If you are looking for a restaurant management software that can help you achieve full control over your business operations, you should consider Abacre Restaurant Point of Sale (POS). This software is a new generation of restaurant management software for Windows that offers a complete solution from taking orders from patrons to billing and tax reports. In this article, we will review what Abacre Restaurant Point of Sale is, what are its main features, how to download and install it, and how to buy it.
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Abacre Restaurant Point of Sale is a complete solution that covers all aspects of restaurant management. It allows you to take orders from patrons using different methods, such as keyboard, mouse, touch screen, or handheld devices. It also allows you to print orders to kitchen printers or send them to kitchen displays. You can also manage your inventory, menu items, recipes, ingredients, modifiers, and prices. You can also generate bills for your guests with customizable layouts and print them or email them. You can also handle payments with cash, credit cards, checks, or gift cards. You can also generate various reports that show you the performance of your restaurant, such as sales, profits, taxes, tips, discounts, refunds, reservations, hours of operation, busiest tables, most active employees, and more.
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-Abacre Restaurant Point of Sale allows you to customize the guest bill layouts according to your preferences and needs. You can choose from different templates or create your own using a built-in editor. You can also add your logo, address, phone number, website URL, and other information to your bills. You can also set up different currency formats, tax rates, and gratuity options for your bills. You can also apply discounts, coupons, or surcharges to your bills.
-Abacre Restaurant Point of Sale supports multiple payment methods for your guests, such as cash, credit cards, checks, or gift cards. You can also split bills among guests or merge bills from different tables. You can also accept partial payments or deposits for reservations or catering orders. You can also integrate with various payment processors, such as PayPal, Stripe, Square, or Authorize.Net. Abacre Restaurant Point of Sale also calculates taxes automatically based on your tax settings and applies them to your bills.
-Abacre Restaurant Point of Sale provides a rich set of reports that show you a complete picture of your restaurant operations and life cycles. You can generate reports on various aspects of your business, such as sales, profits, taxes, tips, discounts, refunds, reservations, hours of operation, busiest tables, most active employees, payment methods, and more. You can also filter and sort the reports by date range, time period, location, employee, table, item category, or any other criteria. You can also export the reports to Excel, PDF, HTML, or text formats or email them to yourself or others.
-By standardizing the entire restaurant management process with Abacre Restaurant Point of Sale, you can improve the efficiency and quality of your service. You can reduce the waiting time for your guests by taking orders faster and sending them directly to the kitchen. You can also avoid errors and confusion by printing clear and accurate bills and receipts. You can also increase customer satisfaