32")(a)) - - @pytest.mark.skipif(not npyv2, reason=( - "could not find a second SIMD extension with NPYV support" - )) - def test_nomix(self): - # mix among submodules isn't allowed - a = npyv.setall_u32(1) - a2 = npyv2.setall_u32(1) - pytest.raises(TypeError, npyv.add_u32, a2, a2) - pytest.raises(TypeError, npyv2.add_u32, a, a) - - @pytest.mark.parametrize('sfx', unsigned_sfx) - def test_unsigned_overflow(self, sfx): - nlanes = getattr(npyv, "nlanes_" + sfx) - maxu = (1 << int(sfx[1:])) - 1 - maxu_72 = (1 << 72) - 1 - lane = getattr(npyv, "setall_" + sfx)(maxu_72)[0] - assert lane == maxu - lanes = getattr(npyv, "load_" + sfx)([maxu_72] * nlanes) - assert lanes == [maxu] * nlanes - lane = getattr(npyv, "setall_" + sfx)(-1)[0] - assert lane == maxu - lanes = getattr(npyv, "load_" + sfx)([-1] * nlanes) - assert lanes == [maxu] * nlanes - - @pytest.mark.parametrize('sfx', signed_sfx) - def test_signed_overflow(self, sfx): - nlanes = getattr(npyv, "nlanes_" + sfx) - maxs_72 = (1 << 71) - 1 - lane = getattr(npyv, "setall_" + sfx)(maxs_72)[0] - assert lane == -1 - lanes = getattr(npyv, "load_" + sfx)([maxs_72] * nlanes) - assert lanes == [-1] * nlanes - mins_72 = -1 << 71 - lane = getattr(npyv, "setall_" + sfx)(mins_72)[0] - assert lane == 0 - lanes = getattr(npyv, "load_" + sfx)([mins_72] * nlanes) - assert lanes == [0] * nlanes - - def test_truncate_f32(self): - if not npyv.simd_f32: - pytest.skip("F32 isn't support by the SIMD extension") - f32 = npyv.setall_f32(0.1)[0] - assert f32 != 0.1 - assert round(f32, 1) == 0.1 - - def test_compare(self): - data_range = range(0, npyv.nlanes_u32) - vdata = npyv.load_u32(data_range) - assert vdata == list(data_range) - assert vdata == tuple(data_range) - for i in data_range: - assert vdata[i] == data_range[i] diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/polynomial/polynomial.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/polynomial/polynomial.py deleted file mode 100644 index ceadff0bf4ed32f8bbbb9f208bf4d84946efe195..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/numpy/polynomial/polynomial.py +++ /dev/null @@ -1,1542 +0,0 @@ -""" -================================================= -Power Series (:mod:`numpy.polynomial.polynomial`) -================================================= - -This module provides a number of objects (mostly functions) useful for -dealing with polynomials, including a `Polynomial` class that -encapsulates the usual arithmetic operations. (General information -on how this module represents and works with polynomial objects is in -the docstring for its "parent" sub-package, `numpy.polynomial`). - -Classes -------- -.. autosummary:: - :toctree: generated/ - - Polynomial - -Constants ---------- -.. autosummary:: - :toctree: generated/ - - polydomain - polyzero - polyone - polyx - -Arithmetic ----------- -.. autosummary:: - :toctree: generated/ - - polyadd - polysub - polymulx - polymul - polydiv - polypow - polyval - polyval2d - polyval3d - polygrid2d - polygrid3d - -Calculus --------- -.. autosummary:: - :toctree: generated/ - - polyder - polyint - -Misc Functions --------------- -.. autosummary:: - :toctree: generated/ - - polyfromroots - polyroots - polyvalfromroots - polyvander - polyvander2d - polyvander3d - polycompanion - polyfit - polytrim - polyline - -See Also --------- -`numpy.polynomial` - -""" -__all__ = [ - 'polyzero', 'polyone', 'polyx', 'polydomain', 'polyline', 'polyadd', - 'polysub', 'polymulx', 'polymul', 'polydiv', 'polypow', 'polyval', - 'polyvalfromroots', 'polyder', 'polyint', 'polyfromroots', 'polyvander', - 'polyfit', 'polytrim', 'polyroots', 'Polynomial', 'polyval2d', 'polyval3d', - 'polygrid2d', 'polygrid3d', 'polyvander2d', 'polyvander3d'] - -import numpy as np -import numpy.linalg as la -from numpy.core.multiarray import normalize_axis_index - -from . import polyutils as pu -from ._polybase import ABCPolyBase - -polytrim = pu.trimcoef - -# -# These are constant arrays are of integer type so as to be compatible -# with the widest range of other types, such as Decimal. -# - -# Polynomial default domain. -polydomain = np.array([-1, 1]) - -# Polynomial coefficients representing zero. -polyzero = np.array([0]) - -# Polynomial coefficients representing one. -polyone = np.array([1]) - -# Polynomial coefficients representing the identity x. -polyx = np.array([0, 1]) - -# -# Polynomial series functions -# - - -def polyline(off, scl): - """ - Returns an array representing a linear polynomial. - - Parameters - ---------- - off, scl : scalars - The "y-intercept" and "slope" of the line, respectively. - - Returns - ------- - y : ndarray - This module's representation of the linear polynomial ``off + - scl*x``. - - See Also - -------- - numpy.polynomial.chebyshev.chebline - numpy.polynomial.legendre.legline - numpy.polynomial.laguerre.lagline - numpy.polynomial.hermite.hermline - numpy.polynomial.hermite_e.hermeline - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> P.polyline(1,-1) - array([ 1, -1]) - >>> P.polyval(1, P.polyline(1,-1)) # should be 0 - 0.0 - - """ - if scl != 0: - return np.array([off, scl]) - else: - return np.array([off]) - - -def polyfromroots(roots): - """ - Generate a monic polynomial with given roots. - - Return the coefficients of the polynomial - - .. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n), - - where the ``r_n`` are the roots specified in `roots`. If a zero has - multiplicity n, then it must appear in `roots` n times. For instance, - if 2 is a root of multiplicity three and 3 is a root of multiplicity 2, - then `roots` looks something like [2, 2, 2, 3, 3]. The roots can appear - in any order. - - If the returned coefficients are `c`, then - - .. math:: p(x) = c_0 + c_1 * x + ... + x^n - - The coefficient of the last term is 1 for monic polynomials in this - form. - - Parameters - ---------- - roots : array_like - Sequence containing the roots. - - Returns - ------- - out : ndarray - 1-D array of the polynomial's coefficients If all the roots are - real, then `out` is also real, otherwise it is complex. (see - Examples below). - - See Also - -------- - numpy.polynomial.chebyshev.chebfromroots - numpy.polynomial.legendre.legfromroots - numpy.polynomial.laguerre.lagfromroots - numpy.polynomial.hermite.hermfromroots - numpy.polynomial.hermite_e.hermefromroots - - Notes - ----- - The coefficients are determined by multiplying together linear factors - of the form ``(x - r_i)``, i.e. - - .. math:: p(x) = (x - r_0) (x - r_1) ... (x - r_n) - - where ``n == len(roots) - 1``; note that this implies that ``1`` is always - returned for :math:`a_n`. - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> P.polyfromroots((-1,0,1)) # x(x - 1)(x + 1) = x^3 - x - array([ 0., -1., 0., 1.]) - >>> j = complex(0,1) - >>> P.polyfromroots((-j,j)) # complex returned, though values are real - array([1.+0.j, 0.+0.j, 1.+0.j]) - - """ - return pu._fromroots(polyline, polymul, roots) - - -def polyadd(c1, c2): - """ - Add one polynomial to another. - - Returns the sum of two polynomials `c1` + `c2`. The arguments are - sequences of coefficients from lowest order term to highest, i.e., - [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``. - - Parameters - ---------- - c1, c2 : array_like - 1-D arrays of polynomial coefficients ordered from low to high. - - Returns - ------- - out : ndarray - The coefficient array representing their sum. - - See Also - -------- - polysub, polymulx, polymul, polydiv, polypow - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> c1 = (1,2,3) - >>> c2 = (3,2,1) - >>> sum = P.polyadd(c1,c2); sum - array([4., 4., 4.]) - >>> P.polyval(2, sum) # 4 + 4(2) + 4(2**2) - 28.0 - - """ - return pu._add(c1, c2) - - -def polysub(c1, c2): - """ - Subtract one polynomial from another. - - Returns the difference of two polynomials `c1` - `c2`. The arguments - are sequences of coefficients from lowest order term to highest, i.e., - [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2``. - - Parameters - ---------- - c1, c2 : array_like - 1-D arrays of polynomial coefficients ordered from low to - high. - - Returns - ------- - out : ndarray - Of coefficients representing their difference. - - See Also - -------- - polyadd, polymulx, polymul, polydiv, polypow - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> c1 = (1,2,3) - >>> c2 = (3,2,1) - >>> P.polysub(c1,c2) - array([-2., 0., 2.]) - >>> P.polysub(c2,c1) # -P.polysub(c1,c2) - array([ 2., 0., -2.]) - - """ - return pu._sub(c1, c2) - - -def polymulx(c): - """Multiply a polynomial by x. - - Multiply the polynomial `c` by x, where x is the independent - variable. - - - Parameters - ---------- - c : array_like - 1-D array of polynomial coefficients ordered from low to - high. - - Returns - ------- - out : ndarray - Array representing the result of the multiplication. - - See Also - -------- - polyadd, polysub, polymul, polydiv, polypow - - Notes - ----- - - .. versionadded:: 1.5.0 - - """ - # c is a trimmed copy - [c] = pu.as_series([c]) - # The zero series needs special treatment - if len(c) == 1 and c[0] == 0: - return c - - prd = np.empty(len(c) + 1, dtype=c.dtype) - prd[0] = c[0]*0 - prd[1:] = c - return prd - - -def polymul(c1, c2): - """ - Multiply one polynomial by another. - - Returns the product of two polynomials `c1` * `c2`. The arguments are - sequences of coefficients, from lowest order term to highest, e.g., - [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2.`` - - Parameters - ---------- - c1, c2 : array_like - 1-D arrays of coefficients representing a polynomial, relative to the - "standard" basis, and ordered from lowest order term to highest. - - Returns - ------- - out : ndarray - Of the coefficients of their product. - - See Also - -------- - polyadd, polysub, polymulx, polydiv, polypow - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> c1 = (1,2,3) - >>> c2 = (3,2,1) - >>> P.polymul(c1,c2) - array([ 3., 8., 14., 8., 3.]) - - """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - ret = np.convolve(c1, c2) - return pu.trimseq(ret) - - -def polydiv(c1, c2): - """ - Divide one polynomial by another. - - Returns the quotient-with-remainder of two polynomials `c1` / `c2`. - The arguments are sequences of coefficients, from lowest order term - to highest, e.g., [1,2,3] represents ``1 + 2*x + 3*x**2``. - - Parameters - ---------- - c1, c2 : array_like - 1-D arrays of polynomial coefficients ordered from low to high. - - Returns - ------- - [quo, rem] : ndarrays - Of coefficient series representing the quotient and remainder. - - See Also - -------- - polyadd, polysub, polymulx, polymul, polypow - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> c1 = (1,2,3) - >>> c2 = (3,2,1) - >>> P.polydiv(c1,c2) - (array([3.]), array([-8., -4.])) - >>> P.polydiv(c2,c1) - (array([ 0.33333333]), array([ 2.66666667, 1.33333333])) # may vary - - """ - # c1, c2 are trimmed copies - [c1, c2] = pu.as_series([c1, c2]) - if c2[-1] == 0: - raise ZeroDivisionError() - - # note: this is more efficient than `pu._div(polymul, c1, c2)` - lc1 = len(c1) - lc2 = len(c2) - if lc1 < lc2: - return c1[:1]*0, c1 - elif lc2 == 1: - return c1/c2[-1], c1[:1]*0 - else: - dlen = lc1 - lc2 - scl = c2[-1] - c2 = c2[:-1]/scl - i = dlen - j = lc1 - 1 - while i >= 0: - c1[i:j] -= c2*c1[j] - i -= 1 - j -= 1 - return c1[j+1:]/scl, pu.trimseq(c1[:j+1]) - - -def polypow(c, pow, maxpower=None): - """Raise a polynomial to a power. - - Returns the polynomial `c` raised to the power `pow`. The argument - `c` is a sequence of coefficients ordered from low to high. i.e., - [1,2,3] is the series ``1 + 2*x + 3*x**2.`` - - Parameters - ---------- - c : array_like - 1-D array of array of series coefficients ordered from low to - high degree. - pow : integer - Power to which the series will be raised - maxpower : integer, optional - Maximum power allowed. This is mainly to limit growth of the series - to unmanageable size. Default is 16 - - Returns - ------- - coef : ndarray - Power series of power. - - See Also - -------- - polyadd, polysub, polymulx, polymul, polydiv - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> P.polypow([1,2,3], 2) - array([ 1., 4., 10., 12., 9.]) - - """ - # note: this is more efficient than `pu._pow(polymul, c1, c2)`, as it - # avoids calling `as_series` repeatedly - return pu._pow(np.convolve, c, pow, maxpower) - - -def polyder(c, m=1, scl=1, axis=0): - """ - Differentiate a polynomial. - - Returns the polynomial coefficients `c` differentiated `m` times along - `axis`. At each iteration the result is multiplied by `scl` (the - scaling factor is for use in a linear change of variable). The - argument `c` is an array of coefficients from low to high degree along - each axis, e.g., [1,2,3] represents the polynomial ``1 + 2*x + 3*x**2`` - while [[1,2],[1,2]] represents ``1 + 1*x + 2*y + 2*x*y`` if axis=0 is - ``x`` and axis=1 is ``y``. - - Parameters - ---------- - c : array_like - Array of polynomial coefficients. If c is multidimensional the - different axis correspond to different variables with the degree - in each axis given by the corresponding index. - m : int, optional - Number of derivatives taken, must be non-negative. (Default: 1) - scl : scalar, optional - Each differentiation is multiplied by `scl`. The end result is - multiplication by ``scl**m``. This is for use in a linear change - of variable. (Default: 1) - axis : int, optional - Axis over which the derivative is taken. (Default: 0). - - .. versionadded:: 1.7.0 - - Returns - ------- - der : ndarray - Polynomial coefficients of the derivative. - - See Also - -------- - polyint - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> c = (1,2,3,4) # 1 + 2x + 3x**2 + 4x**3 - >>> P.polyder(c) # (d/dx)(c) = 2 + 6x + 12x**2 - array([ 2., 6., 12.]) - >>> P.polyder(c,3) # (d**3/dx**3)(c) = 24 - array([24.]) - >>> P.polyder(c,scl=-1) # (d/d(-x))(c) = -2 - 6x - 12x**2 - array([ -2., -6., -12.]) - >>> P.polyder(c,2,-1) # (d**2/d(-x)**2)(c) = 6 + 24x - array([ 6., 24.]) - - """ - c = np.array(c, ndmin=1, copy=True) - if c.dtype.char in '?bBhHiIlLqQpP': - # astype fails with NA - c = c + 0.0 - cdt = c.dtype - cnt = pu._deprecate_as_int(m, "the order of derivation") - iaxis = pu._deprecate_as_int(axis, "the axis") - if cnt < 0: - raise ValueError("The order of derivation must be non-negative") - iaxis = normalize_axis_index(iaxis, c.ndim) - - if cnt == 0: - return c - - c = np.moveaxis(c, iaxis, 0) - n = len(c) - if cnt >= n: - c = c[:1]*0 - else: - for i in range(cnt): - n = n - 1 - c *= scl - der = np.empty((n,) + c.shape[1:], dtype=cdt) - for j in range(n, 0, -1): - der[j - 1] = j*c[j] - c = der - c = np.moveaxis(c, 0, iaxis) - return c - - -def polyint(c, m=1, k=[], lbnd=0, scl=1, axis=0): - """ - Integrate a polynomial. - - Returns the polynomial coefficients `c` integrated `m` times from - `lbnd` along `axis`. At each iteration the resulting series is - **multiplied** by `scl` and an integration constant, `k`, is added. - The scaling factor is for use in a linear change of variable. ("Buyer - beware": note that, depending on what one is doing, one may want `scl` - to be the reciprocal of what one might expect; for more information, - see the Notes section below.) The argument `c` is an array of - coefficients, from low to high degree along each axis, e.g., [1,2,3] - represents the polynomial ``1 + 2*x + 3*x**2`` while [[1,2],[1,2]] - represents ``1 + 1*x + 2*y + 2*x*y`` if axis=0 is ``x`` and axis=1 is - ``y``. - - Parameters - ---------- - c : array_like - 1-D array of polynomial coefficients, ordered from low to high. - m : int, optional - Order of integration, must be positive. (Default: 1) - k : {[], list, scalar}, optional - Integration constant(s). The value of the first integral at zero - is the first value in the list, the value of the second integral - at zero is the second value, etc. If ``k == []`` (the default), - all constants are set to zero. If ``m == 1``, a single scalar can - be given instead of a list. - lbnd : scalar, optional - The lower bound of the integral. (Default: 0) - scl : scalar, optional - Following each integration the result is *multiplied* by `scl` - before the integration constant is added. (Default: 1) - axis : int, optional - Axis over which the integral is taken. (Default: 0). - - .. versionadded:: 1.7.0 - - Returns - ------- - S : ndarray - Coefficient array of the integral. - - Raises - ------ - ValueError - If ``m < 1``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or - ``np.ndim(scl) != 0``. - - See Also - -------- - polyder - - Notes - ----- - Note that the result of each integration is *multiplied* by `scl`. Why - is this important to note? Say one is making a linear change of - variable :math:`u = ax + b` in an integral relative to `x`. Then - :math:`dx = du/a`, so one will need to set `scl` equal to - :math:`1/a` - perhaps not what one would have first thought. - - Examples - -------- - >>> from numpy.polynomial import polynomial as P - >>> c = (1,2,3) - >>> P.polyint(c) # should return array([0, 1, 1, 1]) - array([0., 1., 1., 1.]) - >>> P.polyint(c,3) # should return array([0, 0, 0, 1/6, 1/12, 1/20]) - array([ 0. , 0. , 0. , 0.16666667, 0.08333333, # may vary - 0.05 ]) - >>> P.polyint(c,k=3) # should return array([3, 1, 1, 1]) - array([3., 1., 1., 1.]) - >>> P.polyint(c,lbnd=-2) # should return array([6, 1, 1, 1]) - array([6., 1., 1., 1.]) - >>> P.polyint(c,scl=-2) # should return array([0, -2, -2, -2]) - array([ 0., -2., -2., -2.]) - - """ - c = np.array(c, ndmin=1, copy=True) - if c.dtype.char in '?bBhHiIlLqQpP': - # astype doesn't preserve mask attribute. - c = c + 0.0 - cdt = c.dtype - if not np.iterable(k): - k = [k] - cnt = pu._deprecate_as_int(m, "the order of integration") - iaxis = pu._deprecate_as_int(axis, "the axis") - if cnt < 0: - raise ValueError("The order of integration must be non-negative") - if len(k) > cnt: - raise ValueError("Too many integration constants") - if np.ndim(lbnd) != 0: - raise ValueError("lbnd must be a scalar.") - if np.ndim(scl) != 0: - raise ValueError("scl must be a scalar.") - iaxis = normalize_axis_index(iaxis, c.ndim) - - if cnt == 0: - return c - - k = list(k) + [0]*(cnt - len(k)) - c = np.moveaxis(c, iaxis, 0) - for i in range(cnt): - n = len(c) - c *= scl - if n == 1 and np.all(c[0] == 0): - c[0] += k[i] - else: - tmp = np.empty((n + 1,) + c.shape[1:], dtype=cdt) - tmp[0] = c[0]*0 - tmp[1] = c[0] - for j in range(1, n): - tmp[j + 1] = c[j]/(j + 1) - tmp[0] += k[i] - polyval(lbnd, tmp) - c = tmp - c = np.moveaxis(c, 0, iaxis) - return c - - -def polyval(x, c, tensor=True): - """ - Evaluate a polynomial at points x. - - If `c` is of length `n + 1`, this function returns the value - - .. math:: p(x) = c_0 + c_1 * x + ... + c_n * x^n - - The parameter `x` is converted to an array only if it is a tuple or a - list, otherwise it is treated as a scalar. In either case, either `x` - or its elements must support multiplication and addition both with - themselves and with the elements of `c`. - - If `c` is a 1-D array, then `p(x)` will have the same shape as `x`. If - `c` is multidimensional, then the shape of the result depends on the - value of `tensor`. If `tensor` is true the shape will be c.shape[1:] + - x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that - scalars have shape (,). - - Trailing zeros in the coefficients will be used in the evaluation, so - they should be avoided if efficiency is a concern. - - Parameters - ---------- - x : array_like, compatible object - If `x` is a list or tuple, it is converted to an ndarray, otherwise - it is left unchanged and treated as a scalar. In either case, `x` - or its elements must support addition and multiplication with - with themselves and with the elements of `c`. - c : array_like - Array of coefficients ordered so that the coefficients for terms of - degree n are contained in c[n]. If `c` is multidimensional the - remaining indices enumerate multiple polynomials. In the two - dimensional case the coefficients may be thought of as stored in - the columns of `c`. - tensor : boolean, optional - If True, the shape of the coefficient array is extended with ones - on the right, one for each dimension of `x`. Scalars have dimension 0 - for this action. The result is that every column of coefficients in - `c` is evaluated for every element of `x`. If False, `x` is broadcast - over the columns of `c` for the evaluation. This keyword is useful - when `c` is multidimensional. The default value is True. - - .. versionadded:: 1.7.0 - - Returns - ------- - values : ndarray, compatible object - The shape of the returned array is described above. - - See Also - -------- - polyval2d, polygrid2d, polyval3d, polygrid3d - - Notes - ----- - The evaluation uses Horner's method. - - Examples - -------- - >>> from numpy.polynomial.polynomial import polyval - >>> polyval(1, [1,2,3]) - 6.0 - >>> a = np.arange(4).reshape(2,2) - >>> a - array([[0, 1], - [2, 3]]) - >>> polyval(a, [1,2,3]) - array([[ 1., 6.], - [17., 34.]]) - >>> coef = np.arange(4).reshape(2,2) # multidimensional coefficients - >>> coef - array([[0, 1], - [2, 3]]) - >>> polyval([1,2], coef, tensor=True) - array([[2., 4.], - [4., 7.]]) - >>> polyval([1,2], coef, tensor=False) - array([2., 7.]) - - """ - c = np.array(c, ndmin=1, copy=False) - if c.dtype.char in '?bBhHiIlLqQpP': - # astype fails with NA - c = c + 0.0 - if isinstance(x, (tuple, list)): - x = np.asarray(x) - if isinstance(x, np.ndarray) and tensor: - c = c.reshape(c.shape + (1,)*x.ndim) - - c0 = c[-1] + x*0 - for i in range(2, len(c) + 1): - c0 = c[-i] + c0*x - return c0 - - -def polyvalfromroots(x, r, tensor=True): - """ - Evaluate a polynomial specified by its roots at points x. - - If `r` is of length `N`, this function returns the value - - .. math:: p(x) = \\prod_{n=1}^{N} (x - r_n) - - The parameter `x` is converted to an array only if it is a tuple or a - list, otherwise it is treated as a scalar. In either case, either `x` - or its elements must support multiplication and addition both with - themselves and with the elements of `r`. - - If `r` is a 1-D array, then `p(x)` will have the same shape as `x`. If `r` - is multidimensional, then the shape of the result depends on the value of - `tensor`. If `tensor` is ``True`` the shape will be r.shape[1:] + x.shape; - that is, each polynomial is evaluated at every value of `x`. If `tensor` is - ``False``, the shape will be r.shape[1:]; that is, each polynomial is - evaluated only for the corresponding broadcast value of `x`. Note that - scalars have shape (,). - - .. versionadded:: 1.12 - - Parameters - ---------- - x : array_like, compatible object - If `x` is a list or tuple, it is converted to an ndarray, otherwise - it is left unchanged and treated as a scalar. In either case, `x` - or its elements must support addition and multiplication with - with themselves and with the elements of `r`. - r : array_like - Array of roots. If `r` is multidimensional the first index is the - root index, while the remaining indices enumerate multiple - polynomials. For instance, in the two dimensional case the roots - of each polynomial may be thought of as stored in the columns of `r`. - tensor : boolean, optional - If True, the shape of the roots array is extended with ones on the - right, one for each dimension of `x`. Scalars have dimension 0 for this - action. The result is that every column of coefficients in `r` is - evaluated for every element of `x`. If False, `x` is broadcast over the - columns of `r` for the evaluation. This keyword is useful when `r` is - multidimensional. The default value is True. - - Returns - ------- - values : ndarray, compatible object - The shape of the returned array is described above. - - See Also - -------- - polyroots, polyfromroots, polyval - - Examples - -------- - >>> from numpy.polynomial.polynomial import polyvalfromroots - >>> polyvalfromroots(1, [1,2,3]) - 0.0 - >>> a = np.arange(4).reshape(2,2) - >>> a - array([[0, 1], - [2, 3]]) - >>> polyvalfromroots(a, [-1, 0, 1]) - array([[-0., 0.], - [ 6., 24.]]) - >>> r = np.arange(-2, 2).reshape(2,2) # multidimensional coefficients - >>> r # each column of r defines one polynomial - array([[-2, -1], - [ 0, 1]]) - >>> b = [-2, 1] - >>> polyvalfromroots(b, r, tensor=True) - array([[-0., 3.], - [ 3., 0.]]) - >>> polyvalfromroots(b, r, tensor=False) - array([-0., 0.]) - """ - r = np.array(r, ndmin=1, copy=False) - if r.dtype.char in '?bBhHiIlLqQpP': - r = r.astype(np.double) - if isinstance(x, (tuple, list)): - x = np.asarray(x) - if isinstance(x, np.ndarray): - if tensor: - r = r.reshape(r.shape + (1,)*x.ndim) - elif x.ndim >= r.ndim: - raise ValueError("x.ndim must be < r.ndim when tensor == False") - return np.prod(x - r, axis=0) - - -def polyval2d(x, y, c): - """ - Evaluate a 2-D polynomial at points (x, y). - - This function returns the value - - .. math:: p(x,y) = \\sum_{i,j} c_{i,j} * x^i * y^j - - The parameters `x` and `y` are converted to arrays only if they are - tuples or a lists, otherwise they are treated as a scalars and they - must have the same shape after conversion. In either case, either `x` - and `y` or their elements must support multiplication and addition both - with themselves and with the elements of `c`. - - If `c` has fewer than two dimensions, ones are implicitly appended to - its shape to make it 2-D. The shape of the result will be c.shape[2:] + - x.shape. - - Parameters - ---------- - x, y : array_like, compatible objects - The two dimensional series is evaluated at the points `(x, y)`, - where `x` and `y` must have the same shape. If `x` or `y` is a list - or tuple, it is first converted to an ndarray, otherwise it is left - unchanged and, if it isn't an ndarray, it is treated as a scalar. - c : array_like - Array of coefficients ordered so that the coefficient of the term - of multi-degree i,j is contained in `c[i,j]`. If `c` has - dimension greater than two the remaining indices enumerate multiple - sets of coefficients. - - Returns - ------- - values : ndarray, compatible object - The values of the two dimensional polynomial at points formed with - pairs of corresponding values from `x` and `y`. - - See Also - -------- - polyval, polygrid2d, polyval3d, polygrid3d - - Notes - ----- - - .. versionadded:: 1.7.0 - - """ - return pu._valnd(polyval, c, x, y) - - -def polygrid2d(x, y, c): - """ - Evaluate a 2-D polynomial on the Cartesian product of x and y. - - This function returns the values: - - .. math:: p(a,b) = \\sum_{i,j} c_{i,j} * a^i * b^j - - where the points `(a, b)` consist of all pairs formed by taking - `a` from `x` and `b` from `y`. The resulting points form a grid with - `x` in the first dimension and `y` in the second. - - The parameters `x` and `y` are converted to arrays only if they are - tuples or a lists, otherwise they are treated as a scalars. In either - case, either `x` and `y` or their elements must support multiplication - and addition both with themselves and with the elements of `c`. - - If `c` has fewer than two dimensions, ones are implicitly appended to - its shape to make it 2-D. The shape of the result will be c.shape[2:] + - x.shape + y.shape. - - Parameters - ---------- - x, y : array_like, compatible objects - The two dimensional series is evaluated at the points in the - Cartesian product of `x` and `y`. If `x` or `y` is a list or - tuple, it is first converted to an ndarray, otherwise it is left - unchanged and, if it isn't an ndarray, it is treated as a scalar. - c : array_like - Array of coefficients ordered so that the coefficients for terms of - degree i,j are contained in ``c[i,j]``. If `c` has dimension - greater than two the remaining indices enumerate multiple sets of - coefficients. - - Returns - ------- - values : ndarray, compatible object - The values of the two dimensional polynomial at points in the Cartesian - product of `x` and `y`. - - See Also - -------- - polyval, polyval2d, polyval3d, polygrid3d - - Notes - ----- - - .. versionadded:: 1.7.0 - - """ - return pu._gridnd(polyval, c, x, y) - - -def polyval3d(x, y, z, c): - """ - Evaluate a 3-D polynomial at points (x, y, z). - - This function returns the values: - - .. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * x^i * y^j * z^k - - The parameters `x`, `y`, and `z` are converted to arrays only if - they are tuples or a lists, otherwise they are treated as a scalars and - they must have the same shape after conversion. In either case, either - `x`, `y`, and `z` or their elements must support multiplication and - addition both with themselves and with the elements of `c`. - - If `c` has fewer than 3 dimensions, ones are implicitly appended to its - shape to make it 3-D. The shape of the result will be c.shape[3:] + - x.shape. - - Parameters - ---------- - x, y, z : array_like, compatible object - The three dimensional series is evaluated at the points - `(x, y, z)`, where `x`, `y`, and `z` must have the same shape. If - any of `x`, `y`, or `z` is a list or tuple, it is first converted - to an ndarray, otherwise it is left unchanged and if it isn't an - ndarray it is treated as a scalar. - c : array_like - Array of coefficients ordered so that the coefficient of the term of - multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension - greater than 3 the remaining indices enumerate multiple sets of - coefficients. - - Returns - ------- - values : ndarray, compatible object - The values of the multidimensional polynomial on points formed with - triples of corresponding values from `x`, `y`, and `z`. - - See Also - -------- - polyval, polyval2d, polygrid2d, polygrid3d - - Notes - ----- - - .. versionadded:: 1.7.0 - - """ - return pu._valnd(polyval, c, x, y, z) - - -def polygrid3d(x, y, z, c): - """ - Evaluate a 3-D polynomial on the Cartesian product of x, y and z. - - This function returns the values: - - .. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * a^i * b^j * c^k - - where the points `(a, b, c)` consist of all triples formed by taking - `a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form - a grid with `x` in the first dimension, `y` in the second, and `z` in - the third. - - The parameters `x`, `y`, and `z` are converted to arrays only if they - are tuples or a lists, otherwise they are treated as a scalars. In - either case, either `x`, `y`, and `z` or their elements must support - multiplication and addition both with themselves and with the elements - of `c`. - - If `c` has fewer than three dimensions, ones are implicitly appended to - its shape to make it 3-D. The shape of the result will be c.shape[3:] + - x.shape + y.shape + z.shape. - - Parameters - ---------- - x, y, z : array_like, compatible objects - The three dimensional series is evaluated at the points in the - Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a - list or tuple, it is first converted to an ndarray, otherwise it is - left unchanged and, if it isn't an ndarray, it is treated as a - scalar. - c : array_like - Array of coefficients ordered so that the coefficients for terms of - degree i,j are contained in ``c[i,j]``. If `c` has dimension - greater than two the remaining indices enumerate multiple sets of - coefficients. - - Returns - ------- - values : ndarray, compatible object - The values of the two dimensional polynomial at points in the Cartesian - product of `x` and `y`. - - See Also - -------- - polyval, polyval2d, polygrid2d, polyval3d - - Notes - ----- - - .. versionadded:: 1.7.0 - - """ - return pu._gridnd(polyval, c, x, y, z) - - -def polyvander(x, deg): - """Vandermonde matrix of given degree. - - Returns the Vandermonde matrix of degree `deg` and sample points - `x`. The Vandermonde matrix is defined by - - .. math:: V[..., i] = x^i, - - where `0 <= i <= deg`. The leading indices of `V` index the elements of - `x` and the last index is the power of `x`. - - If `c` is a 1-D array of coefficients of length `n + 1` and `V` is the - matrix ``V = polyvander(x, n)``, then ``np.dot(V, c)`` and - ``polyval(x, c)`` are the same up to roundoff. This equivalence is - useful both for least squares fitting and for the evaluation of a large - number of polynomials of the same degree and sample points. - - Parameters - ---------- - x : array_like - Array of points. The dtype is converted to float64 or complex128 - depending on whether any of the elements are complex. If `x` is - scalar it is converted to a 1-D array. - deg : int - Degree of the resulting matrix. - - Returns - ------- - vander : ndarray. - The Vandermonde matrix. The shape of the returned matrix is - ``x.shape + (deg + 1,)``, where the last index is the power of `x`. - The dtype will be the same as the converted `x`. - - See Also - -------- - polyvander2d, polyvander3d - - """ - ideg = pu._deprecate_as_int(deg, "deg") - if ideg < 0: - raise ValueError("deg must be non-negative") - - x = np.array(x, copy=False, ndmin=1) + 0.0 - dims = (ideg + 1,) + x.shape - dtyp = x.dtype - v = np.empty(dims, dtype=dtyp) - v[0] = x*0 + 1 - if ideg > 0: - v[1] = x - for i in range(2, ideg + 1): - v[i] = v[i-1]*x - return np.moveaxis(v, 0, -1) - - -def polyvander2d(x, y, deg): - """Pseudo-Vandermonde matrix of given degrees. - - Returns the pseudo-Vandermonde matrix of degrees `deg` and sample - points `(x, y)`. The pseudo-Vandermonde matrix is defined by - - .. math:: V[..., (deg[1] + 1)*i + j] = x^i * y^j, - - where `0 <= i <= deg[0]` and `0 <= j <= deg[1]`. The leading indices of - `V` index the points `(x, y)` and the last index encodes the powers of - `x` and `y`. - - If ``V = polyvander2d(x, y, [xdeg, ydeg])``, then the columns of `V` - correspond to the elements of a 2-D coefficient array `c` of shape - (xdeg + 1, ydeg + 1) in the order - - .. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ... - - and ``np.dot(V, c.flat)`` and ``polyval2d(x, y, c)`` will be the same - up to roundoff. This equivalence is useful both for least squares - fitting and for the evaluation of a large number of 2-D polynomials - of the same degrees and sample points. - - Parameters - ---------- - x, y : array_like - Arrays of point coordinates, all of the same shape. The dtypes - will be converted to either float64 or complex128 depending on - whether any of the elements are complex. Scalars are converted to - 1-D arrays. - deg : list of ints - List of maximum degrees of the form [x_deg, y_deg]. - - Returns - ------- - vander2d : ndarray - The shape of the returned matrix is ``x.shape + (order,)``, where - :math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same - as the converted `x` and `y`. - - See Also - -------- - polyvander, polyvander3d, polyval2d, polyval3d - - """ - return pu._vander_nd_flat((polyvander, polyvander), (x, y), deg) - - -def polyvander3d(x, y, z, deg): - """Pseudo-Vandermonde matrix of given degrees. - - Returns the pseudo-Vandermonde matrix of degrees `deg` and sample - points `(x, y, z)`. If `l, m, n` are the given degrees in `x, y, z`, - then The pseudo-Vandermonde matrix is defined by - - .. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = x^i * y^j * z^k, - - where `0 <= i <= l`, `0 <= j <= m`, and `0 <= j <= n`. The leading - indices of `V` index the points `(x, y, z)` and the last index encodes - the powers of `x`, `y`, and `z`. - - If ``V = polyvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns - of `V` correspond to the elements of a 3-D coefficient array `c` of - shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order - - .. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},... - - and ``np.dot(V, c.flat)`` and ``polyval3d(x, y, z, c)`` will be the - same up to roundoff. This equivalence is useful both for least squares - fitting and for the evaluation of a large number of 3-D polynomials - of the same degrees and sample points. - - Parameters - ---------- - x, y, z : array_like - Arrays of point coordinates, all of the same shape. The dtypes will - be converted to either float64 or complex128 depending on whether - any of the elements are complex. Scalars are converted to 1-D - arrays. - deg : list of ints - List of maximum degrees of the form [x_deg, y_deg, z_deg]. - - Returns - ------- - vander3d : ndarray - The shape of the returned matrix is ``x.shape + (order,)``, where - :math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will - be the same as the converted `x`, `y`, and `z`. - - See Also - -------- - polyvander, polyvander3d, polyval2d, polyval3d - - Notes - ----- - - .. versionadded:: 1.7.0 - - """ - return pu._vander_nd_flat((polyvander, polyvander, polyvander), (x, y, z), deg) - - -def polyfit(x, y, deg, rcond=None, full=False, w=None): - """ - Least-squares fit of a polynomial to data. - - Return the coefficients of a polynomial of degree `deg` that is the - least squares fit to the data values `y` given at points `x`. If `y` is - 1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple - fits are done, one for each column of `y`, and the resulting - coefficients are stored in the corresponding columns of a 2-D return. - The fitted polynomial(s) are in the form - - .. math:: p(x) = c_0 + c_1 * x + ... + c_n * x^n, - - where `n` is `deg`. - - Parameters - ---------- - x : array_like, shape (`M`,) - x-coordinates of the `M` sample (data) points ``(x[i], y[i])``. - y : array_like, shape (`M`,) or (`M`, `K`) - y-coordinates of the sample points. Several sets of sample points - sharing the same x-coordinates can be (independently) fit with one - call to `polyfit` by passing in for `y` a 2-D array that contains - one data set per column. - deg : int or 1-D array_like - Degree(s) of the fitting polynomials. If `deg` is a single integer - all terms up to and including the `deg`'th term are included in the - fit. For NumPy versions >= 1.11.0 a list of integers specifying the - degrees of the terms to include may be used instead. - rcond : float, optional - Relative condition number of the fit. Singular values smaller - than `rcond`, relative to the largest singular value, will be - ignored. The default value is ``len(x)*eps``, where `eps` is the - relative precision of the platform's float type, about 2e-16 in - most cases. - full : bool, optional - Switch determining the nature of the return value. When ``False`` - (the default) just the coefficients are returned; when ``True``, - diagnostic information from the singular value decomposition (used - to solve the fit's matrix equation) is also returned. - w : array_like, shape (`M`,), optional - Weights. If not None, the weight ``w[i]`` applies to the unsquared - residual ``y[i] - y_hat[i]`` at ``x[i]``. Ideally the weights are - chosen so that the errors of the products ``w[i]*y[i]`` all have the - same variance. When using inverse-variance weighting, use - ``w[i] = 1/sigma(y[i])``. The default value is None. - - .. versionadded:: 1.5.0 - - Returns - ------- - coef : ndarray, shape (`deg` + 1,) or (`deg` + 1, `K`) - Polynomial coefficients ordered from low to high. If `y` was 2-D, - the coefficients in column `k` of `coef` represent the polynomial - fit to the data in `y`'s `k`-th column. - - [residuals, rank, singular_values, rcond] : list - These values are only returned if ``full == True`` - - - residuals -- sum of squared residuals of the least squares fit - - rank -- the numerical rank of the scaled Vandermonde matrix - - singular_values -- singular values of the scaled Vandermonde matrix - - rcond -- value of `rcond`. - - For more details, see `numpy.linalg.lstsq`. - - Raises - ------ - RankWarning - Raised if the matrix in the least-squares fit is rank deficient. - The warning is only raised if ``full == False``. The warnings can - be turned off by: - - >>> import warnings - >>> warnings.simplefilter('ignore', np.RankWarning) - - See Also - -------- - numpy.polynomial.chebyshev.chebfit - numpy.polynomial.legendre.legfit - numpy.polynomial.laguerre.lagfit - numpy.polynomial.hermite.hermfit - numpy.polynomial.hermite_e.hermefit - polyval : Evaluates a polynomial. - polyvander : Vandermonde matrix for powers. - numpy.linalg.lstsq : Computes a least-squares fit from the matrix. - scipy.interpolate.UnivariateSpline : Computes spline fits. - - Notes - ----- - The solution is the coefficients of the polynomial `p` that minimizes - the sum of the weighted squared errors - - .. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2, - - where the :math:`w_j` are the weights. This problem is solved by - setting up the (typically) over-determined matrix equation: - - .. math:: V(x) * c = w * y, - - where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the - coefficients to be solved for, `w` are the weights, and `y` are the - observed values. This equation is then solved using the singular value - decomposition of `V`. - - If some of the singular values of `V` are so small that they are - neglected (and `full` == ``False``), a `RankWarning` will be raised. - This means that the coefficient values may be poorly determined. - Fitting to a lower order polynomial will usually get rid of the warning - (but may not be what you want, of course; if you have independent - reason(s) for choosing the degree which isn't working, you may have to: - a) reconsider those reasons, and/or b) reconsider the quality of your - data). The `rcond` parameter can also be set to a value smaller than - its default, but the resulting fit may be spurious and have large - contributions from roundoff error. - - Polynomial fits using double precision tend to "fail" at about - (polynomial) degree 20. Fits using Chebyshev or Legendre series are - generally better conditioned, but much can still depend on the - distribution of the sample points and the smoothness of the data. If - the quality of the fit is inadequate, splines may be a good - alternative. - - Examples - -------- - >>> np.random.seed(123) - >>> from numpy.polynomial import polynomial as P - >>> x = np.linspace(-1,1,51) # x "data": [-1, -0.96, ..., 0.96, 1] - >>> y = x**3 - x + np.random.randn(len(x)) # x^3 - x + Gaussian noise - >>> c, stats = P.polyfit(x,y,3,full=True) - >>> np.random.seed(123) - >>> c # c[0], c[2] should be approx. 0, c[1] approx. -1, c[3] approx. 1 - array([ 0.01909725, -1.30598256, -0.00577963, 1.02644286]) # may vary - >>> stats # note the large SSR, explaining the rather poor results - [array([ 38.06116253]), 4, array([ 1.38446749, 1.32119158, 0.50443316, # may vary - 0.28853036]), 1.1324274851176597e-014] - - Same thing without the added noise - - >>> y = x**3 - x - >>> c, stats = P.polyfit(x,y,3,full=True) - >>> c # c[0], c[2] should be "very close to 0", c[1] ~= -1, c[3] ~= 1 - array([-6.36925336e-18, -1.00000000e+00, -4.08053781e-16, 1.00000000e+00]) - >>> stats # note the minuscule SSR - [array([ 7.46346754e-31]), 4, array([ 1.38446749, 1.32119158, # may vary - 0.50443316, 0.28853036]), 1.1324274851176597e-014] - - """ - return pu._fit(polyvander, x, y, deg, rcond, full, w) - - -def polycompanion(c): - """ - Return the companion matrix of c. - - The companion matrix for power series cannot be made symmetric by - scaling the basis, so this function differs from those for the - orthogonal polynomials. - - Parameters - ---------- - c : array_like - 1-D array of polynomial coefficients ordered from low to high - degree. - - Returns - ------- - mat : ndarray - Companion matrix of dimensions (deg, deg). - - Notes - ----- - - .. versionadded:: 1.7.0 - - """ - # c is a trimmed copy - [c] = pu.as_series([c]) - if len(c) < 2: - raise ValueError('Series must have maximum degree of at least 1.') - if len(c) == 2: - return np.array([[-c[0]/c[1]]]) - - n = len(c) - 1 - mat = np.zeros((n, n), dtype=c.dtype) - bot = mat.reshape(-1)[n::n+1] - bot[...] = 1 - mat[:, -1] -= c[:-1]/c[-1] - return mat - - -def polyroots(c): - """ - Compute the roots of a polynomial. - - Return the roots (a.k.a. "zeros") of the polynomial - - .. math:: p(x) = \\sum_i c[i] * x^i. - - Parameters - ---------- - c : 1-D array_like - 1-D array of polynomial coefficients. - - Returns - ------- - out : ndarray - Array of the roots of the polynomial. If all the roots are real, - then `out` is also real, otherwise it is complex. - - See Also - -------- - numpy.polynomial.chebyshev.chebroots - numpy.polynomial.legendre.legroots - numpy.polynomial.laguerre.lagroots - numpy.polynomial.hermite.hermroots - numpy.polynomial.hermite_e.hermeroots - - Notes - ----- - The root estimates are obtained as the eigenvalues of the companion - matrix, Roots far from the origin of the complex plane may have large - errors due to the numerical instability of the power series for such - values. Roots with multiplicity greater than 1 will also show larger - errors as the value of the series near such points is relatively - insensitive to errors in the roots. Isolated roots near the origin can - be improved by a few iterations of Newton's method. - - Examples - -------- - >>> import numpy.polynomial.polynomial as poly - >>> poly.polyroots(poly.polyfromroots((-1,0,1))) - array([-1., 0., 1.]) - >>> poly.polyroots(poly.polyfromroots((-1,0,1))).dtype - dtype('float64') - >>> j = complex(0,1) - >>> poly.polyroots(poly.polyfromroots((-j,0,j))) - array([ 0.00000000e+00+0.j, 0.00000000e+00+1.j, 2.77555756e-17-1.j]) # may vary - - """ - # c is a trimmed copy - [c] = pu.as_series([c]) - if len(c) < 2: - return np.array([], dtype=c.dtype) - if len(c) == 2: - return np.array([-c[0]/c[1]]) - - # rotated companion matrix reduces error - m = polycompanion(c)[::-1,::-1] - r = la.eigvals(m) - r.sort() - return r - - -# -# polynomial class -# - -class Polynomial(ABCPolyBase): - """A power series class. - - The Polynomial class provides the standard Python numerical methods - '+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the - attributes and methods listed in the `ABCPolyBase` documentation. - - Parameters - ---------- - coef : array_like - Polynomial coefficients in order of increasing degree, i.e., - ``(1, 2, 3)`` give ``1 + 2*x + 3*x**2``. - domain : (2,) array_like, optional - Domain to use. The interval ``[domain[0], domain[1]]`` is mapped - to the interval ``[window[0], window[1]]`` by shifting and scaling. - The default value is [-1, 1]. - window : (2,) array_like, optional - Window, see `domain` for its use. The default value is [-1, 1]. - - .. versionadded:: 1.6.0 - symbol : str, optional - Symbol used to represent the independent variable in string - representations of the polynomial expression, e.g. for printing. - The symbol must be a valid Python identifier. Default value is 'x'. - - .. versionadded:: 1.24 - - """ - # Virtual Functions - _add = staticmethod(polyadd) - _sub = staticmethod(polysub) - _mul = staticmethod(polymul) - _div = staticmethod(polydiv) - _pow = staticmethod(polypow) - _val = staticmethod(polyval) - _int = staticmethod(polyint) - _der = staticmethod(polyder) - _fit = staticmethod(polyfit) - _line = staticmethod(polyline) - _roots = staticmethod(polyroots) - _fromroots = staticmethod(polyfromroots) - - # Virtual properties - domain = np.array(polydomain) - window = np.array(polydomain) - basis_name = None - - @classmethod - def _str_term_unicode(cls, i, arg_str): - if i == '1': - return f"·{arg_str}" - else: - return f"·{arg_str}{i.translate(cls._superscript_mapping)}" - - @staticmethod - def _str_term_ascii(i, arg_str): - if i == '1': - return f" {arg_str}" - else: - return f" {arg_str}**{i}" - - @staticmethod - def _repr_latex_term(i, arg_str, needs_parens): - if needs_parens: - arg_str = rf"\left({arg_str}\right)" - if i == 0: - return '1' - elif i == 1: - return arg_str - else: - return f"{arg_str}^{{{i}}}" diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py deleted file mode 100644 index 24ab3888412d08b54543ed22910c67ce9bdf328f..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/indexes/timedeltas/methods/test_factorize.py +++ /dev/null @@ -1,40 +0,0 @@ -import numpy as np - -from pandas import ( - TimedeltaIndex, - factorize, - timedelta_range, -) -import pandas._testing as tm - - -class TestTimedeltaIndexFactorize: - def test_factorize(self): - idx1 = TimedeltaIndex(["1 day", "1 day", "2 day", "2 day", "3 day", "3 day"]) - - exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp) - exp_idx = TimedeltaIndex(["1 day", "2 day", "3 day"]) - - arr, idx = idx1.factorize() - tm.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, exp_idx) - assert idx.freq == exp_idx.freq - - arr, idx = idx1.factorize(sort=True) - tm.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, exp_idx) - assert idx.freq == exp_idx.freq - - def test_factorize_preserves_freq(self): - # GH#38120 freq should be preserved - idx3 = timedelta_range("1 day", periods=4, freq="s") - exp_arr = np.array([0, 1, 2, 3], dtype=np.intp) - arr, idx = idx3.factorize() - tm.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, idx3) - assert idx.freq == idx3.freq - - arr, idx = factorize(idx3) - tm.assert_numpy_array_equal(arr, exp_arr) - tm.assert_index_equal(idx, idx3) - assert idx.freq == idx3.freq diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pandas/tests/io/formats/style/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/html5lib/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/html5lib/__init__.py deleted file mode 100644 index d1d82f157f884dc65160a41b436258d1aaf12e4c..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/html5lib/__init__.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -HTML parsing library based on the `WHATWG HTML specification -`_. The parser is designed to be compatible with -existing HTML found in the wild and implements well-defined error recovery that -is largely compatible with modern desktop web browsers. - -Example usage:: - - from pip._vendor import html5lib - with open("my_document.html", "rb") as f: - tree = html5lib.parse(f) - -For convenience, this module re-exports the following names: - -* :func:`~.html5parser.parse` -* :func:`~.html5parser.parseFragment` -* :class:`~.html5parser.HTMLParser` -* :func:`~.treebuilders.getTreeBuilder` -* :func:`~.treewalkers.getTreeWalker` -* :func:`~.serializer.serialize` -""" - -from __future__ import absolute_import, division, unicode_literals - -from .html5parser import HTMLParser, parse, parseFragment -from .treebuilders import getTreeBuilder -from .treewalkers import getTreeWalker -from .serializer import serialize - -__all__ = ["HTMLParser", "parse", "parseFragment", "getTreeBuilder", - "getTreeWalker", "serialize"] - -# this has to be at the top level, see how setup.py parses this -#: Distribution version number. -__version__ = "1.1" diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/msgpack/__init__.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/msgpack/__init__.py deleted file mode 100644 index d6705e22b79dd53f3896bee29eb1e2f12bf106eb..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/msgpack/__init__.py +++ /dev/null @@ -1,54 +0,0 @@ -# coding: utf-8 -from ._version import version -from .exceptions import * -from .ext import ExtType, Timestamp - -import os -import sys - - -if os.environ.get("MSGPACK_PUREPYTHON") or sys.version_info[0] == 2: - from .fallback import Packer, unpackb, Unpacker -else: - try: - from ._cmsgpack import Packer, unpackb, Unpacker - except ImportError: - from .fallback import Packer, unpackb, Unpacker - - -def pack(o, stream, **kwargs): - """ - Pack object `o` and write it to `stream` - - See :class:`Packer` for options. - """ - packer = Packer(**kwargs) - stream.write(packer.pack(o)) - - -def packb(o, **kwargs): - """ - Pack object `o` and return packed bytes - - See :class:`Packer` for options. - """ - return Packer(**kwargs).pack(o) - - -def unpack(stream, **kwargs): - """ - Unpack an object from `stream`. - - Raises `ExtraData` when `stream` contains extra bytes. - See :class:`Unpacker` for options. - """ - data = stream.read() - return unpackb(data, **kwargs) - - -# alias for compatibility to simplejson/marshal/pickle. -load = unpack -loads = unpackb - -dump = pack -dumps = packb diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/live_render.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/live_render.py deleted file mode 100644 index b90fbf7f35097694f727e201b0b378942d70a443..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pip/_vendor/rich/live_render.py +++ /dev/null @@ -1,113 +0,0 @@ -import sys -from typing import Optional, Tuple - -if sys.version_info >= (3, 8): - from typing import Literal -else: - from pip._vendor.typing_extensions import Literal # pragma: no cover - - -from ._loop import loop_last -from .console import Console, ConsoleOptions, RenderableType, RenderResult -from .control import Control -from .segment import ControlType, Segment -from .style import StyleType -from .text import Text - -VerticalOverflowMethod = Literal["crop", "ellipsis", "visible"] - - -class LiveRender: - """Creates a renderable that may be updated. - - Args: - renderable (RenderableType): Any renderable object. - style (StyleType, optional): An optional style to apply to the renderable. Defaults to "". - """ - - def __init__( - self, - renderable: RenderableType, - style: StyleType = "", - vertical_overflow: VerticalOverflowMethod = "ellipsis", - ) -> None: - self.renderable = renderable - self.style = style - self.vertical_overflow = vertical_overflow - self._shape: Optional[Tuple[int, int]] = None - - def set_renderable(self, renderable: RenderableType) -> None: - """Set a new renderable. - - Args: - renderable (RenderableType): Any renderable object, including str. - """ - self.renderable = renderable - - def position_cursor(self) -> Control: - """Get control codes to move cursor to beginning of live render. - - Returns: - Control: A control instance that may be printed. - """ - if self._shape is not None: - _, height = self._shape - return Control( - ControlType.CARRIAGE_RETURN, - (ControlType.ERASE_IN_LINE, 2), - *( - ( - (ControlType.CURSOR_UP, 1), - (ControlType.ERASE_IN_LINE, 2), - ) - * (height - 1) - ) - ) - return Control() - - def restore_cursor(self) -> Control: - """Get control codes to clear the render and restore the cursor to its previous position. - - Returns: - Control: A Control instance that may be printed. - """ - if self._shape is not None: - _, height = self._shape - return Control( - ControlType.CARRIAGE_RETURN, - *((ControlType.CURSOR_UP, 1), (ControlType.ERASE_IN_LINE, 2)) * height - ) - return Control() - - def __rich_console__( - self, console: Console, options: ConsoleOptions - ) -> RenderResult: - - renderable = self.renderable - style = console.get_style(self.style) - lines = console.render_lines(renderable, options, style=style, pad=False) - shape = Segment.get_shape(lines) - - _, height = shape - if height > options.size.height: - if self.vertical_overflow == "crop": - lines = lines[: options.size.height] - shape = Segment.get_shape(lines) - elif self.vertical_overflow == "ellipsis": - lines = lines[: (options.size.height - 1)] - overflow_text = Text( - "...", - overflow="crop", - justify="center", - end="", - style="live.ellipsis", - ) - lines.append(list(console.render(overflow_text))) - shape = Segment.get_shape(lines) - self._shape = shape - - new_line = Segment.line() - for last, line in loop_last(lines): - yield from line - if not last: - yield new_line diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/tools.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/tools.py deleted file mode 100644 index 45be27704cbc9df2bcc2bf5294b87f2083960980..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/pydantic/v1/tools.py +++ /dev/null @@ -1,92 +0,0 @@ -import json -from functools import lru_cache -from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union - -from .parse import Protocol, load_file, load_str_bytes -from .types import StrBytes -from .typing import display_as_type - -__all__ = ('parse_file_as', 'parse_obj_as', 'parse_raw_as', 'schema_of', 'schema_json_of') - -NameFactory = Union[str, Callable[[Type[Any]], str]] - -if TYPE_CHECKING: - from .typing import DictStrAny - - -def _generate_parsing_type_name(type_: Any) -> str: - return f'ParsingModel[{display_as_type(type_)}]' - - -@lru_cache(maxsize=2048) -def _get_parsing_type(type_: Any, *, type_name: Optional[NameFactory] = None) -> Any: - from .main import create_model - - if type_name is None: - type_name = _generate_parsing_type_name - if not isinstance(type_name, str): - type_name = type_name(type_) - return create_model(type_name, __root__=(type_, ...)) - - -T = TypeVar('T') - - -def parse_obj_as(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = None) -> T: - model_type = _get_parsing_type(type_, type_name=type_name) # type: ignore[arg-type] - return model_type(__root__=obj).__root__ - - -def parse_file_as( - type_: Type[T], - path: Union[str, Path], - *, - content_type: str = None, - encoding: str = 'utf8', - proto: Protocol = None, - allow_pickle: bool = False, - json_loads: Callable[[str], Any] = json.loads, - type_name: Optional[NameFactory] = None, -) -> T: - obj = load_file( - path, - proto=proto, - content_type=content_type, - encoding=encoding, - allow_pickle=allow_pickle, - json_loads=json_loads, - ) - return parse_obj_as(type_, obj, type_name=type_name) - - -def parse_raw_as( - type_: Type[T], - b: StrBytes, - *, - content_type: str = None, - encoding: str = 'utf8', - proto: Protocol = None, - allow_pickle: bool = False, - json_loads: Callable[[str], Any] = json.loads, - type_name: Optional[NameFactory] = None, -) -> T: - obj = load_str_bytes( - b, - proto=proto, - content_type=content_type, - encoding=encoding, - allow_pickle=allow_pickle, - json_loads=json_loads, - ) - return parse_obj_as(type_, obj, type_name=type_name) - - -def schema_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_kwargs: Any) -> 'DictStrAny': - """Generate a JSON schema (as dict) for the passed model or dynamically generated one""" - return _get_parsing_type(type_, type_name=title).schema(**schema_kwargs) - - -def schema_json_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_json_kwargs: Any) -> str: - """Generate a JSON schema (as JSON) for the passed model or dynamically generated one""" - return _get_parsing_type(type_, type_name=title).schema_json(**schema_json_kwargs) diff --git a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/contrib/pyopenssl.py b/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/contrib/pyopenssl.py deleted file mode 100644 index 74b35883bfdd214cb784215a0b83ff2c0f5f23c3..0000000000000000000000000000000000000000 --- a/spaces/profayle/TerrapinTalk/myenv/lib/python3.9/site-packages/urllib3/contrib/pyopenssl.py +++ /dev/null @@ -1,548 +0,0 @@ -""" -Module for using pyOpenSSL as a TLS backend. This module was relevant before -the standard library ``ssl`` module supported SNI, but now that we've dropped -support for Python 2.7 all relevant Python versions support SNI so -**this module is no longer recommended**. - -This needs the following packages installed: - -* `pyOpenSSL`_ (tested with 16.0.0) -* `cryptography`_ (minimum 1.3.4, from pyopenssl) -* `idna`_ (minimum 2.0, from cryptography) - -However, pyOpenSSL depends on cryptography, which depends on idna, so while we -use all three directly here we end up having relatively few packages required. - -You can install them with the following command: - -.. code-block:: bash - - $ python -m pip install pyopenssl cryptography idna - -To activate certificate checking, call -:func:`~urllib3.contrib.pyopenssl.inject_into_urllib3` from your Python code -before you begin making HTTP requests. This can be done in a ``sitecustomize`` -module, or at any other time before your application begins using ``urllib3``, -like this: - -.. code-block:: python - - try: - import urllib3.contrib.pyopenssl - urllib3.contrib.pyopenssl.inject_into_urllib3() - except ImportError: - pass - -.. _pyopenssl: https://www.pyopenssl.org -.. _cryptography: https://cryptography.io -.. _idna: https://github.com/kjd/idna -""" - -from __future__ import annotations - -import OpenSSL.SSL # type: ignore[import] -from cryptography import x509 - -try: - from cryptography.x509 import UnsupportedExtension # type: ignore[attr-defined] -except ImportError: - # UnsupportedExtension is gone in cryptography >= 2.1.0 - class UnsupportedExtension(Exception): # type: ignore[no-redef] - pass - - -import logging -import ssl -import typing -from io import BytesIO -from socket import socket as socket_cls -from socket import timeout - -from .. import util - -if typing.TYPE_CHECKING: - from OpenSSL.crypto import X509 # type: ignore[import] - - -__all__ = ["inject_into_urllib3", "extract_from_urllib3"] - -# Map from urllib3 to PyOpenSSL compatible parameter-values. -_openssl_versions = { - util.ssl_.PROTOCOL_TLS: OpenSSL.SSL.SSLv23_METHOD, # type: ignore[attr-defined] - util.ssl_.PROTOCOL_TLS_CLIENT: OpenSSL.SSL.SSLv23_METHOD, # type: ignore[attr-defined] - ssl.PROTOCOL_TLSv1: OpenSSL.SSL.TLSv1_METHOD, -} - -if hasattr(ssl, "PROTOCOL_TLSv1_1") and hasattr(OpenSSL.SSL, "TLSv1_1_METHOD"): - _openssl_versions[ssl.PROTOCOL_TLSv1_1] = OpenSSL.SSL.TLSv1_1_METHOD - -if hasattr(ssl, "PROTOCOL_TLSv1_2") and hasattr(OpenSSL.SSL, "TLSv1_2_METHOD"): - _openssl_versions[ssl.PROTOCOL_TLSv1_2] = OpenSSL.SSL.TLSv1_2_METHOD - - -_stdlib_to_openssl_verify = { - ssl.CERT_NONE: OpenSSL.SSL.VERIFY_NONE, - ssl.CERT_OPTIONAL: OpenSSL.SSL.VERIFY_PEER, - ssl.CERT_REQUIRED: OpenSSL.SSL.VERIFY_PEER - + OpenSSL.SSL.VERIFY_FAIL_IF_NO_PEER_CERT, -} -_openssl_to_stdlib_verify = {v: k for k, v in _stdlib_to_openssl_verify.items()} - -# The SSLvX values are the most likely to be missing in the future -# but we check them all just to be sure. -_OP_NO_SSLv2_OR_SSLv3: int = getattr(OpenSSL.SSL, "OP_NO_SSLv2", 0) | getattr( - OpenSSL.SSL, "OP_NO_SSLv3", 0 -) -_OP_NO_TLSv1: int = getattr(OpenSSL.SSL, "OP_NO_TLSv1", 0) -_OP_NO_TLSv1_1: int = getattr(OpenSSL.SSL, "OP_NO_TLSv1_1", 0) -_OP_NO_TLSv1_2: int = getattr(OpenSSL.SSL, "OP_NO_TLSv1_2", 0) -_OP_NO_TLSv1_3: int = getattr(OpenSSL.SSL, "OP_NO_TLSv1_3", 0) - -_openssl_to_ssl_minimum_version: dict[int, int] = { - ssl.TLSVersion.MINIMUM_SUPPORTED: _OP_NO_SSLv2_OR_SSLv3, - ssl.TLSVersion.TLSv1: _OP_NO_SSLv2_OR_SSLv3, - ssl.TLSVersion.TLSv1_1: _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1, - ssl.TLSVersion.TLSv1_2: _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1 | _OP_NO_TLSv1_1, - ssl.TLSVersion.TLSv1_3: ( - _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1 | _OP_NO_TLSv1_1 | _OP_NO_TLSv1_2 - ), - ssl.TLSVersion.MAXIMUM_SUPPORTED: ( - _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1 | _OP_NO_TLSv1_1 | _OP_NO_TLSv1_2 - ), -} -_openssl_to_ssl_maximum_version: dict[int, int] = { - ssl.TLSVersion.MINIMUM_SUPPORTED: ( - _OP_NO_SSLv2_OR_SSLv3 - | _OP_NO_TLSv1 - | _OP_NO_TLSv1_1 - | _OP_NO_TLSv1_2 - | _OP_NO_TLSv1_3 - ), - ssl.TLSVersion.TLSv1: ( - _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1_1 | _OP_NO_TLSv1_2 | _OP_NO_TLSv1_3 - ), - ssl.TLSVersion.TLSv1_1: _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1_2 | _OP_NO_TLSv1_3, - ssl.TLSVersion.TLSv1_2: _OP_NO_SSLv2_OR_SSLv3 | _OP_NO_TLSv1_3, - ssl.TLSVersion.TLSv1_3: _OP_NO_SSLv2_OR_SSLv3, - ssl.TLSVersion.MAXIMUM_SUPPORTED: _OP_NO_SSLv2_OR_SSLv3, -} - -# OpenSSL will only write 16K at a time -SSL_WRITE_BLOCKSIZE = 16384 - -orig_util_SSLContext = util.ssl_.SSLContext - - -log = logging.getLogger(__name__) - - -def inject_into_urllib3() -> None: - "Monkey-patch urllib3 with PyOpenSSL-backed SSL-support." - - _validate_dependencies_met() - - util.SSLContext = PyOpenSSLContext # type: ignore[assignment] - util.ssl_.SSLContext = PyOpenSSLContext # type: ignore[assignment] - util.IS_PYOPENSSL = True - util.ssl_.IS_PYOPENSSL = True - - -def extract_from_urllib3() -> None: - "Undo monkey-patching by :func:`inject_into_urllib3`." - - util.SSLContext = orig_util_SSLContext - util.ssl_.SSLContext = orig_util_SSLContext - util.IS_PYOPENSSL = False - util.ssl_.IS_PYOPENSSL = False - - -def _validate_dependencies_met() -> None: - """ - Verifies that PyOpenSSL's package-level dependencies have been met. - Throws `ImportError` if they are not met. - """ - # Method added in `cryptography==1.1`; not available in older versions - from cryptography.x509.extensions import Extensions - - if getattr(Extensions, "get_extension_for_class", None) is None: - raise ImportError( - "'cryptography' module missing required functionality. " - "Try upgrading to v1.3.4 or newer." - ) - - # pyOpenSSL 0.14 and above use cryptography for OpenSSL bindings. The _x509 - # attribute is only present on those versions. - from OpenSSL.crypto import X509 - - x509 = X509() - if getattr(x509, "_x509", None) is None: - raise ImportError( - "'pyOpenSSL' module missing required functionality. " - "Try upgrading to v0.14 or newer." - ) - - -def _dnsname_to_stdlib(name: str) -> str | None: - """ - Converts a dNSName SubjectAlternativeName field to the form used by the - standard library on the given Python version. - - Cryptography produces a dNSName as a unicode string that was idna-decoded - from ASCII bytes. We need to idna-encode that string to get it back, and - then on Python 3 we also need to convert to unicode via UTF-8 (the stdlib - uses PyUnicode_FromStringAndSize on it, which decodes via UTF-8). - - If the name cannot be idna-encoded then we return None signalling that - the name given should be skipped. - """ - - def idna_encode(name: str) -> bytes | None: - """ - Borrowed wholesale from the Python Cryptography Project. It turns out - that we can't just safely call `idna.encode`: it can explode for - wildcard names. This avoids that problem. - """ - import idna - - try: - for prefix in ["*.", "."]: - if name.startswith(prefix): - name = name[len(prefix) :] - return prefix.encode("ascii") + idna.encode(name) - return idna.encode(name) - except idna.core.IDNAError: - return None - - # Don't send IPv6 addresses through the IDNA encoder. - if ":" in name: - return name - - encoded_name = idna_encode(name) - if encoded_name is None: - return None - return encoded_name.decode("utf-8") - - -def get_subj_alt_name(peer_cert: X509) -> list[tuple[str, str]]: - """ - Given an PyOpenSSL certificate, provides all the subject alternative names. - """ - cert = peer_cert.to_cryptography() - - # We want to find the SAN extension. Ask Cryptography to locate it (it's - # faster than looping in Python) - try: - ext = cert.extensions.get_extension_for_class(x509.SubjectAlternativeName).value - except x509.ExtensionNotFound: - # No such extension, return the empty list. - return [] - except ( - x509.DuplicateExtension, - UnsupportedExtension, - x509.UnsupportedGeneralNameType, - UnicodeError, - ) as e: - # A problem has been found with the quality of the certificate. Assume - # no SAN field is present. - log.warning( - "A problem was encountered with the certificate that prevented " - "urllib3 from finding the SubjectAlternativeName field. This can " - "affect certificate validation. The error was %s", - e, - ) - return [] - - # We want to return dNSName and iPAddress fields. We need to cast the IPs - # back to strings because the match_hostname function wants them as - # strings. - # Sadly the DNS names need to be idna encoded and then, on Python 3, UTF-8 - # decoded. This is pretty frustrating, but that's what the standard library - # does with certificates, and so we need to attempt to do the same. - # We also want to skip over names which cannot be idna encoded. - names = [ - ("DNS", name) - for name in map(_dnsname_to_stdlib, ext.get_values_for_type(x509.DNSName)) - if name is not None - ] - names.extend( - ("IP Address", str(name)) for name in ext.get_values_for_type(x509.IPAddress) - ) - - return names - - -class WrappedSocket: - """API-compatibility wrapper for Python OpenSSL's Connection-class.""" - - def __init__( - self, - connection: OpenSSL.SSL.Connection, - socket: socket_cls, - suppress_ragged_eofs: bool = True, - ) -> None: - self.connection = connection - self.socket = socket - self.suppress_ragged_eofs = suppress_ragged_eofs - self._io_refs = 0 - self._closed = False - - def fileno(self) -> int: - return self.socket.fileno() - - # Copy-pasted from Python 3.5 source code - def _decref_socketios(self) -> None: - if self._io_refs > 0: - self._io_refs -= 1 - if self._closed: - self.close() - - def recv(self, *args: typing.Any, **kwargs: typing.Any) -> bytes: - try: - data = self.connection.recv(*args, **kwargs) - except OpenSSL.SSL.SysCallError as e: - if self.suppress_ragged_eofs and e.args == (-1, "Unexpected EOF"): - return b"" - else: - raise OSError(e.args[0], str(e)) from e - except OpenSSL.SSL.ZeroReturnError: - if self.connection.get_shutdown() == OpenSSL.SSL.RECEIVED_SHUTDOWN: - return b"" - else: - raise - except OpenSSL.SSL.WantReadError as e: - if not util.wait_for_read(self.socket, self.socket.gettimeout()): - raise timeout("The read operation timed out") from e - else: - return self.recv(*args, **kwargs) - - # TLS 1.3 post-handshake authentication - except OpenSSL.SSL.Error as e: - raise ssl.SSLError(f"read error: {e!r}") from e - else: - return data # type: ignore[no-any-return] - - def recv_into(self, *args: typing.Any, **kwargs: typing.Any) -> int: - try: - return self.connection.recv_into(*args, **kwargs) # type: ignore[no-any-return] - except OpenSSL.SSL.SysCallError as e: - if self.suppress_ragged_eofs and e.args == (-1, "Unexpected EOF"): - return 0 - else: - raise OSError(e.args[0], str(e)) from e - except OpenSSL.SSL.ZeroReturnError: - if self.connection.get_shutdown() == OpenSSL.SSL.RECEIVED_SHUTDOWN: - return 0 - else: - raise - except OpenSSL.SSL.WantReadError as e: - if not util.wait_for_read(self.socket, self.socket.gettimeout()): - raise timeout("The read operation timed out") from e - else: - return self.recv_into(*args, **kwargs) - - # TLS 1.3 post-handshake authentication - except OpenSSL.SSL.Error as e: - raise ssl.SSLError(f"read error: {e!r}") from e - - def settimeout(self, timeout: float) -> None: - return self.socket.settimeout(timeout) - - def _send_until_done(self, data: bytes) -> int: - while True: - try: - return self.connection.send(data) # type: ignore[no-any-return] - except OpenSSL.SSL.WantWriteError as e: - if not util.wait_for_write(self.socket, self.socket.gettimeout()): - raise timeout() from e - continue - except OpenSSL.SSL.SysCallError as e: - raise OSError(e.args[0], str(e)) from e - - def sendall(self, data: bytes) -> None: - total_sent = 0 - while total_sent < len(data): - sent = self._send_until_done( - data[total_sent : total_sent + SSL_WRITE_BLOCKSIZE] - ) - total_sent += sent - - def shutdown(self) -> None: - # FIXME rethrow compatible exceptions should we ever use this - self.connection.shutdown() - - def close(self) -> None: - self._closed = True - if self._io_refs <= 0: - self._real_close() - - def _real_close(self) -> None: - try: - return self.connection.close() # type: ignore[no-any-return] - except OpenSSL.SSL.Error: - return - - def getpeercert( - self, binary_form: bool = False - ) -> dict[str, list[typing.Any]] | None: - x509 = self.connection.get_peer_certificate() - - if not x509: - return x509 # type: ignore[no-any-return] - - if binary_form: - return OpenSSL.crypto.dump_certificate(OpenSSL.crypto.FILETYPE_ASN1, x509) # type: ignore[no-any-return] - - return { - "subject": ((("commonName", x509.get_subject().CN),),), # type: ignore[dict-item] - "subjectAltName": get_subj_alt_name(x509), - } - - def version(self) -> str: - return self.connection.get_protocol_version_name() # type: ignore[no-any-return] - - -WrappedSocket.makefile = socket_cls.makefile # type: ignore[attr-defined] - - -class PyOpenSSLContext: - """ - I am a wrapper class for the PyOpenSSL ``Context`` object. I am responsible - for translating the interface of the standard library ``SSLContext`` object - to calls into PyOpenSSL. - """ - - def __init__(self, protocol: int) -> None: - self.protocol = _openssl_versions[protocol] - self._ctx = OpenSSL.SSL.Context(self.protocol) - self._options = 0 - self.check_hostname = False - self._minimum_version: int = ssl.TLSVersion.MINIMUM_SUPPORTED - self._maximum_version: int = ssl.TLSVersion.MAXIMUM_SUPPORTED - - @property - def options(self) -> int: - return self._options - - @options.setter - def options(self, value: int) -> None: - self._options = value - self._set_ctx_options() - - @property - def verify_mode(self) -> int: - return _openssl_to_stdlib_verify[self._ctx.get_verify_mode()] - - @verify_mode.setter - def verify_mode(self, value: ssl.VerifyMode) -> None: - self._ctx.set_verify(_stdlib_to_openssl_verify[value], _verify_callback) - - def set_default_verify_paths(self) -> None: - self._ctx.set_default_verify_paths() - - def set_ciphers(self, ciphers: bytes | str) -> None: - if isinstance(ciphers, str): - ciphers = ciphers.encode("utf-8") - self._ctx.set_cipher_list(ciphers) - - def load_verify_locations( - self, - cafile: str | None = None, - capath: str | None = None, - cadata: bytes | None = None, - ) -> None: - if cafile is not None: - cafile = cafile.encode("utf-8") # type: ignore[assignment] - if capath is not None: - capath = capath.encode("utf-8") # type: ignore[assignment] - try: - self._ctx.load_verify_locations(cafile, capath) - if cadata is not None: - self._ctx.load_verify_locations(BytesIO(cadata)) - except OpenSSL.SSL.Error as e: - raise ssl.SSLError(f"unable to load trusted certificates: {e!r}") from e - - def load_cert_chain( - self, - certfile: str, - keyfile: str | None = None, - password: str | None = None, - ) -> None: - try: - self._ctx.use_certificate_chain_file(certfile) - if password is not None: - if not isinstance(password, bytes): - password = password.encode("utf-8") # type: ignore[assignment] - self._ctx.set_passwd_cb(lambda *_: password) - self._ctx.use_privatekey_file(keyfile or certfile) - except OpenSSL.SSL.Error as e: - raise ssl.SSLError(f"Unable to load certificate chain: {e!r}") from e - - def set_alpn_protocols(self, protocols: list[bytes | str]) -> None: - protocols = [util.util.to_bytes(p, "ascii") for p in protocols] - return self._ctx.set_alpn_protos(protocols) # type: ignore[no-any-return] - - def wrap_socket( - self, - sock: socket_cls, - server_side: bool = False, - do_handshake_on_connect: bool = True, - suppress_ragged_eofs: bool = True, - server_hostname: bytes | str | None = None, - ) -> WrappedSocket: - cnx = OpenSSL.SSL.Connection(self._ctx, sock) - - # If server_hostname is an IP, don't use it for SNI, per RFC6066 Section 3 - if server_hostname and not util.ssl_.is_ipaddress(server_hostname): - if isinstance(server_hostname, str): - server_hostname = server_hostname.encode("utf-8") - cnx.set_tlsext_host_name(server_hostname) - - cnx.set_connect_state() - - while True: - try: - cnx.do_handshake() - except OpenSSL.SSL.WantReadError as e: - if not util.wait_for_read(sock, sock.gettimeout()): - raise timeout("select timed out") from e - continue - except OpenSSL.SSL.Error as e: - raise ssl.SSLError(f"bad handshake: {e!r}") from e - break - - return WrappedSocket(cnx, sock) - - def _set_ctx_options(self) -> None: - self._ctx.set_options( - self._options - | _openssl_to_ssl_minimum_version[self._minimum_version] - | _openssl_to_ssl_maximum_version[self._maximum_version] - ) - - @property - def minimum_version(self) -> int: - return self._minimum_version - - @minimum_version.setter - def minimum_version(self, minimum_version: int) -> None: - self._minimum_version = minimum_version - self._set_ctx_options() - - @property - def maximum_version(self) -> int: - return self._maximum_version - - @maximum_version.setter - def maximum_version(self, maximum_version: int) -> None: - self._maximum_version = maximum_version - self._set_ctx_options() - - -def _verify_callback( - cnx: OpenSSL.SSL.Connection, - x509: X509, - err_no: int, - 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processPackageData(arrayBuffer){assert(arrayBuffer,"Loading data file failed.");assert(arrayBuffer instanceof ArrayBuffer,"bad input to processPackageData");var byteArray=new Uint8Array(arrayBuffer);var curr;var 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Module.LZ4==="object","LZ4 not present - was your app build with -s LZ4=1 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\ No newline at end of file diff --git a/spaces/quidiaMuxgu/Expedit-SAM/Adobe Premiere Pro CC 2015 V9.0 Crack Serial Key Keygen _BEST_.md b/spaces/quidiaMuxgu/Expedit-SAM/Adobe Premiere Pro CC 2015 V9.0 Crack Serial Key Keygen _BEST_.md deleted file mode 100644 index 87e8e5b0384b51d1cf927f525ef3a9031630c448..0000000000000000000000000000000000000000 --- a/spaces/quidiaMuxgu/Expedit-SAM/Adobe Premiere Pro CC 2015 V9.0 Crack Serial Key Keygen _BEST_.md +++ /dev/null @@ -1,7 +0,0 @@ -
        -

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        Deset malih crnaca (And Then There Were None)

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        This is one of Agata Kristi's most famous and acclaimed novels, and also one of the best-selling books of all time. It tells the story of ten strangers who are invited to an isolated island by a mysterious host. They soon discover that they have been lured there for a sinister purpose: they are accused of various crimes that they have committed in their pasts, and one by one they are killed off according to a nursery rhyme. Who is behind this deadly game? And who will survive?

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        This is another classic novel by Agata Kristi featuring Hercule Poirot as the main protagonist. It takes place on a luxurious train that travels from Istanbul to London. During the journey, one of the passengers is found stabbed to death in his cabin. Poirot is asked to investigate the case by his friend Monsieur Bouc, who is also on board. He soon realizes that there are twelve suspects with different motives and alibis, and that the murder is connected to a notorious kidnapping case that happened years ago.

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        Smrt na Nilu (Death on the Nile)

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        This is a thrilling novel by Agata Kristi that also features Hercule Poirot as the main protagonist. It takes place on a cruise ship that sails along the Nile River in Egypt. Among the passengers are Linnet Doyle, a beautiful heiress who has recently married Simon Doyle, her best friend's former fiancé; Jacqueline de Bellefort, Linnet's former friend who is obsessed with revenge; and various other characters with secrets and grudges. When Linnet is shot dead during a sightseeing tour, Poirot has to find out who among them is the killer.

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        Ubistvo Rodžera Akrojda (The Murder of Roger Ackroyd)

        -```html the main protagonist. It takes place in a small village where a wealthy man named Roger Ackroyd is found stabbed to death in his study. The narrator of the story is Dr. James Sheppard, who assists Miss Marple in solving the case. The novel has a shocking and unexpected twist that challenges the conventions of detective fiction.

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        Zlo pod suncem (Evil Under the Sun)

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        This is a captivating novel by Agata Kristi that also features Hercule Poirot as the main protagonist. It takes place on a secluded island resort where a group of guests are staying for a holiday. Among them is Arlena Stuart, a beautiful actress who attracts the attention of many men and the jealousy of many women. When she is found strangled on a beach, Poirot has to find out who among the guests had a motive and an opportunity to kill her.

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        In this article, we have introduced you to Agata Kristi Knjige.pdf, a collection of her books in Serbian that you can download for free and read on your device. We have also given you some background information about Agata Kristi, her life and her works. We have also recommended some of her best books that you should not miss if you are a fan of mystery and suspense.

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        If you are interested in reading Agata Kristi Knjige.pdf, you can visit Knjige Online or search for other sources online. You can also check out other books by Agata Kristi in other languages and formats. You will not regret diving into the world of Agata Kristi, where nothing is as it seems and where every puzzle has a brilliant solution.

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        • Q: How many books did Agata Kristi write in total? A: Agata Kristi wrote over 80 novels and collections of short stories, as well as 19 plays and two memoirs.
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        • Q: Which book by Agata Kristi is considered her masterpiece? A: Many critics and readers consider And Then There Were None to be her masterpiece, as it is one of the most original and ingenious novels ever written.
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        • Q: Which book by Agata Kristi was adapted into a movie starring Johnny Depp? A: Murder on the Orient Express was adapted into a movie starring Johnny Depp as one of the suspects in 2017.
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        • Q: Which book by Agata Kristi features both Hercule Poirot and Miss Marple? A: The only book by Agata Kristi that features both Hercule Poirot and Miss Marple is The Thirteen Problems, which is a collection of short stories.
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        • Q: Which book by Agata Kristi was inspired by her own experience of being poisoned? A: The Pale Horse was inspired by Agata Kristi's own experience of being poisoned by thallium during World War II.
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          The final step is to install the FM8 full crack software and activate it using a serial number or a keygen. To do this, follow these steps:

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          3. Wait for the installation to complete and click on Finish. Do not run the FM8 full crack software yet.
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          4. Open the folder where you installed the FM8 full crack software and look for a file named serial.txt or keygen.exe. This file contains the serial number or the keygen that you need to activate the FM8 full crack software.
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          Now that you have installed and activated the FM8 full crack software, you can enjoy its features and presets. You can open it as a standalone application or as a plugin in your DAW. You can explore its interface and its powerful FM matrix, its intuitive arpeggiator, its revolutionary sound morphing, and its large selection of effects. You can also browse through its library of over 1200 presets that cover a wide range of genres and styles. You can tweak them, mix them, or create your own sounds from scratch.

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            • -the terms and conditions of Native Instruments. You may face legal consequences if you are caught using a cracked version of FM8. According to the U.S. Copyright Law, you may have to pay fines of up to $150,000 and spend five years in prison. -
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          If you are not willing to take the risks of downloading FM8 full crack free for windows, or if you want to support the developers of FM8, there are some alternatives that you can try instead. Some of these alternatives include:

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          • Buying FM8 from the official website: The best and most legal way to get FM8 is to buy it from the official website of Native Instruments. You can get FM8 for $199 or as part of a bundle like Komplete 13 for $599. By buying FM8 from the official website, you can enjoy its full features and presets without any restrictions or risks. You can also get updates, technical support, and discounts from Native Instruments.
            • -
          • Using a trial version of FM8: If you want to try out FM8 before buying it, you can use a trial version of FM8 for 30 days. You can download the trial version from the official website of Native Instruments. The trial version has all the features and presets of the full version, but it will stop working after 30 days. You can then decide whether to buy FM8 or not.
            • -
          • Subscribing to a service like Komplete: If you want to access FM8 and other plugins and software by Native Instruments, you can subscribe to a service like Komplete for a monthly or annual fee. Komplete is a collection of over 100 instruments and effects by Native Instruments, including FM8. You can get Komplete for $9.99 per month or $99 per year. By subscribing to Komplete, you can use FM8 and other plugins and software without any limitations or risks. You can also get updates, technical support, and discounts from Native Instruments.
            • -
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          These are some of the alternatives that you can consider instead of downloading FM8 full crack free for windows. They may cost you some money, but they will also give you more benefits and peace of mind.

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          FAQs about Downloading FM8 Full Crack Free for Windows

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          In this section, we will answer some of the most common questions that people have about downloading FM8 full crack free for windows. If you have any other questions that are not answered here, feel free to leave a comment below or contact us through our website.

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          Is FM8 compatible with Windows 11?

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          Yes, according to Native Instruments, FM8 is compatible with Windows 11 as well as Windows 10, Windows 8.1, and Windows 7. However, if you download FM8 full crack free for windows, you may encounter some compatibility issues or errors with Windows 11.

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          Is FM8 worth downloading?

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          Yes, if you are looking for a versatile and powerful FM synthesis plugin that can create a wide range of sounds from classic to modern. FM8 has many features and presets that make it one of the best synthesizers on the market. However, if you download FM8 full crack free for windows, you may not be able to enjoy its full potential or support its developers.

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          Is downloading FM8 full crack free for windows illegal?

          -the terms and conditions of Native Instruments. You may face legal consequences if you are caught using a cracked version of FM8. According to the U.S. Copyright Law, you may have to pay fines of up to $150,000 and spend five years in prison. -
        • Compatibility problems: Downloading FM8 full crack free for windows may cause compatibility problems with your system or your DAW. The cracked version of FM8 may not work properly or may crash frequently. It may also conflict with other plugins or software that you have installed on your computer.
          • -
        -

        These are some of the risks that you should consider before downloading FM8 full crack free for windows. However, there are also some ways to avoid or minimize these risks, such as:

        -
          -
        • Scanning the downloaded file: Before opening or extracting the FM8 full crack file, you should scan it with an antivirus software to make sure it does not contain any viruses, malware, or other threats. You should also scan your computer regularly to detect and remove any potential infections.
          • -
        • Backing up your data: Before installing the FM8 full crack software, you should back up your data to an external drive or a cloud service. This way, you can restore your data in case something goes wrong with the installation or the software damages your files.
          • -
        • Updating your system and software: Before using the FM8 full crack software, you should update your system and software to the latest versions. This can help you avoid compatibility problems and improve your performance and security.
          • -
        -

        What are the alternatives of downloading FM8 full crack free for windows?

        -

        If you are not willing to take the risks of downloading FM8 full crack free for windows, or if you want to support the developers of FM8, there are some alternatives that you can try instead. Some of these alternatives include:

        -
          -
        • Buying FM8 from the official website: The best and most legal way to get FM8 is to buy it from the official website of Native Instruments. You can get FM8 for $199 or as part of a bundle like Komplete 13 for $599. By buying FM8 from the official website, you can enjoy its full features and presets without any restrictions or risks. You can also get updates, technical support, and discounts from Native Instruments.
          • -
        • Using a trial version of FM8: If you want to try out FM8 before buying it, you can use a trial version of FM8 for 30 days. You can download the trial version from the official website of Native Instruments. The trial version has all the features and presets of the full version, but it will stop working after 30 days. You can then decide whether to buy FM8 or not.
          • -
        • Subscribing to a service like Komplete: If you want to access FM8 and other plugins and software by Native Instruments, you can subscribe to a service like Komplete for a monthly or annual fee. Komplete is a collection of over 100 instruments and effects by Native Instruments, including FM8. You can get Komplete for $9.99 per month or $99 per year. By subscribing to Komplete, you can use FM8 and other plugins and software without any limitations or risks. You can also get updates, technical support, and discounts from Native Instruments.
          • -
        -

        These are some of the alternatives that you can consider instead of downloading FM8 full crack free for windows. They may cost you some money, but they will also give you more benefits and peace of mind.

        -

        -

        This concludes our article on how to download FM8 full crack free for windows. We hope that this article has been informative and helpful for you. We have covered what FM8 is, why you might want to download it for free, how to find and install a working FM8 full crack for windows, what are the risks and alternatives of downloading FM8 full crack free for windows, and some FAQs about downloading FM8 full crack free for windows.

        -

        If you have any questions or comments about this article, feel free to leave them below or contact us through our website. We would love to hear from you and help you out with any issues that you may have.

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        Thank you for reading this article and have a great day!

        - : https://www.makeuseof.com/what-is-software-cracking-and-dangers-of-cracked-software/ : https://www.native-instruments.com/en/products/komplete/synths/fm8/

        0a6ba089eb
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        \ No newline at end of file diff --git a/spaces/rahul999r/Rahul_Kannada_TTS/utils/inference/run_gradio.py b/spaces/rahul999r/Rahul_Kannada_TTS/utils/inference/run_gradio.py deleted file mode 100644 index 5b4c46a79fc0b7e1760d1d321a2902e14e3020e3..0000000000000000000000000000000000000000 --- a/spaces/rahul999r/Rahul_Kannada_TTS/utils/inference/run_gradio.py +++ /dev/null @@ -1,53 +0,0 @@ -import gradio as gr -import argparse -import numpy as np -from argparse import Namespace -from advanced_tts import load_all_models, run_tts_paragraph - - -def hit_tts(textbox, slider_noise_scale, slider_length_sclae, choice_transliteration, choice_number_conversion, choice_split_sentences): - inputs_to_gradio = {'text' : textbox, - 'noise_scale': slider_noise_scale, - 'length_scale': slider_length_sclae, - 'transliteration' : 1 if choice_transliteration else 0, - 'number_conversion' : 1 if choice_number_conversion else 0, - 'split_sentences' : 1 if choice_split_sentences else 0 - } - - args = Namespace(**inputs_to_gradio) - args.wav = None - args.lang = lang - - if args.text: - sr, audio = run_tts_paragraph(args) - return (sr, audio) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("-a", "--acoustic", required=True, type=str) - parser.add_argument("-v", "--vocoder", required=True, type=str) - parser.add_argument("-d", "--device", type=str, default="cpu") - parser.add_argument("-L", "--lang", type=str, required=True) - - global lang - - args = parser.parse_args() - lang = args.lang - load_all_models(args) - - textbox = gr.inputs.Textbox(placeholder="Enter Text to run", default="", label="TTS") - slider_noise_scale = gr.inputs.Slider(minimum=0, maximum=1.0, step=0.001, default=0.667, label='Enter Noise Scale') - slider_length_sclae = gr.inputs.Slider(minimum=0, maximum=2.0, step=0.1, default=1.0, label='Enter Slider Scale') - - choice_transliteration = gr.inputs.Checkbox(default=True, label="Transliteration") - choice_number_conversion = gr.inputs.Checkbox(default=True, label="Number Conversion") - choice_split_sentences = gr.inputs.Checkbox(default=True, label="Split Sentences") - - - - op = gr.outputs.Audio(type="numpy", label=None) - - inputs_to_gradio = [textbox, slider_noise_scale, slider_length_sclae, choice_transliteration, choice_number_conversion, choice_split_sentences] - iface = gr.Interface(fn=hit_tts, inputs=inputs_to_gradio, outputs=op, theme='huggingface', title='Run TTS example') - iface.launch(share=True, enable_queue=True) \ No newline at end of file diff --git a/spaces/ramkamal2000/voice-conversion-ddp/wavlm/WavLM.py b/spaces/ramkamal2000/voice-conversion-ddp/wavlm/WavLM.py deleted file mode 100644 index 09af97bfc48c5ecc3558c03666df9f965d2bef0d..0000000000000000000000000000000000000000 --- a/spaces/ramkamal2000/voice-conversion-ddp/wavlm/WavLM.py +++ /dev/null @@ -1,742 +0,0 @@ -# -------------------------------------------------------- -# WavLM: Large-Scale Self-Supervised Pre-training for Full Stack Speech Processing (https://arxiv.org/abs/2110.13900.pdf) -# Github source: https://github.com/microsoft/unilm/tree/master/wavlm -# Copyright (c) 2021 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Based on fairseq code bases -# https://github.com/pytorch/fairseq -# -------------------------------------------------------- - -import math -import logging -from typing import List, Optional, Tuple - -import numpy as np - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn import LayerNorm -from wavlm.modules import ( - Fp32GroupNorm, - Fp32LayerNorm, - GradMultiply, - MultiheadAttention, - SamePad, - init_bert_params, - get_activation_fn, - TransposeLast, - GLU_Linear, -) - -logger = logging.getLogger(__name__) - - -def compute_mask_indices( - shape: Tuple[int, int], - padding_mask: Optional[torch.Tensor], - mask_prob: float, - mask_length: int, - mask_type: str = "static", - mask_other: float = 0.0, - min_masks: int = 0, - no_overlap: bool = False, - min_space: int = 0, -) -> np.ndarray: - """ - Computes random mask spans for a given shape - - Args: - shape: the the shape for which to compute masks. - should be of size 2 where first element is batch size and 2nd is timesteps - padding_mask: optional padding mask of the same size as shape, which will prevent masking padded elements - mask_prob: probability for each token to be chosen as start of the span to be masked. this will be multiplied by - number of timesteps divided by length of mask span to mask approximately this percentage of all elements. - however due to overlaps, the actual number will be smaller (unless no_overlap is True) - mask_type: how to compute mask lengths - static = fixed size - uniform = sample from uniform distribution [mask_other, mask_length*2] - normal = sample from normal distribution with mean mask_length and stdev mask_other. mask is min 1 element - poisson = sample from possion distribution with lambda = mask length - min_masks: minimum number of masked spans - no_overlap: if false, will switch to an alternative recursive algorithm that prevents spans from overlapping - min_space: only used if no_overlap is True, this is how many elements to keep unmasked between spans - """ - - bsz, all_sz = shape - mask = np.full((bsz, all_sz), False) - - all_num_mask = int( - # add a random number for probabilistic rounding - mask_prob * all_sz / float(mask_length) - + np.random.rand() - ) - - all_num_mask = max(min_masks, all_num_mask) - - mask_idcs = [] - for i in range(bsz): - if padding_mask is not None: - sz = all_sz - padding_mask[i].long().sum().item() - num_mask = int( - # add a random number for probabilistic rounding - mask_prob * sz / float(mask_length) - + np.random.rand() - ) - num_mask = max(min_masks, num_mask) - else: - sz = all_sz - num_mask = all_num_mask - - if mask_type == "static": - lengths = np.full(num_mask, mask_length) - elif mask_type == "uniform": - lengths = np.random.randint(mask_other, mask_length * 2 + 1, size=num_mask) - elif mask_type == "normal": - lengths = np.random.normal(mask_length, mask_other, size=num_mask) - lengths = [max(1, int(round(x))) for x in lengths] - elif mask_type == "poisson": - lengths = np.random.poisson(mask_length, size=num_mask) - lengths = [int(round(x)) for x in lengths] - else: - raise Exception("unknown mask selection " + mask_type) - - if sum(lengths) == 0: - lengths[0] = min(mask_length, sz - 1) - - if no_overlap: - mask_idc = [] - - def arrange(s, e, length, keep_length): - span_start = np.random.randint(s, e - length) - mask_idc.extend(span_start + i for i in range(length)) - - new_parts = [] - if span_start - s - min_space >= keep_length: - new_parts.append((s, span_start - min_space + 1)) - if e - span_start - keep_length - min_space > keep_length: - new_parts.append((span_start + length + min_space, e)) - return new_parts - - parts = [(0, sz)] - min_length = min(lengths) - for length in sorted(lengths, reverse=True): - lens = np.fromiter( - (e - s if e - s >= length + min_space else 0 for s, e in parts), - np.int, - ) - l_sum = np.sum(lens) - if l_sum == 0: - break - probs = lens / np.sum(lens) - c = np.random.choice(len(parts), p=probs) - s, e = parts.pop(c) - parts.extend(arrange(s, e, length, min_length)) - mask_idc = np.asarray(mask_idc) - else: - min_len = min(lengths) - if sz - min_len <= num_mask: - min_len = sz - num_mask - 1 - - mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) - - mask_idc = np.asarray( - [ - mask_idc[j] + offset - for j in range(len(mask_idc)) - for offset in range(lengths[j]) - ] - ) - - mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) - - min_len = min([len(m) for m in mask_idcs]) - for i, mask_idc in enumerate(mask_idcs): - if len(mask_idc) > min_len: - mask_idc = np.random.choice(mask_idc, min_len, replace=False) - mask[i, mask_idc] = True - - return mask - - -class WavLMConfig: - def __init__(self, cfg=None): - self.extractor_mode: str = "default" # mode for feature extractor. default has a single group norm with d groups in the first conv block, whereas layer_norm has layer norms in every block (meant to use with normalize=True) - self.encoder_layers: int = 12 # num encoder layers in the transformer - - self.encoder_embed_dim: int = 768 # encoder embedding dimension - self.encoder_ffn_embed_dim: int = 3072 # encoder embedding dimension for FFN - self.encoder_attention_heads: int = 12 # num encoder attention heads - self.activation_fn: str = "gelu" # activation function to use - - self.layer_norm_first: bool = False # apply layernorm first in the transformer - self.conv_feature_layers: str = "[(512,10,5)] + [(512,3,2)] * 4 + [(512,2,2)] * 2" # string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...] - self.conv_bias: bool = False # include bias in conv encoder - self.feature_grad_mult: float = 1.0 # multiply feature extractor var grads by this - - self.normalize: bool = False # normalize input to have 0 mean and unit variance during training - - # dropouts - self.dropout: float = 0.1 # dropout probability for the transformer - self.attention_dropout: float = 0.1 # dropout probability for attention weights - self.activation_dropout: float = 0.0 # dropout probability after activation in FFN - self.encoder_layerdrop: float = 0.0 # probability of dropping a tarnsformer layer - self.dropout_input: float = 0.0 # dropout to apply to the input (after feat extr) - self.dropout_features: float = 0.0 # dropout to apply to the features (after feat extr) - - # masking - self.mask_length: int = 10 # mask length - self.mask_prob: float = 0.65 # probability of replacing a token with mask - self.mask_selection: str = "static" # how to choose mask length - self.mask_other: float = 0 # secondary mask argument (used for more complex distributions), see help in compute_mask_indicesh - self.no_mask_overlap: bool = False # whether to allow masks to overlap - self.mask_min_space: int = 1 # min space between spans (if no overlap is enabled) - - # channel masking - self.mask_channel_length: int = 10 # length of the mask for features (channels) - self.mask_channel_prob: float = 0.0 # probability of replacing a feature with 0 - self.mask_channel_selection: str = "static" # how to choose mask length for channel masking - self.mask_channel_other: float = 0 # secondary mask argument (used for more complex distributions), see help in compute_mask_indices - self.no_mask_channel_overlap: bool = False # whether to allow channel masks to overlap - self.mask_channel_min_space: int = 1 # min space between spans (if no overlap is enabled) - - # positional embeddings - self.conv_pos: int = 128 # number of filters for convolutional positional embeddings - self.conv_pos_groups: int = 16 # number of groups for convolutional positional embedding - - # relative position embedding - self.relative_position_embedding: bool = False # apply relative position embedding - self.num_buckets: int = 320 # number of buckets for relative position embedding - self.max_distance: int = 1280 # maximum distance for relative position embedding - self.gru_rel_pos: bool = False # apply gated relative position embedding - - if cfg is not None: - self.update(cfg) - - def update(self, cfg: dict): - self.__dict__.update(cfg) - - -class WavLM(nn.Module): - def __init__( - self, - cfg: WavLMConfig, - ) -> None: - super().__init__() - logger.info(f"WavLM Config: {cfg.__dict__}") - - self.cfg = cfg - feature_enc_layers = eval(cfg.conv_feature_layers) - self.embed = feature_enc_layers[-1][0] - - self.feature_extractor = ConvFeatureExtractionModel( - conv_layers=feature_enc_layers, - dropout=0.0, - mode=cfg.extractor_mode, - conv_bias=cfg.conv_bias, - ) - - self.post_extract_proj = ( - nn.Linear(self.embed, cfg.encoder_embed_dim) - if self.embed != cfg.encoder_embed_dim - else None - ) - - self.mask_prob = cfg.mask_prob - self.mask_selection = cfg.mask_selection - self.mask_other = cfg.mask_other - self.mask_length = cfg.mask_length - self.no_mask_overlap = cfg.no_mask_overlap - self.mask_min_space = cfg.mask_min_space - - self.mask_channel_prob = cfg.mask_channel_prob - self.mask_channel_selection = cfg.mask_channel_selection - self.mask_channel_other = cfg.mask_channel_other - self.mask_channel_length = cfg.mask_channel_length - self.no_mask_channel_overlap = cfg.no_mask_channel_overlap - self.mask_channel_min_space = cfg.mask_channel_min_space - - self.dropout_input = nn.Dropout(cfg.dropout_input) - self.dropout_features = nn.Dropout(cfg.dropout_features) - - self.feature_grad_mult = cfg.feature_grad_mult - - self.mask_emb = nn.Parameter( - torch.FloatTensor(cfg.encoder_embed_dim).uniform_() - ) - - self.encoder = TransformerEncoder(cfg) - self.layer_norm = LayerNorm(self.embed) - - def apply_mask(self, x, padding_mask): - B, T, C = x.shape - if self.mask_prob > 0: - mask_indices = compute_mask_indices( - (B, T), - padding_mask, - self.mask_prob, - self.mask_length, - self.mask_selection, - self.mask_other, - min_masks=2, - no_overlap=self.no_mask_overlap, - min_space=self.mask_min_space, - ) - mask_indices = torch.from_numpy(mask_indices).to(x.device) - x[mask_indices] = self.mask_emb - else: - mask_indices = None - - if self.mask_channel_prob > 0: - mask_channel_indices = compute_mask_indices( - (B, C), - None, - self.mask_channel_prob, - self.mask_channel_length, - self.mask_channel_selection, - self.mask_channel_other, - no_overlap=self.no_mask_channel_overlap, - min_space=self.mask_channel_min_space, - ) - mask_channel_indices = ( - torch.from_numpy(mask_channel_indices) - .to(x.device) - .unsqueeze(1) - .expand(-1, T, -1) - ) - x[mask_channel_indices] = 0 - - return x, mask_indices - - def forward_padding_mask( - self, features: torch.Tensor, padding_mask: torch.Tensor, - ) -> torch.Tensor: - extra = padding_mask.size(1) % features.size(1) - if extra > 0: - padding_mask = padding_mask[:, :-extra] - padding_mask = padding_mask.view( - padding_mask.size(0), features.size(1), -1 - ) - #padding_mask = padding_mask.all(-1) - padding_mask = padding_mask.any(-1) - return padding_mask - - def extract_features( - self, - source: torch.Tensor, - padding_mask: Optional[torch.Tensor] = None, - mask: bool = False, - ret_conv: bool = False, - output_layer: Optional[int] = None, - ret_layer_results: bool = False, - ): - if self.feature_grad_mult > 0: - features = self.feature_extractor(source) - if self.feature_grad_mult != 1.0: - features = GradMultiply.apply(features, self.feature_grad_mult) - else: - with torch.no_grad(): - features = self.feature_extractor(source) - - features = features.transpose(1, 2) - features = self.layer_norm(features) - - if padding_mask is not None: - padding_mask = self.forward_padding_mask(features, padding_mask) - - if self.post_extract_proj is not None: - features = self.post_extract_proj(features) - - features = self.dropout_input(features) - - if mask: - x, mask_indices = self.apply_mask( - features, padding_mask - ) - else: - x = features - - # feature: (B, T, D), float - # target: (B, T), long - # x: (B, T, D), float - # padding_mask: (B, T), bool - # mask_indices: (B, T), bool - x, layer_results = self.encoder( - x, - padding_mask=padding_mask, - layer=None if output_layer is None else output_layer - 1 - ) - - res = {"x": x, "padding_mask": padding_mask, "features": features, "layer_results": layer_results} - - feature = res["features"] if ret_conv else res["x"] - if ret_layer_results: - feature = (feature, res["layer_results"]) - return feature, res["padding_mask"] - - -class ConvFeatureExtractionModel(nn.Module): - def __init__( - self, - conv_layers: List[Tuple[int, int, int]], - dropout: float = 0.0, - mode: str = "default", - conv_bias: bool = False, - conv_type: str = "default" - ): - super().__init__() - - assert mode in {"default", "layer_norm"} - - def block( - n_in, - n_out, - k, - stride, - is_layer_norm=False, - is_group_norm=False, - conv_bias=False, - ): - def make_conv(): - conv = nn.Conv1d(n_in, n_out, k, stride=stride, bias=conv_bias) - nn.init.kaiming_normal_(conv.weight) - return conv - - assert ( - is_layer_norm and is_group_norm - ) == False, "layer norm and group norm are exclusive" - - if is_layer_norm: - return nn.Sequential( - make_conv(), - nn.Dropout(p=dropout), - nn.Sequential( - TransposeLast(), - Fp32LayerNorm(dim, elementwise_affine=True), - TransposeLast(), - ), - nn.GELU(), - ) - elif is_group_norm: - return nn.Sequential( - make_conv(), - nn.Dropout(p=dropout), - Fp32GroupNorm(dim, dim, affine=True), - nn.GELU(), - ) - else: - return nn.Sequential(make_conv(), nn.Dropout(p=dropout), nn.GELU()) - - self.conv_type = conv_type - if self.conv_type == "default": - in_d = 1 - self.conv_layers = nn.ModuleList() - for i, cl in enumerate(conv_layers): - assert len(cl) == 3, "invalid conv definition: " + str(cl) - (dim, k, stride) = cl - - self.conv_layers.append( - block( - in_d, - dim, - k, - stride, - is_layer_norm=mode == "layer_norm", - is_group_norm=mode == "default" and i == 0, - conv_bias=conv_bias, - ) - ) - in_d = dim - elif self.conv_type == "conv2d": - in_d = 1 - self.conv_layers = nn.ModuleList() - for i, cl in enumerate(conv_layers): - assert len(cl) == 3 - (dim, k, stride) = cl - - self.conv_layers.append( - torch.nn.Conv2d(in_d, dim, k, stride) - ) - self.conv_layers.append(torch.nn.ReLU()) - in_d = dim - elif self.conv_type == "custom": - in_d = 1 - idim = 80 - self.conv_layers = nn.ModuleList() - for i, cl in enumerate(conv_layers): - assert len(cl) == 3 - (dim, k, stride) = cl - self.conv_layers.append( - torch.nn.Conv2d(in_d, dim, k, stride, padding=1) - ) - self.conv_layers.append( - torch.nn.LayerNorm([dim, idim]) - ) - self.conv_layers.append(torch.nn.ReLU()) - in_d = dim - if (i + 1) % 2 == 0: - self.conv_layers.append( - torch.nn.MaxPool2d(2, stride=2, ceil_mode=True) - ) - idim = int(math.ceil(idim / 2)) - else: - pass - - def forward(self, x, mask=None): - - # BxT -> BxCxT - x = x.unsqueeze(1) - if self.conv_type == "custom": - for conv in self.conv_layers: - if isinstance(conv, nn.LayerNorm): - x = x.transpose(1, 2) - x = conv(x).transpose(1, 2) - else: - x = conv(x) - x = x.transpose(2, 3).contiguous() - x = x.view(x.size(0), -1, x.size(-1)) - else: - for conv in self.conv_layers: - x = conv(x) - if self.conv_type == "conv2d": - b, c, t, f = x.size() - x = x.transpose(2, 3).contiguous().view(b, c * f, t) - return x - - -class TransformerEncoder(nn.Module): - def __init__(self, args): - super().__init__() - - self.dropout = args.dropout - self.embedding_dim = args.encoder_embed_dim - - self.pos_conv = nn.Conv1d( - self.embedding_dim, - self.embedding_dim, - kernel_size=args.conv_pos, - padding=args.conv_pos // 2, - groups=args.conv_pos_groups, - ) - dropout = 0 - std = math.sqrt((4 * (1.0 - dropout)) / (args.conv_pos * self.embedding_dim)) - nn.init.normal_(self.pos_conv.weight, mean=0, std=std) - nn.init.constant_(self.pos_conv.bias, 0) - - self.pos_conv = nn.utils.weight_norm(self.pos_conv, name="weight", dim=2) - self.pos_conv = nn.Sequential(self.pos_conv, SamePad(args.conv_pos), nn.GELU()) - - if hasattr(args, "relative_position_embedding"): - self.relative_position_embedding = args.relative_position_embedding - self.num_buckets = args.num_buckets - self.max_distance = args.max_distance - else: - self.relative_position_embedding = False - self.num_buckets = 0 - self.max_distance = 0 - - self.layers = nn.ModuleList( - [ - TransformerSentenceEncoderLayer( - embedding_dim=self.embedding_dim, - ffn_embedding_dim=args.encoder_ffn_embed_dim, - num_attention_heads=args.encoder_attention_heads, - dropout=self.dropout, - attention_dropout=args.attention_dropout, - activation_dropout=args.activation_dropout, - activation_fn=args.activation_fn, - layer_norm_first=args.layer_norm_first, - has_relative_attention_bias=(self.relative_position_embedding and i == 0), - num_buckets=self.num_buckets, - max_distance=self.max_distance, - gru_rel_pos=args.gru_rel_pos, - ) - for i in range(args.encoder_layers) - ] - ) - - self.layer_norm_first = args.layer_norm_first - self.layer_norm = LayerNorm(self.embedding_dim) - self.layerdrop = args.encoder_layerdrop - - self.apply(init_bert_params) - - def forward(self, x, padding_mask=None, streaming_mask=None, layer=None): - x, layer_results = self.extract_features(x, padding_mask, streaming_mask, layer) - - if self.layer_norm_first and layer is None: - x = self.layer_norm(x) - - return x, layer_results - - def extract_features(self, x, padding_mask=None, streaming_mask=None, tgt_layer=None): - - if padding_mask is not None: - x[padding_mask] = 0 - - x_conv = self.pos_conv(x.transpose(1, 2)) - x_conv = x_conv.transpose(1, 2) - x += x_conv - - if not self.layer_norm_first: - x = self.layer_norm(x) - - x = F.dropout(x, p=self.dropout, training=self.training) - - # B x T x C -> T x B x C - x = x.transpose(0, 1) - - layer_results = [] - z = None - if tgt_layer is not None: - layer_results.append((x, z)) - r = None - pos_bias = None - for i, layer in enumerate(self.layers): - dropout_probability = np.random.random() - if not self.training or (dropout_probability > self.layerdrop): - x, z, pos_bias = layer(x, self_attn_padding_mask=padding_mask, need_weights=False, - self_attn_mask=streaming_mask, pos_bias=pos_bias) - if tgt_layer is not None: - layer_results.append((x, z)) - if i == tgt_layer: - r = x - break - - if r is not None: - x = r - - # T x B x C -> B x T x C - x = x.transpose(0, 1) - - return x, layer_results - - -class TransformerSentenceEncoderLayer(nn.Module): - """ - Implements a Transformer Encoder Layer used in BERT/XLM style pre-trained - models. - """ - - def __init__( - self, - embedding_dim: float = 768, - ffn_embedding_dim: float = 3072, - num_attention_heads: float = 8, - dropout: float = 0.1, - attention_dropout: float = 0.1, - activation_dropout: float = 0.1, - activation_fn: str = "relu", - layer_norm_first: bool = False, - has_relative_attention_bias: bool = False, - num_buckets: int = 0, - max_distance: int = 0, - rescale_init: bool = False, - gru_rel_pos: bool = False, - ) -> None: - - super().__init__() - # Initialize parameters - self.embedding_dim = embedding_dim - self.dropout = dropout - self.activation_dropout = activation_dropout - - # Initialize blocks - self.activation_name = activation_fn - self.activation_fn = get_activation_fn(activation_fn) - self.self_attn = MultiheadAttention( - self.embedding_dim, - num_attention_heads, - dropout=attention_dropout, - self_attention=True, - has_relative_attention_bias=has_relative_attention_bias, - num_buckets=num_buckets, - max_distance=max_distance, - rescale_init=rescale_init, - gru_rel_pos=gru_rel_pos, - ) - - self.dropout1 = nn.Dropout(dropout) - self.dropout2 = nn.Dropout(self.activation_dropout) - self.dropout3 = nn.Dropout(dropout) - - self.layer_norm_first = layer_norm_first - - # layer norm associated with the self attention layer - self.self_attn_layer_norm = LayerNorm(self.embedding_dim) - - if self.activation_name == "glu": - self.fc1 = GLU_Linear(self.embedding_dim, ffn_embedding_dim, "swish") - else: - self.fc1 = nn.Linear(self.embedding_dim, ffn_embedding_dim) - self.fc2 = nn.Linear(ffn_embedding_dim, self.embedding_dim) - - # layer norm associated with the position wise feed-forward NN - self.final_layer_norm = LayerNorm(self.embedding_dim) - - def forward( - self, - x: torch.Tensor, - self_attn_mask: torch.Tensor = None, - self_attn_padding_mask: torch.Tensor = None, - need_weights: bool = False, - pos_bias=None - ): - """ - LayerNorm is applied either before or after the self-attention/ffn - modules similar to the original Transformer imlementation. - """ - residual = x - - if self.layer_norm_first: - x = self.self_attn_layer_norm(x) - x, attn, pos_bias = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=self_attn_padding_mask, - need_weights=False, - attn_mask=self_attn_mask, - position_bias=pos_bias - ) - x = self.dropout1(x) - x = residual + x - - residual = x - x = self.final_layer_norm(x) - if self.activation_name == "glu": - x = self.fc1(x) - else: - x = self.activation_fn(self.fc1(x)) - x = self.dropout2(x) - x = self.fc2(x) - x = self.dropout3(x) - x = residual + x - else: - x, attn, pos_bias = self.self_attn( - query=x, - key=x, - value=x, - key_padding_mask=self_attn_padding_mask, - need_weights=need_weights, - attn_mask=self_attn_mask, - position_bias=pos_bias - ) - - x = self.dropout1(x) - x = residual + x - - x = self.self_attn_layer_norm(x) - - residual = x - if self.activation_name == "glu": - x = self.fc1(x) - else: - x = self.activation_fn(self.fc1(x)) - x = self.dropout2(x) - x = self.fc2(x) - x = self.dropout3(x) - x = residual + x - x = self.final_layer_norm(x) - - return x, attn, pos_bias diff --git a/spaces/raseel-zymr/LangChain-Youtube-Script-Generator/app.py b/spaces/raseel-zymr/LangChain-Youtube-Script-Generator/app.py deleted file mode 100644 index 53cdee9b7678b2235e13d8e433a9d08f06f2e1a6..0000000000000000000000000000000000000000 --- a/spaces/raseel-zymr/LangChain-Youtube-Script-Generator/app.py +++ /dev/null @@ -1,61 +0,0 @@ -import os -from apikey import apikey - -import streamlit as st -from langchain.llms import OpenAI -from langchain.prompts import PromptTemplate -from langchain.chains import LLMChain, SequentialChain -from langchain.memory import ConversationBufferMemory -from langchain.utilities import WikipediaAPIWrapper - -os.environ['OPENAI_API_KEY'] = st.secrets["openai_api_key"] - -st.title('🦜🔗 LangChain Tutorial - YouTube GPT Creator - by Raseel ') -prompt = st.text_input('Prompt: Write me a Youtube Video Script about') - -# Prompt Templates -title_template = PromptTemplate( - input_variables = ['topic'], - template='Write me a Youtube Video title about {topic}' -) - -script_template = PromptTemplate( - input_variables = ['title', 'wikipedia_research'], - template='Write me a Youtube Video script based on the title, TITLE {title} while leveraging its Wikipedia researc: {wikipedia_research}' -) - -# Memory -title_memory = ConversationBufferMemory(input_key='topic', memory_key='chat_history') -script_memory = ConversationBufferMemory(input_key='title', memory_key='chat_history') - -# LLMs -llm = OpenAI(temperature=0.9) -title_chain = LLMChain(llm=llm, prompt=title_template, verbose=True, output_key='title', memory=title_memory) -script_chain = LLMChain(llm=llm, prompt=script_template, verbose=True, output_key='script', memory=script_memory) - -# Tools -wiki = WikipediaAPIWrapper() - -# sequential_chain = SequentialChain(chains=[title_chain, script_chain], -# input_variables=['topic'], -# output_variables=['title', 'script'], -# verbose=True) - -if prompt: - title = title_chain.run(prompt) - wiki_research = wiki.run(prompt) - script = script_chain.run(title=title, wikipedia_research=wiki_research) - - st.subheader('Generated Title') - st.write(title) - st.subheader('Generated SCript') - st.write(script) - - with st.expander('Title History:'): - st.info(title_memory.buffer) - - with st.expander('Script History:'): - st.info(script_memory.buffer) - - with st.expander('Wikipedia Research:'): - st.info(wiki_research) \ No newline at end of file diff --git a/spaces/rasmodev/sepsis_prediction/README.md b/spaces/rasmodev/sepsis_prediction/README.md deleted file mode 100644 index 34ae7b9cc6990179efcee1d71379d0085859bea2..0000000000000000000000000000000000000000 --- a/spaces/rasmodev/sepsis_prediction/README.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -title: Sepsis Prediction -emoji: 🐨 -colorFrom: pink -colorTo: purple -sdk: docker -pinned: false -license: mit ---- - 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        There are different types of Facebook auto liker software, such as:

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        • Auto liker apps: These are mobile applications that you can install on your smartphone and connect to your Facebook account. They usually have a simple interface and allow you to choose the posts you want to like, the number of likes you want to send, and the time interval between each like.
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        • Auto liker websites: These are online platforms that you can access from your browser and log in with your Facebook credentials. They usually have more features and options than auto liker apps, such as selecting the target audience, the post type, the reaction type, and the speed of liking.
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        • Auto liker software: These are desktop programs that you can download and install on your PC and link to your Facebook account. They usually have the most advanced features and settings than auto liker apps and websites, such as scheduling the liking activity, filtering the posts by keywords, hashtags, or locations, and using multiple accounts.
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        If you are looking for some of the best options for downloading Facebook auto liker software for free, here are some suggestions:

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        • Facebook Auto Like Platform: This is a free desktop program that enables you to increase your Facebook page likes in very short time. You just need to add your Facebook page address and start getting likes. You can download it from here.
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        Conclusion

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        Facebook auto liker software is a tool that can help you boost your Facebook engagement and get more likes on your posts. However, you need to be careful when choosing and using such software, as some of them might be unsafe or ineffective. You should always follow the best practices of using Facebook auto liker software, such as:

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        • Using it sparingly and moderately: You should not overuse or abuse the software, as this might make your account look spammy or suspicious. You should also respect the limits and rules of Facebook regarding liking activity.
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        • Using it wisely and strategically: You should not use the software randomly or indiscriminately. You should use it only on relevant and quality posts that match your niche or interest. You should also use it only on people who might be interested in your content or offer.
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        If you follow these tips and choose the best Facebook auto liker software for PC from our list above, you can enjoy the benefits of getting more likes on your posts without risking your account or reputation.

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        Using Facebook auto liker software for PC is not very difficult, but you need to follow some steps and precautions to make sure you use it correctly and safely. Here are some general steps to use Facebook auto liker software for PC:

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        1. Download and install the software from a trusted source. You can choose one of the options we mentioned above, or you can search for other alternatives online.
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        Some precautions you need to take when using Facebook auto liker software for PC are:

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        Conclusion

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        Facebook auto liker software for PC is a tool that can help you boost your Facebook engagement and get more likes on your posts. However, you need to be careful when choosing and using such software, as some of them might be unsafe or ineffective. You should always follow the best practices of using Facebook auto liker software for PC, such as:

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        If you follow these tips and choose the best Facebook auto liker software for PC from our list above, you can enjoy the benefits of getting more likes on your posts without risking your account or reputation.

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        Conclusion

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        In this article, we have discussed what Facebook auto liker software for PC is, why you should use it, how to choose it, how to use it, and what are the alternatives to it. We have also provided you with some of the best options for downloading Facebook auto liker software for free.

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        Facebook auto liker software for PC can be a useful tool to boost your Facebook engagement and get more likes on your posts. However, you need to be careful when choosing and using such software, as some of them might be unsafe or ineffective. You should always follow the best practices of using Facebook auto liker software for PC, such as using it sparingly and moderately, using it wisely and strategically, and using it ethically and responsibly.

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        \ No newline at end of file diff --git a/spaces/reha/Stick_Tech/hubert/app.py b/spaces/reha/Stick_Tech/hubert/app.py deleted file mode 100644 index 4cdd8d2485463d458e248c5f95a1431062706a58..0000000000000000000000000000000000000000 --- a/spaces/reha/Stick_Tech/hubert/app.py +++ /dev/null @@ -1,70 +0,0 @@ -import io - -import gradio as gr -import librosa -import numpy as np -import soundfile -import torch -from inference.infer_tool import Svc -import logging - -logging.getLogger('numba').setLevel(logging.WARNING) - -model_name = "logs/32k/G_98000.pth" -config_name = "configs/config.json" - -svc_model = Svc(model_name, config_name) -sid_map = { - "Ztech": "Ztech" -} - - -def vc_fn(sid, input_audio, vc_transform): - 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 = io.BytesIO() - soundfile.write(out_wav_path, audio, 16000, format="wav") - out_wav_path.seek(0) - - sid = sid_map[sid] - out_audio, out_sr = svc_model.infer(sid, vc_transform, out_wav_path) - _audio = out_audio.cpu().numpy() - return "Success", (32000, _audio) - - -app = gr.Blocks() -with app: - with gr.Tabs(): - with gr.TabItem("Basic"): - gr.Markdown(value=""" - 这是sovits 3.0 32khz版本ai草莓猫taffy的在线demo - - 在使用此模型前请阅读[AI粘连科技模型使用协议](https://huggingface.co/spaces/reha/Stick_Tech/blob/main/terms.md) - - 粘连科技Official@bilibili：[点击关注](https://space.bilibili.com/248582596) - - 如果要在本地使用该demo，请使用git lfs clone 该仓库，安装requirements.txt后运行app.py即可 - - 项目改写基于 https://huggingface.co/spaces/innnky/nyaru-svc-3.0 - - 本地合成可以删除26、27两行代码以解除合成45s长度限制""") - sid = gr.Dropdown(label="音色", choices=["taffy"], value="taffy") - 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）", value=0) - 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], [vc_output1, vc_output2]) - - app.launch() diff --git a/spaces/rexoscare/Resume_screener/app.py b/spaces/rexoscare/Resume_screener/app.py deleted file mode 100644 index 43608b8fc995847b1d4b1f446b5ec4c053c63096..0000000000000000000000000000000000000000 --- a/spaces/rexoscare/Resume_screener/app.py +++ /dev/null @@ -1,75 +0,0 @@ -from sentence_transformers import SentenceTransformer -from sklearn.metrics.pairwise import cosine_similarity -import pandas as pd -import gradio as gr -import pdfplumber -import texthero as hero -from texthero import preprocessing as ppe -import re - - -model = SentenceTransformer('sentence-transformers/paraphrase-xlm-r-multilingual-v1') - - -def remove_special_characters(text): - pattern = r'[^a-zA-Z]' - text = re.sub(pattern, ' ', text) - return text - - -#word file (Job Description) -def opentxt(filepath): - file_1 = open(filepath, errors="ignore") - file_2 = file_1.read() - file_2 = file_2.replace('\n', ' ') - file_2 = re.sub('www.\S+|www.\S+', '', file_2) - df_1 = pd.DataFrame([file_2], columns = ['text']) - df_1['text'] = df_1['text'].apply(remove_special_characters) - custom_pipeline = [ppe.fillna, ppe.remove_urls, ppe.remove_whitespace] - df_1['cleaned_text'] = hero.clean(df_1['text'], custom_pipeline) - file_2 = df_1['cleaned_text'].astype(str) - return file_2 - - -#pdf file (Resume) -def pdftotext(filepath): - with pdfplumber.open(filepath) as pdf: - first_page = pdf.pages[0] - list_1 = first_page.extract_text(x_tolerance=3, y_tolerance=3) - list_1 = list_1.replace('\n', ' ') - list_1 = re.sub('www.\S+|www.\S+', '', list_1) - df = pd.DataFrame([list_1], columns = ['text']) - df['text'] = df['text'].apply(remove_special_characters) - custom_pipeline = [ppe.fillna, ppe.remove_urls, ppe.remove_whitespace] - df['cleaned_text'] = hero.clean(df['text'], custom_pipeline) - list_1 = df['cleaned_text'].astype(str) - return list_1 - - -def sent_similarity(filepath_1, filepath_2): - txt_1 = pdftotext(filepath_1.name) - txt_2 = opentxt(filepath_2.name) - sentences = [''.join(txt_1), ''.join(txt_2)] - sentence_embeddings = model.encode(sentences) - similarity = cosine_similarity(sentence_embeddings[0].reshape(1, -1),sentence_embeddings[1].reshape(1, -1))[0][0] - return round(similarity*100, 2) - - -input_1 = gr.inputs.File(file_count="single", type="file", label= 'Upload the Resume (.pdf)', optional=False) -input_2 = gr.inputs.File(file_count="single", type="file", label= 'Upload the Job Description (.txt)', optional=False) - -title = "Resume Screener" -description = "Upload your resume(.pdf) and the job description(.txt) and let the sentence similarity model display the similarity percentage !!!" - -iface = gr.Interface( - sent_similarity, - [input_1, input_2], "label", title = title, description = description) - -if __name__ == "__main__": - iface.launch() - - - - - - diff --git a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/segment_anything/modeling/common.py b/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/segment_anything/modeling/common.py deleted file mode 100644 index 2bf15236a3eb24d8526073bc4fa2b274cccb3f96..0000000000000000000000000000000000000000 --- a/spaces/rockeycoss/Prompt-Segment-Anything-Demo/projects/instance_segment_anything/models/segment_anything/modeling/common.py +++ /dev/null @@ -1,43 +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. - -import torch -import torch.nn as nn - -from typing import Type - - -class MLPBlock(nn.Module): - def __init__( - self, - embedding_dim: int, - mlp_dim: int, - act: Type[nn.Module] = nn.GELU, - ) -> None: - super().__init__() - self.lin1 = nn.Linear(embedding_dim, mlp_dim) - self.lin2 = nn.Linear(mlp_dim, embedding_dim) - self.act = act() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - return self.lin2(self.act(self.lin1(x))) - - -# From https://github.com/facebookresearch/detectron2/blob/main/detectron2/layers/batch_norm.py # noqa -# Itself from https://github.com/facebookresearch/ConvNeXt/blob/d1fa8f6fef0a165b27399986cc2bdacc92777e40/models/convnext.py#L119 # noqa -class LayerNorm2d(nn.Module): - def __init__(self, num_channels: int, eps: float = 1e-6) -> None: - super().__init__() - self.weight = nn.Parameter(torch.ones(num_channels)) - self.bias = nn.Parameter(torch.zeros(num_channels)) - self.eps = eps - - def forward(self, x: torch.Tensor) -> torch.Tensor: - u = x.mean(1, keepdim=True) - s = (x - u).pow(2).mean(1, keepdim=True) - x = (x - u) / torch.sqrt(s + self.eps) - x = self.weight[:, None, None] * x + self.bias[:, None, None] - return x diff --git a/spaces/rorallitri/biomedical-language-models/logs/Blu Suicide Squad (English) Hd Movie 1080p Hindi Movies [WORK].md b/spaces/rorallitri/biomedical-language-models/logs/Blu Suicide Squad (English) Hd Movie 1080p Hindi Movies [WORK].md deleted file mode 100644 index 39d7ca7132e16d0323232f151e2e4e03b19ee035..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/Blu Suicide Squad (English) Hd Movie 1080p Hindi Movies [WORK].md +++ /dev/null @@ -1,8 +0,0 @@ - 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        \ No newline at end of file diff --git a/spaces/rorallitri/biomedical-language-models/logs/CHEMKIN 2 DOWNLOAD Tips and Tricks for Using Chemkin-II for Chemical Kinetics Analysis.md b/spaces/rorallitri/biomedical-language-models/logs/CHEMKIN 2 DOWNLOAD Tips and Tricks for Using Chemkin-II for Chemical Kinetics Analysis.md deleted file mode 100644 index 2ed5631543f73ef3b4291fccbe090a0fe9b0f11b..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/CHEMKIN 2 DOWNLOAD Tips and Tricks for Using Chemkin-II for Chemical Kinetics Analysis.md +++ /dev/null @@ -1,11 +0,0 @@ -
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        To generate graphs your chemkin spreadsheets should be uploaded to a new folder in the src folder. To plot, a graph object must be created in src/main.py, where graph_object.plot_sensitivity functions can be used to plot the sensitivities as follows:

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        There are 4 species-fields in the 0/-directory, which are initially required for the chemistry:O2, N2, C7H16 and Ydefault. Ydefault gives the boundary conditions for all other species than O2, N2, C7H16,that appear in the reaction mechanism.The reactions are defined in the "chem.inp"-file in the chemkin/-subdirectory (there are two more files for more complex mechanisms, which can also be chosen), the thermodynamic properties of the species aredefined in the "therm.dat"-file in the same subdirectory.

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        \ No newline at end of file diff --git a/spaces/rorallitri/biomedical-language-models/logs/Igo Primo 2.4 Wince Free Download 2021.md b/spaces/rorallitri/biomedical-language-models/logs/Igo Primo 2.4 Wince Free Download 2021.md deleted file mode 100644 index 3cb13fd9fb33d28e2abdf908c32fddc2dcf42f05..0000000000000000000000000000000000000000 --- a/spaces/rorallitri/biomedical-language-models/logs/Igo Primo 2.4 Wince Free Download 2021.md +++ /dev/null @@ -1,10 +0,0 @@ - -

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stable_inpiant_model_list, -) -from diffusion_webui.utils.scheduler_list import ( - SCHEDULER_LIST, - get_scheduler_list, -) - -# https://github.com/mikonvergence/ControlNetInpaint - - -class StableDiffusionControlNetInpaintHedGenerator: - 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 = ( - StableDiffusionControlNetInpaintPipeline.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 load_image(self, image_path): - image = np.array(image_path) - image = Image.fromarray(image) - return image - - def controlnet_inpaint_hed(self, image_path: str): - hed = HEDdetector.from_pretrained("lllyasviel/ControlNet") - image = image_path["image"].convert("RGB").resize((512, 512)) - image = np.array(image) - image = hed(image) - - return image - - def generate_image( - self, - image_path: str, - stable_model_path: str, - controlnet_model_path: str, - prompt: str, - negative_prompt: str, - num_images_per_prompt: int, - guidance_scale: int, - num_inference_step: int, - controlnet_conditioning_scale: int, - scheduler: str, - seed_generator: int, - ): - normal_image = image_path["image"].convert("RGB").resize((512, 512)) - mask_image = image_path["mask"].convert("RGB").resize((512, 512)) - - normal_image = self.load_image(image_path=normal_image) - mask_image = self.load_image(image_path=mask_image) - - control_image = self.controlnet_inpaint_hed(image_path=image_path) - - pipe = self.load_model( - stable_model_path=stable_model_path, - controlnet_model_path=controlnet_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=normal_image, - mask_image=mask_image, - control_image=control_image, - negative_prompt=negative_prompt, - num_images_per_prompt=num_images_per_prompt, - num_inference_steps=num_inference_step, - guidance_scale=guidance_scale, - controlnet_conditioning_scale=controlnet_conditioning_scale, - generator=generator, - ).images - - return output - - def app(): - with gr.Blocks(): - with gr.Row(): - with gr.Column(): - controlnet_hed_inpaint_image_file = gr.Image( - source="upload", - tool="sketch", - elem_id="image_upload", - type="pil", - label="Upload", - ) - - controlnet_hed_inpaint_prompt = gr.Textbox( - lines=1, placeholder="Prompt", show_label=False - ) - - controlnet_hed_inpaint_negative_prompt = gr.Textbox( - lines=1, - show_label=False, - placeholder="Negative Prompt", - ) - with gr.Row(): - with gr.Column(): - controlnet_hed_inpaint_stable_model_id = ( - gr.Dropdown( - choices=stable_inpiant_model_list, - value=stable_inpiant_model_list[0], - label="Stable Model Id", - ) - ) - - controlnet_hed_inpaint_guidance_scale = gr.Slider( - minimum=0.1, - maximum=15, - step=0.1, - value=7.5, - label="Guidance Scale", - ) - - controlnet_hed_inpaint_num_inference_step = ( - gr.Slider( - minimum=1, - maximum=100, - step=1, - value=50, - label="Num Inference Step", - ) - ) - controlnet_hed_inpaint_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_hed_inpaint_model_id = gr.Dropdown( - choices=controlnet_hed_model_list, - value=controlnet_hed_model_list[0], - label="Controlnet Model Id", - ) - controlnet_hed_inpaint_scheduler = gr.Dropdown( - choices=SCHEDULER_LIST, - value=SCHEDULER_LIST[0], - label="Scheduler", - ) - controlnet_hed_inpaint_controlnet_conditioning_scale = gr.Slider( - minimum=0.1, - maximum=1.0, - step=0.1, - value=0.5, - label="Controlnet Conditioning Scale", - ) - - controlnet_hed_inpaint_seed_generator = ( - gr.Slider( - minimum=0, - maximum=1000000, - step=1, - value=0, - label="Seed Generator", - ) - ) - - controlnet_hed_inpaint_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_hed_inpaint_predict.click( - fn=StableDiffusionControlNetInpaintHedGenerator().generate_image, - inputs=[ - controlnet_hed_inpaint_image_file, - controlnet_hed_inpaint_stable_model_id, - controlnet_hed_inpaint_model_id, - controlnet_hed_inpaint_prompt, - controlnet_hed_inpaint_negative_prompt, - controlnet_hed_inpaint_num_images_per_prompt, - controlnet_hed_inpaint_guidance_scale, - controlnet_hed_inpaint_num_inference_step, - controlnet_hed_inpaint_controlnet_conditioning_scale, - controlnet_hed_inpaint_scheduler, - controlnet_hed_inpaint_seed_generator, - ], - outputs=[output_image], - ) diff --git a/spaces/samcaicn/bingai/postcss.config.js b/spaces/samcaicn/bingai/postcss.config.js deleted file mode 100644 index 33ad091d26d8a9dc95ebdf616e217d985ec215b8..0000000000000000000000000000000000000000 --- a/spaces/samcaicn/bingai/postcss.config.js +++ /dev/null @@ -1,6 +0,0 @@ -module.exports = { - plugins: { - tailwindcss: {}, - autoprefixer: {}, - }, -} diff --git a/spaces/sarinam/speaker-anonymization/IMSToucan/Layers/Attention.py b/spaces/sarinam/speaker-anonymization/IMSToucan/Layers/Attention.py deleted file mode 100644 index 7e1ee9a1e9995bd65fbe328a652424c6f5d5373f..0000000000000000000000000000000000000000 --- a/spaces/sarinam/speaker-anonymization/IMSToucan/Layers/Attention.py +++ /dev/null @@ -1,324 +0,0 @@ -# Written by Shigeki Karita, 2019 -# Published under Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) -# Adapted by Florian Lux, 2021 - -"""Multi-Head Attention layer definition.""" - -import math - -import numpy -import torch -from torch import nn - -from ..Utility.utils import make_non_pad_mask - - -class MultiHeadedAttention(nn.Module): - """ - Multi-Head Attention layer. - - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - """ - - def __init__(self, n_head, n_feat, dropout_rate): - """ - Construct an MultiHeadedAttention object. - """ - super(MultiHeadedAttention, self).__init__() - assert n_feat % n_head == 0 - # We assume d_v always equals d_k - self.d_k = n_feat // n_head - self.h = n_head - self.linear_q = nn.Linear(n_feat, n_feat) - self.linear_k = nn.Linear(n_feat, n_feat) - self.linear_v = nn.Linear(n_feat, n_feat) - self.linear_out = nn.Linear(n_feat, n_feat) - self.attn = None - self.dropout = nn.Dropout(p=dropout_rate) - - def forward_qkv(self, query, key, value): - """ - Transform query, key and value. - - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - - Returns: - torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k). - torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k). - torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k). - """ - n_batch = query.size(0) - q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k) - k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k) - v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k) - q = q.transpose(1, 2) # (batch, head, time1, d_k) - k = k.transpose(1, 2) # (batch, head, time2, d_k) - v = v.transpose(1, 2) # (batch, head, time2, d_k) - - return q, k, v - - def forward_attention(self, value, scores, mask): - """ - Compute attention context vector. - - Args: - value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k). - scores (torch.Tensor): Attention score (#batch, n_head, time1, time2). - mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2). - - Returns: - torch.Tensor: Transformed value (#batch, time1, d_model) - weighted by the attention score (#batch, time1, time2). - """ - n_batch = value.size(0) - if mask is not None: - mask = mask.unsqueeze(1).eq(0) # (batch, 1, *, time2) - min_value = float(numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min) - scores = scores.masked_fill(mask, min_value) - self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0) # (batch, head, time1, time2) - else: - self.attn = torch.softmax(scores, dim=-1) # (batch, head, time1, time2) - - p_attn = self.dropout(self.attn) - x = torch.matmul(p_attn, value) # (batch, head, time1, d_k) - x = (x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)) # (batch, time1, d_model) - - return self.linear_out(x) # (batch, time1, d_model) - - def forward(self, query, key, value, mask): - """ - Compute scaled dot product attention. - - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2). - - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - """ - q, k, v = self.forward_qkv(query, key, value) - scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k) - return self.forward_attention(v, scores, mask) - - -class RelPositionMultiHeadedAttention(MultiHeadedAttention): - """ - Multi-Head Attention layer with relative position encoding. - Details can be found in https://github.com/espnet/espnet/pull/2816. - Paper: https://arxiv.org/abs/1901.02860 - Args: - n_head (int): The number of heads. - n_feat (int): The number of features. - dropout_rate (float): Dropout rate. - zero_triu (bool): Whether to zero the upper triangular part of attention matrix. - """ - - def __init__(self, n_head, n_feat, dropout_rate, zero_triu=False): - """Construct an RelPositionMultiHeadedAttention object.""" - super().__init__(n_head, n_feat, dropout_rate) - self.zero_triu = zero_triu - # linear transformation for positional encoding - self.linear_pos = nn.Linear(n_feat, n_feat, bias=False) - # these two learnable bias are used in matrix c and matrix d - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k)) - self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k)) - torch.nn.init.xavier_uniform_(self.pos_bias_u) - torch.nn.init.xavier_uniform_(self.pos_bias_v) - - def rel_shift(self, x): - """ - Compute relative positional encoding. - Args: - x (torch.Tensor): Input tensor (batch, head, time1, 2*time1-1). - time1 means the length of query vector. - Returns: - torch.Tensor: Output tensor. - """ - zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype) - x_padded = torch.cat([zero_pad, x], dim=-1) - - x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2)) - x = x_padded[:, :, 1:].view_as(x)[:, :, :, : x.size(-1) // 2 + 1] # only keep the positions from 0 to time2 - - if self.zero_triu: - ones = torch.ones((x.size(2), x.size(3)), device=x.device) - x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :] - - return x - - def forward(self, query, key, value, pos_emb, mask): - """ - Compute 'Scaled Dot Product Attention' with rel. positional encoding. - Args: - query (torch.Tensor): Query tensor (#batch, time1, size). - key (torch.Tensor): Key tensor (#batch, time2, size). - value (torch.Tensor): Value tensor (#batch, time2, size). - pos_emb (torch.Tensor): Positional embedding tensor - (#batch, 2*time1-1, size). - mask (torch.Tensor): Mask tensor (#batch, 1, time2) or - (#batch, time1, time2). - Returns: - torch.Tensor: Output tensor (#batch, time1, d_model). - """ - q, k, v = self.forward_qkv(query, key, value) - q = q.transpose(1, 2) # (batch, time1, head, d_k) - - n_batch_pos = pos_emb.size(0) - p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k) - p = p.transpose(1, 2) # (batch, head, 2*time1-1, d_k) - - # (batch, head, time1, d_k) - q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2) - # (batch, head, time1, d_k) - q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2) - - # compute attention score - # first compute matrix a and matrix c - # as described in https://arxiv.org/abs/1901.02860 Section 3.3 - # (batch, head, time1, time2) - matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1)) - - # compute matrix b and matrix d - # (batch, head, time1, 2*time1-1) - matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1)) - matrix_bd = self.rel_shift(matrix_bd) - - scores = (matrix_ac + matrix_bd) / math.sqrt(self.d_k) # (batch, head, time1, time2) - - return self.forward_attention(v, scores, mask) - - -class GuidedAttentionLoss(torch.nn.Module): - """ - Guided attention loss function module. - - This module calculates the guided attention loss described - in `Efficiently Trainable Text-to-Speech System Based - on Deep Convolutional Networks with Guided Attention`_, - which forces the attention to be diagonal. - - .. _`Efficiently Trainable Text-to-Speech System - Based on Deep Convolutional Networks with Guided Attention`: - https://arxiv.org/abs/1710.08969 - """ - - def __init__(self, sigma=0.4, alpha=1.0): - """ - Initialize guided attention loss module. - - Args: - sigma (float, optional): Standard deviation to control - how close attention to a diagonal. - alpha (float, optional): Scaling coefficient (lambda). - reset_always (bool, optional): Whether to always reset masks. - """ - super(GuidedAttentionLoss, self).__init__() - self.sigma = sigma - self.alpha = alpha - self.guided_attn_masks = None - self.masks = None - - def _reset_masks(self): - self.guided_attn_masks = None - self.masks = None - - def forward(self, att_ws, ilens, olens): - """ - Calculate forward propagation. - - Args: - att_ws (Tensor): Batch of attention weights (B, T_max_out, T_max_in). - ilens (LongTensor): Batch of input lenghts (B,). - olens (LongTensor): Batch of output lenghts (B,). - - Returns: - Tensor: Guided attention loss value. - """ - self._reset_masks() - self.guided_attn_masks = self._make_guided_attention_masks(ilens, olens).to(att_ws.device) - self.masks = self._make_masks(ilens, olens).to(att_ws.device) - losses = self.guided_attn_masks * att_ws - loss = torch.mean(losses.masked_select(self.masks)) - self._reset_masks() - return self.alpha * loss - - def _make_guided_attention_masks(self, ilens, olens): - n_batches = len(ilens) - max_ilen = max(ilens) - max_olen = max(olens) - guided_attn_masks = torch.zeros((n_batches, max_olen, max_ilen), device=ilens.device) - for idx, (ilen, olen) in enumerate(zip(ilens, olens)): - guided_attn_masks[idx, :olen, :ilen] = self._make_guided_attention_mask(ilen, olen, self.sigma) - return guided_attn_masks - - @staticmethod - def _make_guided_attention_mask(ilen, olen, sigma): - """ - Make guided attention mask. - """ - grid_x, grid_y = torch.meshgrid(torch.arange(olen, device=olen.device).float(), torch.arange(ilen, device=ilen.device).float()) - return 1.0 - torch.exp(-((grid_y / ilen - grid_x / olen) ** 2) / (2 * (sigma ** 2))) - - @staticmethod - def _make_masks(ilens, olens): - """ - Make masks indicating non-padded part. - - Args: - ilens (LongTensor or List): Batch of lengths (B,). - olens (LongTensor or List): Batch of lengths (B,). - - Returns: - Tensor: Mask tensor indicating non-padded part. - dtype=torch.uint8 in PyTorch 1.2- - dtype=torch.bool in PyTorch 1.2+ (including 1.2) - """ - in_masks = make_non_pad_mask(ilens, device=ilens.device) # (B, T_in) - out_masks = make_non_pad_mask(olens, device=olens.device) # (B, T_out) - return out_masks.unsqueeze(-1) & in_masks.unsqueeze(-2) # (B, T_out, T_in) - - -class GuidedMultiHeadAttentionLoss(GuidedAttentionLoss): - """ - Guided attention loss function module for multi head attention. - - Args: - sigma (float, optional): Standard deviation to control - how close attention to a diagonal. - alpha (float, optional): Scaling coefficient (lambda). - reset_always (bool, optional): Whether to always reset masks. - """ - - def forward(self, att_ws, ilens, olens): - """ - Calculate forward propagation. - - Args: - att_ws (Tensor): - Batch of multi head attention weights (B, H, T_max_out, T_max_in). - ilens (LongTensor): Batch of input lenghts (B,). - olens (LongTensor): Batch of output lenghts (B,). - - Returns: - Tensor: Guided attention loss value. - """ - if self.guided_attn_masks is None: - self.guided_attn_masks = (self._make_guided_attention_masks(ilens, olens).to(att_ws.device).unsqueeze(1)) - if self.masks is None: - self.masks = self._make_masks(ilens, olens).to(att_ws.device).unsqueeze(1) - losses = self.guided_attn_masks * att_ws - loss = torch.mean(losses.masked_select(self.masks)) - if self.reset_always: - self._reset_masks() - - return self.alpha * loss diff --git a/spaces/scedlatioru/img-to-music/example/Autoclosets Lt 5.0 Full Version.md b/spaces/scedlatioru/img-to-music/example/Autoclosets Lt 5.0 Full Version.md deleted file mode 100644 index d2b441fbfb4a7ba98406baad74b69501930694ee..0000000000000000000000000000000000000000 --- a/spaces/scedlatioru/img-to-music/example/Autoclosets Lt 5.0 Full Version.md +++ /dev/null @@ -1,6 +0,0 @@ - -

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        diff --git a/spaces/sczhou/CodeFormer/CodeFormer/scripts/crop_align_face.py b/spaces/sczhou/CodeFormer/CodeFormer/scripts/crop_align_face.py deleted file mode 100644 index 31e66266ac0e5f818fa18b6409993151086bbc8b..0000000000000000000000000000000000000000 --- a/spaces/sczhou/CodeFormer/CodeFormer/scripts/crop_align_face.py +++ /dev/null @@ -1,192 +0,0 @@ -""" -brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset) -author: lzhbrian (https://lzhbrian.me) -link: https://gist.github.com/lzhbrian/bde87ab23b499dd02ba4f588258f57d5 -date: 2020.1.5 -note: code is heavily borrowed from - https://github.com/NVlabs/ffhq-dataset - http://dlib.net/face_landmark_detection.py.html -requirements: - conda install Pillow numpy scipy - conda install -c conda-forge dlib - # download face landmark model from: - # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 -""" - -import cv2 -import dlib -import glob -import numpy as np -import os -import PIL -import PIL.Image -import scipy -import scipy.ndimage -import sys -import argparse - -# download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2 -predictor = dlib.shape_predictor('weights/dlib/shape_predictor_68_face_landmarks-fbdc2cb8.dat') - - -def get_landmark(filepath, only_keep_largest=True): - """get landmark with dlib - :return: np.array shape=(68, 2) - """ - detector = dlib.get_frontal_face_detector() - - img = dlib.load_rgb_image(filepath) - dets = detector(img, 1) - - # Shangchen modified - print("Number of faces detected: {}".format(len(dets))) - if only_keep_largest: - print('Detect several faces and only keep the largest.') - face_areas = [] - for k, d in enumerate(dets): - face_area = (d.right() - d.left()) * (d.bottom() - d.top()) - face_areas.append(face_area) - - largest_idx = face_areas.index(max(face_areas)) - d = dets[largest_idx] - shape = predictor(img, d) - print("Part 0: {}, Part 1: {} ...".format( - shape.part(0), shape.part(1))) - else: - for k, d in enumerate(dets): - print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format( - k, d.left(), d.top(), d.right(), d.bottom())) - # Get the landmarks/parts for the face in box d. - shape = predictor(img, d) - print("Part 0: {}, Part 1: {} ...".format( - shape.part(0), shape.part(1))) - - t = list(shape.parts()) - a = [] - for tt in t: - a.append([tt.x, tt.y]) - lm = np.array(a) - # lm is a shape=(68,2) np.array - return lm - -def align_face(filepath, out_path): - """ - :param filepath: str - :return: PIL Image - """ - try: - lm = get_landmark(filepath) - except: - print('No landmark ...') - return - - lm_chin = lm[0:17] # left-right - lm_eyebrow_left = lm[17:22] # left-right - lm_eyebrow_right = lm[22:27] # left-right - lm_nose = lm[27:31] # top-down - lm_nostrils = lm[31:36] # top-down - lm_eye_left = lm[36:42] # left-clockwise - lm_eye_right = lm[42:48] # left-clockwise - lm_mouth_outer = lm[48:60] # left-clockwise - lm_mouth_inner = lm[60:68] # left-clockwise - - # Calculate auxiliary vectors. - eye_left = np.mean(lm_eye_left, axis=0) - eye_right = np.mean(lm_eye_right, axis=0) - eye_avg = (eye_left + eye_right) * 0.5 - eye_to_eye = eye_right - eye_left - mouth_left = lm_mouth_outer[0] - mouth_right = lm_mouth_outer[6] - mouth_avg = (mouth_left + mouth_right) * 0.5 - eye_to_mouth = mouth_avg - eye_avg - - # Choose oriented crop rectangle. - x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] - x /= np.hypot(*x) - x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8) - y = np.flipud(x) * [-1, 1] - c = eye_avg + eye_to_mouth * 0.1 - quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) - qsize = np.hypot(*x) * 2 - - # read image - img = PIL.Image.open(filepath) - - output_size = 512 - transform_size = 4096 - enable_padding = False - - # Shrink. - shrink = int(np.floor(qsize / output_size * 0.5)) - if shrink > 1: - rsize = (int(np.rint(float(img.size[0]) / shrink)), - int(np.rint(float(img.size[1]) / shrink))) - img = img.resize(rsize, PIL.Image.ANTIALIAS) - quad /= shrink - qsize /= shrink - - # Crop. - border = max(int(np.rint(qsize * 0.1)), 3) - crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), - int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) - crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), - min(crop[2] + border, - img.size[0]), min(crop[3] + border, img.size[1])) - if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]: - img = img.crop(crop) - quad -= crop[0:2] - - # Pad. - pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), - int(np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1])))) - pad = (max(-pad[0] + border, - 0), max(-pad[1] + border, - 0), max(pad[2] - img.size[0] + border, - 0), max(pad[3] - img.size[1] + border, 0)) - if enable_padding and max(pad) > border - 4: - pad = np.maximum(pad, int(np.rint(qsize * 0.3))) - img = np.pad( - np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), - 'reflect') - h, w, _ = img.shape - y, x, _ = np.ogrid[:h, :w, :1] - mask = np.maximum( - 1.0 - - np.minimum(np.float32(x) / pad[0], - np.float32(w - 1 - x) / pad[2]), 1.0 - - np.minimum(np.float32(y) / pad[1], - np.float32(h - 1 - y) / pad[3])) - blur = qsize * 0.02 - img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) - img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) - img = PIL.Image.fromarray( - np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB') - quad += pad[:2] - - img = img.transform((transform_size, transform_size), PIL.Image.QUAD, - (quad + 0.5).flatten(), PIL.Image.BILINEAR) - - if output_size < transform_size: - img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS) - - # Save aligned image. - print('saveing: ', out_path) - img.save(out_path) - - return img, np.max(quad[:, 0]) - np.min(quad[:, 0]) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--in_dir', type=str, default='./inputs/whole_imgs') - parser.add_argument('--out_dir', type=str, default='./inputs/cropped_faces') - args = parser.parse_args() - - img_list = sorted(glob.glob(f'{args.in_dir}/*.png')) - img_list = sorted(img_list) - - for in_path in img_list: - out_path = os.path.join(args.out_dir, in_path.split("/")[-1]) - out_path = out_path.replace('.jpg', '.png') - size_ = align_face(in_path, out_path) \ No newline at end of file diff --git a/spaces/seduerr/ethical_data/services/bad_words.py b/spaces/seduerr/ethical_data/services/bad_words.py deleted file mode 100644 index e9dc840dd90a14870656e58dc4b38f9b34c095d7..0000000000000000000000000000000000000000 --- a/spaces/seduerr/ethical_data/services/bad_words.py +++ /dev/null @@ -1,11 +0,0 @@ -import re - -def identify_bad_words(text): - bad_words = [] - with open('./src/bad_words.txt', 'r') as filehandle: - bad_words = [current_place.rstrip() - for current_place in filehandle.readlines()] - pattern = re.compile('[A-Z]*[a-z]+') - cleaned_text = pattern.findall(text) - long_word_set = set(bad_words) - return (set(cleaned_text) & long_word_set) diff --git a/spaces/seok07/Voice-Changer1/util.py b/spaces/seok07/Voice-Changer1/util.py deleted file mode 100644 index 8d6bcff1135c2d97e4caad7922f03f05c98484da..0000000000000000000000000000000000000000 --- a/spaces/seok07/Voice-Changer1/util.py +++ /dev/null @@ -1,81 +0,0 @@ -import sys -import asyncio -from io import BytesIO - -from fairseq import checkpoint_utils - -import torch - -import edge_tts -import librosa - - -# https://github.com/fumiama/Retrieval-based-Voice-Conversion-WebUI/blob/main/config.py#L43-L55 # noqa -def has_mps() -> bool: - if sys.platform != "darwin": - return False - else: - if not getattr(torch, 'has_mps', False): - return False - - try: - torch.zeros(1).to(torch.device("mps")) - return True - except Exception: - return False - - -def is_half(device: str) -> bool: - if not device.startswith('cuda'): - return False - else: - gpu_name = torch.cuda.get_device_name( - int(device.split(':')[-1]) - ).upper() - - # ...regex? - if ( - ('16' in gpu_name and 'V100' not in gpu_name) - or 'P40' in gpu_name - or '1060' in gpu_name - or '1070' in gpu_name - or '1080' in gpu_name - ): - return False - - return True - - -def load_hubert_model(device: str, model_path: str = 'hubert_base.pt'): - model = checkpoint_utils.load_model_ensemble_and_task( - [model_path] - )[0][0].to(device) - - if is_half(device): - return model.half() - else: - return model.float() - - -async def call_edge_tts(speaker_name: str, text: str): - tts_com = edge_tts.Communicate(text, speaker_name) - tts_raw = b'' - - # Stream TTS audio to bytes - async for chunk in tts_com.stream(): - if chunk['type'] == 'audio': - tts_raw += chunk['data'] - - # Convert mp3 stream to wav - ffmpeg_proc = await asyncio.create_subprocess_exec( - 'ffmpeg', - '-f', 'mp3', - '-i', '-', - '-f', 'wav', - '-', - stdin=asyncio.subprocess.PIPE, - stdout=asyncio.subprocess.PIPE - ) - (tts_wav, _) = await ffmpeg_proc.communicate(tts_raw) - - return librosa.load(BytesIO(tts_wav)) diff --git a/spaces/shencc/gpt/crazy_functions/test_project/python/dqn/dqn.py b/spaces/shencc/gpt/crazy_functions/test_project/python/dqn/dqn.py deleted file mode 100644 index 6cea64d39baa7ff4c1e549869aaa4b0ae17779a9..0000000000000000000000000000000000000000 --- a/spaces/shencc/gpt/crazy_functions/test_project/python/dqn/dqn.py +++ /dev/null @@ -1,245 +0,0 @@ -from typing import Any, Dict, List, Optional, Tuple, Type, Union - -import gym -import numpy as np -import torch as th -from torch.nn import functional as F - -from stable_baselines3.common import logger -from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm -from stable_baselines3.common.preprocessing import maybe_transpose -from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule -from stable_baselines3.common.utils import get_linear_fn, is_vectorized_observation, polyak_update -from stable_baselines3.dqn.policies import DQNPolicy - - -class DQN(OffPolicyAlgorithm): - """ - Deep Q-Network (DQN) - - Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236 - Default hyperparameters are taken from the nature paper, - except for the optimizer and learning rate that were taken from Stable Baselines defaults. - - :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...) - :param env: The environment to learn from (if registered in Gym, can be str) - :param learning_rate: The learning rate, it can be a function - of the current progress remaining (from 1 to 0) - :param buffer_size: size of the replay buffer - :param learning_starts: how many steps of the model to collect transitions for before learning starts - :param batch_size: Minibatch size for each gradient update - :param tau: the soft update coefficient ("Polyak update", between 0 and 1) default 1 for hard update - :param gamma: the discount factor - :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit - like ``(5, "step")`` or ``(2, "episode")``. - :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``) - Set to ``-1`` means to do as many gradient steps as steps done in the environment - during the rollout. - :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer - at a cost of more complexity. - See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195 - :param target_update_interval: update the target network every ``target_update_interval`` - environment steps. - :param exploration_fraction: fraction of entire training period over which the exploration rate is reduced - :param exploration_initial_eps: initial value of random action probability - :param exploration_final_eps: final value of random action probability - :param max_grad_norm: The maximum value for the gradient clipping - :param tensorboard_log: the log location for tensorboard (if None, no logging) - :param create_eval_env: Whether to create a second environment that will be - used for evaluating the agent periodically. (Only available when passing string for the environment) - :param policy_kwargs: additional arguments to be passed to the policy on creation - :param verbose: the verbosity level: 0 no output, 1 info, 2 debug - :param seed: Seed for the pseudo random generators - :param device: Device (cpu, cuda, ...) on which the code should be run. - Setting it to auto, the code will be run on the GPU if possible. - :param _init_setup_model: Whether or not to build the network at the creation of the instance - """ - - def __init__( - self, - policy: Union[str, Type[DQNPolicy]], - env: Union[GymEnv, str], - learning_rate: Union[float, Schedule] = 1e-4, - buffer_size: int = 1000000, - learning_starts: int = 50000, - batch_size: Optional[int] = 32, - tau: float = 1.0, - gamma: float = 0.99, - train_freq: Union[int, Tuple[int, str]] = 4, - gradient_steps: int = 1, - optimize_memory_usage: bool = False, - target_update_interval: int = 10000, - exploration_fraction: float = 0.1, - exploration_initial_eps: float = 1.0, - exploration_final_eps: float = 0.05, - max_grad_norm: float = 10, - tensorboard_log: Optional[str] = None, - create_eval_env: bool = False, - policy_kwargs: Optional[Dict[str, Any]] = None, - verbose: int = 0, - seed: Optional[int] = None, - device: Union[th.device, str] = "auto", - _init_setup_model: bool = True, - ): - - super(DQN, self).__init__( - policy, - env, - DQNPolicy, - learning_rate, - buffer_size, - learning_starts, - batch_size, - tau, - gamma, - train_freq, - gradient_steps, - action_noise=None, # No action noise - policy_kwargs=policy_kwargs, - tensorboard_log=tensorboard_log, - verbose=verbose, - device=device, - create_eval_env=create_eval_env, - seed=seed, - sde_support=False, - optimize_memory_usage=optimize_memory_usage, - supported_action_spaces=(gym.spaces.Discrete,), - ) - - self.exploration_initial_eps = exploration_initial_eps - self.exploration_final_eps = exploration_final_eps - self.exploration_fraction = exploration_fraction - self.target_update_interval = target_update_interval - self.max_grad_norm = max_grad_norm - # "epsilon" for the epsilon-greedy exploration - self.exploration_rate = 0.0 - # Linear schedule will be defined in `_setup_model()` - self.exploration_schedule = None - self.q_net, self.q_net_target = None, None - - if _init_setup_model: - self._setup_model() - - def _setup_model(self) -> None: - super(DQN, self)._setup_model() - self._create_aliases() - self.exploration_schedule = get_linear_fn( - self.exploration_initial_eps, self.exploration_final_eps, self.exploration_fraction - ) - - def _create_aliases(self) -> None: - self.q_net = self.policy.q_net - self.q_net_target = self.policy.q_net_target - - def _on_step(self) -> None: - """ - Update the exploration rate and target network if needed. - This method is called in ``collect_rollouts()`` after each step in the environment. - """ - if self.num_timesteps % self.target_update_interval == 0: - polyak_update(self.q_net.parameters(), self.q_net_target.parameters(), self.tau) - - self.exploration_rate = self.exploration_schedule(self._current_progress_remaining) - logger.record("rollout/exploration rate", self.exploration_rate) - - def train(self, gradient_steps: int, batch_size: int = 100) -> None: - # Update learning rate according to schedule - self._update_learning_rate(self.policy.optimizer) - - losses = [] - for _ in range(gradient_steps): - # Sample replay buffer - replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env) - - with th.no_grad(): - # Compute the next Q-values using the target network - next_q_values = self.q_net_target(replay_data.next_observations) - # Follow greedy policy: use the one with the highest value - next_q_values, _ = next_q_values.max(dim=1) - # Avoid potential broadcast issue - next_q_values = next_q_values.reshape(-1, 1) - # 1-step TD target - target_q_values = replay_data.rewards + (1 - replay_data.dones) * self.gamma * next_q_values - - # Get current Q-values estimates - current_q_values = self.q_net(replay_data.observations) - - # Retrieve the q-values for the actions from the replay buffer - current_q_values = th.gather(current_q_values, dim=1, index=replay_data.actions.long()) - - # Compute Huber loss (less sensitive to outliers) - loss = F.smooth_l1_loss(current_q_values, target_q_values) - losses.append(loss.item()) - - # Optimize the policy - self.policy.optimizer.zero_grad() - loss.backward() - # Clip gradient norm - th.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm) - self.policy.optimizer.step() - - # Increase update counter - self._n_updates += gradient_steps - - logger.record("train/n_updates", self._n_updates, exclude="tensorboard") - logger.record("train/loss", np.mean(losses)) - - def predict( - self, - observation: np.ndarray, - state: Optional[np.ndarray] = None, - mask: Optional[np.ndarray] = None, - deterministic: bool = False, - ) -> Tuple[np.ndarray, Optional[np.ndarray]]: - """ - Overrides the base_class predict function to include epsilon-greedy exploration. - - :param observation: the input observation - :param state: The last states (can be None, used in recurrent policies) - :param mask: The last masks (can be None, used in recurrent policies) - :param deterministic: Whether or not to return deterministic actions. - :return: the model's action and the next state - (used in recurrent policies) - """ - if not deterministic and np.random.rand() < self.exploration_rate: - if is_vectorized_observation(maybe_transpose(observation, self.observation_space), self.observation_space): - n_batch = observation.shape[0] - action = np.array([self.action_space.sample() for _ in range(n_batch)]) - else: - action = np.array(self.action_space.sample()) - else: - action, state = self.policy.predict(observation, state, mask, deterministic) - return action, state - - def learn( - self, - total_timesteps: int, - callback: MaybeCallback = None, - log_interval: int = 4, - eval_env: Optional[GymEnv] = None, - eval_freq: int = -1, - n_eval_episodes: int = 5, - tb_log_name: str = "DQN", - eval_log_path: Optional[str] = None, - reset_num_timesteps: bool = True, - ) -> OffPolicyAlgorithm: - - return super(DQN, self).learn( - total_timesteps=total_timesteps, - callback=callback, - log_interval=log_interval, - eval_env=eval_env, - eval_freq=eval_freq, - n_eval_episodes=n_eval_episodes, - tb_log_name=tb_log_name, - eval_log_path=eval_log_path, - reset_num_timesteps=reset_num_timesteps, - ) - - def _excluded_save_params(self) -> List[str]: - return super(DQN, self)._excluded_save_params() + ["q_net", "q_net_target"] - - def _get_torch_save_params(self) -> Tuple[List[str], List[str]]: - state_dicts = ["policy", "policy.optimizer"] - - return state_dicts, [] diff --git a/spaces/shgao/EditAnything/utils/stable_diffusion_reference.py b/spaces/shgao/EditAnything/utils/stable_diffusion_reference.py deleted file mode 100644 index 6a2e3a943a1d1dd7c16e7ebf07c851c20120eca7..0000000000000000000000000000000000000000 --- a/spaces/shgao/EditAnything/utils/stable_diffusion_reference.py +++ /dev/null @@ -1,1128 +0,0 @@ -# Based on https://raw.githubusercontent.com/okotaku/diffusers/feature/reference_only_control/examples/community/stable_diffusion_reference.py -# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 -from typing import Any, Callable, Dict, List, Optional, Union, Tuple - -import numpy as np -import PIL.Image -import torch - -from diffusers import StableDiffusionPipeline -from diffusers.models.attention import BasicTransformerBlock -from diffusers.models.unet_2d_blocks import ( - CrossAttnDownBlock2D, - CrossAttnUpBlock2D, - DownBlock2D, - UpBlock2D, -) -from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput -from diffusers.utils import PIL_INTERPOLATION, logging -import torch.nn.functional as F - - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import UniPCMultistepScheduler - >>> from diffusers.utils import load_image - - >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png") - - >>> pipe = StableDiffusionReferencePipeline.from_pretrained( - "runwayml/stable-diffusion-v1-5", - safety_checker=None, - torch_dtype=torch.float16 - ).to('cuda:0') - - >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config) - - >>> result_img = pipe(ref_image=input_image, - prompt="1girl", - num_inference_steps=20, - reference_attn=True, - reference_adain=True).images[0] - - >>> result_img.show() - ``` -""" - - -def torch_dfs(model: torch.nn.Module): - result = [model] - for child in model.children(): - result += torch_dfs(child) - return result - - -class StableDiffusionReferencePipeline: - def prepare_ref_image( - self, - image, - width, - height, - batch_size, - num_images_per_prompt, - device, - dtype, - do_classifier_free_guidance=False, - guess_mode=False, - ): - if not isinstance(image, torch.Tensor): - if isinstance(image, PIL.Image.Image): - image = [image] - - if isinstance(image[0], PIL.Image.Image): - images = [] - - for image_ in image: - image_ = image_.convert("RGB") - image_ = image_.resize( - (width, height), resample=PIL_INTERPOLATION["lanczos"] - ) - image_ = np.array(image_) - image_ = image_[None, :] - images.append(image_) - - image = images - - image = np.concatenate(image, axis=0) - image = np.array(image).astype(np.float32) / 255.0 - image = (image - 0.5) / 0.5 - image = image.transpose(0, 3, 1, 2) - image = torch.from_numpy(image) - elif isinstance(image[0], torch.Tensor): - image = torch.cat(image, dim=0) - - image_batch_size = image.shape[0] - - if image_batch_size == 1: - repeat_by = batch_size - else: - # image batch size is the same as prompt batch size - repeat_by = num_images_per_prompt - - image = image.repeat_interleave(repeat_by, dim=0) - - image = image.to(device=device, dtype=dtype) - - if do_classifier_free_guidance and not guess_mode: - image = torch.cat([image] * 2) - - return image - - def prepare_ref_latents( - self, - refimage, - batch_size, - dtype, - device, - generator, - do_classifier_free_guidance, - ): - refimage = refimage.to(device=device, dtype=dtype) - - # encode the mask image into latents space so we can concatenate it to the latents - if isinstance(generator, list): - ref_image_latents = [ - self.vae.encode(refimage[i: i + 1]).latent_dist.sample( - generator=generator[i] - ) - for i in range(batch_size) - ] - ref_image_latents = torch.cat(ref_image_latents, dim=0) - else: - ref_image_latents = self.vae.encode(refimage).latent_dist.sample( - generator=generator - ) - ref_image_latents = self.vae.config.scaling_factor * ref_image_latents - - # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method - if ref_image_latents.shape[0] < batch_size: - if not batch_size % ref_image_latents.shape[0] == 0: - raise ValueError( - "The passed images and the required batch size don't match. Images are supposed to be duplicated" - f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed." - " Make sure the number of images that you pass is divisible by the total requested batch size." - ) - ref_image_latents = ref_image_latents.repeat( - batch_size // ref_image_latents.shape[0], 1, 1, 1 - ) - - ref_image_latents = ( - torch.cat([ref_image_latents] * 2) - if do_classifier_free_guidance - else ref_image_latents - ) - - # aligning device to prevent device errors when concating it with the latent model input - ref_image_latents = ref_image_latents.to(device=device, dtype=dtype) - return ref_image_latents - - def check_ref_input(self, reference_attn, reference_adain): - assert ( - reference_attn or reference_adain - ), "`reference_attn` or `reference_adain` must be True." - - def redefine_ref_model( - self, model, reference_attn, reference_adain, model_type="unet" - ): - def hacked_basic_transformer_inner_forward( - self, - hidden_states: torch.FloatTensor, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - timestep: Optional[torch.LongTensor] = None, - cross_attention_kwargs: Dict[str, Any] = None, - class_labels: Optional[torch.LongTensor] = None, - ): - if self.use_ada_layer_norm: - norm_hidden_states = self.norm1(hidden_states, timestep) - elif self.use_ada_layer_norm_zero: - ( - norm_hidden_states, - gate_msa, - shift_mlp, - scale_mlp, - gate_mlp, - ) = self.norm1( - hidden_states, - timestep, - class_labels, - hidden_dtype=hidden_states.dtype, - ) - else: - norm_hidden_states = self.norm1(hidden_states) - - # 1. Self-Attention - cross_attention_kwargs = ( - cross_attention_kwargs if cross_attention_kwargs is not None else {} - ) - if self.only_cross_attention: - attn_output = self.attn1( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states - if self.only_cross_attention - else None, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - else: - if self.MODE == "write": - if self.attention_auto_machine_weight > self.attn_weight: - # print("hacked_basic_transformer_inner_forward") - scale_ratio = ( - (self.ref_mask.shape[2] * self.ref_mask.shape[3]) - / norm_hidden_states.shape[1] - ) ** 0.5 - this_ref_mask = F.interpolate( - self.ref_mask.to(norm_hidden_states.device), - scale_factor=1 / scale_ratio, - ) - resize_norm_hidden_states = norm_hidden_states.view( - norm_hidden_states.shape[0], - this_ref_mask.shape[2], - this_ref_mask.shape[3], - -1, - ).permute(0, 3, 1, 2) - - ref_scale = 1.0 - resize_norm_hidden_states = F.interpolate( - resize_norm_hidden_states, - scale_factor=ref_scale, - mode="bilinear", - ) - this_ref_mask = F.interpolate( - this_ref_mask, scale_factor=ref_scale - ) - # print("this_ref_mask",this_ref_mask.shape) - - # this_ref_mask = this_ref_mask.view(1,-1,1) - this_ref_mask = this_ref_mask.repeat( - resize_norm_hidden_states.shape[0], - resize_norm_hidden_states.shape[1], - 1, - 1, - ).bool() - masked_norm_hidden_states = ( - resize_norm_hidden_states[this_ref_mask] - .detach() - .clone() - .view( - resize_norm_hidden_states.shape[0], - resize_norm_hidden_states.shape[1], - -1, - ) - ) - masked_norm_hidden_states = masked_norm_hidden_states.permute( - 0, 2, 1 - ) - self.bank.append(masked_norm_hidden_states) - # self.bank.append(norm_hidden_states.detach().clone()) - attn_output = self.attn1( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states - if self.only_cross_attention - else None, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - if self.MODE == "read": - if self.attention_auto_machine_weight > self.attn_weight: - # scale_ratio = ((self.ref_mask.shape[2] * self.ref_mask.shape[3])/norm_hidden_states.shape[1])**0.5 - # print(scale_ratio) - # this_ref_mask = F.interpolate(self.ref_mask.to(norm_hidden_states.device), scale_factor=1/scale_ratio).view(1,1,-1) - # print("resized mask", this_ref_mask.shape, this_ref_mask.max(), this_ref_mask.min(), this_ref_mask.sum()) - # ref_hidden_states = torch.cat([norm_hidden_states] + self.bank, dim=1) - # if attention_mask is None: - # attention_mask = torch.ones( - # norm_hidden_states.shape[0], norm_hidden_states.shape[1], ref_hidden_states.shape[1], dtype=norm_hidden_states.dtype, device=norm_hidden_states.device - # ) - # this_ref_mask = this_ref_mask.repeat(norm_hidden_states.shape[0], norm_hidden_states.shape[1], 1) - # this_ref_mask = torch.zeros( - # norm_hidden_states.shape[0], norm_hidden_states.shape[1], this_ref_mask.shape[1], dtype=norm_hidden_states.dtype, device=norm_hidden_states.device - # ) - # print(attention_mask.shape, this_ref_mask.shape) - # attention_mask = torch.cat((attention_mask, this_ref_mask), dim=-1) - # print("merge", attention_mask.shape) - ref_hidden_states = torch.cat( - [norm_hidden_states] + self.bank, dim=1 - ) - attn_output_uc = self.attn1( - norm_hidden_states, - encoder_hidden_states=ref_hidden_states, - # attention_mask=attention_mask, - **cross_attention_kwargs, - ) - attn_output_c = attn_output_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - attn_output_c[self.uc_mask] = self.attn1( - norm_hidden_states[self.uc_mask], - encoder_hidden_states=norm_hidden_states[self.uc_mask], - **cross_attention_kwargs, - ) - attn_output = ( - self.style_fidelity * attn_output_c - + (1.0 - self.style_fidelity) * attn_output_uc - ) - self.bank.clear() - else: - attn_output = self.attn1( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states - if self.only_cross_attention - else None, - attention_mask=attention_mask, - **cross_attention_kwargs, - ) - if self.use_ada_layer_norm_zero: - attn_output = gate_msa.unsqueeze(1) * attn_output - hidden_states = attn_output + hidden_states - - if self.attn2 is not None: - norm_hidden_states = ( - self.norm2(hidden_states, timestep) - if self.use_ada_layer_norm - else self.norm2(hidden_states) - ) - - # 2. Cross-Attention - attn_output = self.attn2( - norm_hidden_states, - encoder_hidden_states=encoder_hidden_states, - attention_mask=encoder_attention_mask, - **cross_attention_kwargs, - ) - hidden_states = attn_output + hidden_states - - # 3. Feed-forward - norm_hidden_states = self.norm3(hidden_states) - - if self.use_ada_layer_norm_zero: - norm_hidden_states = ( - norm_hidden_states * - (1 + scale_mlp[:, None]) + shift_mlp[:, None] - ) - - ff_output = self.ff(norm_hidden_states) - - if self.use_ada_layer_norm_zero: - ff_output = gate_mlp.unsqueeze(1) * ff_output - - hidden_states = ff_output + hidden_states - - return hidden_states - - def hacked_mid_forward(self, *args, **kwargs): - eps = 1e-6 - x = self.original_forward(*args, **kwargs) - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # mask var mean - scale_ratio = self.ref_mask.shape[2] / x.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(x.device), scale_factor=1 / scale_ratio - ) - this_ref_mask = this_ref_mask.repeat( - x.shape[0], x.shape[1], 1, 1 - ).bool() - masked_x = ( - x[this_ref_mask] - .detach() - .clone() - .view(x.shape[0], x.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_x, dim=(2, 3), keepdim=True, correction=0 - ) - - self.mean_bank.append(mean) - self.var_bank.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank) > 0 - and len(self.var_bank) > 0 - ): - # print("hacked_mid_forward") - scale_ratio = self.inpaint_mask.shape[2] / x.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(x.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - x.shape[0], x.shape[1], 1, 1 - ).bool() - masked_x = ( - x[this_inpaint_mask] - .detach() - .clone() - .view(x.shape[0], x.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_x, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank) / float(len(self.mean_bank)) - var_acc = sum(self.var_bank) / float(len(self.var_bank)) - std_acc = ( - torch.maximum(var_acc, torch.zeros_like( - var_acc) + eps) ** 0.5 - ) - x_uc = (((masked_x - mean) / std) * std_acc) + mean_acc - x_c = x_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - x_c[self.uc_mask] = masked_x[self.uc_mask] - masked_x = self.style_fidelity * x_c + \ - (1.0 - self.style_fidelity) * x_uc - x[this_inpaint_mask] = masked_x.view(-1) - self.mean_bank = [] - self.var_bank = [] - return x - - def hack_CrossAttnDownBlock2D_forward( - self, - hidden_states: torch.FloatTensor, - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ): - eps = 1e-6 - # TODO(Patrick, William) - attention mask is not used - output_states = () - - for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)): - hidden_states = resnet(hidden_states, temb) - - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0) - # mask var mean - scale_ratio = self.ref_mask.shape[2] / \ - hidden_states.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(hidden_states.device), - scale_factor=1 / scale_ratio, - ) - this_ref_mask = this_ref_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_ref_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - self.mean_bank0.append(mean) - self.var_bank0.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank0) > 0 - and len(self.var_bank0) > 0 - ): - # print("hacked_CrossAttnDownBlock2D_forward0") - scale_ratio = self.inpaint_mask.shape[2] / \ - hidden_states.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(hidden_states.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_inpaint_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank0[i]) / float( - len(self.mean_bank0[i]) - ) - var_acc = sum( - self.var_bank0[i]) / float(len(self.var_bank0[i])) - std_acc = ( - torch.maximum( - var_acc, torch.zeros_like(var_acc) + eps) - ** 0.5 - ) - hidden_states_uc = ( - ((masked_hidden_states - mean) / std) * std_acc - ) + mean_acc - hidden_states_c = hidden_states_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - hidden_states_c[self.uc_mask] = masked_hidden_states[self.uc_mask] - masked_hidden_states = ( - self.style_fidelity * hidden_states_c - + (1.0 - self.style_fidelity) * hidden_states_uc - ) - hidden_states[this_inpaint_mask] = masked_hidden_states.view( - -1) - - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - # attention_mask=attention_mask, - # encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # mask var mean - scale_ratio = self.ref_mask.shape[2] / \ - hidden_states.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(hidden_states.device), - scale_factor=1 / scale_ratio, - ) - this_ref_mask = this_ref_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_ref_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - self.mean_bank.append(mean) - self.var_bank.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank) > 0 - and len(self.var_bank) > 0 - ): - # print("hack_CrossAttnDownBlock2D_forward") - scale_ratio = self.inpaint_mask.shape[2] / \ - hidden_states.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(hidden_states.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_inpaint_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank[i]) / float( - len(self.mean_bank[i]) - ) - var_acc = sum( - self.var_bank[i]) / float(len(self.var_bank[i])) - std_acc = ( - torch.maximum( - var_acc, torch.zeros_like(var_acc) + eps) - ** 0.5 - ) - hidden_states_uc = ( - ((masked_hidden_states - mean) / std) * std_acc - ) + mean_acc - hidden_states_c = hidden_states_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - hidden_states_c[self.uc_mask] = masked_hidden_states[self.uc_mask] - masked_hidden_states = ( - self.style_fidelity * hidden_states_c - + (1.0 - self.style_fidelity) * hidden_states_uc - ) - hidden_states[this_inpaint_mask] = masked_hidden_states.view( - -1) - - output_states = output_states + (hidden_states,) - - if self.MODE == "read": - self.mean_bank0 = [] - self.var_bank0 = [] - self.mean_bank = [] - self.var_bank = [] - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - def hacked_DownBlock2D_forward(self, hidden_states, temb=None): - eps = 1e-6 - - output_states = () - - for i, resnet in enumerate(self.resnets): - hidden_states = resnet(hidden_states, temb) - - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0) - # mask var mean - scale_ratio = self.ref_mask.shape[2] / \ - hidden_states.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(hidden_states.device), - scale_factor=1 / scale_ratio, - ) - this_ref_mask = this_ref_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_ref_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - self.mean_bank.append(mean) - self.var_bank.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank) > 0 - and len(self.var_bank) > 0 - ): - # print("hacked_DownBlock2D_forward") - scale_ratio = self.inpaint_mask.shape[2] / \ - hidden_states.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(hidden_states.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_inpaint_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank[i]) / float( - len(self.mean_bank[i]) - ) - var_acc = sum( - self.var_bank[i]) / float(len(self.var_bank[i])) - std_acc = ( - torch.maximum( - var_acc, torch.zeros_like(var_acc) + eps) - ** 0.5 - ) - hidden_states_uc = ( - ((masked_hidden_states - mean) / std) * std_acc - ) + mean_acc - hidden_states_c = hidden_states_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - hidden_states_c[self.uc_mask] = masked_hidden_states[self.uc_mask] - masked_hidden_states = ( - self.style_fidelity * hidden_states_c - + (1.0 - self.style_fidelity) * hidden_states_uc - ) - hidden_states[this_inpaint_mask] = masked_hidden_states.view( - -1) - - output_states = output_states + (hidden_states,) - - if self.MODE == "read": - self.mean_bank = [] - self.var_bank = [] - - if self.downsamplers is not None: - for downsampler in self.downsamplers: - hidden_states = downsampler(hidden_states) - - output_states = output_states + (hidden_states,) - - return hidden_states, output_states - - def hacked_CrossAttnUpBlock2D_forward( - self, - hidden_states: torch.FloatTensor, - res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], - temb: Optional[torch.FloatTensor] = None, - encoder_hidden_states: Optional[torch.FloatTensor] = None, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - upsample_size: Optional[int] = None, - attention_mask: Optional[torch.FloatTensor] = None, - encoder_attention_mask: Optional[torch.FloatTensor] = None, - ): - eps = 1e-6 - # TODO(Patrick, William) - attention mask is not used - for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat( - [hidden_states, res_hidden_states], dim=1) - hidden_states = resnet(hidden_states, temb) - - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0) - # mask var mean - scale_ratio = self.ref_mask.shape[2] / \ - hidden_states.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(hidden_states.device), - scale_factor=1 / scale_ratio, - ) - this_ref_mask = this_ref_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_ref_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - self.mean_bank0.append(mean) - self.var_bank0.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank0) > 0 - and len(self.var_bank0) > 0 - ): - # print("hacked_CrossAttnUpBlock2D_forward1") - scale_ratio = self.inpaint_mask.shape[2] / \ - hidden_states.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(hidden_states.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_inpaint_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank0[i]) / float( - len(self.mean_bank0[i]) - ) - var_acc = sum( - self.var_bank0[i]) / float(len(self.var_bank0[i])) - std_acc = ( - torch.maximum( - var_acc, torch.zeros_like(var_acc) + eps) - ** 0.5 - ) - hidden_states_uc = ( - ((masked_hidden_states - mean) / std) * std_acc - ) + mean_acc - hidden_states_c = hidden_states_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - hidden_states_c[self.uc_mask] = masked_hidden_states[self.uc_mask] - masked_hidden_states = ( - self.style_fidelity * hidden_states_c - + (1.0 - self.style_fidelity) * hidden_states_uc - ) - hidden_states[this_inpaint_mask] = masked_hidden_states.view( - -1) - - hidden_states = attn( - hidden_states, - encoder_hidden_states=encoder_hidden_states, - cross_attention_kwargs=cross_attention_kwargs, - # attention_mask=attention_mask, - # encoder_attention_mask=encoder_attention_mask, - return_dict=False, - )[0] - - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0) - # mask var mean - scale_ratio = self.ref_mask.shape[2] / \ - hidden_states.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(hidden_states.device), - scale_factor=1 / scale_ratio, - ) - this_ref_mask = this_ref_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_ref_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - self.mean_bank.append(mean) - self.var_bank.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank) > 0 - and len(self.var_bank) > 0 - ): - # print("hacked_CrossAttnUpBlock2D_forward") - scale_ratio = self.inpaint_mask.shape[2] / \ - hidden_states.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(hidden_states.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_inpaint_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank[i]) / float( - len(self.mean_bank[i]) - ) - var_acc = sum( - self.var_bank[i]) / float(len(self.var_bank[i])) - std_acc = ( - torch.maximum( - var_acc, torch.zeros_like(var_acc) + eps) - ** 0.5 - ) - hidden_states_uc = ( - ((masked_hidden_states - mean) / std) * std_acc - ) + mean_acc - hidden_states_c = hidden_states_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - hidden_states_c[self.uc_mask] = masked_hidden_states[self.uc_mask] - masked_hidden_states = ( - self.style_fidelity * hidden_states_c - + (1.0 - self.style_fidelity) * hidden_states_uc - ) - hidden_states[this_inpaint_mask] = masked_hidden_states.view( - -1) - - if self.MODE == "read": - self.mean_bank0 = [] - self.var_bank0 = [] - self.mean_bank = [] - self.var_bank = [] - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - def hacked_UpBlock2D_forward( - self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None - ): - eps = 1e-6 - for i, resnet in enumerate(self.resnets): - # pop res hidden states - res_hidden_states = res_hidden_states_tuple[-1] - res_hidden_states_tuple = res_hidden_states_tuple[:-1] - hidden_states = torch.cat( - [hidden_states, res_hidden_states], dim=1) - hidden_states = resnet(hidden_states, temb) - - if self.MODE == "write": - if self.gn_auto_machine_weight >= self.gn_weight: - # var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0) - # mask var mean - scale_ratio = self.ref_mask.shape[2] / \ - hidden_states.shape[2] - this_ref_mask = F.interpolate( - self.ref_mask.to(hidden_states.device), - scale_factor=1 / scale_ratio, - ) - this_ref_mask = this_ref_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_ref_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - self.mean_bank.append(mean) - self.var_bank.append(var) - if self.MODE == "read": - if ( - self.gn_auto_machine_weight >= self.gn_weight - and len(self.mean_bank) > 0 - and len(self.var_bank) > 0 - ): - # print("hacked_UpBlock2D_forward") - scale_ratio = self.inpaint_mask.shape[2] / \ - hidden_states.shape[2] - this_inpaint_mask = F.interpolate( - self.inpaint_mask.to(hidden_states.device), scale_factor=1 / scale_ratio - ) - this_inpaint_mask = this_inpaint_mask.repeat( - hidden_states.shape[0], hidden_states.shape[1], 1, 1 - ).bool() - masked_hidden_states = ( - hidden_states[this_inpaint_mask] - .detach() - .clone() - .view(hidden_states.shape[0], hidden_states.shape[1], -1, 1) - ) - var, mean = torch.var_mean( - masked_hidden_states, dim=(2, 3), keepdim=True, correction=0 - ) - std = torch.maximum( - var, torch.zeros_like(var) + eps) ** 0.5 - mean_acc = sum(self.mean_bank[i]) / float( - len(self.mean_bank[i]) - ) - var_acc = sum( - self.var_bank[i]) / float(len(self.var_bank[i])) - std_acc = ( - torch.maximum( - var_acc, torch.zeros_like(var_acc) + eps) - ** 0.5 - ) - hidden_states_uc = ( - ((masked_hidden_states - mean) / std) * std_acc - ) + mean_acc - hidden_states_c = hidden_states_uc.clone() - if self.do_classifier_free_guidance and self.style_fidelity > 0: - hidden_states_c[self.uc_mask] = masked_hidden_states[self.uc_mask] - masked_hidden_states = ( - self.style_fidelity * hidden_states_c - + (1.0 - self.style_fidelity) * hidden_states_uc - ) - hidden_states[this_inpaint_mask] = masked_hidden_states.view( - -1) - - if self.MODE == "read": - self.mean_bank = [] - self.var_bank = [] - - if self.upsamplers is not None: - for upsampler in self.upsamplers: - hidden_states = upsampler(hidden_states, upsample_size) - - return hidden_states - - if model_type == "unet": - - if reference_attn: - attn_modules = [ - module - for module in torch_dfs(model) - if isinstance(module, BasicTransformerBlock) - ] - attn_modules = sorted( - attn_modules, key=lambda x: -x.norm1.normalized_shape[0] - ) - - for i, module in enumerate(attn_modules): - module._original_inner_forward = module.forward - module.forward = hacked_basic_transformer_inner_forward.__get__( - module, BasicTransformerBlock - ) - module.bank = [] - module.attn_weight = float(i) / float(len(attn_modules)) - module.attention_auto_machine_weight = ( - self.attention_auto_machine_weight - ) - module.gn_auto_machine_weight = self.gn_auto_machine_weight - module.do_classifier_free_guidance = ( - self.do_classifier_free_guidance - ) - module.do_classifier_free_guidance = ( - self.do_classifier_free_guidance - ) - module.uc_mask = self.uc_mask - module.style_fidelity = self.style_fidelity - module.ref_mask = self.ref_mask - else: - attn_modules = None - if reference_adain: - gn_modules = [model.mid_block] - model.mid_block.gn_weight = 0 - - down_blocks = model.down_blocks - for w, module in enumerate(down_blocks): - module.gn_weight = 1.0 - float(w) / float(len(down_blocks)) - gn_modules.append(module) - # print(module.__class__.__name__,module.gn_weight) - - up_blocks = model.up_blocks - for w, module in enumerate(up_blocks): - module.gn_weight = float(w) / float(len(up_blocks)) - gn_modules.append(module) - # print(module.__class__.__name__,module.gn_weight) - - for i, module in enumerate(gn_modules): - if getattr(module, "original_forward", None) is None: - module.original_forward = module.forward - if i == 0: - # mid_block - module.forward = hacked_mid_forward.__get__( - module, torch.nn.Module - ) - elif isinstance(module, CrossAttnDownBlock2D): - module.forward = hack_CrossAttnDownBlock2D_forward.__get__( - module, CrossAttnDownBlock2D - ) - module.mean_bank0 = [] - module.var_bank0 = [] - elif isinstance(module, DownBlock2D): - module.forward = hacked_DownBlock2D_forward.__get__( - module, DownBlock2D - ) - # elif isinstance(module, CrossAttnUpBlock2D): - # module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D) - # module.mean_bank0 = [] - # module.var_bank0 = [] - elif isinstance(module, UpBlock2D): - module.forward = hacked_UpBlock2D_forward.__get__( - module, UpBlock2D - ) - module.mean_bank0 = [] - module.var_bank0 = [] - module.mean_bank = [] - module.var_bank = [] - module.attention_auto_machine_weight = ( - self.attention_auto_machine_weight - ) - module.gn_auto_machine_weight = self.gn_auto_machine_weight - module.do_classifier_free_guidance = ( - self.do_classifier_free_guidance - ) - module.do_classifier_free_guidance = ( - self.do_classifier_free_guidance - ) - module.uc_mask = self.uc_mask - module.style_fidelity = self.style_fidelity - module.ref_mask = self.ref_mask - module.inpaint_mask = self.inpaint_mask - else: - gn_modules = None - elif model_type == "controlnet": - model = model.nets[-1] # only hack the inpainting controlnet - if reference_attn: - attn_modules = [ - module - for module in torch_dfs(model) - if isinstance(module, BasicTransformerBlock) - ] - attn_modules = sorted( - attn_modules, key=lambda x: -x.norm1.normalized_shape[0] - ) - for i, module in enumerate(attn_modules): - module._original_inner_forward = module.forward - module.forward = hacked_basic_transformer_inner_forward.__get__( - module, BasicTransformerBlock - ) - module.bank = [] - # float(i) / float(len(attn_modules)) - module.attn_weight = 0.0 - module.attention_auto_machine_weight = ( - self.attention_auto_machine_weight - ) - module.gn_auto_machine_weight = self.gn_auto_machine_weight - module.do_classifier_free_guidance = ( - self.do_classifier_free_guidance - ) - module.do_classifier_free_guidance = ( - self.do_classifier_free_guidance - ) - module.uc_mask = self.uc_mask - module.style_fidelity = self.style_fidelity - module.ref_mask = self.ref_mask - else: - attn_modules = None - gn_modules = None - - return attn_modules, gn_modules - - def change_module_mode(self, mode, attn_modules, gn_modules): - if attn_modules is not None: - for i, module in enumerate(attn_modules): - module.MODE = mode - if gn_modules is not None: - for i, module in enumerate(gn_modules): - module.MODE = mode diff --git a/spaces/shi-labs/Matting-Anything/utils/copy_data.py b/spaces/shi-labs/Matting-Anything/utils/copy_data.py deleted file mode 100644 index 44a81926af41d8c5aa8c83aca5af596e3e6b1f5f..0000000000000000000000000000000000000000 --- a/spaces/shi-labs/Matting-Anything/utils/copy_data.py +++ /dev/null @@ -1,24 +0,0 @@ -import os -from shutil import copyfile -#copyfile(src, dst) - -### list based on https://github.com/senguptaumd/Background-Matting/blob/master/Data_adobe/train_data_list.txt -train_list = open('train_data_list.txt').read().splitlines() - -### training -src_fg_path = '/export/ccvl12b/qihang/MGMatting/data/Combined_Dataset/Training_set/fg/' -src_alpha_path = '/export/ccvl12b/qihang/MGMatting/data/Combined_Dataset/Training_set/alpha/' - -dst_fg_path = '/export/ccvl12b/qihang/MGMatting/data/Combined_Dataset_Solid/Training_set/fg/' -dst_alpha_path = '/export/ccvl12b/qihang/MGMatting/data/Combined_Dataset_Solid/Training_set/alpha/' - -if not os.path.exists(dst_fg_path): - os.makedirs(dst_fg_path) -if not os.path.exists(dst_alpha_path): - os.makedirs(dst_alpha_path) - -for f in train_list: - copyfile(src_fg_path + f, dst_fg_path + f) - copyfile(src_alpha_path + f, dst_alpha_path + f) - - diff --git a/spaces/sidharthism/fashion-eye/netdissect/upsegmodel/resnext.py b/spaces/sidharthism/fashion-eye/netdissect/upsegmodel/resnext.py deleted file mode 100644 index 4c618c9da5be17feb975833532e19474fca82dba..0000000000000000000000000000000000000000 --- a/spaces/sidharthism/fashion-eye/netdissect/upsegmodel/resnext.py +++ /dev/null @@ -1,183 +0,0 @@ -import os -import sys -import torch -import torch.nn as nn -import math -try: - from lib.nn import SynchronizedBatchNorm2d -except ImportError: - from torch.nn import BatchNorm2d as SynchronizedBatchNorm2d - -try: - from urllib import urlretrieve -except ImportError: - from urllib.request import urlretrieve - - -__all__ = ['ResNeXt', 'resnext101'] # support resnext 101 - - -model_urls = { - #'resnext50': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext50-imagenet.pth', - 'resnext101': 'http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnext101-imagenet.pth' -} - - -def conv3x3(in_planes, out_planes, stride=1): - "3x3 convolution with padding" - return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, - padding=1, bias=False) - - -class GroupBottleneck(nn.Module): - expansion = 2 - - def __init__(self, inplanes, planes, stride=1, groups=1, downsample=None): - super(GroupBottleneck, self).__init__() - self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) - self.bn1 = SynchronizedBatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, - padding=1, groups=groups, bias=False) - self.bn2 = SynchronizedBatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, planes * 2, kernel_size=1, bias=False) - self.bn3 = SynchronizedBatchNorm2d(planes * 2) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - residual = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - residual = self.downsample(x) - - out += residual - out = self.relu(out) - - return out - - -class ResNeXt(nn.Module): - - def __init__(self, block, layers, groups=32, num_classes=1000): - self.inplanes = 128 - super(ResNeXt, self).__init__() - self.conv1 = conv3x3(3, 64, stride=2) - self.bn1 = SynchronizedBatchNorm2d(64) - self.relu1 = nn.ReLU(inplace=True) - self.conv2 = conv3x3(64, 64) - self.bn2 = SynchronizedBatchNorm2d(64) - self.relu2 = nn.ReLU(inplace=True) - self.conv3 = conv3x3(64, 128) - self.bn3 = SynchronizedBatchNorm2d(128) - self.relu3 = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - - self.layer1 = self._make_layer(block, 128, layers[0], groups=groups) - self.layer2 = self._make_layer(block, 256, layers[1], stride=2, groups=groups) - self.layer3 = self._make_layer(block, 512, layers[2], stride=2, groups=groups) - self.layer4 = self._make_layer(block, 1024, layers[3], stride=2, groups=groups) - self.avgpool = nn.AvgPool2d(7, stride=1) - self.fc = nn.Linear(1024 * block.expansion, num_classes) - - for m in self.modules(): - if isinstance(m, nn.Conv2d): - n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels // m.groups - m.weight.data.normal_(0, math.sqrt(2. / n)) - elif isinstance(m, SynchronizedBatchNorm2d): - m.weight.data.fill_(1) - m.bias.data.zero_() - - def _make_layer(self, block, planes, blocks, stride=1, groups=1): - downsample = None - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - nn.Conv2d(self.inplanes, planes * block.expansion, - kernel_size=1, stride=stride, bias=False), - SynchronizedBatchNorm2d(planes * block.expansion), - ) - - layers = [] - layers.append(block(self.inplanes, planes, stride, groups, downsample)) - self.inplanes = planes * block.expansion - for i in range(1, blocks): - layers.append(block(self.inplanes, planes, groups=groups)) - - return nn.Sequential(*layers) - - def forward(self, x): - x = self.relu1(self.bn1(self.conv1(x))) - x = self.relu2(self.bn2(self.conv2(x))) - x = self.relu3(self.bn3(self.conv3(x))) - x = self.maxpool(x) - - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - - x = self.avgpool(x) - x = x.view(x.size(0), -1) - x = self.fc(x) - - return x - - -''' -def resnext50(pretrained=False, **kwargs): - """Constructs a ResNet-50 model. - - Args: - pretrained (bool): If True, returns a model pre-trained on Places - """ - model = ResNeXt(GroupBottleneck, [3, 4, 6, 3], **kwargs) - if pretrained: - model.load_state_dict(load_url(model_urls['resnext50']), strict=False) - return model -''' - - -def resnext101(pretrained=False, **kwargs): - """Constructs a ResNet-101 model. - - Args: - pretrained (bool): If True, returns a model pre-trained on Places - """ - model = ResNeXt(GroupBottleneck, [3, 4, 23, 3], **kwargs) - if pretrained: - model.load_state_dict(load_url(model_urls['resnext101']), strict=False) - return model - - -# def resnext152(pretrained=False, **kwargs): -# """Constructs a ResNeXt-152 model. -# -# Args: -# pretrained (bool): If True, returns a model pre-trained on Places -# """ -# model = ResNeXt(GroupBottleneck, [3, 8, 36, 3], **kwargs) -# if pretrained: -# model.load_state_dict(load_url(model_urls['resnext152'])) -# return model - - -def load_url(url, model_dir='./pretrained', map_location=None): - if not os.path.exists(model_dir): - os.makedirs(model_dir) - filename = url.split('/')[-1] - cached_file = os.path.join(model_dir, filename) - if not os.path.exists(cached_file): - sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file)) - urlretrieve(url, cached_file) - return torch.load(cached_file, map_location=map_location) diff --git a/spaces/simonduerr/ProteinMPNN/ProteinMPNN/colab_notebooks/README.md b/spaces/simonduerr/ProteinMPNN/ProteinMPNN/colab_notebooks/README.md deleted file mode 100644 index 894645f04740b0dc56805ab58ad2df0556fe29a2..0000000000000000000000000000000000000000 --- a/spaces/simonduerr/ProteinMPNN/ProteinMPNN/colab_notebooks/README.md +++ /dev/null @@ -1 +0,0 @@ -Open In Colab diff --git a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/hmmbuild.py b/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/hmmbuild.py deleted file mode 100644 index f3c573047450f5f17e791ad9a54f1b436e71b095..0000000000000000000000000000000000000000 --- a/spaces/simonduerr/ProteinMPNN/af_backprop/alphafold/data/tools/hmmbuild.py +++ /dev/null @@ -1,138 +0,0 @@ -# Copyright 2021 DeepMind Technologies Limited -# -# 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. - -"""A Python wrapper for hmmbuild - construct HMM profiles from MSA.""" - -import os -import re -import subprocess - -from absl import logging -from alphafold.data.tools import utils -# Internal import (7716). - - -class Hmmbuild(object): - """Python wrapper of the hmmbuild binary.""" - - def __init__(self, - *, - binary_path: str, - singlemx: bool = False): - """Initializes the Python hmmbuild wrapper. - - Args: - binary_path: The path to the hmmbuild executable. - singlemx: Whether to use --singlemx flag. If True, it forces HMMBuild to - just use a common substitution score matrix. - - Raises: - RuntimeError: If hmmbuild binary not found within the path. - """ - self.binary_path = binary_path - self.singlemx = singlemx - - def build_profile_from_sto(self, sto: str, model_construction='fast') -> str: - """Builds a HHM for the aligned sequences given as an A3M string. - - Args: - sto: A string with the aligned sequences in the Stockholm format. - model_construction: Whether to use reference annotation in the msa to - determine consensus columns ('hand') or default ('fast'). - - Returns: - A string with the profile in the HMM format. - - Raises: - RuntimeError: If hmmbuild fails. - """ - return self._build_profile(sto, model_construction=model_construction) - - def build_profile_from_a3m(self, a3m: str) -> str: - """Builds a HHM for the aligned sequences given as an A3M string. - - Args: - a3m: A string with the aligned sequences in the A3M format. - - Returns: - A string with the profile in the HMM format. - - Raises: - RuntimeError: If hmmbuild fails. - """ - lines = [] - for line in a3m.splitlines(): - if not line.startswith('>'): - line = re.sub('[a-z]+', '', line) # Remove inserted residues. - lines.append(line + '\n') - msa = ''.join(lines) - return self._build_profile(msa, model_construction='fast') - - def _build_profile(self, msa: str, model_construction: str = 'fast') -> str: - """Builds a HMM for the aligned sequences given as an MSA string. - - Args: - msa: A string with the aligned sequences, in A3M or STO format. - model_construction: Whether to use reference annotation in the msa to - determine consensus columns ('hand') or default ('fast'). - - Returns: - A string with the profile in the HMM format. - - Raises: - RuntimeError: If hmmbuild fails. - ValueError: If unspecified arguments are provided. - """ - if model_construction not in {'hand', 'fast'}: - raise ValueError(f'Invalid model_construction {model_construction} - only' - 'hand and fast supported.') - - with utils.tmpdir_manager(base_dir='/tmp') as query_tmp_dir: - input_query = os.path.join(query_tmp_dir, 'query.msa') - output_hmm_path = os.path.join(query_tmp_dir, 'output.hmm') - - with open(input_query, 'w') as f: - f.write(msa) - - cmd = [self.binary_path] - # If adding flags, we have to do so before the output and input: - - if model_construction == 'hand': - cmd.append(f'--{model_construction}') - if self.singlemx: - cmd.append('--singlemx') - cmd.extend([ - '--amino', - output_hmm_path, - input_query, - ]) - - logging.info('Launching subprocess %s', cmd) - process = subprocess.Popen(cmd, stdout=subprocess.PIPE, - stderr=subprocess.PIPE) - - with utils.timing('hmmbuild query'): - stdout, stderr = process.communicate() - retcode = process.wait() - logging.info('hmmbuild stdout:\n%s\n\nstderr:\n%s\n', - stdout.decode('utf-8'), stderr.decode('utf-8')) - - if retcode: - raise RuntimeError('hmmbuild failed\nstdout:\n%s\n\nstderr:\n%s\n' - % (stdout.decode('utf-8'), stderr.decode('utf-8'))) - - with open(output_hmm_path, encoding='utf-8') as f: - hmm = f.read() - - return hmm diff --git a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Discover the Secrets of the Runes in Rune Factory 5 PC.md b/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Discover the Secrets of the Runes in Rune Factory 5 PC.md deleted file mode 100644 index 01e6b77a8c725303e9da4be7f5bb12840d9f15b8..0000000000000000000000000000000000000000 --- a/spaces/simple0urra/skops-model-card-creator-2a23515a-d54e-4804-b365-27ed6e938735/example/Discover the Secrets of the Runes in Rune Factory 5 PC.md +++ /dev/null @@ -1,190 +0,0 @@ - -

        How to Download Rune Factory 5 PC

        -

        If you are looking for a game that combines farming, combat, crafting, dating, and exploration in a fantasy world, then you might want to check out Rune Factory 5. This game is the latest entry in the popular Rune Factory series, which is a spin-off of the Harvest Moon franchise. In this article, we will show you how to download Rune Factory 5 PC, and give you some useful information and tips on how to play and enjoy this game.

        -

        What is Rune Factory 5?

        -

        Rune Factory 5 is a hybrid action-JRPG and farming sim game developed by Marvelous Inc. and published by XSEED Games. It was released for Nintendo Switch in Japan in May 2021, and for PC via Steam in July 2022. The game follows the story of a young man or woman who joins a peacekeeping organization called SEED in a town called Rigbarth. There, they discover that they have the power to control runes, which are mysterious forces that govern nature. They also meet various characters who become their friends, allies, or potential love interests.

        -

        download rune factory 5 pc


        Download https://ssurll.com/2uNQo8



        -

        The game features a variety of activities that you can do in your daily life. You can grow crops on your farm, tame monsters that can help you with chores or fight by your side, craft items and equipment using materials you gather or buy, date and marry one of the twelve eligible bachelors or bachelorettes in town, explore dungeons and other locations full of enemies and treasures, complete missions for SEED or other NPCs, participate in festivals and events, and more. The game also has a rich story that unfolds as you progress through the main quests and interact with the characters.

        -

        Why Play Rune Factory 5 on PC?

        -

        Rune Factory 5 was originally designed for Nintendo Switch, but it also has a PC version that offers some advantages over the console version. Here are some reasons why you might want to play Rune Factory 5 on PC:

        -
          -
        • Better graphics: The PC version of Rune Factory 5 supports higher resolutions (up to 4K), higher framerates (up to 120 fps or more), and more graphical options than the Switch version. This means that you can enjoy sharper textures, smoother animations, and more detailed environments on your PC.
          • -
        • Better performance: The Switch version of Rune Factory 5 suffers from some performance issues such as low framerates, long loading times, and occasional crashes. The PC version is more stable and optimized for different hardware configurations. You can also tweak the settings to suit your preferences and

          system requirements. You can also use mods or cheats to enhance your gameplay experience.

          • -
        • Better controls: The PC version of Rune Factory 5 supports both keyboard and mouse and gamepad controls. You can customize the key bindings and sensitivity to your liking. You can also use the mouse to navigate the menus and the UI more easily. The gamepad controls are similar to the Switch version, but you can also use other controllers such as Xbox or PlayStation ones.
          • -
        • Better accessibility: The PC version of Rune Factory 5 is more accessible to players who live in regions where the Switch version is not available or is too expensive. You can also play the game on different devices such as laptops or desktops, as long as they meet the minimum requirements. You can also use Steam's features such as cloud saves, achievements, and community forums.
          • -
        -

        Of course, playing Rune Factory 5 on PC also has some drawbacks, such as needing a decent PC to run the game, having to deal with potential bugs or compatibility issues, and missing out on some exclusive features of the Switch version such as touch screen support and amiibo compatibility. Ultimately, the choice is up to you and your personal preference.

        -

        Where to Buy Rune Factory 5 PC?

        -

        If you decide to play Rune Factory 5 on PC, you need to buy the game from a legitimate and authorized platform. The official and recommended platform is Steam, which is a digital distribution service that offers a variety of games and software. You can buy Rune Factory 5 PC from Steam for $59.99 USD, or your regional equivalent. You can also get a 10% discount if you pre-order the game before its release date.

        -

        To buy Rune Factory 5 PC from Steam, you need to have a Steam account and a valid payment method. You can create a Steam account for free by visiting https://store.steampowered.com/join/ and following the instructions. You can also download the Steam client from https://store.steampowered.com/about/ and install it on your PC. Once you have a Steam account and the Steam client, you can buy Rune Factory 5 PC by visiting https://store.steampowered.com/app/1567890/Rune_Factory_5/ and clicking on the "Add to Cart" button. You can then proceed to checkout and pay with your preferred payment method.

        -

        There are also other platforms that sell Rune Factory 5 PC, such as GOG.com, Humble Bundle, Green Man Gaming, and Fanatical. However, these platforms are not officially endorsed by the developers or publishers of the game, and they may not offer the same features or support as Steam. Therefore, we advise you to be careful and do your research before buying from these platforms. Make sure that they are reputable and trustworthy, and that they provide you with a valid Steam key that you can activate on your Steam account.

        -

        How to Install Rune Factory 5 PC?

        -

        After you buy Rune Factory 5 PC from Steam or another platform, you need to install the game on your PC. The installation process is simple and straightforward, but it may take some time depending on your internet speed and PC specifications. Here are the steps to install Rune Factory 5 PC:

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        -
          -
        1. Launch the Steam client on your PC and log in with your Steam account.
          2. -
        2. Go to your Library tab and find Rune Factory 5 in your list of games.
          3. -
        3. Click on the game's name and then click on the "Install" button.
          4. -
        4. Select the destination folder where you want to install the game and click on "Next".
          5. -
        5. Wait for the game to download and install on your PC. You can check the progress bar and the estimated time remaining on the bottom of the screen.
          6. -
        6. Once the installation is complete, click on "Play" to launch the game.
          7. -
        -

        Congratulations! You have successfully installed Rune Factory 5 PC on your PC. You are now ready to start your adventure in Rigbarth.

        -

        How to Optimize Rune Factory 5 PC?

        -

        Rune Factory 5 PC is a relatively demanding game that requires a decent PC to run smoothly and look good. However, you can optimize the game's performance and quality by adjusting some graphical options and settings. Here are some tips on how to optimize Rune Factory 5 PC:

        -
          -
        • Check your system requirements: Before you play Rune Factory 5 PC, make sure that your PC meets the minimum or recommended system requirements for the game. You can

          check the system requirements by visiting https://store.steampowered.com/app/1567890/Rune_Factory_5/ and scrolling down to the bottom of the page. If your PC does not meet the minimum requirements, you may experience low framerates, poor graphics, or crashes. If your PC meets or exceeds the recommended requirements, you can enjoy the game at its best quality and performance.

          -
        • Update your drivers: Make sure that your graphics card and sound card drivers are up to date. You can update your drivers by visiting the official website of your hardware manufacturer and downloading the latest version. Updating your drivers can improve your PC's compatibility and stability with the game.
          • -
        • Adjust your settings: You can access the game's settings by launching the game and clicking on the "Options" button on the main menu. You can then choose from various categories such as "Graphics", "Sound", "Controls", and "Language". You can change the settings to suit your preferences and needs. Here are some of the most important settings that affect the game's performance and quality:

          -
            -
          • Resolution: This setting determines the size and clarity of the game's display on your screen. The higher the resolution, the sharper and more detailed the game will look, but it will also consume more resources and lower your framerate. The lower the resolution, the blurrier and less detailed the game will look, but it will also use less resources and increase your framerate. You should choose a resolution that matches your monitor's native resolution, or a lower one if you have a weak PC.
            • -
          • Fullscreen: This setting determines whether the game will run in fullscreen mode or windowed mode. Fullscreen mode means that the game will occupy the entire screen and block out any other applications or programs. Windowed mode means that the game will run in a smaller window that you can resize or move around. Fullscreen mode usually offers better performance and immersion, but it may also cause some compatibility or alt-tabbing issues. Windowed mode usually offers more flexibility and convenience, but it may also cause some distractions or input lag.
            • -
          • V-sync: This setting determines whether the game will synchronize its framerate with your monitor's refresh rate. V-sync is a feature that prevents screen tearing, which is a visual artifact that occurs when the game's framerate does not match your monitor's refresh rate. Screen tearing can make the game look choppy or distorted. V-sync can eliminate screen tearing, but it can also introduce input lag or stuttering. You should enable v-sync if you experience screen tearing, but disable it if you experience input lag or stuttering.
            • -
          • Anti-aliasing: This setting determines whether the game will smooth out the jagged edges of objects and textures in the game. Anti-aliasing is a technique that reduces aliasing, which is a visual artifact that occurs when the game's resolution is not high enough to render smooth curves and lines. Aliasing can make the game look pixelated or jagged. Anti-aliasing can improve the game's appearance, but it can also reduce its performance. You should enable anti-aliasing if you want a smoother and more realistic look, but disable it if you want a faster and more responsive gameplay.
            • -
          • Shadow quality: This setting determines how detailed and realistic the shadows in the game will be. Shadows are effects that simulate how light interacts with objects and surfaces in the game. Shadows can add depth and realism to the game's graphics, but they can also lower its framerate. You should choose a shadow quality that balances between aesthetics and performance.
            • -
          -

          You can experiment with different settings until you find the optimal combination for your PC and preference. You can also use the "Auto" option to let the game automatically adjust the settings based on your PC's capabilities.

          -
        -

        How to Play Rune Factory 5 PC?

        -

        Rune Factory 5 PC is a fun and addictive game that offers a lot of content and variety for you to enjoy. However, it can also be overwhelming and confusing for new players who are not familiar with its mechanics and features. Here are some basic tips on how to play Rune Factory 5 PC:

        -

        Farming

        -

        Farming is one of the core aspects of Rune Factory 5 PC, as it allows you to grow crops, tame monsters, and manage your farm. Farming is not only a source of income, but also a way to improve your skills, relationships, and reputation in town.

        -

        To start farming, you need to have some seeds, tools, and land. You can buy seeds from shops or get them from other sources such as monsters or chests. You can get tools from quests or crafting them yourself. You

        You can use the land on your farm or rent some fields from other NPCs. To plant seeds, you need to till the soil with your hoe, place the seeds on the tilled spots, and water them with your watering can. You can also use fertilizer or magic to enhance your crops' growth and quality. To harvest crops, you need to use your sickle or your hands, depending on the type of crop. You can then sell your crops, use them for cooking or crafting, or give them as gifts.

        -

        To tame monsters, you need to have some monster treats, which you can buy from shops or make yourself. You can also use other items that the monsters like, such as fruits or flowers. To tame a monster, you need to approach it carefully and throw a treat at it. If the monster likes the treat, it will eat it and become friendly. You can then pet it and talk to it until it agrees to join you. You can also fight the monster until it becomes weak and then throw a treat at it. You can then bring the tamed monster to your barn and name it. You can have up to four monsters in your party at a time, and they will help you with farming or combat.

        -

        To manage your farm, you need to take care of your crops and monsters regularly. You also need to upgrade your tools, barn, and house to unlock more features and options. You can also decorate your farm with furniture, plants, and other items. You can also interact with other farmers and join the Farm Association, which will give you access to more fields, seeds, and events.

        -

        Combat

        -

        Combat is another core aspect of Rune Factory 5 PC, as it allows you to fight enemies, use weapons, and unleash link attacks. Combat is not only a source of excitement, but also a way to improve your skills, obtain materials, and advance the story.

        -

        To start combat, you need to have some weapons, armor, and accessories. You can buy them from shops or craft them yourself. You can also get them from other sources such as monsters or chests. You can equip up to two weapons at a time, one for each hand. You can switch between them by pressing the Q key on your keyboard or the Y button on your gamepad. You can also equip armor and accessories that will increase your defense and provide other benefits.

        -

        To fight enemies, you need to use your weapons or magic. You can attack by pressing the left mouse button or the X button on your gamepad. You can also use special skills by pressing the right mouse button or the A button on your gamepad. You can also use magic by pressing the E key on your keyboard or the B button on your gamepad. You can also dodge by pressing the spacebar on your keyboard or the L1 button on your gamepad. You can also block by pressing the shift key on your keyboard or the R1 button on your gamepad.

        -

        To unleash link attacks, you need to have some allies in your party. You can have up to three allies at a time, which can be either monsters or humans. You can switch between them by pressing the 1-4 keys on your keyboard or the D-pad on your gamepad. You can also command them by pressing the F key on your keyboard or the L2 button on your gamepad. You can also link with them by pressing the R key on your keyboard or the R2 button on your gamepad. Linking with an ally will increase your stats and allow you to perform powerful combo attacks.

        -

        Crafting

        -

        Crafting is another core aspect of Rune Factory 5 PC, as it allows you to create items, equipment, and accessories. Crafting is not only a source of creativity, but also a way to improve your skills, customize your gear, and save money.

        -

        To start crafting, you need to have some materials, recipes, and facilities. You can get materials from shops or gather them from nature or enemies. You can get recipes from books or NPCs or learn them by yourself. You can use facilities such as forge, workshop, kitchen, pharmacy

        and laboratory on your farm or in town. To craft an item, you need to select the facility and the recipe, and then add the required materials. You can also add optional materials to enhance the item's quality or effects. You can then confirm the crafting and wait for the result. You can then use the crafted item, equip it, or sell it.

        -

        Dating

        -

        Dating is another core aspect of Rune Factory 5 PC, as it allows you to romance characters, go on dates, and get married. Dating is not only a source of romance, but also a way to improve your relationships, unlock events, and start a family.

        -

        To start dating, you need to have some gifts, dialogues, and events. You can get gifts from shops or make them yourself. You can also use other items that the characters like, such as flowers or food. To date a character, you need to talk to them regularly and give them gifts until you raise their friendship level to at least 7 hearts. You can also trigger events by visiting certain locations or times with them. You can then confess your feelings to them by giving them a ring or a pendant. If they accept, you will become lovers and unlock more events and options.

        -

        To go on dates, you need to have some invitations, locations, and outfits. You can get invitations from your lover or make them yourself. You can also use other items that they like, such as tickets or letters. To go on a date, you need to give an invitation to your lover and choose a location and a time. You can also choose an outfit for yourself and your lover. You can then enjoy the date and interact with your lover by choosing dialogue options or actions. You can also take pictures or selfies with them.

        -

        To get married, you need to have some requirements, preparations, and ceremonies. You need to meet the following requirements to get married: have at least 10 hearts with your lover, have a double bed in your house, have seen all of their events, have completed the main story, and have obtained a marriage license from the church. To prepare for the wedding, you need to choose a date and a venue for the ceremony. You can also invite guests and choose outfits for yourself and your lover. You can then attend the ceremony and exchange vows with your lover. You can also have a honeymoon and move in together.

        -

        Exploration

        -

        Exploration is another core aspect of Rune Factory 5 PC, as it allows you to discover new areas, complete quests, and uncover secrets. Exploration is not only a source of adventure, but also a way to improve your skills, obtain rewards, and learn more about the world.

        -

        To start exploring, you need to have some maps, equipment, and transportation. You can get maps from shops or NPCs or find them in chests or other places. You can use maps to see the layout of the areas and mark points of interest. You can also use equipment such as weapons, armor, accessories

        and items to protect yourself and enhance your abilities. You can also use transportation such as horses, carts, or airships to travel faster and easier. You can get equipment and transportation from shops or crafting them yourself. You can also get them from other sources such as monsters or events.

        -

        To explore areas, you need to use your senses, skills, and tools. You can use your senses to observe the environment and find clues or hints. You can use your skills to overcome obstacles or challenges. You can use your tools to interact with objects or items. You can also use your map to navigate and locate your destination. You can also encounter enemies, NPCs, or events along the way.

        -

        To complete quests, you need to have some objectives, rewards, and reputation. You can get quests from SEED or other NPCs or find them on bulletin boards or other places. You can also create your own quests by using the quest editor. Quests are tasks that require you to do something specific, such as defeating a certain enemy, delivering an item, or finding a person. Quests will give you rewards such as money, items, or experience. Quests will also affect your reputation with SEED or other factions, which will influence how they treat you and what opportunities they offer you.

        -

        To uncover secrets, you need to have some curiosity, intuition, and luck. You can find secrets by exploring hidden paths, solving puzzles, or triggering events. Secrets are things that are not obvious or easy to find, such as secret rooms, hidden chests, or rare items. Secrets will give you surprises such as new information, extra rewards, or special effects.

        -

        How to Troubleshoot Rune Factory 5 PC?

        -

        Rune Factory 5 PC is a complex and sophisticated game that may encounter some issues or errors during its operation. However, you can troubleshoot these problems by following some simple steps and solutions. Here are some of the common issues and errors that may occur in Rune Factory 5 PC, and how to fix them:

        -
          -
        • Game won't launch: This issue may occur if your PC does not meet the system requirements, if your drivers are outdated or corrupted, if your antivirus software is blocking the game, or if the game files are missing or damaged. To fix this issue, you can try the following solutions:

          -
            -
          • Check your system requirements and make sure that your PC meets the minimum or recommended specifications for the game.
            • -
          • Update your drivers and make sure that they are compatible with the game.
            • -
          • Disable your antivirus software temporarily and see if the game launches. If it does, add the game to the exception list of your antivirus software.
            • -
          • Verify the integrity of the game files by right-clicking on the game in your Steam library, selecting "Properties", clicking on the "Local Files" tab, and clicking on the "Verify Integrity of Game Files" button.
            • -
          -

          If none of these solutions work, you may need to reinstall the game or contact the customer support for further assistance.

          -
          • -
        • Game crashes: This issue may occur if your PC is overheating, if your settings are too high for your hardware, if your mods or cheats are conflicting with the game, or if there is a bug or glitch in the game. To fix this issue, you can try the following solutions:

          -
            -
          • Clean your PC and make sure that it is well-ventilated and cool.
            • -
          • Lower your settings and see if the game runs more smoothly and stably.
            • -
          • Disable any mods or cheats that you are using and see if the game works normally.
            • -
          • Update the game to the latest version and see if there are any patches or fixes for the issue.
            • -
          -

          If none of these solutions work, you may need to report the issue to the developers or wait for a future update that addresses it.

          -
          • -
        • Game lags: This issue may occur if your internet connection is slow or unstable

          that are not essential for your PC's functionality. You can also use a cleaner or optimizer tool to remove any junk or temporary files that are slowing down your PC.

          • -
        • Lower your settings and see if the game runs more smoothly and stably. You can also use the "Auto" option to let the game automatically adjust the settings based on your PC's capabilities.
          • -
        • Check the game's network status and see if there are any issues or maintenance with the servers or the online features. You can also contact the customer support or the community forums for more information or help.
          • -
        -

        If none of these solutions work, you may need to upgrade your PC or internet connection to meet the game's requirements or expectations.

        -
        • -
      -

      How to Enjoy Rune Factory 5 PC?

      -

      Rune Factory 5 PC is a game that offers a lot of fun and enjoyment for players who love farming, combat, crafting, dating, and exploration. However, you can also enhance your gameplay experience by following some tips and advice from experts and veterans. Here are some of the best tips, tricks, and advice on how to enjoy Rune Factory 5 PC:

      -
        -
      • Take your time: Rune Factory 5 PC is a game that does not have a strict time limit or deadline. You can play at your own pace and do whatever you want. You do not have to rush through the main story or complete all the quests. You can also skip or ignore any activities or features that you do not like or care about. You can also take breaks and save your progress whenever you want. The game is meant to be relaxing and enjoyable, not stressful or frustrating.
        • -
      • Experiment with different options: Rune Factory 5 PC is a game that offers a lot of choices and possibilities for you to explore. You can try different weapons, skills, items, equipment, and accessories to see what suits your style and preference. You can also try different crops, monsters, recipes, facilities, and decorations to see what works best for your farm. You can also try different characters, dialogues, events, and outfits to see what sparks your interest and romance. You can also try different areas, quests, secrets, and rewards to see what surprises and challenges you.
        • -
      • Interact with other players: Rune Factory 5 PC is a game that supports online multiplayer features that allow you to interact with other players around the world. You can join or host online sessions where you can chat, trade, cooperate, or compete with other players. You can also join or create online communities where you can share your experiences, opinions, tips, screenshots, videos, fan art, fan fiction, or anything related to the game. You can also participate in online events or contests where you can win prizes or recognition.
        • -
      • Have fun: Rune Factory 5 PC is a game that is designed to be fun and entertaining for players of all ages and backgrounds. You do not have to follow any rules or guidelines on how to play the game. You do not have to worry about any consequences or penalties for your actions or decisions. You do not have to compare yourself or compete with other players or standards. You just have to enjoy the game and have fun.
        • -
      -

      Conclusion

      -

      Rune Factory 5 PC is a game that combines farming, combat, crafting, dating, and exploration in a fantasy world. It is a game that offers a lot of content and variety for you to enjoy. It is a game that allows you to download it from Steam or other platforms, install it on your PC, optimize it for your hardware and preference, play it with your skills and tools, troubleshoot it if you encounter any issues or errors, and enjoy it with your choices and options.

      -

      If you are looking for a game that will keep you entertained and engaged for hours on end, then you should definitely try Rune Factory 5 PC. It is a game that will make you feel happy and satisfied with your gaming experience.

      -

      Frequently Asked Questions

      -

      Here are some of the frequently asked questions about Rune Factory 5 PC:

      -
        -
      • Q: How long is Rune Factory 5 PC?
        • -
      • A: Rune Factory 5 PC does not have a fixed length or duration. It depends on how you play the game and what you do in it. However, according to some estimates from players who have completed the game, it takes about 40-60 hours to finish the main story, about 80-100 hours to complete all the quests and events, and about 120-150 hours to do everything in the game.
        • -
      • Q: How many endings are there in Rune Factory 5 PC?
        • -
      • A: Rune Factory 5 PC has two main endings: a normal ending and a true ending. The normal ending is the default ending that you get by completing the main story. The true ending is a secret ending that you get by fulfilling certain conditions, such as having at least 10 hearts with your spouse, having a child, and completing all the town events. There are also different variations of the endings depending on your spouse and your choices.
        • -
      • Q: How many characters can you date in Rune Factory 5 PC?
        • -
      • A: Rune Factory 5 PC has 12 romanceable characters: six male and six female. You can date any of them regardless of your gender, but you can only marry one of them. The male characters are Lucas, Murakumo, Fuuka, Ludmilla, Reinhard, and Martin. The female characters are Scarlett, Beatrice, Lucy, Misasagi, Livia, and Simone.
        • -
      • Q: How can you get more money in Rune Factory 5 PC?
        • -
      • A: Rune Factory 5 PC has several ways to earn money, such as selling crops, items, or equipment, completing quests or missions, participating in festivals or events, or finding treasures or secrets. However, some of the best and fastest ways to get more money are:

        -
          -
        • Growing and selling golden crops, which are rare and valuable crops that you can get by using golden seeds or fertilizer.
          • -
        • Taming and selling rare monsters, which are powerful and unique monsters that you can find in certain dungeons or locations.
          • -
        • Crafting and selling high-quality equipment or accessories, which are enhanced and customized gear that you can make by using rare materials or skills.
          • -
        -
        • -
      • Q: How can you get more runes in Rune Factory 5 PC?
        • -
      • A: Runes are the energy that you use to perform actions such as farming, combat, crafting, or dating. You have a limited amount of runes that you can replenish by resting, eating, or using items. However, some of the best and easiest ways to get more runes are:

        -
          -
        • Using rune flowers, which are special flowers that grow on your farm or in other areas that restore your runes when you walk over them.
          • -
        • Using rune abilities, which are special skills that you can learn from books or NPCs that increase your rune capacity or regeneration.
          • -
        • Using rune stones, which are rare items that you can find in dungeons or other places that grant you a permanent boost to your runes.
          • -
        -
        • -
      -

      I hope this article has helped you learn how to download Rune Factory 5 PC and enjoy it to the fullest. If you have any questions or feedback, please feel free to leave a comment below. Thank you for reading and have fun!

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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. -# from fengshen.models.zen1 import ZenModel -from dataclasses import dataclass -from fengshen.models.megatron_t5 import T5EncoderModel -from fengshen.models.roformer import RoFormerModel -from fengshen.models.longformer import LongformerModel -# from fengshen.models.cocolm.modeling_cocolm import COCOLMForSequenceClassification -import numpy as np -import os -from tqdm import tqdm -import json -import torch -import pytorch_lightning as pl -import argparse -from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, LearningRateMonitor -from torch.utils.data import Dataset, DataLoader -from torch.utils.data._utils.collate import default_collate -from transformers import ( - BertModel, - BertConfig, - MegatronBertModel, - MegatronBertConfig, - AutoModel, - AutoConfig, - AutoTokenizer, - AutoModelForSequenceClassification, -) -# os.environ["CUDA_VISIBLE_DEVICES"] = '6' - - -model_dict = {'huggingface-bert': BertModel, - 'fengshen-roformer': RoFormerModel, - 'huggingface-megatron_bert': MegatronBertModel, - 'fengshen-megatron_t5': T5EncoderModel, - 'fengshen-longformer': LongformerModel, - # 'fengshen-zen1': ZenModel, - 'huggingface-auto': AutoModelForSequenceClassification, - } - - -class TaskDataset(Dataset): - def __init__(self, data_path, args, label2id): - super().__init__() - self.args = args - self.label2id = label2id - self.max_length = args.max_length - self.data = self.load_data(data_path, args) - - def __len__(self): - return len(self.data) - - def __getitem__(self, index): - return self.data[index] - - def load_data(self, data_path, args): - with open(data_path, 'r', encoding='utf8') as f: - lines = f.readlines() - samples = [] - for line in tqdm(lines): - data = json.loads(line) - text_id = int(data[args.id_name] - ) if args.id_name in data.keys() else 0 - texta = data[args.texta_name] if args.texta_name in data.keys( - ) else '' - textb = data[args.textb_name] if args.textb_name in data.keys( - ) else '' - labels = self.label2id[data[args.label_name] - ] if args.label_name in data.keys() else 0 - samples.append({args.texta_name: texta, args.textb_name: textb, - args.label_name: labels, 'id': text_id}) - return samples - - -@dataclass -class TaskCollator: - args = None - tokenizer = None - - def __call__(self, samples): - sample_list = [] - for item in samples: - if item[self.args.texta_name] != '' and item[self.args.textb_name] != '': - if self.args.model_type != 'fengshen-roformer': - encode_dict = self.tokenizer.encode_plus( - [item[self.args.texta_name], item[self.args.textb_name]], - max_length=self.args.max_length, - padding='max_length', - truncation='longest_first') - else: - encode_dict = self.tokenizer.encode_plus( - [item[self.args.texta_name] + - self.tokenizer.eos_token+item[self.args.textb_name]], - max_length=self.args.max_length, - padding='max_length', - truncation='longest_first') - else: - encode_dict = self.tokenizer.encode_plus( - item[self.args.texta_name], - max_length=self.args.max_length, - padding='max_length', - truncation='longest_first') - sample = {} - for k, v in encode_dict.items(): - sample[k] = torch.tensor(v) - sample['labels'] = torch.tensor(item[self.args.label_name]).long() - sample['id'] = item['id'] - sample_list.append(sample) - return default_collate(sample_list) - - -class TaskDataModel(pl.LightningDataModule): - @staticmethod - def add_data_specific_args(parent_args): - parser = parent_args.add_argument_group('TASK NAME DataModel') - parser.add_argument('--data_dir', default='./data', type=str) - parser.add_argument('--num_workers', default=8, type=int) - parser.add_argument('--train_data', default='train.json', type=str) - parser.add_argument('--valid_data', default='dev.json', type=str) - parser.add_argument('--test_data', default='test.json', type=str) - parser.add_argument('--train_batchsize', default=16, type=int) - parser.add_argument('--valid_batchsize', default=32, type=int) - parser.add_argument('--max_length', default=128, type=int) - - parser.add_argument('--texta_name', default='text', type=str) - parser.add_argument('--textb_name', default='sentence2', type=str) - parser.add_argument('--label_name', default='label', type=str) - parser.add_argument('--id_name', default='id', type=str) - - parser.add_argument('--dataset_name', default=None, type=str) - - return parent_args - - def __init__(self, args): - super().__init__() - self.train_batchsize = args.train_batchsize - self.valid_batchsize = args.valid_batchsize - self.tokenizer = AutoTokenizer.from_pretrained( - args.pretrained_model_path) - self.collator = TaskCollator() - self.collator.args = args - self.collator.tokenizer = self.tokenizer - if args.dataset_name is None: - self.label2id, self.id2label = self.load_schema(os.path.join( - args.data_dir, args.train_data), args) - self.train_data = TaskDataset(os.path.join( - args.data_dir, args.train_data), args, self.label2id) - self.valid_data = TaskDataset(os.path.join( - args.data_dir, args.valid_data), args, self.label2id) - self.test_data = TaskDataset(os.path.join( - args.data_dir, args.test_data), args, self.label2id) - else: - import datasets - ds = datasets.load_dataset(args.dataset_name) - self.train_data = ds['train'] - self.valid_data = ds['validation'] - self.test_data = ds['test'] - self.save_hyperparameters(args) - - def train_dataloader(self): - return DataLoader(self.train_data, shuffle=True, batch_size=self.train_batchsize, pin_memory=False, - collate_fn=self.collator) - - def val_dataloader(self): - return DataLoader(self.valid_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False, - collate_fn=self.collator) - - def predict_dataloader(self): - return DataLoader(self.test_data, shuffle=False, batch_size=self.valid_batchsize, pin_memory=False, - collate_fn=self.collator) - - def load_schema(self, data_path, args): - with open(data_path, 'r', encoding='utf8') as f: - lines = f.readlines() - label_list = [] - for line in tqdm(lines): - data = json.loads(line) - labels = data[args.label_name] if args.label_name in data.keys( - ) else 0 - if labels not in label_list: - label_list.append(labels) - - label2id, id2label = {}, {} - for i, k in enumerate(label_list): - label2id[k] = i - id2label[i] = k - return label2id, id2label - - -class taskModel(torch.nn.Module): - def __init__(self, args): - super().__init__() - self.args = args - print('args mode type:', args.model_type) - self.bert_encoder = model_dict[args.model_type].from_pretrained( - args.pretrained_model_path) - self.config = self.bert_encoder.config - self.cls_layer = torch.nn.Linear( - in_features=self.config.hidden_size, out_features=self.args.num_labels) - self.loss_func = torch.nn.CrossEntropyLoss() - - def forward(self, input_ids, attention_mask, token_type_ids, labels=None): - if self.args.model_type == 'fengshen-megatron_t5': - bert_output = self.bert_encoder( - input_ids=input_ids, attention_mask=attention_mask) # (bsz, seq, dim) - encode = bert_output.last_hidden_state[:, 0, :] - else: - bert_output = self.bert_encoder( - input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) # (bsz, seq, dim) - encode = bert_output[1] - logits = self.cls_layer(encode) - if labels is not None: - loss = self.loss_func(logits, labels.view(-1,)) - return loss, logits - else: - return 0, logits - - -class LitModel(pl.LightningModule): - - @staticmethod - def add_model_specific_args(parent_args): - parser = parent_args.add_argument_group('BaseModel') - parser.add_argument('--num_labels', default=2, type=int) - - return parent_args - - def __init__(self, args, num_data): - super().__init__() - self.args = args - self.num_data = num_data - self.model = model_dict[args.model_type].from_pretrained( - args.pretrained_model_path) - self.save_hyperparameters(args) - - def setup(self, stage) -> None: - train_loader = self.trainer._data_connector._train_dataloader_source.dataloader() - - # Calculate total steps - if self.trainer.max_epochs > 0: - world_size = self.trainer.world_size - tb_size = self.hparams.train_batchsize * max(1, world_size) - ab_size = self.trainer.accumulate_grad_batches - self.total_steps = (len(train_loader.dataset) * - self.trainer.max_epochs // tb_size) // ab_size - else: - self.total_steps = self.trainer.max_steps // self.trainer.accumulate_grad_batches - - print('Total steps: {}' .format(self.total_steps)) - - def training_step(self, batch, batch_idx): - del batch['id'] - output = self.model(**batch) - loss, logits = output[0], output[1] - acc = self.comput_metrix(logits, batch['labels']) - self.log('train_loss', loss) - self.log('train_acc', acc) - return loss - - def comput_metrix(self, logits, labels): - y_pred = torch.argmax(logits, dim=-1) - y_pred = y_pred.view(size=(-1,)) - y_true = labels.view(size=(-1,)).float() - corr = torch.eq(y_pred, y_true) - acc = torch.sum(corr.float())/labels.size()[0] - return acc - - def validation_step(self, batch, batch_idx): - del batch['id'] - output = self.model(**batch) - loss, logits = output[0], output[1] - acc = self.comput_metrix(logits, batch['labels']) - self.log('val_loss', loss) - self.log('val_acc', acc, sync_dist=True) - - def predict_step(self, batch, batch_idx): - ids = batch['id'] - del batch['id'] - output = self.model(**batch) - return {ids, output.logits} - - def configure_optimizers(self): - from fengshen.models.model_utils import configure_optimizers - return configure_optimizers(self) - - -class TaskModelCheckpoint: - @staticmethod - def add_argparse_args(parent_args): - parser = parent_args.add_argument_group('BaseModel') - - parser.add_argument('--monitor', default='train_loss', type=str) - parser.add_argument('--mode', default='min', type=str) - parser.add_argument('--dirpath', default='./log/', type=str) - parser.add_argument( - '--filename', default='model-{epoch:02d}-{train_loss:.4f}', type=str) - - parser.add_argument('--save_top_k', default=3, type=float) - parser.add_argument('--every_n_train_steps', default=100, type=float) - parser.add_argument('--save_weights_only', default=True, type=bool) - - return parent_args - - def __init__(self, args): - self.callbacks = ModelCheckpoint(monitor=args.monitor, - save_top_k=args.save_top_k, - mode=args.mode, - every_n_train_steps=args.every_n_train_steps, - save_weights_only=args.save_weights_only, - dirpath=args.dirpath, - every_n_epochs=1, - filename=args.filename) - - -def save_test(data, args, data_model, rank): - file_name = args.output_save_path + f'.{rank}' - with open(file_name, 'w', encoding='utf-8') as f: - idx = 0 - for i in range(len(data)): - ids, batch = data[i] - for id, sample in zip(ids, batch): - tmp_result = dict() - label_id = np.argmax(sample.cpu().numpy()) - tmp_result['id'] = id.item() - tmp_result['label'] = data_model.id2label[label_id] - json_data = json.dumps(tmp_result, ensure_ascii=False) - f.write(json_data+'\n') - idx += 1 - print('save the result to '+file_name) - - -def main(): - pl.seed_everything(42) - - total_parser = argparse.ArgumentParser("TASK NAME") - total_parser.add_argument('--pretrained_model_path', default='', type=str) - total_parser.add_argument('--output_save_path', - default='./predict.json', type=str) - total_parser.add_argument('--model_type', - default='huggingface-bert', type=str) - - # * Args for data preprocessing - total_parser = TaskDataModel.add_data_specific_args(total_parser) - # * Args for training - total_parser = pl.Trainer.add_argparse_args(total_parser) - total_parser = TaskModelCheckpoint.add_argparse_args(total_parser) - - # * Args for base model - from fengshen.models.model_utils import add_module_args - total_parser = add_module_args(total_parser) - total_parser = LitModel.add_model_specific_args(total_parser) - - args = total_parser.parse_args() - print(args.pretrained_model_path) - - checkpoint_callback = TaskModelCheckpoint(args).callbacks - early_stop_callback = EarlyStopping( - monitor="val_acc", min_delta=0.00, patience=5, verbose=False, mode="max") - lr_monitor = LearningRateMonitor(logging_interval='step') - trainer = pl.Trainer.from_argparse_args(args, - callbacks=[ - checkpoint_callback, - lr_monitor, - early_stop_callback] - ) - - data_model = TaskDataModel(args) - model = LitModel(args, len(data_model.train_dataloader())) - - trainer.fit(model, data_model) - result = trainer.predict( - model, data_model, ckpt_path=trainer.checkpoint_callback.best_model_path) - save_test(result, args, data_model, trainer.global_rank) - - -if __name__ == "__main__": - main() diff --git a/spaces/sklearn-docs/Nearest_Neighbor_Regression/README.md b/spaces/sklearn-docs/Nearest_Neighbor_Regression/README.md deleted file mode 100644 index 6f1723139a613525d829f29682aae444c2809ab9..0000000000000000000000000000000000000000 --- a/spaces/sklearn-docs/Nearest_Neighbor_Regression/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Nearest Neighbor Regression -emoji: 🏢 -colorFrom: green -colorTo: red -sdk: gradio -sdk_version: 3.29.0 -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/spark-ds549/fal2022-videoanalysis-v2/slowfast.py b/spaces/spark-ds549/fal2022-videoanalysis-v2/slowfast.py deleted file mode 100644 index b438c4344c8583c5e0872b1612582c3dfeddb58a..0000000000000000000000000000000000000000 --- a/spaces/spark-ds549/fal2022-videoanalysis-v2/slowfast.py +++ /dev/null @@ -1,191 +0,0 @@ -import numpy as np -import pandas as pd -import cv2 -import torch -import warnings -from detectron2.config import get_cfg -from detectron2 import model_zoo -from detectron2.engine import DefaultPredictor -import ffmpeg -import pytorchvideo -from pytorchvideo.transforms.functional import ( - uniform_temporal_subsample, - short_side_scale_with_boxes, - clip_boxes_to_image -) -from torchvision.transforms._functional_video import normalize -from pytorchvideo.data.ava import AvaLabeledVideoFramePaths -from pytorchvideo.models.hub import slowfast_r50_detection # Another option is slow_r50_detection -from visualization import VideoVisualizer - - -# This method takes in an image and generates the bounding boxes for people in the image. -def get_person_bboxes(inp_img, predictor): - predictions = predictor(inp_img.cpu().detach().numpy())['instances'].to('cpu') - boxes = predictions.pred_boxes if predictions.has("pred_boxes") else None - scores = predictions.scores if predictions.has("scores") else None - classes = np.array(predictions.pred_classes.tolist() if predictions.has("pred_classes") else None) - predicted_boxes = boxes[np.logical_and(classes==0, scores>0.75 )].tensor.cpu() # only person - return predicted_boxes - - -def ava_inference_transform( - clip, - boxes, - num_frames = 32, # 4 if using slowfast_r50_detection, change this to 32 - crop_size = 256, - data_mean = [0.45, 0.45, 0.45], - data_std = [0.225, 0.225, 0.225], - slow_fast_alpha = 4, # if using slowfast_r50_detection, change None to 4 - device = 'cpu'): - - boxes = np.array(boxes) - ori_boxes = boxes.copy() - - # Image [0, 255] -> [0, 1]. - clip = uniform_temporal_subsample(clip, num_frames) - clip = clip.float() - clip = clip / 255.0 - - height, width = clip.shape[2], clip.shape[3] - # The format of boxes is [x1, y1, x2, y2]. The input boxes are in the - # range of [0, width] for x and [0,height] for y - boxes = clip_boxes_to_image(boxes, height, width) - - # Resize short side to crop_size. Non-local and STRG uses 256. - clip, boxes = short_side_scale_with_boxes(clip, size=crop_size, boxes=boxes) - - # Normalize images by mean and std. - clip = normalize(clip, np.array(data_mean, dtype=np.float32), np.array(data_std, dtype=np.float32)) - - boxes = clip_boxes_to_image(boxes, clip.shape[2], clip.shape[3]) - - # Incase of slowfast, generate both pathways - if slow_fast_alpha is not None: - fast_pathway = clip - # Perform temporal sampling from the fast pathway. - slow_pathway = torch.index_select(clip, 1, torch.linspace( - 0, clip.shape[1] - 1, clip.shape[1] // slow_fast_alpha).long()) - clip = [slow_pathway.unsqueeze(0).to(device), fast_pathway.unsqueeze(0).to(device)] - - return clip, torch.from_numpy(boxes), ori_boxes - -# get video info -def with_opencv(filename): - video = cv2.VideoCapture(filename) - frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT) - fps = video.get(cv2.CAP_PROP_FPS) - s = round(frame_count / fps) - video.release() - return int(s), fps - - -def slow_fast_train(file_path, gpu=False): - device = 'cuda' if gpu else 'cpu' - top_k = 1 - - video_model = slowfast_r50_detection(True) # Another option is slow_r50_detection(True) - video_model = video_model.eval().to(device) - cfg = get_cfg() - cfg.merge_from_file(model_zoo.get_config_file("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml")) - cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.55 # set threshold for this model - cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml") - cfg.MODEL.DEVICE = device - predictor = DefaultPredictor(cfg) - # Create an id to label name mapping - label_map, allowed_class_ids = AvaLabeledVideoFramePaths.read_label_map('ava_action_list.pbtxt') - # Create a video visualizer that can plot bounding boxes and visualize actions on bboxes. - video_visualizer = VideoVisualizer(81, label_map, top_k=top_k, mode="thres",thres=0.5) #get top3 predictions show in each bounding box - - #preprocess video data - encoded_vid = pytorchvideo.data.encoded_video.EncodedVideo.from_path(file_path) - - # Video predictions are generated each frame/second for the wholevideo. - total_sec, fps = with_opencv(file_path) - time_stamp_range = range(0, total_sec) # time stamps in video for which clip is sampled - clip_duration = 1.0 # Duration of clip used for each inference step. - gif_imgs = [] - xleft, ytop, xright, ybottom = [], [], [], [] - labels = [] - time_frame = [] - scores = [] - - for time_stamp in time_stamp_range: - - # Generate clip around the designated time stamps - inp_imgs = encoded_vid.get_clip( - time_stamp - clip_duration/2.0, - time_stamp + clip_duration/2.0) - inp_imgs = inp_imgs['video'] - - #if time_stamp % 15 == 0: - # Generate people bbox predictions using Detectron2's off the self pre-trained predictor - # We use the the middle image in each clip to generate the bounding boxes. - inp_img = inp_imgs[:,inp_imgs.shape[1]//2,:,:] - inp_img = inp_img.permute(1,2,0) - - # Predicted boxes are of the form List[(x_1, y_1, x_2, y_2)] - predicted_boxes = get_person_bboxes(inp_img, predictor) - if len(predicted_boxes) == 0: - print("Skipping clip no frames detected at time stamp: ", time_stamp) - continue - - # Preprocess clip and bounding boxes for video action recognition. - inputs, inp_boxes, _ = ava_inference_transform(inp_imgs, predicted_boxes.numpy(), device=device) - # Prepend data sample id for each bounding box. - # For more details refere to the RoIAlign in Detectron2 - inp_boxes = torch.cat([torch.zeros(inp_boxes.shape[0],1), inp_boxes], dim=1) - - # Generate actions predictions for the bounding boxes in the clip. - # The model here takes in the pre-processed video clip and the detected bounding boxes. - preds = video_model(inputs, inp_boxes.to(device)) #change inputs to inputs.unsqueeze(0).to(device) if using slow_r50 - - preds = preds.to('cpu') - # The model is trained on AVA and AVA labels are 1 indexed so, prepend 0 to convert to 0 index. - preds = torch.cat([torch.zeros(preds.shape[0],1), preds], dim=1) - - # Plot predictions on the video and save for later visualization. - inp_imgs = inp_imgs.permute(1,2,3,0) - inp_imgs = inp_imgs/255.0 - out_img_pred = video_visualizer.draw_clip_range(inp_imgs, preds, predicted_boxes) - gif_imgs += out_img_pred - - #format of bboxes(x_left, y_top, x_right, y_bottom) - predicted_boxes_lst = predicted_boxes.tolist() - topscores, topclasses = torch.topk(preds, k=1) - topscores, topclasses = topscores.tolist(), topclasses.tolist() - topclasses = np.concatenate(topclasses) - topscores = np.concatenate(topscores) - - #add top 1 prediction of behaviors in each time step - for i in range(len(predicted_boxes_lst)): - xleft.append(predicted_boxes_lst[i][0]) - ytop.append(predicted_boxes_lst[i][1]) - xright.append(predicted_boxes_lst[i][2]) - ybottom.append(predicted_boxes_lst[i][3]) - labels.append(label_map.get(topclasses[i])) - time_frame.append(time_stamp) - scores.append(topscores[i]) - - print("Finished generating predictions.") - # Generate Metadata file - metadata = pd.DataFrame() - metadata['frame'] = time_frame - metadata['x_left'] = xleft - metadata['y_top'] = ytop - metadata['x_right'] = xright - metadata['y_bottom'] = ybottom - metadata['label'] = labels - metadata['confidence'] = scores - - height, width = gif_imgs[0].shape[0], gif_imgs[0].shape[1] - video_save_path = 'activity_recognition.mp4' - video = cv2.VideoWriter(video_save_path, cv2.VideoWriter_fourcc(*'mp4v'), int(fps), (width, height)) - - for image in gif_imgs: - img = (255*image).astype(np.uint8) - img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) - video.write(img) - video.release() - - return video_save_path, metadata diff --git a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py b/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py deleted file mode 100644 index 2c87445d810cd790f887d1a135287a334cbdf223..0000000000000000000000000000000000000000 --- a/spaces/sriramelango/Social_Classification_Public/fairseq/examples/textless_nlp/gslm/speech2unit/clustering/quantize_with_kmeans.py +++ /dev/null @@ -1,125 +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. - -import argparse -import logging -import os - -import numpy as np - -import joblib -from examples.textless_nlp.gslm.speech2unit.clustering.utils import ( - get_audio_files, -) -from examples.textless_nlp.gslm.speech2unit.pretrained.utils import ( - get_features, -) - - -def get_logger(): - log_format = "[%(asctime)s] [%(levelname)s]: %(message)s" - logging.basicConfig(format=log_format, level=logging.INFO) - logger = logging.getLogger(__name__) - return logger - - -def get_parser(): - parser = argparse.ArgumentParser( - description="Quantize using K-means clustering over acoustic features." - ) - parser.add_argument( - "--feature_type", - type=str, - choices=["logmel", "hubert", "w2v2", "cpc"], - default=None, - required=True, - help="Acoustic feature type", - ) - parser.add_argument( - "--acoustic_model_path", - type=str, - help="Pretrained acoustic model checkpoint" - ) - parser.add_argument( - "--layer", - type=int, - help="The layer of the pretrained model to extract features from", - default=-1, - ) - parser.add_argument( - "--kmeans_model_path", - type=str, - required=True, - help="K-means model file path to use for inference", - ) - parser.add_argument( - "--features_path", - type=str, - default=None, - help="Features file path. You don't need to enter acoustic model details if you have dumped features", - ) - parser.add_argument( - "--manifest_path", - type=str, - default=None, - help="Manifest file containing the root dir and file names", - ) - parser.add_argument( - "--out_quantized_file_path", - required=True, - type=str, - help="File path of quantized output.", - ) - parser.add_argument( - "--extension", type=str, default=".flac", help="Features file path" - ) - return parser - - -def main(args, logger): - # Feature extraction - if args.features_path is not None: - logger.info(f"Loading acoustic features from {args.features_path}...") - features_batch = np.load(args.features_path) - else: - logger.info(f"Extracting {args.feature_type} acoustic features...") - features_batch = get_features( - feature_type=args.feature_type, - checkpoint_path=args.acoustic_model_path, - layer=args.layer, - manifest_path=args.manifest_path, - sample_pct=1.0, - flatten=False, - ) - logger.info( - f"Features extracted for {len(features_batch)} utterances.\n" - ) - logger.info( - f"Dimensionality of representation = {features_batch[0].shape[1]}" - ) - - # K-means model - logger.info(f"Loading K-means model from {args.kmeans_model_path} ...") - kmeans_model = joblib.load(open(args.kmeans_model_path, "rb")) - kmeans_model.verbose = False - - _, fnames, _ = get_audio_files(args.manifest_path) - - os.makedirs(os.path.dirname(args.out_quantized_file_path), exist_ok=True) - print(f"Writing quantized predictions to {args.out_quantized_file_path}") - with open(args.out_quantized_file_path, "w") as fout: - for i, feats in enumerate(features_batch): - pred = kmeans_model.predict(feats) - pred_str = " ".join(str(p) for p in pred) - base_fname = os.path.basename(fnames[i]).rstrip(args.extension) - fout.write(f"{base_fname}|{pred_str}\n") - - -if __name__ == "__main__": - parser = get_parser() - args = parser.parse_args() - logger = get_logger() - logger.info(args) - main(args, logger) diff --git a/spaces/stabilityai/stable-diffusion/share_btn.py b/spaces/stabilityai/stable-diffusion/share_btn.py deleted file mode 100644 index 4c9aa8a91b1d0f86746fb118c19b03df86d424a3..0000000000000000000000000000000000000000 --- a/spaces/stabilityai/stable-diffusion/share_btn.py +++ /dev/null @@ -1,60 +0,0 @@ -community_icon_html = """""" - -loading_icon_html = """""" - -share_js = """async () => { - async function uploadFile(file){ - const UPLOAD_URL = 'https://huggingface.co/uploads'; - const response = await fetch(UPLOAD_URL, { - method: 'POST', - headers: { - 'Content-Type': file.type, - 'X-Requested-With': 'XMLHttpRequest', - }, - body: file, /// <- File inherits from Blob - }); - const url = await response.text(); - return url; - } - const gradioEl = document.querySelector('body > gradio-app'); - const imgEls = gradioEl.querySelectorAll('#gallery img'); - const promptTxt = gradioEl.querySelector('#prompt-text-input input').value; - const shareBtnEl = gradioEl.querySelector('#share-btn'); - const shareIconEl = gradioEl.querySelector('#share-btn-share-icon'); - const loadingIconEl = gradioEl.querySelector('#share-btn-loading-icon'); - if(!imgEls.length){ - return; - }; - shareBtnEl.style.pointerEvents = 'none'; - shareIconEl.style.display = 'none'; - loadingIconEl.style.removeProperty('display'); - const files = await Promise.all( - [...imgEls].map(async (imgEl) => { - const res = await fetch(imgEl.src); - const blob = await res.blob(); - const imgId = Date.now() % 200; - const fileName = `diffuse-the-rest-${{imgId}}.jpg`; - return new File([blob], fileName, { type: 'image/jpeg' }); - }) - ); - const urls = await Promise.all(files.map((f) => uploadFile(f))); - const htmlImgs = urls.map(url => ``); - const descriptionMd = `
      -${htmlImgs.join(`\n`)} -
      `; - const params = new URLSearchParams({ - title: promptTxt, - description: descriptionMd, - }); - const paramsStr = params.toString(); - window.open(`https://huggingface.co/spaces/stabilityai/stable-diffusion/discussions/new?${paramsStr}`, '_blank'); - shareBtnEl.style.removeProperty('pointer-events'); - shareIconEl.style.removeProperty('display'); - loadingIconEl.style.display = 'none'; -}""" \ No newline at end of file diff --git a/spaces/stomexserde/gpt4-ui/Examples/Bhaag Milkha Bhaag Hindi Dubbed 720p Movies.md b/spaces/stomexserde/gpt4-ui/Examples/Bhaag Milkha Bhaag Hindi Dubbed 720p Movies.md deleted file mode 100644 index fea62ab5a4793d6bd67ef9eb3ce80b161e3d748f..0000000000000000000000000000000000000000 --- a/spaces/stomexserde/gpt4-ui/Examples/Bhaag Milkha Bhaag Hindi Dubbed 720p Movies.md +++ /dev/null @@ -1,25 +0,0 @@ -
      -Here is a possible title and article with SEO optimization and HTML formatting for the keyword "Bhaag Milkha Bhaag Hindi Dubbed 720p Movies": - -

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      \ No newline at end of file diff --git a/spaces/stratussox/yolov5_inference/utils/aws/resume.py b/spaces/stratussox/yolov5_inference/utils/aws/resume.py deleted file mode 100644 index b21731c979a121ab8227280351b70d6062efd983..0000000000000000000000000000000000000000 --- a/spaces/stratussox/yolov5_inference/utils/aws/resume.py +++ /dev/null @@ -1,40 +0,0 @@ -# Resume all interrupted trainings in yolov5/ dir including DDP trainings -# Usage: $ python utils/aws/resume.py - -import os -import sys -from pathlib import Path - -import torch -import yaml - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[2] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -port = 0 # --master_port -path = Path('').resolve() -for last in path.rglob('*/**/last.pt'): - ckpt = torch.load(last) - if ckpt['optimizer'] is None: - continue - - # Load opt.yaml - with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: - opt = yaml.safe_load(f) - - # Get device count - d = opt['device'].split(',') # devices - nd = len(d) # number of devices - ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel - - if ddp: # multi-GPU - port += 1 - cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' - else: # single-GPU - cmd = f'python train.py --resume {last}' - - cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread - print(cmd) - os.system(cmd) diff --git a/spaces/sub314xxl/MusicGen-Continuation/audiocraft/models/__init__.py b/spaces/sub314xxl/MusicGen-Continuation/audiocraft/models/__init__.py deleted file mode 100644 index 92c7a48a200eba455044cd66e0d2c1efe6494f5c..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/MusicGen-Continuation/audiocraft/models/__init__.py +++ /dev/null @@ -1,10 +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. - -# flake8: noqa -from .musicgen import MusicGen -from .lm import LMModel -from .encodec import CompressionModel, EncodecModel diff --git a/spaces/sub314xxl/MusicGen-Continuation/audiocraft/modules/streaming.py b/spaces/sub314xxl/MusicGen-Continuation/audiocraft/modules/streaming.py deleted file mode 100644 index fdbdf5e90fc0c6560873d66bf273460b38e5ed7e..0000000000000000000000000000000000000000 --- a/spaces/sub314xxl/MusicGen-Continuation/audiocraft/modules/streaming.py +++ /dev/null @@ -1,135 +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. - -""" -Streaming module API that should be implemented by all Streaming components, -""" - -from contextlib import contextmanager -import typing as tp -from torch import nn -import torch - - -State = tp.Dict[str, torch.Tensor] - - -class StreamingModule(nn.Module): - """Common API for streaming components. - - Each streaming component has a streaming state, which is just a dict[str, Tensor]. - By convention, the first dim of each tensor must be the batch size. - Don't use dots in the key names, as this would clash with submodules - (like in state_dict). - - If `self._is_streaming` is True, the component should use and remember - the proper state inside `self._streaming_state`. - - To set a streaming component in streaming state, use - - with module.streaming(): - ... - - This will automatically reset the streaming state when exiting the context manager. - This also automatically propagates to all streaming children module. - - Some module might also implement the `StreamingModule.flush` method, although - this one is trickier, as all parents module must be StreamingModule and implement - it as well for it to work properly. See `StreamingSequential` after. - """ - def __init__(self) -> None: - super().__init__() - self._streaming_state: State = {} - self._is_streaming = False - - def _apply_named_streaming(self, fn: tp.Any): - for name, module in self.named_modules(): - if isinstance(module, StreamingModule): - fn(name, module) - - def _set_streaming(self, streaming: bool): - def _set_streaming(name, module): - module._is_streaming = streaming - self._apply_named_streaming(_set_streaming) - - @contextmanager - def streaming(self): - """Context manager to enter streaming mode. Reset streaming state on exit. - """ - self._set_streaming(True) - try: - yield - finally: - self._set_streaming(False) - self.reset_streaming() - - def reset_streaming(self): - """Reset the streaming state. - """ - def _reset(name: str, module: StreamingModule): - module._streaming_state.clear() - - self._apply_named_streaming(_reset) - - def get_streaming_state(self) -> State: - """Return the streaming state, including that of sub-modules. - """ - state: State = {} - - def _add(name: str, module: StreamingModule): - if name: - name += "." - for key, value in module._streaming_state.items(): - state[name + key] = value - - self._apply_named_streaming(_add) - return state - - def set_streaming_state(self, state: State): - """Set the streaming state, including that of sub-modules. - """ - state = dict(state) - - def _set(name: str, module: StreamingModule): - if name: - name += "." - module._streaming_state.clear() - for key, value in list(state.items()): - # complexity is not ideal here, but probably fine. - if key.startswith(name): - local_key = key[len(name):] - if '.' not in local_key: - module._streaming_state[local_key] = value - del state[key] - - self._apply_named_streaming(_set) - assert len(state) == 0, list(state.keys()) - - def flush(self, x: tp.Optional[torch.Tensor] = None): - """Flush any remaining outputs that were waiting for completion. - Typically, for convolutions, this will add the final padding - and process the last buffer. - - This should take an optional argument `x`, which will be provided - if a module before this one in the streaming pipeline has already - spitted out a flushed out buffer. - """ - if x is None: - return None - else: - return self(x) - - -class StreamingSequential(StreamingModule, nn.Sequential): - """A streaming compatible alternative of `nn.Sequential`. - """ - def flush(self, x: tp.Optional[torch.Tensor] = None): - for module in self: - if isinstance(module, StreamingModule): - x = module.flush(x) - elif x is not None: - x = module(x) - return x diff --git a/spaces/suppsumstagza/text-to-image-stable-diffusion-v1-5/scripts/Ample Sound Keygen Challenge Code. 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return canvas diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/openpose/body.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/openpose/body.py deleted file mode 100644 index 7c3cf7a388b4ac81004524e64125e383bdd455bd..0000000000000000000000000000000000000000 --- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/openpose/body.py +++ /dev/null @@ -1,219 +0,0 @@ -import cv2 -import numpy as np -import math -import time -from scipy.ndimage.filters import gaussian_filter -import matplotlib.pyplot as plt -import matplotlib -import torch -from torchvision import transforms - -from . import util -from .model import bodypose_model - -class Body(object): - def __init__(self, model_path): - self.model = bodypose_model() - if torch.cuda.is_available(): - self.model = self.model.cuda() - print('cuda') - model_dict = util.transfer(self.model, torch.load(model_path)) - self.model.load_state_dict(model_dict) - self.model.eval() - - def __call__(self, oriImg): - # scale_search = [0.5, 1.0, 1.5, 2.0] - scale_search = [0.5] - boxsize = 368 - stride = 8 - padValue = 128 - thre1 = 0.1 - thre2 = 0.05 - multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search] - heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19)) - paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38)) - - for m in range(len(multiplier)): - scale = multiplier[m] - imageToTest = cv2.resize(oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC) - imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue) - im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5 - im = np.ascontiguousarray(im) - - data = torch.from_numpy(im).float() - if torch.cuda.is_available(): - data = data.cuda() - # data = data.permute([2, 0, 1]).unsqueeze(0).float() - with torch.no_grad(): - Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data) - Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy() - Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy() - - # extract outputs, resize, and remove padding - # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0)) # output 1 is heatmaps - heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0)) # output 1 is heatmaps - heatmap = cv2.resize(heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) - heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] - heatmap = cv2.resize(heatmap, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) - - # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0)) # output 0 is PAFs - paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0)) # output 0 is PAFs - paf = cv2.resize(paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC) - paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :] - paf = cv2.resize(paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC) - - heatmap_avg += heatmap_avg + heatmap / len(multiplier) - paf_avg += + paf / len(multiplier) - - all_peaks = [] - peak_counter = 0 - - for part in range(18): - map_ori = heatmap_avg[:, :, part] - one_heatmap = gaussian_filter(map_ori, sigma=3) - - map_left = np.zeros(one_heatmap.shape) - map_left[1:, :] = one_heatmap[:-1, :] - map_right = np.zeros(one_heatmap.shape) - map_right[:-1, :] = one_heatmap[1:, :] - map_up = np.zeros(one_heatmap.shape) - map_up[:, 1:] = one_heatmap[:, :-1] - map_down = np.zeros(one_heatmap.shape) - map_down[:, :-1] = one_heatmap[:, 1:] - - peaks_binary = np.logical_and.reduce( - (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1)) - peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])) # note reverse - peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks] - peak_id = range(peak_counter, peak_counter + len(peaks)) - peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))] - - all_peaks.append(peaks_with_score_and_id) - peak_counter += len(peaks) - - # find connection in the specified sequence, center 29 is in the position 15 - limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ - [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ - [1, 16], [16, 18], [3, 17], [6, 18]] - # the middle joints heatmap correpondence - mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \ - [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \ - [55, 56], [37, 38], [45, 46]] - - connection_all = [] - special_k = [] - mid_num = 10 - - for k in range(len(mapIdx)): - score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]] - candA = all_peaks[limbSeq[k][0] - 1] - candB = all_peaks[limbSeq[k][1] - 1] - nA = len(candA) - nB = len(candB) - indexA, indexB = limbSeq[k] - if (nA != 0 and nB != 0): - connection_candidate = [] - for i in range(nA): - for j in range(nB): - vec = np.subtract(candB[j][:2], candA[i][:2]) - norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1]) - norm = max(0.001, norm) - vec = np.divide(vec, norm) - - startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \ - np.linspace(candA[i][1], candB[j][1], num=mid_num))) - - vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \ - for I in range(len(startend))]) - vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \ - for I in range(len(startend))]) - - score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1]) - score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min( - 0.5 * oriImg.shape[0] / norm - 1, 0) - criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts) - criterion2 = score_with_dist_prior > 0 - if criterion1 and criterion2: - connection_candidate.append( - [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]]) - - connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True) - connection = np.zeros((0, 5)) - for c in range(len(connection_candidate)): - i, j, s = connection_candidate[c][0:3] - if (i not in connection[:, 3] and j not in connection[:, 4]): - connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]]) - if (len(connection) >= min(nA, nB)): - break - - connection_all.append(connection) - else: - special_k.append(k) - connection_all.append([]) - - # last number in each row is the total parts number of that person - # the second last number in each row is the score of the overall configuration - subset = -1 * np.ones((0, 20)) - candidate = np.array([item for sublist in all_peaks for item in sublist]) - - for k in range(len(mapIdx)): - if k not in special_k: - partAs = connection_all[k][:, 0] - partBs = connection_all[k][:, 1] - indexA, indexB = np.array(limbSeq[k]) - 1 - - for i in range(len(connection_all[k])): # = 1:size(temp,1) - found = 0 - subset_idx = [-1, -1] - for j in range(len(subset)): # 1:size(subset,1): - if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]: - subset_idx[found] = j - found += 1 - - if found == 1: - j = subset_idx[0] - if subset[j][indexB] != partBs[i]: - subset[j][indexB] = partBs[i] - subset[j][-1] += 1 - subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] - elif found == 2: # if found 2 and disjoint, merge them - j1, j2 = subset_idx - membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2] - if len(np.nonzero(membership == 2)[0]) == 0: # merge - subset[j1][:-2] += (subset[j2][:-2] + 1) - subset[j1][-2:] += subset[j2][-2:] - subset[j1][-2] += connection_all[k][i][2] - subset = np.delete(subset, j2, 0) - else: # as like found == 1 - subset[j1][indexB] = partBs[i] - subset[j1][-1] += 1 - subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2] - - # if find no partA in the subset, create a new subset - elif not found and k < 17: - row = -1 * np.ones(20) - row[indexA] = partAs[i] - row[indexB] = partBs[i] - row[-1] = 2 - row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2] - subset = np.vstack([subset, row]) - # delete some rows of subset which has few parts occur - deleteIdx = [] - for i in range(len(subset)): - if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4: - deleteIdx.append(i) - subset = np.delete(subset, deleteIdx, axis=0) - - # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts - # candidate: x, y, score, id - return candidate, subset - -if __name__ == "__main__": - body_estimation = Body('../model/body_pose_model.pth') - - test_image = '../images/ski.jpg' - oriImg = cv2.imread(test_image) # B,G,R order - candidate, subset = body_estimation(oriImg) - canvas = util.draw_bodypose(oriImg, candidate, subset) - plt.imshow(canvas[:, :, [2, 1, 0]]) - plt.show() diff --git a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/builder.py b/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/builder.py deleted file mode 100644 index 77c96ba0b2f30ead9da23f293c5dc84dd3e4a74f..0000000000000000000000000000000000000000 --- a/spaces/svjack/ControlNet-Pose-Chinese/annotator/uniformer/mmcv/runner/builder.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -import copy - -from ..utils import Registry - -RUNNERS = Registry('runner') -RUNNER_BUILDERS = Registry('runner builder') - - -def build_runner_constructor(cfg): - return RUNNER_BUILDERS.build(cfg) - - -def build_runner(cfg, default_args=None): - runner_cfg = copy.deepcopy(cfg) - constructor_type = runner_cfg.pop('constructor', - 'DefaultRunnerConstructor') - runner_constructor = build_runner_constructor( - dict( - type=constructor_type, - runner_cfg=runner_cfg, - default_args=default_args)) - runner = runner_constructor() - return runner diff --git a/spaces/t110-ai-admin/InspectLens/video_llama/tasks/image_text_pretrain.py b/spaces/t110-ai-admin/InspectLens/video_llama/tasks/image_text_pretrain.py deleted file mode 100644 index db955f27bb7dc8093cffd95b3a26917bb681c846..0000000000000000000000000000000000000000 --- a/spaces/t110-ai-admin/InspectLens/video_llama/tasks/image_text_pretrain.py +++ /dev/null @@ -1,18 +0,0 @@ -""" - Copyright (c) 2022, salesforce.com, inc. - All rights reserved. - SPDX-License-Identifier: BSD-3-Clause - For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause -""" - -from video_llama.common.registry import registry -from video_llama.tasks.base_task import BaseTask - - -@registry.register_task("image_text_pretrain") -class ImageTextPretrainTask(BaseTask): - def __init__(self): - super().__init__() - - def evaluation(self, model, data_loader, cuda_enabled=True): - pass diff --git a/spaces/t13718236382/bingoGPT4/src/lib/hooks/chat-history.ts b/spaces/t13718236382/bingoGPT4/src/lib/hooks/chat-history.ts deleted file mode 100644 index c6fbf3fecfa86fe553f56acc8253236b8f22a775..0000000000000000000000000000000000000000 --- a/spaces/t13718236382/bingoGPT4/src/lib/hooks/chat-history.ts +++ /dev/null @@ -1,62 +0,0 @@ -import { zip } from 'lodash-es' -import { ChatMessageModel, BotId } from '@/lib/bots/bing/types' -import { Storage } from '../storage' - -/** - * conversations:$botId => Conversation[] - * conversation:$botId:$cid:messages => ChatMessageModel[] - */ - -interface Conversation { - id: string - createdAt: number -} - -type ConversationWithMessages = Conversation & { messages: ChatMessageModel[] } - -async function loadHistoryConversations(botId: BotId): Promise { - const key = `conversations:${botId}` - const { [key]: value } = await Storage.get(key) - return value || [] -} - -async function deleteHistoryConversation(botId: BotId, cid: string) { - const conversations = await loadHistoryConversations(botId) - const newConversations = conversations.filter((c) => c.id !== cid) - await Storage.set({ [`conversations:${botId}`]: newConversations }) -} - -async function loadConversationMessages(botId: BotId, cid: string): Promise { - const key = `conversation:${botId}:${cid}:messages` - const { [key]: value } = await Storage.get(key) - return value || [] -} - -export async function setConversationMessages(botId: BotId, cid: string, messages: ChatMessageModel[]) { - const conversations = await loadHistoryConversations(botId) - if (!conversations.some((c) => c.id === cid)) { - conversations.unshift({ id: cid, createdAt: Date.now() }) - await Storage.set({ [`conversations:${botId}`]: conversations }) - } - const key = `conversation:${botId}:${cid}:messages` - await Storage.set({ [key]: messages }) -} - -export async function loadHistoryMessages(botId: BotId): Promise { - const conversations = await loadHistoryConversations(botId) - const messagesList = await Promise.all(conversations.map((c) => loadConversationMessages(botId, c.id))) - return zip(conversations, messagesList).map(([c, messages]) => ({ - id: c!.id, - createdAt: c!.createdAt, - messages: messages!, - })) -} - -export async function deleteHistoryMessage(botId: BotId, conversationId: string, messageId: string) { - const messages = await loadConversationMessages(botId, conversationId) - const newMessages = messages.filter((m) => m.id !== messageId) - await setConversationMessages(botId, conversationId, newMessages) - if (!newMessages.length) { - await deleteHistoryConversation(botId, conversationId) - 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      Aankhon Mein Tera Hi Chehra: A Romantic Song by Kumar Sanu

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      Aankhon Mein Tera Hi Chehra is a classic Hindi song from the film Aryan: Unbreakable, released in 2006. The song is sung by the legendary singer Kumar Sanu, who is known for his melodious voice and romantic songs. The song features Shahid Kapoor and Hrishitaa Bhatt as the lead pair, who fall in love at first sight. The song is composed by Anand Raj Anand and written by Anand Bakshi.

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      Blade And Soul Command List: A Guide to Emotes and Chat Commands

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      Blade And Soul is a popular MMORPG that features a dynamic combat system and a rich world of martial arts. Whether you want to express yourself, communicate with other players, or perform some actions, you can use various commands in the game. In this article, we will show you a Blade And Soul command list that covers emotes and chat commands.

      - -

      Emotes

      - -

      Emotes are animations that your character can perform to show different emotions or gestures. You can use emotes by typing a slash (/) followed by the name of the emote, or by using some keywords without a slash in the chat. You can also press F12 to open the emote menu and select an emote from there.

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      Blade And Soul Command List


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      Here is a Blade And Soul command list of emotes and their triggers:

      - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
      EmoteCommandTrigger
      Bow/bow or /greetHi or Hello
      Applause/applauseN/A
      Tease/flirt or /loveLove or Kiss
      Love/loveN/A
      Wave/waveN/A
      Kiss/kissN/A
      Sorry/sorrySorry
      Cry or Sad/cry or /sadN/A
      Sigh or Bored/sigh or /boredBored or Sigh
      Scare or Afraid/scare or /afraid or /scary N/A
      Cheer or Bravo /cheer or /bravo Haha, Lol, Yes, Yay, Woot
      Grr or Angry /grr Angry or Mad
      Scary /scary N/A
      Charge /charge Charge
      Stop or Wait < - td>/stop < - td>Stop or Wait < - tr>< - td>Look < - td>/look < - td>N/A < - tr>< - td>Honor < - td>/honor < - td>Honor < - tr>< - td>Dance 1 < - td>/dance < - td>N/A < - tr>< - td>Dance 2 < - td>/dance2 < - td>N/A
      - -

      Chat Commands< - -/h2> - -

      Chat commands are commands that you can use in the chat window to perform some actions or access some features. You can use chat commands by typing a slash (/) followed by the name of the command and any parameters if needed.

      - -

      Here is a Blade And Soul command list of chat commands and their functions:

      - - - - - - - - - -
      Command - -/th> - -Function - -/th> - -/tr> - -
      /w [name] [message] - -/td> - -Sends a whisper message to another player. - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/ - -/tr> - -
      / - -

      Gameplay Tips

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      Blade And Soul is not your typical MMORPG that relies on stats and gear to determine your success. Instead, it requires skill, timing, and strategy to master the combat system and overcome the challenges. Here are some gameplay tips that can help you improve your performance and enjoy the game more.

      - -
        -
      • Learn your class skills and combos. Each class in Blade And Soul has a unique set of skills and combos that can be customized and upgraded through the skill tree. You can view your skill tree in icon mode by pressing K and selecting the icon layout on the top right corner of the screen. You should experiment with different skills and combos to find out what works best for your playstyle and situation. Some skills and combos are triggered by certain conditions, such as knocking down or stunning your enemy, or blocking or evading their attacks. You should pay attention to these conditions and react accordingly to unleash powerful attacks.
        • -
      • Use your environment and movement abilities. Blade And Soul features a fluid and dynamic movement system that allows you to run, jump, glide, dash, and wall-run across the game world. You can use these movement abilities to explore the environment, find hidden secrets, reach advantageous positions, or escape from danger. You can also use the environment to your advantage, such as hiding behind objects, luring enemies into traps, or knocking them off cliffs.
        • -
      • Engage with all pop-up content. As you progress through the game, you will encounter various pop-up content that can offer you extra rewards, challenges, or fun activities. These include dynamic quests, world bosses, faction wars, PvP arenas, dungeons, raids, events, and more. You should try to engage with all pop-up content as much as possible, as they can provide you with valuable experience, items, gold, reputation, achievements, and fun.
        • -
      • Be thrifty with coins and resources. Blade And Soul has a complex economy system that involves various currencies and resources that are used for different purposes. Some of these include coins, NCoins, Hongmoon Coins, gold bars, soulstones, moonstones, sacred orbs, elysian orbs, unsealing charms, keys, transformation stones, and more. You should be careful with how you spend your coins and resources, as they can be hard to come by or expensive to buy. You should also look for ways to earn more coins and resources, such as completing quests, selling items, crafting goods, participating in events, or exchanging currencies.
        • -
      • Get a job and join a clan. Blade And Soul offers you the opportunity to learn various professions and join a clan. Professions allow you to craft or gather useful items that can help you or other players in the game. There are four types of professions: gathering guilds (collect materials), crafting guilds (produce goods), combat guilds (provide buffs), and social guilds (offer services). You can join up to four guilds at a time (two gathering and two crafting), but you can only craft one item per guild at a time. Clans are player-made groups that can offer you social interaction, cooperation, support, benefits, and fun. You can join a clan by applying to one or creating your own.
        • -
      - -

      These are some of the basic tips that can help you get started in Blade And Soul. There are many more aspects of the game that you can discover and enjoy as you play. Blade And Soul is a game that rewards skillful play and exploration, so don't be afraid to try new things and challenge yourself.

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        \ No newline at end of file diff --git a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Clash Royale Simulator APK The Ultimate Guide to Mastering the Game.md b/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Clash Royale Simulator APK The Ultimate Guide to Mastering the Game.md deleted file mode 100644 index e07f8a8b901ea0325cfa504978df52fe50543c71..0000000000000000000000000000000000000000 --- a/spaces/ticomspire/turkey-syria-earthquake-tweets/logs/Clash Royale Simulator APK The Ultimate Guide to Mastering the Game.md +++ /dev/null @@ -1,155 +0,0 @@ - -

        Clash Royale Simulator Download APK: How to Test Your Strategies in a Sandbox Mode

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        Clash Royale is one of the most popular and addictive mobile games in the world, with millions of players competing in real-time battles. But what if you want to test your strategies and skills without risking your trophies, chests, or gems? What if you want to experiment with different cards, decks, and scenarios without spending any money or time? That's where Clash Royale Simulator comes in.

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        What is Clash Royale Simulator?

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        A fan-made app that lets you simulate battles in Clash Royale

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        Clash Royale Simulator is a fan-made app that lets you simulate battles in Clash Royale. It is not affiliated with or endorsed by Supercell, the developer of the original game. It is a standalone app that does not require you to have Clash Royale installed on your device. You can use it to create and customize your own decks and cards, simulate battles against different opponents and scenarios, analyze your performance and improve your skills, and more.

        -

        The features and benefits of using Clash Royale Simulator

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        Some of the features and benefits of using Clash Royale Simulator are:

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        • You can access all the cards in Clash Royale, including the ones you don't have or haven't unlocked yet.
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        • You can upgrade your cards to any level you want, without spending any gold or gems.
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        • You can create unlimited decks and save them for later use.
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        • You can simulate battles against different types of opponents, such as bots, friends, or top players.
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        • You can simulate battles in different arenas, game modes, challenges, and events.
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        • You can pause, resume, rewind, fast-forward, or replay any battle at any time.
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        • You can view detailed statistics and analytics of your battles, such as elixir usage, damage dealt, damage received, cards played, cards cycle, etc.
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        • You can learn from the best players by watching their replays and copying their decks.
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        • You can have fun and enjoy the game without any pressure, stress, or frustration.
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        How to Download and Install Clash Royale Simulator APK on Your Android Device

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        The steps to download the APK file from a trusted source

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        An APK file is an Android application package file that contains all the files needed to install an app on your device. To download and install Clash Royale Simulator APK on your Android device, you need to follow these steps:

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        1. Go to your device settings and enable the option to install apps from unknown sources. This may vary depending on your device model and Android version, but you can usually find it under security or privacy settings.
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        2. Locate the APK file on your device using a file manager app or your browser's downloads folder.
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        3. Tap on the APK file and follow the instructions to install it. You may need to grant some permissions to the app during the installation process.
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        4. Once the installation is complete, you can launch the app from your app drawer or home screen.
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        The precautions and risks of installing APK files from unknown sources

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        While installing APK files from unknown sources can be useful for accessing apps that are not available on the official app store, it also comes with some risks and precautions. Some of them are:

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        Therefore, you should always be careful and cautious when installing APK files from unknown sources. You should only download them from trusted and reputable sources, scan them with an antivirus app before installing them, and backup your device data regularly.

        How to Use Clash Royale Simulator to Test Your Strategies

        -

        How to create and customize your own decks and cards

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        One of the main advantages of using Clash Royale Simulator is that you can create and customize your own decks and cards. You can access all the cards in the game, including the ones you don't have or haven't unlocked yet. You can also upgrade your cards to any level you want, without spending any gold or gems. Here is how you can create and customize your own decks and cards:

        -
          -
        1. Open the app and tap on the "Decks" button on the bottom menu.
          2. -
        2. Tap on the "+" button on the top right corner to create a new deck.
          3. -
        3. Select the cards you want to add to your deck from the list. You can filter the cards by rarity, type, or elixir cost.
          4. -
        4. Tap on the "Save" button to save your deck. You can also edit, delete, or rename your deck by tapping on the "Edit" button.
          5. -
        5. To customize your cards, tap on the "Cards" button on the bottom menu.
          6. -
        6. Select the card you want to customize from the list. You can filter the cards by rarity, type, or elixir cost.
          7. -
        7. Tap on the "Upgrade" button to upgrade your card to any level you want. You can also view the stats and description of your card by tapping on the "Info" button.
          8. -
        -

        How to simulate battles against different opponents and scenarios

        -

        Another advantage of using Clash Royale Simulator is that you can simulate battles against different opponents and scenarios. You can choose from different types of opponents, such as bots, friends, or top players. You can also choose from different arenas, game modes, challenges, and events. Here is how you can simulate battles against different opponents and scenarios:

        -
          -
        1. Open the app and tap on the "Battle" button on the bottom menu.
          2. -
        2. Select the deck you want to use for your battle from the list. You can also create a new deck by tapping on the "+" button.
          3. -
        3. Select the opponent you want to face from the list. You can choose from bots, friends, or top players. You can also filter the opponents by arena, level, or trophies.
          4. -
        4. Select the arena you want to battle in from the list. You can choose from any arena in the game, including special arenas for events and challenges.
          5. -
        5. Select the game mode you want to play from the list. You can choose from any game mode in the game, such as 1v1, 2v2, draft, touchdown, etc.
          6. -
        6. Select the challenge or event you want to participate in from the list. You can choose from any challenge or event in the game, such as clan wars, global tournaments, special challenges, etc.
          7. -
        7. Tap on the "Start" button to start your battle. You can also adjust some settings for your battle by tapping on the "Settings" button.
          8. -

        How to analyze your performance and improve your skills

        -

        The final advantage of using Clash Royale Simulator is that you can analyze your performance and improve your skills. You can view detailed statistics and analytics of your battles, such as elixir usage, damage dealt, damage received, cards played, cards cycle, etc. You can also pause, resume, rewind, fast-forward, or replay any battle at any time. Here is how you can analyze your performance and improve your skills:

        -
          -
        1. Open the app and tap on the "History" button on the bottom menu.
          2. -
        2. Select the battle you want to analyze from the list. You can also filter the battles by date, opponent, arena, game mode, challenge, or event.
          3. -
        3. Tap on the "Stats" button to view the statistics and analytics of your battle. You can see the elixir usage, damage dealt, damage received, cards played, cards cycle, etc. of both you and your opponent.
          4. -
        4. Tap on the "Replay" button to watch the replay of your battle. You can pause, resume, rewind, fast-forward, or replay any battle at any time. You can also change the speed and perspective of the replay.
          5. -
        5. Use the statistics and analytics to identify your strengths and weaknesses, and use the replay to learn from your mistakes and successes.
          6. -
        6. Apply what you have learned to your next battles and see how you improve over time.
          7. -
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        Tips and Tricks for Using Clash Royale Simulator Effectively

        -

        How to use the simulator to practice different game modes and challenges

        -

        One of the tips and tricks for using Clash Royale Simulator effectively is to use it to practice different game modes and challenges. You can choose from any game mode in the game, such as 1v1, 2v2, draft, touchdown, etc. You can also choose from any challenge or event in the game, such as clan wars, global tournaments, special challenges, etc. Here is how you can use the simulator to practice different game modes and challenges:

        -
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        • Use the simulator to familiarize yourself with the rules and objectives of each game mode and challenge.
          • -
        • Use the simulator to test different decks and strategies for each game mode and challenge.
          • -
        • Use the simulator to practice against different types of opponents and scenarios for each game mode and challenge.
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        • Use the simulator to prepare yourself for the real battles in the original game.
          • -
        -

        How to use the simulator to experiment with new cards and combinations

        -

        Another tip and trick for using Clash Royale Simulator effectively is to use it to experiment with new cards and combinations. You can access all the cards in the game, including the ones you don't have or haven't unlocked yet. You can also upgrade your cards to any level you want, without spending any gold or gems. Here is how you can use the simulator to experiment with new cards and combinations:

        -
          -
        • Use the simulator to explore all the cards in the game and learn their stats, abilities, synergies, counters, etc.
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        • Use the simulator to create new decks with different cards and combinations that suit your playstyle and preferences.
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        • Use the simulator to test your new decks against different opponents and scenarios and see how they perform.
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        • Use the simulator to fine-tune your new decks by making adjustments and improvements based on your results.
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        How to use the simulator to learn from the best players and watch replays

        -

        The final tip and trick for using Clash Royale Simulator effectively is to use it to learn from the best players and watch replays. You can choose from different types of opponents, such as bots, friends, or top players. You can also watch their replays and copy their decks. Here is how you can use the simulator to learn from the best players and watch replays:

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        • Use the simulator to find and select the top players in the game. You can filter them by arena, level, or trophies.
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        • Use the simulator to watch their replays and observe their strategies, tactics, moves, and decisions.
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        • Use the simulator to copy their decks and try them out for yourself.
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        Conclusion

        -

        Clash Royale Simulator is a fan-made app that lets you simulate battles in Clash Royale. It is a great tool for testing your strategies and skills without risking your trophies, chests, or gems. It is also a fun way to experiment with different cards, decks, and scenarios without spending any money or time. You can download and install Clash Royale Simulator APK on your Android device from one of the trusted sources mentioned above. You can also use the tips and tricks provided in this article to use Clash Royale Simulator effectively and improve your game. So what are you waiting for? Download Clash Royale Simulator APK today and unleash your creativity and potential in Clash Royale!

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        FAQs

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        Is Clash Royale Simulator safe and legal to use?

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        Clash Royale Simulator is safe and legal to use as long as you download it from a trusted source and scan it with an antivirus app before installing it. However, you should be aware that it is not affiliated with or endorsed by Supercell, the developer of the original game. You should also be careful not to violate the terms of service or end-user license agreement of the original game by using Clash Royale Simulator.

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        Does Clash Royale Simulator require an internet connection?

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        Clash Royale Simulator does not require an internet connection to run. You can use it offline without any problem. However, you may need an internet connection to download the APK file from a trusted source or to update the app to the latest version.

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        Can I use Clash Royale Simulator on other devices besides Android?

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        Clash Royale Simulator is currently only available for Android devices. There is no official version for iOS, Windows, Mac, or other platforms. However, you may be able to use an emulator or a virtual machine to run Clash Royale Simulator on other devices. However, this may not be optimal or stable and may cause compatibility issues or performance problems.

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        Will using Clash Royale Simulator affect my progress in the original game?

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        No, using Clash Royale Simulator will not affect your progress in the original game. Clash Royale Simulator is a standalone app that does not require you to have Clash Royale installed on your device. It does not sync with your Clash Royale account or data. It does not interfere with your trophies, chests, gems, cards, decks, or anything else in the original game. You can use Clash Royale Simulator independently without any worry.

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        You can find more information and support for Clash Royale Simulator by visiting their official website, their GitHub repository, their Uptodown page, or their apkonline.net page. You can also contact them by email at clashroyalesimulator@gmail.com or by joining their Discord server. You can also leave a comment or a review on their website or app page and share your feedback and suggestions.

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        \ No newline at end of file diff --git a/spaces/timbrooks/instruct-pix2pix/edit_app.py b/spaces/timbrooks/instruct-pix2pix/edit_app.py deleted file mode 100644 index 0359e815ad51b1a2291dd8943555568e452981ad..0000000000000000000000000000000000000000 --- a/spaces/timbrooks/instruct-pix2pix/edit_app.py +++ /dev/null @@ -1,192 +0,0 @@ -from __future__ import annotations - -import math -import random - -import gradio as gr -import torch -from PIL import Image, ImageOps -from diffusers import StableDiffusionInstructPix2PixPipeline - - -help_text = """ -If you're not getting what you want, there may be a few reasons: -1. Is the image not changing enough? Your Image CFG weight may be too high. This value dictates how similar the output should be to the input. It's possible your edit requires larger changes from the original image, and your Image CFG weight isn't allowing that. Alternatively, your Text CFG weight may be too low. This value dictates how much to listen to the text instruction. The default Image CFG of 1.5 and Text CFG of 7.5 are a good starting point, but aren't necessarily optimal for each edit. Try: - * Decreasing the Image CFG weight, or - * Increasing the Text CFG weight, or -2. Conversely, is the image changing too much, such that the details in the original image aren't preserved? Try: - * Increasing the Image CFG weight, or - * Decreasing the Text CFG weight -3. Try generating results with different random seeds by setting "Randomize Seed" and running generation multiple times. You can also try setting "Randomize CFG" to sample new Text CFG and Image CFG values each time. -4. Rephrasing the instruction sometimes improves results (e.g., "turn him into a dog" vs. "make him a dog" vs. "as a dog"). -5. Increasing the number of steps sometimes improves results. -6. Do faces look weird? The Stable Diffusion autoencoder has a hard time with faces that are small in the image. Try: - * Cropping the image so the face takes up a larger portion of the frame. -""" - - -example_instructions = [ - "Make it a picasso painting", - "as if it were by modigliani", - "convert to a bronze statue", - "Turn it into an anime.", - "have it look like a graphic novel", - "make him gain weight", - "what would he look like bald?", - "Have him smile", - "Put him in a cocktail party.", - "move him at the beach.", - "add dramatic lighting", - "Convert to black and white", - "What if it were snowing?", - "Give him a leather jacket", - "Turn him into a cyborg!", - "make him wear a beanie", -] - -model_id = "timbrooks/instruct-pix2pix" - -def main(): - pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None).to("cuda") - example_image = Image.open("imgs/example.jpg").convert("RGB") - - def load_example( - steps: int, - randomize_seed: bool, - seed: int, - randomize_cfg: bool, - text_cfg_scale: float, - image_cfg_scale: float, - ): - example_instruction = random.choice(example_instructions) - return [example_image, example_instruction] + generate( - example_image, - example_instruction, - steps, - randomize_seed, - seed, - randomize_cfg, - text_cfg_scale, - image_cfg_scale, - ) - - def generate( - input_image: Image.Image, - instruction: str, - steps: int, - randomize_seed: bool, - seed: int, - randomize_cfg: bool, - text_cfg_scale: float, - image_cfg_scale: float, - ): - seed = random.randint(0, 100000) if randomize_seed else seed - text_cfg_scale = round(random.uniform(6.0, 9.0), ndigits=2) if randomize_cfg else text_cfg_scale - image_cfg_scale = round(random.uniform(1.2, 1.8), ndigits=2) if randomize_cfg else image_cfg_scale - - width, height = input_image.size - factor = 512 / max(width, height) - factor = math.ceil(min(width, height) * factor / 64) * 64 / min(width, height) - width = int((width * factor) // 64) * 64 - height = int((height * factor) // 64) * 64 - input_image = ImageOps.fit(input_image, (width, height), method=Image.Resampling.LANCZOS) - - if instruction == "": - return [input_image, seed] - - generator = torch.manual_seed(seed) - edited_image = pipe( - instruction, image=input_image, - guidance_scale=text_cfg_scale, image_guidance_scale=image_cfg_scale, - num_inference_steps=steps, generator=generator, - ).images[0] - return [seed, text_cfg_scale, image_cfg_scale, edited_image] - - def reset(): - return [0, "Randomize Seed", 1371, "Fix CFG", 7.5, 1.5, None] - - with gr.Blocks() as demo: - gr.HTML("""

        - InstructPix2Pix: Learning to Follow Image Editing Instructions -

        -

        For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. -
        - -Duplicate Space -

        """) - with gr.Row(): - with gr.Column(scale=1, min_width=100): - generate_button = gr.Button("Generate") - with gr.Column(scale=1, min_width=100): - load_button = gr.Button("Load Example") - with gr.Column(scale=1, min_width=100): - reset_button = gr.Button("Reset") - with gr.Column(scale=3): - instruction = gr.Textbox(lines=1, label="Edit Instruction", interactive=True) - - with gr.Row(): - input_image = gr.Image(label="Input Image", type="pil", interactive=True) - edited_image = gr.Image(label=f"Edited Image", type="pil", interactive=False) - input_image.style(height=512, width=512) - edited_image.style(height=512, width=512) - - with gr.Row(): - steps = gr.Number(value=50, precision=0, label="Steps", interactive=True) - randomize_seed = gr.Radio( - ["Fix Seed", "Randomize Seed"], - value="Randomize Seed", - type="index", - show_label=False, - interactive=True, - ) - seed = gr.Number(value=1371, precision=0, label="Seed", interactive=True) - randomize_cfg = gr.Radio( - ["Fix CFG", "Randomize CFG"], - value="Fix CFG", - type="index", - show_label=False, - interactive=True, - ) - text_cfg_scale = gr.Number(value=7.5, label=f"Text CFG", interactive=True) - image_cfg_scale = gr.Number(value=1.5, label=f"Image CFG", interactive=True) - - gr.Markdown(help_text) - - load_button.click( - fn=load_example, - inputs=[ - steps, - randomize_seed, - seed, - randomize_cfg, - text_cfg_scale, - image_cfg_scale, - ], - outputs=[input_image, instruction, seed, text_cfg_scale, image_cfg_scale, edited_image], - ) - generate_button.click( - fn=generate, - inputs=[ - input_image, - instruction, - steps, - randomize_seed, - seed, - randomize_cfg, - text_cfg_scale, - image_cfg_scale, - ], - outputs=[seed, text_cfg_scale, image_cfg_scale, edited_image], - ) - reset_button.click( - fn=reset, - inputs=[], - outputs=[steps, randomize_seed, seed, randomize_cfg, text_cfg_scale, image_cfg_scale, edited_image], - ) - - demo.queue(concurrency_count=1) - demo.launch(share=False) - - -if __name__ == "__main__": - main() diff --git a/spaces/tioseFevbu/cartoon-converter/IEC INTERNATIONAL 27031 STANDARDpdf.md b/spaces/tioseFevbu/cartoon-converter/IEC INTERNATIONAL 27031 STANDARDpdf.md deleted file mode 100644 index 20cab06b0b61fe8d1f9f89fe4ac1235f362b4941..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/IEC INTERNATIONAL 27031 STANDARDpdf.md +++ /dev/null @@ -1,70 +0,0 @@ -## IEC INTERNATIONAL 27031 STANDARDpdf - - - - - - ![IEC INTERNATIONAL 27031 STANDARDpdf](https://encrypted-tbn3.gstatic.com/images?q=tbn:ANd9GcSiS5zphrJkpL-BXnkRmPL1O6B-xHsuUALz00wKG-278jEnAegztTTgPoE) - - - - - -**CLICK HERE >>> [https://tinourl.com/2txBqE](https://tinourl.com/2txBqE)** - - - - - - - - - - - - - -# What is IEC International 27031 Standard and Why You Need It - - - -IEC International 27031 Standard is a global standard that provides guidelines for information and communication technology (ICT) readiness for business continuity. 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ICT readiness for business continuity is the capability of the ICT infrastructure and systems to be prepared for and to withstand any disruption, and to recover quickly and resume normal operations. - - - -IEC International 27031 Standard describes the concepts and principles of ICT readiness for business continuity, and provides a framework of methods and processes to identify and specify all aspects (such as performance criteria, design and implementation) for improving an organization's ICT readiness to ensure business continuity. It applies to any organization (private, governmental or non-governmental, irrespective of size) developing its ICT readiness for business continuity program (IRBC), and requiring its ICT services/infrastructures to be ready to support business operations in the event of emerging events and incidents, and related disruptions, that could affect continuity (including security) of critical business functions[^1^]. - - - -Some of the benefits of implementing IEC International 27031 Standard are: - - - -- It helps organizations to align their ICT strategy with their business continuity strategy, and to integrate their ICT readiness activities with their overall business continuity management system. - -- It helps organizations to identify and prioritize their critical ICT assets and dependencies, and to assess their current level of ICT readiness. - -- It helps organizations to develop and implement appropriate ICT readiness strategies and plans, based on their specific needs and objectives. - -- It helps organizations to monitor and measure their ICT readiness performance, and to continually improve their ICT readiness capability. - -- It helps organizations to demonstrate their compliance with relevant laws, regulations and standards, and to enhance their reputation and trust among their stakeholders. - - - -If you want to learn more about IEC International 27031 Standard, you can download the PDF version from the official website of ISO[^2^], the International Organization for Standardization. ISO is an independent, non-governmental organization that develops and publishes international standards in collaboration with IEC, the International Electrotechnical Commission. ISO/IEC 27031:2011 is the current edition of the standard[^1^], which was last reviewed and confirmed in 2020. However, a new edition is under development as ISO/IEC DIS 27031[^3^], which is expected to be published soon. - - - -IEC International 27031 Standard is a valuable tool for any organization that wants to ensure its ICT readiness for business continuity. By following its guidelines, you can improve your resilience, reduce your risks and protect your assets from any disruption. Don't wait until it's too late. Start your IRBC journey today! - - dfd1c89656 - - - - - diff --git a/spaces/tioseFevbu/cartoon-converter/Tracktion Software Collective V1.2.2 Incl Patched And Keygen-R2R [VERIFIED].md b/spaces/tioseFevbu/cartoon-converter/Tracktion Software Collective V1.2.2 Incl Patched And Keygen-R2R [VERIFIED].md deleted file mode 100644 index 1626a13cfc49787a0a949d586d3282840d50d048..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/Tracktion Software Collective V1.2.2 Incl Patched And Keygen-R2R [VERIFIED].md +++ /dev/null @@ -1,82 +0,0 @@ -## Tracktion Software Collective V1.2.2 Incl Patched And Keygen-R2R - - - - - - - - - -**CLICK HERE ☆ [https://tinourl.com/2txBqG](https://tinourl.com/2txBqG)** - - - - - - - - - - - - - -# Tracktion Software Collective v1.2.2: A Powerful Sampler and Synth - - - -Tracktion Software Collective v1.2.2 is the latest version of the versatile and creative sampler and synth plugin from Tracktion Software. It includes a patch and a keygen to unlock the full potential of this amazing instrument. - - - -Collective v1.2.2 offers over 600 presets, ranging from acoustic and electric pianos, guitars, basses, drums, orchestral instruments, synths, pads, leads, effects and more. You can also create your own sounds by combining up to 256 layers of samples and synthesis, with extensive modulation and processing options. - - - -Collective v1.2.2 also features a new browser interface that makes it easier to find and load sounds, as well as a redesigned mixer section that gives you more control over the levels, panning, filters and effects of each layer. You can also drag and drop samples from your computer or other plugins into Collective v1.2.2 and use them as sound sources. - - - -Collective v1.2.2 is compatible with Windows and Mac OS X, and supports VST, AU and AAX formats. It requires a 64-bit host application and at least 4 GB of RAM. You can download it from the official website or from various torrent sites. - - - -If you are looking for a powerful sampler and synth plugin that can handle any genre and style of music, you should definitely check out Tracktion Software Collective v1.2.2. - - - -In this article, we will explore some of the features and benefits of Tracktion Software Collective v1.2.2 in more detail. We will also provide some tips and tricks on how to use it effectively and creatively. - - - -## Features and Benefits of Tracktion Software Collective v1.2.2 - - - -Tracktion Software Collective v1.2.2 is a sampler and synth plugin that can produce a wide range of sounds, from realistic acoustic instruments to futuristic electronic sounds. Here are some of the main features and benefits of this plugin: - - - -- It has over 600 presets that cover various musical genres and styles. You can use them as they are or tweak them to suit your needs. - -- It allows you to create your own sounds by combining up to 256 layers of samples and synthesis. You can choose from different types of synthesis engines, such as wavetable, FM, subtractive, additive and granular. You can also import your own samples or use samples from other plugins as sound sources. - -- It has a flexible modulation system that lets you modulate almost any parameter with various sources, such as envelopes, LFOs, step sequencers, MIDI controllers and more. You can also assign macros to control multiple parameters at once. - -- It has a powerful processing section that includes filters, distortion, compression, EQ, chorus, flanger, phaser, delay, reverb and more. You can apply effects to each layer individually or to the whole sound globally. - -- It has a new browser interface that makes it easy to find and load sounds. You can browse by category, subcategory, tag or keyword. You can also create your own folders and favorites for quick access. - -- It has a redesigned mixer section that gives you more control over the levels, panning, filters and effects of each layer. You can also solo and mute layers, as well as adjust the overall volume and balance of the sound. - -- It has a drag and drop feature that lets you drag and drop samples from your computer or other plugins into Collective v1.2.2 and use them as sound sources. You can also drag and drop presets from Collective v1.2.2 into your host application or other plugins. - - - - 1b8d091108 - - - - - diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Adobe.dreamweaver.cc.crack.amtlib.dll.md b/spaces/tioseFevbu/cartoon-converter/scripts/Adobe.dreamweaver.cc.crack.amtlib.dll.md deleted file mode 100644 index 6f459d4acc7319da3665be35a278b0809c367017..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Adobe.dreamweaver.cc.crack.amtlib.dll.md +++ /dev/null @@ -1,85 +0,0 @@ - - - - - -
        -

        Adobe.dreamweaver.cc.crack.amtlib.dll: What Is It and How to Use It?

        -

        Adobe Dreamweaver is a popular web design and development software that allows you to create, code, and manage websites with ease. It supports various web standards such as HTML, CSS, JavaScript, PHP, and more. It also offers a visual interface that lets you see your web pages as you create them.

        -

        Adobe.dreamweaver.cc.crack.amtlib.dll


        Download Zip > https://urlcod.com/2uHyRY



        -

        amtlib.dll is a dynamic link library file that is part of the AMT Licensing system of Adobe products. It is responsible for verifying that your Adobe software is genuine and activated.

        -

        Cracking is a process of modifying or bypassing the software's security features and activation mechanisms. Cracking allows you to use the software without paying for it or following the license terms. Cracking is illegal and unethical, and it may expose your computer to malware or viruses.

        -

        In this article, we will explain what Adobe.dreamweaver.cc.crack.amtlib.dll is and how to use it to crack Adobe Dreamweaver CC. We will also discuss the risks of cracking Adobe Dreamweaver CC using amtlib.dll and the alternatives to cracking Adobe Dreamweaver CC using amtlib.dll.

        -

        Why Do People Crack Adobe Dreamweaver?

        -

        Adobe Dreamweaver is a premium software that requires a subscription fee to use. The subscription fee varies depending on the plan you choose, but it can range from $9.99 to $52.99 per month. Some people may find this fee too expensive or unreasonable, especially if they only use Adobe Dreamweaver occasionally or for personal projects.

        -

        -

        By cracking Adobe Dreamweaver using amtlib.dll, people can save money and access the full features of the software without any limitations or restrictions. They can also bypass the activation process and avoid registering their software with Adobe. Cracking Adobe Dreamweaver using amtlib.dll may seem like a convenient and easy way to use the software for free, but it comes with many drawbacks and dangers that we will discuss later in this article.

        -

        How to Crack Adobe Dreamweaver Using amtlib.dll?

        -

        The basic steps to crack Adobe Dreamweaver using amtlib.dll are as follows:

        -

        Step 1: Download Adobe Dreamweaver CC from the official website.

        -

        The first step is to download Adobe Dreamweaver CC from the official website. You can choose the trial version or the full version, depending on your preference. The trial version will give you access to the software for 7 days, while the full version will require you to sign in with your Adobe ID and enter your payment details.

        -

        After downloading the software, install it on your computer by following the instructions on the screen. You may need to restart your computer after the installation is complete.

        -

        Step 2: Download amtlib.dll from a reliable source.

        -

        The next step is to download amtlib.dll from a reliable source. amtlib.dll is a file that contains the cracked code that will override the original code of the AMT Licensing system of Adobe products. There are many websites that offer amtlib.dll files for different Adobe products, but not all of them are trustworthy or safe.

        -

        You should be careful when downloading amtlib.dll files from unknown or suspicious sources, as they may contain malware or viruses that can harm your computer or steal your personal information. You should also scan the file with an antivirus program before using it.

        -

        One of the websites that claims to provide safe and working amtlib.dll files for various Adobe products is [amtlibdll.com]. You can visit this website and search for Adobe Dreamweaver CC in the list of products. You will see different versions of Adobe Dreamweaver CC, such as 2020, 2019, 2018, etc. Choose the version that matches your installed software and click on the download button.

        -

        You will be redirected to another page where you will see a download link for amtlib.dll. Click on the link and save the file on your computer. Remember the location where you saved the file, as you will need it in the next step.

        -

        Step 3: Replace the original amtlib.dll file with the cracked one.

        -

        The third step is to replace the original amtlib.dll file with the cracked one. The original amtlib.dll file is located in the installation folder of Adobe Dreamweaver CC on your computer. The installation folder may vary depending on your operating system and settings, but it is usually found in one of these paths:

        -
          -
        • C:\Program Files\Adobe\Adobe Dreamweaver CC
          • -
        • C:\Program Files (x86)\Adobe\Adobe Dreamweaver CC
          • -
        -

        Navigate to the installation folder of Adobe Dreamweaver CC and look for a file named amtlib.dll. This is the file that you need to replace with the cracked one that you downloaded in the previous step.

        -

        Before you replace the file, make sure that you backup or rename the original file, in case something goes wrong or you want to restore it later. You can either copy and paste the file to another location, or rename it to something like amtlib.dll.bak or amtlib.dll.old.

        -

        After you backup or rename the original file, copy and paste the cracked file that you downloaded in the previous step to the installation folder of Adobe Dreamweaver CC. You may need to provide administrative permission to do this. Make sure that the cracked file has the same name as the original file, which is amtlib.dll.

        -

        Step 4: Launch Adobe Dreamweaver CC and enjoy its full features.

        -

        The final step is to launch Adobe Dreamweaver CC and enjoy its full features. You can open the software from your desktop shortcut or your start menu. You should not see any activation or registration prompts when you launch the software. You should also be able to access all the premium features and tools of Adobe Dreamweaver CC without any limitations or restrictions.

        -

        You can verify that your software is cracked successfully by checking the Help menu and clicking on About Adobe Dreamweaver CC. You should see a message that says "Adobe Dreamweaver CC - Cracked by amtlib.dll" or something similar.

        -

        Congratulations! You have successfully cracked Adobe Dreamweaver CC using amtlib.dll. You can now use the software for free and create amazing websites with ease.

        -

        What Are the Risks of Cracking Adobe Dreamweaver Using amtlib.dll?

        -

        While cracking Adobe Dreamweaver using amtlib.dll may seem like a tempting and convenient option, it is not without risks and consequences. Here are some of the risks of cracking Adobe Dreamweaver using amtlib.dll:

        -
          -
        • Legal issues: Cracking Adobe Dreamweaver using amtlib.dll is illegal and violates the terms and conditions of Adobe. By cracking Adobe Dreamweaver using amtlib.dll, you are infringing on the intellectual property rights of Adobe and committing software piracy. Software piracy is a serious crime that can result in fines, lawsuits, or even jail time. You are also exposing yourself to potential legal action from Adobe or other parties who may detect your cracked software.
          • -
        • Security threats: Cracking Adobe Dreamweaver using amtlib.dll is risky and unsafe for your computer and your personal information. By downloading and using amtlib.dll files from unknown or suspicious sources, you are putting your computer at risk of malware or viruses that can damage your system, corrupt your files, steal your data, or hijack your online accounts. You are also compromising the security of your software, as cracked software may contain backdoors, spyware, or trojans that can allow hackers or cybercriminals to access your computer remotely or monitor your online activities.
          • -
        • Performance issues: Cracking Adobe Dreamweaver using amtlib.dll is unreliable and unstable for your software and your web projects. By replacing the original amtlib.dll file with a cracked one, you are altering the functionality and compatibility of your software, which may cause errors, bugs, crashes, or glitches. You may also experience reduced performance, speed, or quality of your software, as cracked software may not be optimized or updated for your system or web standards. You may also lose some features or functions of your software, as cracked software may not support all the plugins, extensions, or integrations that you need for your web projects.
          • -
        • Update issues: Cracking Adobe Dreamweaver using amtlib.dll is problematic and inconvenient for your software and your web projects. By cracking Adobe Dreamweaver using amtlib.dll, you are preventing your software from receiving updates from Adobe, which may include bug fixes, security patches, feature enhancements, or performance improvements. You may also miss out on new features or tools that Adobe may release for its software in the future. You may also face compatibility issues with other software or web standards that may change or evolve over time. You may also have to repeat the cracking process every time you update your software, which can be tedious and time-consuming.
          • -
        -

        As you can see, cracking Adobe Dreamweaver using amtlib.dll is not worth it in the long run. It may save you some money in the short term, but it will cost you more in terms of legal troubles, security risks, performance problems, and update difficulties in the long term. It is better to use Adobe Dreamweaver legally and ethically than to crack it illegally and unethically.

        -

        What Are the Alternatives to Cracking Adobe Dreamweaver Using amtlib.dll?

        -

        If you want to use Adobe Dreamweaver without cracking it using amtlib.dll, you have some alternatives that are legal and ethical. Here are some of the alternatives to cracking Adobe Dreamweaver using amtlib.dll :

        -

        Alternative 1: Use a free trial version of Adobe Dreamweaver CC.

        -

        One of the alternatives to cracking Adobe Dreamweaver using amtlib.dll is to use a free trial version of Adobe Dreamweaver CC. Adobe offers a 7-day free trial for its software, which you can download from the official website. The free trial version will give you access to all the features and tools of Adobe Dreamweaver CC, but it will expire after 7 days. You can use the free trial version to test the software and see if it meets your needs and expectations.

        -

        The free trial version is a legal and ethical way to use Adobe Dreamweaver CC, but it has some limitations. You can only use the free trial version once per Adobe ID, and you cannot extend or renew it. You also need to have an internet connection and an Adobe ID to use the free trial version. You also need to uninstall the free trial version after it expires, or else it may interfere with other Adobe products on your computer.

        -

        Alternative 2: Use a student or teacher discount for Adobe Dreamweaver CC.

        -

        Another alternative to cracking Adobe Dreamweaver using amtlib.dll is to use a student or teacher discount for Adobe Dreamweaver CC. Adobe offers a special discount for students and teachers who want to use its software for educational purposes. The student or teacher discount will give you access to the full version of Adobe Dreamweaver CC, as well as other Adobe products, for a reduced price. The student or teacher discount can save you up to 60% off the regular price of the software.

        -

        The student or teacher discount is a legal and ethical way to use Adobe Dreamweaver CC, but it has some requirements and benefits. You need to be at least 13 years old and enrolled in an accredited school or institution to qualify for the student or teacher discount. You also need to provide proof of eligibility, such as a school email address, a school ID card, or a transcript. You also need to renew your subscription every year to maintain your eligibility. The student or teacher discount will give you access to the latest updates and features of Adobe Dreamweaver CC, as well as online storage, cloud services, and technical support.

        -

        Alternative 3: Use a free or open-source web design software.

        -

        A third alternative to cracking Adobe Dreamweaver using amtlib.dll is to use a free or open-source web design software. There are many web design software that are similar to Adobe Dreamweaver CC, but they are free or open-source, which means that you can use them without paying any fees or following any license terms. Some examples of free or open-source web design software are:

        -
          -
        • Brackets: Brackets is a lightweight and powerful web design software that is developed by Adobe. It supports HTML, CSS, JavaScript, and other web languages. It also offers a live preview feature that lets you see your web pages as you code them.
          • -
        • BlueGriffon: BlueGriffon is a user-friendly and versatile web design software that is based on the Firefox browser engine. It supports HTML5, CSS3, SVG, and other web standards. It also offers a visual interface that lets you create and edit your web pages with ease.
          • -
        • KompoZer: KompoZer is a simple and reliable web design software that is based on the Mozilla Composer project. It supports HTML4, CSS2, JavaScript, and other web languages. It also offers a visual interface that lets you see your web pages as you create them.
          • -
        -

        The free or open-source web design software are legal and ethical ways to use web design software, but they have some advantages and disadvantages. The advantages are that they are free or low-cost, easy to use, compatible with various platforms, and customizable with plugins or extensions. The disadvantages are that they may not have all the features or tools that Adobe Dreamweaver CC has, they may not be updated or supported regularly, they may have bugs or errors, and they may not be compatible with some web standards or browsers.

        -

        Conclusion

        -

        In conclusion, Adobe.dreamweaver.cc.crack.amtlib.dll is a file that can be used to crack Adobe Dreamweaver CC using amtlib.dll. Cracking Adobe Dreamweaver CC using amtlib.dll can give you access to the full features of the software without paying for it or following the license terms. However, cracking Adobe Dreamweaver CC using amtlib.dll is illegal and unethical, and it comes with many risks and consequences, such as legal issues, security threats, performance issues, and update issues.

        -

        If you want to use Adobe Dreamweaver without cracking it using amtlib.dll, you have some alternatives that are legal and ethical, such as using a free trial version of Adobe Dreamweaver CC, using a student or teacher discount for Adobe Dreamweaver CC, or using a free or open-source web design software. These alternatives may not have all the benefits of cracking Adobe Dreamweaver CC using amtlib.dll, but they will also not have all the drawbacks and dangers of cracking Adobe Dreamweaver CC using amtlib.dll.

        -

        We hope that this article has helped you understand what Adobe.dreamweaver.cc.crack.amtlib.dll is and how to use it to crack Adobe Dreamweaver CC. We also hope that this article has helped you realize the risks of cracking Adobe Dreamweaver CC using amtlib.dll and the alternatives to cracking Adobe Dreamweaver CC using amtlib.dll. We recommend that you use Adobe Dreamweaver legally and ethically, and respect the intellectual property rights of Adobe and other software developers.

        -

        FAQs

        -

        Here are some frequently asked questions related to the topic of this article and their answers:

        -
          -
        • Q: Is cracking Adobe Dreamweaver CC using amtlib.dll safe?
          • -
        • A: No, cracking Adobe Dreamweaver CC using amtlib.dll is not safe. It is illegal and unethical, and it may expose your computer to malware or viruses. It may also compromise the security, performance, and compatibility of your software and your web projects.
          • -
        • Q: How can I get Adobe Dreamweaver CC for free?
          • -
        • A: You can get Adobe Dreamweaver CC for free by using a free trial version of the software, which you can download from the official website. The free trial version will give you access to all the features and tools of the software for 7 days, but it will expire after that. You can also use a free or open-source web design software that is similar to Adobe Dreamweaver CC, such as Brackets, BlueGriffon, or KompoZer.
          • -
        • Q: How can I get Adobe Dreamweaver CC for cheap?
          • -
        • A: You can get Adobe Dreamweaver CC for cheap by using a student or teacher discount for the software, which you can apply for on the official website. The student or teacher discount will give you access to the full version of the software, as well as other Adobe products, for a reduced price. The student or teacher discount can save you up to 60% off the regular price of the software.
          • -
        • Q: How can I update my cracked Adobe Dreamweaver CC using amtlib.dll?
          • -
        • A: You cannot update your cracked Adobe Dreamweaver CC using amtlib.dll, as cracking prevents your software from receiving updates from Adobe. If you try to update your cracked software, you may lose your crack or face errors or crashes. You may also have to repeat the cracking process every time you update your software, which can be tedious and time-consuming.
          • -
        • Q: How can I restore my original amtlib.dll file after cracking Adobe Dreamweaver CC using amtlib.dll?
          • -
        • A: You can restore your original amtlib.dll file after cracking Adobe Dreamweaver CC using amtlib.dll by deleting or renaming the cracked file and replacing it with the original file that you backed up or renamed before cracking. You may need to provide administrative permission to do this. You may also need to uninstall and reinstall your software to restore its original functionality and compatibility.
          • -
        -

        b2dd77e56b
        -
        -
        \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Autodesk Inventor HSM Pro 2017 Crack 101 __EXCLUSIVE__.md b/spaces/tioseFevbu/cartoon-converter/scripts/Autodesk Inventor HSM Pro 2017 Crack 101 __EXCLUSIVE__.md deleted file mode 100644 index 8b085dde381c701bec74a54ba56a28b6e8d2a7f8..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Autodesk Inventor HSM Pro 2017 Crack 101 __EXCLUSIVE__.md +++ /dev/null @@ -1,38 +0,0 @@ -
        -Here is a possible article: - -

        How to Crack Autodesk Inventor HSM Pro 2017 in 5 Easy Steps

        -

        Autodesk Inventor HSM Pro 2017 is a powerful software for computer-aided design and manufacturing (CAD/CAM) that integrates seamlessly with Autodesk Inventor. It allows you to create high-quality toolpaths for 2.5D, 3D, and 5-axis milling, turning, and mill-turn applications. However, it is also a pricey software that requires a license to use. If you want to use it for free, you might be tempted to look for a crack online. But beware, cracking software is illegal and risky, and you might end up with malware, viruses, or legal troubles.

        -

        Autodesk Inventor HSM Pro 2017 Crack~ 101


        Download ✵ https://urlcod.com/2uHxq0



        -

        That being said, if you are still determined to crack Autodesk Inventor HSM Pro 2017, here are the steps you need to follow:

        -
          -
        1. Download the trial version of Autodesk Inventor HSM Pro 2017 from the official website and install it on your computer.
          2. -
        2. Download the crack file from a reliable source. Make sure it is compatible with your system and has positive feedback from other users.
          3. -
        3. Disable your antivirus and firewall software temporarily. This is to prevent them from interfering with the cracking process or deleting the crack file.
          4. -
        4. Extract the crack file and copy the contents to the installation folder of Autodesk Inventor HSM Pro 2017. Usually, this is located at C:\Program Files\Autodesk\Inventor HSM Pro 2017. Overwrite any existing files if prompted.
          5. -
        5. Run the crack file as administrator and follow the instructions on the screen. Wait for the cracking process to finish and then restart your computer.
          6. -
        -

        Congratulations! You have successfully cracked Autodesk Inventor HSM Pro 2017 and can now use it without any limitations. However, remember that this is not a legal or ethical way of using software and you might face serious consequences if you get caught. Therefore, we recommend that you purchase a legitimate license from Autodesk or use an alternative software that is free or open-source.

        - -

        Why Crack Autodesk Inventor HSM Pro 2017?

        -

        Autodesk Inventor HSM Pro 2017 is a popular software among engineers, designers, and manufacturers who need to create complex and precise parts and products. It offers a range of features and benefits that make it a superior choice for CAD/CAM. Some of these are:

        -
          -
        • It integrates seamlessly with Autodesk Inventor, allowing you to design and manufacture in one environment.
          • -
        • It supports multiple types of machining operations, including 2.5D, 3D, and 5-axis milling, turning, and mill-turn.
          • -
        • It provides advanced simulation and verification tools that help you optimize your toolpaths and avoid errors and collisions.
          • -
        • It generates high-quality NC code that is compatible with most CNC machines and controllers.
          • -
        • It has a user-friendly interface and a comprehensive online help system that guides you through the workflow.
          • -
        -

        However, Autodesk Inventor HSM Pro 2017 is also a very expensive software that requires a yearly subscription or a perpetual license to use. Depending on the type of license and the number of users, the cost can range from hundreds to thousands of dollars. For many individuals and small businesses, this can be a prohibitive expense that limits their access to the software. Therefore, some people resort to cracking the software to bypass the license activation and use it for free.

        - -

        The Risks of Cracking Autodesk Inventor HSM Pro 2017

        -

        While cracking Autodesk Inventor HSM Pro 2017 might seem like a tempting option to save money and enjoy the software without any restrictions, it is also a very risky and illegal activity that can have serious consequences. Some of the risks of cracking Autodesk Inventor HSM Pro 2017 are:

        -
          -
        • You might violate the intellectual property rights of Autodesk and face legal action from them. Autodesk has a strict policy against piracy and cracking and actively monitors the use of its software. If they detect that you are using a cracked version of their software, they might sue you for damages or report you to the authorities.
          • -
        • You might expose your computer and data to malware, viruses, or hackers. The crack file that you download from the internet might contain malicious code that can harm your computer or steal your information. You might also compromise your security by disabling your antivirus and firewall software during the cracking process.
          • -
        • You might experience poor performance, errors, or crashes while using the software. The crack file might not be compatible with your system or might interfere with the normal functioning of the software. You might also miss out on important updates, patches, or bug fixes that Autodesk releases for its software.
          • -
        • You might lose access to customer support, online resources, or community forums that Autodesk provides for its software. If you encounter any problems or need any assistance while using the software, you might not be able to get help from Autodesk or other users who have legitimate licenses.
          • -
        -

        Therefore, cracking Autodesk Inventor HSM Pro 2017 is not worth the risk and hassle. You might end up losing more than you gain by doing so. Instead, you should consider buying a legitimate license from Autodesk or using an alternative software that is free or open-source.

        e93f5a0c3f
        -
        -
        \ No newline at end of file diff --git a/spaces/tioseFevbu/cartoon-converter/scripts/Ebp Devis Facturation 2012 Crack.md b/spaces/tioseFevbu/cartoon-converter/scripts/Ebp Devis Facturation 2012 Crack.md deleted file mode 100644 index 2aff95d7b8dd75bb6f30c7db66467ff4bee6b293..0000000000000000000000000000000000000000 --- a/spaces/tioseFevbu/cartoon-converter/scripts/Ebp Devis Facturation 2012 Crack.md +++ /dev/null @@ -1,20 +0,0 @@ -
        -

        Ebp Devis Facturation 2012: A Simple and Effective Solution for Invoicing and Business Management

        -

        Ebp Devis Facturation 2012 is a software that helps you create invoices and manage your commercial activity. It is designed to be easy to use and to adapt to your specific needs. With Ebp Devis Facturation 2012, you can:

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        Ebp Devis Facturation 2012 Crack


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        -
          -
        • Create professional invoices and quotes with customizable templates and logos.
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        • Track your payments, reminders, and outstanding balances.
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        • Manage your stock, purchases, and suppliers.
          • -
        • Analyze your sales, margins, and profitability with various reports and statistics.
          • -
        • Export your data to other Ebp software or to Excel, Word, or PDF formats.
          • -
        -

        Ebp Devis Facturation 2012 is a reliable and secure solution that complies with the legal requirements and the anti-fraud law. It also offers a technical support service and regular updates. Ebp Devis Facturation 2012 is compatible with Windows XP, Vista, 7, 8, and 10 operating systems.

        -

        If you are looking for a simple and effective solution for invoicing and business management, Ebp Devis Facturation 2012 is the software for you. You can download a free trial version from the official website[^2^] or buy the full version for 149€ (VAT included).

        Ebp Devis Facturation 2012 is a software that can help you save time and money in your invoicing and business management. You can create invoices and quotes in a few clicks, and send them by email or print them. You can also automate your reminders and follow-ups, and avoid late payments and bad debts. You can also manage your stock and purchases efficiently, and avoid stock-outs and overstocks. You can also monitor your business performance and make informed decisions with the help of various reports and statistics.

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        Ebp Devis Facturation 2012 is a software that can help you comply with the legal requirements and the anti-fraud law. You can generate invoices that respect the mandatory mentions and the VAT rules. You can also secure your data with encryption and backups, and prevent any fraud or manipulation. You can also benefit from the technical support service and the regular updates from Ebp, and stay up to date with the latest changes and features.

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        Ebp Devis Facturation 2012 is a software that can help you comply with the legal obligations and the anti-fraud law in your invoicing and business management. You can create invoices that follow the legal standards and the VAT regulations. You can also protect your data with encryption and backups, and prevent any fraud or tampering. You can also benefit from the technical support service and the regular updates from Ebp, and stay current with the latest changes and features.

        7196e7f11a
        -
        -
        \ No newline at end of file diff --git a/spaces/tobiascz/demotime/pytorch_grad_cam/activations_and_gradients.py b/spaces/tobiascz/demotime/pytorch_grad_cam/activations_and_gradients.py deleted file mode 100644 index f6f2c09d8c2941757e0356a619d0b4e7ed44a64f..0000000000000000000000000000000000000000 --- a/spaces/tobiascz/demotime/pytorch_grad_cam/activations_and_gradients.py +++ /dev/null @@ -1,46 +0,0 @@ -class ActivationsAndGradients: - """ Class for extracting activations and - registering gradients from targetted intermediate layers """ - - def __init__(self, model, target_layers, reshape_transform): - self.model = model - self.gradients = [] - self.activations = [] - self.reshape_transform = reshape_transform - self.handles = [] - for target_layer in target_layers: - self.handles.append( - target_layer.register_forward_hook(self.save_activation)) - # Because of https://github.com/pytorch/pytorch/issues/61519, - # we don't use backward hook to record gradients. - self.handles.append( - target_layer.register_forward_hook(self.save_gradient)) - - def save_activation(self, module, input, output): - activation = output - - if self.reshape_transform is not None: - activation = self.reshape_transform(activation) - self.activations.append(activation.cpu().detach()) - - def save_gradient(self, module, input, output): - if not hasattr(output, "requires_grad") or not output.requires_grad: - # You can only register hooks on tensor requires grad. - return - - # Gradients are computed in reverse order - def _store_grad(grad): - if self.reshape_transform is not None: - grad = self.reshape_transform(grad) - self.gradients = [grad.cpu().detach()] + self.gradients - - output.register_hook(_store_grad) - - def __call__(self, x): - self.gradients = [] - self.activations = [] - return self.model(x) - - def release(self): - for handle in self.handles: - handle.remove() diff --git a/spaces/tomandandy/MusicGen3/tests/data/test_audio.py b/spaces/tomandandy/MusicGen3/tests/data/test_audio.py deleted file mode 100644 index 40c0d5ed69eff92a766dc6d176e532f0df6c2b5e..0000000000000000000000000000000000000000 --- a/spaces/tomandandy/MusicGen3/tests/data/test_audio.py +++ /dev/null @@ -1,239 +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. - -from itertools import product -import random - -import numpy as np -import torch -import torchaudio - -from audiocraft.data.audio import audio_info, audio_read, audio_write, _av_read - -from ..common_utils import TempDirMixin, get_white_noise, save_wav - - -class TestInfo(TempDirMixin): - - def test_info_mp3(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - wav = get_white_noise(ch, int(sample_rate * duration)) - path = self.get_temp_path('sample_wav.mp3') - save_wav(path, wav, sample_rate) - info = audio_info(path) - assert info.sample_rate == sample_rate - assert info.channels == ch - # we cannot trust torchaudio for num_frames, so we don't check - - def _test_info_format(self, ext: str): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'sample_wav{ext}') - save_wav(path, wav, sample_rate) - info = audio_info(path) - assert info.sample_rate == sample_rate - assert info.channels == ch - assert np.isclose(info.duration, duration, atol=1e-5) - - def test_info_wav(self): - self._test_info_format('.wav') - - def test_info_flac(self): - self._test_info_format('.flac') - - def test_info_ogg(self): - self._test_info_format('.ogg') - - def test_info_m4a(self): - # TODO: generate m4a file programmatically - # self._test_info_format('.m4a') - pass - - -class TestRead(TempDirMixin): - - def test_read_full_wav(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - read_wav, read_sr = audio_read(path) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == wav.shape[1] - assert torch.allclose(read_wav, wav, rtol=1e-03, atol=1e-04) - - def test_read_partial_wav(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - read_duration = torch.rand(1).item() - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - read_frames = int(sample_rate * read_duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - read_wav, read_sr = audio_read(path, 0, read_duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == read_frames - assert torch.allclose(read_wav[..., 0:read_frames], wav[..., 0:read_frames], rtol=1e-03, atol=1e-04) - - def test_read_seek_time_wav(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - read_duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - seek_time = torch.rand(1).item() - read_wav, read_sr = audio_read(path, seek_time, read_duration) - seek_frames = int(sample_rate * seek_time) - expected_frames = n_frames - seek_frames - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == expected_frames - assert torch.allclose(read_wav, wav[..., seek_frames:], rtol=1e-03, atol=1e-04) - - def test_read_seek_time_wav_padded(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - read_duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - read_frames = int(sample_rate * read_duration) - wav = get_white_noise(ch, n_frames).clamp(-0.99, 0.99) - path = self.get_temp_path('sample_wav.wav') - save_wav(path, wav, sample_rate) - seek_time = torch.rand(1).item() - seek_frames = int(sample_rate * seek_time) - expected_frames = n_frames - seek_frames - read_wav, read_sr = audio_read(path, seek_time, read_duration, pad=True) - expected_pad_wav = torch.zeros(wav.shape[0], read_frames - expected_frames) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[1] == read_frames - assert torch.allclose(read_wav[..., :expected_frames], wav[..., seek_frames:], rtol=1e-03, atol=1e-04) - assert torch.allclose(read_wav[..., expected_frames:], expected_pad_wav) - - -class TestAvRead(TempDirMixin): - - def test_avread_seek_base(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 2. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'reference_a_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - for _ in range(100): - # seek will always load a full duration segment in the file - seek_time = random.uniform(0.0, 1.0) - seek_duration = random.uniform(0.001, 1.0) - read_wav, read_sr = _av_read(path, seek_time, seek_duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == int(seek_duration * sample_rate) - - def test_avread_seek_partial(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'reference_b_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - for _ in range(100): - # seek will always load a partial segment - seek_time = random.uniform(0.5, 1.) - seek_duration = 1. - expected_num_frames = n_frames - int(seek_time * sample_rate) - read_wav, read_sr = _av_read(path, seek_time, seek_duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == expected_num_frames - - def test_avread_seek_outofbound(self): - sample_rates = [8000, 16_000] - channels = [1, 2] - duration = 1. - for sample_rate, ch in product(sample_rates, channels): - n_frames = int(sample_rate * duration) - wav = get_white_noise(ch, n_frames) - path = self.get_temp_path(f'reference_c_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - seek_time = 1.5 - read_wav, read_sr = _av_read(path, seek_time, 1.) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == 0 - - def test_avread_seek_edge(self): - sample_rates = [8000, 16_000] - # some of these values will have - # int(((frames - 1) / sample_rate) * sample_rate) != (frames - 1) - n_frames = [1000, 1001, 1002] - channels = [1, 2] - for sample_rate, ch, frames in product(sample_rates, channels, n_frames): - duration = frames / sample_rate - wav = get_white_noise(ch, frames) - path = self.get_temp_path(f'reference_d_{sample_rate}_{ch}.wav') - save_wav(path, wav, sample_rate) - seek_time = (frames - 1) / sample_rate - seek_frames = int(seek_time * sample_rate) - read_wav, read_sr = _av_read(path, seek_time, duration) - assert read_sr == sample_rate - assert read_wav.shape[0] == wav.shape[0] - assert read_wav.shape[-1] == (frames - seek_frames) - - -class TestAudioWrite(TempDirMixin): - - def test_audio_write_wav(self): - torch.manual_seed(1234) - sample_rates = [8000, 16_000] - n_frames = [1000, 1001, 1002] - channels = [1, 2] - strategies = ["peak", "clip", "rms"] - formats = ["wav", "mp3"] - for sample_rate, ch, frames in product(sample_rates, channels, n_frames): - for format_, strategy in product(formats, strategies): - wav = get_white_noise(ch, frames) - path = self.get_temp_path(f'pred_{sample_rate}_{ch}') - audio_write(path, wav, sample_rate, format_, strategy=strategy) - read_wav, read_sr = torchaudio.load(f'{path}.{format_}') - if format_ == "wav": - assert read_wav.shape == wav.shape - - if format_ == "wav" and strategy in ["peak", "rms"]: - rescaled_read_wav = read_wav / read_wav.abs().max() * wav.abs().max() - # for a Gaussian, the typical max scale will be less than ~5x the std. - # The error when writing to disk will ~ 1/2**15, and when rescaling, 5x that. - # For RMS target, rescaling leaves more headroom by default, leading - # to a 20x rescaling typically - atol = (5 if strategy == "peak" else 20) / 2**15 - delta = (rescaled_read_wav - wav).abs().max() - assert torch.allclose(wav, rescaled_read_wav, rtol=0, atol=atol), (delta, atol) - formats = ["wav"] # faster unit tests diff --git a/spaces/tomofi/MMOCR/configs/_base_/recog_models/robust_scanner.py b/spaces/tomofi/MMOCR/configs/_base_/recog_models/robust_scanner.py deleted file mode 100644 index 4cc2fa108855a102e1f4e48b6f94bac3b7f7d644..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/configs/_base_/recog_models/robust_scanner.py +++ /dev/null @@ -1,24 +0,0 @@ -label_convertor = dict( - type='AttnConvertor', dict_type='DICT90', with_unknown=True) - -hybrid_decoder = dict(type='SequenceAttentionDecoder') - -position_decoder = dict(type='PositionAttentionDecoder') - -model = dict( - type='RobustScanner', - backbone=dict(type='ResNet31OCR'), - encoder=dict( - type='ChannelReductionEncoder', - in_channels=512, - out_channels=128, - ), - decoder=dict( - type='RobustScannerDecoder', - dim_input=512, - dim_model=128, - hybrid_decoder=hybrid_decoder, - position_decoder=position_decoder), - loss=dict(type='SARLoss'), - label_convertor=label_convertor, - max_seq_len=30) diff --git a/spaces/tomofi/MMOCR/docs/zh_cn/stats.py b/spaces/tomofi/MMOCR/docs/zh_cn/stats.py deleted file mode 100644 index 0d2ece5dd6910446fc896e52027afff78db3e450..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/docs/zh_cn/stats.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python -# Copyright (c) OpenMMLab. All rights reserved. -import functools as func -import glob -import re -from os.path import basename, splitext - -import numpy as np -import titlecase - - -def title2anchor(name): - return re.sub(r'-+', '-', re.sub(r'[^a-zA-Z0-9]', '-', - name.strip().lower())).strip('-') - - -# Count algorithms - -files = sorted(glob.glob('*_models.md')) - -stats = [] - -for f in files: - with open(f, 'r') as content_file: - content = content_file.read() - - # Remove the blackquote notation from the paper link under the title - # for better layout in readthedocs - expr = r'(^## \s*?.*?\s+?)>\s*?(\[.*?\]\(.*?\))' - content = re.sub(expr, r'\1\2', content, flags=re.MULTILINE) - with open(f, 'w') as content_file: - content_file.write(content) - - # title - title = content.split('\n')[0].replace('#', '') - - # count papers - exclude_papertype = ['ABSTRACT', 'IMAGE'] - exclude_expr = ''.join(f'(?!{s})' for s in exclude_papertype) - expr = rf''\ - r'\s*\n.*?\btitle\s*=\s*{(.*?)}' - papers = set( - (papertype, titlecase.titlecase(paper.lower().strip())) - for (papertype, paper) in re.findall(expr, content, re.DOTALL)) - print(papers) - # paper links - revcontent = '\n'.join(list(reversed(content.splitlines()))) - paperlinks = {} - for _, p in papers: - q = p.replace('\\', '\\\\').replace('?', '\\?') - paper_link = title2anchor( - re.search( - rf'\btitle\s*=\s*{{\s*{q}\s*}}.*?\n## (.*?)\s*[,;]?\s*\n', - revcontent, re.DOTALL | re.IGNORECASE).group(1)) - paperlinks[p] = f'[{p}]({splitext(basename(f))[0]}.html#{paper_link})' - paperlist = '\n'.join( - sorted(f' - [{t}] {paperlinks[x]}' for t, x in papers)) - # count configs - configs = set(x.lower().strip() - for x in re.findall(r'https.*configs/.*\.py', content)) - - # count ckpts - ckpts = set(x.lower().strip() - for x in re.findall(r'https://download.*\.pth', content) - if 'mmocr' in x) - - statsmsg = f""" -## [{title}]({f}) - -* 模型权重文件数量： {len(ckpts)} -* 配置文件数量： {len(configs)} -* 论文数量： {len(papers)} -{paperlist} - - """ - - stats.append((papers, configs, ckpts, statsmsg)) - -allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _, _ in stats]) -allconfigs = func.reduce(lambda a, b: a.union(b), [c for _, c, _, _ in stats]) -allckpts = func.reduce(lambda a, b: a.union(b), [c for _, _, c, _ in stats]) -msglist = '\n'.join(x for _, _, _, x in stats) - -papertypes, papercounts = np.unique([t for t, _ in allpapers], - return_counts=True) -countstr = '\n'.join( - [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)]) - -modelzoo = f""" -# 统计数据 - -* 模型权重文件数量： {len(allckpts)} -* 配置文件数量： {len(allconfigs)} -* 论文数量： {len(allpapers)} -{countstr} - -{msglist} -""" - -with open('modelzoo.md', 'w') as f: - f.write(modelzoo) diff --git a/spaces/tomofi/MMOCR/mmocr/models/textrecog/decoders/__init__.py b/spaces/tomofi/MMOCR/mmocr/models/textrecog/decoders/__init__.py deleted file mode 100644 index ae91b8bb8571736d74a63a820257dc42700a725f..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MMOCR/mmocr/models/textrecog/decoders/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) OpenMMLab. All rights reserved. -from .abinet_language_decoder import ABILanguageDecoder -from .abinet_vision_decoder import ABIVisionDecoder -from .base_decoder import BaseDecoder -from .crnn_decoder import CRNNDecoder -from .nrtr_decoder import NRTRDecoder -from .position_attention_decoder import PositionAttentionDecoder -from .robust_scanner_decoder import RobustScannerDecoder -from .sar_decoder import ParallelSARDecoder, SequentialSARDecoder -from .sar_decoder_with_bs import ParallelSARDecoderWithBS -from .sequence_attention_decoder import SequenceAttentionDecoder - -__all__ = [ - 'CRNNDecoder', 'ParallelSARDecoder', 'SequentialSARDecoder', - 'ParallelSARDecoderWithBS', 'NRTRDecoder', 'BaseDecoder', - 'SequenceAttentionDecoder', 'PositionAttentionDecoder', - 'RobustScannerDecoder', 'ABILanguageDecoder', 'ABIVisionDecoder' -] diff --git a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/structures/segmentation_mask.py b/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/structures/segmentation_mask.py deleted file mode 100644 index 0d1b0722d4f3366de11166dca7803dd914ef6bd7..0000000000000000000000000000000000000000 --- a/spaces/tomofi/MaskTextSpotterV3-OCR/maskrcnn_benchmark/structures/segmentation_mask.py +++ /dev/null @@ -1,766 +0,0 @@ -#!/usr/bin/env python3 -# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. -import cv2 -import numpy as np -import pycocotools.mask as mask_utils -import torch -from maskrcnn_benchmark.utils.chars import char2num -import pyclipper -# from PIL import Image -from shapely import affinity -from shapely.geometry import Polygon as ShapePolygon - - -# transpose -FLIP_LEFT_RIGHT = 0 -FLIP_TOP_BOTTOM = 1 - - -def convert_2d_tuple(t): - a = [] - for i in t: - a.extend(list(i)) - return a - - -class Mask(object): - """ - This class is unfinished and not meant for use yet - It is supposed to contain the mask for an object as - a 2d tensor - """ - - def __init__(self, masks, size, mode): - self.masks = masks - self.size = size - self.mode = mode - - def transpose(self, method): - if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): - raise NotImplementedError( - "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" - ) - - width, height = self.size - if method == FLIP_LEFT_RIGHT: - dim = width - # idx = 2 - elif method == FLIP_TOP_BOTTOM: - dim = height - # idx = 1 - - flip_idx = list(range(dim)[::-1]) - flipped_masks = self.masks.index_select(dim, flip_idx) - return Mask(flipped_masks, self.size, self.mode) - - def crop(self, box): - w, h = box[2] - box[0], box[3] - box[1] - - cropped_masks = self.masks[:, box[1] : box[3], box[0] : box[2]] - return Mask(cropped_masks, size=(w, h), mode=self.mode) - - def resize(self, size, *args, **kwargs): - pass - - -class SegmentationMask(object): - """ - This class stores the segmentations for all objects in the image - """ - - def __init__(self, polygons, size, mode=None): - """ - Arguments: - polygons: a list of list of lists of numbers. The first - level of the list correspond to individual instances, - the second level to all the polygons that compose the - object, and the third level to the polygon coordinates. - """ - assert isinstance(polygons, list) - - self.polygons = [Polygons(p, size, mode) for p in polygons] - self.size = size - self.mode = mode - - def transpose(self, method): - if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): - raise NotImplementedError( - "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" - ) - - flipped = [] - for polygon in self.polygons: - flipped.append(polygon.transpose(method)) - return SegmentationMask(flipped, size=self.size, mode=self.mode) - - def crop(self, box, keep_ind=None): - w, h = box[2] - box[0], box[3] - box[1] - if keep_ind is not None: - self.polygons = np.array(self.polygons) - self.polygons = self.polygons[keep_ind] - cropped = [] - for polygon in self.polygons: - cropped.append(polygon.crop(box)) - return SegmentationMask(cropped, size=(w, h), mode=self.mode) - - def rotate(self, angle, r_c, start_h, start_w): - rotated = [] - for polygon in self.polygons: - rotated.append(polygon.rotate(angle, r_c, start_h, start_w)) - return SegmentationMask(rotated, size=(r_c[0] * 2, r_c[1] * 2), mode=self.mode) - - def resize(self, size, *args, **kwargs): - scaled = [] - for polygon in self.polygons: - scaled.append(polygon.resize(size, *args, **kwargs)) - return SegmentationMask(scaled, size=size, mode=self.mode) - - def set_size(self, size): - self.size = size - for polygon in self.polygons: - polygon.set_size(size) - - def to(self, *args, **kwargs): - return self - - def __getitem__(self, item): - if isinstance(item, (int, slice)): - selected_polygons = [self.polygons[item]] - else: - # advanced indexing on a single dimension - selected_polygons = [] - if isinstance(item, torch.Tensor) and item.dtype == torch.bool: - item = item.nonzero() - item = item.squeeze(1) if item.numel() > 0 else item - item = item.tolist() - for i in item: - selected_polygons.append(self.polygons[i]) - return SegmentationMask(selected_polygons, size=self.size, mode=self.mode) - - def __iter__(self): - return iter(self.polygons) - - def __repr__(self): - s = self.__class__.__name__ + "(" - s += "num_instances={}, ".format(len(self.polygons)) - s += "image_width={}, ".format(self.size[0]) - s += "image_height={})".format(self.size[1]) - return s - - def size(self): - return self.size - - def get_polygons(self): - return self.polygons - - def to_np_polygon(self): - np_polygons = [] - for polygon in self.polygons: - polys = polygon.get_polygons() - for poly in polys: - np_poly = poly.numpy() - np_polygons.append(np_poly) - return np_polygons - - - def convert_seg_map(self, labels, shrink_ratio, seg_size, ignore_difficult=True): - # width, height = self.size - # assert self.size[0] == seg_size[1] - # assert self.size[1] == seg_size[0] - height, width = seg_size[0], seg_size[1] - seg_map = np.zeros((1, height, width), dtype=np.uint8) - training_mask = np.ones((height, width), dtype=np.uint8) - for poly, label in zip(self.polygons, labels): - poly = poly.get_polygons()[0] - poly = poly.reshape((-1, 2)).numpy() - if ignore_difficult and label.item() == -1: - cv2.fillPoly(training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0) - continue - if poly.shape[0] < 4: - continue - p = ShapePolygon(poly) - if p.length == 0: - continue - try: - d = p.area * (1 - np.power(shrink_ratio, 2)) / p.length - except: - continue - subj = [tuple(s) for s in poly] - pco = pyclipper.PyclipperOffset() - pco.AddPath(subj, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) - s = pco.Execute(-d) - if s == []: - cv2.fillPoly(training_mask, poly.astype(np.int32)[np.newaxis, :, :], 0) - continue - out = convert_2d_tuple(s[0]) - out = np.array(out).reshape(-1, 2) - cv2.fillPoly(seg_map[0, :, :], [out.astype(np.int32)], 1) - return seg_map, training_mask - - -class Polygons(object): - """ - This class holds a set of polygons that represents a single instance - of an object mask. The object can be represented as a set of - polygons - """ - - def __init__(self, polygons, size, mode): - # assert isinstance(polygons, list), '{}'.format(polygons) - if isinstance(polygons, list): - polygons = [torch.as_tensor(p, dtype=torch.float32) for p in polygons] - elif isinstance(polygons, Polygons): - polygons = polygons.polygons - - self.polygons = polygons - self.size = size - self.mode = mode - - def transpose(self, method): - if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): - raise NotImplementedError( - "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" - ) - - flipped_polygons = [] - width, height = self.size - if method == FLIP_LEFT_RIGHT: - dim = width - idx = 0 - elif method == FLIP_TOP_BOTTOM: - dim = height - idx = 1 - - for poly in self.polygons: - p = poly.clone() - TO_REMOVE = 1 - p[idx::2] = dim - poly[idx::2] - TO_REMOVE - flipped_polygons.append(p) - - return Polygons(flipped_polygons, size=self.size, mode=self.mode) - - def rotate(self, angle, r_c, start_h, start_w): - poly = self.polygons[0].numpy().reshape(-1, 2) - poly[:, 0] += start_w - poly[:, 1] += start_h - polys = ShapePolygon(poly) - r_polys = list(affinity.rotate(polys, angle, r_c).boundary.coords[:-1]) - p = [] - for r in r_polys: - p += list(r) - return Polygons([p], size=self.size, mode=self.mode) - - def crop(self, box): - w, h = box[2] - box[0], box[3] - box[1] - - # TODO chck if necessary - w = max(w, 1) - h = max(h, 1) - - cropped_polygons = [] - - for poly in self.polygons: - p = poly.clone() - p[0::2] = p[0::2] - box[0] # .clamp(min=0, max=w) - p[1::2] = p[1::2] - box[1] # .clamp(min=0, max=h) - cropped_polygons.append(p) - - return Polygons(cropped_polygons, size=(w, h), mode=self.mode) - - def resize(self, size, *args, **kwargs): - ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size)) - if ratios[0] == ratios[1]: - ratio = ratios[0] - scaled_polys = [p * ratio for p in self.polygons] - return Polygons(scaled_polys, size, mode=self.mode) - - ratio_w, ratio_h = ratios - scaled_polygons = [] - for poly in self.polygons: - p = poly.clone() - p[0::2] *= ratio_w - p[1::2] *= ratio_h - scaled_polygons.append(p) - - return Polygons(scaled_polygons, size=size, mode=self.mode) - - def convert(self, mode): - width, height = self.size - if mode == "mask": - # print([p.numpy() for p in self.polygons]) - try: - rles = mask_utils.frPyObjects( - [p.numpy() for p in self.polygons], height, width - ) - except: - print([p.numpy() for p in self.polygons]) - mask = torch.ones((height, width), dtype=torch.uint8) - return mask - rle = mask_utils.merge(rles) - mask = mask_utils.decode(rle) - mask = torch.from_numpy(mask) - # TODO add squeeze? - return mask - - def set_size(self, size): - self.size = size - - def get_polygons(self): - return self.polygons - - def __repr__(self): - s = self.__class__.__name__ + "(" - s += "num_polygons={}, ".format(len(self.polygons)) - s += "image_width={}, ".format(self.size[0]) - s += "image_height={}, ".format(self.size[1]) - s += "mode={})".format(self.mode) - return s - - -class CharPolygons(object): - """ - This class holds a set of polygons that represents a single instance - of an object mask. The object can be represented as a set of - polygons - """ - - def __init__( - self, - char_boxes, - word=None, - use_char_ann=False, - char_classes=None, - size=None, - mode=None, - char_num_classes=37, - ): - if isinstance(char_boxes, CharPolygons): - if char_classes is None: - char_classes = char_boxes.char_classes - self.word = char_boxes.word - char_boxes = char_boxes.char_boxes - else: - if char_classes is None: - char_classes = [ - torch.as_tensor(p[8], dtype=torch.float32) for p in char_boxes - ] - char_boxes = [ - torch.as_tensor(p[:8], dtype=torch.float32) for p in char_boxes - ] - self.word = word - self.char_boxes = char_boxes - self.char_classes = char_classes - self.size = size - self.mode = mode - self.use_char_ann = use_char_ann - self.char_num_classes = char_num_classes - - def transpose(self, method): - if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): - raise NotImplementedError( - "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" - ) - - flipped_polygons = [] - width, height = self.size - if method == FLIP_LEFT_RIGHT: - dim = width - idx = 0 - elif method == FLIP_TOP_BOTTOM: - dim = height - idx = 1 - - for char_box in self.char_boxes: - p = char_box.clone() - TO_REMOVE = 1 - p[idx::2] = dim - char_box[idx::2] - TO_REMOVE - flipped_polygons.append(p) - - return CharPolygons( - flipped_polygons, - word=self.word, - use_char_ann=self.use_char_ann, - char_classes=self.char_classes, - size=self.size, - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - def crop(self, box): - w, h = box[2] - box[0], box[3] - box[1] - - # TODO chck if necessary - w = max(w, 1) - h = max(h, 1) - cropped_polygons = [] - for char_box in self.char_boxes: - p = char_box.clone() - p[0::2] = p[0::2] - box[0] # .clamp(min=0, max=w) - p[1::2] = p[1::2] - box[1] # .clamp(min=0, max=h) - cropped_polygons.append(p) - - return CharPolygons( - cropped_polygons, - word=self.word, - use_char_ann=self.use_char_ann, - char_classes=self.char_classes, - size=(w, h), - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - def rotate(self, angle, r_c, start_h, start_w): - r_polys = [] - for poly in self.char_boxes: - poly = poly.numpy() - poly[0::2] += start_w - poly[1::2] += start_h - poly = ShapePolygon(np.array(poly).reshape(4, 2)) - r_poly = np.array( - list(affinity.rotate(poly, angle, r_c).boundary.coords[:-1]) - ).reshape(-1, 8) - r_polys.append(r_poly[0]) - return CharPolygons( - r_polys, - word=self.word, - use_char_ann=self.use_char_ann, - char_classes=self.char_classes, - size=(r_c[0] * 2, r_c[1] * 2), - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - def resize(self, size, *args, **kwargs): - ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size)) - if ratios[0] == ratios[1]: - ratio = ratios[0] - scaled_polys = [p * ratio for p in self.char_boxes] - return CharPolygons( - scaled_polys, - word=self.word, - use_char_ann=self.use_char_ann, - char_classes=self.char_classes, - size=size, - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - ratio_w, ratio_h = ratios - scaled_polygons = [] - for poly in self.char_boxes: - p = poly.clone() - p[0::2] *= ratio_w - p[1::2] *= ratio_h - scaled_polygons.append(p) - - return CharPolygons( - scaled_polygons, - word=self.word, - use_char_ann=self.use_char_ann, - char_classes=self.char_classes, - size=size, - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - def set_size(self, size): - self.size = size - - def convert(self, mode): - width, height = self.size - if mode == "char_mask": - if not self.use_char_ann: - char_map = -np.ones((height, width)) - char_map_weight = np.zeros((self.char_num_classes,)) - else: - char_map = np.zeros((height, width)) - char_map_weight = np.ones((self.char_num_classes,)) - for i, p in enumerate(self.char_boxes): - poly = p.numpy().reshape(4, 2) - poly = shrink_poly(poly, 0.25) - cv2.fillPoly( - char_map, [poly.astype(np.int32)], int(self.char_classes[i]) - ) - pos_index = np.where(char_map > 0) - pos_num = pos_index[0].size - if pos_num > 0: - pos_weight = 1.0 * (height * width - pos_num) / pos_num - char_map_weight[1:] = pos_weight - return torch.from_numpy(char_map), torch.from_numpy(char_map_weight) - elif mode == "seq_char_mask": - decoder_target = self.char_num_classes * np.ones((32,)) - word_target = -np.ones((32,)) - if not self.use_char_ann: - char_map = -np.ones((height, width)) - char_map_weight = np.zeros((self.char_num_classes,)) - for i, char in enumerate(self.word): - if i > 31: - break - decoder_target[i] = char2num(char) - word_target[i] = char2num(char) - end_point = min(max(1, len(self.word)), 31) - word_target[end_point] = self.char_num_classes - else: - char_map = np.zeros((height, width)) - char_map_weight = np.ones((self.char_num_classes,)) - word_length = 0 - for i, p in enumerate(self.char_boxes): - poly = p.numpy().reshape(4, 2) - if i < 32: - decoder_target[i] = int(self.char_classes[i]) - word_target[i] = int(self.char_classes[i]) - word_length += 1 - poly = shrink_poly(poly, 0.25) - cv2.fillPoly( - char_map, [poly.astype(np.int32)], int(self.char_classes[i]) - ) - end_point = min(max(1, word_length), 31) - word_target[end_point] = self.char_num_classes - pos_index = np.where(char_map > 0) - pos_num = pos_index[0].size - if pos_num > 0: - pos_weight = 1.0 * (height * width - pos_num) / pos_num - char_map_weight[1:] = pos_weight - return ( - torch.from_numpy(char_map), - torch.from_numpy(char_map_weight), - torch.from_numpy(decoder_target), - torch.from_numpy(word_target), - ) - - def creat_color_map(self, n_class, width): - splits = int(np.ceil(np.power((n_class * 1.0), 1.0 / 3))) - maps = [] - for i in range(splits): - r = int(i * width * 1.0 / (splits - 1)) - for j in range(splits): - g = int(j * width * 1.0 / (splits - 1)) - for k in range(splits - 1): - b = int(k * width * 1.0 / (splits - 1)) - maps.append([r, g, b]) - return np.array(maps) - - def __repr__(self): - s = self.__class__.__name__ + "(" - s += "num_char_boxes={}, ".format(len(self.char_boxes)) - s += "num_char_classes={}, ".format(len(self.char_classes)) - s += "image_width={}, ".format(self.size[0]) - s += "image_height={}, ".format(self.size[1]) - s += "mode={})".format(self.mode) - return s - - -class SegmentationCharMask(object): - def __init__( - self, chars_boxes, words=None, use_char_ann=True, size=None, mode=None, char_num_classes=37 - ): - # self.chars_boxes=[CharPolygons(char_boxes, word=word, use_char_ann=use_char_ann, size=size, mode=mode) for char_boxes, word in zip(chars_boxes, words)] - if words is None: - self.chars_boxes = [ - CharPolygons( - char_boxes, - word=None, - use_char_ann=use_char_ann, - size=size, - mode=mode, - char_num_classes=char_num_classes, - ) - for char_boxes in chars_boxes - ] - else: - self.chars_boxes = [ - CharPolygons( - char_boxes, - word=words[i], - use_char_ann=use_char_ann, - size=size, - mode=mode, - char_num_classes=char_num_classes, - ) - for i, char_boxes in enumerate(chars_boxes) - ] - self.size = size - self.mode = mode - self.use_char_ann = use_char_ann - self.char_num_classes = char_num_classes - - def transpose(self, method): - if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): - raise NotImplementedError( - "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" - ) - - flipped = [] - for char_boxes in self.chars_boxes: - flipped.append(char_boxes.transpose(method)) - return SegmentationCharMask( - flipped, use_char_ann=self.use_char_ann, size=self.size, mode=self.mode, char_num_classes=self.char_num_classes - ) - - def crop(self, box, keep_ind): - cropped = [] - w, h = box[2] - box[0], box[3] - box[1] - if keep_ind is not None: - self.chars_boxes = np.array(self.chars_boxes) - self.chars_boxes = self.chars_boxes[keep_ind] - for char_boxes in self.chars_boxes: - cropped.append(char_boxes.crop(box)) - return SegmentationCharMask( - cropped, use_char_ann=self.use_char_ann, size=(w, h), mode=self.mode - ) - - def resize(self, size, *args, **kwargs): - scaled = [] - for char_boxes in self.chars_boxes: - scaled.append(char_boxes.resize(size, *args, **kwargs)) - return SegmentationCharMask( - scaled, use_char_ann=self.use_char_ann, size=size, mode=self.mode, char_num_classes=self.char_num_classes - ) - - def set_size(self, size): - self.size = size - for char_box in self.chars_boxes: - char_box.set_size(size) - - def rotate(self, angle, r_c, start_h, start_w): - rotated = [] - for char_boxes in self.chars_boxes: - rotated.append(char_boxes.rotate(angle, r_c, start_h, start_w)) - return SegmentationCharMask( - rotated, - use_char_ann=self.use_char_ann, - size=(r_c[0] * 2, r_c[1] * 2), - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - def __iter__(self): - return iter(self.chars_boxes) - - def __getitem__(self, item): - if isinstance(item, (int, slice)): - selected_chars_boxes = [self.chars_boxes[item]] - else: - # advanced indexing on a single dimension - selected_chars_boxes = [] - if isinstance(item, torch.Tensor) and item.dtype == torch.bool: - item = item.nonzero() - item = item.squeeze(1) if item.numel() > 0 else item - item = item.tolist() - for i in item: - if i >= len(self.chars_boxes): - print(i) - print("chars_boxes.shape: ", len(self.chars_boxes)) - input() - selected_chars_boxes.append(self.chars_boxes[i]) - return SegmentationCharMask( - selected_chars_boxes, - use_char_ann=self.use_char_ann, - size=self.size, - mode=self.mode, - char_num_classes=self.char_num_classes, - ) - - def __repr__(self): - s = self.__class__.__name__ + "(" - s += "num_char_boxes={}, ".format(len(self.chars_boxes)) - s += "image_width={}, ".format(self.size[0]) - s += "image_height={})".format(self.size[1]) - return s - - -def shrink_poly(poly, shrink): - # shrink ratio - R = shrink - r = [None, None, None, None] - for i in range(4): - r[i] = min( - np.linalg.norm(poly[i] - poly[(i + 1) % 4]), - np.linalg.norm(poly[i] - poly[(i - 1) % 4]), - ) - # find the longer pair - if np.linalg.norm(poly[0] - poly[1]) + np.linalg.norm( - poly[2] - poly[3] - ) > np.linalg.norm(poly[0] - poly[3]) + np.linalg.norm(poly[1] - poly[2]): - # first move (p0, p1), (p2, p3), then (p0, p3), (p1, p2) - ## p0, p1 - theta = np.arctan2((poly[1][1] - poly[0][1]), (poly[1][0] - poly[0][0])) - poly[0][0] += R * r[0] * np.cos(theta) - poly[0][1] += R * r[0] * np.sin(theta) - poly[1][0] -= R * r[1] * np.cos(theta) - poly[1][1] -= R * r[1] * np.sin(theta) - ## p2, p3 - theta = np.arctan2((poly[2][1] - poly[3][1]), (poly[2][0] - poly[3][0])) - poly[3][0] += R * r[3] * np.cos(theta) - poly[3][1] += R * r[3] * np.sin(theta) - poly[2][0] -= R * r[2] * np.cos(theta) - poly[2][1] -= R * r[2] * np.sin(theta) - ## p0, p3 - theta = np.arctan2((poly[3][0] - poly[0][0]), (poly[3][1] - poly[0][1])) - poly[0][0] += R * r[0] * np.sin(theta) - poly[0][1] += R * r[0] * np.cos(theta) - poly[3][0] -= R * r[3] * np.sin(theta) - poly[3][1] -= R * r[3] * np.cos(theta) - ## p1, p2 - theta = np.arctan2((poly[2][0] - poly[1][0]), (poly[2][1] - poly[1][1])) - poly[1][0] += R * r[1] * np.sin(theta) - poly[1][1] += R * r[1] * np.cos(theta) - poly[2][0] -= R * r[2] * np.sin(theta) - poly[2][1] -= R * r[2] * np.cos(theta) - else: - ## p0, p3 - # print poly - theta = np.arctan2((poly[3][0] - poly[0][0]), (poly[3][1] - poly[0][1])) - poly[0][0] += R * r[0] * np.sin(theta) - poly[0][1] += R * r[0] * np.cos(theta) - poly[3][0] -= R * r[3] * np.sin(theta) - poly[3][1] -= R * r[3] * np.cos(theta) - ## p1, p2 - theta = np.arctan2((poly[2][0] - poly[1][0]), (poly[2][1] - poly[1][1])) - poly[1][0] += R * r[1] * np.sin(theta) - poly[1][1] += R * r[1] * np.cos(theta) - poly[2][0] -= R * r[2] * np.sin(theta) - poly[2][1] -= R * r[2] * np.cos(theta) - ## p0, p1 - theta = np.arctan2((poly[1][1] - poly[0][1]), (poly[1][0] - poly[0][0])) - poly[0][0] += R * r[0] * np.cos(theta) - poly[0][1] += R * r[0] * np.sin(theta) - poly[1][0] -= R * r[1] * np.cos(theta) - poly[1][1] -= R * r[1] * np.sin(theta) - ## p2, p3 - theta = np.arctan2((poly[2][1] - poly[3][1]), (poly[2][0] - poly[3][0])) - poly[3][0] += R * r[3] * np.cos(theta) - poly[3][1] += R * r[3] * np.sin(theta) - poly[2][0] -= R * r[2] * np.cos(theta) - poly[2][1] -= R * r[2] * np.sin(theta) - return poly - - -def shrink_rect(poly, shrink): - xmin = min(poly[:, 0]) - xmax = max(poly[:, 0]) - ymin = min(poly[:, 1]) - ymax = max(poly[:, 1]) - # assert xmax > xmin and ymax > ymin - xc = (xmax + xmin) / 2 - yc = (ymax + ymin) / 2 - w = xmax - xmin - h = ymax - ymin - sxmin = xc - w / 2 * shrink - sxmax = xc + w / 2 * shrink - symin = yc - h / 2 * shrink - symax = yc + h / 2 * shrink - return np.array([sxmin, symin, sxmax, symin, sxmax, symax, sxmin, symax]).reshape( - (4, 2) - ) - - -def is_poly_inbox(poly, height, width): - min_x = min(poly[:, 0]) - min_y = min(poly[:, 1]) - max_x = max(poly[:, 0]) - max_y = max(poly[:, 1]) - if (max_x < 0 and max_y < 0) or (min_x > width and min_y > height): - return False - else: - return True diff --git a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/instaboost/README.md b/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/instaboost/README.md deleted file mode 100644 index 4755073f4b77091a4e992c626f9129f96359829f..0000000000000000000000000000000000000000 --- a/spaces/tomofi/NDLOCR/src/ndl_layout/mmdetection/configs/instaboost/README.md +++ /dev/null @@ -1,44 +0,0 @@ -# InstaBoost for MMDetection - - - -Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801). - -```latex -@inproceedings{fang2019instaboost, - title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting}, - author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu}, - booktitle={Proceedings of the IEEE International Conference on Computer Vision}, - pages={682--691}, - year={2019} -} -``` - -## Usage - -### Requirements - -You need to install `instaboostfast` before using it. - -```shell -pip install instaboostfast -``` - -The code and more details can be found [here](https://github.com/GothicAi/Instaboost). - -### Integration with MMDetection - -InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change **InstaBoost** configurations after **LoadImageFromFile**. We have provided examples like [this](mask_rcnn_r50_fpn_instaboost_4x#L121). You can refer to [`InstaBoostConfig`](https://github.com/GothicAi/InstaBoost-pypi#instaboostconfig) for more details. - -## Results and Models - -- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for conveinience of evaluation and comparison. In the paper, the results are obtained from `test-dev`. -- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework. -- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost). - -| Network | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | -| :-------------: | :--------: | :-----: | :------: | :------------: | :------:| :-----: | :------: | :-----------------: | -| Mask R-CNN | R-50-FPN | 4x | 4.4 | 17.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) | -| Mask R-CNN | R-101-FPN | 4x | 6.4 | | 42.5 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) | -| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) | -| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) | diff --git a/spaces/tracinginsights/F1-analysis/pages/Top_Speed_at_Speed_Trap.py b/spaces/tracinginsights/F1-analysis/pages/Top_Speed_at_Speed_Trap.py deleted file mode 100644 index a87d65873bb61728a4409ceb19c1a0a07b1e281e..0000000000000000000000000000000000000000 --- a/spaces/tracinginsights/F1-analysis/pages/Top_Speed_at_Speed_Trap.py +++ /dev/null @@ -1,24 +0,0 @@ -import streamlit as st -from repo_directory import Top_Speed_at_Speed_Trap -from repo_directory import button - -YEAR = st.selectbox( - 'Select Year', - (2023, 2022, 2021, 2020, 2019, 2018)) - -RACE = st.selectbox( - 'Select Race', - (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23)) - - -SESSION = st.selectbox( - 'Select Session', - ('FP1', 'FP2', 'FP3', 'Q', 'SQ', 'R')) - -laps = Top_Speed_at_Speed_Trap.get_laps(YEAR, RACE, SESSION) - -fastest_st = Top_Speed_at_Speed_Trap.get_fastest_speedtrap(laps) - -max_speeds = Top_Speed_at_Speed_Trap.get_max_speeds(fastest_st, laps,YEAR) - -Top_Speed_at_Speed_Trap.plot(max_speeds, fastest_st, YEAR, RACE, SESSION) \ No newline at end of file diff --git a/spaces/trttung1610/musicgen/audiocraft/data/audio.py b/spaces/trttung1610/musicgen/audiocraft/data/audio.py deleted file mode 100644 index 39c87047f5033d0016200df77004a9536e06e81a..0000000000000000000000000000000000000000 --- a/spaces/trttung1610/musicgen/audiocraft/data/audio.py +++ /dev/null @@ -1,216 +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. - -""" -Audio IO methods are defined in this module (info, read, write), -We rely on av library for faster read when possible, otherwise on torchaudio. -""" - -from dataclasses import dataclass -from pathlib import Path -import logging -import typing as tp - -import numpy as np -import soundfile -import torch -from torch.nn import functional as F -import torchaudio as ta - -import av - -from .audio_utils import f32_pcm, i16_pcm, normalize_audio - - -_av_initialized = False - - -def _init_av(): - global _av_initialized - if _av_initialized: - return - logger = logging.getLogger('libav.mp3') - logger.setLevel(logging.ERROR) - _av_initialized = True - - -@dataclass(frozen=True) -class AudioFileInfo: - sample_rate: int - duration: float - channels: int - - -def _av_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - _init_av() - with av.open(str(filepath)) as af: - stream = af.streams.audio[0] - sample_rate = stream.codec_context.sample_rate - duration = float(stream.duration * stream.time_base) - channels = stream.channels - return AudioFileInfo(sample_rate, duration, channels) - - -def _soundfile_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - info = soundfile.info(filepath) - return AudioFileInfo(info.samplerate, info.duration, info.channels) - - -def audio_info(filepath: tp.Union[str, Path]) -> AudioFileInfo: - # torchaudio no longer returns useful duration informations for some formats like mp3s. - filepath = Path(filepath) - if filepath.suffix in ['.flac', '.ogg']: # TODO: Validate .ogg can be safely read with av_info - # ffmpeg has some weird issue with flac. - return _soundfile_info(filepath) - else: - return _av_info(filepath) - - -def _av_read(filepath: tp.Union[str, Path], seek_time: float = 0, duration: float = -1.) -> tp.Tuple[torch.Tensor, int]: - """FFMPEG-based audio file reading using PyAV bindings. - Soundfile cannot read mp3 and av_read is more efficient than torchaudio. - - Args: - filepath (str or Path): Path to audio file to read. - seek_time (float): Time at which to start reading in the file. - duration (float): Duration to read from the file. If set to -1, the whole file is read. - Returns: - tuple of torch.Tensor, int: Tuple containing audio data and sample rate - """ - _init_av() - with av.open(str(filepath)) as af: - stream = af.streams.audio[0] - sr = stream.codec_context.sample_rate - num_frames = int(sr * duration) if duration >= 0 else -1 - frame_offset = int(sr * seek_time) - # we need a small negative offset otherwise we get some edge artifact - # from the mp3 decoder. - af.seek(int(max(0, (seek_time - 0.1)) / stream.time_base), stream=stream) - frames = [] - length = 0 - for frame in af.decode(streams=stream.index): - current_offset = int(frame.rate * frame.pts * frame.time_base) - strip = max(0, frame_offset - current_offset) - buf = torch.from_numpy(frame.to_ndarray()) - if buf.shape[0] != stream.channels: - buf = buf.view(-1, stream.channels).t() - buf = buf[:, strip:] - frames.append(buf) - length += buf.shape[1] - if num_frames > 0 and length >= num_frames: - break - assert frames - # If the above assert fails, it is likely because we seeked past the end of file point, - # in which case ffmpeg returns a single frame with only zeros, and a weird timestamp. - # This will need proper debugging, in due time. - wav = torch.cat(frames, dim=1) - assert wav.shape[0] == stream.channels - if num_frames > 0: - wav = wav[:, :num_frames] - return f32_pcm(wav), sr - - -def audio_read(filepath: tp.Union[str, Path], seek_time: float = 0., - duration: float = -1., pad: bool = False) -> tp.Tuple[torch.Tensor, int]: - """Read audio by picking the most appropriate backend tool based on the audio format. - - Args: - filepath (str or Path): Path to audio file to read. - seek_time (float): Time at which to start reading in the file. - duration (float): Duration to read from the file. If set to -1, the whole file is read. - pad (bool): Pad output audio if not reaching expected duration. - Returns: - tuple of torch.Tensor, int: Tuple containing audio data and sample rate. - """ - fp = Path(filepath) - if fp.suffix in ['.flac', '.ogg']: # TODO: check if we can safely use av_read for .ogg - # There is some bug with ffmpeg and reading flac - info = _soundfile_info(filepath) - frames = -1 if duration <= 0 else int(duration * info.sample_rate) - frame_offset = int(seek_time * info.sample_rate) - wav, sr = soundfile.read(filepath, start=frame_offset, frames=frames, dtype=np.float32) - assert info.sample_rate == sr, f"Mismatch of sample rates {info.sample_rate} {sr}" - wav = torch.from_numpy(wav).t().contiguous() - if len(wav.shape) == 1: - wav = torch.unsqueeze(wav, 0) - elif ( - fp.suffix in ['.wav', '.mp3'] and fp.suffix[1:] in ta.utils.sox_utils.list_read_formats() - and duration <= 0 and seek_time == 0 - ): - # Torchaudio is faster if we load an entire file at once. - wav, sr = ta.load(fp) - else: - wav, sr = _av_read(filepath, seek_time, duration) - if pad and duration > 0: - expected_frames = int(duration * sr) - wav = F.pad(wav, (0, expected_frames - wav.shape[-1])) - return wav, sr - - -def audio_write(stem_name: tp.Union[str, Path], - wav: torch.Tensor, sample_rate: int, - format: str = 'wav', mp3_rate: int = 320, normalize: bool = True, - strategy: str = 'peak', peak_clip_headroom_db: float = 1, - rms_headroom_db: float = 18, loudness_headroom_db: float = 14, - loudness_compressor: bool = False, - log_clipping: bool = True, make_parent_dir: bool = True, - add_suffix: bool = True) -> Path: - """Convenience function for saving audio to disk. Returns the filename the audio was written to. - - Args: - stem_name (str or Path): Filename without extension which will be added automatically. - format (str): Either "wav" or "mp3". - mp3_rate (int): kbps when using mp3s. - normalize (bool): if `True` (default), normalizes according to the prescribed - strategy (see after). If `False`, the strategy is only used in case clipping - would happen. - strategy (str): Can be either 'clip', 'peak', or 'rms'. Default is 'peak', - i.e. audio is normalized by its largest value. RMS normalizes by root-mean-square - with extra headroom to avoid clipping. 'clip' just clips. - peak_clip_headroom_db (float): Headroom in dB when doing 'peak' or 'clip' strategy. - rms_headroom_db (float): Headroom in dB when doing 'rms' strategy. This must be much larger - than the `peak_clip` one to avoid further clipping. - loudness_headroom_db (float): Target loudness for loudness normalization. - loudness_compressor (bool): Uses tanh for soft clipping when strategy is 'loudness'. - when strategy is 'loudness' log_clipping (bool): If True, basic logging on stderr when clipping still - occurs despite strategy (only for 'rms'). - make_parent_dir (bool): Make parent directory if it doesn't exist. - Returns: - Path: Path of the saved audio. - """ - assert wav.dtype.is_floating_point, "wav is not floating point" - if wav.dim() == 1: - wav = wav[None] - elif wav.dim() > 2: - raise ValueError("Input wav should be at most 2 dimension.") - assert wav.isfinite().all() - wav = normalize_audio(wav, normalize, strategy, peak_clip_headroom_db, - rms_headroom_db, loudness_headroom_db, loudness_compressor, - log_clipping=log_clipping, sample_rate=sample_rate, - stem_name=str(stem_name)) - kwargs: dict = {} - if format == 'mp3': - suffix = '.mp3' - kwargs.update({"compression": mp3_rate}) - elif format == 'wav': - wav = i16_pcm(wav) - suffix = '.wav' - kwargs.update({"encoding": "PCM_S", "bits_per_sample": 16}) - else: - raise RuntimeError(f"Invalid format {format}. Only wav or mp3 are supported.") - if not add_suffix: - suffix = '' - path = Path(str(stem_name) + suffix) - if make_parent_dir: - path.parent.mkdir(exist_ok=True, parents=True) - try: - ta.save(path, wav, sample_rate, **kwargs) - except Exception: - if path.exists(): - # we do not want to leave half written files around. - path.unlink() - raise - return path diff --git a/spaces/ucalyptus/PTI/dnnlib/util.py b/spaces/ucalyptus/PTI/dnnlib/util.py deleted file mode 100644 index 76725336d01e75e1c68daa88be47f4fde0bbc63b..0000000000000000000000000000000000000000 --- a/spaces/ucalyptus/PTI/dnnlib/util.py +++ /dev/null @@ -1,477 +0,0 @@ -# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Miscellaneous utility classes and functions.""" - -import ctypes -import fnmatch -import importlib -import inspect -import numpy as np -import os -import shutil -import sys -import types -import io -import pickle -import re -import requests -import html -import hashlib -import glob -import tempfile -import urllib -import urllib.request -import uuid - -from distutils.util import strtobool -from typing import Any, List, Tuple, Union - - -# Util classes -# ------------------------------------------------------------------------------------------ - - -class EasyDict(dict): - """Convenience class that behaves like a dict but allows access with the attribute syntax.""" - - 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] - - -class Logger(object): - """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.""" - - def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True): - self.file = None - - if file_name is not None: - self.file = open(file_name, file_mode) - - self.should_flush = should_flush - self.stdout = sys.stdout - self.stderr = sys.stderr - - sys.stdout = self - sys.stderr = self - - def __enter__(self) -> "Logger": - return self - - def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: - self.close() - - def write(self, text: Union[str, bytes]) -> None: - """Write text to stdout (and a file) and optionally flush.""" - if isinstance(text, bytes): - text = text.decode() - if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash - return - - if self.file is not None: - self.file.write(text) - - self.stdout.write(text) - - if self.should_flush: - self.flush() - - def flush(self) -> None: - """Flush written text to both stdout and a file, if open.""" - if self.file is not None: - self.file.flush() - - self.stdout.flush() - - def close(self) -> None: - """Flush, close possible files, and remove stdout/stderr mirroring.""" - self.flush() - - # if using multiple loggers, prevent closing in wrong order - if sys.stdout is self: - sys.stdout = self.stdout - if sys.stderr is self: - sys.stderr = self.stderr - - if self.file is not None: - self.file.close() - self.file = None - - -# Cache directories -# ------------------------------------------------------------------------------------------ - -_dnnlib_cache_dir = None - -def set_cache_dir(path: str) -> None: - global _dnnlib_cache_dir - _dnnlib_cache_dir = path - -def make_cache_dir_path(*paths: str) -> str: - if _dnnlib_cache_dir is not None: - return os.path.join(_dnnlib_cache_dir, *paths) - if 'DNNLIB_CACHE_DIR' in os.environ: - return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths) - if 'HOME' in os.environ: - return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths) - if 'USERPROFILE' in os.environ: - return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths) - return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths) - -# Small util functions -# ------------------------------------------------------------------------------------------ - - -def format_time(seconds: Union[int, float]) -> str: - """Convert the seconds to human readable string with days, hours, minutes and seconds.""" - s = int(np.rint(seconds)) - - if s < 60: - return "{0}s".format(s) - elif s < 60 * 60: - return "{0}m {1:02}s".format(s // 60, s % 60) - elif s < 24 * 60 * 60: - return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60) - else: - return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60) - - -def ask_yes_no(question: str) -> bool: - """Ask the user the question until the user inputs a valid answer.""" - while True: - try: - print("{0} [y/n]".format(question)) - return strtobool(input().lower()) - except ValueError: - pass - - -def tuple_product(t: Tuple) -> Any: - """Calculate the product of the tuple elements.""" - result = 1 - - for v in t: - result *= v - - return result - - -_str_to_ctype = { - "uint8": ctypes.c_ubyte, - "uint16": ctypes.c_uint16, - "uint32": ctypes.c_uint32, - "uint64": ctypes.c_uint64, - "int8": ctypes.c_byte, - "int16": ctypes.c_int16, - "int32": ctypes.c_int32, - "int64": ctypes.c_int64, - "float32": ctypes.c_float, - "float64": ctypes.c_double -} - - -def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]: - """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes.""" - type_str = None - - if isinstance(type_obj, str): - type_str = type_obj - elif hasattr(type_obj, "__name__"): - type_str = type_obj.__name__ - elif hasattr(type_obj, "name"): - type_str = type_obj.name - else: - raise RuntimeError("Cannot infer type name from input") - - assert type_str in _str_to_ctype.keys() - - my_dtype = np.dtype(type_str) - my_ctype = _str_to_ctype[type_str] - - assert my_dtype.itemsize == ctypes.sizeof(my_ctype) - - return my_dtype, my_ctype - - -def is_pickleable(obj: Any) -> bool: - try: - with io.BytesIO() as stream: - pickle.dump(obj, stream) - return True - except: - return False - - -# Functionality to import modules/objects by name, and call functions by name -# ------------------------------------------------------------------------------------------ - -def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]: - """Searches for the underlying module behind the name to some python object. - Returns the module and the object name (original name with module part removed).""" - - # allow convenience shorthands, substitute them by full names - obj_name = re.sub("^np.", "numpy.", obj_name) - obj_name = re.sub("^tf.", "tensorflow.", obj_name) - - # list alternatives for (module_name, local_obj_name) - parts = obj_name.split(".") - name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)] - - # try each alternative in turn - for module_name, local_obj_name in name_pairs: - try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError - return module, local_obj_name - except: - pass - - # maybe some of the modules themselves contain errors? - for module_name, _local_obj_name in name_pairs: - try: - importlib.import_module(module_name) # may raise ImportError - except ImportError: - if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"): - raise - - # maybe the requested attribute is missing? - for module_name, local_obj_name in name_pairs: - try: - module = importlib.import_module(module_name) # may raise ImportError - get_obj_from_module(module, local_obj_name) # may raise AttributeError - except ImportError: - pass - - # we are out of luck, but we have no idea why - raise ImportError(obj_name) - - -def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any: - """Traverses the object name and returns the last (rightmost) python object.""" - if obj_name == '': - return module - obj = module - for part in obj_name.split("."): - obj = getattr(obj, part) - return obj - - -def get_obj_by_name(name: str) -> Any: - """Finds the python object with the given name.""" - module, obj_name = get_module_from_obj_name(name) - return get_obj_from_module(module, obj_name) - - -def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any: - """Finds the python object with the given name and calls it as a function.""" - assert func_name is not None - func_obj = get_obj_by_name(func_name) - assert callable(func_obj) - return func_obj(*args, **kwargs) - - -def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any: - """Finds the python class with the given name and constructs it with the given arguments.""" - return call_func_by_name(*args, func_name=class_name, **kwargs) - - -def get_module_dir_by_obj_name(obj_name: str) -> str: - """Get the directory path of the module containing the given object name.""" - module, _ = get_module_from_obj_name(obj_name) - return os.path.dirname(inspect.getfile(module)) - - -def is_top_level_function(obj: Any) -> bool: - """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.""" - return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__ - - -def get_top_level_function_name(obj: Any) -> str: - """Return the fully-qualified name of a top-level function.""" - assert is_top_level_function(obj) - module = obj.__module__ - if module == '__main__': - module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0] - return module + "." + obj.__name__ - - -# File system helpers -# ------------------------------------------------------------------------------------------ - -def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]: - """List all files recursively in a given directory while ignoring given file and directory names. - Returns list of tuples containing both absolute and relative paths.""" - assert os.path.isdir(dir_path) - base_name = os.path.basename(os.path.normpath(dir_path)) - - if ignores is None: - ignores = [] - - result = [] - - for root, dirs, files in os.walk(dir_path, topdown=True): - for ignore_ in ignores: - dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)] - - # dirs need to be edited in-place - for d in dirs_to_remove: - dirs.remove(d) - - files = [f for f in files if not fnmatch.fnmatch(f, ignore_)] - - absolute_paths = [os.path.join(root, f) for f in files] - relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths] - - if add_base_to_relative: - relative_paths = [os.path.join(base_name, p) for p in relative_paths] - - assert len(absolute_paths) == len(relative_paths) - result += zip(absolute_paths, relative_paths) - - return result - - -def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None: - """Takes in a list of tuples of (src, dst) paths and copies files. - Will create all necessary directories.""" - for file in files: - target_dir_name = os.path.dirname(file[1]) - - # will create all intermediate-level directories - if not os.path.exists(target_dir_name): - os.makedirs(target_dir_name) - - shutil.copyfile(file[0], file[1]) - - -# URL helpers -# ------------------------------------------------------------------------------------------ - -def is_url(obj: Any, allow_file_urls: bool = False) -> bool: - """Determine whether the given object is a valid URL string.""" - if not isinstance(obj, str) or not "://" in obj: - return False - if allow_file_urls and obj.startswith('file://'): - return True - try: - res = requests.compat.urlparse(obj) - if not res.scheme or not res.netloc or not "." in res.netloc: - return False - res = requests.compat.urlparse(requests.compat.urljoin(obj, "/")) - if not res.scheme or not res.netloc or not "." in res.netloc: - return False - except: - return False - return True - - -def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any: - """Download the given URL and return a binary-mode file object to access the data.""" - assert num_attempts >= 1 - assert not (return_filename and (not cache)) - - # Doesn't look like an URL scheme so interpret it as a local filename. - if not re.match('^[a-z]+://', url): - return url if return_filename else open(url, "rb") - - # Handle file URLs. This code handles unusual file:// patterns that - # arise on Windows: - # - # file:///c:/foo.txt - # - # which would translate to a local '/c:/foo.txt' filename that's - # invalid. Drop the forward slash for such pathnames. - # - # If you touch this code path, you should test it on both Linux and - # Windows. - # - # Some internet resources suggest using urllib.request.url2pathname() but - # but that converts forward slashes to backslashes and this causes - # its own set of problems. - if url.startswith('file://'): - filename = urllib.parse.urlparse(url).path - if re.match(r'^/[a-zA-Z]:', filename): - filename = filename[1:] - return filename if return_filename else open(filename, "rb") - - assert is_url(url) - - # Lookup from cache. - if cache_dir is None: - cache_dir = make_cache_dir_path('downloads') - - url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest() - if cache: - cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*")) - if len(cache_files) == 1: - filename = cache_files[0] - return filename if return_filename else open(filename, "rb") - - # Download. - url_name = None - url_data = None - with requests.Session() as session: - if verbose: - print("Downloading %s ..." % url, end="", flush=True) - for attempts_left in reversed(range(num_attempts)): - try: - with session.get(url) as res: - res.raise_for_status() - if len(res.content) == 0: - raise IOError("No data received") - - if len(res.content) < 8192: - content_str = res.content.decode("utf-8") - if "download_warning" in res.headers.get("Set-Cookie", ""): - links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link] - if len(links) == 1: - url = requests.compat.urljoin(url, links[0]) - raise IOError("Google Drive virus checker nag") - if "Google Drive - Quota exceeded" in content_str: - raise IOError("Google Drive download quota exceeded -- please try again later") - - match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", "")) - url_name = match[1] if match else url - url_data = res.content - if verbose: - print(" done") - break - except KeyboardInterrupt: - raise - except: - if not attempts_left: - if verbose: - print(" failed") - raise - if verbose: - print(".", end="", flush=True) - - # Save to cache. - if cache: - safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name) - cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name) - temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name) - os.makedirs(cache_dir, exist_ok=True) - with open(temp_file, "wb") as f: - f.write(url_data) - os.replace(temp_file, cache_file) # atomic - if return_filename: - return cache_file - - # Return data as file object. - assert not return_filename - return io.BytesIO(url_data) diff --git a/spaces/ucalyptus/PTI/scripts/latent_creators/__init__.py b/spaces/ucalyptus/PTI/scripts/latent_creators/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/callbacks/wb.py b/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/callbacks/wb.py deleted file mode 100644 index 4b4c29b77d6c193c29f01224fdd102366afd22f1..0000000000000000000000000000000000000000 --- a/spaces/vaishanthr/Simultaneous-Segmented-Depth-Prediction/yolov8/ultralytics/yolo/utils/callbacks/wb.py +++ /dev/null @@ -1,60 +0,0 @@ -# Ultralytics YOLO 🚀, AGPL-3.0 license -from ultralytics.yolo.utils import TESTS_RUNNING -from ultralytics.yolo.utils.torch_utils import model_info_for_loggers - -try: - import wandb as wb - - assert hasattr(wb, '__version__') - assert not TESTS_RUNNING # do not log pytest -except (ImportError, AssertionError): - wb = None - -_processed_plots = {} - - -def _log_plots(plots, step): - for name, params in plots.items(): - timestamp = params['timestamp'] - if _processed_plots.get(name, None) != timestamp: - wb.run.log({name.stem: wb.Image(str(name))}, step=step) - _processed_plots[name] = timestamp - - -def on_pretrain_routine_start(trainer): - """Initiate and start project if module is present.""" - wb.run or wb.init(project=trainer.args.project or 'YOLOv8', name=trainer.args.name, config=vars(trainer.args)) - - -def on_fit_epoch_end(trainer): - """Logs training metrics and model information at the end of an epoch.""" - wb.run.log(trainer.metrics, step=trainer.epoch + 1) - _log_plots(trainer.plots, step=trainer.epoch + 1) - _log_plots(trainer.validator.plots, step=trainer.epoch + 1) - if trainer.epoch == 0: - wb.run.log(model_info_for_loggers(trainer), step=trainer.epoch + 1) - - -def on_train_epoch_end(trainer): - """Log metrics and save images at the end of each training epoch.""" - wb.run.log(trainer.label_loss_items(trainer.tloss, prefix='train'), step=trainer.epoch + 1) - wb.run.log(trainer.lr, step=trainer.epoch + 1) - if trainer.epoch == 1: - _log_plots(trainer.plots, step=trainer.epoch + 1) - - -def on_train_end(trainer): - """Save the best model as an artifact at end of training.""" - _log_plots(trainer.validator.plots, step=trainer.epoch + 1) - _log_plots(trainer.plots, step=trainer.epoch + 1) - art = wb.Artifact(type='model', name=f'run_{wb.run.id}_model') - if trainer.best.exists(): - art.add_file(trainer.best) - wb.run.log_artifact(art, aliases=['best']) - - -callbacks = { - 'on_pretrain_routine_start': on_pretrain_routine_start, - 'on_train_epoch_end': on_train_epoch_end, - 'on_fit_epoch_end': on_fit_epoch_end, - 'on_train_end': on_train_end} if wb else {} diff --git a/spaces/videfikri/aicover/train/data_utils.py b/spaces/videfikri/aicover/train/data_utils.py deleted file mode 100644 index 4cf75e1c9a2658499ac51e60fd3efa755cd7c271..0000000000000000000000000000000000000000 --- a/spaces/videfikri/aicover/train/data_utils.py +++ /dev/null @@ -1,512 +0,0 @@ -import os, traceback -import numpy as np -import torch -import torch.utils.data - -from mel_processing import spectrogram_torch -from utils import load_wav_to_torch, load_filepaths_and_text - - -class TextAudioLoaderMultiNSFsid(torch.utils.data.Dataset): - """ - 1) loads audio, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths_and_text, hparams): - self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 5000) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - audiopaths_and_text_new = [] - lengths = [] - for audiopath, text, pitch, pitchf, dv in self.audiopaths_and_text: - if self.min_text_len <= len(text) and len(text) <= self.max_text_len: - audiopaths_and_text_new.append([audiopath, text, pitch, pitchf, dv]) - lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) - self.audiopaths_and_text = audiopaths_and_text_new - self.lengths = lengths - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def get_audio_text_pair(self, audiopath_and_text): - # separate filename and text - file = audiopath_and_text[0] - phone = audiopath_and_text[1] - pitch = audiopath_and_text[2] - pitchf = audiopath_and_text[3] - dv = audiopath_and_text[4] - - phone, pitch, pitchf = self.get_labels(phone, pitch, pitchf) - spec, wav = self.get_audio(file) - dv = self.get_sid(dv) - - len_phone = phone.size()[0] - len_spec = spec.size()[-1] - # print(123,phone.shape,pitch.shape,spec.shape) - if len_phone != len_spec: - len_min = min(len_phone, len_spec) - # amor - len_wav = len_min * self.hop_length - - spec = spec[:, :len_min] - wav = wav[:, :len_wav] - - phone = phone[:len_min, :] - pitch = pitch[:len_min] - pitchf = pitchf[:len_min] - - return (spec, wav, phone, pitch, pitchf, dv) - - def get_labels(self, phone, pitch, pitchf): - phone = np.load(phone) - phone = np.repeat(phone, 2, axis=0) - pitch = np.load(pitch) - pitchf = np.load(pitchf) - n_num = min(phone.shape[0], 900) # DistributedBucketSampler - # print(234,phone.shape,pitch.shape) - phone = phone[:n_num, :] - pitch = pitch[:n_num] - pitchf = pitchf[:n_num] - phone = torch.FloatTensor(phone) - pitch = torch.LongTensor(pitch) - pitchf = torch.FloatTensor(pitchf) - return phone, pitch, pitchf - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError( - "{} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate - ) - ) - audio_norm = audio - # audio_norm = audio / self.max_wav_value - # audio_norm = audio / np.abs(audio).max() - - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - try: - spec = torch.load(spec_filename) - except: - print(spec_filename, traceback.format_exc()) - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - else: - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - return spec, audio_norm - - def __getitem__(self, index): - return self.get_audio_text_pair(self.audiopaths_and_text[index]) - - def __len__(self): - return len(self.audiopaths_and_text) - - -class TextAudioCollateMultiNSFsid: - """Zero-pads model inputs and targets""" - - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text and aduio - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True - ) - - max_spec_len = max([x[0].size(1) for x in batch]) - max_wave_len = max([x[1].size(1) for x in batch]) - spec_lengths = torch.LongTensor(len(batch)) - wave_lengths = torch.LongTensor(len(batch)) - spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len) - wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len) - spec_padded.zero_() - wave_padded.zero_() - - max_phone_len = max([x[2].size(0) for x in batch]) - phone_lengths = torch.LongTensor(len(batch)) - phone_padded = torch.FloatTensor( - len(batch), max_phone_len, batch[0][2].shape[1] - ) # (spec, wav, phone, pitch) - pitch_padded = torch.LongTensor(len(batch), max_phone_len) - pitchf_padded = torch.FloatTensor(len(batch), max_phone_len) - phone_padded.zero_() - pitch_padded.zero_() - pitchf_padded.zero_() - # dv = torch.FloatTensor(len(batch), 256)#gin=256 - sid = torch.LongTensor(len(batch)) - - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - spec = row[0] - spec_padded[i, :, : spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wave = row[1] - wave_padded[i, :, : wave.size(1)] = wave - wave_lengths[i] = wave.size(1) - - phone = row[2] - phone_padded[i, : phone.size(0), :] = phone - phone_lengths[i] = phone.size(0) - - pitch = row[3] - pitch_padded[i, : pitch.size(0)] = pitch - pitchf = row[4] - pitchf_padded[i, : pitchf.size(0)] = pitchf - - # dv[i] = row[5] - sid[i] = row[5] - - return ( - phone_padded, - phone_lengths, - pitch_padded, - pitchf_padded, - spec_padded, - spec_lengths, - wave_padded, - wave_lengths, - # dv - sid, - ) - - -class TextAudioLoader(torch.utils.data.Dataset): - """ - 1) loads audio, text pairs - 2) normalizes text and converts them to sequences of integers - 3) computes spectrograms from audio files. - """ - - def __init__(self, audiopaths_and_text, hparams): - self.audiopaths_and_text = load_filepaths_and_text(audiopaths_and_text) - self.max_wav_value = hparams.max_wav_value - self.sampling_rate = hparams.sampling_rate - self.filter_length = hparams.filter_length - self.hop_length = hparams.hop_length - self.win_length = hparams.win_length - self.sampling_rate = hparams.sampling_rate - self.min_text_len = getattr(hparams, "min_text_len", 1) - self.max_text_len = getattr(hparams, "max_text_len", 5000) - self._filter() - - def _filter(self): - """ - Filter text & store spec lengths - """ - # Store spectrogram lengths for Bucketing - # wav_length ~= file_size / (wav_channels * Bytes per dim) = file_size / (1 * 2) - # spec_length = wav_length // hop_length - audiopaths_and_text_new = [] - lengths = [] - for audiopath, text, dv in self.audiopaths_and_text: - if self.min_text_len <= len(text) and len(text) <= self.max_text_len: - audiopaths_and_text_new.append([audiopath, text, dv]) - lengths.append(os.path.getsize(audiopath) // (2 * self.hop_length)) - self.audiopaths_and_text = audiopaths_and_text_new - self.lengths = lengths - - def get_sid(self, sid): - sid = torch.LongTensor([int(sid)]) - return sid - - def get_audio_text_pair(self, audiopath_and_text): - # separate filename and text - file = audiopath_and_text[0] - phone = audiopath_and_text[1] - dv = audiopath_and_text[2] - - phone = self.get_labels(phone) - spec, wav = self.get_audio(file) - dv = self.get_sid(dv) - - len_phone = phone.size()[0] - len_spec = spec.size()[-1] - if len_phone != len_spec: - len_min = min(len_phone, len_spec) - len_wav = len_min * self.hop_length - spec = spec[:, :len_min] - wav = wav[:, :len_wav] - phone = phone[:len_min, :] - return (spec, wav, phone, dv) - - def get_labels(self, phone): - phone = np.load(phone) - phone = np.repeat(phone, 2, axis=0) - n_num = min(phone.shape[0], 900) # DistributedBucketSampler - phone = phone[:n_num, :] - phone = torch.FloatTensor(phone) - return phone - - def get_audio(self, filename): - audio, sampling_rate = load_wav_to_torch(filename) - if sampling_rate != self.sampling_rate: - raise ValueError( - "{} SR doesn't match target {} SR".format( - sampling_rate, self.sampling_rate - ) - ) - audio_norm = audio - # audio_norm = audio / self.max_wav_value - # audio_norm = audio / np.abs(audio).max() - - audio_norm = audio_norm.unsqueeze(0) - spec_filename = filename.replace(".wav", ".spec.pt") - if os.path.exists(spec_filename): - try: - spec = torch.load(spec_filename) - except: - print(spec_filename, traceback.format_exc()) - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - else: - spec = spectrogram_torch( - audio_norm, - self.filter_length, - self.sampling_rate, - self.hop_length, - self.win_length, - center=False, - ) - spec = torch.squeeze(spec, 0) - torch.save(spec, spec_filename, _use_new_zipfile_serialization=False) - return spec, audio_norm - - def __getitem__(self, index): - return self.get_audio_text_pair(self.audiopaths_and_text[index]) - - def __len__(self): - return len(self.audiopaths_and_text) - - -class TextAudioCollate: - """Zero-pads model inputs and targets""" - - def __init__(self, return_ids=False): - self.return_ids = return_ids - - def __call__(self, batch): - """Collate's training batch from normalized text and aduio - PARAMS - ------ - batch: [text_normalized, spec_normalized, wav_normalized] - """ - # Right zero-pad all one-hot text sequences to max input length - _, ids_sorted_decreasing = torch.sort( - torch.LongTensor([x[0].size(1) for x in batch]), dim=0, descending=True - ) - - max_spec_len = max([x[0].size(1) for x in batch]) - max_wave_len = max([x[1].size(1) for x in batch]) - spec_lengths = torch.LongTensor(len(batch)) - wave_lengths = torch.LongTensor(len(batch)) - spec_padded = torch.FloatTensor(len(batch), batch[0][0].size(0), max_spec_len) - wave_padded = torch.FloatTensor(len(batch), 1, max_wave_len) - spec_padded.zero_() - wave_padded.zero_() - - max_phone_len = max([x[2].size(0) for x in batch]) - phone_lengths = torch.LongTensor(len(batch)) - phone_padded = torch.FloatTensor( - len(batch), max_phone_len, batch[0][2].shape[1] - ) - phone_padded.zero_() - sid = torch.LongTensor(len(batch)) - - for i in range(len(ids_sorted_decreasing)): - row = batch[ids_sorted_decreasing[i]] - - spec = row[0] - spec_padded[i, :, : spec.size(1)] = spec - spec_lengths[i] = spec.size(1) - - wave = row[1] - wave_padded[i, :, : wave.size(1)] = wave - wave_lengths[i] = wave.size(1) - - phone = row[2] - phone_padded[i, : phone.size(0), :] = phone - phone_lengths[i] = phone.size(0) - - sid[i] = row[3] - - return ( - phone_padded, - phone_lengths, - spec_padded, - spec_lengths, - wave_padded, - wave_lengths, - sid, - ) - - -class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): - """ - Maintain similar input lengths in a batch. - Length groups are specified by boundaries. - Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. - - It removes samples which are not included in the boundaries. - Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. - """ - - def __init__( - self, - dataset, - batch_size, - boundaries, - num_replicas=None, - rank=None, - shuffle=True, - ): - super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) - self.lengths = dataset.lengths - self.batch_size = batch_size - self.boundaries = boundaries - - self.buckets, self.num_samples_per_bucket = self._create_buckets() - self.total_size = sum(self.num_samples_per_bucket) - self.num_samples = self.total_size // self.num_replicas - - def _create_buckets(self): - buckets = [[] for _ in range(len(self.boundaries) - 1)] - for i in range(len(self.lengths)): - length = self.lengths[i] - idx_bucket = self._bisect(length) - if idx_bucket != -1: - buckets[idx_bucket].append(i) - - for i in range(len(buckets) - 1, -1, -1): # - if len(buckets[i]) == 0: - buckets.pop(i) - self.boundaries.pop(i + 1) - - num_samples_per_bucket = [] - for i in range(len(buckets)): - len_bucket = len(buckets[i]) - total_batch_size = self.num_replicas * self.batch_size - rem = ( - total_batch_size - (len_bucket % total_batch_size) - ) % total_batch_size - num_samples_per_bucket.append(len_bucket + rem) - return buckets, num_samples_per_bucket - - def __iter__(self): - # deterministically shuffle based on epoch - g = torch.Generator() - g.manual_seed(self.epoch) - - indices = [] - if self.shuffle: - for bucket in self.buckets: - indices.append(torch.randperm(len(bucket), generator=g).tolist()) - else: - for bucket in self.buckets: - indices.append(list(range(len(bucket)))) - - batches = [] - for i in range(len(self.buckets)): - bucket = self.buckets[i] - len_bucket = len(bucket) - ids_bucket = indices[i] - num_samples_bucket = self.num_samples_per_bucket[i] - - # add extra samples to make it evenly divisible - rem = num_samples_bucket - len_bucket - ids_bucket = ( - ids_bucket - + ids_bucket * (rem // len_bucket) - + ids_bucket[: (rem % len_bucket)] - ) - - # subsample - ids_bucket = ids_bucket[self.rank :: self.num_replicas] - - # batching - for j in range(len(ids_bucket) // self.batch_size): - batch = [ - bucket[idx] - for idx in ids_bucket[ - j * self.batch_size : (j + 1) * self.batch_size - ] - ] - batches.append(batch) - - if self.shuffle: - batch_ids = torch.randperm(len(batches), generator=g).tolist() - batches = [batches[i] for i in batch_ids] - self.batches = batches - - assert len(self.batches) * self.batch_size == self.num_samples - return iter(self.batches) - - def _bisect(self, x, lo=0, hi=None): - if hi is None: - hi = len(self.boundaries) - 1 - - if hi > lo: - mid = (hi + lo) // 2 - if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: - return mid - elif x <= self.boundaries[mid]: - return self._bisect(x, lo, mid) - else: - return self._bisect(x, mid + 1, hi) - else: - return -1 - - def __len__(self): - return self.num_samples // self.batch_size diff --git a/spaces/vishnu0001/text2mesh/shap_e/models/nn/camera.py b/spaces/vishnu0001/text2mesh/shap_e/models/nn/camera.py deleted file mode 100644 index 61d98426ade534daf1edd9f9d0d1bc52b9b884f4..0000000000000000000000000000000000000000 --- a/spaces/vishnu0001/text2mesh/shap_e/models/nn/camera.py +++ /dev/null @@ -1,207 +0,0 @@ -from abc import ABC, abstractmethod -from dataclasses import dataclass -from typing import Optional, Tuple, Union - -import numpy as np -import torch - -from shap_e.rendering.view_data import ProjectiveCamera - - -@dataclass -class DifferentiableCamera(ABC): - """ - An object describing how a camera corresponds to pixels in an image. - """ - - @abstractmethod - def camera_rays(self, coords: torch.Tensor) -> torch.Tensor: - """ - For every (x, y) coordinate in a rendered image, compute the ray of the - corresponding pixel. - - :param coords: an [N x ... x 2] integer array of 2D image coordinates. - :return: an [N x ... x 2 x 3] array of [2 x 3] (origin, direction) tuples. - The direction should always be unit length. - """ - - @abstractmethod - def resize_image(self, width: int, height: int) -> "DifferentiableCamera": - """ - Creates a new camera with the same intrinsics and direction as this one, - but with resized image dimensions. - """ - - -@dataclass -class DifferentiableProjectiveCamera(DifferentiableCamera): - """ - Implements a batch, differentiable, standard pinhole camera - """ - - origin: torch.Tensor # [batch_size x 3] - x: torch.Tensor # [batch_size x 3] - y: torch.Tensor # [batch_size x 3] - z: torch.Tensor # [batch_size x 3] - width: int - height: int - x_fov: float - y_fov: float - - def __post_init__(self): - assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0] - assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3 - assert ( - len(self.x.shape) - == len(self.y.shape) - == len(self.z.shape) - == len(self.origin.shape) - == 2 - ) - - def resolution(self): - return torch.from_numpy(np.array([self.width, self.height], dtype=np.float32)) - - def fov(self): - return torch.from_numpy(np.array([self.x_fov, self.y_fov], dtype=np.float32)) - - def image_coords(self) -> torch.Tensor: - """ - :return: coords of shape (width * height, 2) - """ - pixel_indices = torch.arange(self.height * self.width) - coords = torch.stack( - [ - pixel_indices % self.width, - torch.div(pixel_indices, self.width, rounding_mode="trunc"), - ], - axis=1, - ) - return coords - - def camera_rays(self, coords: torch.Tensor) -> torch.Tensor: - batch_size, *shape, n_coords = coords.shape - assert n_coords == 2 - assert batch_size == self.origin.shape[0] - flat = coords.view(batch_size, -1, 2) - - res = self.resolution().to(flat.device) - fov = self.fov().to(flat.device) - - fracs = (flat.float() / (res - 1)) * 2 - 1 - fracs = fracs * torch.tan(fov / 2) - - fracs = fracs.view(batch_size, -1, 2) - directions = ( - self.z.view(batch_size, 1, 3) - + self.x.view(batch_size, 1, 3) * fracs[:, :, :1] - + self.y.view(batch_size, 1, 3) * fracs[:, :, 1:] - ) - directions = directions / directions.norm(dim=-1, keepdim=True) - rays = torch.stack( - [ - torch.broadcast_to( - self.origin.view(batch_size, 1, 3), [batch_size, directions.shape[1], 3] - ), - directions, - ], - dim=2, - ) - return rays.view(batch_size, *shape, 2, 3) - - def resize_image(self, width: int, height: int) -> "DifferentiableProjectiveCamera": - """ - Creates a new camera for the resized view assuming the aspect ratio does not change. - """ - assert width * self.height == height * self.width, "The aspect ratio should not change." - return DifferentiableProjectiveCamera( - origin=self.origin, - x=self.x, - y=self.y, - z=self.z, - width=width, - height=height, - x_fov=self.x_fov, - y_fov=self.y_fov, - ) - - -@dataclass -class DifferentiableCameraBatch(ABC): - """ - Annotate a differentiable camera with a multi-dimensional batch shape. - """ - - shape: Tuple[int] - flat_camera: DifferentiableCamera - - -def normalize(vec: torch.Tensor) -> torch.Tensor: - return vec / vec.norm(dim=-1, keepdim=True) - - -def project_out(vec1: torch.Tensor, vec2: torch.Tensor) -> torch.Tensor: - """ - Removes the vec2 component from vec1 - """ - vec2 = normalize(vec2) - proj = (vec1 * vec2).sum(dim=-1, keepdim=True) - return vec1 - proj * vec2 - - -def camera_orientation(toward: torch.Tensor, up: Optional[torch.Tensor] = None) -> torch.Tensor: - """ - :param toward: [batch_size x 3] unit vector from camera position to the object - :param up: Optional [batch_size x 3] specifying the physical up direction in the world frame. - :return: [batch_size x 3 x 3] - """ - - if up is None: - up = torch.zeros_like(toward) - up[:, 2] = 1 - - assert len(toward.shape) == 2 - assert toward.shape[1] == 3 - - assert len(up.shape) == 2 - assert up.shape[1] == 3 - - z = toward / toward.norm(dim=-1, keepdim=True) - y = -normalize(project_out(up, toward)) - x = torch.cross(y, z, dim=1) - return torch.stack([x, y, z], dim=1) - - -def projective_camera_frame( - origin: torch.Tensor, - toward: torch.Tensor, - camera_params: Union[ProjectiveCamera, DifferentiableProjectiveCamera], -) -> DifferentiableProjectiveCamera: - """ - Given the origin and the direction of a view, return a differentiable - projective camera with the given parameters. - - TODO: We need to support the rotation of the camera frame about the - `toward` vector to fully implement 6 degrees of freedom. - """ - rot = camera_orientation(toward) - camera = DifferentiableProjectiveCamera( - origin=origin, - x=rot[:, 0], - y=rot[:, 1], - z=rot[:, 2], - width=camera_params.width, - height=camera_params.height, - x_fov=camera_params.x_fov, - y_fov=camera_params.y_fov, - ) - return camera - - -@torch.no_grad() -def get_image_coords(width, height) -> torch.Tensor: - pixel_indices = torch.arange(height * width) - # torch throws warnings for pixel_indices // width - pixel_indices_div = torch.div(pixel_indices, width, rounding_mode="trunc") - coords = torch.stack([pixel_indices % width, pixel_indices_div], dim=1) - return coords diff --git a/spaces/vonewman/my-sentiment-analyzer-app/app.py b/spaces/vonewman/my-sentiment-analyzer-app/app.py deleted file mode 100644 index 9703bc937eba94c933666509d97a4c2186b3601b..0000000000000000000000000000000000000000 --- a/spaces/vonewman/my-sentiment-analyzer-app/app.py +++ /dev/null @@ -1,14 +0,0 @@ -import gradio as gr - -from transformers import pipeline - -sentiment = pipeline("sentiment-analysis") - -def get_sentiment(input_text): - return sentiment(input_text) - -iface = gr.Interface(fn = get_sentiment, inputs = 'text', outputs = ['text'], - title = 'Sentiment Analysis', - description="Get sentiment Negatives/Positives for the given input.") - -iface.launch(inline = False) \ No newline at end of file diff --git a/spaces/vumichien/Generate_human_motion/pyrender/pyrender/camera.py b/spaces/vumichien/Generate_human_motion/pyrender/pyrender/camera.py deleted file mode 100644 index e019358039033c3a372c990ebad3151258c3651d..0000000000000000000000000000000000000000 --- a/spaces/vumichien/Generate_human_motion/pyrender/pyrender/camera.py +++ /dev/null @@ -1,437 +0,0 @@ -"""Virtual cameras compliant with the glTF 2.0 specification as described at -https://github.com/KhronosGroup/glTF/tree/master/specification/2.0#reference-camera - -Author: Matthew Matl -""" -import abc -import numpy as np -import six -import sys - -from .constants import DEFAULT_Z_NEAR, DEFAULT_Z_FAR - - -@six.add_metaclass(abc.ABCMeta) -class Camera(object): - """Abstract base class for all cameras. - - Note - ---- - Camera poses are specified in the OpenGL format, - where the z axis points away from the view direction and the - x and y axes point to the right and up in the image plane, respectively. - - Parameters - ---------- - znear : float - The floating-point distance to the near clipping plane. - zfar : float - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - znear=DEFAULT_Z_NEAR, - zfar=DEFAULT_Z_FAR, - name=None): - self.name = name - self.znear = znear - self.zfar = zfar - - @property - def name(self): - """str : The user-defined name of this object. - """ - return self._name - - @name.setter - def name(self, value): - if value is not None: - value = str(value) - self._name = value - - @property - def znear(self): - """float : The distance to the near clipping plane. - """ - return self._znear - - @znear.setter - def znear(self, value): - value = float(value) - if value < 0: - raise ValueError('z-near must be >= 0.0') - self._znear = value - - @property - def zfar(self): - """float : The distance to the far clipping plane. - """ - return self._zfar - - @zfar.setter - def zfar(self, value): - value = float(value) - if value <= 0 or value <= self.znear: - raise ValueError('zfar must be >0 and >znear') - self._zfar = value - - @abc.abstractmethod - def get_projection_matrix(self, width=None, height=None): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - height : int - Height of the current viewport, in pixels. - """ - pass - - -class PerspectiveCamera(Camera): - - """A perspective camera for perspective projection. - - Parameters - ---------- - yfov : float - The floating-point vertical field of view in radians. - znear : float - The floating-point distance to the near clipping plane. - If not specified, defaults to 0.05. - zfar : float, optional - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - If None, the camera uses an infinite projection matrix. - aspectRatio : float, optional - The floating-point aspect ratio of the field of view. - If not specified, the camera uses the viewport's aspect ratio. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - yfov, - znear=DEFAULT_Z_NEAR, - zfar=None, - aspectRatio=None, - name=None): - super(PerspectiveCamera, self).__init__( - znear=znear, - zfar=zfar, - name=name, - ) - - self.yfov = yfov - self.aspectRatio = aspectRatio - - @property - def yfov(self): - """float : The vertical field of view in radians. - """ - return self._yfov - - @yfov.setter - def yfov(self, value): - value = float(value) - if value <= 0.0: - raise ValueError('Field of view must be positive') - self._yfov = value - - @property - def zfar(self): - """float : The distance to the far clipping plane. - """ - return self._zfar - - @zfar.setter - def zfar(self, value): - if value is not None: - value = float(value) - if value <= 0 or value <= self.znear: - raise ValueError('zfar must be >0 and >znear') - self._zfar = value - - @property - def aspectRatio(self): - """float : The ratio of the width to the height of the field of view. - """ - return self._aspectRatio - - @aspectRatio.setter - def aspectRatio(self, value): - if value is not None: - value = float(value) - if value <= 0.0: - raise ValueError('Aspect ratio must be positive') - self._aspectRatio = value - - def get_projection_matrix(self, width=None, height=None): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - height : int - Height of the current viewport, in pixels. - """ - aspect_ratio = self.aspectRatio - if aspect_ratio is None: - if width is None or height is None: - raise ValueError('Aspect ratio of camera must be defined') - aspect_ratio = float(width) / float(height) - - a = aspect_ratio - t = np.tan(self.yfov / 2.0) - n = self.znear - f = self.zfar - - P = np.zeros((4,4)) - P[0][0] = 1.0 / (a * t) - P[1][1] = 1.0 / t - P[3][2] = -1.0 - - if f is None: - P[2][2] = -1.0 - P[2][3] = -2.0 * n - else: - P[2][2] = (f + n) / (n - f) - P[2][3] = (2 * f * n) / (n - f) - - return P - - -class OrthographicCamera(Camera): - """An orthographic camera for orthographic projection. - - Parameters - ---------- - xmag : float - The floating-point horizontal magnification of the view. - ymag : float - The floating-point vertical magnification of the view. - znear : float - The floating-point distance to the near clipping plane. - If not specified, defaults to 0.05. - zfar : float - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - If not specified, defaults to 100.0. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - xmag, - ymag, - znear=DEFAULT_Z_NEAR, - zfar=DEFAULT_Z_FAR, - name=None): - super(OrthographicCamera, self).__init__( - znear=znear, - zfar=zfar, - name=name, - ) - - self.xmag = xmag - self.ymag = ymag - - @property - def xmag(self): - """float : The horizontal magnification of the view. - """ - return self._xmag - - @xmag.setter - def xmag(self, value): - value = float(value) - if value <= 0.0: - raise ValueError('X magnification must be positive') - self._xmag = value - - @property - def ymag(self): - """float : The vertical magnification of the view. - """ - return self._ymag - - @ymag.setter - def ymag(self, value): - value = float(value) - if value <= 0.0: - raise ValueError('Y magnification must be positive') - self._ymag = value - - @property - def znear(self): - """float : The distance to the near clipping plane. - """ - return self._znear - - @znear.setter - def znear(self, value): - value = float(value) - if value <= 0: - raise ValueError('z-near must be > 0.0') - self._znear = value - - def get_projection_matrix(self, width=None, height=None): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - Unused in this function. - height : int - Height of the current viewport, in pixels. - Unused in this function. - """ - xmag = self.xmag - ymag = self.ymag - - # If screen width/height defined, rescale xmag - if width is not None and height is not None: - xmag = width / height * ymag - - n = self.znear - f = self.zfar - P = np.zeros((4,4)) - P[0][0] = 1.0 / xmag - P[1][1] = 1.0 / ymag - P[2][2] = 2.0 / (n - f) - P[2][3] = (f + n) / (n - f) - P[3][3] = 1.0 - return P - - -class IntrinsicsCamera(Camera): - """A perspective camera with custom intrinsics. - - Parameters - ---------- - fx : float - X-axis focal length in pixels. - fy : float - Y-axis focal length in pixels. - cx : float - X-axis optical center in pixels. - cy : float - Y-axis optical center in pixels. - znear : float - The floating-point distance to the near clipping plane. - If not specified, defaults to 0.05. - zfar : float - The floating-point distance to the far clipping plane. - ``zfar`` must be greater than ``znear``. - If not specified, defaults to 100.0. - name : str, optional - The user-defined name of this object. - """ - - def __init__(self, - fx, - fy, - cx, - cy, - znear=DEFAULT_Z_NEAR, - zfar=DEFAULT_Z_FAR, - name=None): - super(IntrinsicsCamera, self).__init__( - znear=znear, - zfar=zfar, - name=name, - ) - - self.fx = fx - self.fy = fy - self.cx = cx - self.cy = cy - - @property - def fx(self): - """float : X-axis focal length in meters. - """ - return self._fx - - @fx.setter - def fx(self, value): - self._fx = float(value) - - @property - def fy(self): - """float : Y-axis focal length in meters. - """ - return self._fy - - @fy.setter - def fy(self, value): - self._fy = float(value) - - @property - def cx(self): - """float : X-axis optical center in pixels. - """ - return self._cx - - @cx.setter - def cx(self, value): - self._cx = float(value) - - @property - def cy(self): - """float : Y-axis optical center in pixels. - """ - return self._cy - - @cy.setter - def cy(self, value): - self._cy = float(value) - - def get_projection_matrix(self, width, height): - """Return the OpenGL projection matrix for this camera. - - Parameters - ---------- - width : int - Width of the current viewport, in pixels. - height : int - Height of the current viewport, in pixels. - """ - width = float(width) - height = float(height) - - cx, cy = self.cx, self.cy - fx, fy = self.fx, self.fy - if sys.platform == 'darwin': - cx = self.cx * 2.0 - cy = self.cy * 2.0 - fx = self.fx * 2.0 - fy = self.fy * 2.0 - - P = np.zeros((4,4)) - P[0][0] = 2.0 * fx / width - P[1][1] = 2.0 * fy / height - P[0][2] = 1.0 - 2.0 * cx / width - P[1][2] = 2.0 * cy / height - 1.0 - P[3][2] = -1.0 - - n = self.znear - f = self.zfar - if f is None: - P[2][2] = -1.0 - P[2][3] = -2.0 * n - else: - P[2][2] = (f + n) / (n - f) - P[2][3] = (2 * f * n) / (n - f) - - return P - - -__all__ = ['Camera', 'PerspectiveCamera', 'OrthographicCamera', - 'IntrinsicsCamera'] diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/core/evaluation/__init__.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/core/evaluation/__init__.py deleted file mode 100644 index f7cc4b23413a0639e9de00eeb0bf600632d2c6cd..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/core/evaluation/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .class_names import get_classes, get_palette -from .eval_hooks import DistEvalHook, EvalHook -from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou - -__all__ = [ - 'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore', - 'eval_metrics', 'get_classes', 'get_palette' -] diff --git a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pipelines/transforms.py b/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pipelines/transforms.py deleted file mode 100644 index 94e869b252ef6d8b43604add2bbc02f034614bfb..0000000000000000000000000000000000000000 --- a/spaces/vumichien/canvas_controlnet/annotator/uniformer/mmseg/datasets/pipelines/transforms.py +++ /dev/null @@ -1,889 +0,0 @@ -import annotator.uniformer.mmcv as mmcv -import numpy as np -from annotator.uniformer.mmcv.utils import deprecated_api_warning, is_tuple_of -from numpy import random - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class Resize(object): - """Resize images & seg. - - This transform resizes the input image to some scale. If the input dict - contains the key "scale", then the scale in the input dict is used, - otherwise the specified scale in the init method is used. - - ``img_scale`` can be None, a tuple (single-scale) or a list of tuple - (multi-scale). There are 4 multiscale modes: - - - ``ratio_range is not None``: - 1. When img_scale is None, img_scale is the shape of image in results - (img_scale = results['img'].shape[:2]) and the image is resized based - on the original size. (mode 1) - 2. When img_scale is a tuple (single-scale), randomly sample a ratio from - the ratio range and multiply it with the image scale. (mode 2) - - - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a - scale from the a range. (mode 3) - - - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a - scale from multiple scales. (mode 4) - - Args: - img_scale (tuple or list[tuple]): Images scales for resizing. - multiscale_mode (str): Either "range" or "value". - ratio_range (tuple[float]): (min_ratio, max_ratio) - keep_ratio (bool): Whether to keep the aspect ratio when resizing the - image. - """ - - def __init__(self, - img_scale=None, - multiscale_mode='range', - ratio_range=None, - keep_ratio=True): - if img_scale is None: - self.img_scale = None - else: - if isinstance(img_scale, list): - self.img_scale = img_scale - else: - self.img_scale = [img_scale] - assert mmcv.is_list_of(self.img_scale, tuple) - - if ratio_range is not None: - # mode 1: given img_scale=None and a range of image ratio - # mode 2: given a scale and a range of image ratio - assert self.img_scale is None or len(self.img_scale) == 1 - else: - # mode 3 and 4: given multiple scales or a range of scales - assert multiscale_mode in ['value', 'range'] - - self.multiscale_mode = multiscale_mode - self.ratio_range = ratio_range - self.keep_ratio = keep_ratio - - @staticmethod - def random_select(img_scales): - """Randomly select an img_scale from given candidates. - - Args: - img_scales (list[tuple]): Images scales for selection. - - Returns: - (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, - where ``img_scale`` is the selected image scale and - ``scale_idx`` is the selected index in the given candidates. - """ - - assert mmcv.is_list_of(img_scales, tuple) - scale_idx = np.random.randint(len(img_scales)) - img_scale = img_scales[scale_idx] - return img_scale, scale_idx - - @staticmethod - def random_sample(img_scales): - """Randomly sample an img_scale when ``multiscale_mode=='range'``. - - Args: - img_scales (list[tuple]): Images scale range for sampling. - There must be two tuples in img_scales, which specify the lower - and upper bound of image scales. - - Returns: - (tuple, None): Returns a tuple ``(img_scale, None)``, where - ``img_scale`` is sampled scale and None is just a placeholder - to be consistent with :func:`random_select`. - """ - - assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2 - img_scale_long = [max(s) for s in img_scales] - img_scale_short = [min(s) for s in img_scales] - long_edge = np.random.randint( - min(img_scale_long), - max(img_scale_long) + 1) - short_edge = np.random.randint( - min(img_scale_short), - max(img_scale_short) + 1) - img_scale = (long_edge, short_edge) - return img_scale, None - - @staticmethod - def random_sample_ratio(img_scale, ratio_range): - """Randomly sample an img_scale when ``ratio_range`` is specified. - - A ratio will be randomly sampled from the range specified by - ``ratio_range``. Then it would be multiplied with ``img_scale`` to - generate sampled scale. - - Args: - img_scale (tuple): Images scale base to multiply with ratio. - ratio_range (tuple[float]): The minimum and maximum ratio to scale - the ``img_scale``. - - Returns: - (tuple, None): Returns a tuple ``(scale, None)``, where - ``scale`` is sampled ratio multiplied with ``img_scale`` and - None is just a placeholder to be consistent with - :func:`random_select`. - """ - - assert isinstance(img_scale, tuple) and len(img_scale) == 2 - min_ratio, max_ratio = ratio_range - assert min_ratio <= max_ratio - ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio - scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio) - return scale, None - - def _random_scale(self, results): - """Randomly sample an img_scale according to ``ratio_range`` and - ``multiscale_mode``. - - If ``ratio_range`` is specified, a ratio will be sampled and be - multiplied with ``img_scale``. - If multiple scales are specified by ``img_scale``, a scale will be - sampled according to ``multiscale_mode``. - Otherwise, single scale will be used. - - Args: - results (dict): Result dict from :obj:`dataset`. - - Returns: - dict: Two new keys 'scale` and 'scale_idx` are added into - ``results``, which would be used by subsequent pipelines. - """ - - if self.ratio_range is not None: - if self.img_scale is None: - h, w = results['img'].shape[:2] - scale, scale_idx = self.random_sample_ratio((w, h), - self.ratio_range) - else: - scale, scale_idx = self.random_sample_ratio( - self.img_scale[0], self.ratio_range) - elif len(self.img_scale) == 1: - scale, scale_idx = self.img_scale[0], 0 - elif self.multiscale_mode == 'range': - scale, scale_idx = self.random_sample(self.img_scale) - elif self.multiscale_mode == 'value': - scale, scale_idx = self.random_select(self.img_scale) - else: - raise NotImplementedError - - results['scale'] = scale - results['scale_idx'] = scale_idx - - def _resize_img(self, results): - """Resize images with ``results['scale']``.""" - if self.keep_ratio: - img, scale_factor = mmcv.imrescale( - results['img'], results['scale'], return_scale=True) - # the w_scale and h_scale has minor difference - # a real fix should be done in the mmcv.imrescale in the future - new_h, new_w = img.shape[:2] - h, w = results['img'].shape[:2] - w_scale = new_w / w - h_scale = new_h / h - else: - img, w_scale, h_scale = mmcv.imresize( - results['img'], results['scale'], return_scale=True) - scale_factor = np.array([w_scale, h_scale, w_scale, h_scale], - dtype=np.float32) - results['img'] = img - results['img_shape'] = img.shape - results['pad_shape'] = img.shape # in case that there is no padding - results['scale_factor'] = scale_factor - results['keep_ratio'] = self.keep_ratio - - def _resize_seg(self, results): - """Resize semantic segmentation map with ``results['scale']``.""" - for key in results.get('seg_fields', []): - if self.keep_ratio: - gt_seg = mmcv.imrescale( - results[key], results['scale'], interpolation='nearest') - else: - gt_seg = mmcv.imresize( - results[key], results['scale'], interpolation='nearest') - results[key] = gt_seg - - def __call__(self, results): - """Call function to resize images, bounding boxes, masks, semantic - segmentation map. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', - 'keep_ratio' keys are added into result dict. - """ - - if 'scale' not in results: - self._random_scale(results) - self._resize_img(results) - self._resize_seg(results) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += (f'(img_scale={self.img_scale}, ' - f'multiscale_mode={self.multiscale_mode}, ' - f'ratio_range={self.ratio_range}, ' - f'keep_ratio={self.keep_ratio})') - return repr_str - - -@PIPELINES.register_module() -class RandomFlip(object): - """Flip the image & seg. - - If the input dict contains the key "flip", then the flag will be used, - otherwise it will be randomly decided by a ratio specified in the init - method. - - Args: - prob (float, optional): The flipping probability. Default: None. - direction(str, optional): The flipping direction. Options are - 'horizontal' and 'vertical'. Default: 'horizontal'. - """ - - @deprecated_api_warning({'flip_ratio': 'prob'}, cls_name='RandomFlip') - def __init__(self, prob=None, direction='horizontal'): - self.prob = prob - self.direction = direction - if prob is not None: - assert prob >= 0 and prob <= 1 - assert direction in ['horizontal', 'vertical'] - - def __call__(self, results): - """Call function to flip bounding boxes, masks, semantic segmentation - maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Flipped results, 'flip', 'flip_direction' keys are added into - result dict. - """ - - if 'flip' not in results: - flip = True if np.random.rand() < self.prob else False - results['flip'] = flip - if 'flip_direction' not in results: - results['flip_direction'] = self.direction - if results['flip']: - # flip image - results['img'] = mmcv.imflip( - results['img'], direction=results['flip_direction']) - - # flip segs - for key in results.get('seg_fields', []): - # use copy() to make numpy stride positive - results[key] = mmcv.imflip( - results[key], direction=results['flip_direction']).copy() - return results - - def __repr__(self): - return self.__class__.__name__ + f'(prob={self.prob})' - - -@PIPELINES.register_module() -class Pad(object): - """Pad the image & mask. - - There are two padding modes: (1) pad to a fixed size and (2) pad to the - minimum size that is divisible by some number. - Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor", - - Args: - size (tuple, optional): Fixed padding size. - size_divisor (int, optional): The divisor of padded size. - pad_val (float, optional): Padding value. Default: 0. - seg_pad_val (float, optional): Padding value of segmentation map. - Default: 255. - """ - - def __init__(self, - size=None, - size_divisor=None, - pad_val=0, - seg_pad_val=255): - self.size = size - self.size_divisor = size_divisor - self.pad_val = pad_val - self.seg_pad_val = seg_pad_val - # only one of size and size_divisor should be valid - assert size is not None or size_divisor is not None - assert size is None or size_divisor is None - - def _pad_img(self, results): - """Pad images according to ``self.size``.""" - if self.size is not None: - padded_img = mmcv.impad( - results['img'], shape=self.size, pad_val=self.pad_val) - elif self.size_divisor is not None: - padded_img = mmcv.impad_to_multiple( - results['img'], self.size_divisor, pad_val=self.pad_val) - results['img'] = padded_img - results['pad_shape'] = padded_img.shape - results['pad_fixed_size'] = self.size - results['pad_size_divisor'] = self.size_divisor - - def _pad_seg(self, results): - """Pad masks according to ``results['pad_shape']``.""" - for key in results.get('seg_fields', []): - results[key] = mmcv.impad( - results[key], - shape=results['pad_shape'][:2], - pad_val=self.seg_pad_val) - - def __call__(self, results): - """Call function to pad images, masks, semantic segmentation maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Updated result dict. - """ - - self._pad_img(results) - self._pad_seg(results) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(size={self.size}, size_divisor={self.size_divisor}, ' \ - f'pad_val={self.pad_val})' - return repr_str - - -@PIPELINES.register_module() -class Normalize(object): - """Normalize the image. - - Added key is "img_norm_cfg". - - Args: - mean (sequence): Mean values of 3 channels. - std (sequence): Std values of 3 channels. - to_rgb (bool): Whether to convert the image from BGR to RGB, - default is true. - """ - - def __init__(self, mean, std, to_rgb=True): - self.mean = np.array(mean, dtype=np.float32) - self.std = np.array(std, dtype=np.float32) - self.to_rgb = to_rgb - - def __call__(self, results): - """Call function to normalize images. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Normalized results, 'img_norm_cfg' key is added into - result dict. - """ - - results['img'] = mmcv.imnormalize(results['img'], self.mean, self.std, - self.to_rgb) - results['img_norm_cfg'] = dict( - mean=self.mean, std=self.std, to_rgb=self.to_rgb) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(mean={self.mean}, std={self.std}, to_rgb=' \ - f'{self.to_rgb})' - return repr_str - - -@PIPELINES.register_module() -class Rerange(object): - """Rerange the image pixel value. - - Args: - min_value (float or int): Minimum value of the reranged image. - Default: 0. - max_value (float or int): Maximum value of the reranged image. - Default: 255. - """ - - def __init__(self, min_value=0, max_value=255): - assert isinstance(min_value, float) or isinstance(min_value, int) - assert isinstance(max_value, float) or isinstance(max_value, int) - assert min_value < max_value - self.min_value = min_value - self.max_value = max_value - - def __call__(self, results): - """Call function to rerange images. - - Args: - results (dict): Result dict from loading pipeline. - Returns: - dict: Reranged results. - """ - - img = results['img'] - img_min_value = np.min(img) - img_max_value = np.max(img) - - assert img_min_value < img_max_value - # rerange to [0, 1] - img = (img - img_min_value) / (img_max_value - img_min_value) - # rerange to [min_value, max_value] - img = img * (self.max_value - self.min_value) + self.min_value - results['img'] = img - - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(min_value={self.min_value}, max_value={self.max_value})' - return repr_str - - -@PIPELINES.register_module() -class CLAHE(object): - """Use CLAHE method to process the image. - - See `ZUIDERVELD,K. Contrast Limited Adaptive Histogram Equalization[J]. - Graphics Gems, 1994:474-485.` for more information. - - Args: - clip_limit (float): Threshold for contrast limiting. Default: 40.0. - tile_grid_size (tuple[int]): Size of grid for histogram equalization. - Input image will be divided into equally sized rectangular tiles. - It defines the number of tiles in row and column. Default: (8, 8). - """ - - def __init__(self, clip_limit=40.0, tile_grid_size=(8, 8)): - assert isinstance(clip_limit, (float, int)) - self.clip_limit = clip_limit - assert is_tuple_of(tile_grid_size, int) - assert len(tile_grid_size) == 2 - self.tile_grid_size = tile_grid_size - - def __call__(self, results): - """Call function to Use CLAHE method process images. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Processed results. - """ - - for i in range(results['img'].shape[2]): - results['img'][:, :, i] = mmcv.clahe( - np.array(results['img'][:, :, i], dtype=np.uint8), - self.clip_limit, self.tile_grid_size) - - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(clip_limit={self.clip_limit}, '\ - f'tile_grid_size={self.tile_grid_size})' - return repr_str - - -@PIPELINES.register_module() -class RandomCrop(object): - """Random crop the image & seg. - - Args: - crop_size (tuple): Expected size after cropping, (h, w). - cat_max_ratio (float): The maximum ratio that single category could - occupy. - """ - - def __init__(self, crop_size, cat_max_ratio=1., ignore_index=255): - assert crop_size[0] > 0 and crop_size[1] > 0 - self.crop_size = crop_size - self.cat_max_ratio = cat_max_ratio - self.ignore_index = ignore_index - - def get_crop_bbox(self, img): - """Randomly get a crop bounding box.""" - margin_h = max(img.shape[0] - self.crop_size[0], 0) - margin_w = max(img.shape[1] - self.crop_size[1], 0) - offset_h = np.random.randint(0, margin_h + 1) - offset_w = np.random.randint(0, margin_w + 1) - crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0] - crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1] - - return crop_y1, crop_y2, crop_x1, crop_x2 - - def crop(self, img, crop_bbox): - """Crop from ``img``""" - crop_y1, crop_y2, crop_x1, crop_x2 = crop_bbox - img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...] - return img - - def __call__(self, results): - """Call function to randomly crop images, semantic segmentation maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Randomly cropped results, 'img_shape' key in result dict is - updated according to crop size. - """ - - img = results['img'] - crop_bbox = self.get_crop_bbox(img) - if self.cat_max_ratio < 1.: - # Repeat 10 times - for _ in range(10): - seg_temp = self.crop(results['gt_semantic_seg'], crop_bbox) - labels, cnt = np.unique(seg_temp, return_counts=True) - cnt = cnt[labels != self.ignore_index] - if len(cnt) > 1 and np.max(cnt) / np.sum( - cnt) < self.cat_max_ratio: - break - crop_bbox = self.get_crop_bbox(img) - - # crop the image - img = self.crop(img, crop_bbox) - img_shape = img.shape - results['img'] = img - results['img_shape'] = img_shape - - # crop semantic seg - for key in results.get('seg_fields', []): - results[key] = self.crop(results[key], crop_bbox) - - return results - - def __repr__(self): - return self.__class__.__name__ + f'(crop_size={self.crop_size})' - - -@PIPELINES.register_module() -class RandomRotate(object): - """Rotate the image & seg. - - Args: - prob (float): The rotation probability. - degree (float, tuple[float]): Range of degrees to select from. If - degree is a number instead of tuple like (min, max), - the range of degree will be (``-degree``, ``+degree``) - pad_val (float, optional): Padding value of image. Default: 0. - seg_pad_val (float, optional): Padding value of segmentation map. - Default: 255. - center (tuple[float], optional): Center point (w, h) of the rotation in - the source image. If not specified, the center of the image will be - used. Default: None. - auto_bound (bool): Whether to adjust the image size to cover the whole - rotated image. Default: False - """ - - def __init__(self, - prob, - degree, - pad_val=0, - seg_pad_val=255, - center=None, - auto_bound=False): - self.prob = prob - assert prob >= 0 and prob <= 1 - if isinstance(degree, (float, int)): - assert degree > 0, f'degree {degree} should be positive' - self.degree = (-degree, degree) - else: - self.degree = degree - assert len(self.degree) == 2, f'degree {self.degree} should be a ' \ - f'tuple of (min, max)' - self.pal_val = pad_val - self.seg_pad_val = seg_pad_val - self.center = center - self.auto_bound = auto_bound - - def __call__(self, results): - """Call function to rotate image, semantic segmentation maps. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Rotated results. - """ - - rotate = True if np.random.rand() < self.prob else False - degree = np.random.uniform(min(*self.degree), max(*self.degree)) - if rotate: - # rotate image - results['img'] = mmcv.imrotate( - results['img'], - angle=degree, - border_value=self.pal_val, - center=self.center, - auto_bound=self.auto_bound) - - # rotate segs - for key in results.get('seg_fields', []): - results[key] = mmcv.imrotate( - results[key], - angle=degree, - border_value=self.seg_pad_val, - center=self.center, - auto_bound=self.auto_bound, - interpolation='nearest') - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(prob={self.prob}, ' \ - f'degree={self.degree}, ' \ - f'pad_val={self.pal_val}, ' \ - f'seg_pad_val={self.seg_pad_val}, ' \ - f'center={self.center}, ' \ - f'auto_bound={self.auto_bound})' - return repr_str - - -@PIPELINES.register_module() -class RGB2Gray(object): - """Convert RGB image to grayscale image. - - This transform calculate the weighted mean of input image channels with - ``weights`` and then expand the channels to ``out_channels``. When - ``out_channels`` is None, the number of output channels is the same as - input channels. - - Args: - out_channels (int): Expected number of output channels after - transforming. Default: None. - weights (tuple[float]): The weights to calculate the weighted mean. - Default: (0.299, 0.587, 0.114). - """ - - def __init__(self, out_channels=None, weights=(0.299, 0.587, 0.114)): - assert out_channels is None or out_channels > 0 - self.out_channels = out_channels - assert isinstance(weights, tuple) - for item in weights: - assert isinstance(item, (float, int)) - self.weights = weights - - def __call__(self, results): - """Call function to convert RGB image to grayscale image. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Result dict with grayscale image. - """ - img = results['img'] - assert len(img.shape) == 3 - assert img.shape[2] == len(self.weights) - weights = np.array(self.weights).reshape((1, 1, -1)) - img = (img * weights).sum(2, keepdims=True) - if self.out_channels is None: - img = img.repeat(weights.shape[2], axis=2) - else: - img = img.repeat(self.out_channels, axis=2) - - results['img'] = img - results['img_shape'] = img.shape - - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(out_channels={self.out_channels}, ' \ - f'weights={self.weights})' - return repr_str - - -@PIPELINES.register_module() -class AdjustGamma(object): - """Using gamma correction to process the image. - - Args: - gamma (float or int): Gamma value used in gamma correction. - Default: 1.0. - """ - - def __init__(self, gamma=1.0): - assert isinstance(gamma, float) or isinstance(gamma, int) - assert gamma > 0 - self.gamma = gamma - inv_gamma = 1.0 / gamma - self.table = np.array([(i / 255.0)**inv_gamma * 255 - for i in np.arange(256)]).astype('uint8') - - def __call__(self, results): - """Call function to process the image with gamma correction. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Processed results. - """ - - results['img'] = mmcv.lut_transform( - np.array(results['img'], dtype=np.uint8), self.table) - - return results - - def __repr__(self): - return self.__class__.__name__ + f'(gamma={self.gamma})' - - -@PIPELINES.register_module() -class SegRescale(object): - """Rescale semantic segmentation maps. - - Args: - scale_factor (float): The scale factor of the final output. - """ - - def __init__(self, scale_factor=1): - self.scale_factor = scale_factor - - def __call__(self, results): - """Call function to scale the semantic segmentation map. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Result dict with semantic segmentation map scaled. - """ - for key in results.get('seg_fields', []): - if self.scale_factor != 1: - results[key] = mmcv.imrescale( - results[key], self.scale_factor, interpolation='nearest') - return results - - def __repr__(self): - return self.__class__.__name__ + f'(scale_factor={self.scale_factor})' - - -@PIPELINES.register_module() -class PhotoMetricDistortion(object): - """Apply photometric distortion to image sequentially, every transformation - is applied with a probability of 0.5. The position of random contrast is in - second or second to last. - - 1. random brightness - 2. random contrast (mode 0) - 3. convert color from BGR to HSV - 4. random saturation - 5. random hue - 6. convert color from HSV to BGR - 7. random contrast (mode 1) - - Args: - brightness_delta (int): delta of brightness. - contrast_range (tuple): range of contrast. - saturation_range (tuple): range of saturation. - hue_delta (int): delta of hue. - """ - - def __init__(self, - brightness_delta=32, - contrast_range=(0.5, 1.5), - saturation_range=(0.5, 1.5), - hue_delta=18): - self.brightness_delta = brightness_delta - self.contrast_lower, self.contrast_upper = contrast_range - self.saturation_lower, self.saturation_upper = saturation_range - self.hue_delta = hue_delta - - def convert(self, img, alpha=1, beta=0): - """Multiple with alpha and add beat with clip.""" - img = img.astype(np.float32) * alpha + beta - img = np.clip(img, 0, 255) - return img.astype(np.uint8) - - def brightness(self, img): - """Brightness distortion.""" - if random.randint(2): - return self.convert( - img, - beta=random.uniform(-self.brightness_delta, - self.brightness_delta)) - return img - - def contrast(self, img): - """Contrast distortion.""" - if random.randint(2): - return self.convert( - img, - alpha=random.uniform(self.contrast_lower, self.contrast_upper)) - return img - - def saturation(self, img): - """Saturation distortion.""" - if random.randint(2): - img = mmcv.bgr2hsv(img) - img[:, :, 1] = self.convert( - img[:, :, 1], - alpha=random.uniform(self.saturation_lower, - self.saturation_upper)) - img = mmcv.hsv2bgr(img) - return img - - def hue(self, img): - """Hue distortion.""" - if random.randint(2): - img = mmcv.bgr2hsv(img) - img[:, :, - 0] = (img[:, :, 0].astype(int) + - random.randint(-self.hue_delta, self.hue_delta)) % 180 - img = mmcv.hsv2bgr(img) - return img - - def __call__(self, results): - """Call function to perform photometric distortion on images. - - Args: - results (dict): Result dict from loading pipeline. - - Returns: - dict: Result dict with images distorted. - """ - - img = results['img'] - # random brightness - img = self.brightness(img) - - # mode == 0 --> do random contrast first - # mode == 1 --> do random contrast last - mode = random.randint(2) - if mode == 1: - img = self.contrast(img) - - # random saturation - img = self.saturation(img) - - # random hue - img = self.hue(img) - - # random contrast - if mode == 0: - img = self.contrast(img) - - results['img'] = img - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += (f'(brightness_delta={self.brightness_delta}, ' - f'contrast_range=({self.contrast_lower}, ' - f'{self.contrast_upper}), ' - f'saturation_range=({self.saturation_lower}, ' - f'{self.saturation_upper}), ' - f'hue_delta={self.hue_delta})') - return repr_str diff --git a/spaces/weibinke/vits-simple-api/vits/text/mandarin.py b/spaces/weibinke/vits-simple-api/vits/text/mandarin.py deleted file mode 100644 index 80742a394f52165409bd820dc14e3cea6589454b..0000000000000000000000000000000000000000 --- a/spaces/weibinke/vits-simple-api/vits/text/mandarin.py +++ /dev/null @@ -1,365 +0,0 @@ -import config -import re -from pypinyin import lazy_pinyin, BOPOMOFO -import jieba -import cn2an -import logging - -logging.getLogger('jieba').setLevel(logging.WARNING) -jieba.set_dictionary(config.ABS_PATH + '/vits/text/jieba/dict.txt') -jieba.initialize() - -# List of (Latin alphabet, bopomofo) pairs: -_latin_to_bopomofo = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('a', 'ㄟˉ'), - ('b', 'ㄅㄧˋ'), - ('c', 'ㄙㄧˉ'), - ('d', 'ㄉㄧˋ'), - ('e', 'ㄧˋ'), - ('f', 'ㄝˊㄈㄨˋ'), - ('g', 'ㄐㄧˋ'), - ('h', 'ㄝˇㄑㄩˋ'), - ('i', 'ㄞˋ'), - ('j', 'ㄐㄟˋ'), - ('k', 'ㄎㄟˋ'), - ('l', 'ㄝˊㄛˋ'), - ('m', 'ㄝˊㄇㄨˋ'), - ('n', 'ㄣˉ'), - ('o', 'ㄡˉ'), - ('p', 'ㄆㄧˉ'), - ('q', 'ㄎㄧㄡˉ'), - ('r', 'ㄚˋ'), - ('s', 'ㄝˊㄙˋ'), - ('t', 'ㄊㄧˋ'), - ('u', 'ㄧㄡˉ'), - ('v', 'ㄨㄧˉ'), - ('w', 'ㄉㄚˋㄅㄨˋㄌㄧㄡˋ'), - ('x', 'ㄝˉㄎㄨˋㄙˋ'), - ('y', 'ㄨㄞˋ'), - ('z', 'ㄗㄟˋ') -]] - -# List of (bopomofo, romaji) pairs: -_bopomofo_to_romaji = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ㄅㄛ', 'p⁼wo'), - ('ㄆㄛ', 'pʰwo'), - ('ㄇㄛ', 'mwo'), - ('ㄈㄛ', 'fwo'), - ('ㄅ', 'p⁼'), - ('ㄆ', 'pʰ'), - ('ㄇ', 'm'), - ('ㄈ', 'f'), - ('ㄉ', 't⁼'), - ('ㄊ', 'tʰ'), - ('ㄋ', 'n'), - ('ㄌ', 'l'), - ('ㄍ', 'k⁼'), - ('ㄎ', 'kʰ'), - ('ㄏ', 'h'), - ('ㄐ', 'ʧ⁼'), - ('ㄑ', 'ʧʰ'), - ('ㄒ', 'ʃ'), - ('ㄓ', 'ʦ`⁼'), - ('ㄔ', 'ʦ`ʰ'), - ('ㄕ', 's`'), - ('ㄖ', 'ɹ`'), - ('ㄗ', 'ʦ⁼'), - ('ㄘ', 'ʦʰ'), - ('ㄙ', 's'), - ('ㄚ', 'a'), - ('ㄛ', 'o'), - ('ㄜ', 'ə'), - ('ㄝ', 'e'), - ('ㄞ', 'ai'), - ('ㄟ', 'ei'), - ('ㄠ', 'au'), - ('ㄡ', 'ou'), - ('ㄧㄢ', 'yeNN'), - ('ㄢ', 'aNN'), - ('ㄧㄣ', 'iNN'), - ('ㄣ', 'əNN'), - ('ㄤ', 'aNg'), - ('ㄧㄥ', 'iNg'), - ('ㄨㄥ', 'uNg'), - ('ㄩㄥ', 'yuNg'), - ('ㄥ', 'əNg'), - ('ㄦ', 'əɻ'), - ('ㄧ', 'i'), - ('ㄨ', 'u'), - ('ㄩ', 'ɥ'), - ('ˉ', '→'), - ('ˊ', '↑'), - ('ˇ', '↓↑'), - ('ˋ', '↓'), - ('˙', ''), - ('，', ','), - ('。', '.'), - ('！', '!'), - ('？', '?'), - ('—', '-') -]] - -# List of (romaji, ipa) pairs: -_romaji_to_ipa = [(re.compile('%s' % x[0], re.IGNORECASE), x[1]) for x in [ - ('ʃy', 'ʃ'), - ('ʧʰy', 'ʧʰ'), - ('ʧ⁼y', 'ʧ⁼'), - ('NN', 'n'), - ('Ng', 'ŋ'), - ('y', 'j'), - ('h', 'x') -]] - -# List of (bopomofo, ipa) pairs: -_bopomofo_to_ipa = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ㄅㄛ', 'p⁼wo'), - ('ㄆㄛ', 'pʰwo'), - ('ㄇㄛ', 'mwo'), - ('ㄈㄛ', 'fwo'), - ('ㄅ', 'p⁼'), - ('ㄆ', 'pʰ'), - ('ㄇ', 'm'), - ('ㄈ', 'f'), - ('ㄉ', 't⁼'), - ('ㄊ', 'tʰ'), - ('ㄋ', 'n'), - ('ㄌ', 'l'), - ('ㄍ', 'k⁼'), - ('ㄎ', 'kʰ'), - ('ㄏ', 'x'), - ('ㄐ', 'tʃ⁼'), - ('ㄑ', 'tʃʰ'), - ('ㄒ', 'ʃ'), - ('ㄓ', 'ts`⁼'), - ('ㄔ', 'ts`ʰ'), - ('ㄕ', 's`'), - ('ㄖ', 'ɹ`'), - ('ㄗ', 'ts⁼'), - ('ㄘ', 'tsʰ'), - ('ㄙ', 's'), - ('ㄚ', 'a'), - ('ㄛ', 'o'), - ('ㄜ', 'ə'), - ('ㄝ', 'ɛ'), - ('ㄞ', 'aɪ'), - ('ㄟ', 'eɪ'), - ('ㄠ', 'ɑʊ'), - ('ㄡ', 'oʊ'), - ('ㄧㄢ', 'jɛn'), - ('ㄩㄢ', 'ɥæn'), - ('ㄢ', 'an'), - ('ㄧㄣ', 'in'), - ('ㄩㄣ', 'ɥn'), - ('ㄣ', 'ən'), - ('ㄤ', 'ɑŋ'), - ('ㄧㄥ', 'iŋ'), - ('ㄨㄥ', 'ʊŋ'), - ('ㄩㄥ', 'jʊŋ'), - ('ㄥ', 'əŋ'), - ('ㄦ', 'əɻ'), - ('ㄧ', 'i'), - ('ㄨ', 'u'), - ('ㄩ', 'ɥ'), - ('ˉ', '→'), - ('ˊ', '↑'), - ('ˇ', '↓↑'), - ('ˋ', '↓'), - ('˙', ''), - ('，', ','), - ('。', '.'), - ('！', '!'), - ('？', '?'), - ('—', '-') -]] - -# List of (bopomofo, ipa2) pairs: -_bopomofo_to_ipa2 = [(re.compile('%s' % x[0]), x[1]) for x in [ - ('ㄅㄛ', 'pwo'), - ('ㄆㄛ', 'pʰwo'), - ('ㄇㄛ', 'mwo'), - ('ㄈㄛ', 'fwo'), - ('ㄅ', 'p'), - ('ㄆ', 'pʰ'), - ('ㄇ', 'm'), - ('ㄈ', 'f'), - ('ㄉ', 't'), - ('ㄊ', 'tʰ'), - ('ㄋ', 'n'), - ('ㄌ', 'l'), - ('ㄍ', 'k'), - ('ㄎ', 'kʰ'), - ('ㄏ', 'h'), - ('ㄐ', 'tɕ'), - ('ㄑ', 'tɕʰ'), - ('ㄒ', 'ɕ'), - ('ㄓ', 'tʂ'), - ('ㄔ', 'tʂʰ'), - ('ㄕ', 'ʂ'), - ('ㄖ', 'ɻ'), - ('ㄗ', 'ts'), - ('ㄘ', 'tsʰ'), - ('ㄙ', 's'), - ('ㄚ', 'a'), - ('ㄛ', 'o'), - ('ㄜ', 'ɤ'), - ('ㄝ', 'ɛ'), - ('ㄞ', 'aɪ'), - ('ㄟ', 'eɪ'), - ('ㄠ', 'ɑʊ'), - ('ㄡ', 'oʊ'), - ('ㄧㄢ', 'jɛn'), - ('ㄩㄢ', 'yæn'), - ('ㄢ', 'an'), - ('ㄧㄣ', 'in'), - ('ㄩㄣ', 'yn'), - ('ㄣ', 'ən'), - ('ㄤ', 'ɑŋ'), - ('ㄧㄥ', 'iŋ'), - ('ㄨㄥ', 'ʊŋ'), - ('ㄩㄥ', 'jʊŋ'), - ('ㄥ', 'ɤŋ'), - ('ㄦ', 'əɻ'), - ('ㄧ', 'i'), - ('ㄨ', 'u'), - ('ㄩ', 'y'), - ('ˉ', '˥'), - ('ˊ', '˧˥'), - ('ˇ', '˨˩˦'), - ('ˋ', '˥˩'), - ('˙', ''), - ('，', ','), - ('。', '.'), - ('！', '!'), - ('？', '?'), - ('—', '-') -]] - -_symbols_to_chinese = [(re.compile(f'{x[0]}'), x[1]) for x in [ - ('([0-9]+(?:\.?[0-9]+)?)%', r'百分之\1'), - ('([0-9]+)/([0-9]+)', r'\2分之\1'), - ('\+', r'加'), - ('([0-9]+)-([0-9]+)', r'\1减\2'), - ('×', r'乘以'), - ('([0-9]+)x([0-9]+)', r'\1乘以\2'), - ('([0-9]+)\*([0-9]+)', r'\1乘以\2'), - ('÷', r'除以'), - ('=', r'等于'), - ('≠', r'不等于'), -]] - - -def symbols_to_chinese(text): - for regex, replacement in _symbols_to_chinese: - text = re.sub(regex, replacement, text) - return text - - -def number_to_chinese(text): - numbers = re.findall(r'[0-9]+(?:\.?[0-9]+)?', text) - for number in numbers: - text = text.replace(number, cn2an.an2cn(number), 1) - return text - - -def number_transform_to_chinese(text): - text = cn2an.transform(text, "an2cn") - return text - - -def chinese_to_bopomofo(text): - text = text.replace('、', '，').replace('；', '，').replace('：', '，') - words = jieba.lcut(text, cut_all=False) - text = '' - for word in words: - bopomofos = lazy_pinyin(word, BOPOMOFO) - if not re.search('[\u4e00-\u9fff]', word): - text += word - continue - for i in range(len(bopomofos)): - bopomofos[i] = re.sub(r'([\u3105-\u3129])$', r'\1ˉ', bopomofos[i]) - if text != '': - text += ' ' - text += ''.join(bopomofos) - return text - - -def latin_to_bopomofo(text): - for regex, replacement in _latin_to_bopomofo: - text = re.sub(regex, replacement, text) - return text - - -def bopomofo_to_romaji(text): - for regex, replacement in _bopomofo_to_romaji: - text = re.sub(regex, replacement, text) - return text - - -def bopomofo_to_ipa(text): - for regex, replacement in _bopomofo_to_ipa: - text = re.sub(regex, replacement, text) - return text - - -def bopomofo_to_ipa2(text): - for regex, replacement in _bopomofo_to_ipa2: - text = re.sub(regex, replacement, text) - return text - - -def chinese_to_romaji(text): - text = symbols_to_chinese(text) - text = number_transform_to_chinese(text) - text = chinese_to_bopomofo(text) - text = latin_to_bopomofo(text) - text = bopomofo_to_romaji(text) - text = re.sub('i([aoe])', r'y\1', text) - text = re.sub('u([aoəe])', r'w\1', text) - text = re.sub('([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)', - r'\1ɹ`\2', text).replace('ɻ', 'ɹ`') - text = re.sub('([ʦs][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text) - return text - - -def chinese_to_lazy_ipa(text): - text = chinese_to_romaji(text) - for regex, replacement in _romaji_to_ipa: - text = re.sub(regex, replacement, text) - return text - - -def chinese_to_ipa(text): - text = symbols_to_chinese(text) - text = number_transform_to_chinese(text) - text = chinese_to_bopomofo(text) - text = latin_to_bopomofo(text) - text = bopomofo_to_ipa(text) - text = re.sub('i([aoe])', r'j\1', text) - text = re.sub('u([aoəe])', r'w\1', text) - text = re.sub('([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)', - r'\1ɹ`\2', text).replace('ɻ', 'ɹ`') - text = re.sub('([s][⁼ʰ]?)([→↓↑ ]+|$)', r'\1ɹ\2', text) - return text - - -def chinese_to_ipa2(text): - text = symbols_to_chinese(text) - text = number_transform_to_chinese(text) - text = chinese_to_bopomofo(text) - text = latin_to_bopomofo(text) - text = bopomofo_to_ipa2(text) - text = re.sub(r'i([aoe])', r'j\1', text) - text = re.sub(r'u([aoəe])', r'w\1', text) - text = re.sub(r'([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)', r'\1ʅ\2', text) - text = re.sub(r'(sʰ?)([˩˨˧˦˥ ]+|$)', r'\1ɿ\2', text) - return text - - -def VITS_PinYin_model(): - import torch - import config - from vits.text.vits_pinyin import VITS_PinYin - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - # pinyin - tts_front = VITS_PinYin(f"{config.ABS_PATH}/vits/bert", device) - return tts_front diff --git a/spaces/wffcyrus/MetaGPT-v1/metagpt/static/assets/home-a1d5c9a6.js b/spaces/wffcyrus/MetaGPT-v1/metagpt/static/assets/home-a1d5c9a6.js deleted file mode 100644 index 850fa15fdf043ed58411c15e63fbeb6d7ca0895a..0000000000000000000000000000000000000000 --- 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-`,sre="∄",lre="∄",cre="𝔑",ure="𝔫",dre="≧̸",_re="≱",pre="≱",mre="≧̸",gre="⩾̸",Ere="⩾̸",fre="⋙̸",Sre="≵",bre="≫⃒",hre="≯",Tre="≯",vre="≫̸",Cre="↮",Rre="⇎",Nre="⫲",Ore="∋",Are="⋼",yre="⋺",Ire="∋",Dre="Њ",xre="њ",wre="↚",Mre="⇍",Lre="‥",Pre="≦̸",kre="≰",Ure="↚",Fre="⇍",Bre="↮",Gre="⇎",Yre="≰",qre="≦̸",$re="⩽̸",Hre="⩽̸",zre="≮",Vre="⋘̸",Wre="≴",Kre="≪⃒",Qre="≮",Xre="⋪",Zre="⋬",Jre="≪̸",jre="∤",eie="⁠",tie=" ",nie="𝕟",rie="ℕ",iie="⫬",aie="¬",oie="≢",sie="≭",lie="∦",cie="∉",uie="≠",die="≂̸",_ie="∄",pie="≯",mie="≱",gie="≧̸",Eie="≫̸",fie="≹",Sie="⩾̸",bie="≵",hie="≎̸",Tie="≏̸",vie="∉",Cie="⋵̸",Rie="⋹̸",Nie="∉",Oie="⋷",Aie="⋶",yie="⧏̸",Iie="⋪",Die="⋬",xie="≮",wie="≰",Mie="≸",Lie="≪̸",Pie="⩽̸",kie="≴",Uie="⪢̸",Fie="⪡̸",Bie="∌",Gie="∌",Yie="⋾",qie="⋽",$ie="⊀",Hie="⪯̸",zie="⋠",Vie="∌",Wie="⧐̸",Kie="⋫",Qie="⋭",Xie="⊏̸",Zie="⋢",Jie="⊐̸",jie="⋣",eae="⊂⃒",tae="⊈",nae="⊁",rae="⪰̸",iae="⋡",aae="≿̸",oae="⊃⃒",sae="⊉",lae="≁",cae="≄",uae="≇",dae="≉",_ae="∤",pae="∦",mae="∦",gae="⫽⃥",Eae="∂̸",fae="⨔",Sae="⊀",bae="⋠",hae="⊀",Tae="⪯̸",vae="⪯̸",Cae="⤳̸",Rae="↛",Nae="⇏",Oae="↝̸",Aae="↛",yae="⇏",Iae="⋫",Dae="⋭",xae="⊁",wae="⋡",Mae="⪰̸",Lae="𝒩",Pae="𝓃",kae="∤",Uae="∦",Fae="≁",Bae="≄",Gae="≄",Yae="∤",qae="∦",$ae="⋢",Hae="⋣",zae="⊄",Vae="⫅̸",Wae="⊈",Kae="⊂⃒",Qae="⊈",Xae="⫅̸",Zae="⊁",Jae="⪰̸",jae="⊅",eoe="⫆̸",toe="⊉",noe="⊃⃒",roe="⊉",ioe="⫆̸",aoe="≹",ooe="Ñ",soe="ñ",loe="≸",coe="⋪",uoe="⋬",doe="⋫",_oe="⋭",poe="Ν",moe="ν",goe="#",Eoe="№",foe=" ",Soe="≍⃒",boe="⊬",hoe="⊭",Toe="⊮",voe="⊯",Coe="≥⃒",Roe=">⃒",Noe="⤄",Ooe="⧞",Aoe="⤂",yoe="≤⃒",Ioe="<⃒",Doe="⊴⃒",xoe="⤃",woe="⊵⃒",Moe="∼⃒",Loe="⤣",Poe="↖",koe="⇖",Uoe="↖",Foe="⤧",Boe="Ó",Goe="ó",Yoe="⊛",qoe="Ô",$oe="ô",Hoe="⊚",zoe="О",Voe="о",Woe="⊝",Koe="Ő",Qoe="ő",Xoe="⨸",Zoe="⊙",Joe="⦼",joe="Œ",ese="œ",tse="⦿",nse="𝔒",rse="𝔬",ise="˛",ase="Ò",ose="ò",sse="⧁",lse="⦵",cse="Ω",use="∮",dse="↺",_se="⦾",pse="⦻",mse="‾",gse="⧀",Ese="Ō",fse="ō",Sse="Ω",bse="ω",hse="Ο",Tse="ο",vse="⦶",Cse="⊖",Rse="𝕆",Nse="𝕠",Ose="⦷",Ase="“",yse="‘",Ise="⦹",Dse="⊕",xse="↻",wse="⩔",Mse="∨",Lse="⩝",Pse="ℴ",kse="ℴ",Use="ª",Fse="º",Bse="⊶",Gse="⩖",Yse="⩗",qse="⩛",$se="Ⓢ",Hse="𝒪",zse="ℴ",Vse="Ø",Wse="ø",Kse="⊘",Qse="Õ",Xse="õ",Zse="⨶",Jse="⨷",jse="⊗",ele="Ö",tle="ö",nle="⌽",rle="‾",ile="⏞",ale="⎴",ole="⏜",sle="¶",lle="∥",cle="∥",ule="⫳",dle="⫽",_le="∂",ple="∂",mle="П",gle="п",Ele="%",fle=".",Sle="‰",ble="⊥",hle="‱",Tle="𝔓",vle="𝔭",Cle="Φ",Rle="φ",Nle="ϕ",Ole="ℳ",Ale="☎",yle="Π",Ile="π",Dle="⋔",xle="ϖ",wle="ℏ",Mle="ℎ",Lle="ℏ",Ple="⨣",kle="⊞",Ule="⨢",Fle="+",Ble="∔",Gle="⨥",Yle="⩲",qle="±",$le="±",Hle="⨦",zle="⨧",Vle="±",Wle="ℌ",Kle="⨕",Qle="𝕡",Xle="ℙ",Zle="£",Jle="⪷",jle="⪻",ece="≺",tce="≼",nce="⪷",rce="≺",ice="≼",ace="≺",oce="⪯",sce="≼",lce="≾",cce="⪯",uce="⪹",dce="⪵",_ce="⋨",pce="⪯",mce="⪳",gce="≾",Ece="′",fce="″",Sce="ℙ",bce="⪹",hce="⪵",Tce="⋨",vce="∏",Cce="∏",Rce="⌮",Nce="⌒",Oce="⌓",Ace="∝",yce="∝",Ice="∷",Dce="∝",xce="≾",wce="⊰",Mce="𝒫",Lce="𝓅",Pce="Ψ",kce="ψ",Uce=" ",Fce="𝔔",Bce="𝔮",Gce="⨌",Yce="𝕢",qce="ℚ",$ce="⁗",Hce="𝒬",zce="𝓆",Vce="ℍ",Wce="⨖",Kce="?",Qce="≟",Xce='"',Zce='"',Jce="⇛",jce="∽̱",eue="Ŕ",tue="ŕ",nue="√",rue="⦳",iue="⟩",aue="⟫",oue="⦒",sue="⦥",lue="⟩",cue="»",uue="⥵",due="⇥",_ue="⤠",pue="⤳",mue="→",gue="↠",Eue="⇒",fue="⤞",Sue="↪",bue="↬",hue="⥅",Tue="⥴",vue="⤖",Cue="↣",Rue="↝",Nue="⤚",Oue="⤜",Aue="∶",yue="ℚ",Iue="⤍",Due="⤏",xue="⤐",wue="❳",Mue="}",Lue="]",Pue="⦌",kue="⦎",Uue="⦐",Fue="Ř",Bue="ř",Gue="Ŗ",Yue="ŗ",que="⌉",$ue="}",Hue="Р",zue="р",Vue="⤷",Wue="⥩",Kue="”",Que="”",Xue="↳",Zue="ℜ",Jue="ℛ",jue="ℜ",ede="ℝ",tde="ℜ",nde="▭",rde="®",ide="®",ade="∋",ode="⇋",sde="⥯",lde="⥽",cde="⌋",ude="𝔯",dde="ℜ",_de="⥤",pde="⇁",mde="⇀",gde="⥬",Ede="Ρ",fde="ρ",Sde="ϱ",bde="⟩",hde="⇥",Tde="→",vde="→",Cde="⇒",Rde="⇄",Nde="↣",Ode="⌉",Ade="⟧",yde="⥝",Ide="⥕",Dde="⇂",xde="⌋",wde="⇁",Mde="⇀",Lde="⇄",Pde="⇌",kde="⇉",Ude="↝",Fde="↦",Bde="⊢",Gde="⥛",Yde="⋌",qde="⧐",$de="⊳",Hde="⊵",zde="⥏",Vde="⥜",Wde="⥔",Kde="↾",Qde="⥓",Xde="⇀",Zde="˚",Jde="≓",jde="⇄",e_e="⇌",t_e="‏",n_e="⎱",r_e="⎱",i_e="⫮",a_e="⟭",o_e="⇾",s_e="⟧",l_e="⦆",c_e="𝕣",u_e="ℝ",d_e="⨮",__e="⨵",p_e="⥰",m_e=")",g_e="⦔",E_e="⨒",f_e="⇉",S_e="⇛",b_e="›",h_e="𝓇",T_e="ℛ",v_e="↱",C_e="↱",R_e="]",N_e="’",O_e="’",A_e="⋌",y_e="⋊",I_e="▹",D_e="⊵",x_e="▸",w_e="⧎",M_e="⧴",L_e="⥨",P_e="℞",k_e="Ś",U_e="ś",F_e="‚",B_e="⪸",G_e="Š",Y_e="š",q_e="⪼",$_e="≻",H_e="≽",z_e="⪰",V_e="⪴",W_e="Ş",K_e="ş",Q_e="Ŝ",X_e="ŝ",Z_e="⪺",J_e="⪶",j_e="⋩",epe="⨓",tpe="≿",npe="С",rpe="с",ipe="⊡",ape="⋅",ope="⩦",spe="⤥",lpe="↘",cpe="⇘",upe="↘",dpe="§",_pe=";",ppe="⤩",mpe="∖",gpe="∖",Epe="✶",fpe="𝔖",Spe="𝔰",bpe="⌢",hpe="♯",Tpe="Щ",vpe="щ",Cpe="Ш",Rpe="ш",Npe="↓",Ope="←",Ape="∣",ype="∥",Ipe="→",Dpe="↑",xpe="­",wpe="Σ",Mpe="σ",Lpe="ς",Ppe="ς",kpe="∼",Upe="⩪",Fpe="≃",Bpe="≃",Gpe="⪞",Ype="⪠",qpe="⪝",$pe="⪟",Hpe="≆",zpe="⨤",Vpe="⥲",Wpe="←",Kpe="∘",Qpe="∖",Xpe="⨳",Zpe="⧤",Jpe="∣",jpe="⌣",eme="⪪",tme="⪬",nme="⪬︀",rme="Ь",ime="ь",ame="⌿",ome="⧄",sme="/",lme="𝕊",cme="𝕤",ume="♠",dme="♠",_me="∥",pme="⊓",mme="⊓︀",gme="⊔",Eme="⊔︀",fme="√",Sme="⊏",bme="⊑",hme="⊏",Tme="⊑",vme="⊐",Cme="⊒",Rme="⊐",Nme="⊒",Ome="□",Ame="□",yme="⊓",Ime="⊏",Dme="⊑",xme="⊐",wme="⊒",Mme="⊔",Lme="▪",Pme="□",kme="▪",Ume="→",Fme="𝒮",Bme="𝓈",Gme="∖",Yme="⌣",qme="⋆",$me="⋆",Hme="☆",zme="★",Vme="ϵ",Wme="ϕ",Kme="¯",Qme="⊂",Xme="⋐",Zme="⪽",Jme="⫅",jme="⊆",ege="⫃",tge="⫁",nge="⫋",rge="⊊",ige="⪿",age="⥹",oge="⊂",sge="⋐",lge="⊆",cge="⫅",uge="⊆",dge="⊊",_ge="⫋",pge="⫇",mge="⫕",gge="⫓",Ege="⪸",fge="≻",Sge="≽",bge="≻",hge="⪰",Tge="≽",vge="≿",Cge="⪰",Rge="⪺",Nge="⪶",Oge="⋩",Age="≿",yge="∋",Ige="∑",Dge="∑",xge="♪",wge="¹",Mge="²",Lge="³",Pge="⊃",kge="⋑",Uge="⪾",Fge="⫘",Bge="⫆",Gge="⊇",Yge="⫄",qge="⊃",$ge="⊇",Hge="⟉",zge="⫗",Vge="⥻",Wge="⫂",Kge="⫌",Qge="⊋",Xge="⫀",Zge="⊃",Jge="⋑",jge="⊇",eEe="⫆",tEe="⊋",nEe="⫌",rEe="⫈",iEe="⫔",aEe="⫖",oEe="⤦",sEe="↙",lEe="⇙",cEe="↙",uEe="⤪",dEe="ß",_Ee=" ",pEe="⌖",mEe="Τ",gEe="τ",EEe="⎴",fEe="Ť",SEe="ť",bEe="Ţ",hEe="ţ",TEe="Т",vEe="т",CEe="⃛",REe="⌕",NEe="𝔗",OEe="𝔱",AEe="∴",yEe="∴",IEe="∴",DEe="Θ",xEe="θ",wEe="ϑ",MEe="ϑ",LEe="≈",PEe="∼",kEe="  ",UEe=" ",FEe=" ",BEe="≈",GEe="∼",YEe="Þ",qEe="þ",$Ee="˜",HEe="∼",zEe="≃",VEe="≅",WEe="≈",KEe="⨱",QEe="⊠",XEe="×",ZEe="⨰",JEe="∭",jEe="⤨",efe="⌶",tfe="⫱",nfe="⊤",rfe="𝕋",ife="𝕥",afe="⫚",ofe="⤩",sfe="‴",lfe="™",cfe="™",ufe="▵",dfe="▿",_fe="◃",pfe="⊴",mfe="≜",gfe="▹",Efe="⊵",ffe="◬",Sfe="≜",bfe="⨺",hfe="⃛",Tfe="⨹",vfe="⧍",Cfe="⨻",Rfe="⏢",Nfe="𝒯",Ofe="𝓉",Afe="Ц",yfe="ц",Ife="Ћ",Dfe="ћ",xfe="Ŧ",wfe="ŧ",Mfe="≬",Lfe="↞",Pfe="↠",kfe="Ú",Ufe="ú",Ffe="↑",Bfe="↟",Gfe="⇑",Yfe="⥉",qfe="Ў",$fe="ў",Hfe="Ŭ",zfe="ŭ",Vfe="Û",Wfe="û",Kfe="У",Qfe="у",Xfe="⇅",Zfe="Ű",Jfe="ű",jfe="⥮",eSe="⥾",tSe="𝔘",nSe="𝔲",rSe="Ù",iSe="ù",aSe="⥣",oSe="↿",sSe="↾",lSe="▀",cSe="⌜",uSe="⌜",dSe="⌏",_Se="◸",pSe="Ū",mSe="ū",gSe="¨",ESe="_",fSe="⏟",SSe="⎵",bSe="⏝",hSe="⋃",TSe="⊎",vSe="Ų",CSe="ų",RSe="𝕌",NSe="𝕦",OSe="⤒",ASe="↑",ySe="↑",ISe="⇑",DSe="⇅",xSe="↕",wSe="↕",MSe="⇕",LSe="⥮",PSe="↿",kSe="↾",USe="⊎",FSe="↖",BSe="↗",GSe="υ",YSe="ϒ",qSe="ϒ",$Se="Υ",HSe="υ",zSe="↥",VSe="⊥",WSe="⇈",KSe="⌝",QSe="⌝",XSe="⌎",ZSe="Ů",JSe="ů",jSe="◹",ebe="𝒰",tbe="𝓊",nbe="⋰",rbe="Ũ",ibe="ũ",abe="▵",obe="▴",sbe="⇈",lbe="Ü",cbe="ü",ube="⦧",dbe="⦜",_be="ϵ",pbe="ϰ",mbe="∅",gbe="ϕ",Ebe="ϖ",fbe="∝",Sbe="↕",bbe="⇕",hbe="ϱ",Tbe="ς",vbe="⊊︀",Cbe="⫋︀",Rbe="⊋︀",Nbe="⫌︀",Obe="ϑ",Abe="⊲",ybe="⊳",Ibe="⫨",Dbe="⫫",xbe="⫩",wbe="В",Mbe="в",Lbe="⊢",Pbe="⊨",kbe="⊩",Ube="⊫",Fbe="⫦",Bbe="⊻",Gbe="∨",Ybe="⋁",qbe="≚",$be="⋮",Hbe="|",zbe="‖",Vbe="|",Wbe="‖",Kbe="∣",Qbe="|",Xbe="❘",Zbe="≀",Jbe=" ",jbe="𝔙",ehe="𝔳",the="⊲",nhe="⊂⃒",rhe="⊃⃒",ihe="𝕍",ahe="𝕧",ohe="∝",she="⊳",lhe="𝒱",che="𝓋",uhe="⫋︀",dhe="⊊︀",_he="⫌︀",phe="⊋︀",mhe="⊪",ghe="⦚",Ehe="Ŵ",fhe="ŵ",She="⩟",bhe="∧",hhe="⋀",The="≙",vhe="℘",Che="𝔚",Rhe="𝔴",Nhe="𝕎",Ohe="𝕨",Ahe="℘",yhe="≀",Ihe="≀",Dhe="𝒲",xhe="𝓌",whe="⋂",Mhe="◯",Lhe="⋃",Phe="▽",khe="𝔛",Uhe="𝔵",Fhe="⟷",Bhe="⟺",Ghe="Ξ",Yhe="ξ",qhe="⟵",$he="⟸",Hhe="⟼",zhe="⋻",Vhe="⨀",Whe="𝕏",Khe="𝕩",Qhe="⨁",Xhe="⨂",Zhe="⟶",Jhe="⟹",jhe="𝒳",eTe="𝓍",tTe="⨆",nTe="⨄",rTe="△",iTe="⋁",aTe="⋀",oTe="Ý",sTe="ý",lTe="Я",cTe="я",uTe="Ŷ",dTe="ŷ",_Te="Ы",pTe="ы",mTe="¥",gTe="𝔜",ETe="𝔶",fTe="Ї",STe="ї",bTe="𝕐",hTe="𝕪",TTe="𝒴",vTe="𝓎",CTe="Ю",RTe="ю",NTe="ÿ",OTe="Ÿ",ATe="Ź",yTe="ź",ITe="Ž",DTe="ž",xTe="З",wTe="з",MTe="Ż",LTe="ż",PTe="ℨ",kTe="​",UTe="Ζ",FTe="ζ",BTe="𝔷",GTe="ℨ",YTe="Ж",qTe="ж",$Te="⇝",HTe="𝕫",zTe="ℤ",VTe="𝒵",WTe="𝓏",KTe="‍",QTe="‌",XTe={Aacute:M$,aacute:L$,Abreve:P$,abreve:k$,ac:U$,acd:F$,acE:B$,Acirc:G$,acirc:Y$,acute:q$,Acy:$$,acy:H$,AElig:z$,aelig:V$,af:W$,Afr:K$,afr:Q$,Agrave:X$,agrave:Z$,alefsym:J$,aleph:j$,Alpha:eH,alpha:tH,Amacr:nH,amacr:rH,amalg:iH,amp:aH,AMP:oH,andand:sH,And:lH,and:cH,andd:uH,andslope:dH,andv:_H,ang:pH,ange:mH,angle:gH,angmsdaa:EH,angmsdab:fH,angmsdac:SH,angmsdad:bH,angmsdae:hH,angmsdaf:TH,angmsdag:vH,angmsdah:CH,angmsd:RH,angrt:NH,angrtvb:OH,angrtvbd:AH,angsph:yH,angst:IH,angzarr:DH,Aogon:xH,aogon:wH,Aopf:MH,aopf:LH,apacir:PH,ap:kH,apE:UH,ape:FH,apid:BH,apos:GH,ApplyFunction:YH,approx:qH,approxeq:$H,Aring:HH,aring:zH,Ascr:VH,ascr:WH,Assign:KH,ast:QH,asymp:XH,asympeq:ZH,Atilde:JH,atilde:jH,Auml:ez,auml:tz,awconint:nz,awint:rz,backcong:iz,backepsilon:az,backprime:oz,backsim:sz,backsimeq:lz,Backslash:cz,Barv:uz,barvee:dz,barwed:_z,Barwed:pz,barwedge:mz,bbrk:gz,bbrktbrk:Ez,bcong:fz,Bcy:Sz,bcy:bz,bdquo:hz,becaus:Tz,because:vz,Because:Cz,bemptyv:Rz,bepsi:Nz,bernou:Oz,Bernoullis:Az,Beta:yz,beta:Iz,beth:Dz,between:xz,Bfr:wz,bfr:Mz,bigcap:Lz,bigcirc:Pz,bigcup:kz,bigodot:Uz,bigoplus:Fz,bigotimes:Bz,bigsqcup:Gz,bigstar:Yz,bigtriangledown:qz,bigtriangleup:$z,biguplus:Hz,bigvee:zz,bigwedge:Vz,bkarow:Wz,blacklozenge:Kz,blacksquare:Qz,blacktriangle:Xz,blacktriangledown:Zz,blacktriangleleft:Jz,blacktriangleright:jz,blank:eV,blk12:tV,blk14:nV,blk34:rV,block:iV,bne:aV,bnequiv:oV,bNot:sV,bnot:lV,Bopf:cV,bopf:uV,bot:dV,bottom:_V,bowtie:pV,boxbox:mV,boxdl:gV,boxdL:EV,boxDl:fV,boxDL:SV,boxdr:bV,boxdR:hV,boxDr:TV,boxDR:vV,boxh:CV,boxH:RV,boxhd:NV,boxHd:OV,boxhD:AV,boxHD:yV,boxhu:IV,boxHu:DV,boxhU:xV,boxHU:wV,boxminus:MV,boxplus:LV,boxtimes:PV,boxul:kV,boxuL:UV,boxUl:FV,boxUL:BV,boxur:GV,boxuR:YV,boxUr:qV,boxUR:$V,boxv:HV,boxV:zV,boxvh:VV,boxvH:WV,boxVh:KV,boxVH:QV,boxvl:XV,boxvL:ZV,boxVl:JV,boxVL:jV,boxvr:eW,boxvR:tW,boxVr:nW,boxVR:rW,bprime:iW,breve:aW,Breve:oW,brvbar:sW,bscr:lW,Bscr:cW,bsemi:uW,bsim:dW,bsime:_W,bsolb:pW,bsol:mW,bsolhsub:gW,bull:EW,bullet:fW,bump:SW,bumpE:bW,bumpe:hW,Bumpeq:TW,bumpeq:vW,Cacute:CW,cacute:RW,capand:NW,capbrcup:OW,capcap:AW,cap:yW,Cap:IW,capcup:DW,capdot:xW,CapitalDifferentialD:wW,caps:MW,caret:LW,caron:PW,Cayleys:kW,ccaps:UW,Ccaron:FW,ccaron:BW,Ccedil:GW,ccedil:YW,Ccirc:qW,ccirc:$W,Cconint:HW,ccups:zW,ccupssm:VW,Cdot:WW,cdot:KW,cedil:QW,Cedilla:XW,cemptyv:ZW,cent:JW,centerdot:jW,CenterDot:e3,cfr:t3,Cfr:n3,CHcy:r3,chcy:i3,check:a3,checkmark:o3,Chi:s3,chi:l3,circ:c3,circeq:u3,circlearrowleft:d3,circlearrowright:_3,circledast:p3,circledcirc:m3,circleddash:g3,CircleDot:E3,circledR:f3,circledS:S3,CircleMinus:b3,CirclePlus:h3,CircleTimes:T3,cir:v3,cirE:C3,cire:R3,cirfnint:N3,cirmid:O3,cirscir:A3,ClockwiseContourIntegral:y3,CloseCurlyDoubleQuote:I3,CloseCurlyQuote:D3,clubs:x3,clubsuit:w3,colon:M3,Colon:L3,Colone:P3,colone:k3,coloneq:U3,comma:F3,commat:B3,comp:G3,compfn:Y3,complement:q3,complexes:$3,cong:H3,congdot:z3,Congruent:V3,conint:W3,Conint:K3,ContourIntegral:Q3,copf:X3,Copf:Z3,coprod:J3,Coproduct:j3,copy:eK,COPY:tK,copysr:nK,CounterClockwiseContourIntegral:rK,crarr:iK,cross:aK,Cross:oK,Cscr:sK,cscr:lK,csub:cK,csube:uK,csup:dK,csupe:_K,ctdot:pK,cudarrl:mK,cudarrr:gK,cuepr:EK,cuesc:fK,cularr:SK,cularrp:bK,cupbrcap:hK,cupcap:TK,CupCap:vK,cup:CK,Cup:RK,cupcup:NK,cupdot:OK,cupor:AK,cups:yK,curarr:IK,curarrm:DK,curlyeqprec:xK,curlyeqsucc:wK,curlyvee:MK,curlywedge:LK,curren:PK,curvearrowleft:kK,curvearrowright:UK,cuvee:FK,cuwed:BK,cwconint:GK,cwint:YK,cylcty:qK,dagger:$K,Dagger:HK,daleth:zK,darr:VK,Darr:WK,dArr:KK,dash:QK,Dashv:XK,dashv:ZK,dbkarow:JK,dblac:jK,Dcaron:eQ,dcaron:tQ,Dcy:nQ,dcy:rQ,ddagger:iQ,ddarr:aQ,DD:oQ,dd:sQ,DDotrahd:lQ,ddotseq:cQ,deg:uQ,Del:dQ,Delta:_Q,delta:pQ,demptyv:mQ,dfisht:gQ,Dfr:EQ,dfr:fQ,dHar:SQ,dharl:bQ,dharr:hQ,DiacriticalAcute:TQ,DiacriticalDot:vQ,DiacriticalDoubleAcute:CQ,DiacriticalGrave:RQ,DiacriticalTilde:NQ,diam:OQ,diamond:AQ,Diamond:yQ,diamondsuit:IQ,diams:DQ,die:xQ,DifferentialD:wQ,digamma:MQ,disin:LQ,div:PQ,divide:kQ,divideontimes:UQ,divonx:FQ,DJcy:BQ,djcy:GQ,dlcorn:YQ,dlcrop:qQ,dollar:$Q,Dopf:HQ,dopf:zQ,Dot:VQ,dot:WQ,DotDot:KQ,doteq:QQ,doteqdot:XQ,DotEqual:ZQ,dotminus:JQ,dotplus:jQ,dotsquare:e4,doublebarwedge:t4,DoubleContourIntegral:n4,DoubleDot:r4,DoubleDownArrow:i4,DoubleLeftArrow:a4,DoubleLeftRightArrow:o4,DoubleLeftTee:s4,DoubleLongLeftArrow:l4,DoubleLongLeftRightArrow:c4,DoubleLongRightArrow:u4,DoubleRightArrow:d4,DoubleRightTee:_4,DoubleUpArrow:p4,DoubleUpDownArrow:m4,DoubleVerticalBar:g4,DownArrowBar:E4,downarrow:f4,DownArrow:S4,Downarrow:b4,DownArrowUpArrow:h4,DownBreve:T4,downdownarrows:v4,downharpoonleft:C4,downharpoonright:R4,DownLeftRightVector:N4,DownLeftTeeVector:O4,DownLeftVectorBar:A4,DownLeftVector:y4,DownRightTeeVector:I4,DownRightVectorBar:D4,DownRightVector:x4,DownTeeArrow:w4,DownTee:M4,drbkarow:L4,drcorn:P4,drcrop:k4,Dscr:U4,dscr:F4,DScy:B4,dscy:G4,dsol:Y4,Dstrok:q4,dstrok:$4,dtdot:H4,dtri:z4,dtrif:V4,duarr:W4,duhar:K4,dwangle:Q4,DZcy:X4,dzcy:Z4,dzigrarr:J4,Eacute:j4,eacute:e5,easter:t5,Ecaron:n5,ecaron:r5,Ecirc:i5,ecirc:a5,ecir:o5,ecolon:s5,Ecy:l5,ecy:c5,eDDot:u5,Edot:d5,edot:_5,eDot:p5,ee:m5,efDot:g5,Efr:E5,efr:f5,eg:S5,Egrave:b5,egrave:h5,egs:T5,egsdot:v5,el:C5,Element:R5,elinters:N5,ell:O5,els:A5,elsdot:y5,Emacr:I5,emacr:D5,empty:x5,emptyset:w5,EmptySmallSquare:M5,emptyv:L5,EmptyVerySmallSquare:P5,emsp13:k5,emsp14:U5,emsp:F5,ENG:B5,eng:G5,ensp:Y5,Eogon:q5,eogon:$5,Eopf:H5,eopf:z5,epar:V5,eparsl:W5,eplus:K5,epsi:Q5,Epsilon:X5,epsilon:Z5,epsiv:J5,eqcirc:j5,eqcolon:e6,eqsim:t6,eqslantgtr:n6,eqslantless:r6,Equal:i6,equals:a6,EqualTilde:o6,equest:s6,Equilibrium:l6,equiv:c6,equivDD:u6,eqvparsl:d6,erarr:_6,erDot:p6,escr:m6,Escr:g6,esdot:E6,Esim:f6,esim:S6,Eta:b6,eta:h6,ETH:T6,eth:v6,Euml:C6,euml:R6,euro:N6,excl:O6,exist:A6,Exists:y6,expectation:I6,exponentiale:D6,ExponentialE:x6,fallingdotseq:w6,Fcy:M6,fcy:L6,female:P6,ffilig:k6,fflig:U6,ffllig:F6,Ffr:B6,ffr:G6,filig:Y6,FilledSmallSquare:q6,FilledVerySmallSquare:$6,fjlig:H6,flat:z6,fllig:V6,fltns:W6,fnof:K6,Fopf:Q6,fopf:X6,forall:Z6,ForAll:J6,fork:j6,forkv:e9,Fouriertrf:t9,fpartint:n9,frac12:r9,frac13:i9,frac14:a9,frac15:o9,frac16:s9,frac18:l9,frac23:c9,frac25:u9,frac34:d9,frac35:_9,frac38:p9,frac45:m9,frac56:g9,frac58:E9,frac78:f9,frasl:S9,frown:b9,fscr:h9,Fscr:T9,gacute:v9,Gamma:C9,gamma:R9,Gammad:N9,gammad:O9,gap:A9,Gbreve:y9,gbreve:I9,Gcedil:D9,Gcirc:x9,gcirc:w9,Gcy:M9,gcy:L9,Gdot:P9,gdot:k9,ge:U9,gE:F9,gEl:B9,gel:G9,geq:Y9,geqq:q9,geqslant:$9,gescc:H9,ges:z9,gesdot:V9,gesdoto:W9,gesdotol:K9,gesl:Q9,gesles:X9,Gfr:Z9,gfr:J9,gg:j9,Gg:e8,ggg:t8,gimel:n8,GJcy:r8,gjcy:i8,gla:a8,gl:o8,glE:s8,glj:l8,gnap:c8,gnapprox:u8,gne:d8,gnE:_8,gneq:p8,gneqq:m8,gnsim:g8,Gopf:E8,gopf:f8,grave:S8,GreaterEqual:b8,GreaterEqualLess:h8,GreaterFullEqual:T8,GreaterGreater:v8,GreaterLess:C8,GreaterSlantEqual:R8,GreaterTilde:N8,Gscr:O8,gscr:A8,gsim:y8,gsime:I8,gsiml:D8,gtcc:x8,gtcir:w8,gt:M8,GT:L8,Gt:P8,gtdot:k8,gtlPar:U8,gtquest:F8,gtrapprox:B8,gtrarr:G8,gtrdot:Y8,gtreqless:q8,gtreqqless:$8,gtrless:H8,gtrsim:z8,gvertneqq:V8,gvnE:W8,Hacek:K8,hairsp:Q8,half:X8,hamilt:Z8,HARDcy:J8,hardcy:j8,harrcir:e7,harr:t7,hArr:n7,harrw:r7,Hat:i7,hbar:a7,Hcirc:o7,hcirc:s7,hearts:l7,heartsuit:c7,hellip:u7,hercon:d7,hfr:_7,Hfr:p7,HilbertSpace:m7,hksearow:g7,hkswarow:E7,hoarr:f7,homtht:S7,hookleftarrow:b7,hookrightarrow:h7,hopf:T7,Hopf:v7,horbar:C7,HorizontalLine:R7,hscr:N7,Hscr:O7,hslash:A7,Hstrok:y7,hstrok:I7,HumpDownHump:D7,HumpEqual:x7,hybull:w7,hyphen:M7,Iacute:L7,iacute:P7,ic:k7,Icirc:U7,icirc:F7,Icy:B7,icy:G7,Idot:Y7,IEcy:q7,iecy:$7,iexcl:H7,iff:z7,ifr:V7,Ifr:W7,Igrave:K7,igrave:Q7,ii:X7,iiiint:Z7,iiint:J7,iinfin:j7,iiota:eX,IJlig:tX,ijlig:nX,Imacr:rX,imacr:iX,image:aX,ImaginaryI:oX,imagline:sX,imagpart:lX,imath:cX,Im:uX,imof:dX,imped:_X,Implies:pX,incare:mX,in:"∈",infin:gX,infintie:EX,inodot:fX,intcal:SX,int:bX,Int:hX,integers:TX,Integral:vX,intercal:CX,Intersection:RX,intlarhk:NX,intprod:OX,InvisibleComma:AX,InvisibleTimes:yX,IOcy:IX,iocy:DX,Iogon:xX,iogon:wX,Iopf:MX,iopf:LX,Iota:PX,iota:kX,iprod:UX,iquest:FX,iscr:BX,Iscr:GX,isin:YX,isindot:qX,isinE:$X,isins:HX,isinsv:zX,isinv:VX,it:WX,Itilde:KX,itilde:QX,Iukcy:XX,iukcy:ZX,Iuml:JX,iuml:jX,Jcirc:eZ,jcirc:tZ,Jcy:nZ,jcy:rZ,Jfr:iZ,jfr:aZ,jmath:oZ,Jopf:sZ,jopf:lZ,Jscr:cZ,jscr:uZ,Jsercy:dZ,jsercy:_Z,Jukcy:pZ,jukcy:mZ,Kappa:gZ,kappa:EZ,kappav:fZ,Kcedil:SZ,kcedil:bZ,Kcy:hZ,kcy:TZ,Kfr:vZ,kfr:CZ,kgreen:RZ,KHcy:NZ,khcy:OZ,KJcy:AZ,kjcy:yZ,Kopf:IZ,kopf:DZ,Kscr:xZ,kscr:wZ,lAarr:MZ,Lacute:LZ,lacute:PZ,laemptyv:kZ,lagran:UZ,Lambda:FZ,lambda:BZ,lang:GZ,Lang:YZ,langd:qZ,langle:$Z,lap:HZ,Laplacetrf:zZ,laquo:VZ,larrb:WZ,larrbfs:KZ,larr:QZ,Larr:XZ,lArr:ZZ,larrfs:JZ,larrhk:jZ,larrlp:eJ,larrpl:tJ,larrsim:nJ,larrtl:rJ,latail:iJ,lAtail:aJ,lat:oJ,late:sJ,lates:lJ,lbarr:cJ,lBarr:uJ,lbbrk:dJ,lbrace:_J,lbrack:pJ,lbrke:mJ,lbrksld:gJ,lbrkslu:EJ,Lcaron:fJ,lcaron:SJ,Lcedil:bJ,lcedil:hJ,lceil:TJ,lcub:vJ,Lcy:CJ,lcy:RJ,ldca:NJ,ldquo:OJ,ldquor:AJ,ldrdhar:yJ,ldrushar:IJ,ldsh:DJ,le:xJ,lE:wJ,LeftAngleBracket:MJ,LeftArrowBar:LJ,leftarrow:PJ,LeftArrow:kJ,Leftarrow:UJ,LeftArrowRightArrow:FJ,leftarrowtail:BJ,LeftCeiling:GJ,LeftDoubleBracket:YJ,LeftDownTeeVector:qJ,LeftDownVectorBar:$J,LeftDownVector:HJ,LeftFloor:zJ,leftharpoondown:VJ,leftharpoonup:WJ,leftleftarrows:KJ,leftrightarrow:QJ,LeftRightArrow:XJ,Leftrightarrow:ZJ,leftrightarrows:JJ,leftrightharpoons:jJ,leftrightsquigarrow:ej,LeftRightVector:tj,LeftTeeArrow:nj,LeftTee:rj,LeftTeeVector:ij,leftthreetimes:aj,LeftTriangleBar:oj,LeftTriangle:sj,LeftTriangleEqual:lj,LeftUpDownVector:cj,LeftUpTeeVector:uj,LeftUpVectorBar:dj,LeftUpVector:_j,LeftVectorBar:pj,LeftVector:mj,lEg:gj,leg:Ej,leq:fj,leqq:Sj,leqslant:bj,lescc:hj,les:Tj,lesdot:vj,lesdoto:Cj,lesdotor:Rj,lesg:Nj,lesges:Oj,lessapprox:Aj,lessdot:yj,lesseqgtr:Ij,lesseqqgtr:Dj,LessEqualGreater:xj,LessFullEqual:wj,LessGreater:Mj,lessgtr:Lj,LessLess:Pj,lesssim:kj,LessSlantEqual:Uj,LessTilde:Fj,lfisht:Bj,lfloor:Gj,Lfr:Yj,lfr:qj,lg:$j,lgE:Hj,lHar:zj,lhard:Vj,lharu:Wj,lharul:Kj,lhblk:Qj,LJcy:Xj,ljcy:Zj,llarr:Jj,ll:jj,Ll:eee,llcorner:tee,Lleftarrow:nee,llhard:ree,lltri:iee,Lmidot:aee,lmidot:oee,lmoustache:see,lmoust:lee,lnap:cee,lnapprox:uee,lne:dee,lnE:_ee,lneq:pee,lneqq:mee,lnsim:gee,loang:Eee,loarr:fee,lobrk:See,longleftarrow:bee,LongLeftArrow:hee,Longleftarrow:Tee,longleftrightarrow:vee,LongLeftRightArrow:Cee,Longleftrightarrow:Ree,longmapsto:Nee,longrightarrow:Oee,LongRightArrow:Aee,Longrightarrow:yee,looparrowleft:Iee,looparrowright:Dee,lopar:xee,Lopf:wee,lopf:Mee,loplus:Lee,lotimes:Pee,lowast:kee,lowbar:Uee,LowerLeftArrow:Fee,LowerRightArrow:Bee,loz:Gee,lozenge:Yee,lozf:qee,lpar:$ee,lparlt:Hee,lrarr:zee,lrcorner:Vee,lrhar:Wee,lrhard:Kee,lrm:Qee,lrtri:Xee,lsaquo:Zee,lscr:Jee,Lscr:jee,lsh:ete,Lsh:tte,lsim:nte,lsime:rte,lsimg:ite,lsqb:ate,lsquo:ote,lsquor:ste,Lstrok:lte,lstrok:cte,ltcc:ute,ltcir:dte,lt:_te,LT:pte,Lt:mte,ltdot:gte,lthree:Ete,ltimes:fte,ltlarr:Ste,ltquest:bte,ltri:hte,ltrie:Tte,ltrif:vte,ltrPar:Cte,lurdshar:Rte,luruhar:Nte,lvertneqq:Ote,lvnE:Ate,macr:yte,male:Ite,malt:Dte,maltese:xte,Map:"⤅",map:wte,mapsto:Mte,mapstodown:Lte,mapstoleft:Pte,mapstoup:kte,marker:Ute,mcomma:Fte,Mcy:Bte,mcy:Gte,mdash:Yte,mDDot:qte,measuredangle:$te,MediumSpace:Hte,Mellintrf:zte,Mfr:Vte,mfr:Wte,mho:Kte,micro:Qte,midast:Xte,midcir:Zte,mid:Jte,middot:jte,minusb:ene,minus:tne,minusd:nne,minusdu:rne,MinusPlus:ine,mlcp:ane,mldr:one,mnplus:sne,models:lne,Mopf:cne,mopf:une,mp:dne,mscr:_ne,Mscr:pne,mstpos:mne,Mu:gne,mu:Ene,multimap:fne,mumap:Sne,nabla:bne,Nacute:hne,nacute:Tne,nang:vne,nap:Cne,napE:Rne,napid:Nne,napos:One,napprox:Ane,natural:yne,naturals:Ine,natur:Dne,nbsp:xne,nbump:wne,nbumpe:Mne,ncap:Lne,Ncaron:Pne,ncaron:kne,Ncedil:Une,ncedil:Fne,ncong:Bne,ncongdot:Gne,ncup:Yne,Ncy:qne,ncy:$ne,ndash:Hne,nearhk:zne,nearr:Vne,neArr:Wne,nearrow:Kne,ne:Qne,nedot:Xne,NegativeMediumSpace:Zne,NegativeThickSpace:Jne,NegativeThinSpace:jne,NegativeVeryThinSpace:ere,nequiv:tre,nesear:nre,nesim:rre,NestedGreaterGreater:ire,NestedLessLess:are,NewLine:ore,nexist:sre,nexists:lre,Nfr:cre,nfr:ure,ngE:dre,nge:_re,ngeq:pre,ngeqq:mre,ngeqslant:gre,nges:Ere,nGg:fre,ngsim:Sre,nGt:bre,ngt:hre,ngtr:Tre,nGtv:vre,nharr:Cre,nhArr:Rre,nhpar:Nre,ni:Ore,nis:Are,nisd:yre,niv:Ire,NJcy:Dre,njcy:xre,nlarr:wre,nlArr:Mre,nldr:Lre,nlE:Pre,nle:kre,nleftarrow:Ure,nLeftarrow:Fre,nleftrightarrow:Bre,nLeftrightarrow:Gre,nleq:Yre,nleqq:qre,nleqslant:$re,nles:Hre,nless:zre,nLl:Vre,nlsim:Wre,nLt:Kre,nlt:Qre,nltri:Xre,nltrie:Zre,nLtv:Jre,nmid:jre,NoBreak:eie,NonBreakingSpace:tie,nopf:nie,Nopf:rie,Not:iie,not:aie,NotCongruent:oie,NotCupCap:sie,NotDoubleVerticalBar:lie,NotElement:cie,NotEqual:uie,NotEqualTilde:die,NotExists:_ie,NotGreater:pie,NotGreaterEqual:mie,NotGreaterFullEqual:gie,NotGreaterGreater:Eie,NotGreaterLess:fie,NotGreaterSlantEqual:Sie,NotGreaterTilde:bie,NotHumpDownHump:hie,NotHumpEqual:Tie,notin:vie,notindot:Cie,notinE:Rie,notinva:Nie,notinvb:Oie,notinvc:Aie,NotLeftTriangleBar:yie,NotLeftTriangle:Iie,NotLeftTriangleEqual:Die,NotLess:xie,NotLessEqual:wie,NotLessGreater:Mie,NotLessLess:Lie,NotLessSlantEqual:Pie,NotLessTilde:kie,NotNestedGreaterGreater:Uie,NotNestedLessLess:Fie,notni:Bie,notniva:Gie,notnivb:Yie,notnivc:qie,NotPrecedes:$ie,NotPrecedesEqual:Hie,NotPrecedesSlantEqual:zie,NotReverseElement:Vie,NotRightTriangleBar:Wie,NotRightTriangle:Kie,NotRightTriangleEqual:Qie,NotSquareSubset:Xie,NotSquareSubsetEqual:Zie,NotSquareSuperset:Jie,NotSquareSupersetEqual:jie,NotSubset:eae,NotSubsetEqual:tae,NotSucceeds:nae,NotSucceedsEqual:rae,NotSucceedsSlantEqual:iae,NotSucceedsTilde:aae,NotSuperset:oae,NotSupersetEqual:sae,NotTilde:lae,NotTildeEqual:cae,NotTildeFullEqual:uae,NotTildeTilde:dae,NotVerticalBar:_ae,nparallel:pae,npar:mae,nparsl:gae,npart:Eae,npolint:fae,npr:Sae,nprcue:bae,nprec:hae,npreceq:Tae,npre:vae,nrarrc:Cae,nrarr:Rae,nrArr:Nae,nrarrw:Oae,nrightarrow:Aae,nRightarrow:yae,nrtri:Iae,nrtrie:Dae,nsc:xae,nsccue:wae,nsce:Mae,Nscr:Lae,nscr:Pae,nshortmid:kae,nshortparallel:Uae,nsim:Fae,nsime:Bae,nsimeq:Gae,nsmid:Yae,nspar:qae,nsqsube:$ae,nsqsupe:Hae,nsub:zae,nsubE:Vae,nsube:Wae,nsubset:Kae,nsubseteq:Qae,nsubseteqq:Xae,nsucc:Zae,nsucceq:Jae,nsup:jae,nsupE:eoe,nsupe:toe,nsupset:noe,nsupseteq:roe,nsupseteqq:ioe,ntgl:aoe,Ntilde:ooe,ntilde:soe,ntlg:loe,ntriangleleft:coe,ntrianglelefteq:uoe,ntriangleright:doe,ntrianglerighteq:_oe,Nu:poe,nu:moe,num:goe,numero:Eoe,numsp:foe,nvap:Soe,nvdash:boe,nvDash:hoe,nVdash:Toe,nVDash:voe,nvge:Coe,nvgt:Roe,nvHarr:Noe,nvinfin:Ooe,nvlArr:Aoe,nvle:yoe,nvlt:Ioe,nvltrie:Doe,nvrArr:xoe,nvrtrie:woe,nvsim:Moe,nwarhk:Loe,nwarr:Poe,nwArr:koe,nwarrow:Uoe,nwnear:Foe,Oacute:Boe,oacute:Goe,oast:Yoe,Ocirc:qoe,ocirc:$oe,ocir:Hoe,Ocy:zoe,ocy:Voe,odash:Woe,Odblac:Koe,odblac:Qoe,odiv:Xoe,odot:Zoe,odsold:Joe,OElig:joe,oelig:ese,ofcir:tse,Ofr:nse,ofr:rse,ogon:ise,Ograve:ase,ograve:ose,ogt:sse,ohbar:lse,ohm:cse,oint:use,olarr:dse,olcir:_se,olcross:pse,oline:mse,olt:gse,Omacr:Ese,omacr:fse,Omega:Sse,omega:bse,Omicron:hse,omicron:Tse,omid:vse,ominus:Cse,Oopf:Rse,oopf:Nse,opar:Ose,OpenCurlyDoubleQuote:Ase,OpenCurlyQuote:yse,operp:Ise,oplus:Dse,orarr:xse,Or:wse,or:Mse,ord:Lse,order:Pse,orderof:kse,ordf:Use,ordm:Fse,origof:Bse,oror:Gse,orslope:Yse,orv:qse,oS:$se,Oscr:Hse,oscr:zse,Oslash:Vse,oslash:Wse,osol:Kse,Otilde:Qse,otilde:Xse,otimesas:Zse,Otimes:Jse,otimes:jse,Ouml:ele,ouml:tle,ovbar:nle,OverBar:rle,OverBrace:ile,OverBracket:ale,OverParenthesis:ole,para:sle,parallel:lle,par:cle,parsim:ule,parsl:dle,part:_le,PartialD:ple,Pcy:mle,pcy:gle,percnt:Ele,period:fle,permil:Sle,perp:ble,pertenk:hle,Pfr:Tle,pfr:vle,Phi:Cle,phi:Rle,phiv:Nle,phmmat:Ole,phone:Ale,Pi:yle,pi:Ile,pitchfork:Dle,piv:xle,planck:wle,planckh:Mle,plankv:Lle,plusacir:Ple,plusb:kle,pluscir:Ule,plus:Fle,plusdo:Ble,plusdu:Gle,pluse:Yle,PlusMinus:qle,plusmn:$le,plussim:Hle,plustwo:zle,pm:Vle,Poincareplane:Wle,pointint:Kle,popf:Qle,Popf:Xle,pound:Zle,prap:Jle,Pr:jle,pr:ece,prcue:tce,precapprox:nce,prec:rce,preccurlyeq:ice,Precedes:ace,PrecedesEqual:oce,PrecedesSlantEqual:sce,PrecedesTilde:lce,preceq:cce,precnapprox:uce,precneqq:dce,precnsim:_ce,pre:pce,prE:mce,precsim:gce,prime:Ece,Prime:fce,primes:Sce,prnap:bce,prnE:hce,prnsim:Tce,prod:vce,Product:Cce,profalar:Rce,profline:Nce,profsurf:Oce,prop:Ace,Proportional:yce,Proportion:Ice,propto:Dce,prsim:xce,prurel:wce,Pscr:Mce,pscr:Lce,Psi:Pce,psi:kce,puncsp:Uce,Qfr:Fce,qfr:Bce,qint:Gce,qopf:Yce,Qopf:qce,qprime:$ce,Qscr:Hce,qscr:zce,quaternions:Vce,quatint:Wce,quest:Kce,questeq:Qce,quot:Xce,QUOT:Zce,rAarr:Jce,race:jce,Racute:eue,racute:tue,radic:nue,raemptyv:rue,rang:iue,Rang:aue,rangd:oue,range:sue,rangle:lue,raquo:cue,rarrap:uue,rarrb:due,rarrbfs:_ue,rarrc:pue,rarr:mue,Rarr:gue,rArr:Eue,rarrfs:fue,rarrhk:Sue,rarrlp:bue,rarrpl:hue,rarrsim:Tue,Rarrtl:vue,rarrtl:Cue,rarrw:Rue,ratail:Nue,rAtail:Oue,ratio:Aue,rationals:yue,rbarr:Iue,rBarr:Due,RBarr:xue,rbbrk:wue,rbrace:Mue,rbrack:Lue,rbrke:Pue,rbrksld:kue,rbrkslu:Uue,Rcaron:Fue,rcaron:Bue,Rcedil:Gue,rcedil:Yue,rceil:que,rcub:$ue,Rcy:Hue,rcy:zue,rdca:Vue,rdldhar:Wue,rdquo:Kue,rdquor:Que,rdsh:Xue,real:Zue,realine:Jue,realpart:jue,reals:ede,Re:tde,rect:nde,reg:rde,REG:ide,ReverseElement:ade,ReverseEquilibrium:ode,ReverseUpEquilibrium:sde,rfisht:lde,rfloor:cde,rfr:ude,Rfr:dde,rHar:_de,rhard:pde,rharu:mde,rharul:gde,Rho:Ede,rho:fde,rhov:Sde,RightAngleBracket:bde,RightArrowBar:hde,rightarrow:Tde,RightArrow:vde,Rightarrow:Cde,RightArrowLeftArrow:Rde,rightarrowtail:Nde,RightCeiling:Ode,RightDoubleBracket:Ade,RightDownTeeVector:yde,RightDownVectorBar:Ide,RightDownVector:Dde,RightFloor:xde,rightharpoondown:wde,rightharpoonup:Mde,rightleftarrows:Lde,rightleftharpoons:Pde,rightrightarrows:kde,rightsquigarrow:Ude,RightTeeArrow:Fde,RightTee:Bde,RightTeeVector:Gde,rightthreetimes:Yde,RightTriangleBar:qde,RightTriangle:$de,RightTriangleEqual:Hde,RightUpDownVector:zde,RightUpTeeVector:Vde,RightUpVectorBar:Wde,RightUpVector:Kde,RightVectorBar:Qde,RightVector:Xde,ring:Zde,risingdotseq:Jde,rlarr:jde,rlhar:e_e,rlm:t_e,rmoustache:n_e,rmoust:r_e,rnmid:i_e,roang:a_e,roarr:o_e,robrk:s_e,ropar:l_e,ropf:c_e,Ropf:u_e,roplus:d_e,rotimes:__e,RoundImplies:p_e,rpar:m_e,rpargt:g_e,rppolint:E_e,rrarr:f_e,Rrightarrow:S_e,rsaquo:b_e,rscr:h_e,Rscr:T_e,rsh:v_e,Rsh:C_e,rsqb:R_e,rsquo:N_e,rsquor:O_e,rthree:A_e,rtimes:y_e,rtri:I_e,rtrie:D_e,rtrif:x_e,rtriltri:w_e,RuleDelayed:M_e,ruluhar:L_e,rx:P_e,Sacute:k_e,sacute:U_e,sbquo:F_e,scap:B_e,Scaron:G_e,scaron:Y_e,Sc:q_e,sc:$_e,sccue:H_e,sce:z_e,scE:V_e,Scedil:W_e,scedil:K_e,Scirc:Q_e,scirc:X_e,scnap:Z_e,scnE:J_e,scnsim:j_e,scpolint:epe,scsim:tpe,Scy:npe,scy:rpe,sdotb:ipe,sdot:ape,sdote:ope,searhk:spe,searr:lpe,seArr:cpe,searrow:upe,sect:dpe,semi:_pe,seswar:ppe,setminus:mpe,setmn:gpe,sext:Epe,Sfr:fpe,sfr:Spe,sfrown:bpe,sharp:hpe,SHCHcy:Tpe,shchcy:vpe,SHcy:Cpe,shcy:Rpe,ShortDownArrow:Npe,ShortLeftArrow:Ope,shortmid:Ape,shortparallel:ype,ShortRightArrow:Ipe,ShortUpArrow:Dpe,shy:xpe,Sigma:wpe,sigma:Mpe,sigmaf:Lpe,sigmav:Ppe,sim:kpe,simdot:Upe,sime:Fpe,simeq:Bpe,simg:Gpe,simgE:Ype,siml:qpe,simlE:$pe,simne:Hpe,simplus:zpe,simrarr:Vpe,slarr:Wpe,SmallCircle:Kpe,smallsetminus:Qpe,smashp:Xpe,smeparsl:Zpe,smid:Jpe,smile:jpe,smt:eme,smte:tme,smtes:nme,SOFTcy:rme,softcy:ime,solbar:ame,solb:ome,sol:sme,Sopf:lme,sopf:cme,spades:ume,spadesuit:dme,spar:_me,sqcap:pme,sqcaps:mme,sqcup:gme,sqcups:Eme,Sqrt:fme,sqsub:Sme,sqsube:bme,sqsubset:hme,sqsubseteq:Tme,sqsup:vme,sqsupe:Cme,sqsupset:Rme,sqsupseteq:Nme,square:Ome,Square:Ame,SquareIntersection:yme,SquareSubset:Ime,SquareSubsetEqual:Dme,SquareSuperset:xme,SquareSupersetEqual:wme,SquareUnion:Mme,squarf:Lme,squ:Pme,squf:kme,srarr:Ume,Sscr:Fme,sscr:Bme,ssetmn:Gme,ssmile:Yme,sstarf:qme,Star:$me,star:Hme,starf:zme,straightepsilon:Vme,straightphi:Wme,strns:Kme,sub:Qme,Sub:Xme,subdot:Zme,subE:Jme,sube:jme,subedot:ege,submult:tge,subnE:nge,subne:rge,subplus:ige,subrarr:age,subset:oge,Subset:sge,subseteq:lge,subseteqq:cge,SubsetEqual:uge,subsetneq:dge,subsetneqq:_ge,subsim:pge,subsub:mge,subsup:gge,succapprox:Ege,succ:fge,succcurlyeq:Sge,Succeeds:bge,SucceedsEqual:hge,SucceedsSlantEqual:Tge,SucceedsTilde:vge,succeq:Cge,succnapprox:Rge,succneqq:Nge,succnsim:Oge,succsim:Age,SuchThat:yge,sum:Ige,Sum:Dge,sung:xge,sup1:wge,sup2:Mge,sup3:Lge,sup:Pge,Sup:kge,supdot:Uge,supdsub:Fge,supE:Bge,supe:Gge,supedot:Yge,Superset:qge,SupersetEqual:$ge,suphsol:Hge,suphsub:zge,suplarr:Vge,supmult:Wge,supnE:Kge,supne:Qge,supplus:Xge,supset:Zge,Supset:Jge,supseteq:jge,supseteqq:eEe,supsetneq:tEe,supsetneqq:nEe,supsim:rEe,supsub:iEe,supsup:aEe,swarhk:oEe,swarr:sEe,swArr:lEe,swarrow:cEe,swnwar:uEe,szlig:dEe,Tab:_Ee,target:pEe,Tau:mEe,tau:gEe,tbrk:EEe,Tcaron:fEe,tcaron:SEe,Tcedil:bEe,tcedil:hEe,Tcy:TEe,tcy:vEe,tdot:CEe,telrec:REe,Tfr:NEe,tfr:OEe,there4:AEe,therefore:yEe,Therefore:IEe,Theta:DEe,theta:xEe,thetasym:wEe,thetav:MEe,thickapprox:LEe,thicksim:PEe,ThickSpace:kEe,ThinSpace:UEe,thinsp:FEe,thkap:BEe,thksim:GEe,THORN:YEe,thorn:qEe,tilde:$Ee,Tilde:HEe,TildeEqual:zEe,TildeFullEqual:VEe,TildeTilde:WEe,timesbar:KEe,timesb:QEe,times:XEe,timesd:ZEe,tint:JEe,toea:jEe,topbot:efe,topcir:tfe,top:nfe,Topf:rfe,topf:ife,topfork:afe,tosa:ofe,tprime:sfe,trade:lfe,TRADE:cfe,triangle:ufe,triangledown:dfe,triangleleft:_fe,trianglelefteq:pfe,triangleq:mfe,triangleright:gfe,trianglerighteq:Efe,tridot:ffe,trie:Sfe,triminus:bfe,TripleDot:hfe,triplus:Tfe,trisb:vfe,tritime:Cfe,trpezium:Rfe,Tscr:Nfe,tscr:Ofe,TScy:Afe,tscy:yfe,TSHcy:Ife,tshcy:Dfe,Tstrok:xfe,tstrok:wfe,twixt:Mfe,twoheadleftarrow:Lfe,twoheadrightarrow:Pfe,Uacute:kfe,uacute:Ufe,uarr:Ffe,Uarr:Bfe,uArr:Gfe,Uarrocir:Yfe,Ubrcy:qfe,ubrcy:$fe,Ubreve:Hfe,ubreve:zfe,Ucirc:Vfe,ucirc:Wfe,Ucy:Kfe,ucy:Qfe,udarr:Xfe,Udblac:Zfe,udblac:Jfe,udhar:jfe,ufisht:eSe,Ufr:tSe,ufr:nSe,Ugrave:rSe,ugrave:iSe,uHar:aSe,uharl:oSe,uharr:sSe,uhblk:lSe,ulcorn:cSe,ulcorner:uSe,ulcrop:dSe,ultri:_Se,Umacr:pSe,umacr:mSe,uml:gSe,UnderBar:ESe,UnderBrace:fSe,UnderBracket:SSe,UnderParenthesis:bSe,Union:hSe,UnionPlus:TSe,Uogon:vSe,uogon:CSe,Uopf:RSe,uopf:NSe,UpArrowBar:OSe,uparrow:ASe,UpArrow:ySe,Uparrow:ISe,UpArrowDownArrow:DSe,updownarrow:xSe,UpDownArrow:wSe,Updownarrow:MSe,UpEquilibrium:LSe,upharpoonleft:PSe,upharpoonright:kSe,uplus:USe,UpperLeftArrow:FSe,UpperRightArrow:BSe,upsi:GSe,Upsi:YSe,upsih:qSe,Upsilon:$Se,upsilon:HSe,UpTeeArrow:zSe,UpTee:VSe,upuparrows:WSe,urcorn:KSe,urcorner:QSe,urcrop:XSe,Uring:ZSe,uring:JSe,urtri:jSe,Uscr:ebe,uscr:tbe,utdot:nbe,Utilde:rbe,utilde:ibe,utri:abe,utrif:obe,uuarr:sbe,Uuml:lbe,uuml:cbe,uwangle:ube,vangrt:dbe,varepsilon:_be,varkappa:pbe,varnothing:mbe,varphi:gbe,varpi:Ebe,varpropto:fbe,varr:Sbe,vArr:bbe,varrho:hbe,varsigma:Tbe,varsubsetneq:vbe,varsubsetneqq:Cbe,varsupsetneq:Rbe,varsupsetneqq:Nbe,vartheta:Obe,vartriangleleft:Abe,vartriangleright:ybe,vBar:Ibe,Vbar:Dbe,vBarv:xbe,Vcy:wbe,vcy:Mbe,vdash:Lbe,vDash:Pbe,Vdash:kbe,VDash:Ube,Vdashl:Fbe,veebar:Bbe,vee:Gbe,Vee:Ybe,veeeq:qbe,vellip:$be,verbar:Hbe,Verbar:zbe,vert:Vbe,Vert:Wbe,VerticalBar:Kbe,VerticalLine:Qbe,VerticalSeparator:Xbe,VerticalTilde:Zbe,VeryThinSpace:Jbe,Vfr:jbe,vfr:ehe,vltri:the,vnsub:nhe,vnsup:rhe,Vopf:ihe,vopf:ahe,vprop:ohe,vrtri:she,Vscr:lhe,vscr:che,vsubnE:uhe,vsubne:dhe,vsupnE:_he,vsupne:phe,Vvdash:mhe,vzigzag:ghe,Wcirc:Ehe,wcirc:fhe,wedbar:She,wedge:bhe,Wedge:hhe,wedgeq:The,weierp:vhe,Wfr:Che,wfr:Rhe,Wopf:Nhe,wopf:Ohe,wp:Ahe,wr:yhe,wreath:Ihe,Wscr:Dhe,wscr:xhe,xcap:whe,xcirc:Mhe,xcup:Lhe,xdtri:Phe,Xfr:khe,xfr:Uhe,xharr:Fhe,xhArr:Bhe,Xi:Ghe,xi:Yhe,xlarr:qhe,xlArr:$he,xmap:Hhe,xnis:zhe,xodot:Vhe,Xopf:Whe,xopf:Khe,xoplus:Qhe,xotime:Xhe,xrarr:Zhe,xrArr:Jhe,Xscr:jhe,xscr:eTe,xsqcup:tTe,xuplus:nTe,xutri:rTe,xvee:iTe,xwedge:aTe,Yacute:oTe,yacute:sTe,YAcy:lTe,yacy:cTe,Ycirc:uTe,ycirc:dTe,Ycy:_Te,ycy:pTe,yen:mTe,Yfr:gTe,yfr:ETe,YIcy:fTe,yicy:STe,Yopf:bTe,yopf:hTe,Yscr:TTe,yscr:vTe,YUcy:CTe,yucy:RTe,yuml:NTe,Yuml:OTe,Zacute:ATe,zacute:yTe,Zcaron:ITe,zcaron:DTe,Zcy:xTe,zcy:wTe,Zdot:MTe,zdot:LTe,zeetrf:PTe,ZeroWidthSpace:kTe,Zeta:UTe,zeta:FTe,zfr:BTe,Zfr:GTe,ZHcy:YTe,zhcy:qTe,zigrarr:$Te,zopf:HTe,Zopf:zTe,Zscr:VTe,zscr:WTe,zwj:KTe,zwnj:QTe};var 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s,l,c,d,_,p,g,E=!1,f=e.bMarks[n]+e.tShift[n],S=e.eMarks[n];if(e.sCount[n]-e.blkIndent>=4||f+3>S||(s=e.src.charCodeAt(f),s!==126&&s!==96)||(_=f,f=e.skipChars(f,s),l=f-_,l<3)||(g=e.src.slice(_,f),c=e.src.slice(f,S),s===96&&c.indexOf(String.fromCharCode(s))>=0))return!1;if(o)return!0;for(d=n;d++,!(d>=i||(f=_=e.bMarks[d]+e.tShift[d],S=e.eMarks[d],f=4)&&(f=e.skipChars(f,s),!(f-_=4||e.src.charCodeAt(z++)!==62)return!1;if(o)return!0;for(d=f=e.sCount[n]+1,e.src.charCodeAt(z)===32?(z++,d++,f++,s=!1,x=!0):e.src.charCodeAt(z)===9?(x=!0,(e.bsCount[n]+f)%4===3?(z++,d++,f++,s=!1):s=!0):x=!1,S=[e.bMarks[n]],e.bMarks[n]=z;z=K,N=[e.sCount[n]],e.sCount[n]=f-d,y=[e.tShift[n]],e.tShift[n]=z-e.bMarks[n],D=e.md.block.ruler.getRules("blockquote"),T=e.parentType,e.parentType="blockquote",E=n+1;E=K));E++){if(e.src.charCodeAt(z++)===62&&!U){for(d=f=e.sCount[E]+1,e.src.charCodeAt(z)===32?(z++,d++,f++,s=!1,x=!0):e.src.charCodeAt(z)===9?(x=!0,(e.bsCount[E]+f)%4===3?(z++,d++,f++,s=!1):s=!0):x=!1,S.push(e.bMarks[E]),e.bMarks[E]=z;z=K,C.push(e.bsCount[E]),e.bsCount[E]=e.sCount[E]+1+(x?1:0),N.push(e.sCount[E]),e.sCount[E]=f-d,y.push(e.tShift[E]),e.tShift[E]=z-e.bMarks[E];continue}if(p)break;for(P=!1,c=0,_=D.length;c<_;c++)if(D[c](e,E,i,!0)){P=!0;break}if(P){e.lineMax=E,e.blkIndent!==0&&(S.push(e.bMarks[E]),C.push(e.bsCount[E]),y.push(e.tShift[E]),N.push(e.sCount[E]),e.sCount[E]-=e.blkIndent);break}S.push(e.bMarks[E]),C.push(e.bsCount[E]),y.push(e.tShift[E]),N.push(e.sCount[E]),e.sCount[E]=-1}for(h=e.blkIndent,e.blkIndent=0,k=e.push("blockquote_open","blockquote",1),k.markup=">",k.map=g=[n,0],e.md.block.tokenize(e,n,E),k=e.push("blockquote_close","blockquote",-1),k.markup=">",e.lineMax=W,e.parentType=T,g[1]=e.line,c=0;c=4||(s=e.src.charCodeAt(_++),s!==42&&s!==45&&s!==95))return!1;for(l=1;_=s||(n=t.src.charCodeAt(o++),n<48||n>57))return-1;for(;;){if(o>=s)return-1;if(n=t.src.charCodeAt(o++),n>=48&&n<=57){if(o-i>=10)return-1;continue}if(n===41||n===46)break;return-1}return o=4||e.listIndent>=0&&e.sCount[n]-e.listIndent>=4&&e.sCount[n]=e.blkIndent&&(G=!0),(K=Cb(e,n))>=0){if(g=!0,oe=e.bMarks[n]+e.tShift[n],T=Number(e.src.slice(oe,K-1)),G&&T!==1)return!1}else if((K=vb(e,n))>=0)g=!1;else 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e.env.references[E]>"u"&&(e.env.references[E]={title:x,href:_}),e.parentType=S,e.line=n+P+1),!0)},tCe=["address","article","aside","base","basefont","blockquote","body","caption","center","col","colgroup","dd","details","dialog","dir","div","dl","dt","fieldset","figcaption","figure","footer","form","frame","frameset","h1","h2","h3","h4","h5","h6","head","header","hr","html","iframe","legend","li","link","main","menu","menuitem","nav","noframes","ol","optgroup","option","p","param","section","source","summary","table","tbody","td","tfoot","th","thead","title","tr","track","ul"],sl={},nCe="[a-zA-Z_:][a-zA-Z0-9:._-]*",rCe="[^\"'=<>`\\x00-\\x20]+",iCe="'[^']*'",aCe='"[^"]*"',oCe="(?:"+rCe+"|"+iCe+"|"+aCe+")",sCe="(?:\\s+"+nCe+"(?:\\s*=\\s*"+oCe+")?)",TN="<[A-Za-z][A-Za-z0-9\\-]*"+sCe+"*\\s*\\/?>",vN="<\\/[A-Za-z][A-Za-z0-9\\-]*\\s*>",lCe="|",cCe="<[?][\\s\\S]*?[?]>",uCe="]*>",dCe="",_Ce=new RegExp("^(?:"+TN+"|"+vN+"|"+lCe+"|"+cCe+"|"+uCe+"|"+dCe+")"),pCe=new RegExp("^(?:"+TN+"|"+vN+")");sl.HTML_TAG_RE=_Ce;sl.HTML_OPEN_CLOSE_TAG_RE=pCe;var mCe=tCe,gCe=sl.HTML_OPEN_CLOSE_TAG_RE,Gi=[[/^<(script|pre|style|textarea)(?=(\s|>|$))/i,/<\/(script|pre|style|textarea)>/i,!0],[/^/,!0],[/^<\?/,/\?>/,!0],[/^/,!0],[/^/,!0],[new RegExp("^|$))","i"),/^$/,!0],[new RegExp(gCe.source+"\\s*$"),/^$/,!1]],ECe=function(e,n,i,o){var s,l,c,d,_=e.bMarks[n]+e.tShift[n],p=e.eMarks[n];if(e.sCount[n]-e.blkIndent>=4||!e.md.options.html||e.src.charCodeAt(_)!==60)return!1;for(d=e.src.slice(_,p),s=0;s=4||(s=e.src.charCodeAt(_),s!==35||_>=p))return!1;for(l=1,s=e.src.charCodeAt(++_);s===35&&_6||__&&Rb(e.src.charCodeAt(c-1))&&(p=c),e.line=n+1,d=e.push("heading_open","h"+String(l),1),d.markup="########".slice(0,l),d.map=[n,e.line],d=e.push("inline","",0),d.content=e.src.slice(_,p).trim(),d.map=[n,e.line],d.children=[],d=e.push("heading_close","h"+String(l),-1),d.markup="########".slice(0,l)),!0)},SCe=function(e,n,i){var o,s,l,c,d,_,p,g,E,f=n+1,S,C=e.md.block.ruler.getRules("paragraph");if(e.sCount[n]-e.blkIndent>=4)return!1;for(S=e.parentType,e.parentType="paragraph";f3)){if(e.sCount[f]>=e.blkIndent&&(_=e.bMarks[f]+e.tShift[f],p=e.eMarks[f],_=p)))){g=E===61?1:2;break}if(!(e.sCount[f]<0)){for(s=!1,l=0,c=C.length;l3)&&!(e.sCount[_]<0)){for(o=!1,s=0,l=p.length;s0&&this.level++,this.tokens.push(i),i};Jn.prototype.isEmpty=function(e){return this.bMarks[e]+this.tShift[e]>=this.eMarks[e]};Jn.prototype.skipEmptyLines=function(e){for(var n=this.lineMax;en;)if(!ll(this.src.charCodeAt(--e)))return e+1;return e};Jn.prototype.skipChars=function(e,n){for(var i=this.src.length;ei;)if(n!==this.src.charCodeAt(--e))return e+1;return e};Jn.prototype.getLines=function(e,n,i,o){var s,l,c,d,_,p,g,E=e;if(e>=n)return"";for(p=new Array(n-e),s=0;Ei?p[s]=new Array(l-i+1).join(" ")+this.src.slice(d,_):p[s]=this.src.slice(d,_)}return p.join("")};Jn.prototype.Token=CN;var hCe=Jn,TCe=Eg,xs=[["table",zve,["paragraph","reference"]],["code",Vve],["fence",Wve,["paragraph","reference","blockquote","list"]],["blockquote",Kve,["paragraph","reference","blockquote","list"]],["hr",Xve,["paragraph","reference","blockquote","list"]],["list",Jve,["paragraph","reference","blockquote"]],["reference",eCe],["html_block",ECe,["paragraph","reference","blockquote"]],["heading",fCe,["paragraph","reference","blockquote"]],["lheading",SCe],["paragraph",bCe]];function cl(){this.ruler=new TCe;for(var t=0;t=n||t.sCount[c]=_){t.line=n;break}for(o=0;o0||(i=e.pos,o=e.posMax,i+3>o)||e.src.charCodeAt(i)!==58||e.src.charCodeAt(i+1)!==47||e.src.charCodeAt(i+2)!==47||(s=e.pending.match(NCe),!s)||(l=s[1],c=e.md.linkify.matchAtStart(e.src.slice(i-l.length)),!c)||(d=c.url,d=d.replace(/\*+$/,""),_=e.md.normalizeLink(d),!e.md.validateLink(_))?!1:(n||(e.pending=e.pending.slice(0,-l.length),p=e.push("link_open","a",1),p.attrs=[["href",_]],p.markup="linkify",p.info="auto",p=e.push("text","",0),p.content=e.md.normalizeLinkText(d),p=e.push("link_close","a",-1),p.markup="linkify",p.info="auto"),e.pos+=d.length-l.length,!0)},ACe=Je.isSpace,yCe=function(e,n){var i,o,s,l=e.pos;if(e.src.charCodeAt(l)!==10)return!1;if(i=e.pending.length-1,o=e.posMax,!n)if(i>=0&&e.pending.charCodeAt(i)===32)if(i>=1&&e.pending.charCodeAt(i-1)===32){for(s=i-1;s>=1&&e.pending.charCodeAt(s-1)===32;)s--;e.pending=e.pending.slice(0,s),e.push("hardbreak","br",0)}else e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0);else e.push("softbreak","br",0);for(l++;l?@[]^_`{|}~-".split("").forEach(function(t){bg[t.charCodeAt(0)]=1});var DCe=function(e,n){var i,o,s,l,c,d=e.pos,_=e.posMax;if(e.src.charCodeAt(d)!==92||(d++,d>=_))return!1;if(i=e.src.charCodeAt(d),i===10){for(n||e.push("hardbreak","br",0),d++;d<_&&(i=e.src.charCodeAt(d),!!ICe(i));)d++;return e.pos=d,!0}return l=e.src[d],i>=55296&&i<=56319&&d+1<_&&(o=e.src.charCodeAt(d+1),o>=56320&&o<=57343&&(l+=e.src[d+1],d++)),s="\\"+l,n||(c=e.push("text_special","",0),i<256&&bg[i]!==0?c.content=l:c.content=s,c.markup=s,c.info="escape"),e.pos=d+1,!0},xCe=function(e,n){var i,o,s,l,c,d,_,p,g=e.pos,E=e.src.charCodeAt(g);if(E!==96)return!1;for(i=g,g++,o=e.posMax;g=0;n--)i=e[n],!(i.marker!==95&&i.marker!==42)&&i.end!==-1&&(o=e[i.end],c=n>0&&e[n-1].end===i.end+1&&e[n-1].marker===i.marker&&e[n-1].token===i.token-1&&e[i.end+1].token===o.token+1,l=String.fromCharCode(i.marker),s=t.tokens[i.token],s.type=c?"strong_open":"em_open",s.tag=c?"strong":"em",s.nesting=1,s.markup=c?l+l:l,s.content="",s=t.tokens[o.token],s.type=c?"strong_close":"em_close",s.tag=c?"strong":"em",s.nesting=-1,s.markup=c?l+l:l,s.content="",c&&(t.tokens[e[n-1].token].content="",t.tokens[e[i.end+1].token].content="",n--))}dl.postProcess=function(e){var n,i=e.tokens_meta,o=e.tokens_meta.length;for(Ab(e,e.delimiters),n=0;n=C)return!1;if(h=d,_=e.md.helpers.parseLinkDestination(e.src,d,e.posMax),_.ok){for(E=e.md.normalizeLink(_.str),e.md.validateLink(E)?d=_.pos:E="",h=d;d=C||e.src.charCodeAt(d)!==41)&&(T=!0),d++}if(T){if(typeof 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e.pos=T,!1;h=p.href,E=p.title}return n||(s=e.src.slice(d,c),e.md.inline.parse(s,e.md,e.env,S=[]),f=e.push("image","img",0),f.attrs=i=[["src",h],["alt",""]],f.children=S,f.content=s,E&&i.push(["title",E])),e.pos=_,e.posMax=N,!0},kCe=/^([a-zA-Z0-9.!#$%&'*+\/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)$/,UCe=/^([a-zA-Z][a-zA-Z0-9+.\-]{1,31}):([^<>\x00-\x20]*)$/,FCe=function(e,n){var i,o,s,l,c,d,_=e.pos;if(e.src.charCodeAt(_)!==60)return!1;for(c=e.pos,d=e.posMax;;){if(++_>=d||(l=e.src.charCodeAt(_),l===60))return!1;if(l===62)break}return 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представлениекодалокализации представлениепериода представлениеправа представлениеприложения представлениесобытияжурналарегистрации представлениечасовогопояса предупреждение прекратитьработусистемы привилегированныйрежим продолжитьвызов прочитатьjson прочитатьxml прочитатьдатуjson пустаястрока рабочийкаталогданныхпользователя разблокироватьданныедляредактирования разделитьфайл разорватьсоединениесвнешнимисточникомданных раскодироватьстроку рольдоступна секунда сигнал символ скопироватьжурналрегистрации смещениелетнеговремени смещениестандартноговремени соединитьбуферыдвоичныхданных создатькаталог создатьфабрикуxdto сокрл сокрлп сокрп сообщить состояние сохранитьзначение сохранитьнастройкипользователя сред стрдлина стрзаканчиваетсяна стрзаменить стрнайти стрначинаетсяс строка строкасоединенияинформационнойбазы стрполучитьстроку стрразделить стрсоединить стрсравнить стрчисловхождений стрчислострок стршаблон текущаядата текущаядатасеанса текущаяуниверсальнаядата 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gl_ObjectPlaneS gl_ObjectPlaneT gl_PatchVerticesIn gl_Point gl_PointCoord gl_PointSize gl_Position gl_PrimitiveID gl_PrimitiveIDIn gl_ProjectionMatrix gl_ProjectionMatrixInverse gl_ProjectionMatrixInverseTranspose gl_ProjectionMatrixTranspose gl_SampleID gl_SampleMask gl_SampleMaskIn gl_SamplePosition gl_SecondaryColor gl_TessCoord gl_TessLevelInner gl_TessLevelOuter gl_TexCoord gl_TextureEnvColor gl_TextureMatrix gl_TextureMatrixInverse gl_TextureMatrixInverseTranspose gl_TextureMatrixTranspose gl_Vertex gl_VertexID gl_ViewportIndex gl_WorkGroupID gl_WorkGroupSize gl_in gl_out EmitStreamVertex EmitVertex EndPrimitive EndStreamPrimitive abs acos acosh all any asin asinh atan atanh atomicAdd atomicAnd atomicCompSwap atomicCounter atomicCounterDecrement atomicCounterIncrement atomicExchange atomicMax atomicMin atomicOr atomicXor barrier bitCount bitfieldExtract bitfieldInsert bitfieldReverse ceil clamp cos cosh cross dFdx dFdy degrees determinant distance dot equal exp exp2 faceforward findLSB findMSB floatBitsToInt floatBitsToUint floor fma fract frexp ftransform fwidth greaterThan greaterThanEqual groupMemoryBarrier imageAtomicAdd imageAtomicAnd imageAtomicCompSwap imageAtomicExchange imageAtomicMax imageAtomicMin imageAtomicOr imageAtomicXor imageLoad imageSize imageStore imulExtended intBitsToFloat interpolateAtCentroid interpolateAtOffset interpolateAtSample inverse inversesqrt isinf isnan ldexp length lessThan lessThanEqual log log2 matrixCompMult max memoryBarrier memoryBarrierAtomicCounter memoryBarrierBuffer memoryBarrierImage memoryBarrierShared min mix mod modf noise1 noise2 noise3 noise4 normalize not notEqual outerProduct packDouble2x32 packHalf2x16 packSnorm2x16 packSnorm4x8 packUnorm2x16 packUnorm4x8 pow radians reflect refract round roundEven shadow1D shadow1DLod shadow1DProj shadow1DProjLod shadow2D shadow2DLod shadow2DProj shadow2DProjLod sign sin sinh smoothstep sqrt step tan tanh texelFetch texelFetchOffset texture texture1D texture1DLod texture1DProj texture1DProjLod texture2D texture2DLod texture2DProj texture2DProjLod texture3D texture3DLod texture3DProj texture3DProjLod textureCube textureCubeLod textureGather textureGatherOffset textureGatherOffsets textureGrad textureGradOffset textureLod textureLodOffset textureOffset textureProj textureProjGrad textureProjGradOffset textureProjLod textureProjLodOffset textureProjOffset textureQueryLevels textureQueryLod textureSize transpose trunc uaddCarry uintBitsToFloat umulExtended unpackDouble2x32 unpackHalf2x16 unpackSnorm2x16 unpackSnorm4x8 unpackUnorm2x16 unpackUnorm4x8 usubBorrow",literal:"true false"},illegal:'"',contains:[e.C_LINE_COMMENT_MODE,e.C_BLOCK_COMMENT_MODE,e.C_NUMBER_MODE,{className:"meta",begin:"#",end:"$"}]}}return n_=t,n_}var r_,lT;function lAe(){if(lT)return r_;lT=1;function 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SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS SYSRES_CONST_ALL_USERS_GROUP SYSRES_CONST_ALL_USERS_GROUP_NAME SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE SYSRES_CONST_APPROVING_SIGNATURE_NAME SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN SYSRES_CONST_ATTACH_TYPE_DOC SYSRES_CONST_ATTACH_TYPE_EDOC SYSRES_CONST_ATTACH_TYPE_FOLDER SYSRES_CONST_ATTACH_TYPE_JOB SYSRES_CONST_ATTACH_TYPE_REFERENCE SYSRES_CONST_ATTACH_TYPE_TASK SYSRES_CONST_AUTH_ENCODED_PASSWORD SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE SYSRES_CONST_AUTH_NOVELL SYSRES_CONST_AUTH_PASSWORD SYSRES_CONST_AUTH_PASSWORD_CODE SYSRES_CONST_AUTH_WINDOWS SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE SYSRES_CONST_AUTO_ENUM_METHOD_FLAG SYSRES_CONST_AUTO_NUMERATION_CODE SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE SYSRES_CONST_AUTOTEXT_USAGE_ALL SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE SYSRES_CONST_AUTOTEXT_USAGE_SIGN SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE SYSRES_CONST_AUTOTEXT_USAGE_WORK SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_BTN_PART SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT SYSRES_CONST_CARD_PART SYSRES_CONST_CARD_REFERENCE_MODE_NAME SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT SYSRES_CONST_CODE_COMPONENT_TYPE_URL SYSRES_CONST_CODE_REQUISITE_ACCESS SYSRES_CONST_CODE_REQUISITE_CODE SYSRES_CONST_CODE_REQUISITE_COMPONENT SYSRES_CONST_CODE_REQUISITE_DESCRIPTION SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT SYSRES_CONST_CODE_REQUISITE_RECORD SYSRES_CONST_COMMENT_REQ_CODE SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE SYSRES_CONST_COMP_CODE_GRD SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS SYSRES_CONST_COMPONENT_TYPE_DOCS SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS SYSRES_CONST_COMPONENT_TYPE_EDOCS SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE SYSRES_CONST_COMPONENT_TYPE_OTHER SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES SYSRES_CONST_COMPONENT_TYPE_REFERENCES SYSRES_CONST_COMPONENT_TYPE_REPORTS SYSRES_CONST_COMPONENT_TYPE_SCRIPTS SYSRES_CONST_COMPONENT_TYPE_URL SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION SYSRES_CONST_CONST_FIRM_STATUS_COMMON SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL SYSRES_CONST_CONST_NEGATIVE_VALUE SYSRES_CONST_CONST_POSITIVE_VALUE SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE SYSRES_CONST_CONTENTS_REQUISITE_CODE SYSRES_CONST_DATA_TYPE_BOOLEAN SYSRES_CONST_DATA_TYPE_DATE SYSRES_CONST_DATA_TYPE_FLOAT SYSRES_CONST_DATA_TYPE_INTEGER SYSRES_CONST_DATA_TYPE_PICK SYSRES_CONST_DATA_TYPE_REFERENCE SYSRES_CONST_DATA_TYPE_STRING SYSRES_CONST_DATA_TYPE_TEXT SYSRES_CONST_DATA_TYPE_VARIANT SYSRES_CONST_DATE_CLOSE_REQ_CODE SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR SYSRES_CONST_DATE_OPEN_REQ_CODE SYSRES_CONST_DATE_REQUISITE SYSRES_CONST_DATE_REQUISITE_CODE SYSRES_CONST_DATE_REQUISITE_NAME SYSRES_CONST_DATE_REQUISITE_TYPE SYSRES_CONST_DATE_TYPE_CHAR SYSRES_CONST_DATETIME_FORMAT_VALUE SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_DET1_PART SYSRES_CONST_DET2_PART SYSRES_CONST_DET3_PART SYSRES_CONST_DET4_PART SYSRES_CONST_DET5_PART SYSRES_CONST_DET6_PART SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE SYSRES_CONST_DETAIL_REQ_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME SYSRES_CONST_DOCUMENT_STORAGES_CODE SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME SYSRES_CONST_DOUBLE_REQUISITE_CODE SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE SYSRES_CONST_EDITORS_REFERENCE_CODE SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE SYSRES_CONST_EDOC_DATE_REQUISITE_CODE SYSRES_CONST_EDOC_KIND_REFERENCE_CODE SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE SYSRES_CONST_EDOC_NONE_ENCODE_CODE SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE SYSRES_CONST_EDOC_READONLY_ACCESS_CODE SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE SYSRES_CONST_EDOC_WRITE_ACCES_CODE SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_END_DATE_REQUISITE_CODE SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE SYSRES_CONST_EXIST_CONST SYSRES_CONST_EXIST_VALUE SYSRES_CONST_EXPORT_LOCK_TYPE_ASK SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK SYSRES_CONST_EXPORT_VERSION_TYPE_ASK SYSRES_CONST_EXPORT_VERSION_TYPE_LAST SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE SYSRES_CONST_EXTENSION_REQUISITE_CODE SYSRES_CONST_FILTER_NAME_REQUISITE_CODE SYSRES_CONST_FILTER_REQUISITE_CODE SYSRES_CONST_FILTER_TYPE_COMMON_CODE SYSRES_CONST_FILTER_TYPE_COMMON_NAME SYSRES_CONST_FILTER_TYPE_USER_CODE SYSRES_CONST_FILTER_TYPE_USER_NAME SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR SYSRES_CONST_FLOAT_REQUISITE_TYPE SYSRES_CONST_FOLDER_AUTHOR_VALUE SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS SYSRES_CONST_FOLDER_KIND_COMPONENTS SYSRES_CONST_FOLDER_KIND_EDOCS SYSRES_CONST_FOLDER_KIND_JOBS SYSRES_CONST_FOLDER_KIND_TASKS SYSRES_CONST_FOLDER_TYPE_COMMON SYSRES_CONST_FOLDER_TYPE_COMPONENT SYSRES_CONST_FOLDER_TYPE_FAVORITES SYSRES_CONST_FOLDER_TYPE_INBOX SYSRES_CONST_FOLDER_TYPE_OUTBOX SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH SYSRES_CONST_FOLDER_TYPE_SEARCH SYSRES_CONST_FOLDER_TYPE_SHORTCUTS SYSRES_CONST_FOLDER_TYPE_USER SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG SYSRES_CONST_FULL_SUBSTITUTE_TYPE SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE SYSRES_CONST_FUNCTION_CANCEL_RESULT SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM SYSRES_CONST_FUNCTION_CATEGORY_USER SYSRES_CONST_FUNCTION_FAILURE_RESULT SYSRES_CONST_FUNCTION_SAVE_RESULT SYSRES_CONST_GENERATED_REQUISITE SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE SYSRES_CONST_GROUP_USER_REQUISITE_CODE SYSRES_CONST_GROUPS_REFERENCE_CODE SYSRES_CONST_GROUPS_REQUISITE_CODE SYSRES_CONST_HIDDEN_MODE_NAME SYSRES_CONST_HIGH_LVL_REQUISITE_CODE SYSRES_CONST_HISTORY_ACTION_CREATE_CODE SYSRES_CONST_HISTORY_ACTION_DELETE_CODE SYSRES_CONST_HISTORY_ACTION_EDIT_CODE SYSRES_CONST_HOUR_CHAR SYSRES_CONST_ID_REQUISITE_CODE SYSRES_CONST_IDSPS_REQUISITE_CODE SYSRES_CONST_IMAGE_MODE_COLOR SYSRES_CONST_IMAGE_MODE_GREYSCALE SYSRES_CONST_IMAGE_MODE_MONOCHROME SYSRES_CONST_IMPORTANCE_HIGH SYSRES_CONST_IMPORTANCE_LOW SYSRES_CONST_IMPORTANCE_NORMAL SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE SYSRES_CONST_INT_REQUISITE SYSRES_CONST_INT_REQUISITE_TYPE SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR SYSRES_CONST_INTEGER_TYPE_CHAR SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_JOB_BLOCK_DESCRIPTION SYSRES_CONST_JOB_KIND_CONTROL_JOB SYSRES_CONST_JOB_KIND_JOB SYSRES_CONST_JOB_KIND_NOTICE SYSRES_CONST_JOB_STATE_ABORTED SYSRES_CONST_JOB_STATE_COMPLETE SYSRES_CONST_JOB_STATE_WORKING SYSRES_CONST_KIND_REQUISITE_CODE SYSRES_CONST_KIND_REQUISITE_NAME SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE SYSRES_CONST_KOD_INPUT_TYPE SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT SYSRES_CONST_LINK_OBJECT_KIND_EDOC SYSRES_CONST_LINK_OBJECT_KIND_FOLDER SYSRES_CONST_LINK_OBJECT_KIND_JOB SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE SYSRES_CONST_LINK_OBJECT_KIND_TASK SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE SYSRES_CONST_LIST_REFERENCE_MODE_NAME SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE SYSRES_CONST_MAIN_VIEW_CODE SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE SYSRES_CONST_MAXIMIZED_MODE_NAME SYSRES_CONST_ME_VALUE SYSRES_CONST_MESSAGE_ATTENTION_CAPTION SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION SYSRES_CONST_MESSAGE_ERROR_CAPTION SYSRES_CONST_MESSAGE_INFORMATION_CAPTION SYSRES_CONST_MINIMIZED_MODE_NAME SYSRES_CONST_MINUTE_CHAR SYSRES_CONST_MODULE_REQUISITE_CODE SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION SYSRES_CONST_MONTH_FORMAT_VALUE SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE SYSRES_CONST_NAME_REQUISITE_CODE SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE SYSRES_CONST_NAMEAN_INPUT_TYPE SYSRES_CONST_NEGATIVE_PICK_VALUE SYSRES_CONST_NEGATIVE_VALUE SYSRES_CONST_NO SYSRES_CONST_NO_PICK_VALUE SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE SYSRES_CONST_NO_VALUE SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_NORMAL_MODE_NAME SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME SYSRES_CONST_NOTE_REQUISITE_CODE SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION SYSRES_CONST_NUM_REQUISITE SYSRES_CONST_NUM_STR_REQUISITE_CODE SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG SYSRES_CONST_NUMERATION_AUTO_STRONG SYSRES_CONST_NUMERATION_FROM_DICTONARY SYSRES_CONST_NUMERATION_MANUAL SYSRES_CONST_NUMERIC_TYPE_CHAR SYSRES_CONST_NUMREQ_REQUISITE_CODE SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_ORIGINALREF_REQUISITE_CODE SYSRES_CONST_OURFIRM_REF_CODE SYSRES_CONST_OURFIRM_REQUISITE_CODE SYSRES_CONST_OURFIRM_VAR SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE SYSRES_CONST_PICK_NEGATIVE_RESULT SYSRES_CONST_PICK_POSITIVE_RESULT SYSRES_CONST_PICK_REQUISITE SYSRES_CONST_PICK_REQUISITE_TYPE SYSRES_CONST_PICK_TYPE_CHAR SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE SYSRES_CONST_PLATFORM_VERSION_COMMENT SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_POSITIVE_PICK_VALUE SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE SYSRES_CONST_PRIORITY_REQUISITE_CODE SYSRES_CONST_QUALIFIED_TASK_TYPE SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE SYSRES_CONST_RECSTAT_REQUISITE_CODE SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_REF_REQUISITE SYSRES_CONST_REF_REQUISITE_TYPE SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE SYSRES_CONST_REFERENCE_TYPE_CHAR SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE SYSRES_CONST_REQ_MODE_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_EDIT_CODE SYSRES_CONST_REQ_MODE_HIDDEN_CODE SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE SYSRES_CONST_REQ_MODE_VIEW_CODE SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE SYSRES_CONST_REQ_SECTION_VALUE SYSRES_CONST_REQ_TYPE_VALUE SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME SYSRES_CONST_REQUISITE_FORMAT_LEFT SYSRES_CONST_REQUISITE_FORMAT_RIGHT SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_REQUISITE_SECTION_ACTIONS SYSRES_CONST_REQUISITE_SECTION_BUTTON SYSRES_CONST_REQUISITE_SECTION_BUTTONS SYSRES_CONST_REQUISITE_SECTION_CARD SYSRES_CONST_REQUISITE_SECTION_TABLE SYSRES_CONST_REQUISITE_SECTION_TABLE10 SYSRES_CONST_REQUISITE_SECTION_TABLE11 SYSRES_CONST_REQUISITE_SECTION_TABLE12 SYSRES_CONST_REQUISITE_SECTION_TABLE13 SYSRES_CONST_REQUISITE_SECTION_TABLE14 SYSRES_CONST_REQUISITE_SECTION_TABLE15 SYSRES_CONST_REQUISITE_SECTION_TABLE16 SYSRES_CONST_REQUISITE_SECTION_TABLE17 SYSRES_CONST_REQUISITE_SECTION_TABLE18 SYSRES_CONST_REQUISITE_SECTION_TABLE19 SYSRES_CONST_REQUISITE_SECTION_TABLE2 SYSRES_CONST_REQUISITE_SECTION_TABLE20 SYSRES_CONST_REQUISITE_SECTION_TABLE21 SYSRES_CONST_REQUISITE_SECTION_TABLE22 SYSRES_CONST_REQUISITE_SECTION_TABLE23 SYSRES_CONST_REQUISITE_SECTION_TABLE24 SYSRES_CONST_REQUISITE_SECTION_TABLE3 SYSRES_CONST_REQUISITE_SECTION_TABLE4 SYSRES_CONST_REQUISITE_SECTION_TABLE5 SYSRES_CONST_REQUISITE_SECTION_TABLE6 SYSRES_CONST_REQUISITE_SECTION_TABLE7 SYSRES_CONST_REQUISITE_SECTION_TABLE8 SYSRES_CONST_REQUISITE_SECTION_TABLE9 SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE SYSRES_CONST_RIGHT_ALIGNMENT_CODE SYSRES_CONST_ROLES_REFERENCE_CODE SYSRES_CONST_ROUTE_STEP_AFTER_RUS SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS SYSRES_CONST_ROUTE_TYPE_COMPLEX SYSRES_CONST_ROUTE_TYPE_PARALLEL SYSRES_CONST_ROUTE_TYPE_SERIAL SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE SYSRES_CONST_SEARCHES_COMPONENT_CONTENT SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME SYSRES_CONST_SEARCHES_EDOC_CONTENT SYSRES_CONST_SEARCHES_FOLDER_CONTENT SYSRES_CONST_SEARCHES_JOB_CONTENT SYSRES_CONST_SEARCHES_REFERENCE_CODE SYSRES_CONST_SEARCHES_TASK_CONTENT SYSRES_CONST_SECOND_CHAR SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE SYSRES_CONST_SECTION_REQUISITE_CODE SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE SYSRES_CONST_SELECT_REFERENCE_MODE_NAME SYSRES_CONST_SELECT_TYPE_SELECTABLE SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD SYSRES_CONST_SELECT_TYPE_UNSLECTABLE SYSRES_CONST_SERVER_TYPE_MAIN SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE SYSRES_CONST_STATE_REQ_NAME SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE SYSRES_CONST_STATE_REQUISITE_CODE SYSRES_CONST_STATIC_ROLE_TYPE_CODE SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE SYSRES_CONST_STATUS_VALUE_AUTOCLEANING SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE SYSRES_CONST_STATUS_VALUE_COMPLETE SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE SYSRES_CONST_STATUS_VALUE_RED_SQUARE SYSRES_CONST_STATUS_VALUE_SUSPEND SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE SYSRES_CONST_STORAGE_TYPE_FILE SYSRES_CONST_STORAGE_TYPE_SQL_SERVER SYSRES_CONST_STR_REQUISITE SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR SYSRES_CONST_STRING_REQUISITE_CODE SYSRES_CONST_STRING_REQUISITE_TYPE SYSRES_CONST_STRING_TYPE_CHAR SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE SYSRES_CONST_SYSTEM_VERSION_COMMENT SYSRES_CONST_TASK_ACCESS_TYPE_ALL SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD SYSRES_CONST_TASK_ENCODE_TYPE_NONE SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD SYSRES_CONST_TASK_ROUTE_ALL_CONDITION SYSRES_CONST_TASK_ROUTE_AND_CONDITION SYSRES_CONST_TASK_ROUTE_OR_CONDITION SYSRES_CONST_TASK_STATE_ABORTED SYSRES_CONST_TASK_STATE_COMPLETE SYSRES_CONST_TASK_STATE_CONTINUED SYSRES_CONST_TASK_STATE_CONTROL SYSRES_CONST_TASK_STATE_INIT SYSRES_CONST_TASK_STATE_WORKING SYSRES_CONST_TASK_TITLE SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE SYSRES_CONST_TASK_TYPES_REFERENCE_CODE SYSRES_CONST_TEMPLATES_REFERENCE_CODE SYSRES_CONST_TEST_DATE_REQUISITE_NAME SYSRES_CONST_TEST_DEV_DATABASE_NAME SYSRES_CONST_TEST_DEV_SYSTEM_CODE SYSRES_CONST_TEST_EDMS_DATABASE_NAME SYSRES_CONST_TEST_EDMS_MAIN_CODE SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME SYSRES_CONST_TEST_EDMS_SECOND_CODE SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME SYSRES_CONST_TEST_EDMS_SYSTEM_CODE SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME SYSRES_CONST_TEXT_REQUISITE SYSRES_CONST_TEXT_REQUISITE_CODE SYSRES_CONST_TEXT_REQUISITE_TYPE SYSRES_CONST_TEXT_TYPE_CHAR SYSRES_CONST_TYPE_CODE_REQUISITE_CODE SYSRES_CONST_TYPE_REQUISITE_CODE SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME SYSRES_CONST_USE_ACCESS_TYPE_CODE SYSRES_CONST_USE_ACCESS_TYPE_NAME SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE SYSRES_CONST_USER_CATEGORY_NORMAL SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE SYSRES_CONST_USER_COMMON_CATEGORY SYSRES_CONST_USER_COMMON_CATEGORY_CODE SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE SYSRES_CONST_USER_LOGIN_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE SYSRES_CONST_USER_SERVICE_CATEGORY SYSRES_CONST_USER_SERVICE_CATEGORY_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME SYSRES_CONST_USER_STATUS_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_DEVELOPER_NAME SYSRES_CONST_USER_STATUS_DISABLED_CODE SYSRES_CONST_USER_STATUS_DISABLED_NAME SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE SYSRES_CONST_USER_STATUS_USER_CODE SYSRES_CONST_USER_STATUS_USER_NAME SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER SYSRES_CONST_USER_TYPE_REQUISITE_CODE SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE SYSRES_CONST_USERS_REFERENCE_CODE SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME SYSRES_CONST_USERS_REQUISITE_CODE SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME SYSRES_CONST_VIEW_DEFAULT_CODE SYSRES_CONST_VIEW_DEFAULT_NAME SYSRES_CONST_VIEWER_REQUISITE_CODE SYSRES_CONST_WAITING_BLOCK_DESCRIPTION SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT SYSRES_CONST_XML_ENCODING SYSRES_CONST_XREC_STAT_REQUISITE_CODE SYSRES_CONST_XRECID_FIELD_NAME SYSRES_CONST_YES SYSRES_CONST_YES_NO_2_REQUISITE_CODE SYSRES_CONST_YES_NO_REQUISITE_CODE SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE SYSRES_CONST_YES_PICK_VALUE 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JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY JOB_BLOCK_JOB_TEXT_PROPERTY JOB_BLOCK_NAME_PROPERTY JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY JOB_BLOCK_PERFORMER_PROPERTY JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY JOB_BLOCK_SUBJECT_PROPERTY ",g="ENGLISH_LANGUAGE_CODE RUSSIAN_LANGUAGE_CODE ",E="smHidden smMaximized smMinimized smNormal wmNo wmYes ",f="COMPONENT_TOKEN_LINK_KIND DOCUMENT_LINK_KIND EDOCUMENT_LINK_KIND FOLDER_LINK_KIND JOB_LINK_KIND REFERENCE_LINK_KIND TASK_LINK_KIND ",S="COMPONENT_TOKEN_LOCK_TYPE EDOCUMENT_VERSION_LOCK_TYPE ",C="MONITOR_BLOCK_AFTER_FINISH_EVENT MONITOR_BLOCK_BEFORE_START_EVENT MONITOR_BLOCK_DEADLINE_PROPERTY MONITOR_BLOCK_INTERVAL_PROPERTY MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY MONITOR_BLOCK_NAME_PROPERTY MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY ",h="NOTICE_BLOCK_AFTER_FINISH_EVENT NOTICE_BLOCK_ATTACHMENT_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY NOTICE_BLOCK_BEFORE_START_EVENT NOTICE_BLOCK_CREATED_NOTICES_PROPERTY NOTICE_BLOCK_DEADLINE_PROPERTY NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY NOTICE_BLOCK_NAME_PROPERTY NOTICE_BLOCK_NOTICE_TEXT_PROPERTY NOTICE_BLOCK_PERFORMER_PROPERTY NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY NOTICE_BLOCK_SUBJECT_PROPERTY ",T="dseAfterCancel dseAfterClose dseAfterDelete dseAfterDeleteOutOfTransaction dseAfterInsert dseAfterOpen dseAfterScroll dseAfterUpdate dseAfterUpdateOutOfTransaction dseBeforeCancel dseBeforeClose dseBeforeDelete dseBeforeDetailUpdate dseBeforeInsert dseBeforeOpen dseBeforeUpdate dseOnAnyRequisiteChange dseOnCloseRecord dseOnDeleteError dseOnOpenRecord dseOnPrepareUpdate dseOnUpdateError dseOnUpdateRatifiedRecord dseOnValidDelete dseOnValidUpdate reOnChange reOnChangeValues SELECTION_BEGIN_ROUTE_EVENT SELECTION_END_ROUTE_EVENT ",N="CURRENT_PERIOD_IS_REQUIRED PREVIOUS_CARD_TYPE_NAME SHOW_RECORD_PROPERTIES_FORM ",y="ACCESS_RIGHTS_SETTING_DIALOG_CODE ADMINISTRATOR_USER_CODE ANALYTIC_REPORT_TYPE asrtHideLocal asrtHideRemote CALCULATED_ROLE_TYPE_CODE COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE DCTS_TEST_PROTOCOLS_FOLDER_PATH E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER E_EDOC_VERSION_ALREDY_SIGNED E_EDOC_VERSION_ALREDY_SIGNED_BY_USER EDOC_TYPES_CODE_REQUISITE_FIELD_NAME EDOCUMENTS_ALIAS_NAME FILES_FOLDER_PATH FILTER_OPERANDS_DELIMITER FILTER_OPERATIONS_DELIMITER FORMCARD_NAME FORMLIST_NAME GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE INTEGRATED_REPORT_TYPE IS_BUILDER_APPLICATION_ROLE IS_BUILDER_APPLICATION_ROLE2 IS_BUILDER_USERS ISBSYSDEV LOG_FOLDER_PATH mbCancel mbNo mbNoToAll mbOK mbYes mbYesToAll MEMORY_DATASET_DESRIPTIONS_FILENAME mrNo mrNoToAll mrYes mrYesToAll MULTIPLE_SELECT_DIALOG_CODE NONOPERATING_RECORD_FLAG_FEMININE NONOPERATING_RECORD_FLAG_MASCULINE OPERATING_RECORD_FLAG_FEMININE OPERATING_RECORD_FLAG_MASCULINE PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE PROGRAM_INITIATED_LOOKUP_ACTION ratDelete ratEdit ratInsert REPORT_TYPE REQUIRED_PICK_VALUES_VARIABLE rmCard rmList SBRTE_PROGID_DEV SBRTE_PROGID_RELEASE STATIC_ROLE_TYPE_CODE SUPPRESS_EMPTY_TEMPLATE_CREATION SYSTEM_USER_CODE UPDATE_DIALOG_DATASET USED_IN_OBJECT_HINT_PARAM USER_INITIATED_LOOKUP_ACTION USER_NAME_FORMAT USER_SELECTION_RESTRICTIONS WORKFLOW_TEST_PROTOCOLS_FOLDER_PATH ELS_SUBTYPE_CONTROL_NAME ELS_FOLDER_KIND_CONTROL_NAME REPEAT_PROCESS_CURRENT_OBJECT_EXCEPTION_NAME ",x="PRIVILEGE_COMPONENT_FULL_ACCESS PRIVILEGE_DEVELOPMENT_EXPORT PRIVILEGE_DEVELOPMENT_IMPORT PRIVILEGE_DOCUMENT_DELETE PRIVILEGE_ESD PRIVILEGE_FOLDER_DELETE PRIVILEGE_MANAGE_ACCESS_RIGHTS PRIVILEGE_MANAGE_REPLICATION PRIVILEGE_MANAGE_SESSION_SERVER PRIVILEGE_OBJECT_FULL_ACCESS PRIVILEGE_OBJECT_VIEW PRIVILEGE_RESERVE_LICENSE PRIVILEGE_SYSTEM_CUSTOMIZE PRIVILEGE_SYSTEM_DEVELOP PRIVILEGE_SYSTEM_INSTALL PRIVILEGE_TASK_DELETE PRIVILEGE_USER_PLUGIN_SETTINGS_CUSTOMIZE PRIVILEGES_PSEUDOREFERENCE_CODE ",P="ACCESS_TYPES_PSEUDOREFERENCE_CODE ALL_AVAILABLE_COMPONENTS_PSEUDOREFERENCE_CODE ALL_AVAILABLE_PRIVILEGES_PSEUDOREFERENCE_CODE ALL_REPLICATE_COMPONENTS_PSEUDOREFERENCE_CODE AVAILABLE_DEVELOPERS_COMPONENTS_PSEUDOREFERENCE_CODE COMPONENTS_PSEUDOREFERENCE_CODE FILTRATER_SETTINGS_CONFLICTS_PSEUDOREFERENCE_CODE GROUPS_PSEUDOREFERENCE_CODE RECEIVE_PROTOCOL_PSEUDOREFERENCE_CODE REFERENCE_REQUISITE_PSEUDOREFERENCE_CODE REFERENCE_REQUISITES_PSEUDOREFERENCE_CODE REFTYPES_PSEUDOREFERENCE_CODE REPLICATION_SEANCES_DIARY_PSEUDOREFERENCE_CODE SEND_PROTOCOL_PSEUDOREFERENCE_CODE SUBSTITUTES_PSEUDOREFERENCE_CODE SYSTEM_SETTINGS_PSEUDOREFERENCE_CODE UNITS_PSEUDOREFERENCE_CODE USERS_PSEUDOREFERENCE_CODE VIEWERS_PSEUDOREFERENCE_CODE ",D="CERTIFICATE_TYPE_ENCRYPT CERTIFICATE_TYPE_SIGN CERTIFICATE_TYPE_SIGN_AND_ENCRYPT ",k="STORAGE_TYPE_FILE STORAGE_TYPE_NAS_CIFS STORAGE_TYPE_SAPERION STORAGE_TYPE_SQL_SERVER ",U="COMPTYPE2_REQUISITE_DOCUMENTS_VALUE COMPTYPE2_REQUISITE_TASKS_VALUE COMPTYPE2_REQUISITE_FOLDERS_VALUE COMPTYPE2_REQUISITE_REFERENCES_VALUE ",W="SYSREQ_CODE SYSREQ_COMPTYPE2 SYSREQ_CONST_AVAILABLE_FOR_WEB SYSREQ_CONST_COMMON_CODE SYSREQ_CONST_COMMON_VALUE SYSREQ_CONST_FIRM_CODE SYSREQ_CONST_FIRM_STATUS SYSREQ_CONST_FIRM_VALUE SYSREQ_CONST_SERVER_STATUS SYSREQ_CONTENTS SYSREQ_DATE_OPEN SYSREQ_DATE_CLOSE SYSREQ_DESCRIPTION SYSREQ_DESCRIPTION_LOCALIZE_ID SYSREQ_DOUBLE SYSREQ_EDOC_ACCESS_TYPE SYSREQ_EDOC_AUTHOR SYSREQ_EDOC_CREATED SYSREQ_EDOC_DELEGATE_RIGHTS_REQUISITE_CODE SYSREQ_EDOC_EDITOR SYSREQ_EDOC_ENCODE_TYPE SYSREQ_EDOC_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_EXPORT_DATE SYSREQ_EDOC_EXPORTER SYSREQ_EDOC_KIND SYSREQ_EDOC_LIFE_STAGE_NAME SYSREQ_EDOC_LOCKED_FOR_SERVER_CODE SYSREQ_EDOC_MODIFIED SYSREQ_EDOC_NAME SYSREQ_EDOC_NOTE SYSREQ_EDOC_QUALIFIED_ID SYSREQ_EDOC_SESSION_KEY SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_NAME SYSREQ_EDOC_SESSION_KEY_ENCRYPTION_PLUGIN_VERSION SYSREQ_EDOC_SIGNATURE_TYPE SYSREQ_EDOC_SIGNED SYSREQ_EDOC_STORAGE SYSREQ_EDOC_STORAGES_ARCHIVE_STORAGE SYSREQ_EDOC_STORAGES_CHECK_RIGHTS SYSREQ_EDOC_STORAGES_COMPUTER_NAME SYSREQ_EDOC_STORAGES_EDIT_IN_STORAGE SYSREQ_EDOC_STORAGES_EXECUTIVE_STORAGE SYSREQ_EDOC_STORAGES_FUNCTION SYSREQ_EDOC_STORAGES_INITIALIZED SYSREQ_EDOC_STORAGES_LOCAL_PATH SYSREQ_EDOC_STORAGES_SAPERION_DATABASE_NAME SYSREQ_EDOC_STORAGES_SEARCH_BY_TEXT SYSREQ_EDOC_STORAGES_SERVER_NAME SYSREQ_EDOC_STORAGES_SHARED_SOURCE_NAME SYSREQ_EDOC_STORAGES_TYPE SYSREQ_EDOC_TEXT_MODIFIED SYSREQ_EDOC_TYPE_ACT_CODE SYSREQ_EDOC_TYPE_ACT_DESCRIPTION SYSREQ_EDOC_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE SYSREQ_EDOC_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_EDOC_TYPE_ACT_SECTION SYSREQ_EDOC_TYPE_ADD_PARAMS SYSREQ_EDOC_TYPE_COMMENT SYSREQ_EDOC_TYPE_EVENT_TEXT SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR SYSREQ_EDOC_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_EDOC_TYPE_NAME_LOCALIZE_ID SYSREQ_EDOC_TYPE_NUMERATION_METHOD SYSREQ_EDOC_TYPE_PSEUDO_REQUISITE_CODE SYSREQ_EDOC_TYPE_REQ_CODE SYSREQ_EDOC_TYPE_REQ_DESCRIPTION SYSREQ_EDOC_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_EDOC_TYPE_REQ_IS_LEADING SYSREQ_EDOC_TYPE_REQ_IS_REQUIRED SYSREQ_EDOC_TYPE_REQ_NUMBER SYSREQ_EDOC_TYPE_REQ_ON_CHANGE SYSREQ_EDOC_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_EDOC_TYPE_REQ_ON_SELECT SYSREQ_EDOC_TYPE_REQ_ON_SELECT_KIND SYSREQ_EDOC_TYPE_REQ_SECTION SYSREQ_EDOC_TYPE_VIEW_CARD SYSREQ_EDOC_TYPE_VIEW_CODE SYSREQ_EDOC_TYPE_VIEW_COMMENT SYSREQ_EDOC_TYPE_VIEW_IS_MAIN SYSREQ_EDOC_TYPE_VIEW_NAME SYSREQ_EDOC_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_EDOC_VERSION_AUTHOR SYSREQ_EDOC_VERSION_CRC SYSREQ_EDOC_VERSION_DATA SYSREQ_EDOC_VERSION_EDITOR SYSREQ_EDOC_VERSION_EXPORT_DATE SYSREQ_EDOC_VERSION_EXPORTER SYSREQ_EDOC_VERSION_HIDDEN SYSREQ_EDOC_VERSION_LIFE_STAGE SYSREQ_EDOC_VERSION_MODIFIED SYSREQ_EDOC_VERSION_NOTE SYSREQ_EDOC_VERSION_SIGNATURE_TYPE SYSREQ_EDOC_VERSION_SIGNED SYSREQ_EDOC_VERSION_SIZE SYSREQ_EDOC_VERSION_SOURCE SYSREQ_EDOC_VERSION_TEXT_MODIFIED SYSREQ_EDOCKIND_DEFAULT_VERSION_STATE_CODE SYSREQ_FOLDER_KIND SYSREQ_FUNC_CATEGORY SYSREQ_FUNC_COMMENT SYSREQ_FUNC_GROUP SYSREQ_FUNC_GROUP_COMMENT SYSREQ_FUNC_GROUP_NUMBER SYSREQ_FUNC_HELP SYSREQ_FUNC_PARAM_DEF_VALUE SYSREQ_FUNC_PARAM_IDENT SYSREQ_FUNC_PARAM_NUMBER SYSREQ_FUNC_PARAM_TYPE SYSREQ_FUNC_TEXT SYSREQ_GROUP_CATEGORY SYSREQ_ID SYSREQ_LAST_UPDATE SYSREQ_LEADER_REFERENCE SYSREQ_LINE_NUMBER SYSREQ_MAIN_RECORD_ID SYSREQ_NAME SYSREQ_NAME_LOCALIZE_ID SYSREQ_NOTE SYSREQ_ORIGINAL_RECORD SYSREQ_OUR_FIRM SYSREQ_PROFILING_SETTINGS_BATCH_LOGING SYSREQ_PROFILING_SETTINGS_BATCH_SIZE SYSREQ_PROFILING_SETTINGS_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_SQL_PROFILING_ENABLED SYSREQ_PROFILING_SETTINGS_START_LOGGED SYSREQ_RECORD_STATUS SYSREQ_REF_REQ_FIELD_NAME SYSREQ_REF_REQ_FORMAT SYSREQ_REF_REQ_GENERATED SYSREQ_REF_REQ_LENGTH SYSREQ_REF_REQ_PRECISION SYSREQ_REF_REQ_REFERENCE SYSREQ_REF_REQ_SECTION SYSREQ_REF_REQ_STORED SYSREQ_REF_REQ_TOKENS SYSREQ_REF_REQ_TYPE SYSREQ_REF_REQ_VIEW SYSREQ_REF_TYPE_ACT_CODE SYSREQ_REF_TYPE_ACT_DESCRIPTION SYSREQ_REF_TYPE_ACT_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_ACT_ON_EXECUTE SYSREQ_REF_TYPE_ACT_ON_EXECUTE_EXISTS SYSREQ_REF_TYPE_ACT_SECTION SYSREQ_REF_TYPE_ADD_PARAMS SYSREQ_REF_TYPE_COMMENT SYSREQ_REF_TYPE_COMMON_SETTINGS SYSREQ_REF_TYPE_DISPLAY_REQUISITE_NAME SYSREQ_REF_TYPE_EVENT_TEXT SYSREQ_REF_TYPE_MAIN_LEADING_REF SYSREQ_REF_TYPE_NAME_IN_SINGULAR SYSREQ_REF_TYPE_NAME_IN_SINGULAR_LOCALIZE_ID SYSREQ_REF_TYPE_NAME_LOCALIZE_ID SYSREQ_REF_TYPE_NUMERATION_METHOD SYSREQ_REF_TYPE_REQ_CODE SYSREQ_REF_TYPE_REQ_DESCRIPTION SYSREQ_REF_TYPE_REQ_DESCRIPTION_LOCALIZE_ID SYSREQ_REF_TYPE_REQ_IS_CONTROL SYSREQ_REF_TYPE_REQ_IS_FILTER SYSREQ_REF_TYPE_REQ_IS_LEADING SYSREQ_REF_TYPE_REQ_IS_REQUIRED SYSREQ_REF_TYPE_REQ_NUMBER SYSREQ_REF_TYPE_REQ_ON_CHANGE SYSREQ_REF_TYPE_REQ_ON_CHANGE_EXISTS SYSREQ_REF_TYPE_REQ_ON_SELECT SYSREQ_REF_TYPE_REQ_ON_SELECT_KIND SYSREQ_REF_TYPE_REQ_SECTION SYSREQ_REF_TYPE_VIEW_CARD SYSREQ_REF_TYPE_VIEW_CODE SYSREQ_REF_TYPE_VIEW_COMMENT SYSREQ_REF_TYPE_VIEW_IS_MAIN SYSREQ_REF_TYPE_VIEW_NAME SYSREQ_REF_TYPE_VIEW_NAME_LOCALIZE_ID SYSREQ_REFERENCE_TYPE_ID SYSREQ_STATE SYSREQ_STATЕ SYSREQ_SYSTEM_SETTINGS_VALUE SYSREQ_TYPE SYSREQ_UNIT SYSREQ_UNIT_ID SYSREQ_USER_GROUPS_GROUP_FULL_NAME SYSREQ_USER_GROUPS_GROUP_NAME SYSREQ_USER_GROUPS_GROUP_SERVER_NAME SYSREQ_USERS_ACCESS_RIGHTS SYSREQ_USERS_AUTHENTICATION SYSREQ_USERS_CATEGORY SYSREQ_USERS_COMPONENT SYSREQ_USERS_COMPONENT_USER_IS_PUBLIC SYSREQ_USERS_DOMAIN SYSREQ_USERS_FULL_USER_NAME SYSREQ_USERS_GROUP SYSREQ_USERS_IS_MAIN_SERVER SYSREQ_USERS_LOGIN SYSREQ_USERS_REFERENCE_USER_IS_PUBLIC SYSREQ_USERS_STATUS SYSREQ_USERS_USER_CERTIFICATE SYSREQ_USERS_USER_CERTIFICATE_INFO SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_NAME SYSREQ_USERS_USER_CERTIFICATE_PLUGIN_VERSION SYSREQ_USERS_USER_CERTIFICATE_STATE SYSREQ_USERS_USER_CERTIFICATE_SUBJECT_NAME SYSREQ_USERS_USER_CERTIFICATE_THUMBPRINT 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logcontract log_gamma lopow lorentz_gauge lowercasep lpart lratsubst lreduce lriemann lsquares_estimates lsquares_estimates_approximate lsquares_estimates_exact lsquares_mse lsquares_residual_mse lsquares_residuals lsum ltreillis lu_backsub lucas lu_factor %m macroexpand macroexpand1 make_array makebox makefact makegamma make_graph make_level_picture makelist makeOrders make_poly_continent make_poly_country make_polygon make_random_state make_rgb_picture makeset make_string_input_stream make_string_output_stream make_transform mandelbrot mandelbrot_set map mapatom maplist matchdeclare matchfix mat_cond mat_fullunblocker mat_function mathml_display mat_norm matrix matrixmap matrixp matrix_size mattrace mat_trace mat_unblocker max max_clique max_degree max_flow maximize_lp max_independent_set max_matching maybe md5sum mean mean_bernoulli mean_beta mean_binomial mean_chi2 mean_continuous_uniform mean_deviation mean_discrete_uniform mean_exp mean_f mean_gamma mean_general_finite_discrete 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pdf_noncentral_chi2 pdf_noncentral_student_t pdf_normal pdf_pareto pdf_poisson pdf_rank_sum pdf_rayleigh pdf_signed_rank pdf_student_t pdf_weibull pearson_skewness permanent permut permutation permutations petersen_graph petrov pickapart picture_equalp picturep piechart piechart_description planar_embedding playback plog plot2d plot3d plotdf ploteq plsquares pochhammer points poisdiff poisexpt poisint poismap poisplus poissimp poissubst poistimes poistrim polar polarform polartorect polar_to_xy poly_add poly_buchberger poly_buchberger_criterion poly_colon_ideal poly_content polydecomp poly_depends_p poly_elimination_ideal poly_exact_divide poly_expand poly_expt poly_gcd polygon poly_grobner poly_grobner_equal poly_grobner_member poly_grobner_subsetp poly_ideal_intersection poly_ideal_polysaturation poly_ideal_polysaturation1 poly_ideal_saturation poly_ideal_saturation1 poly_lcm poly_minimization polymod poly_multiply polynome2ele polynomialp poly_normal_form poly_normalize poly_normalize_list poly_polysaturation_extension poly_primitive_part poly_pseudo_divide poly_reduced_grobner poly_reduction poly_saturation_extension poly_s_polynomial poly_subtract polytocompanion pop postfix potential power_mod powerseries powerset prefix prev_prime primep primes principal_components print printf printfile print_graph printpois printprops prodrac product properties propvars psi psubst ptriangularize pui pui2comp pui2ele pui2polynome pui_direct puireduc push put pv qput qrange qty quad_control quad_qag quad_qagi quad_qagp quad_qags quad_qawc quad_qawf quad_qawo quad_qaws quadrilateral quantile quantile_bernoulli quantile_beta quantile_binomial quantile_cauchy quantile_chi2 quantile_continuous_uniform quantile_discrete_uniform quantile_exp quantile_f quantile_gamma quantile_general_finite_discrete quantile_geometric quantile_gumbel quantile_hypergeometric quantile_laplace quantile_logistic quantile_lognormal quantile_negative_binomial quantile_noncentral_chi2 quantile_noncentral_student_t quantile_normal quantile_pareto quantile_poisson quantile_rayleigh quantile_student_t quantile_weibull quartile_skewness quit qunit quotient racah_v racah_w radcan radius random random_bernoulli random_beta random_binomial random_bipartite_graph random_cauchy random_chi2 random_continuous_uniform random_digraph random_discrete_uniform random_exp random_f random_gamma random_general_finite_discrete random_geometric random_graph random_graph1 random_gumbel random_hypergeometric random_laplace random_logistic random_lognormal random_negative_binomial random_network random_noncentral_chi2 random_noncentral_student_t random_normal random_pareto random_permutation random_poisson random_rayleigh random_regular_graph random_student_t random_tournament random_tree random_weibull range rank rat ratcoef ratdenom ratdiff ratdisrep ratexpand ratinterpol rational rationalize ratnumer ratnump ratp ratsimp ratsubst ratvars ratweight read read_array read_binary_array read_binary_list read_binary_matrix readbyte readchar read_hashed_array readline read_list read_matrix read_nested_list readonly read_xpm real_imagpart_to_conjugate realpart realroots rearray rectangle rectform rectform_log_if_constant recttopolar rediff reduce_consts reduce_order region region_boundaries region_boundaries_plus rem remainder remarray rembox remcomps remcon remcoord remfun remfunction remlet remove remove_constvalue remove_dimensions remove_edge remove_fundamental_dimensions remove_fundamental_units remove_plot_option remove_vertex rempart remrule remsym remvalue rename rename_file reset reset_displays residue resolvante resolvante_alternee1 resolvante_bipartite resolvante_diedrale resolvante_klein resolvante_klein3 resolvante_produit_sym resolvante_unitaire resolvante_vierer rest resultant return reveal reverse revert revert2 rgb2level rhs ricci riemann rinvariant risch rk rmdir rncombine romberg room rootscontract round row rowop rowswap rreduce run_testsuite %s save 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error eval evalDeferred evalEcho event exactWorldBoundingBox exclusiveLightCheckBox exec executeForEachObject exists exp expression expressionEditorListen extendCurve extendSurface extrude fcheck fclose feof fflush fgetline fgetword file fileBrowserDialog fileDialog fileExtension fileInfo filetest filletCurve filter filterCurve filterExpand filterStudioImport findAllIntersections findAnimCurves findKeyframe findMenuItem findRelatedSkinCluster finder firstParentOf fitBspline flexor floatEq floatField floatFieldGrp floatScrollBar floatSlider floatSlider2 floatSliderButtonGrp floatSliderGrp floor flow fluidCacheInfo fluidEmitter fluidVoxelInfo flushUndo fmod fontDialog fopen formLayout format fprint frameLayout fread freeFormFillet frewind fromNativePath fwrite gamma gauss geometryConstraint getApplicationVersionAsFloat getAttr getClassification getDefaultBrush getFileList getFluidAttr getInputDeviceRange getMayaPanelTypes getModifiers getPanel getParticleAttr getPluginResource getenv getpid glRender glRenderEditor globalStitch gmatch goal gotoBindPose grabColor gradientControl gradientControlNoAttr graphDollyCtx graphSelectContext graphTrackCtx gravity grid gridLayout group groupObjectsByName HfAddAttractorToAS HfAssignAS HfBuildEqualMap HfBuildFurFiles HfBuildFurImages HfCancelAFR HfConnectASToHF HfCreateAttractor HfDeleteAS HfEditAS HfPerformCreateAS HfRemoveAttractorFromAS HfSelectAttached HfSelectAttractors HfUnAssignAS hardenPointCurve hardware hardwareRenderPanel headsUpDisplay headsUpMessage help helpLine hermite hide hilite hitTest hotBox hotkey hotkeyCheck hsv_to_rgb hudButton hudSlider hudSliderButton hwReflectionMap hwRender hwRenderLoad hyperGraph hyperPanel hyperShade hypot iconTextButton iconTextCheckBox iconTextRadioButton iconTextRadioCollection iconTextScrollList iconTextStaticLabel ikHandle ikHandleCtx ikHandleDisplayScale ikSolver ikSplineHandleCtx ikSystem ikSystemInfo ikfkDisplayMethod illustratorCurves image imfPlugins inheritTransform 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All rights reserved. -# -# NVIDIA CORPORATION and its licensors retain all intellectual property -# and proprietary rights in and to this software, related documentation -# and any modifications thereto. Any use, reproduction, disclosure or -# distribution of this software and related documentation without an express -# license agreement from NVIDIA CORPORATION is strictly prohibited. - -"""Custom replacement for `torch.nn.functional.conv2d` that supports -arbitrarily high order gradients with zero performance penalty.""" - -import warnings -import contextlib -import torch - -# pylint: disable=redefined-builtin -# pylint: disable=arguments-differ -# pylint: disable=protected-access - -#---------------------------------------------------------------------------- - -enabled = False # Enable the custom op by setting this to true. -weight_gradients_disabled = False # Forcefully disable computation of gradients with respect to the weights. - -@contextlib.contextmanager -def no_weight_gradients(): - global weight_gradients_disabled - old = weight_gradients_disabled - weight_gradients_disabled = True - yield - weight_gradients_disabled = old - -#---------------------------------------------------------------------------- - -def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): - if _should_use_custom_op(input): - return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias) - return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups) - -def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1): - if _should_use_custom_op(input): - return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias) - return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation) - -#---------------------------------------------------------------------------- - -def _should_use_custom_op(input): - assert isinstance(input, torch.Tensor) - if (not enabled) or (not torch.backends.cudnn.enabled): - return False - if input.device.type != 'cuda': - return False - if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']): - return True - warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().') - return False - -def _tuple_of_ints(xs, ndim): - xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim - assert len(xs) == ndim - assert all(isinstance(x, int) for x in xs) - return xs - -#---------------------------------------------------------------------------- - -_conv2d_gradfix_cache = dict() - -def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups): - # Parse arguments. - ndim = 2 - weight_shape = tuple(weight_shape) - stride = _tuple_of_ints(stride, ndim) - padding = _tuple_of_ints(padding, ndim) - output_padding = _tuple_of_ints(output_padding, ndim) - dilation = _tuple_of_ints(dilation, ndim) - - # Lookup from cache. - key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups) - if key in _conv2d_gradfix_cache: - return _conv2d_gradfix_cache[key] - - # Validate arguments. - assert groups >= 1 - assert len(weight_shape) == ndim + 2 - assert all(stride[i] >= 1 for i in range(ndim)) - assert all(padding[i] >= 0 for i in range(ndim)) - assert all(dilation[i] >= 0 for i in range(ndim)) - if not transpose: - assert all(output_padding[i] == 0 for i in range(ndim)) - else: # transpose - assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim)) - - # Helpers. - common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups) - def calc_output_padding(input_shape, output_shape): - if transpose: - return [0, 0] - return [ - input_shape[i + 2] - - (output_shape[i + 2] - 1) * stride[i] - - (1 - 2 * padding[i]) - - dilation[i] * (weight_shape[i + 2] - 1) - for i in range(ndim) - ] - - # Forward & backward. - class Conv2d(torch.autograd.Function): - @staticmethod - def forward(ctx, input, weight, bias): - assert weight.shape == weight_shape - if not transpose: - output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs) - else: # transpose - output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs) - ctx.save_for_backward(input, weight) - return output - - @staticmethod - def backward(ctx, grad_output): - input, weight = ctx.saved_tensors - grad_input = None - grad_weight = None - grad_bias = None - - if ctx.needs_input_grad[0]: - p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) - grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None) - assert grad_input.shape == input.shape - - if ctx.needs_input_grad[1] and not weight_gradients_disabled: - grad_weight = Conv2dGradWeight.apply(grad_output, input) - assert grad_weight.shape == weight_shape - - if ctx.needs_input_grad[2]: - grad_bias = grad_output.sum([0, 2, 3]) - - return grad_input, grad_weight, grad_bias - - # Gradient with respect to the weights. - class Conv2dGradWeight(torch.autograd.Function): - @staticmethod - def forward(ctx, grad_output, input): - op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight') - flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32] - grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags) - assert grad_weight.shape == weight_shape - ctx.save_for_backward(grad_output, input) - return grad_weight - - @staticmethod - def backward(ctx, grad2_grad_weight): - grad_output, input = ctx.saved_tensors - grad2_grad_output = None - grad2_input = None - - if ctx.needs_input_grad[0]: - grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None) - assert grad2_grad_output.shape == grad_output.shape - - if ctx.needs_input_grad[1]: - p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape) - grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None) - assert grad2_input.shape == input.shape - - return grad2_grad_output, grad2_input - - _conv2d_gradfix_cache[key] = Conv2d - return Conv2d - -#---------------------------------------------------------------------------- diff --git a/spaces/xdecoder/Demo/tasks/ref_in.py b/spaces/xdecoder/Demo/tasks/ref_in.py deleted file mode 100644 index d872a993eadae5cbd6c37e821232149fa2e3de16..0000000000000000000000000000000000000000 --- a/spaces/xdecoder/Demo/tasks/ref_in.py +++ /dev/null @@ -1,77 +0,0 @@ -# -------------------------------------------------------- -# X-Decoder -- Generalized Decoding for Pixel, Image, and Language -# Copyright (c) 2022 Microsoft -# Licensed under The MIT License [see LICENSE for details] -# Written by Jianwei Yang (jianwyan@microsoft.com), Xueyan Zou (xueyan@cs.wisc.edu) -# -------------------------------------------------------- - -import torch -import numpy as np -from PIL import Image -from utils.inpainting import pad_image -from torchvision import transforms -from utils.visualizer import Visualizer -from diffusers import StableDiffusionInpaintPipeline -from detectron2.utils.colormap import random_color -from detectron2.data import MetadataCatalog -from scipy import ndimage - - -t = [] -t.append(transforms.Resize(512, interpolation=Image.BICUBIC)) -transform = transforms.Compose(t) -metadata = MetadataCatalog.get('ade20k_panoptic_train') - -pipe = StableDiffusionInpaintPipeline.from_pretrained( - # "stabilityai/stable-diffusion-2-inpainting", - "runwayml/stable-diffusion-inpainting", - revision="fp16", - torch_dtype=torch.float16, -).to("cuda") - -def crop_image(input_image): - crop_w, crop_h = np.floor(np.array(input_image.size) / 64).astype(int) * 64 - im_cropped = Image.fromarray(np.array(input_image)[:crop_h, :crop_w]) - return im_cropped - -def referring_inpainting(model, image, texts, inpainting_text, *args, **kwargs): - model.model.metadata = metadata - texts = [[texts if texts.strip().endswith('.') else (texts.strip() + '.')]] - image_ori = crop_image(transform(image)) - - with torch.no_grad(): - width = image_ori.size[0] - height = image_ori.size[1] - image = np.asarray(image_ori) - image_ori_np = np.asarray(image_ori) - images = torch.from_numpy(image.copy()).permute(2,0,1).cuda() - - batch_inputs = [{'image': images, 'height': height, 'width': width, 'groundings': {'texts': texts}}] - outputs = model.model.evaluate_grounding(batch_inputs, None) - visual = Visualizer(image_ori_np, metadata=metadata) - - grd_mask = (outputs[0]['grounding_mask'] > 0).float().cpu().numpy() - for idx, mask in enumerate(grd_mask): - color = random_color(rgb=True, maximum=1).astype(np.int32).tolist() - demo = visual.draw_binary_mask(mask, color=color, text=texts[idx]) - res = demo.get_image() - - if inpainting_text not in ['no', '']: - # if we want to do inpainting - image_crop = image_ori - struct2 = ndimage.generate_binary_structure(2, 2) - mask_dilated = ndimage.binary_dilation(grd_mask[0], structure=struct2, iterations=3).astype(grd_mask[0].dtype) - mask = Image.fromarray(mask_dilated * 255).convert('RGB') - image_and_mask = { - "image": image_crop, - "mask": mask, - } - width = image_crop.size[0]; height = image_crop.size[1] - images_inpainting = pipe(prompt = inpainting_text.strip(), image=image_and_mask['image'], mask_image=image_and_mask['mask'], height=height, width=width).images[0] - # put images_inpainting back to original image - # image_ori.paste(images_inpainting) - torch.cuda.empty_cache() - return Image.fromarray(res) ,'' , images_inpainting - else: - torch.cuda.empty_cache() - return image_ori, 'text', Image.fromarray(res) \ No newline at end of file diff --git a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/resnet.py b/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/resnet.py deleted file mode 100644 index 63d7f43ff43373d28c45de3930da0bdbee817b61..0000000000000000000000000000000000000000 --- a/spaces/xfys/yolov5_tracking/trackers/strong_sort/deep/models/resnet.py +++ /dev/null @@ -1,530 +0,0 @@ -""" -Code source: https://github.com/pytorch/vision -""" -from __future__ import division, absolute_import -import torch.utils.model_zoo as model_zoo -from torch import nn - -__all__ = [ - 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', - 'resnext50_32x4d', 'resnext101_32x8d', 'resnet50_fc512' -] - -model_urls = { - 'resnet18': - 'https://download.pytorch.org/models/resnet18-5c106cde.pth', - 'resnet34': - 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', - 'resnet50': - 'https://download.pytorch.org/models/resnet50-19c8e357.pth', - 'resnet101': - 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', - 'resnet152': - 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', - 'resnext50_32x4d': - 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', - 'resnext101_32x8d': - 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', -} - - -def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1): - """3x3 convolution with padding""" - return nn.Conv2d( - in_planes, - out_planes, - kernel_size=3, - stride=stride, - padding=dilation, - groups=groups, - bias=False, - dilation=dilation - ) - - -def conv1x1(in_planes, out_planes, stride=1): - """1x1 convolution""" - return nn.Conv2d( - in_planes, out_planes, kernel_size=1, stride=stride, bias=False - ) - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__( - self, - inplanes, - planes, - stride=1, - downsample=None, - groups=1, - base_width=64, - dilation=1, - norm_layer=None - ): - super(BasicBlock, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - if groups != 1 or base_width != 64: - raise ValueError( - 'BasicBlock only supports groups=1 and base_width=64' - ) - if dilation > 1: - raise NotImplementedError( - "Dilation > 1 not supported in BasicBlock" - ) - # Both self.conv1 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv3x3(inplanes, planes, stride) - self.bn1 = norm_layer(planes) - self.relu = nn.ReLU(inplace=True) - self.conv2 = conv3x3(planes, planes) - self.bn2 = norm_layer(planes) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - - return out - - -class Bottleneck(nn.Module): - expansion = 4 - - def __init__( - self, - inplanes, - planes, - stride=1, - downsample=None, - groups=1, - base_width=64, - dilation=1, - norm_layer=None - ): - super(Bottleneck, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - width = int(planes * (base_width/64.)) * groups - # Both self.conv2 and self.downsample layers downsample the input when stride != 1 - self.conv1 = conv1x1(inplanes, width) - self.bn1 = norm_layer(width) - self.conv2 = conv3x3(width, width, stride, groups, dilation) - self.bn2 = norm_layer(width) - self.conv3 = conv1x1(width, planes * self.expansion) - self.bn3 = norm_layer(planes * self.expansion) - self.relu = nn.ReLU(inplace=True) - self.downsample = downsample - self.stride = stride - - def forward(self, x): - identity = x - - out = self.conv1(x) - out = self.bn1(out) - out = self.relu(out) - - out = self.conv2(out) - out = self.bn2(out) - out = self.relu(out) - - out = self.conv3(out) - out = self.bn3(out) - - if self.downsample is not None: - identity = self.downsample(x) - - out += identity - out = self.relu(out) - - return out - - -class ResNet(nn.Module): - """Residual network. - - Reference: - - He et al. Deep Residual Learning for Image Recognition. CVPR 2016. - - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017. - - Public keys: - - ``resnet18``: ResNet18. - - ``resnet34``: ResNet34. - - ``resnet50``: ResNet50. - - ``resnet101``: ResNet101. - - ``resnet152``: ResNet152. - - ``resnext50_32x4d``: ResNeXt50. - - ``resnext101_32x8d``: ResNeXt101. - - ``resnet50_fc512``: ResNet50 + FC. - """ - - def __init__( - self, - num_classes, - loss, - block, - layers, - zero_init_residual=False, - groups=1, - width_per_group=64, - replace_stride_with_dilation=None, - norm_layer=None, - last_stride=2, - fc_dims=None, - dropout_p=None, - **kwargs - ): - super(ResNet, self).__init__() - if norm_layer is None: - norm_layer = nn.BatchNorm2d - self._norm_layer = norm_layer - self.loss = loss - self.feature_dim = 512 * block.expansion - self.inplanes = 64 - self.dilation = 1 - if replace_stride_with_dilation is None: - # each element in the tuple indicates if we should replace - # the 2x2 stride with a dilated convolution instead - replace_stride_with_dilation = [False, False, False] - if len(replace_stride_with_dilation) != 3: - raise ValueError( - "replace_stride_with_dilation should be None " - "or a 3-element tuple, got {}". - format(replace_stride_with_dilation) - ) - self.groups = groups - self.base_width = width_per_group - self.conv1 = nn.Conv2d( - 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False - ) - self.bn1 = norm_layer(self.inplanes) - self.relu = nn.ReLU(inplace=True) - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - self.layer1 = self._make_layer(block, 64, layers[0]) - self.layer2 = self._make_layer( - block, - 128, - layers[1], - stride=2, - dilate=replace_stride_with_dilation[0] - ) - self.layer3 = self._make_layer( - block, - 256, - layers[2], - stride=2, - dilate=replace_stride_with_dilation[1] - ) - self.layer4 = self._make_layer( - block, - 512, - layers[3], - stride=last_stride, - dilate=replace_stride_with_dilation[2] - ) - self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1)) - self.fc = self._construct_fc_layer( - fc_dims, 512 * block.expansion, dropout_p - ) - self.classifier = nn.Linear(self.feature_dim, num_classes) - - self._init_params() - - # Zero-initialize the last BN in each residual branch, - # so that the residual branch starts with zeros, and each residual block behaves like an identity. - # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 - if zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - nn.init.constant_(m.bn3.weight, 0) - elif isinstance(m, BasicBlock): - nn.init.constant_(m.bn2.weight, 0) - - def _make_layer(self, block, planes, blocks, stride=1, dilate=False): - norm_layer = self._norm_layer - downsample = None - previous_dilation = self.dilation - if dilate: - self.dilation *= stride - stride = 1 - if stride != 1 or self.inplanes != planes * block.expansion: - downsample = nn.Sequential( - conv1x1(self.inplanes, planes * block.expansion, stride), - norm_layer(planes * block.expansion), - ) - - layers = [] - layers.append( - block( - self.inplanes, planes, stride, downsample, self.groups, - self.base_width, previous_dilation, norm_layer - ) - ) - self.inplanes = planes * block.expansion - for _ in range(1, blocks): - layers.append( - block( - self.inplanes, - planes, - groups=self.groups, - base_width=self.base_width, - dilation=self.dilation, - norm_layer=norm_layer - ) - ) - - return nn.Sequential(*layers) - - def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None): - """Constructs fully connected layer - - Args: - fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed - input_dim (int): input dimension - dropout_p (float): dropout probability, if None, dropout is unused - """ - if fc_dims is None: - self.feature_dim = input_dim - return None - - assert isinstance( - fc_dims, (list, tuple) - ), 'fc_dims must be either list or tuple, but got {}'.format( - type(fc_dims) - ) - - layers = [] - for dim in fc_dims: - layers.append(nn.Linear(input_dim, dim)) - layers.append(nn.BatchNorm1d(dim)) - layers.append(nn.ReLU(inplace=True)) - if dropout_p is not None: - layers.append(nn.Dropout(p=dropout_p)) - input_dim = dim - - self.feature_dim = fc_dims[-1] - - return nn.Sequential(*layers) - - def _init_params(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_( - m.weight, mode='fan_out', nonlinearity='relu' - ) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.BatchNorm1d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - if m.bias is not None: - nn.init.constant_(m.bias, 0) - - def featuremaps(self, x): - x = self.conv1(x) - x = self.bn1(x) - x = self.relu(x) - x = self.maxpool(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - return x - - def forward(self, x): - f = self.featuremaps(x) - v = self.global_avgpool(f) - v = v.view(v.size(0), -1) - - if self.fc is not None: - v = self.fc(v) - - if not self.training: - return v - - y = self.classifier(v) - - if self.loss == 'softmax': - return y - elif self.loss == 'triplet': - return y, v - else: - raise KeyError("Unsupported loss: {}".format(self.loss)) - - -def init_pretrained_weights(model, model_url): - """Initializes model with pretrained weights. - - Layers that don't match with pretrained layers in name or size are kept unchanged. - """ - pretrain_dict = model_zoo.load_url(model_url) - model_dict = model.state_dict() - pretrain_dict = { - k: v - for k, v in pretrain_dict.items() - if k in model_dict and model_dict[k].size() == v.size() - } - model_dict.update(pretrain_dict) - model.load_state_dict(model_dict) - - -"""ResNet""" - - -def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=BasicBlock, - layers=[2, 2, 2, 2], - last_stride=2, - fc_dims=None, - dropout_p=None, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnet18']) - return model - - -def resnet34(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=BasicBlock, - layers=[3, 4, 6, 3], - last_stride=2, - fc_dims=None, - dropout_p=None, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnet34']) - return model - - -def resnet50(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=Bottleneck, - layers=[3, 4, 6, 3], - last_stride=2, - fc_dims=None, - dropout_p=None, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnet50']) - return model - - -def resnet101(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=Bottleneck, - layers=[3, 4, 23, 3], - last_stride=2, - fc_dims=None, - dropout_p=None, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnet101']) - return model - - -def resnet152(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=Bottleneck, - layers=[3, 8, 36, 3], - last_stride=2, - fc_dims=None, - dropout_p=None, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnet152']) - return model - - -"""ResNeXt""" - - -def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=Bottleneck, - layers=[3, 4, 6, 3], - last_stride=2, - fc_dims=None, - dropout_p=None, - groups=32, - width_per_group=4, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnext50_32x4d']) - return model - - -def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=Bottleneck, - layers=[3, 4, 23, 3], - last_stride=2, - fc_dims=None, - dropout_p=None, - groups=32, - width_per_group=8, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnext101_32x8d']) - return model - - -""" -ResNet + FC -""" - - -def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs): - model = ResNet( - num_classes=num_classes, - loss=loss, - block=Bottleneck, - layers=[3, 4, 6, 3], - last_stride=1, - fc_dims=[512], - dropout_p=None, - **kwargs - ) - if pretrained: - init_pretrained_weights(model, model_urls['resnet50']) - return model diff --git a/spaces/xfys/yolov5_tracking/yolov5/utils/augmentations.py b/spaces/xfys/yolov5_tracking/yolov5/utils/augmentations.py deleted file mode 100644 index 6cbbc990501d8aa902117369aa6f5c4ee0a244be..0000000000000000000000000000000000000000 --- a/spaces/xfys/yolov5_tracking/yolov5/utils/augmentations.py +++ /dev/null @@ -1,397 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license -""" -Image augmentation functions -""" - -import math -import random - -import cv2 -import numpy as np -import torch -import torchvision.transforms as T -import torchvision.transforms.functional as TF - -from yolov5.utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy -from yolov5.utils.metrics import bbox_ioa - -IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean -IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation - - -class Albumentations: - # YOLOv5 Albumentations class (optional, only used if package is installed) - def __init__(self, size=640): - self.transform = None - prefix = colorstr('albumentations: ') - try: - import albumentations as A - check_version(A.__version__, '1.0.3', hard=True) # version requirement - - T = [ - A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), - A.Blur(p=0.01), - A.MedianBlur(p=0.01), - A.ToGray(p=0.01), - A.CLAHE(p=0.01), - A.RandomBrightnessContrast(p=0.0), - A.RandomGamma(p=0.0), - A.ImageCompression(quality_lower=75, p=0.0)] # transforms - self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) - - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) - except ImportError: # package not installed, skip - pass - except Exception as e: - LOGGER.info(f'{prefix}{e}') - - def __call__(self, im, labels, p=1.0): - if self.transform and random.random() < p: - new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed - im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) - return im, labels - - -def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): - # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std - return TF.normalize(x, mean, std, inplace=inplace) - - -def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): - # Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean - for i in range(3): - x[:, i] = x[:, i] * std[i] + mean[i] - return x - - -def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): - # HSV color-space augmentation - if hgain or sgain or vgain: - r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains - hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) - dtype = im.dtype # uint8 - - x = np.arange(0, 256, dtype=r.dtype) - lut_hue = ((x * r[0]) % 180).astype(dtype) - lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) - lut_val = np.clip(x * r[2], 0, 255).astype(dtype) - - im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) - cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed - - -def hist_equalize(im, clahe=True, bgr=False): - # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 - yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) - if clahe: - c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) - yuv[:, :, 0] = c.apply(yuv[:, :, 0]) - else: - yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram - return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB - - -def replicate(im, labels): - # Replicate labels - h, w = im.shape[:2] - boxes = labels[:, 1:].astype(int) - x1, y1, x2, y2 = boxes.T - s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) - for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices - x1b, y1b, x2b, y2b = boxes[i] - bh, bw = y2b - y1b, x2b - x1b - yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y - x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] - im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] - labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) - - return im, labels - - -def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): - # Resize and pad image while meeting stride-multiple constraints - shape = im.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better val mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - elif scaleFill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border - return im, ratio, (dw, dh) - - -def random_perspective(im, - targets=(), - segments=(), - degrees=10, - translate=.1, - scale=.1, - shear=10, - perspective=0.0, - border=(0, 0)): - # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) - # targets = [cls, xyxy] - - height = im.shape[0] + border[0] * 2 # shape(h,w,c) - width = im.shape[1] + border[1] * 2 - - # Center - C = np.eye(3) - C[0, 2] = -im.shape[1] / 2 # x translation (pixels) - C[1, 2] = -im.shape[0] / 2 # y translation (pixels) - - # Perspective - P = np.eye(3) - P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) - P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) - - # Rotation and Scale - R = np.eye(3) - a = random.uniform(-degrees, degrees) - # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations - s = random.uniform(1 - scale, 1 + scale) - # s = 2 ** random.uniform(-scale, scale) - R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) - - # Shear - S = np.eye(3) - S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) - S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) - - # Translation - T = np.eye(3) - T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) - T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) - - # Combined rotation matrix - M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT - if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed - if perspective: - im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) - else: # affine - im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) - - # Visualize - # import matplotlib.pyplot as plt - # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() - # ax[0].imshow(im[:, :, ::-1]) # base - # ax[1].imshow(im2[:, :, ::-1]) # warped - - # Transform label coordinates - n = len(targets) - if n: - use_segments = any(x.any() for x in segments) and len(segments) == n - new = np.zeros((n, 4)) - if use_segments: # warp segments - segments = resample_segments(segments) # upsample - for i, segment in enumerate(segments): - xy = np.ones((len(segment), 3)) - xy[:, :2] = segment - xy = xy @ M.T # transform - xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine - - # clip - new[i] = segment2box(xy, width, height) - - else: # warp boxes - xy = np.ones((n * 4, 3)) - xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 - xy = xy @ M.T # transform - xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine - - # create new boxes - x = xy[:, [0, 2, 4, 6]] - y = xy[:, [1, 3, 5, 7]] - new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T - - # clip - new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) - new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) - - # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) - targets = targets[i] - targets[:, 1:5] = new[i] - - return im, targets - - -def copy_paste(im, labels, segments, p=0.5): - # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) - n = len(segments) - if p and n: - h, w, c = im.shape # height, width, channels - im_new = np.zeros(im.shape, np.uint8) - for j in random.sample(range(n), k=round(p * n)): - l, s = labels[j], segments[j] - box = w - l[3], l[2], w - l[1], l[4] - ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area - if (ioa < 0.30).all(): # allow 30% obscuration of existing labels - labels = np.concatenate((labels, [[l[0], *box]]), 0) - segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) - cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) - - result = cv2.flip(im, 1) # augment segments (flip left-right) - i = cv2.flip(im_new, 1).astype(bool) - im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug - - return im, labels, segments - - -def cutout(im, labels, p=0.5): - # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 - if random.random() < p: - h, w = im.shape[:2] - scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction - for s in scales: - mask_h = random.randint(1, int(h * s)) # create random masks - mask_w = random.randint(1, int(w * s)) - - # box - xmin = max(0, random.randint(0, w) - mask_w // 2) - ymin = max(0, random.randint(0, h) - mask_h // 2) - xmax = min(w, xmin + mask_w) - ymax = min(h, ymin + mask_h) - - # apply random color mask - im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] - - # return unobscured labels - if len(labels) and s > 0.03: - box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) - ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area - labels = labels[ioa < 0.60] # remove >60% obscured labels - - return labels - - -def mixup(im, labels, im2, labels2): - # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf - r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 - im = (im * r + im2 * (1 - r)).astype(np.uint8) - labels = np.concatenate((labels, labels2), 0) - return im, labels - - -def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) - # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio - w1, h1 = box1[2] - box1[0], box1[3] - box1[1] - w2, h2 = box2[2] - box2[0], box2[3] - box2[1] - ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio - return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates - - -def classify_albumentations( - augment=True, - size=224, - scale=(0.08, 1.0), - ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 - hflip=0.5, - vflip=0.0, - jitter=0.4, - mean=IMAGENET_MEAN, - std=IMAGENET_STD, - auto_aug=False): - # YOLOv5 classification Albumentations (optional, only used if package is installed) - prefix = colorstr('albumentations: ') - try: - import albumentations as A - from albumentations.pytorch import ToTensorV2 - check_version(A.__version__, '1.0.3', hard=True) # version requirement - if augment: # Resize and crop - T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] - if auto_aug: - # TODO: implement AugMix, AutoAug & RandAug in albumentation - LOGGER.info(f'{prefix}auto augmentations are currently not supported') - else: - if hflip > 0: - T += [A.HorizontalFlip(p=hflip)] - if vflip > 0: - T += [A.VerticalFlip(p=vflip)] - if jitter > 0: - color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue - T += [A.ColorJitter(*color_jitter, 0)] - else: # Use fixed crop for eval set (reproducibility) - T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] - T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor - LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p)) - return A.Compose(T) - - except ImportError: # package not installed, skip - LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)') - except Exception as e: - LOGGER.info(f'{prefix}{e}') - - -def classify_transforms(size=224): - # Transforms to apply if albumentations not installed - assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)' - # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) - return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) - - -class LetterBox: - # YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) - def __init__(self, size=(640, 640), auto=False, stride=32): - super().__init__() - self.h, self.w = (size, size) if isinstance(size, int) else size - self.auto = auto # pass max size integer, automatically solve for short side using stride - self.stride = stride # used with auto - - def __call__(self, im): # im = np.array HWC - imh, imw = im.shape[:2] - r = min(self.h / imh, self.w / imw) # ratio of new/old - h, w = round(imh * r), round(imw * r) # resized image - hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w - top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) - im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) - im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) - return im_out - - -class CenterCrop: - # YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()]) - def __init__(self, size=640): - super().__init__() - self.h, self.w = (size, size) if isinstance(size, int) else size - - def __call__(self, im): # im = np.array HWC - imh, imw = im.shape[:2] - m = min(imh, imw) # min dimension - top, left = (imh - m) // 2, (imw - m) // 2 - return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) - - -class ToTensor: - # YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()]) - def __init__(self, half=False): - super().__init__() - self.half = half - - def __call__(self, im): # im = np.array HWC in BGR order - im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous - im = torch.from_numpy(im) # to torch - im = im.half() if self.half else im.float() # uint8 to fp16/32 - im /= 255.0 # 0-255 to 0.0-1.0 - return im diff --git a/spaces/xinhai/Spleeter/README.md b/spaces/xinhai/Spleeter/README.md deleted file mode 100644 index c35291f4dce76daf61d33a687a81796fc210749b..0000000000000000000000000000000000000000 --- a/spaces/xinhai/Spleeter/README.md +++ /dev/null @@ -1,34 +0,0 @@ ---- -title: Spleeter -emoji: 💻 -colorFrom: purple -colorTo: blue -sdk: gradio -app_file: app.py -pinned: false -duplicated_from: akhaliq/Spleeter ---- - -# 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` - -`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/xinyu2/anime-remove-background/README.md b/spaces/xinyu2/anime-remove-background/README.md deleted file mode 100644 index 1ba3cb5ea0e994e246d57b7d62b8aa5a6331901c..0000000000000000000000000000000000000000 --- a/spaces/xinyu2/anime-remove-background/README.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -title: Anime Remove Background -emoji: 🪄🖼️ -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.1.4 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: skytnt/anime-remove-background ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/xwsm/gpt/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/xwsm/gpt/crazy_functions/test_project/cpp/libJPG/jpgd.cpp deleted file mode 100644 index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000 --- a/spaces/xwsm/gpt/crazy_functions/test_project/cpp/libJPG/jpgd.cpp +++ /dev/null @@ -1,3276 +0,0 @@ -// jpgd.cpp - C++ class for JPEG decompression. -// Public domain, Rich Geldreich -// Last updated Apr. 16, 2011 -// Alex Evans: Linear memory allocator (taken from jpge.h). -// -// Supports progressive and baseline sequential JPEG image files, and the most common chroma subsampling factors: Y, H1V1, H2V1, H1V2, and H2V2. -// -// Chroma upsampling quality: H2V2 is upsampled in the frequency domain, H2V1 and H1V2 are upsampled using point sampling. -// Chroma upsampling reference: "Fast Scheme for Image Size Change in the Compressed Domain" -// http://vision.ai.uiuc.edu/~dugad/research/dct/index.html - -#include "jpgd.h" -#include - -#include -// BEGIN EPIC MOD -#define JPGD_ASSERT(x) { assert(x); CA_ASSUME(x); } (void)0 -// END EPIC MOD - -#ifdef _MSC_VER -#pragma warning (disable : 4611) // warning C4611: interaction between '_setjmp' and C++ object destruction is non-portable -#endif - -// Set to 1 to enable freq. domain chroma upsampling on images using H2V2 subsampling (0=faster nearest neighbor sampling). -// This is slower, but results in higher quality on images with highly saturated colors. -#define JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING 1 - -#define JPGD_TRUE (1) -#define JPGD_FALSE (0) - -#define JPGD_MAX(a,b) (((a)>(b)) ? (a) : (b)) -#define JPGD_MIN(a,b) (((a)<(b)) ? (a) : (b)) - -namespace jpgd { - - static inline void *jpgd_malloc(size_t nSize) { return FMemory::Malloc(nSize); } - static inline void jpgd_free(void *p) { FMemory::Free(p); } - -// BEGIN EPIC MOD -//@UE3 - use UE3 BGRA encoding instead of assuming RGBA - // stolen from IImageWrapper.h - enum ERGBFormatJPG - { - Invalid = -1, - RGBA = 0, - BGRA = 1, - Gray = 2, - }; - static ERGBFormatJPG jpg_format; -// END EPIC MOD - - // DCT coefficients are stored in this sequence. - static int g_ZAG[64] = { 0,1,8,16,9,2,3,10,17,24,32,25,18,11,4,5,12,19,26,33,40,48,41,34,27,20,13,6,7,14,21,28,35,42,49,56,57,50,43,36,29,22,15,23,30,37,44,51,58,59,52,45,38,31,39,46,53,60,61,54,47,55,62,63 }; - - enum JPEG_MARKER - { - M_SOF0 = 0xC0, M_SOF1 = 0xC1, M_SOF2 = 0xC2, M_SOF3 = 0xC3, M_SOF5 = 0xC5, M_SOF6 = 0xC6, M_SOF7 = 0xC7, M_JPG = 0xC8, - M_SOF9 = 0xC9, M_SOF10 = 0xCA, M_SOF11 = 0xCB, M_SOF13 = 0xCD, M_SOF14 = 0xCE, M_SOF15 = 0xCF, M_DHT = 0xC4, M_DAC = 0xCC, - M_RST0 = 0xD0, M_RST1 = 0xD1, M_RST2 = 0xD2, M_RST3 = 0xD3, M_RST4 = 0xD4, M_RST5 = 0xD5, M_RST6 = 0xD6, M_RST7 = 0xD7, - M_SOI = 0xD8, M_EOI = 0xD9, M_SOS = 0xDA, M_DQT = 0xDB, M_DNL = 0xDC, M_DRI = 0xDD, M_DHP = 0xDE, M_EXP = 0xDF, - M_APP0 = 0xE0, M_APP15 = 0xEF, M_JPG0 = 0xF0, M_JPG13 = 0xFD, M_COM = 0xFE, M_TEM = 0x01, M_ERROR = 0x100, RST0 = 0xD0 - }; - - enum JPEG_SUBSAMPLING { JPGD_GRAYSCALE = 0, JPGD_YH1V1, JPGD_YH2V1, JPGD_YH1V2, JPGD_YH2V2 }; - -#define CONST_BITS 13 -#define PASS1_BITS 2 -#define SCALEDONE ((int32)1) - -#define FIX_0_298631336 ((int32)2446) /* FIX(0.298631336) */ -#define FIX_0_390180644 ((int32)3196) /* FIX(0.390180644) */ -#define FIX_0_541196100 ((int32)4433) /* FIX(0.541196100) */ -#define FIX_0_765366865 ((int32)6270) /* FIX(0.765366865) */ -#define FIX_0_899976223 ((int32)7373) /* FIX(0.899976223) */ -#define FIX_1_175875602 ((int32)9633) /* FIX(1.175875602) */ -#define FIX_1_501321110 ((int32)12299) /* FIX(1.501321110) */ -#define FIX_1_847759065 ((int32)15137) /* FIX(1.847759065) */ -#define FIX_1_961570560 ((int32)16069) /* FIX(1.961570560) */ -#define FIX_2_053119869 ((int32)16819) /* FIX(2.053119869) */ -#define FIX_2_562915447 ((int32)20995) /* FIX(2.562915447) */ -#define FIX_3_072711026 ((int32)25172) /* FIX(3.072711026) */ - -#define DESCALE(x,n) (((x) + (SCALEDONE << ((n)-1))) >> (n)) -#define DESCALE_ZEROSHIFT(x,n) (((x) + (128 << (n)) + (SCALEDONE << ((n)-1))) >> (n)) - -#define MULTIPLY(var, cnst) ((var) * (cnst)) - -#define CLAMP(i) ((static_cast(i) > 255) ? (((~i) >> 31) & 0xFF) : (i)) - - // Compiler creates a fast path 1D IDCT for X non-zero columns - template - struct Row - { - static void idct(int* pTemp, const jpgd_block_t* pSrc) - { - // ACCESS_COL() will be optimized at compile time to either an array access, or 0. -#define ACCESS_COL(x) (((x) < NONZERO_COLS) ? (int)pSrc[x] : 0) - - const int z2 = ACCESS_COL(2), z3 = ACCESS_COL(6); - - const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100); - const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065); - const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865); - - const int tmp0 = (ACCESS_COL(0) + ACCESS_COL(4)) << CONST_BITS; - const int tmp1 = (ACCESS_COL(0) - ACCESS_COL(4)) << CONST_BITS; - - const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2; - - const int atmp0 = ACCESS_COL(7), atmp1 = ACCESS_COL(5), atmp2 = ACCESS_COL(3), atmp3 = ACCESS_COL(1); - - const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3; - const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602); - - const int az1 = MULTIPLY(bz1, - FIX_0_899976223); - const int az2 = MULTIPLY(bz2, - FIX_2_562915447); - const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5; - const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5; - - const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3; - const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4; - const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3; - const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4; - - pTemp[0] = DESCALE(tmp10 + btmp3, CONST_BITS-PASS1_BITS); - pTemp[7] = DESCALE(tmp10 - btmp3, CONST_BITS-PASS1_BITS); - pTemp[1] = DESCALE(tmp11 + btmp2, CONST_BITS-PASS1_BITS); - pTemp[6] = DESCALE(tmp11 - btmp2, CONST_BITS-PASS1_BITS); - pTemp[2] = DESCALE(tmp12 + btmp1, CONST_BITS-PASS1_BITS); - pTemp[5] = DESCALE(tmp12 - btmp1, CONST_BITS-PASS1_BITS); - pTemp[3] = DESCALE(tmp13 + btmp0, CONST_BITS-PASS1_BITS); - pTemp[4] = DESCALE(tmp13 - btmp0, CONST_BITS-PASS1_BITS); - } - }; - - template <> - struct Row<0> - { - static void idct(int* pTemp, const jpgd_block_t* pSrc) - { -#ifdef _MSC_VER - pTemp; pSrc; -#endif - } - }; - - template <> - struct Row<1> - { - static void idct(int* pTemp, const jpgd_block_t* pSrc) - { - const int dcval = (pSrc[0] << PASS1_BITS); - - pTemp[0] = dcval; - pTemp[1] = dcval; - pTemp[2] = dcval; - pTemp[3] = dcval; - pTemp[4] = dcval; - pTemp[5] = dcval; - pTemp[6] = dcval; - pTemp[7] = dcval; - } - }; - - // Compiler creates a fast path 1D IDCT for X non-zero rows - template - struct Col - { - static void idct(uint8* pDst_ptr, const int* pTemp) - { - // ACCESS_ROW() will be optimized at compile time to either an array access, or 0. -#define ACCESS_ROW(x) (((x) < NONZERO_ROWS) ? pTemp[x * 8] : 0) - - const int z2 = ACCESS_ROW(2); - const int z3 = ACCESS_ROW(6); - - const int z1 = MULTIPLY(z2 + z3, FIX_0_541196100); - const int tmp2 = z1 + MULTIPLY(z3, - FIX_1_847759065); - const int tmp3 = z1 + MULTIPLY(z2, FIX_0_765366865); - - const int tmp0 = (ACCESS_ROW(0) + ACCESS_ROW(4)) << CONST_BITS; - const int tmp1 = (ACCESS_ROW(0) - ACCESS_ROW(4)) << CONST_BITS; - - const int tmp10 = tmp0 + tmp3, tmp13 = tmp0 - tmp3, tmp11 = tmp1 + tmp2, tmp12 = tmp1 - tmp2; - - const int atmp0 = ACCESS_ROW(7), atmp1 = ACCESS_ROW(5), atmp2 = ACCESS_ROW(3), atmp3 = ACCESS_ROW(1); - - const int bz1 = atmp0 + atmp3, bz2 = atmp1 + atmp2, bz3 = atmp0 + atmp2, bz4 = atmp1 + atmp3; - const int bz5 = MULTIPLY(bz3 + bz4, FIX_1_175875602); - - const int az1 = MULTIPLY(bz1, - FIX_0_899976223); - const int az2 = MULTIPLY(bz2, - FIX_2_562915447); - const int az3 = MULTIPLY(bz3, - FIX_1_961570560) + bz5; - const int az4 = MULTIPLY(bz4, - FIX_0_390180644) + bz5; - - const int btmp0 = MULTIPLY(atmp0, FIX_0_298631336) + az1 + az3; - const int btmp1 = MULTIPLY(atmp1, FIX_2_053119869) + az2 + az4; - const int btmp2 = MULTIPLY(atmp2, FIX_3_072711026) + az2 + az3; - const int btmp3 = MULTIPLY(atmp3, FIX_1_501321110) + az1 + az4; - - int i = DESCALE_ZEROSHIFT(tmp10 + btmp3, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*0] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp10 - btmp3, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*7] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp11 + btmp2, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*1] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp11 - btmp2, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*6] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp12 + btmp1, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*2] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp12 - btmp1, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*5] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp13 + btmp0, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*3] = (uint8)CLAMP(i); - - i = DESCALE_ZEROSHIFT(tmp13 - btmp0, CONST_BITS+PASS1_BITS+3); - pDst_ptr[8*4] = (uint8)CLAMP(i); - } - }; - - template <> - struct Col<1> - { - static void idct(uint8* pDst_ptr, const int* pTemp) - { - int dcval = DESCALE_ZEROSHIFT(pTemp[0], PASS1_BITS+3); - const uint8 dcval_clamped = (uint8)CLAMP(dcval); - pDst_ptr[0*8] = dcval_clamped; - pDst_ptr[1*8] = dcval_clamped; - pDst_ptr[2*8] = dcval_clamped; - pDst_ptr[3*8] = dcval_clamped; - pDst_ptr[4*8] = dcval_clamped; - pDst_ptr[5*8] = dcval_clamped; - pDst_ptr[6*8] = dcval_clamped; - pDst_ptr[7*8] = dcval_clamped; - } - }; - - static const uint8 s_idct_row_table[] = - { - 1,0,0,0,0,0,0,0, 2,0,0,0,0,0,0,0, 2,1,0,0,0,0,0,0, 2,1,1,0,0,0,0,0, 2,2,1,0,0,0,0,0, 3,2,1,0,0,0,0,0, 4,2,1,0,0,0,0,0, 4,3,1,0,0,0,0,0, - 4,3,2,0,0,0,0,0, 4,3,2,1,0,0,0,0, 4,3,2,1,1,0,0,0, 4,3,2,2,1,0,0,0, 4,3,3,2,1,0,0,0, 4,4,3,2,1,0,0,0, 5,4,3,2,1,0,0,0, 6,4,3,2,1,0,0,0, - 6,5,3,2,1,0,0,0, 6,5,4,2,1,0,0,0, 6,5,4,3,1,0,0,0, 6,5,4,3,2,0,0,0, 6,5,4,3,2,1,0,0, 6,5,4,3,2,1,1,0, 6,5,4,3,2,2,1,0, 6,5,4,3,3,2,1,0, - 6,5,4,4,3,2,1,0, 6,5,5,4,3,2,1,0, 6,6,5,4,3,2,1,0, 7,6,5,4,3,2,1,0, 8,6,5,4,3,2,1,0, 8,7,5,4,3,2,1,0, 8,7,6,4,3,2,1,0, 8,7,6,5,3,2,1,0, - 8,7,6,5,4,2,1,0, 8,7,6,5,4,3,1,0, 8,7,6,5,4,3,2,0, 8,7,6,5,4,3,2,1, 8,7,6,5,4,3,2,2, 8,7,6,5,4,3,3,2, 8,7,6,5,4,4,3,2, 8,7,6,5,5,4,3,2, - 8,7,6,6,5,4,3,2, 8,7,7,6,5,4,3,2, 8,8,7,6,5,4,3,2, 8,8,8,6,5,4,3,2, 8,8,8,7,5,4,3,2, 8,8,8,7,6,4,3,2, 8,8,8,7,6,5,3,2, 8,8,8,7,6,5,4,2, - 8,8,8,7,6,5,4,3, 8,8,8,7,6,5,4,4, 8,8,8,7,6,5,5,4, 8,8,8,7,6,6,5,4, 8,8,8,7,7,6,5,4, 8,8,8,8,7,6,5,4, 8,8,8,8,8,6,5,4, 8,8,8,8,8,7,5,4, - 8,8,8,8,8,7,6,4, 8,8,8,8,8,7,6,5, 8,8,8,8,8,7,6,6, 8,8,8,8,8,7,7,6, 8,8,8,8,8,8,7,6, 8,8,8,8,8,8,8,6, 8,8,8,8,8,8,8,7, 8,8,8,8,8,8,8,8, - }; - - static const uint8 s_idct_col_table[] = { 1, 1, 2, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8 }; - - void idct(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr, int block_max_zag) - { - JPGD_ASSERT(block_max_zag >= 1); - JPGD_ASSERT(block_max_zag <= 64); - - if (block_max_zag == 1) - { - int k = ((pSrc_ptr[0] + 4) >> 3) + 128; - k = CLAMP(k); - k = k | (k<<8); - k = k | (k<<16); - - for (int i = 8; i > 0; i--) - { - *(int*)&pDst_ptr[0] = k; - *(int*)&pDst_ptr[4] = k; - pDst_ptr += 8; - } - return; - } - - int temp[64]; - - const jpgd_block_t* pSrc = pSrc_ptr; - int* pTemp = temp; - - const uint8* pRow_tab = &s_idct_row_table[(block_max_zag - 1) * 8]; - int i; - for (i = 8; i > 0; i--, pRow_tab++) - { - switch (*pRow_tab) - { - case 0: Row<0>::idct(pTemp, pSrc); break; - case 1: Row<1>::idct(pTemp, pSrc); break; - case 2: Row<2>::idct(pTemp, pSrc); break; - case 3: Row<3>::idct(pTemp, pSrc); break; - case 4: Row<4>::idct(pTemp, pSrc); break; - case 5: Row<5>::idct(pTemp, pSrc); break; - case 6: Row<6>::idct(pTemp, pSrc); break; - case 7: Row<7>::idct(pTemp, pSrc); break; - case 8: Row<8>::idct(pTemp, pSrc); break; - } - - pSrc += 8; - pTemp += 8; - } - - pTemp = temp; - - const int nonzero_rows = s_idct_col_table[block_max_zag - 1]; - for (i = 8; i > 0; i--) - { - switch (nonzero_rows) - { - case 1: Col<1>::idct(pDst_ptr, pTemp); break; - case 2: Col<2>::idct(pDst_ptr, pTemp); break; - case 3: Col<3>::idct(pDst_ptr, pTemp); break; - case 4: Col<4>::idct(pDst_ptr, pTemp); break; - case 5: Col<5>::idct(pDst_ptr, pTemp); break; - case 6: Col<6>::idct(pDst_ptr, pTemp); break; - case 7: Col<7>::idct(pDst_ptr, pTemp); break; - case 8: Col<8>::idct(pDst_ptr, pTemp); break; - } - - pTemp++; - pDst_ptr++; - } - } - - void idct_4x4(const jpgd_block_t* pSrc_ptr, uint8* pDst_ptr) - { - int temp[64]; - int* pTemp = temp; - const jpgd_block_t* pSrc = pSrc_ptr; - - for (int i = 4; i > 0; i--) - { - Row<4>::idct(pTemp, pSrc); - pSrc += 8; - pTemp += 8; - } - - pTemp = temp; - for (int i = 8; i > 0; i--) - { - Col<4>::idct(pDst_ptr, pTemp); - pTemp++; - pDst_ptr++; - } - } - - // Retrieve one character from the input stream. - inline uint jpeg_decoder::get_char() - { - // Any bytes remaining in buffer? - if (!m_in_buf_left) - { - // Try to get more bytes. - prep_in_buffer(); - // Still nothing to get? - if (!m_in_buf_left) - { - // Pad the end of the stream with 0xFF 0xD9 (EOI marker) - int t = m_tem_flag; - m_tem_flag ^= 1; - if (t) - return 0xD9; - else - return 0xFF; - } - } - - uint c = *m_pIn_buf_ofs++; - m_in_buf_left--; - - return c; - } - - // Same as previous method, except can indicate if the character is a pad character or not. - inline uint jpeg_decoder::get_char(bool *pPadding_flag) - { - if (!m_in_buf_left) - { - prep_in_buffer(); - if (!m_in_buf_left) - { - *pPadding_flag = true; - int t = m_tem_flag; - m_tem_flag ^= 1; - if (t) - return 0xD9; - else - return 0xFF; - } - } - - *pPadding_flag = false; - - uint c = *m_pIn_buf_ofs++; - m_in_buf_left--; - - return c; - } - - // Inserts a previously retrieved character back into the input buffer. - inline void jpeg_decoder::stuff_char(uint8 q) - { - *(--m_pIn_buf_ofs) = q; - m_in_buf_left++; - } - - // Retrieves one character from the input stream, but does not read past markers. Will continue to return 0xFF when a marker is encountered. - inline uint8 jpeg_decoder::get_octet() - { - bool padding_flag; - int c = get_char(&padding_flag); - - if (c == 0xFF) - { - if (padding_flag) - return 0xFF; - - c = get_char(&padding_flag); - if (padding_flag) - { - stuff_char(0xFF); - return 0xFF; - } - - if (c == 0x00) - return 0xFF; - else - { - stuff_char(static_cast(c)); - stuff_char(0xFF); - return 0xFF; - } - } - - return static_cast(c); - } - - // Retrieves a variable number of bits from the input stream. Does not recognize markers. - inline uint jpeg_decoder::get_bits(int num_bits) - { - if (!num_bits) - return 0; - - uint i = m_bit_buf >> (32 - num_bits); - - if ((m_bits_left -= num_bits) <= 0) - { - m_bit_buf <<= (num_bits += m_bits_left); - - uint c1 = get_char(); - uint c2 = get_char(); - m_bit_buf = (m_bit_buf & 0xFFFF0000) | (c1 << 8) | c2; - - m_bit_buf <<= -m_bits_left; - - m_bits_left += 16; - - JPGD_ASSERT(m_bits_left >= 0); - } - else - m_bit_buf <<= num_bits; - - return i; - } - - // Retrieves a variable number of bits from the input stream. Markers will not be read into the input bit buffer. Instead, an infinite number of all 1's will be returned when a marker is encountered. - inline uint jpeg_decoder::get_bits_no_markers(int num_bits) - { - if (!num_bits) - return 0; - - uint i = m_bit_buf >> (32 - num_bits); - - if ((m_bits_left -= num_bits) <= 0) - { - m_bit_buf <<= (num_bits += m_bits_left); - - if ((m_in_buf_left < 2) || (m_pIn_buf_ofs[0] == 0xFF) || (m_pIn_buf_ofs[1] == 0xFF)) - { - uint c1 = get_octet(); - uint c2 = get_octet(); - m_bit_buf |= (c1 << 8) | c2; - } - else - { - m_bit_buf |= ((uint)m_pIn_buf_ofs[0] << 8) | m_pIn_buf_ofs[1]; - m_in_buf_left -= 2; - m_pIn_buf_ofs += 2; - } - - m_bit_buf <<= -m_bits_left; - - m_bits_left += 16; - - JPGD_ASSERT(m_bits_left >= 0); - } - else - m_bit_buf <<= num_bits; - - return i; - } - - // Decodes a Huffman encoded symbol. - inline int jpeg_decoder::huff_decode(huff_tables *pH) - { - int symbol; - - // Check first 8-bits: do we have a complete symbol? - if ((symbol = pH->look_up[m_bit_buf >> 24]) < 0) - { - // Decode more bits, use a tree traversal to find symbol. - int ofs = 23; - do - { - symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))]; - ofs--; - } while (symbol < 0); - - get_bits_no_markers(8 + (23 - ofs)); - } - else - get_bits_no_markers(pH->code_size[symbol]); - - return symbol; - } - - // Decodes a Huffman encoded symbol. - inline int jpeg_decoder::huff_decode(huff_tables *pH, int& extra_bits) - { - int symbol; - - // Check first 8-bits: do we have a complete symbol? - if ((symbol = pH->look_up2[m_bit_buf >> 24]) < 0) - { - // Use a tree traversal to find symbol. - int ofs = 23; - do - { - symbol = pH->tree[-(int)(symbol + ((m_bit_buf >> ofs) & 1))]; - ofs--; - } while (symbol < 0); - - get_bits_no_markers(8 + (23 - ofs)); - - extra_bits = get_bits_no_markers(symbol & 0xF); - } - else - { - JPGD_ASSERT(((symbol >> 8) & 31) == pH->code_size[symbol & 255] + ((symbol & 0x8000) ? (symbol & 15) : 0)); - - if (symbol & 0x8000) - { - get_bits_no_markers((symbol >> 8) & 31); - extra_bits = symbol >> 16; - } - else - { - int code_size = (symbol >> 8) & 31; - int num_extra_bits = symbol & 0xF; - int bits = code_size + num_extra_bits; - if (bits <= (m_bits_left + 16)) - extra_bits = get_bits_no_markers(bits) & ((1 << num_extra_bits) - 1); - else - { - get_bits_no_markers(code_size); - extra_bits = get_bits_no_markers(num_extra_bits); - } - } - - symbol &= 0xFF; - } - - return symbol; - } - - // Tables and macro used to fully decode the DPCM differences. - static const int s_extend_test[16] = { 0, 0x0001, 0x0002, 0x0004, 0x0008, 0x0010, 0x0020, 0x0040, 0x0080, 0x0100, 0x0200, 0x0400, 0x0800, 0x1000, 0x2000, 0x4000 }; - static const int s_extend_offset[16] = { 0, -1, -3, -7, -15, -31, -63, -127, -255, -511, -1023, -2047, -4095, -8191, -16383, -32767 }; - static const int s_extend_mask[] = { 0, (1<<0), (1<<1), (1<<2), (1<<3), (1<<4), (1<<5), (1<<6), (1<<7), (1<<8), (1<<9), (1<<10), (1<<11), (1<<12), (1<<13), (1<<14), (1<<15), (1<<16) }; -#define HUFF_EXTEND(x,s) ((x) < s_extend_test[s] ? (x) + s_extend_offset[s] : (x)) - - // Clamps a value between 0-255. - inline uint8 jpeg_decoder::clamp(int i) - { - if (static_cast(i) > 255) - i = (((~i) >> 31) & 0xFF); - - return static_cast(i); - } - - namespace DCT_Upsample - { - struct Matrix44 - { - typedef int Element_Type; - enum { NUM_ROWS = 4, NUM_COLS = 4 }; - - Element_Type v[NUM_ROWS][NUM_COLS]; - - inline int rows() const { return NUM_ROWS; } - inline int cols() const { return NUM_COLS; } - - inline const Element_Type & at(int r, int c) const { return v[r][c]; } - inline Element_Type & at(int r, int c) { return v[r][c]; } - - inline Matrix44() { } - - inline Matrix44& operator += (const Matrix44& a) - { - for (int r = 0; r < NUM_ROWS; r++) - { - at(r, 0) += a.at(r, 0); - at(r, 1) += a.at(r, 1); - at(r, 2) += a.at(r, 2); - at(r, 3) += a.at(r, 3); - } - return *this; - } - - inline Matrix44& operator -= (const Matrix44& a) - { - for (int r = 0; r < NUM_ROWS; r++) - { - at(r, 0) -= a.at(r, 0); - at(r, 1) -= a.at(r, 1); - at(r, 2) -= a.at(r, 2); - at(r, 3) -= a.at(r, 3); - } - return *this; - } - - friend inline Matrix44 operator + (const Matrix44& a, const Matrix44& b) - { - Matrix44 ret; - for (int r = 0; r < NUM_ROWS; r++) - { - ret.at(r, 0) = a.at(r, 0) + b.at(r, 0); - ret.at(r, 1) = a.at(r, 1) + b.at(r, 1); - ret.at(r, 2) = a.at(r, 2) + b.at(r, 2); - ret.at(r, 3) = a.at(r, 3) + b.at(r, 3); - } - return ret; - } - - friend inline Matrix44 operator - (const Matrix44& a, const Matrix44& b) - { - Matrix44 ret; - for (int r = 0; r < NUM_ROWS; r++) - { - ret.at(r, 0) = a.at(r, 0) - b.at(r, 0); - ret.at(r, 1) = a.at(r, 1) - b.at(r, 1); - ret.at(r, 2) = a.at(r, 2) - b.at(r, 2); - ret.at(r, 3) = a.at(r, 3) - b.at(r, 3); - } - return ret; - } - - static inline void add_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b) - { - for (int r = 0; r < 4; r++) - { - pDst[0*8 + r] = static_cast(a.at(r, 0) + b.at(r, 0)); - pDst[1*8 + r] = static_cast(a.at(r, 1) + b.at(r, 1)); - pDst[2*8 + r] = static_cast(a.at(r, 2) + b.at(r, 2)); - pDst[3*8 + r] = static_cast(a.at(r, 3) + b.at(r, 3)); - } - } - - static inline void sub_and_store(jpgd_block_t* pDst, const Matrix44& a, const Matrix44& b) - { - for (int r = 0; r < 4; r++) - { - pDst[0*8 + r] = static_cast(a.at(r, 0) - b.at(r, 0)); - pDst[1*8 + r] = static_cast(a.at(r, 1) - b.at(r, 1)); - pDst[2*8 + r] = static_cast(a.at(r, 2) - b.at(r, 2)); - pDst[3*8 + r] = static_cast(a.at(r, 3) - b.at(r, 3)); - } - } - }; - - const int FRACT_BITS = 10; - const int SCALE = 1 << FRACT_BITS; - - typedef int Temp_Type; -#define D(i) (((i) + (SCALE >> 1)) >> FRACT_BITS) -#define F(i) ((int)((i) * SCALE + .5f)) - - // Any decent C++ compiler will optimize this at compile time to a 0, or an array access. -#define AT(c, r) ((((c)>=NUM_COLS)||((r)>=NUM_ROWS)) ? 0 : pSrc[(c)+(r)*8]) - - // NUM_ROWS/NUM_COLS = # of non-zero rows/cols in input matrix - template - struct P_Q - { - static void calc(Matrix44& P, Matrix44& Q, const jpgd_block_t* pSrc) - { - // 4x8 = 4x8 times 8x8, matrix 0 is constant - const Temp_Type X000 = AT(0, 0); - const Temp_Type X001 = AT(0, 1); - const Temp_Type X002 = AT(0, 2); - const Temp_Type X003 = AT(0, 3); - const Temp_Type X004 = AT(0, 4); - const Temp_Type X005 = AT(0, 5); - const Temp_Type X006 = AT(0, 6); - const Temp_Type X007 = AT(0, 7); - const Temp_Type X010 = D(F(0.415735f) * AT(1, 0) + F(0.791065f) * AT(3, 0) + F(-0.352443f) * AT(5, 0) + F(0.277785f) * AT(7, 0)); - const Temp_Type X011 = D(F(0.415735f) * AT(1, 1) + F(0.791065f) * AT(3, 1) + F(-0.352443f) * AT(5, 1) + F(0.277785f) * AT(7, 1)); - const Temp_Type X012 = D(F(0.415735f) * AT(1, 2) + F(0.791065f) * AT(3, 2) + F(-0.352443f) * AT(5, 2) + F(0.277785f) * AT(7, 2)); - const Temp_Type X013 = D(F(0.415735f) * AT(1, 3) + F(0.791065f) * AT(3, 3) + F(-0.352443f) * AT(5, 3) + F(0.277785f) * AT(7, 3)); - const Temp_Type X014 = D(F(0.415735f) * AT(1, 4) + F(0.791065f) * AT(3, 4) + F(-0.352443f) * AT(5, 4) + F(0.277785f) * AT(7, 4)); - const Temp_Type X015 = D(F(0.415735f) * AT(1, 5) + F(0.791065f) * AT(3, 5) + F(-0.352443f) * AT(5, 5) + F(0.277785f) * AT(7, 5)); - const Temp_Type X016 = D(F(0.415735f) * AT(1, 6) + F(0.791065f) * AT(3, 6) + F(-0.352443f) * AT(5, 6) + F(0.277785f) * AT(7, 6)); - const Temp_Type X017 = D(F(0.415735f) * AT(1, 7) + F(0.791065f) * AT(3, 7) + F(-0.352443f) * AT(5, 7) + F(0.277785f) * AT(7, 7)); - const Temp_Type X020 = AT(4, 0); - const Temp_Type X021 = AT(4, 1); - const Temp_Type X022 = AT(4, 2); - const Temp_Type X023 = AT(4, 3); - const Temp_Type X024 = AT(4, 4); - const Temp_Type X025 = AT(4, 5); - const Temp_Type X026 = AT(4, 6); - const Temp_Type X027 = AT(4, 7); - const Temp_Type X030 = D(F(0.022887f) * AT(1, 0) + F(-0.097545f) * AT(3, 0) + F(0.490393f) * AT(5, 0) + F(0.865723f) * AT(7, 0)); - const Temp_Type X031 = D(F(0.022887f) * AT(1, 1) + F(-0.097545f) * AT(3, 1) + F(0.490393f) * AT(5, 1) + F(0.865723f) * AT(7, 1)); - const Temp_Type X032 = D(F(0.022887f) * AT(1, 2) + F(-0.097545f) * AT(3, 2) + F(0.490393f) * AT(5, 2) + F(0.865723f) * AT(7, 2)); - const Temp_Type X033 = D(F(0.022887f) * AT(1, 3) + F(-0.097545f) * AT(3, 3) + F(0.490393f) * AT(5, 3) + F(0.865723f) * AT(7, 3)); - const Temp_Type X034 = D(F(0.022887f) * AT(1, 4) + F(-0.097545f) * AT(3, 4) + F(0.490393f) * AT(5, 4) + F(0.865723f) * AT(7, 4)); - const Temp_Type X035 = D(F(0.022887f) * AT(1, 5) + F(-0.097545f) * AT(3, 5) + F(0.490393f) * AT(5, 5) + F(0.865723f) * AT(7, 5)); - const Temp_Type X036 = D(F(0.022887f) * AT(1, 6) + F(-0.097545f) * AT(3, 6) + F(0.490393f) * AT(5, 6) + F(0.865723f) * AT(7, 6)); - const Temp_Type X037 = D(F(0.022887f) * AT(1, 7) + F(-0.097545f) * AT(3, 7) + F(0.490393f) * AT(5, 7) + F(0.865723f) * AT(7, 7)); - - // 4x4 = 4x8 times 8x4, matrix 1 is constant - P.at(0, 0) = X000; - P.at(0, 1) = D(X001 * F(0.415735f) + X003 * F(0.791065f) + X005 * F(-0.352443f) + X007 * F(0.277785f)); - P.at(0, 2) = X004; - P.at(0, 3) = D(X001 * F(0.022887f) + X003 * F(-0.097545f) + X005 * F(0.490393f) + X007 * F(0.865723f)); - P.at(1, 0) = X010; - P.at(1, 1) = D(X011 * F(0.415735f) + X013 * F(0.791065f) + X015 * F(-0.352443f) + X017 * F(0.277785f)); - P.at(1, 2) = X014; - P.at(1, 3) = D(X011 * F(0.022887f) + X013 * F(-0.097545f) + X015 * F(0.490393f) + X017 * F(0.865723f)); - P.at(2, 0) = X020; - P.at(2, 1) = D(X021 * F(0.415735f) + X023 * F(0.791065f) + X025 * F(-0.352443f) + X027 * F(0.277785f)); - P.at(2, 2) = X024; - P.at(2, 3) = D(X021 * F(0.022887f) + X023 * F(-0.097545f) + X025 * F(0.490393f) + X027 * F(0.865723f)); - P.at(3, 0) = X030; - P.at(3, 1) = D(X031 * F(0.415735f) + X033 * F(0.791065f) + X035 * F(-0.352443f) + X037 * F(0.277785f)); - P.at(3, 2) = X034; - P.at(3, 3) = D(X031 * F(0.022887f) + X033 * F(-0.097545f) + X035 * F(0.490393f) + X037 * F(0.865723f)); - // 40 muls 24 adds - - // 4x4 = 4x8 times 8x4, matrix 1 is constant - Q.at(0, 0) = D(X001 * F(0.906127f) + X003 * F(-0.318190f) + X005 * F(0.212608f) + X007 * F(-0.180240f)); - Q.at(0, 1) = X002; - Q.at(0, 2) = D(X001 * F(-0.074658f) + X003 * F(0.513280f) + X005 * F(0.768178f) + X007 * F(-0.375330f)); - Q.at(0, 3) = X006; - Q.at(1, 0) = D(X011 * F(0.906127f) + X013 * F(-0.318190f) + X015 * F(0.212608f) + X017 * F(-0.180240f)); - Q.at(1, 1) = X012; - Q.at(1, 2) = D(X011 * F(-0.074658f) + X013 * F(0.513280f) + X015 * F(0.768178f) + X017 * F(-0.375330f)); - Q.at(1, 3) = X016; - Q.at(2, 0) = D(X021 * F(0.906127f) + X023 * F(-0.318190f) + X025 * F(0.212608f) + X027 * F(-0.180240f)); - Q.at(2, 1) = X022; - Q.at(2, 2) = D(X021 * F(-0.074658f) + X023 * F(0.513280f) + X025 * F(0.768178f) + X027 * F(-0.375330f)); - Q.at(2, 3) = X026; - Q.at(3, 0) = D(X031 * F(0.906127f) + X033 * F(-0.318190f) + X035 * F(0.212608f) + X037 * F(-0.180240f)); - Q.at(3, 1) = X032; - Q.at(3, 2) = D(X031 * F(-0.074658f) + X033 * F(0.513280f) + X035 * F(0.768178f) + X037 * F(-0.375330f)); - Q.at(3, 3) = X036; - // 40 muls 24 adds - } - }; - - template - struct R_S - { - static void calc(Matrix44& R, Matrix44& S, const jpgd_block_t* pSrc) - { - // 4x8 = 4x8 times 8x8, matrix 0 is constant - const Temp_Type X100 = D(F(0.906127f) * AT(1, 0) + F(-0.318190f) * AT(3, 0) + F(0.212608f) * AT(5, 0) + F(-0.180240f) * AT(7, 0)); - const Temp_Type X101 = D(F(0.906127f) * AT(1, 1) + F(-0.318190f) * AT(3, 1) + F(0.212608f) * AT(5, 1) + F(-0.180240f) * AT(7, 1)); - const Temp_Type X102 = D(F(0.906127f) * AT(1, 2) + F(-0.318190f) * AT(3, 2) + F(0.212608f) * AT(5, 2) + F(-0.180240f) * AT(7, 2)); - const Temp_Type X103 = D(F(0.906127f) * AT(1, 3) + F(-0.318190f) * AT(3, 3) + F(0.212608f) * AT(5, 3) + F(-0.180240f) * AT(7, 3)); - const Temp_Type X104 = D(F(0.906127f) * AT(1, 4) + F(-0.318190f) * AT(3, 4) + F(0.212608f) * AT(5, 4) + F(-0.180240f) * AT(7, 4)); - const Temp_Type X105 = D(F(0.906127f) * AT(1, 5) + F(-0.318190f) * AT(3, 5) + F(0.212608f) * AT(5, 5) + F(-0.180240f) * AT(7, 5)); - const Temp_Type X106 = D(F(0.906127f) * AT(1, 6) + F(-0.318190f) * AT(3, 6) + F(0.212608f) * AT(5, 6) + F(-0.180240f) * AT(7, 6)); - const Temp_Type X107 = D(F(0.906127f) * AT(1, 7) + F(-0.318190f) * AT(3, 7) + F(0.212608f) * AT(5, 7) + F(-0.180240f) * AT(7, 7)); - const Temp_Type X110 = AT(2, 0); - const Temp_Type X111 = AT(2, 1); - const Temp_Type X112 = AT(2, 2); - const Temp_Type X113 = AT(2, 3); - const Temp_Type X114 = AT(2, 4); - const Temp_Type X115 = AT(2, 5); - const Temp_Type X116 = AT(2, 6); - const Temp_Type X117 = AT(2, 7); - const Temp_Type X120 = D(F(-0.074658f) * AT(1, 0) + F(0.513280f) * AT(3, 0) + F(0.768178f) * AT(5, 0) + F(-0.375330f) * AT(7, 0)); - const Temp_Type X121 = D(F(-0.074658f) * AT(1, 1) + F(0.513280f) * AT(3, 1) + F(0.768178f) * AT(5, 1) + F(-0.375330f) * AT(7, 1)); - const Temp_Type X122 = D(F(-0.074658f) * AT(1, 2) + F(0.513280f) * AT(3, 2) + F(0.768178f) * AT(5, 2) + F(-0.375330f) * AT(7, 2)); - const Temp_Type X123 = D(F(-0.074658f) * AT(1, 3) + F(0.513280f) * AT(3, 3) + F(0.768178f) * AT(5, 3) + F(-0.375330f) * AT(7, 3)); - const Temp_Type X124 = D(F(-0.074658f) * AT(1, 4) + F(0.513280f) * AT(3, 4) + F(0.768178f) * AT(5, 4) + F(-0.375330f) * AT(7, 4)); - const Temp_Type X125 = D(F(-0.074658f) * AT(1, 5) + F(0.513280f) * AT(3, 5) + F(0.768178f) * AT(5, 5) + F(-0.375330f) * AT(7, 5)); - const Temp_Type X126 = D(F(-0.074658f) * AT(1, 6) + F(0.513280f) * AT(3, 6) + F(0.768178f) * AT(5, 6) + F(-0.375330f) * AT(7, 6)); - const Temp_Type X127 = D(F(-0.074658f) * AT(1, 7) + F(0.513280f) * AT(3, 7) + F(0.768178f) * AT(5, 7) + F(-0.375330f) * AT(7, 7)); - const Temp_Type X130 = AT(6, 0); - const Temp_Type X131 = AT(6, 1); - const Temp_Type X132 = AT(6, 2); - const Temp_Type X133 = AT(6, 3); - const Temp_Type X134 = AT(6, 4); - const Temp_Type X135 = AT(6, 5); - const Temp_Type X136 = AT(6, 6); - const Temp_Type X137 = AT(6, 7); - // 80 muls 48 adds - - // 4x4 = 4x8 times 8x4, matrix 1 is constant - R.at(0, 0) = X100; - R.at(0, 1) = D(X101 * F(0.415735f) + X103 * F(0.791065f) + X105 * F(-0.352443f) + X107 * F(0.277785f)); - R.at(0, 2) = X104; - R.at(0, 3) = D(X101 * F(0.022887f) + X103 * F(-0.097545f) + X105 * F(0.490393f) + X107 * F(0.865723f)); - R.at(1, 0) = X110; - R.at(1, 1) = D(X111 * F(0.415735f) + X113 * F(0.791065f) + X115 * F(-0.352443f) + X117 * F(0.277785f)); - R.at(1, 2) = X114; - R.at(1, 3) = D(X111 * F(0.022887f) + X113 * F(-0.097545f) + X115 * F(0.490393f) + X117 * F(0.865723f)); - R.at(2, 0) = X120; - R.at(2, 1) = D(X121 * F(0.415735f) + X123 * F(0.791065f) + X125 * F(-0.352443f) + X127 * F(0.277785f)); - R.at(2, 2) = X124; - R.at(2, 3) = D(X121 * F(0.022887f) + X123 * F(-0.097545f) + X125 * F(0.490393f) + X127 * F(0.865723f)); - R.at(3, 0) = X130; - R.at(3, 1) = D(X131 * F(0.415735f) + X133 * F(0.791065f) + X135 * F(-0.352443f) + X137 * F(0.277785f)); - R.at(3, 2) = X134; - R.at(3, 3) = D(X131 * F(0.022887f) + X133 * F(-0.097545f) + X135 * F(0.490393f) + X137 * F(0.865723f)); - // 40 muls 24 adds - // 4x4 = 4x8 times 8x4, matrix 1 is constant - S.at(0, 0) = D(X101 * F(0.906127f) + X103 * F(-0.318190f) + X105 * F(0.212608f) + X107 * F(-0.180240f)); - S.at(0, 1) = X102; - S.at(0, 2) = D(X101 * F(-0.074658f) + X103 * F(0.513280f) + X105 * F(0.768178f) + X107 * F(-0.375330f)); - S.at(0, 3) = X106; - S.at(1, 0) = D(X111 * F(0.906127f) + X113 * F(-0.318190f) + X115 * F(0.212608f) + X117 * F(-0.180240f)); - S.at(1, 1) = X112; - S.at(1, 2) = D(X111 * F(-0.074658f) + X113 * F(0.513280f) + X115 * F(0.768178f) + X117 * F(-0.375330f)); - S.at(1, 3) = X116; - S.at(2, 0) = D(X121 * F(0.906127f) + X123 * F(-0.318190f) + X125 * F(0.212608f) + X127 * F(-0.180240f)); - S.at(2, 1) = X122; - S.at(2, 2) = D(X121 * F(-0.074658f) + X123 * F(0.513280f) + X125 * F(0.768178f) + X127 * F(-0.375330f)); - S.at(2, 3) = X126; - S.at(3, 0) = D(X131 * F(0.906127f) + X133 * F(-0.318190f) + X135 * F(0.212608f) + X137 * F(-0.180240f)); - S.at(3, 1) = X132; - S.at(3, 2) = D(X131 * F(-0.074658f) + X133 * F(0.513280f) + X135 * F(0.768178f) + X137 * F(-0.375330f)); - S.at(3, 3) = X136; - // 40 muls 24 adds - } - }; - } // end namespace DCT_Upsample - - // Unconditionally frees all allocated m_blocks. - void jpeg_decoder::free_all_blocks() - { - m_pStream = NULL; - for (mem_block *b = m_pMem_blocks; b; ) - { - mem_block *n = b->m_pNext; - jpgd_free(b); - b = n; - } - m_pMem_blocks = NULL; - } - - // This method handles all errors. - // It could easily be changed to use C++ exceptions. - void jpeg_decoder::stop_decoding(jpgd_status status) - { - m_error_code = status; - free_all_blocks(); - longjmp(m_jmp_state, status); - - // we shouldn't get here as longjmp shouldn't return, but we put it here to make it explicit - // that this function doesn't return, otherwise we get this error: - // - // error : function declared 'noreturn' should not return - exit(1); - } - - void *jpeg_decoder::alloc(size_t nSize, bool zero) - { - nSize = (JPGD_MAX(nSize, 1) + 3) & ~3; - char *rv = NULL; - for (mem_block *b = m_pMem_blocks; b; b = b->m_pNext) - { - if ((b->m_used_count + nSize) <= b->m_size) - { - rv = b->m_data + b->m_used_count; - b->m_used_count += nSize; - break; - } - } - if (!rv) - { - int capacity = JPGD_MAX(32768 - 256, (nSize + 2047) & ~2047); - mem_block *b = (mem_block*)jpgd_malloc(sizeof(mem_block) + capacity); - if (!b) stop_decoding(JPGD_NOTENOUGHMEM); - b->m_pNext = m_pMem_blocks; m_pMem_blocks = b; - b->m_used_count = nSize; - b->m_size = capacity; - rv = b->m_data; - } - if (zero) memset(rv, 0, nSize); - return rv; - } - - void jpeg_decoder::word_clear(void *p, uint16 c, uint n) - { - uint8 *pD = (uint8*)p; - const uint8 l = c & 0xFF, h = (c >> 8) & 0xFF; - while (n) - { - pD[0] = l; pD[1] = h; pD += 2; - n--; - } - } - - // Refill the input buffer. - // This method will sit in a loop until (A) the buffer is full or (B) - // the stream's read() method reports and end of file condition. - void jpeg_decoder::prep_in_buffer() - { - m_in_buf_left = 0; - m_pIn_buf_ofs = m_in_buf; - - if (m_eof_flag) - return; - - do - { - int bytes_read = m_pStream->read(m_in_buf + m_in_buf_left, JPGD_IN_BUF_SIZE - m_in_buf_left, &m_eof_flag); - if (bytes_read == -1) - stop_decoding(JPGD_STREAM_READ); - - m_in_buf_left += bytes_read; - } while ((m_in_buf_left < JPGD_IN_BUF_SIZE) && (!m_eof_flag)); - - m_total_bytes_read += m_in_buf_left; - - // Pad the end of the block with M_EOI (prevents the decompressor from going off the rails if the stream is invalid). - // (This dates way back to when this decompressor was written in C/asm, and the all-asm Huffman decoder did some fancy things to increase perf.) - word_clear(m_pIn_buf_ofs + m_in_buf_left, 0xD9FF, 64); - } - - // Read a Huffman code table. - void jpeg_decoder::read_dht_marker() - { - int i, index, count; - uint8 huff_num[17]; - uint8 huff_val[256]; - - uint num_left = get_bits(16); - - if (num_left < 2) - stop_decoding(JPGD_BAD_DHT_MARKER); - - num_left -= 2; - - while (num_left) - { - index = get_bits(8); - - huff_num[0] = 0; - - count = 0; - - for (i = 1; i <= 16; i++) - { - huff_num[i] = static_cast(get_bits(8)); - count += huff_num[i]; - } - - if (count > 255) - stop_decoding(JPGD_BAD_DHT_COUNTS); - - for (i = 0; i < count; i++) - huff_val[i] = static_cast(get_bits(8)); - - i = 1 + 16 + count; - - if (num_left < (uint)i) - stop_decoding(JPGD_BAD_DHT_MARKER); - - num_left -= i; - - if ((index & 0x10) > 0x10) - stop_decoding(JPGD_BAD_DHT_INDEX); - - index = (index & 0x0F) + ((index & 0x10) >> 4) * (JPGD_MAX_HUFF_TABLES >> 1); - - if (index >= JPGD_MAX_HUFF_TABLES) - stop_decoding(JPGD_BAD_DHT_INDEX); - - if (!m_huff_num[index]) - m_huff_num[index] = (uint8 *)alloc(17); - - if (!m_huff_val[index]) - m_huff_val[index] = (uint8 *)alloc(256); - - m_huff_ac[index] = (index & 0x10) != 0; - memcpy(m_huff_num[index], huff_num, 17); - memcpy(m_huff_val[index], huff_val, 256); - } - } - - // Read a quantization table. - void jpeg_decoder::read_dqt_marker() - { - int n, i, prec; - uint num_left; - uint temp; - - num_left = get_bits(16); - - if (num_left < 2) - stop_decoding(JPGD_BAD_DQT_MARKER); - - num_left -= 2; - - while (num_left) - { - n = get_bits(8); - prec = n >> 4; - n &= 0x0F; - - if (n >= JPGD_MAX_QUANT_TABLES) - stop_decoding(JPGD_BAD_DQT_TABLE); - - if (!m_quant[n]) - m_quant[n] = (jpgd_quant_t *)alloc(64 * sizeof(jpgd_quant_t)); - - // read quantization entries, in zag order - for (i = 0; i < 64; i++) - { - temp = get_bits(8); - - if (prec) - temp = (temp << 8) + get_bits(8); - - m_quant[n][i] = static_cast(temp); - } - - i = 64 + 1; - - if (prec) - i += 64; - - if (num_left < (uint)i) - stop_decoding(JPGD_BAD_DQT_LENGTH); - - num_left -= i; - } - } - - // Read the start of frame (SOF) marker. - void jpeg_decoder::read_sof_marker() - { - int i; - uint num_left; - - num_left = get_bits(16); - - if (get_bits(8) != 8) /* precision: sorry, only 8-bit precision is supported right now */ - stop_decoding(JPGD_BAD_PRECISION); - - m_image_y_size = get_bits(16); - - if ((m_image_y_size < 1) || (m_image_y_size > JPGD_MAX_HEIGHT)) - stop_decoding(JPGD_BAD_HEIGHT); - - m_image_x_size = get_bits(16); - - if ((m_image_x_size < 1) || (m_image_x_size > JPGD_MAX_WIDTH)) - stop_decoding(JPGD_BAD_WIDTH); - - m_comps_in_frame = get_bits(8); - - if (m_comps_in_frame > JPGD_MAX_COMPONENTS) - stop_decoding(JPGD_TOO_MANY_COMPONENTS); - - if (num_left != (uint)(m_comps_in_frame * 3 + 8)) - stop_decoding(JPGD_BAD_SOF_LENGTH); - - for (i = 0; i < m_comps_in_frame; i++) - { - m_comp_ident[i] = get_bits(8); - m_comp_h_samp[i] = get_bits(4); - m_comp_v_samp[i] = get_bits(4); - m_comp_quant[i] = get_bits(8); - } - } - - // Used to skip unrecognized markers. - void jpeg_decoder::skip_variable_marker() - { - uint num_left; - - num_left = get_bits(16); - - if (num_left < 2) - stop_decoding(JPGD_BAD_VARIABLE_MARKER); - - num_left -= 2; - - while (num_left) - { - get_bits(8); - num_left--; - } - } - - // Read a define restart interval (DRI) marker. - void jpeg_decoder::read_dri_marker() - { - if (get_bits(16) != 4) - stop_decoding(JPGD_BAD_DRI_LENGTH); - - m_restart_interval = get_bits(16); - } - - // Read a start of scan (SOS) marker. - void jpeg_decoder::read_sos_marker() - { - uint num_left; - int i, ci, n, c, cc; - - num_left = get_bits(16); - - n = get_bits(8); - - m_comps_in_scan = n; - - num_left -= 3; - - if ( (num_left != (uint)(n * 2 + 3)) || (n < 1) || (n > JPGD_MAX_COMPS_IN_SCAN) ) - stop_decoding(JPGD_BAD_SOS_LENGTH); - - for (i = 0; i < n; i++) - { - cc = get_bits(8); - c = get_bits(8); - num_left -= 2; - - for (ci = 0; ci < m_comps_in_frame; ci++) - if (cc == m_comp_ident[ci]) - break; - - if (ci >= m_comps_in_frame) - stop_decoding(JPGD_BAD_SOS_COMP_ID); - - m_comp_list[i] = ci; - m_comp_dc_tab[ci] = (c >> 4) & 15; - m_comp_ac_tab[ci] = (c & 15) + (JPGD_MAX_HUFF_TABLES >> 1); - } - - m_spectral_start = get_bits(8); - m_spectral_end = get_bits(8); - m_successive_high = get_bits(4); - m_successive_low = get_bits(4); - - if (!m_progressive_flag) - { - m_spectral_start = 0; - m_spectral_end = 63; - } - - num_left -= 3; - - while (num_left) /* read past whatever is num_left */ - { - get_bits(8); - num_left--; - } - } - - // Finds the next marker. - int jpeg_decoder::next_marker() - { - uint c, bytes; - - bytes = 0; - - do - { - do - { - bytes++; - c = get_bits(8); - } while (c != 0xFF); - - do - { - c = get_bits(8); - } while (c == 0xFF); - - } while (c == 0); - - // If bytes > 0 here, there where extra bytes before the marker (not good). - - return c; - } - - // Process markers. Returns when an SOFx, SOI, EOI, or SOS marker is - // encountered. - int jpeg_decoder::process_markers() - { - int c; - - for ( ; ; ) - { - c = next_marker(); - - switch (c) - { - case M_SOF0: - case M_SOF1: - case M_SOF2: - case M_SOF3: - case M_SOF5: - case M_SOF6: - case M_SOF7: - // case M_JPG: - case M_SOF9: - case M_SOF10: - case M_SOF11: - case M_SOF13: - case M_SOF14: - case M_SOF15: - case M_SOI: - case M_EOI: - case M_SOS: - { - return c; - } - case M_DHT: - { - read_dht_marker(); - break; - } - // No arithmitic support - dumb patents! - case M_DAC: - { - stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT); - break; - } - case M_DQT: - { - read_dqt_marker(); - break; - } - case M_DRI: - { - read_dri_marker(); - break; - } - //case M_APP0: /* no need to read the JFIF marker */ - - case M_JPG: - case M_RST0: /* no parameters */ - case M_RST1: - case M_RST2: - case M_RST3: - case M_RST4: - case M_RST5: - case M_RST6: - case M_RST7: - case M_TEM: - { - stop_decoding(JPGD_UNEXPECTED_MARKER); - break; - } - default: /* must be DNL, DHP, EXP, APPn, JPGn, COM, or RESn or APP0 */ - { - skip_variable_marker(); - break; - } - } - } - } - - // Finds the start of image (SOI) marker. - // This code is rather defensive: it only checks the first 512 bytes to avoid - // false positives. - void jpeg_decoder::locate_soi_marker() - { - uint lastchar, thischar; - uint bytesleft; - - lastchar = get_bits(8); - - thischar = get_bits(8); - - /* ok if it's a normal JPEG file without a special header */ - - if ((lastchar == 0xFF) && (thischar == M_SOI)) - return; - - bytesleft = 4096; //512; - - for ( ; ; ) - { - if (--bytesleft == 0) - stop_decoding(JPGD_NOT_JPEG); - - lastchar = thischar; - - thischar = get_bits(8); - - if (lastchar == 0xFF) - { - if (thischar == M_SOI) - break; - else if (thischar == M_EOI) // get_bits will keep returning M_EOI if we read past the end - stop_decoding(JPGD_NOT_JPEG); - } - } - - // Check the next character after marker: if it's not 0xFF, it can't be the start of the next marker, so the file is bad. - thischar = (m_bit_buf >> 24) & 0xFF; - - if (thischar != 0xFF) - stop_decoding(JPGD_NOT_JPEG); - } - - // Find a start of frame (SOF) marker. - void jpeg_decoder::locate_sof_marker() - { - locate_soi_marker(); - - int c = process_markers(); - - switch (c) - { - case M_SOF2: - m_progressive_flag = JPGD_TRUE; - case M_SOF0: /* baseline DCT */ - case M_SOF1: /* extended sequential DCT */ - { - read_sof_marker(); - break; - } - case M_SOF9: /* Arithmitic coding */ - { - stop_decoding(JPGD_NO_ARITHMITIC_SUPPORT); - break; - } - default: - { - stop_decoding(JPGD_UNSUPPORTED_MARKER); - break; - } - } - } - - // Find a start of scan (SOS) marker. - int jpeg_decoder::locate_sos_marker() - { - int c; - - c = process_markers(); - - if (c == M_EOI) - return JPGD_FALSE; - else if (c != M_SOS) - stop_decoding(JPGD_UNEXPECTED_MARKER); - - read_sos_marker(); - - return JPGD_TRUE; - } - - // Reset everything to default/uninitialized state. - void jpeg_decoder::init(jpeg_decoder_stream *pStream) - { - m_pMem_blocks = NULL; - m_error_code = JPGD_SUCCESS; - m_ready_flag = false; - m_image_x_size = m_image_y_size = 0; - m_pStream = pStream; - m_progressive_flag = JPGD_FALSE; - - memset(m_huff_ac, 0, sizeof(m_huff_ac)); - memset(m_huff_num, 0, sizeof(m_huff_num)); - memset(m_huff_val, 0, sizeof(m_huff_val)); - memset(m_quant, 0, sizeof(m_quant)); - - m_scan_type = 0; - m_comps_in_frame = 0; - - memset(m_comp_h_samp, 0, sizeof(m_comp_h_samp)); - memset(m_comp_v_samp, 0, sizeof(m_comp_v_samp)); - memset(m_comp_quant, 0, sizeof(m_comp_quant)); - memset(m_comp_ident, 0, sizeof(m_comp_ident)); - memset(m_comp_h_blocks, 0, sizeof(m_comp_h_blocks)); - memset(m_comp_v_blocks, 0, sizeof(m_comp_v_blocks)); - - m_comps_in_scan = 0; - memset(m_comp_list, 0, sizeof(m_comp_list)); - memset(m_comp_dc_tab, 0, sizeof(m_comp_dc_tab)); - memset(m_comp_ac_tab, 0, sizeof(m_comp_ac_tab)); - - m_spectral_start = 0; - m_spectral_end = 0; - m_successive_low = 0; - m_successive_high = 0; - m_max_mcu_x_size = 0; - m_max_mcu_y_size = 0; - m_blocks_per_mcu = 0; - m_max_blocks_per_row = 0; - m_mcus_per_row = 0; - m_mcus_per_col = 0; - m_expanded_blocks_per_component = 0; - m_expanded_blocks_per_mcu = 0; - m_expanded_blocks_per_row = 0; - m_freq_domain_chroma_upsample = false; - - memset(m_mcu_org, 0, sizeof(m_mcu_org)); - - m_total_lines_left = 0; - m_mcu_lines_left = 0; - m_real_dest_bytes_per_scan_line = 0; - m_dest_bytes_per_scan_line = 0; - m_dest_bytes_per_pixel = 0; - - memset(m_pHuff_tabs, 0, sizeof(m_pHuff_tabs)); - - memset(m_dc_coeffs, 0, sizeof(m_dc_coeffs)); - memset(m_ac_coeffs, 0, sizeof(m_ac_coeffs)); - memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu)); - - m_eob_run = 0; - - memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu)); - - m_pIn_buf_ofs = m_in_buf; - m_in_buf_left = 0; - m_eof_flag = false; - m_tem_flag = 0; - - memset(m_in_buf_pad_start, 0, sizeof(m_in_buf_pad_start)); - memset(m_in_buf, 0, sizeof(m_in_buf)); - memset(m_in_buf_pad_end, 0, sizeof(m_in_buf_pad_end)); - - m_restart_interval = 0; - m_restarts_left = 0; - m_next_restart_num = 0; - - m_max_mcus_per_row = 0; - m_max_blocks_per_mcu = 0; - m_max_mcus_per_col = 0; - - memset(m_last_dc_val, 0, sizeof(m_last_dc_val)); - m_pMCU_coefficients = NULL; - m_pSample_buf = NULL; - - m_total_bytes_read = 0; - - m_pScan_line_0 = NULL; - m_pScan_line_1 = NULL; - - // Ready the input buffer. - prep_in_buffer(); - - // Prime the bit buffer. - m_bits_left = 16; - m_bit_buf = 0; - - get_bits(16); - get_bits(16); - - for (int i = 0; i < JPGD_MAX_BLOCKS_PER_MCU; i++) - m_mcu_block_max_zag[i] = 64; - } - -#define SCALEBITS 16 -#define ONE_HALF ((int) 1 << (SCALEBITS-1)) -#define FIX(x) ((int) ((x) * (1L<> SCALEBITS; - m_cbb[i] = ( FIX(1.77200f) * k + ONE_HALF) >> SCALEBITS; - m_crg[i] = (-FIX(0.71414f)) * k; - m_cbg[i] = (-FIX(0.34414f)) * k + ONE_HALF; - } - } - - // This method throws back into the stream any bytes that where read - // into the bit buffer during initial marker scanning. - void jpeg_decoder::fix_in_buffer() - { - // In case any 0xFF's where pulled into the buffer during marker scanning. - JPGD_ASSERT((m_bits_left & 7) == 0); - - if (m_bits_left == 16) - stuff_char( (uint8)(m_bit_buf & 0xFF)); - - if (m_bits_left >= 8) - stuff_char( (uint8)((m_bit_buf >> 8) & 0xFF)); - - stuff_char((uint8)((m_bit_buf >> 16) & 0xFF)); - stuff_char((uint8)((m_bit_buf >> 24) & 0xFF)); - - m_bits_left = 16; - get_bits_no_markers(16); - get_bits_no_markers(16); - } - - void jpeg_decoder::transform_mcu(int mcu_row) - { - jpgd_block_t* pSrc_ptr = m_pMCU_coefficients; - uint8* pDst_ptr = m_pSample_buf + mcu_row * m_blocks_per_mcu * 64; - - for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++) - { - idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]); - pSrc_ptr += 64; - pDst_ptr += 64; - } - } - - static const uint8 s_max_rc[64] = - { - 17, 18, 34, 50, 50, 51, 52, 52, 52, 68, 84, 84, 84, 84, 85, 86, 86, 86, 86, 86, - 102, 118, 118, 118, 118, 118, 118, 119, 120, 120, 120, 120, 120, 120, 120, 136, - 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, - 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136, 136 - }; - - void jpeg_decoder::transform_mcu_expand(int mcu_row) - { - jpgd_block_t* pSrc_ptr = m_pMCU_coefficients; - uint8* pDst_ptr = m_pSample_buf + mcu_row * m_expanded_blocks_per_mcu * 64; - - // Y IDCT - int mcu_block; - for (mcu_block = 0; mcu_block < m_expanded_blocks_per_component; mcu_block++) - { - idct(pSrc_ptr, pDst_ptr, m_mcu_block_max_zag[mcu_block]); - pSrc_ptr += 64; - pDst_ptr += 64; - } - - // Chroma IDCT, with upsampling - jpgd_block_t temp_block[64]; - - for (int i = 0; i < 2; i++) - { - DCT_Upsample::Matrix44 P, Q, R, S; - - JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] >= 1); - JPGD_ASSERT(m_mcu_block_max_zag[mcu_block] <= 64); - - switch (s_max_rc[m_mcu_block_max_zag[mcu_block++] - 1]) - { - case 1*16+1: - DCT_Upsample::P_Q<1, 1>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<1, 1>::calc(R, S, pSrc_ptr); - break; - case 1*16+2: - DCT_Upsample::P_Q<1, 2>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<1, 2>::calc(R, S, pSrc_ptr); - break; - case 2*16+2: - DCT_Upsample::P_Q<2, 2>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<2, 2>::calc(R, S, pSrc_ptr); - break; - case 3*16+2: - DCT_Upsample::P_Q<3, 2>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<3, 2>::calc(R, S, pSrc_ptr); - break; - case 3*16+3: - DCT_Upsample::P_Q<3, 3>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<3, 3>::calc(R, S, pSrc_ptr); - break; - case 3*16+4: - DCT_Upsample::P_Q<3, 4>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<3, 4>::calc(R, S, pSrc_ptr); - break; - case 4*16+4: - DCT_Upsample::P_Q<4, 4>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<4, 4>::calc(R, S, pSrc_ptr); - break; - case 5*16+4: - DCT_Upsample::P_Q<5, 4>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<5, 4>::calc(R, S, pSrc_ptr); - break; - case 5*16+5: - DCT_Upsample::P_Q<5, 5>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<5, 5>::calc(R, S, pSrc_ptr); - break; - case 5*16+6: - DCT_Upsample::P_Q<5, 6>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<5, 6>::calc(R, S, pSrc_ptr); - break; - case 6*16+6: - DCT_Upsample::P_Q<6, 6>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<6, 6>::calc(R, S, pSrc_ptr); - break; - case 7*16+6: - DCT_Upsample::P_Q<7, 6>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<7, 6>::calc(R, S, pSrc_ptr); - break; - case 7*16+7: - DCT_Upsample::P_Q<7, 7>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<7, 7>::calc(R, S, pSrc_ptr); - break; - case 7*16+8: - DCT_Upsample::P_Q<7, 8>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<7, 8>::calc(R, S, pSrc_ptr); - break; - case 8*16+8: - DCT_Upsample::P_Q<8, 8>::calc(P, Q, pSrc_ptr); - DCT_Upsample::R_S<8, 8>::calc(R, S, pSrc_ptr); - break; - default: - JPGD_ASSERT(false); - } - - DCT_Upsample::Matrix44 a(P + Q); P -= Q; - DCT_Upsample::Matrix44& b = P; - DCT_Upsample::Matrix44 c(R + S); R -= S; - DCT_Upsample::Matrix44& d = R; - - DCT_Upsample::Matrix44::add_and_store(temp_block, a, c); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - DCT_Upsample::Matrix44::sub_and_store(temp_block, a, c); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - DCT_Upsample::Matrix44::add_and_store(temp_block, b, d); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - DCT_Upsample::Matrix44::sub_and_store(temp_block, b, d); - idct_4x4(temp_block, pDst_ptr); - pDst_ptr += 64; - - pSrc_ptr += 64; - } - } - - // Loads and dequantizes the next row of (already decoded) coefficients. - // Progressive images only. - void jpeg_decoder::load_next_row() - { - int i; - jpgd_block_t *p; - jpgd_quant_t *q; - int mcu_row, mcu_block, row_block = 0; - int component_num, component_id; - int block_x_mcu[JPGD_MAX_COMPONENTS]; - - memset(block_x_mcu, 0, JPGD_MAX_COMPONENTS * sizeof(int)); - - for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++) - { - int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0; - - for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++) - { - component_id = m_mcu_org[mcu_block]; - q = m_quant[m_comp_quant[component_id]]; - - p = m_pMCU_coefficients + 64 * mcu_block; - - jpgd_block_t* pAC = coeff_buf_getp(m_ac_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs); - jpgd_block_t* pDC = coeff_buf_getp(m_dc_coeffs[component_id], block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs); - p[0] = pDC[0]; - memcpy(&p[1], &pAC[1], 63 * sizeof(jpgd_block_t)); - - for (i = 63; i > 0; i--) - if (p[g_ZAG[i]]) - break; - - m_mcu_block_max_zag[mcu_block] = i + 1; - - for ( ; i >= 0; i--) - if (p[g_ZAG[i]]) - p[g_ZAG[i]] = static_cast(p[g_ZAG[i]] * q[i]); - - row_block++; - - if (m_comps_in_scan == 1) - block_x_mcu[component_id]++; - else - { - if (++block_x_mcu_ofs == m_comp_h_samp[component_id]) - { - block_x_mcu_ofs = 0; - - if (++block_y_mcu_ofs == m_comp_v_samp[component_id]) - { - block_y_mcu_ofs = 0; - - block_x_mcu[component_id] += m_comp_h_samp[component_id]; - } - } - } - } - - if (m_freq_domain_chroma_upsample) - transform_mcu_expand(mcu_row); - else - transform_mcu(mcu_row); - } - - if (m_comps_in_scan == 1) - m_block_y_mcu[m_comp_list[0]]++; - else - { - for (component_num = 0; component_num < m_comps_in_scan; component_num++) - { - component_id = m_comp_list[component_num]; - - m_block_y_mcu[component_id] += m_comp_v_samp[component_id]; - } - } - } - - // Restart interval processing. - void jpeg_decoder::process_restart() - { - int i; - int c = 0; - - // Align to a byte boundry - // FIXME: Is this really necessary? get_bits_no_markers() never reads in markers! - //get_bits_no_markers(m_bits_left & 7); - - // Let's scan a little bit to find the marker, but not _too_ far. - // 1536 is a "fudge factor" that determines how much to scan. - for (i = 1536; i > 0; i--) - if (get_char() == 0xFF) - break; - - if (i == 0) - stop_decoding(JPGD_BAD_RESTART_MARKER); - - for ( ; i > 0; i--) - if ((c = get_char()) != 0xFF) - break; - - if (i == 0) - stop_decoding(JPGD_BAD_RESTART_MARKER); - - // Is it the expected marker? If not, something bad happened. - if (c != (m_next_restart_num + M_RST0)) - stop_decoding(JPGD_BAD_RESTART_MARKER); - - // Reset each component's DC prediction values. - memset(&m_last_dc_val, 0, m_comps_in_frame * sizeof(uint)); - - m_eob_run = 0; - - m_restarts_left = m_restart_interval; - - m_next_restart_num = (m_next_restart_num + 1) & 7; - - // Get the bit buffer going again... - - m_bits_left = 16; - get_bits_no_markers(16); - get_bits_no_markers(16); - } - - static inline int dequantize_ac(int c, int q) { c *= q; return c; } - - // Decodes and dequantizes the next row of coefficients. - void jpeg_decoder::decode_next_row() - { - int row_block = 0; - - for (int mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++) - { - if ((m_restart_interval) && (m_restarts_left == 0)) - process_restart(); - - jpgd_block_t* p = m_pMCU_coefficients; - for (int mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++, p += 64) - { - int component_id = m_mcu_org[mcu_block]; - jpgd_quant_t* q = m_quant[m_comp_quant[component_id]]; - - int r, s; - s = huff_decode(m_pHuff_tabs[m_comp_dc_tab[component_id]], r); - s = HUFF_EXTEND(r, s); - - m_last_dc_val[component_id] = (s += m_last_dc_val[component_id]); - - p[0] = static_cast(s * q[0]); - - int prev_num_set = m_mcu_block_max_zag[mcu_block]; - - huff_tables *pH = m_pHuff_tabs[m_comp_ac_tab[component_id]]; - - int k; - for (k = 1; k < 64; k++) - { - int extra_bits; - s = huff_decode(pH, extra_bits); - - r = s >> 4; - s &= 15; - - if (s) - { - if (r) - { - if ((k + r) > 63) - stop_decoding(JPGD_DECODE_ERROR); - - if (k < prev_num_set) - { - int n = JPGD_MIN(r, prev_num_set - k); - int kt = k; - while (n--) - p[g_ZAG[kt++]] = 0; - } - - k += r; - } - - s = HUFF_EXTEND(extra_bits, s); - - JPGD_ASSERT(k < 64); - - p[g_ZAG[k]] = static_cast(dequantize_ac(s, q[k])); //s * q[k]; - } - else - { - if (r == 15) - { - if ((k + 16) > 64) - stop_decoding(JPGD_DECODE_ERROR); - - if (k < prev_num_set) - { - int n = JPGD_MIN(16, prev_num_set - k); - int kt = k; - while (n--) - { - JPGD_ASSERT(kt <= 63); - p[g_ZAG[kt++]] = 0; - } - } - - k += 16 - 1; // - 1 because the loop counter is k - // BEGIN EPIC MOD - JPGD_ASSERT(k < 64 && p[g_ZAG[k]] == 0); - // END EPIC MOD - } - else - break; - } - } - - if (k < prev_num_set) - { - int kt = k; - while (kt < prev_num_set) - p[g_ZAG[kt++]] = 0; - } - - m_mcu_block_max_zag[mcu_block] = k; - - row_block++; - } - - if (m_freq_domain_chroma_upsample) - transform_mcu_expand(mcu_row); - else - transform_mcu(mcu_row); - - m_restarts_left--; - } - } - - // YCbCr H1V1 (1x1:1:1, 3 m_blocks per MCU) to RGB - void jpeg_decoder::H1V1Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d = m_pScan_line_0; - uint8 *s = m_pSample_buf + row * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int j = 0; j < 8; j++) - { - int y = s[j]; - int cb = s[64+j]; - int cr = s[128+j]; - - if (jpg_format == ERGBFormatJPG::BGRA) - { - d[0] = clamp(y + m_cbb[cb]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_crr[cr]); - d[3] = 255; - } - else - { - d[0] = clamp(y + m_crr[cr]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_cbb[cb]); - d[3] = 255; - } - d += 4; - } - - s += 64*3; - } - } - - // YCbCr H2V1 (2x1:1:1, 4 m_blocks per MCU) to RGB - void jpeg_decoder::H2V1Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d0 = m_pScan_line_0; - uint8 *y = m_pSample_buf + row * 8; - uint8 *c = m_pSample_buf + 2*64 + row * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int l = 0; l < 2; l++) - { - for (int j = 0; j < 4; j++) - { - int cb = c[0]; - int cr = c[64]; - - int rc = m_crr[cr]; - int gc = ((m_crg[cr] + m_cbg[cb]) >> 16); - int bc = m_cbb[cb]; - - int yy = y[j<<1]; - if (jpg_format == ERGBFormatJPG::BGRA) - { - d0[0] = clamp(yy+bc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+rc); - d0[3] = 255; - yy = y[(j<<1)+1]; - d0[4] = clamp(yy+bc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+rc); - d0[7] = 255; - } - else - { - d0[0] = clamp(yy+rc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+bc); - d0[3] = 255; - yy = y[(j<<1)+1]; - d0[4] = clamp(yy+rc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+bc); - d0[7] = 255; - } - - d0 += 8; - - c++; - } - y += 64; - } - - y += 64*4 - 64*2; - c += 64*4 - 8; - } - } - - // YCbCr H2V1 (1x2:1:1, 4 m_blocks per MCU) to RGB - void jpeg_decoder::H1V2Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d0 = m_pScan_line_0; - uint8 *d1 = m_pScan_line_1; - uint8 *y; - uint8 *c; - - if (row < 8) - y = m_pSample_buf + row * 8; - else - y = m_pSample_buf + 64*1 + (row & 7) * 8; - - c = m_pSample_buf + 64*2 + (row >> 1) * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int j = 0; j < 8; j++) - { - int cb = c[0+j]; - int cr = c[64+j]; - - int rc = m_crr[cr]; - int gc = ((m_crg[cr] + m_cbg[cb]) >> 16); - int bc = m_cbb[cb]; - - int yy = y[j]; - if (jpg_format == ERGBFormatJPG::BGRA) - { - d0[0] = clamp(yy+bc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+rc); - d0[3] = 255; - yy = y[8+j]; - d1[0] = clamp(yy+bc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+rc); - d1[3] = 255; - } - else - { - d0[0] = clamp(yy+rc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+bc); - d0[3] = 255; - yy = y[8+j]; - d1[0] = clamp(yy+rc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+bc); - d1[3] = 255; - } - - d0 += 4; - d1 += 4; - } - - y += 64*4; - c += 64*4; - } - } - - // YCbCr H2V2 (2x2:1:1, 6 m_blocks per MCU) to RGB - void jpeg_decoder::H2V2Convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d0 = m_pScan_line_0; - uint8 *d1 = m_pScan_line_1; - uint8 *y; - uint8 *c; - - if (row < 8) - y = m_pSample_buf + row * 8; - else - y = m_pSample_buf + 64*2 + (row & 7) * 8; - - c = m_pSample_buf + 64*4 + (row >> 1) * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int l = 0; l < 2; l++) - { - for (int j = 0; j < 8; j += 2) - { - int cb = c[0]; - int cr = c[64]; - - int rc = m_crr[cr]; - int gc = ((m_crg[cr] + m_cbg[cb]) >> 16); - int bc = m_cbb[cb]; - - int yy = y[j]; - if (jpg_format == ERGBFormatJPG::BGRA) - { - d0[0] = clamp(yy+bc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+rc); - d0[3] = 255; - yy = y[j+1]; - d0[4] = clamp(yy+bc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+rc); - d0[7] = 255; - yy = y[j+8]; - d1[0] = clamp(yy+bc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+rc); - d1[3] = 255; - yy = y[j+8+1]; - d1[4] = clamp(yy+bc); - d1[5] = clamp(yy+gc); - d1[6] = clamp(yy+rc); - d1[7] = 255; - } - else - { - d0[0] = clamp(yy+rc); - d0[1] = clamp(yy+gc); - d0[2] = clamp(yy+bc); - d0[3] = 255; - yy = y[j+1]; - d0[4] = clamp(yy+rc); - d0[5] = clamp(yy+gc); - d0[6] = clamp(yy+bc); - d0[7] = 255; - yy = y[j+8]; - d1[0] = clamp(yy+rc); - d1[1] = clamp(yy+gc); - d1[2] = clamp(yy+bc); - d1[3] = 255; - yy = y[j+8+1]; - d1[4] = clamp(yy+rc); - d1[5] = clamp(yy+gc); - d1[6] = clamp(yy+bc); - d1[7] = 255; - } - - d0 += 8; - d1 += 8; - - c++; - } - y += 64; - } - - y += 64*6 - 64*2; - c += 64*6 - 8; - } - } - - // Y (1 block per MCU) to 8-bit grayscale - void jpeg_decoder::gray_convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - uint8 *d = m_pScan_line_0; - uint8 *s = m_pSample_buf + row * 8; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - *(uint *)d = *(uint *)s; - *(uint *)(&d[4]) = *(uint *)(&s[4]); - - s += 64; - d += 8; - } - } - - void jpeg_decoder::expanded_convert() - { - int row = m_max_mcu_y_size - m_mcu_lines_left; - - uint8* Py = m_pSample_buf + (row / 8) * 64 * m_comp_h_samp[0] + (row & 7) * 8; - - uint8* d = m_pScan_line_0; - - for (int i = m_max_mcus_per_row; i > 0; i--) - { - for (int k = 0; k < m_max_mcu_x_size; k += 8) - { - const int Y_ofs = k * 8; - const int Cb_ofs = Y_ofs + 64 * m_expanded_blocks_per_component; - const int Cr_ofs = Y_ofs + 64 * m_expanded_blocks_per_component * 2; - for (int j = 0; j < 8; j++) - { - int y = Py[Y_ofs + j]; - int cb = Py[Cb_ofs + j]; - int cr = Py[Cr_ofs + j]; - - if (jpg_format == ERGBFormatJPG::BGRA) - { - d[0] = clamp(y + m_cbb[cb]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_crr[cr]); - d[3] = 255; - } - else - { - d[0] = clamp(y + m_crr[cr]); - d[1] = clamp(y + ((m_crg[cr] + m_cbg[cb]) >> 16)); - d[2] = clamp(y + m_cbb[cb]); - d[3] = 255; - } - - d += 4; - } - } - - Py += 64 * m_expanded_blocks_per_mcu; - } - } - - // Find end of image (EOI) marker, so we can return to the user the exact size of the input stream. - void jpeg_decoder::find_eoi() - { - if (!m_progressive_flag) - { - // Attempt to read the EOI marker. - //get_bits_no_markers(m_bits_left & 7); - - // Prime the bit buffer - m_bits_left = 16; - get_bits(16); - get_bits(16); - - // The next marker _should_ be EOI - process_markers(); - } - - m_total_bytes_read -= m_in_buf_left; - } - - int jpeg_decoder::decode(const void** pScan_line, uint* pScan_line_len) - { - if ((m_error_code) || (!m_ready_flag)) - return JPGD_FAILED; - - if (m_total_lines_left == 0) - return JPGD_DONE; - - if (m_mcu_lines_left == 0) - { - if (setjmp(m_jmp_state)) - return JPGD_FAILED; - - if (m_progressive_flag) - load_next_row(); - else - decode_next_row(); - - // Find the EOI marker if that was the last row. - if (m_total_lines_left <= m_max_mcu_y_size) - find_eoi(); - - m_mcu_lines_left = m_max_mcu_y_size; - } - - if (m_freq_domain_chroma_upsample) - { - expanded_convert(); - *pScan_line = m_pScan_line_0; - } - else - { - switch (m_scan_type) - { - case JPGD_YH2V2: - { - if ((m_mcu_lines_left & 1) == 0) - { - H2V2Convert(); - *pScan_line = m_pScan_line_0; - } - else - *pScan_line = m_pScan_line_1; - - break; - } - case JPGD_YH2V1: - { - H2V1Convert(); - *pScan_line = m_pScan_line_0; - break; - } - case JPGD_YH1V2: - { - if ((m_mcu_lines_left & 1) == 0) - { - H1V2Convert(); - *pScan_line = m_pScan_line_0; - } - else - *pScan_line = m_pScan_line_1; - - break; - } - case JPGD_YH1V1: - { - H1V1Convert(); - *pScan_line = m_pScan_line_0; - break; - } - case JPGD_GRAYSCALE: - { - gray_convert(); - *pScan_line = m_pScan_line_0; - - break; - } - } - } - - *pScan_line_len = m_real_dest_bytes_per_scan_line; - - m_mcu_lines_left--; - m_total_lines_left--; - - return JPGD_SUCCESS; - } - - // Creates the tables needed for efficient Huffman decoding. - void jpeg_decoder::make_huff_table(int index, huff_tables *pH) - { - int p, i, l, si; - uint8 huffsize[257]; - uint huffcode[257]; - uint code; - uint subtree; - int code_size; - int lastp; - int nextfreeentry; - int currententry; - - pH->ac_table = m_huff_ac[index] != 0; - - p = 0; - - for (l = 1; l <= 16; l++) - { - for (i = 1; i <= m_huff_num[index][l]; i++) - huffsize[p++] = static_cast(l); - } - - huffsize[p] = 0; - - lastp = p; - - code = 0; - si = huffsize[0]; - p = 0; - - while (huffsize[p]) - { - while (huffsize[p] == si) - { - huffcode[p++] = code; - code++; - } - - code <<= 1; - si++; - } - - memset(pH->look_up, 0, sizeof(pH->look_up)); - memset(pH->look_up2, 0, sizeof(pH->look_up2)); - memset(pH->tree, 0, sizeof(pH->tree)); - memset(pH->code_size, 0, sizeof(pH->code_size)); - - nextfreeentry = -1; - - p = 0; - - while (p < lastp) - { - i = m_huff_val[index][p]; - code = huffcode[p]; - code_size = huffsize[p]; - - pH->code_size[i] = static_cast(code_size); - - if (code_size <= 8) - { - code <<= (8 - code_size); - - for (l = 1 << (8 - code_size); l > 0; l--) - { - JPGD_ASSERT(i < 256); - - pH->look_up[code] = i; - - bool has_extrabits = false; - int extra_bits = 0; - int num_extra_bits = i & 15; - - int bits_to_fetch = code_size; - if (num_extra_bits) - { - int total_codesize = code_size + num_extra_bits; - if (total_codesize <= 8) - { - has_extrabits = true; - extra_bits = ((1 << num_extra_bits) - 1) & (code >> (8 - total_codesize)); - JPGD_ASSERT(extra_bits <= 0x7FFF); - bits_to_fetch += num_extra_bits; - } - } - - if (!has_extrabits) - pH->look_up2[code] = i | (bits_to_fetch << 8); - else - pH->look_up2[code] = i | 0x8000 | (extra_bits << 16) | (bits_to_fetch << 8); - - code++; - } - } - else - { - subtree = (code >> (code_size - 8)) & 0xFF; - - currententry = pH->look_up[subtree]; - - if (currententry == 0) - { - pH->look_up[subtree] = currententry = nextfreeentry; - pH->look_up2[subtree] = currententry = nextfreeentry; - - nextfreeentry -= 2; - } - - code <<= (16 - (code_size - 8)); - - for (l = code_size; l > 9; l--) - { - if ((code & 0x8000) == 0) - currententry--; - - if (pH->tree[-currententry - 1] == 0) - { - pH->tree[-currententry - 1] = nextfreeentry; - - currententry = nextfreeentry; - - nextfreeentry -= 2; - } - else - currententry = pH->tree[-currententry - 1]; - - code <<= 1; - } - - if ((code & 0x8000) == 0) - currententry--; - - pH->tree[-currententry - 1] = i; - } - - p++; - } - } - - // Verifies the quantization tables needed for this scan are available. - void jpeg_decoder::check_quant_tables() - { - for (int i = 0; i < m_comps_in_scan; i++) - if (m_quant[m_comp_quant[m_comp_list[i]]] == NULL) - stop_decoding(JPGD_UNDEFINED_QUANT_TABLE); - } - - // Verifies that all the Huffman tables needed for this scan are available. - void jpeg_decoder::check_huff_tables() - { - for (int i = 0; i < m_comps_in_scan; i++) - { - if ((m_spectral_start == 0) && (m_huff_num[m_comp_dc_tab[m_comp_list[i]]] == NULL)) - stop_decoding(JPGD_UNDEFINED_HUFF_TABLE); - - if ((m_spectral_end > 0) && (m_huff_num[m_comp_ac_tab[m_comp_list[i]]] == NULL)) - stop_decoding(JPGD_UNDEFINED_HUFF_TABLE); - } - - for (int i = 0; i < JPGD_MAX_HUFF_TABLES; i++) - if (m_huff_num[i]) - { - if (!m_pHuff_tabs[i]) - m_pHuff_tabs[i] = (huff_tables *)alloc(sizeof(huff_tables)); - - make_huff_table(i, m_pHuff_tabs[i]); - } - } - - // Determines the component order inside each MCU. - // Also calcs how many MCU's are on each row, etc. - void jpeg_decoder::calc_mcu_block_order() - { - int component_num, component_id; - int max_h_samp = 0, max_v_samp = 0; - - for (component_id = 0; component_id < m_comps_in_frame; component_id++) - { - if (m_comp_h_samp[component_id] > max_h_samp) - max_h_samp = m_comp_h_samp[component_id]; - - if (m_comp_v_samp[component_id] > max_v_samp) - max_v_samp = m_comp_v_samp[component_id]; - } - - for (component_id = 0; component_id < m_comps_in_frame; component_id++) - { - m_comp_h_blocks[component_id] = ((((m_image_x_size * m_comp_h_samp[component_id]) + (max_h_samp - 1)) / max_h_samp) + 7) / 8; - m_comp_v_blocks[component_id] = ((((m_image_y_size * m_comp_v_samp[component_id]) + (max_v_samp - 1)) / max_v_samp) + 7) / 8; - } - - if (m_comps_in_scan == 1) - { - m_mcus_per_row = m_comp_h_blocks[m_comp_list[0]]; - m_mcus_per_col = m_comp_v_blocks[m_comp_list[0]]; - } - else - { - m_mcus_per_row = (((m_image_x_size + 7) / 8) + (max_h_samp - 1)) / max_h_samp; - m_mcus_per_col = (((m_image_y_size + 7) / 8) + (max_v_samp - 1)) / max_v_samp; - } - - if (m_comps_in_scan == 1) - { - m_mcu_org[0] = m_comp_list[0]; - - m_blocks_per_mcu = 1; - } - else - { - m_blocks_per_mcu = 0; - - for (component_num = 0; component_num < m_comps_in_scan; component_num++) - { - int num_blocks; - - component_id = m_comp_list[component_num]; - - num_blocks = m_comp_h_samp[component_id] * m_comp_v_samp[component_id]; - - while (num_blocks--) - m_mcu_org[m_blocks_per_mcu++] = component_id; - } - } - } - - // Starts a new scan. - int jpeg_decoder::init_scan() - { - if (!locate_sos_marker()) - return JPGD_FALSE; - - calc_mcu_block_order(); - - check_huff_tables(); - - check_quant_tables(); - - memset(m_last_dc_val, 0, m_comps_in_frame * sizeof(uint)); - - m_eob_run = 0; - - if (m_restart_interval) - { - m_restarts_left = m_restart_interval; - m_next_restart_num = 0; - } - - fix_in_buffer(); - - return JPGD_TRUE; - } - - // Starts a frame. Determines if the number of components or sampling factors - // are supported. - void jpeg_decoder::init_frame() - { - int i; - - if (m_comps_in_frame == 1) - { - if ((m_comp_h_samp[0] != 1) || (m_comp_v_samp[0] != 1)) - stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS); - - m_scan_type = JPGD_GRAYSCALE; - m_max_blocks_per_mcu = 1; - m_max_mcu_x_size = 8; - m_max_mcu_y_size = 8; - } - else if (m_comps_in_frame == 3) - { - if ( ((m_comp_h_samp[1] != 1) || (m_comp_v_samp[1] != 1)) || - ((m_comp_h_samp[2] != 1) || (m_comp_v_samp[2] != 1)) ) - stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS); - - if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 1)) - { - m_scan_type = JPGD_YH1V1; - - m_max_blocks_per_mcu = 3; - m_max_mcu_x_size = 8; - m_max_mcu_y_size = 8; - } - else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 1)) - { - m_scan_type = JPGD_YH2V1; - m_max_blocks_per_mcu = 4; - m_max_mcu_x_size = 16; - m_max_mcu_y_size = 8; - } - else if ((m_comp_h_samp[0] == 1) && (m_comp_v_samp[0] == 2)) - { - m_scan_type = JPGD_YH1V2; - m_max_blocks_per_mcu = 4; - m_max_mcu_x_size = 8; - m_max_mcu_y_size = 16; - } - else if ((m_comp_h_samp[0] == 2) && (m_comp_v_samp[0] == 2)) - { - m_scan_type = JPGD_YH2V2; - m_max_blocks_per_mcu = 6; - m_max_mcu_x_size = 16; - m_max_mcu_y_size = 16; - } - else - stop_decoding(JPGD_UNSUPPORTED_SAMP_FACTORS); - } - else - stop_decoding(JPGD_UNSUPPORTED_COLORSPACE); - - m_max_mcus_per_row = (m_image_x_size + (m_max_mcu_x_size - 1)) / m_max_mcu_x_size; - m_max_mcus_per_col = (m_image_y_size + (m_max_mcu_y_size - 1)) / m_max_mcu_y_size; - - // These values are for the *destination* pixels: after conversion. - if (m_scan_type == JPGD_GRAYSCALE) - m_dest_bytes_per_pixel = 1; - else - m_dest_bytes_per_pixel = 4; - - m_dest_bytes_per_scan_line = ((m_image_x_size + 15) & 0xFFF0) * m_dest_bytes_per_pixel; - - m_real_dest_bytes_per_scan_line = (m_image_x_size * m_dest_bytes_per_pixel); - - // Initialize two scan line buffers. - m_pScan_line_0 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true); - if ((m_scan_type == JPGD_YH1V2) || (m_scan_type == JPGD_YH2V2)) - m_pScan_line_1 = (uint8 *)alloc(m_dest_bytes_per_scan_line, true); - - m_max_blocks_per_row = m_max_mcus_per_row * m_max_blocks_per_mcu; - - // Should never happen - if (m_max_blocks_per_row > JPGD_MAX_BLOCKS_PER_ROW) - stop_decoding(JPGD_ASSERTION_ERROR); - - // Allocate the coefficient buffer, enough for one MCU - m_pMCU_coefficients = (jpgd_block_t*)alloc(m_max_blocks_per_mcu * 64 * sizeof(jpgd_block_t)); - - for (i = 0; i < m_max_blocks_per_mcu; i++) - m_mcu_block_max_zag[i] = 64; - - m_expanded_blocks_per_component = m_comp_h_samp[0] * m_comp_v_samp[0]; - m_expanded_blocks_per_mcu = m_expanded_blocks_per_component * m_comps_in_frame; - m_expanded_blocks_per_row = m_max_mcus_per_row * m_expanded_blocks_per_mcu; - // Freq. domain chroma upsampling is only supported for H2V2 subsampling factor. -// BEGIN EPIC MOD -#if JPGD_SUPPORT_FREQ_DOMAIN_UPSAMPLING - m_freq_domain_chroma_upsample = (m_expanded_blocks_per_mcu == 4*3); -#else - m_freq_domain_chroma_upsample = 0; -#endif -// END EPIC MOD - - if (m_freq_domain_chroma_upsample) - m_pSample_buf = (uint8 *)alloc(m_expanded_blocks_per_row * 64); - else - m_pSample_buf = (uint8 *)alloc(m_max_blocks_per_row * 64); - - m_total_lines_left = m_image_y_size; - - m_mcu_lines_left = 0; - - create_look_ups(); - } - - // The coeff_buf series of methods originally stored the coefficients - // into a "virtual" file which was located in EMS, XMS, or a disk file. A cache - // was used to make this process more efficient. Now, we can store the entire - // thing in RAM. - jpeg_decoder::coeff_buf* jpeg_decoder::coeff_buf_open(int block_num_x, int block_num_y, int block_len_x, int block_len_y) - { - coeff_buf* cb = (coeff_buf*)alloc(sizeof(coeff_buf)); - - cb->block_num_x = block_num_x; - cb->block_num_y = block_num_y; - cb->block_len_x = block_len_x; - cb->block_len_y = block_len_y; - cb->block_size = (block_len_x * block_len_y) * sizeof(jpgd_block_t); - cb->pData = (uint8 *)alloc(cb->block_size * block_num_x * block_num_y, true); - return cb; - } - - inline jpgd_block_t *jpeg_decoder::coeff_buf_getp(coeff_buf *cb, int block_x, int block_y) - { - JPGD_ASSERT((block_x < cb->block_num_x) && (block_y < cb->block_num_y)); - return (jpgd_block_t *)(cb->pData + block_x * cb->block_size + block_y * (cb->block_size * cb->block_num_x)); - } - - // The following methods decode the various types of m_blocks encountered - // in progressively encoded images. - void jpeg_decoder::decode_block_dc_first(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - int s, r; - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y); - - if ((s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_dc_tab[component_id]])) != 0) - { - r = pD->get_bits_no_markers(s); - s = HUFF_EXTEND(r, s); - } - - pD->m_last_dc_val[component_id] = (s += pD->m_last_dc_val[component_id]); - - p[0] = static_cast(s << pD->m_successive_low); - } - - void jpeg_decoder::decode_block_dc_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - if (pD->get_bits_no_markers(1)) - { - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_dc_coeffs[component_id], block_x, block_y); - - p[0] |= (1 << pD->m_successive_low); - } - } - - void jpeg_decoder::decode_block_ac_first(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - int k, s, r; - - if (pD->m_eob_run) - { - pD->m_eob_run--; - return; - } - - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y); - - for (k = pD->m_spectral_start; k <= pD->m_spectral_end; k++) - { - s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]); - - r = s >> 4; - s &= 15; - - if (s) - { - if ((k += r) > 63) - pD->stop_decoding(JPGD_DECODE_ERROR); - - r = pD->get_bits_no_markers(s); - s = HUFF_EXTEND(r, s); - - p[g_ZAG[k]] = static_cast(s << pD->m_successive_low); - } - else - { - if (r == 15) - { - if ((k += 15) > 63) - pD->stop_decoding(JPGD_DECODE_ERROR); - } - else - { - pD->m_eob_run = 1 << r; - - if (r) - pD->m_eob_run += pD->get_bits_no_markers(r); - - pD->m_eob_run--; - - break; - } - } - } - } - - void jpeg_decoder::decode_block_ac_refine(jpeg_decoder *pD, int component_id, int block_x, int block_y) - { - int s, k, r; - int p1 = 1 << pD->m_successive_low; - int m1 = (-1) << pD->m_successive_low; - jpgd_block_t *p = pD->coeff_buf_getp(pD->m_ac_coeffs[component_id], block_x, block_y); - - k = pD->m_spectral_start; - - if (pD->m_eob_run == 0) - { - for ( ; k <= pD->m_spectral_end; k++) - { - s = pD->huff_decode(pD->m_pHuff_tabs[pD->m_comp_ac_tab[component_id]]); - - r = s >> 4; - s &= 15; - - if (s) - { - if (s != 1) - pD->stop_decoding(JPGD_DECODE_ERROR); - - if (pD->get_bits_no_markers(1)) - s = p1; - else - s = m1; - } - else - { - if (r != 15) - { - pD->m_eob_run = 1 << r; - - if (r) - pD->m_eob_run += pD->get_bits_no_markers(r); - - break; - } - } - - do - { - // BEGIN EPIC MOD - JPGD_ASSERT(k < 64); - // END EPIC MOD - - jpgd_block_t *this_coef = p + g_ZAG[k]; - - if (*this_coef != 0) - { - if (pD->get_bits_no_markers(1)) - { - if ((*this_coef & p1) == 0) - { - if (*this_coef >= 0) - *this_coef = static_cast(*this_coef + p1); - else - *this_coef = static_cast(*this_coef + m1); - } - } - } - else - { - if (--r < 0) - break; - } - - k++; - - } while (k <= pD->m_spectral_end); - - if ((s) && (k < 64)) - { - p[g_ZAG[k]] = static_cast(s); - } - } - } - - if (pD->m_eob_run > 0) - { - for ( ; k <= pD->m_spectral_end; k++) - { - // BEGIN EPIC MOD - JPGD_ASSERT(k < 64); - // END EPIC MOD - - jpgd_block_t *this_coef = p + g_ZAG[k]; - - if (*this_coef != 0) - { - if (pD->get_bits_no_markers(1)) - { - if ((*this_coef & p1) == 0) - { - if (*this_coef >= 0) - *this_coef = static_cast(*this_coef + p1); - else - *this_coef = static_cast(*this_coef + m1); - } - } - } - } - - pD->m_eob_run--; - } - } - - // Decode a scan in a progressively encoded image. - void jpeg_decoder::decode_scan(pDecode_block_func decode_block_func) - { - int mcu_row, mcu_col, mcu_block; - int block_x_mcu[JPGD_MAX_COMPONENTS], m_block_y_mcu[JPGD_MAX_COMPONENTS]; - - memset(m_block_y_mcu, 0, sizeof(m_block_y_mcu)); - - for (mcu_col = 0; mcu_col < m_mcus_per_col; mcu_col++) - { - int component_num, component_id; - - memset(block_x_mcu, 0, sizeof(block_x_mcu)); - - for (mcu_row = 0; mcu_row < m_mcus_per_row; mcu_row++) - { - int block_x_mcu_ofs = 0, block_y_mcu_ofs = 0; - - if ((m_restart_interval) && (m_restarts_left == 0)) - process_restart(); - - for (mcu_block = 0; mcu_block < m_blocks_per_mcu; mcu_block++) - { - component_id = m_mcu_org[mcu_block]; - - decode_block_func(this, component_id, block_x_mcu[component_id] + block_x_mcu_ofs, m_block_y_mcu[component_id] + block_y_mcu_ofs); - - if (m_comps_in_scan == 1) - block_x_mcu[component_id]++; - else - { - if (++block_x_mcu_ofs == m_comp_h_samp[component_id]) - { - block_x_mcu_ofs = 0; - - if (++block_y_mcu_ofs == m_comp_v_samp[component_id]) - { - block_y_mcu_ofs = 0; - block_x_mcu[component_id] += m_comp_h_samp[component_id]; - } - } - } - } - - m_restarts_left--; - } - - if (m_comps_in_scan == 1) - m_block_y_mcu[m_comp_list[0]]++; - else - { - for (component_num = 0; component_num < m_comps_in_scan; component_num++) - { - component_id = m_comp_list[component_num]; - m_block_y_mcu[component_id] += m_comp_v_samp[component_id]; - } - } - } - } - - // Decode a progressively encoded image. - void jpeg_decoder::init_progressive() - { - int i; - - if (m_comps_in_frame == 4) - stop_decoding(JPGD_UNSUPPORTED_COLORSPACE); - - // Allocate the coefficient buffers. - for (i = 0; i < m_comps_in_frame; i++) - { - m_dc_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 1, 1); - m_ac_coeffs[i] = coeff_buf_open(m_max_mcus_per_row * m_comp_h_samp[i], m_max_mcus_per_col * m_comp_v_samp[i], 8, 8); - } - - for ( ; ; ) - { - int dc_only_scan, refinement_scan; - pDecode_block_func decode_block_func; - - if (!init_scan()) - break; - - dc_only_scan = (m_spectral_start == 0); - refinement_scan = (m_successive_high != 0); - - if ((m_spectral_start > m_spectral_end) || (m_spectral_end > 63)) - stop_decoding(JPGD_BAD_SOS_SPECTRAL); - - if (dc_only_scan) - { - if (m_spectral_end) - stop_decoding(JPGD_BAD_SOS_SPECTRAL); - } - else if (m_comps_in_scan != 1) /* AC scans can only contain one component */ - stop_decoding(JPGD_BAD_SOS_SPECTRAL); - - if ((refinement_scan) && (m_successive_low != m_successive_high - 1)) - stop_decoding(JPGD_BAD_SOS_SUCCESSIVE); - - if (dc_only_scan) - { - if (refinement_scan) - decode_block_func = decode_block_dc_refine; - else - decode_block_func = decode_block_dc_first; - } - else - { - if (refinement_scan) - decode_block_func = decode_block_ac_refine; - else - decode_block_func = decode_block_ac_first; - } - - decode_scan(decode_block_func); - - m_bits_left = 16; - get_bits(16); - get_bits(16); - } - - m_comps_in_scan = m_comps_in_frame; - - for (i = 0; i < m_comps_in_frame; i++) - m_comp_list[i] = i; - - calc_mcu_block_order(); - } - - void jpeg_decoder::init_sequential() - { - if (!init_scan()) - stop_decoding(JPGD_UNEXPECTED_MARKER); - } - - void jpeg_decoder::decode_start() - { - init_frame(); - - if (m_progressive_flag) - init_progressive(); - else - init_sequential(); - } - - void jpeg_decoder::decode_init(jpeg_decoder_stream *pStream) - { - init(pStream); - locate_sof_marker(); - } - - jpeg_decoder::jpeg_decoder(jpeg_decoder_stream *pStream) - { - if (setjmp(m_jmp_state)) - return; - decode_init(pStream); - } - - int jpeg_decoder::begin_decoding() - { - if (m_ready_flag) - return JPGD_SUCCESS; - - if (m_error_code) - return JPGD_FAILED; - - if (setjmp(m_jmp_state)) - return JPGD_FAILED; - - decode_start(); - - m_ready_flag = true; - - return JPGD_SUCCESS; - } - - jpeg_decoder::~jpeg_decoder() - { - free_all_blocks(); - } - - jpeg_decoder_file_stream::jpeg_decoder_file_stream() - { - m_pFile = NULL; - m_eof_flag = false; - m_error_flag = false; - } - - void jpeg_decoder_file_stream::close() - { - if (m_pFile) - { - fclose(m_pFile); - m_pFile = NULL; - } - - m_eof_flag = false; - m_error_flag = false; - } - - jpeg_decoder_file_stream::~jpeg_decoder_file_stream() - { - close(); - } - - bool jpeg_decoder_file_stream::open(const char *Pfilename) - { - close(); - - m_eof_flag = false; - m_error_flag = false; - -#if defined(_MSC_VER) - m_pFile = NULL; - fopen_s(&m_pFile, Pfilename, "rb"); -#else - m_pFile = fopen(Pfilename, "rb"); -#endif - return m_pFile != NULL; - } - - int jpeg_decoder_file_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) - { - if (!m_pFile) - return -1; - - if (m_eof_flag) - { - *pEOF_flag = true; - return 0; - } - - if (m_error_flag) - return -1; - - int bytes_read = static_cast(fread(pBuf, 1, max_bytes_to_read, m_pFile)); - if (bytes_read < max_bytes_to_read) - { - if (ferror(m_pFile)) - { - m_error_flag = true; - return -1; - } - - m_eof_flag = true; - *pEOF_flag = true; - } - - return bytes_read; - } - - bool jpeg_decoder_mem_stream::open(const uint8 *pSrc_data, uint size) - { - close(); - m_pSrc_data = pSrc_data; - m_ofs = 0; - m_size = size; - return true; - } - - int jpeg_decoder_mem_stream::read(uint8 *pBuf, int max_bytes_to_read, bool *pEOF_flag) - { - *pEOF_flag = false; - - if (!m_pSrc_data) - return -1; - - uint bytes_remaining = m_size - m_ofs; - if ((uint)max_bytes_to_read > bytes_remaining) - { - max_bytes_to_read = bytes_remaining; - *pEOF_flag = true; - } - - memcpy(pBuf, m_pSrc_data + m_ofs, max_bytes_to_read); - m_ofs += max_bytes_to_read; - - return max_bytes_to_read; - } - - unsigned char *decompress_jpeg_image_from_stream(jpeg_decoder_stream *pStream, int *width, int *height, int *actual_comps, int req_comps) - { - if (!actual_comps) - return NULL; - *actual_comps = 0; - - if ((!pStream) || (!width) || (!height) || (!req_comps)) - return NULL; - - if ((req_comps != 1) && (req_comps != 3) && (req_comps != 4)) - return NULL; - - jpeg_decoder decoder(pStream); - if (decoder.get_error_code() != JPGD_SUCCESS) - return NULL; - - const int image_width = decoder.get_width(), image_height = decoder.get_height(); - *width = image_width; - *height = image_height; - *actual_comps = decoder.get_num_components(); - - if (decoder.begin_decoding() != JPGD_SUCCESS) - return NULL; - - const int dst_bpl = image_width * req_comps; - - uint8 *pImage_data = (uint8*)jpgd_malloc(dst_bpl * image_height); - if (!pImage_data) - return NULL; - - for (int y = 0; y < image_height; y++) - { - const uint8* pScan_line = 0; - uint scan_line_len; - if (decoder.decode((const void**)&pScan_line, &scan_line_len) != JPGD_SUCCESS) - { - jpgd_free(pImage_data); - return NULL; - } - - uint8 *pDst = pImage_data + y * dst_bpl; - - if (((req_comps == 4) && (decoder.get_num_components() == 3)) || - ((req_comps == 1) && (decoder.get_num_components() == 1))) - { - memcpy(pDst, pScan_line, dst_bpl); - } - else if (decoder.get_num_components() == 1) - { - if (req_comps == 3) - { - for (int x = 0; x < image_width; x++) - { - uint8 luma = pScan_line[x]; - pDst[0] = luma; - pDst[1] = luma; - pDst[2] = luma; - pDst += 3; - } - } - else - { - for (int x = 0; x < image_width; x++) - { - uint8 luma = pScan_line[x]; - pDst[0] = luma; - pDst[1] = luma; - pDst[2] = luma; - pDst[3] = 255; - pDst += 4; - } - } - } - else if (decoder.get_num_components() == 3) - { - if (req_comps == 1) - { - const int YR = 19595, YG = 38470, YB = 7471; - for (int x = 0; x < image_width; x++) - { - int r = pScan_line[x*4+0]; - int g = pScan_line[x*4+1]; - int b = pScan_line[x*4+2]; - *pDst++ = static_cast((r * YR + g * YG + b * YB + 32768) >> 16); - } - } - else - { - for (int x = 0; x < image_width; x++) - { - pDst[0] = pScan_line[x*4+0]; - pDst[1] = pScan_line[x*4+1]; - pDst[2] = pScan_line[x*4+2]; - pDst += 3; - } - } - } - } - - return pImage_data; - } - -// BEGIN EPIC MOD - unsigned char *decompress_jpeg_image_from_memory(const unsigned char *pSrc_data, int src_data_size, int *width, int *height, int *actual_comps, int req_comps, int format) - { - jpg_format = (ERGBFormatJPG)format; -// EMD EPIC MOD - jpgd::jpeg_decoder_mem_stream mem_stream(pSrc_data, src_data_size); - return decompress_jpeg_image_from_stream(&mem_stream, width, height, actual_comps, req_comps); - } - - unsigned char *decompress_jpeg_image_from_file(const char *pSrc_filename, int *width, int *height, int *actual_comps, int req_comps) - { - jpgd::jpeg_decoder_file_stream file_stream; - if (!file_stream.open(pSrc_filename)) - return NULL; - return decompress_jpeg_image_from_stream(&file_stream, width, height, actual_comps, req_comps); - } - -} // namespace jpgd diff --git "a/spaces/xxccc/gpt-academic/crazy_functions/\346\225\260\345\255\246\345\212\250\347\224\273\347\224\237\346\210\220manim.py" "b/spaces/xxccc/gpt-academic/crazy_functions/\346\225\260\345\255\246\345\212\250\347\224\273\347\224\237\346\210\220manim.py" deleted file mode 100644 index 5851b9c67110ddcdb2ada0bb4d32e4c0154bb272..0000000000000000000000000000000000000000 --- "a/spaces/xxccc/gpt-academic/crazy_functions/\346\225\260\345\255\246\345\212\250\347\224\273\347\224\237\346\210\220manim.py" +++ /dev/null @@ -1,187 +0,0 @@ -from toolbox import CatchException, update_ui, gen_time_str -from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive -from .crazy_utils import input_clipping - -def inspect_dependency(chatbot, history): - # 尝试导入依赖，如果缺少依赖，则给出安装建议 - try: - import manim - return True - except: - chatbot.append(["导入依赖失败", "使用该模块需要额外依赖，安装方法:```pip install manimgl```"]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - return False - -def eval_manim(code): - import subprocess, sys, os, shutil - - with open('gpt_log/MyAnimation.py', 'w', encoding='utf8') as f: - f.write(code) - - def get_class_name(class_string): - import re - # Use regex to extract the class name - class_name = re.search(r'class (\w+)\(', class_string).group(1) - return class_name - - class_name = get_class_name(code) - - try: - subprocess.check_output([sys.executable, '-c', f"from gpt_log.MyAnimation import {class_name}; {class_name}().render()"]) - shutil.move('media/videos/1080p60/{class_name}.mp4', f'gpt_log/{class_name}-{gen_time_str()}.mp4') - return f'gpt_log/{gen_time_str()}.mp4' - except subprocess.CalledProcessError as e: - output = e.output.decode() - print(f"Command returned non-zero exit status {e.returncode}: {output}.") - return f"Evaluating python script failed: {e.output}." - except: - print('generating mp4 failed') - return "Generating mp4 failed." - - -def get_code_block(reply): - import re - pattern = r"```([\s\S]*?)```" # regex pattern to match code blocks - matches = re.findall(pattern, reply) # find all code blocks in text - if len(matches) != 1: - raise RuntimeError("GPT is not generating proper code.") - return matches[0].strip('python') # code block - -@CatchException -def 动画生成(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port): - """ - txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径 - llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行 - plugin_kwargs 插件模型的参数，暂时没有用武之地 - chatbot 聊天显示框的句柄，用于显示给用户 - history 聊天历史，前情提要 - system_prompt 给gpt的静默提醒 - web_port 当前软件运行的端口号 - """ - # 清空历史，以免输入溢出 - history = [] - - # 基本信息：功能、贡献者 - chatbot.append([ - "函数插件功能？", - "生成数学动画, 此插件处于开发阶段, 建议暂时不要使用, 作者: binary-husky, 插件初始化中 ..." - ]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 - - # 尝试导入依赖, 如果缺少依赖, 则给出安装建议 - dep_ok = yield from inspect_dependency(chatbot=chatbot, history=history) # 刷新界面 - if not dep_ok: return - - # 输入 - i_say = f'Generate a animation to show: ' + txt - demo = ["Here is some examples of manim", examples_of_manim()] - _, demo = input_clipping(inputs="", history=demo, max_token_limit=2560) - # 开始 - gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive( - inputs=i_say, inputs_show_user=i_say, - llm_kwargs=llm_kwargs, chatbot=chatbot, history=demo, - sys_prompt= - r"Write a animation script with 3blue1brown's manim. "+ - r"Please begin with `from manim import *`. " + - r"Answer me with a code block wrapped by ```." - ) - chatbot.append(["开始生成动画", "..."]) - history.extend([i_say, gpt_say]) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 - - # 将代码转为动画 - code = get_code_block(gpt_say) - res = eval_manim(code) - - chatbot.append(("生成的视频文件路径", res)) - yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新 - -# 在这里放一些网上搜集的demo，辅助gpt生成代码 -def examples_of_manim(): - return r""" - - -``` - -class MovingGroupToDestination(Scene): - def construct(self): - group = VGroup(Dot(LEFT), Dot(ORIGIN), Dot(RIGHT, color=RED), Dot(2 * RIGHT)).scale(1.4) - dest = Dot([4, 3, 0], color=YELLOW) - self.add(group, dest) - self.play(group.animate.shift(dest.get_center() - group[2].get_center())) - self.wait(0.5) - -``` - - -``` - -class LatexWithMovingFramebox(Scene): - def construct(self): - text=MathTex( - "\\frac{d}{dx}f(x)g(x)=","f(x)\\frac{d}{dx}g(x)","+", - "g(x)\\frac{d}{dx}f(x)" - ) - self.play(Write(text)) - framebox1 = SurroundingRectangle(text[1], buff = .1) - framebox2 = SurroundingRectangle(text[3], buff = .1) - self.play( - Create(framebox1), - ) - self.wait() - self.play( - ReplacementTransform(framebox1,framebox2), - ) - self.wait() - -``` - - - -``` - -class PointWithTrace(Scene): - def construct(self): - path = VMobject() - dot = Dot() - path.set_points_as_corners([dot.get_center(), dot.get_center()]) - def update_path(path): - previous_path = path.copy() - previous_path.add_points_as_corners([dot.get_center()]) - path.become(previous_path) - path.add_updater(update_path) - self.add(path, dot) - self.play(Rotating(dot, radians=PI, about_point=RIGHT, run_time=2)) - self.wait() - self.play(dot.animate.shift(UP)) - self.play(dot.animate.shift(LEFT)) - self.wait() - -``` - -``` - -# do not use get_graph, this funciton is deprecated - -class ExampleFunctionGraph(Scene): - def construct(self): - cos_func = FunctionGraph( - lambda t: np.cos(t) + 0.5 * np.cos(7 * t) + (1 / 7) * np.cos(14 * t), - color=RED, - ) - - sin_func_1 = FunctionGraph( - lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t), - color=BLUE, - ) - - sin_func_2 = FunctionGraph( - lambda t: np.sin(t) + 0.5 * np.sin(7 * t) + (1 / 7) * np.sin(14 * t), - x_range=[-4, 4], - color=GREEN, - ).move_to([0, 1, 0]) - - self.add(cos_func, sin_func_1, sin_func_2) - -``` -""" \ No newline at end of file diff --git a/spaces/xxie92/antibody_visulization/diffab/utils/transforms/select_atom.py b/spaces/xxie92/antibody_visulization/diffab/utils/transforms/select_atom.py deleted file mode 100644 index 7d067ecb50873dc3ec3c5626bc8d1a836258780a..0000000000000000000000000000000000000000 --- a/spaces/xxie92/antibody_visulization/diffab/utils/transforms/select_atom.py +++ /dev/null @@ -1,20 +0,0 @@ - -from ._base import register_transform - - -@register_transform('select_atom') -class SelectAtom(object): - - def __init__(self, resolution): - super().__init__() - assert resolution in ('full', 'backbone') - self.resolution = resolution - - def __call__(self, data): - if self.resolution == 'full': - data['pos_atoms'] = data['pos_heavyatom'][:, :] - data['mask_atoms'] = data['mask_heavyatom'][:, :] - elif self.resolution == 'backbone': - data['pos_atoms'] = data['pos_heavyatom'][:, :5] - data['mask_atoms'] = data['mask_heavyatom'][:, :5] - return data diff --git a/spaces/ybelkada/interfacegan_pp/torch_utils/ops/filtered_lrelu.h b/spaces/ybelkada/interfacegan_pp/torch_utils/ops/filtered_lrelu.h deleted file mode 100644 index 2c403e3f275f472315662321cad54dd0dbc56d00..0000000000000000000000000000000000000000 --- a/spaces/ybelkada/interfacegan_pp/torch_utils/ops/filtered_lrelu.h +++ /dev/null @@ -1,90 +0,0 @@ -// Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. -// -// NVIDIA CORPORATION and its licensors retain all intellectual property -// and proprietary rights in and to this software, related documentation -// and any modifications thereto. Any use, reproduction, disclosure or -// distribution of this software and related documentation without an express -// license agreement from NVIDIA CORPORATION is strictly prohibited. - -#include - -//------------------------------------------------------------------------ -// CUDA kernel parameters. - -struct filtered_lrelu_kernel_params -{ - // These parameters decide which kernel to use. - int up; // upsampling ratio (1, 2, 4) - int down; // downsampling ratio (1, 2, 4) - int2 fuShape; // [size, 1] | [size, size] - int2 fdShape; // [size, 1] | [size, size] - - int _dummy; // Alignment. - - // Rest of the parameters. - const void* x; // Input tensor. - void* y; // Output tensor. - const void* b; // Bias tensor. - unsigned char* s; // Sign tensor in/out. NULL if unused. - const float* fu; // Upsampling filter. - const float* fd; // Downsampling filter. - - int2 pad0; // Left/top padding. - float gain; // Additional gain factor. - float slope; // Leaky ReLU slope on negative side. - float clamp; // Clamp after nonlinearity. - int flip; // Filter kernel flip for gradient computation. - - int tilesXdim; // Original number of horizontal output tiles. - int tilesXrep; // Number of horizontal tiles per CTA. - int blockZofs; // Block z offset to support large minibatch, channel dimensions. - - int4 xShape; // [width, height, channel, batch] - int4 yShape; // [width, height, channel, batch] - int2 sShape; // [width, height] - width is in bytes. Contiguous. Zeros if unused. - int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. - int swLimit; // Active width of sign tensor in bytes. - - longlong4 xStride; // Strides of all tensors except signs, same component order as shapes. - longlong4 yStride; // - int64_t bStride; // - longlong3 fuStride; // - longlong3 fdStride; // -}; - -struct filtered_lrelu_act_kernel_params -{ - void* x; // Input/output, modified in-place. - unsigned char* s; // Sign tensor in/out. NULL if unused. - - float gain; // Additional gain factor. - float slope; // Leaky ReLU slope on negative side. - float clamp; // Clamp after nonlinearity. - - int4 xShape; // [width, height, channel, batch] - longlong4 xStride; // Input/output tensor strides, same order as in shape. - int2 sShape; // [width, height] - width is in elements. Contiguous. Zeros if unused. - int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. -}; - -//------------------------------------------------------------------------ -// CUDA kernel specialization. - -struct filtered_lrelu_kernel_spec -{ - void* setup; // Function for filter kernel setup. - void* exec; // Function for main operation. - int2 tileOut; // Width/height of launch tile. - int numWarps; // Number of warps per thread block, determines launch block size. - int xrep; // For processing multiple horizontal tiles per thread block. - int dynamicSharedKB; // How much dynamic shared memory the exec kernel wants. -}; - -//------------------------------------------------------------------------ -// CUDA kernel selection. - -template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); -template void* choose_filtered_lrelu_act_kernel(void); -template cudaError_t copy_filters(cudaStream_t stream); - -//------------------------------------------------------------------------ diff --git a/spaces/yfyangd/PictureBookUnderstanding/BLIP/data/__init__.py b/spaces/yfyangd/PictureBookUnderstanding/BLIP/data/__init__.py deleted file mode 100644 index 0be209acf415855ea6ef753efedf903b5decb6b9..0000000000000000000000000000000000000000 --- a/spaces/yfyangd/PictureBookUnderstanding/BLIP/data/__init__.py +++ /dev/null @@ -1,101 +0,0 @@ -import torch -from torch.utils.data import DataLoader -from torchvision import transforms -from torchvision.transforms.functional import InterpolationMode - -from data.coco_karpathy_dataset import coco_karpathy_train, coco_karpathy_caption_eval, coco_karpathy_retrieval_eval -from data.nocaps_dataset import nocaps_eval -from data.flickr30k_dataset import flickr30k_train, flickr30k_retrieval_eval -from data.vqa_dataset import vqa_dataset -from data.nlvr_dataset import nlvr_dataset -from data.pretrain_dataset import pretrain_dataset -from transform.randaugment import RandomAugment - -def create_dataset(dataset, config, min_scale=0.5): - - normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) - - transform_train = transforms.Compose([ - transforms.RandomResizedCrop(config['image_size'],scale=(min_scale, 1.0),interpolation=InterpolationMode.BICUBIC), - transforms.RandomHorizontalFlip(), - RandomAugment(2,5,isPIL=True,augs=['Identity','AutoContrast','Brightness','Sharpness','Equalize', - 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Rotate']), - transforms.ToTensor(), - normalize, - ]) - transform_test = transforms.Compose([ - transforms.Resize((config['image_size'],config['image_size']),interpolation=InterpolationMode.BICUBIC), - transforms.ToTensor(), - normalize, - ]) - - if dataset=='pretrain': - dataset = pretrain_dataset(config['train_file'], config['laion_path'], transform_train) - return dataset - - elif dataset=='caption_coco': - train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root'], prompt=config['prompt']) - val_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = coco_karpathy_caption_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return train_dataset, val_dataset, test_dataset - - elif dataset=='nocaps': - val_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = nocaps_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return val_dataset, test_dataset - - elif dataset=='retrieval_coco': - train_dataset = coco_karpathy_train(transform_train, config['image_root'], config['ann_root']) - val_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = coco_karpathy_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return train_dataset, val_dataset, test_dataset - - elif dataset=='retrieval_flickr': - train_dataset = flickr30k_train(transform_train, config['image_root'], config['ann_root']) - val_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'val') - test_dataset = flickr30k_retrieval_eval(transform_test, config['image_root'], config['ann_root'], 'test') - return train_dataset, val_dataset, test_dataset - - elif dataset=='vqa': - train_dataset = vqa_dataset(transform_train, config['ann_root'], config['vqa_root'], config['vg_root'], - train_files = config['train_files'], split='train') - test_dataset = vqa_dataset(transform_test, config['ann_root'], config['vqa_root'], config['vg_root'], split='test') - return train_dataset, test_dataset - - elif dataset=='nlvr': - train_dataset = nlvr_dataset(transform_train, config['image_root'], config['ann_root'],'train') - val_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'val') - test_dataset = nlvr_dataset(transform_test, config['image_root'], config['ann_root'],'test') - return train_dataset, val_dataset, test_dataset - - -def create_sampler(datasets, shuffles, num_tasks, global_rank): - samplers = [] - for dataset,shuffle in zip(datasets,shuffles): - sampler = torch.utils.data.DistributedSampler(dataset, num_replicas=num_tasks, rank=global_rank, shuffle=shuffle) - samplers.append(sampler) - return samplers - - -def create_loader(datasets, samplers, batch_size, num_workers, is_trains, collate_fns): - loaders = [] - for dataset,sampler,bs,n_worker,is_train,collate_fn in zip(datasets,samplers,batch_size,num_workers,is_trains,collate_fns): - if is_train: - shuffle = (sampler is None) - drop_last = True - else: - shuffle = False - drop_last = False - loader = DataLoader( - dataset, - batch_size=bs, - num_workers=n_worker, - pin_memory=True, - sampler=sampler, - shuffle=shuffle, - collate_fn=collate_fn, - drop_last=drop_last, - ) - loaders.append(loader) - return loaders - diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/time_counter.py b/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/time_counter.py deleted file mode 100644 index 0aedb2e4d61bfbe7571dca9d50053f0fedaa1359..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/GroundingDINO/groundingdino/util/time_counter.py +++ /dev/null @@ -1,62 +0,0 @@ -import json -import time - - -class TimeCounter: - def __init__(self) -> None: - pass - - def clear(self): - self.timedict = {} - self.basetime = time.perf_counter() - - def timeit(self, name): - nowtime = time.perf_counter() - self.basetime - self.timedict[name] = nowtime - self.basetime = time.perf_counter() - - -class TimeHolder: - def __init__(self) -> None: - self.timedict = {} - - def update(self, _timedict: dict): - for k, v in _timedict.items(): - if k not in self.timedict: - self.timedict[k] = AverageMeter(name=k, val_only=True) - self.timedict[k].update(val=v) - - def final_res(self): - return {k: v.avg for k, v in self.timedict.items()} - - def __str__(self): - return json.dumps(self.final_res(), indent=2) - - -class AverageMeter(object): - """Computes and stores the average and current value""" - - def __init__(self, name, fmt=":f", val_only=False): - self.name = name - self.fmt = fmt - self.val_only = val_only - self.reset() - - def reset(self): - self.val = 0 - self.avg = 0 - self.sum = 0 - self.count = 0 - - def update(self, val, n=1): - self.val = val - self.sum += val * n - self.count += n - self.avg = self.sum / self.count - - def __str__(self): - if self.val_only: - fmtstr = "{name} {val" + self.fmt + "}" - else: - fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})" - return fmtstr.format(**self.__dict__) diff --git a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt2/tokenization_gpt2_tf.py b/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt2/tokenization_gpt2_tf.py deleted file mode 100644 index 4ab4af5b9d66f306b179e6b1e2158be83be5e330..0000000000000000000000000000000000000000 --- a/spaces/yizhangliu/Grounded-Segment-Anything/transformers_4_35_0/models/gpt2/tokenization_gpt2_tf.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -from typing import Dict, List, Union - -import tensorflow as tf -from keras_nlp.tokenizers import BytePairTokenizer -from tensorflow_text import pad_model_inputs - -from .tokenization_gpt2 import GPT2Tokenizer - - -class TFGPT2Tokenizer(tf.keras.layers.Layer): - """ - This is an in-graph tokenizer for GPT2. It should be initialized similarly to other tokenizers, using the - `from_pretrained()` method. It can also be initialized with the `from_tokenizer()` method, which imports settings - from an existing standard tokenizer object. - - In-graph tokenizers, unlike other Hugging Face tokenizers, are actually Keras layers and are designed to be run - when the model is called, rather than during preprocessing. As a result, they have somewhat more limited options - than standard tokenizer classes. They are most useful when you want to create an end-to-end model that goes - straight from `tf.string` inputs to outputs. - - Args: - vocab (Dict[str, int]): Vocabulary dict for Byte Pair Tokenizer - merges (List[str]): Merges list for Byte Pair Tokenizer - """ - - def __init__(self, vocab: Dict[str, int], merges: List[str], max_length: int = None, pad_token_id: int = None): - super().__init__() - self.pad_token_id = pad_token_id - self.max_length = max_length - self.vocab = vocab - self.merges = merges - self.tf_tokenizer = BytePairTokenizer(vocab, merges, sequence_length=max_length) - - @classmethod - def from_tokenizer(cls, tokenizer: GPT2Tokenizer, *args, **kwargs): - """Creates TFGPT2Tokenizer from GPT2Tokenizer - - Args: - tokenizer (GPT2Tokenizer) - - Examples: - - ```python - from transformers import AutoTokenizer, TFGPT2Tokenizer - - tokenizer = AutoTokenizer.from_pretrained("gpt2") - tf_tokenizer = TFGPT2Tokenizer.from_tokenizer(tokenizer) - ``` - """ - merges = [" ".join(m) for m in tokenizer.bpe_ranks.keys()] - vocab = tokenizer.get_vocab() - return cls(vocab, merges, *args, **kwargs) - - @classmethod - def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], *init_inputs, **kwargs): - """Creates TFGPT2Tokenizer from pretrained GPT2Tokenizer - - Args: - pretrained_model_name_or_path (Union[str, os.PathLike]): Path to pretrained model - - Examples: - - ```python - from transformers import TFGPT2Tokenizer - - tf_tokenizer = TFGPT2Tokenizer.from_pretrained("gpt2") - ``` - """ - tokenizer = GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *init_inputs, **kwargs) - return cls.from_tokenizer(tokenizer, *init_inputs, **kwargs) - - @classmethod - def from_config(cls, config): - """Creates TFGPT2Tokenizer from configurations - - Args: - config (Dict): Dictionary with keys such as stated in `get_config`. - """ - return cls(**config) - - def get_config(self): - return { - "vocab": self.vocab, - "merges": self.merges, - "max_length": self.max_length, - "pad_token_id": self.pad_token_id, - } - - def call(self, x, max_length: int = None): - input_ids = self.tf_tokenizer(x) - attention_mask = tf.ones_like(input_ids) - - if self.pad_token_id is not None: - # pad the tokens up to max length - max_length = max_length if max_length is not None else self.max_length - - if max_length is not None: - input_ids, attention_mask = pad_model_inputs( - input_ids, max_seq_length=max_length, pad_value=self.pad_token_id - ) - - return {"attention_mask": attention_mask, "input_ids": input_ids} diff --git a/spaces/yl12053/so-vits-4.1-Grass-Wonder/vencoder/ContentVec768L12_Onnx.py b/spaces/yl12053/so-vits-4.1-Grass-Wonder/vencoder/ContentVec768L12_Onnx.py deleted file mode 100644 index 8dde0f173ed60169282128cc51eb1c200c5d82c5..0000000000000000000000000000000000000000 --- a/spaces/yl12053/so-vits-4.1-Grass-Wonder/vencoder/ContentVec768L12_Onnx.py +++ /dev/null @@ -1,28 +0,0 @@ -from vencoder.encoder import SpeechEncoder -import onnxruntime -import torch - -class ContentVec768L12_Onnx(SpeechEncoder): - def __init__(self,vec_path = "pretrain/vec-768-layer-12.onnx",device=None): - print("load model(s) from {}".format(vec_path)) - self.hidden_dim = 768 - if device is None: - self.dev = torch.device("cpu") - else: - self.dev = torch.device(device) - if device == 'cpu' or device == torch.device("cpu") or device is None: - providers = ['CPUExecutionProvider'] - elif device == 'cuda' or device == torch.device("cuda"): - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] - self.model = onnxruntime.InferenceSession(vec_path, providers=providers) - - def encoder(self, wav): - feats = wav - if feats.dim() == 2: # double channels - feats = feats.mean(-1) - assert feats.dim() == 1, feats.dim() - feats = feats.view(1, -1) - feats = feats.unsqueeze(0).cpu().detach().numpy() - onnx_input = {self.model.get_inputs()[0].name: feats} - logits = self.model.run(None, onnx_input) - return torch.tensor(logits[0]).transpose(1, 2).to(self.dev) \ No newline at end of file diff --git a/spaces/yulet1de/StableDiffusion2/README.md b/spaces/yulet1de/StableDiffusion2/README.md deleted file mode 100644 index ab81bd442aeb772cb95d25ab1a1f1f0fad3777db..0000000000000000000000000000000000000000 --- a/spaces/yulet1de/StableDiffusion2/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: StableDiffusion2 -emoji: 🌖 -colorFrom: indigo -colorTo: pink -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false -license: openrail ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/onnxexport/model_onnx.py b/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/onnxexport/model_onnx.py deleted file mode 100644 index e28bae95ec1e53aa05d06fc784ff86d55f228d60..0000000000000000000000000000000000000000 --- a/spaces/zdxiaoda/sovits-4.0-V1-anime-character-model/so-vits-svc/onnxexport/model_onnx.py +++ /dev/null @@ -1,335 +0,0 @@ -import torch -from torch import nn -from torch.nn import functional as F - -import modules.attentions as attentions -import modules.commons as commons -import modules.modules as modules - -from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d -from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm - -import utils -from modules.commons import init_weights, get_padding -from vdecoder.hifigan.models import Generator -from utils import f0_to_coarse - - -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 Encoder(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): - # print(x.shape,x_lengths.shape) - 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 TextEncoder(nn.Module): - def __init__(self, - out_channels, - hidden_channels, - kernel_size, - n_layers, - gin_channels=0, - filter_channels=None, - n_heads=None, - p_dropout=None): - super().__init__() - self.out_channels = out_channels - self.hidden_channels = hidden_channels - self.kernel_size = kernel_size - self.n_layers = n_layers - self.gin_channels = gin_channels - self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1) - self.f0_emb = nn.Embedding(256, hidden_channels) - - self.enc_ = attentions.Encoder( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - - def forward(self, x, x_mask, f0=None, z=None): - x = x + self.f0_emb(f0).transpose(1, 2) - x = self.enc_(x * x_mask, x_mask) - stats = self.proj(x) * x_mask - m, logs = torch.split(stats, self.out_channels, dim=1) - z = (m + z * torch.exp(logs)) * x_mask - return z, m, logs, x_mask - - -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 F0Decoder(nn.Module): - def __init__(self, - out_channels, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - spk_channels=0): - 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.spk_channels = spk_channels - - self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1) - self.decoder = attentions.FFT( - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout) - self.proj = nn.Conv1d(hidden_channels, out_channels, 1) - self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1) - self.cond = nn.Conv1d(spk_channels, hidden_channels, 1) - - def forward(self, x, norm_f0, x_mask, spk_emb=None): - x = torch.detach(x) - if spk_emb is not None: - x = x + self.cond(spk_emb) - x += self.f0_prenet(norm_f0) - x = self.prenet(x) * x_mask - x = self.decoder(x * x_mask, x_mask) - x = self.proj(x) * x_mask - return x - - -class SynthesizerTrn(nn.Module): - """ - Synthesizer for Training - """ - - 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, - gin_channels, - ssl_dim, - n_speakers, - sampling_rate=44100, - **kwargs): - super().__init__() - 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.ssl_dim = ssl_dim - self.emb_g = nn.Embedding(n_speakers, gin_channels) - - self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2) - - self.enc_p = TextEncoder( - inter_channels, - hidden_channels, - filter_channels=filter_channels, - n_heads=n_heads, - n_layers=n_layers, - kernel_size=kernel_size, - p_dropout=p_dropout - ) - hps = { - "sampling_rate": sampling_rate, - "inter_channels": inter_channels, - "resblock": resblock, - "resblock_kernel_sizes": resblock_kernel_sizes, - "resblock_dilation_sizes": resblock_dilation_sizes, - "upsample_rates": upsample_rates, - "upsample_initial_channel": upsample_initial_channel, - "upsample_kernel_sizes": upsample_kernel_sizes, - "gin_channels": gin_channels, - } - self.dec = Generator(h=hps) - self.enc_q = Encoder(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) - self.f0_decoder = F0Decoder( - 1, - hidden_channels, - filter_channels, - n_heads, - n_layers, - kernel_size, - p_dropout, - spk_channels=gin_channels - ) - self.emb_uv = nn.Embedding(2, hidden_channels) - self.predict_f0 = False - - def forward(self, c, f0, mel2ph, uv, noise=None, g=None): - - decoder_inp = F.pad(c, [0, 0, 1, 0]) - mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]]) - c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H] - - c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device) - g = g.unsqueeze(0) - g = self.emb_g(g).transpose(1, 2) - x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype) - x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2) - - if self.predict_f0: - lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500 - norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False) - pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g) - f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1) - - z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise) - z = self.flow(z_p, c_mask, g=g, reverse=True) - o = self.dec(z * c_mask, g=g, f0=f0) - return o diff --git a/spaces/zhang-wei-jian/docker/node_modules/ee-first/index.js b/spaces/zhang-wei-jian/docker/node_modules/ee-first/index.js deleted file mode 100644 index 501287cd3b7024435d85a872bb1ba0b234db8e7f..0000000000000000000000000000000000000000 --- a/spaces/zhang-wei-jian/docker/node_modules/ee-first/index.js +++ /dev/null @@ -1,95 +0,0 @@ -/*! - * ee-first - * Copyright(c) 2014 Jonathan Ong - * MIT Licensed - */ - -'use strict' - -/** - * Module exports. - * @public - */ - -module.exports = first - -/** - * Get the first event in a set of event emitters and event pairs. - * - * @param {array} stuff - * @param {function} done - * @public - */ - -function first(stuff, done) { - if (!Array.isArray(stuff)) - throw new TypeError('arg must be an array of [ee, events...] arrays') - - var cleanups = [] - - for (var i = 0; i < stuff.length; i++) { - var arr = stuff[i] - - if (!Array.isArray(arr) || arr.length < 2) - throw new TypeError('each array member must be [ee, events...]') - - var ee = arr[0] - - for (var j = 1; j < arr.length; j++) { - var event = arr[j] - var fn = listener(event, callback) - - // listen to the event - ee.on(event, fn) - // push this listener to the list of cleanups - cleanups.push({ - ee: ee, - event: event, - fn: fn, - }) - } - } - - function callback() { - cleanup() - done.apply(null, arguments) - } - - function cleanup() { - var x - for (var i = 0; i < cleanups.length; i++) { - x = cleanups[i] - x.ee.removeListener(x.event, x.fn) - } - } - - function thunk(fn) { - done = fn - } - - thunk.cancel = cleanup - - return thunk -} - -/** - * Create the event listener. - * @private - */ - -function listener(event, done) { - return function onevent(arg1) { - var args = new Array(arguments.length) - var ee = this - var err = event === 'error' - ? arg1 - : null - - // copy args to prevent arguments escaping scope - for (var i = 0; i < args.length; i++) { - args[i] = arguments[i] - } - - done(err, ee, event, args) - } -} diff --git a/spaces/zhang-wei-jian/docker/node_modules/mime-db/HISTORY.md b/spaces/zhang-wei-jian/docker/node_modules/mime-db/HISTORY.md deleted file mode 100644 index 7436f64146e87d2ebe6cacac33af0aeedcc798fb..0000000000000000000000000000000000000000 --- a/spaces/zhang-wei-jian/docker/node_modules/mime-db/HISTORY.md +++ /dev/null @@ -1,507 +0,0 @@ -1.52.0 / 2022-02-21 -=================== - - * Add extensions from IANA for more `image/*` types - * Add extension `.asc` to `application/pgp-keys` - * Add extensions to various XML types - * Add new upstream MIME types - -1.51.0 / 2021-11-08 -=================== - - * Add new upstream MIME types - * Mark `image/vnd.microsoft.icon` as compressible - * Mark `image/vnd.ms-dds` as compressible - -1.50.0 / 2021-09-15 -=================== - - * Add deprecated iWorks mime types and extensions - * Add new upstream MIME types - -1.49.0 / 2021-07-26 -=================== - - * Add extension `.trig` to `application/trig` - * Add new upstream MIME types - -1.48.0 / 2021-05-30 -=================== - - * Add extension `.mvt` to `application/vnd.mapbox-vector-tile` - * Add new upstream MIME types - * Mark `text/yaml` as compressible - -1.47.0 / 2021-04-01 -=================== - - * Add new upstream MIME types - * Remove ambigious extensions from IANA for `application/*+xml` types - * Update primary extension to `.es` for `application/ecmascript` - -1.46.0 / 2021-02-13 -=================== - - * Add extension `.amr` to `audio/amr` - * Add extension `.m4s` to `video/iso.segment` - * Add extension `.opus` to `audio/ogg` - * Add new upstream MIME types - -1.45.0 / 2020-09-22 -=================== - - * Add `application/ubjson` with extension `.ubj` - * Add `image/avif` with extension `.avif` - * Add `image/ktx2` with extension `.ktx2` - * Add extension `.dbf` to `application/vnd.dbf` - * Add extension `.rar` to `application/vnd.rar` - * Add extension `.td` to `application/urc-targetdesc+xml` - * Add new upstream MIME types - * Fix extension of `application/vnd.apple.keynote` to be `.key` - -1.44.0 / 2020-04-22 -=================== - - * Add charsets from IANA - * Add extension `.cjs` to `application/node` - * Add new upstream MIME types - -1.43.0 / 2020-01-05 -=================== - - * Add `application/x-keepass2` with extension `.kdbx` - * Add extension `.mxmf` to `audio/mobile-xmf` - * Add extensions from IANA for `application/*+xml` types - * Add new upstream MIME types - -1.42.0 / 2019-09-25 -=================== - - * Add `image/vnd.ms-dds` with extension `.dds` - * Add new upstream MIME types - * Remove compressible from `multipart/mixed` - -1.41.0 / 2019-08-30 -=================== - - * Add new upstream MIME types - * Add `application/toml` with extension `.toml` - * Mark `font/ttf` as compressible - -1.40.0 / 2019-04-20 -=================== - - * Add extensions from IANA for `model/*` types - * Add `text/mdx` with extension `.mdx` - -1.39.0 / 2019-04-04 -=================== - - * Add extensions `.siv` and `.sieve` to `application/sieve` - * Add new upstream MIME types - -1.38.0 / 2019-02-04 -=================== - - * Add extension `.nq` to `application/n-quads` - * Add extension `.nt` to `application/n-triples` - * Add new upstream MIME types - * Mark `text/less` as compressible - -1.37.0 / 2018-10-19 -=================== - - * Add extensions to HEIC image types - * Add new upstream MIME types - -1.36.0 / 2018-08-20 -=================== - - * Add Apple file extensions from IANA - * Add extensions from IANA for `image/*` types - * Add new upstream MIME types - -1.35.0 / 2018-07-15 -=================== - - * Add extension `.owl` to `application/rdf+xml` - * Add new upstream MIME types - - Removes extension `.woff` from `application/font-woff` - -1.34.0 / 2018-06-03 -=================== - - * Add extension `.csl` to `application/vnd.citationstyles.style+xml` - * Add extension `.es` to `application/ecmascript` - * Add new upstream MIME types - * Add `UTF-8` as default charset for `text/turtle` - * Mark all XML-derived types as compressible - -1.33.0 / 2018-02-15 -=================== - - * Add extensions from IANA for `message/*` types - * Add new upstream MIME types - * Fix some incorrect OOXML types - * Remove `application/font-woff2` - -1.32.0 / 2017-11-29 -=================== - - * Add new upstream MIME types - * Update `text/hjson` to registered `application/hjson` - * Add `text/shex` with extension `.shex` - -1.31.0 / 2017-10-25 -=================== - - * Add `application/raml+yaml` with extension `.raml` - * Add `application/wasm` with extension `.wasm` - * Add new `font` type from IANA - * Add new upstream font extensions - * Add new upstream MIME types - * Add extensions for JPEG-2000 images - -1.30.0 / 2017-08-27 -=================== - - * Add `application/vnd.ms-outlook` - * Add `application/x-arj` - * Add extension `.mjs` to `application/javascript` - * Add glTF types and extensions - * Add new upstream MIME types - * Add `text/x-org` - * Add VirtualBox MIME types - * Fix `source` records for `video/*` types that are IANA - * Update `font/opentype` to registered `font/otf` - -1.29.0 / 2017-07-10 -=================== - - * Add `application/fido.trusted-apps+json` - * Add extension `.wadl` to `application/vnd.sun.wadl+xml` - * Add new upstream MIME types - * Add `UTF-8` as default charset for `text/css` - -1.28.0 / 2017-05-14 -=================== - - * Add new upstream MIME types - * Add extension `.gz` to `application/gzip` - * Update extensions `.md` and `.markdown` to be `text/markdown` - -1.27.0 / 2017-03-16 -=================== - - * Add new upstream MIME types - * Add `image/apng` with extension `.apng` - -1.26.0 / 2017-01-14 -=================== - - * Add new upstream MIME types - * Add extension `.geojson` to `application/geo+json` - -1.25.0 / 2016-11-11 -=================== - - * Add new upstream MIME types - -1.24.0 / 2016-09-18 -=================== - - * Add `audio/mp3` - * Add new upstream MIME types - -1.23.0 / 2016-05-01 -=================== - - * Add new upstream MIME types - * Add extension `.3gpp` to `audio/3gpp` - -1.22.0 / 2016-02-15 -=================== - - * Add `text/slim` - * Add extension `.rng` to `application/xml` - * Add new upstream MIME types - * Fix extension of `application/dash+xml` to be `.mpd` - * Update primary extension to `.m4a` for `audio/mp4` - -1.21.0 / 2016-01-06 -=================== - - * Add Google document types - * Add new upstream MIME types - -1.20.0 / 2015-11-10 -=================== - - * Add `text/x-suse-ymp` - * Add new upstream MIME types - -1.19.0 / 2015-09-17 -=================== - - * Add `application/vnd.apple.pkpass` - * Add new upstream MIME types - -1.18.0 / 2015-09-03 -=================== - - * Add new upstream MIME types - -1.17.0 / 2015-08-13 -=================== - - * Add `application/x-msdos-program` - * Add `audio/g711-0` - * Add `image/vnd.mozilla.apng` - * Add extension `.exe` to `application/x-msdos-program` - -1.16.0 / 2015-07-29 -=================== - - * Add `application/vnd.uri-map` - -1.15.0 / 2015-07-13 -=================== - - * Add `application/x-httpd-php` - -1.14.0 / 2015-06-25 -=================== - - * Add `application/scim+json` - * Add `application/vnd.3gpp.ussd+xml` - * Add `application/vnd.biopax.rdf+xml` - * Add `text/x-processing` - -1.13.0 / 2015-06-07 -=================== - - * Add nginx as a source - * Add `application/x-cocoa` - * Add `application/x-java-archive-diff` - * Add `application/x-makeself` - * Add `application/x-perl` - * Add `application/x-pilot` - * Add `application/x-redhat-package-manager` - * Add `application/x-sea` - * Add `audio/x-m4a` - * Add `audio/x-realaudio` - * Add `image/x-jng` - * Add `text/mathml` - -1.12.0 / 2015-06-05 -=================== - - * Add `application/bdoc` - * Add `application/vnd.hyperdrive+json` - * Add `application/x-bdoc` - * Add extension `.rtf` to `text/rtf` - -1.11.0 / 2015-05-31 -=================== - - * Add `audio/wav` - * Add `audio/wave` - * Add extension `.litcoffee` to `text/coffeescript` - * Add extension `.sfd-hdstx` to `application/vnd.hydrostatix.sof-data` - * Add extension `.n-gage` to `application/vnd.nokia.n-gage.symbian.install` - -1.10.0 / 2015-05-19 -=================== - - * Add `application/vnd.balsamiq.bmpr` - * Add `application/vnd.microsoft.portable-executable` - * Add `application/x-ns-proxy-autoconfig` - -1.9.1 / 2015-04-19 -================== - - * Remove `.json` extension from `application/manifest+json` - - This is causing bugs downstream - -1.9.0 / 2015-04-19 -================== - - * Add `application/manifest+json` - * Add `application/vnd.micro+json` - * Add `image/vnd.zbrush.pcx` - * Add `image/x-ms-bmp` - -1.8.0 / 2015-03-13 -================== - - * Add `application/vnd.citationstyles.style+xml` - * Add `application/vnd.fastcopy-disk-image` - * Add `application/vnd.gov.sk.xmldatacontainer+xml` - * Add extension `.jsonld` to `application/ld+json` - -1.7.0 / 2015-02-08 -================== - - * Add `application/vnd.gerber` - * Add `application/vnd.msa-disk-image` - -1.6.1 / 2015-02-05 -================== - - * Community extensions ownership transferred from `node-mime` - -1.6.0 / 2015-01-29 -================== - - * Add `application/jose` - * Add `application/jose+json` - * Add `application/json-seq` - * Add `application/jwk+json` - * Add `application/jwk-set+json` - * Add `application/jwt` - * Add `application/rdap+json` - * Add `application/vnd.gov.sk.e-form+xml` - * Add `application/vnd.ims.imsccv1p3` - -1.5.0 / 2014-12-30 -================== - - * Add `application/vnd.oracle.resource+json` - * Fix various invalid MIME type entries - - `application/mbox+xml` - - `application/oscp-response` - - `application/vwg-multiplexed` - - `audio/g721` - -1.4.0 / 2014-12-21 -================== - - * Add `application/vnd.ims.imsccv1p2` - * Fix various invalid MIME type entries - - `application/vnd-acucobol` - - `application/vnd-curl` - - `application/vnd-dart` - - `application/vnd-dxr` - - `application/vnd-fdf` - - `application/vnd-mif` - - `application/vnd-sema` - - `application/vnd-wap-wmlc` - - `application/vnd.adobe.flash-movie` - - `application/vnd.dece-zip` - - `application/vnd.dvb_service` - - `application/vnd.micrografx-igx` - - `application/vnd.sealed-doc` - - `application/vnd.sealed-eml` - - `application/vnd.sealed-mht` - - `application/vnd.sealed-ppt` - - `application/vnd.sealed-tiff` - - `application/vnd.sealed-xls` - - `application/vnd.sealedmedia.softseal-html` - - `application/vnd.sealedmedia.softseal-pdf` - - `application/vnd.wap-slc` - - `application/vnd.wap-wbxml` - - `audio/vnd.sealedmedia.softseal-mpeg` - - `image/vnd-djvu` - - `image/vnd-svf` - - `image/vnd-wap-wbmp` - - `image/vnd.sealed-png` - - `image/vnd.sealedmedia.softseal-gif` - - `image/vnd.sealedmedia.softseal-jpg` - - `model/vnd-dwf` - - `model/vnd.parasolid.transmit-binary` - - `model/vnd.parasolid.transmit-text` - - `text/vnd-a` - - `text/vnd-curl` - - `text/vnd.wap-wml` - * Remove example template MIME types - - `application/example` - - `audio/example` - - `image/example` - - `message/example` - - `model/example` - - `multipart/example` - - `text/example` - - `video/example` - -1.3.1 / 2014-12-16 -================== - - * Fix missing extensions - - `application/json5` - - `text/hjson` - -1.3.0 / 2014-12-07 -================== - - * Add `application/a2l` - * Add `application/aml` - * Add `application/atfx` - * Add `application/atxml` - * Add `application/cdfx+xml` - * Add `application/dii` - * Add `application/json5` - * Add `application/lxf` - * Add `application/mf4` - * Add `application/vnd.apache.thrift.compact` - * Add `application/vnd.apache.thrift.json` - * Add `application/vnd.coffeescript` - * Add `application/vnd.enphase.envoy` - * Add `application/vnd.ims.imsccv1p1` - * Add `text/csv-schema` - * Add `text/hjson` - * Add `text/markdown` - * Add `text/yaml` - -1.2.0 / 2014-11-09 -================== - - * Add `application/cea` - * Add `application/dit` - * Add `application/vnd.gov.sk.e-form+zip` - * Add `application/vnd.tmd.mediaflex.api+xml` - * Type `application/epub+zip` is now IANA-registered - -1.1.2 / 2014-10-23 -================== - - * Rebuild database for `application/x-www-form-urlencoded` change - -1.1.1 / 2014-10-20 -================== - - * Mark `application/x-www-form-urlencoded` as compressible. - -1.1.0 / 2014-09-28 -================== - - * Add `application/font-woff2` - -1.0.3 / 2014-09-25 -================== - - * Fix engine requirement in package - -1.0.2 / 2014-09-25 -================== - - * Add `application/coap-group+json` - * Add `application/dcd` - * Add `application/vnd.apache.thrift.binary` - * Add `image/vnd.tencent.tap` - * Mark all JSON-derived types as compressible - * Update `text/vtt` data - -1.0.1 / 2014-08-30 -================== - - * Fix extension ordering - -1.0.0 / 2014-08-30 -================== - - * Add `application/atf` - * Add `application/merge-patch+json` - * Add `multipart/x-mixed-replace` - * Add `source: 'apache'` metadata - * Add `source: 'iana'` metadata - * Remove badly-assumed charset data diff --git a/spaces/zixian/Zhenhuan-VITS/voice_upload.py b/spaces/zixian/Zhenhuan-VITS/voice_upload.py deleted file mode 100644 index 5c825a933a7970e17e57c381b59a5fc4e06ea569..0000000000000000000000000000000000000000 --- a/spaces/zixian/Zhenhuan-VITS/voice_upload.py +++ /dev/null @@ -1,28 +0,0 @@ -from google.colab import files -import shutil -import os -import argparse -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--type", type=str, required=True, help="type of file to upload") - args = parser.parse_args() - file_type = args.type - - basepath = os.getcwd() - uploaded = files.upload() # 上传文件 - assert(file_type in ['zip', 'audio', 'video']) - if file_type == "zip": - upload_path = "./custom_character_voice/" - for filename in uploaded.keys(): - #将上传的文件移动到指定的位置上 - shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, "custom_character_voice.zip")) - elif file_type == "audio": - upload_path = "./raw_audio/" - for filename in uploaded.keys(): - #将上传的文件移动到指定的位置上 - shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename)) - elif file_type == "video": - upload_path = "./video_data/" - for filename in uploaded.keys(): - # 将上传的文件移动到指定的位置上 - shutil.move(os.path.join(basepath, filename), os.path.join(upload_path, filename)) \ No newline at end of file diff --git a/spaces/zomehwh/sovits-goldship/cluster/__init__.py b/spaces/zomehwh/sovits-goldship/cluster/__init__.py deleted file mode 100644 index f1b9bde04e73e9218a5d534227caa4c25332f424..0000000000000000000000000000000000000000 --- a/spaces/zomehwh/sovits-goldship/cluster/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -import numpy as np -import torch -from sklearn.cluster import KMeans - -def get_cluster_model(ckpt_path): - checkpoint = torch.load(ckpt_path) - kmeans_dict = {} - for spk, ckpt in checkpoint.items(): - km = KMeans(ckpt["n_features_in_"]) - km.__dict__["n_features_in_"] = ckpt["n_features_in_"] - km.__dict__["_n_threads"] = ckpt["_n_threads"] - km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"] - kmeans_dict[spk] = km - return kmeans_dict - -def get_cluster_result(model, x, speaker): - """ - x: np.array [t, 256] - return cluster class result - """ - return model[speaker].predict(x) - -def get_cluster_center_result(model, x,speaker): - """x: np.array [t, 256]""" - predict = model[speaker].predict(x) - return model[speaker].cluster_centers_[predict] - -def get_center(model, x,speaker): - return model[speaker].cluster_centers_[x]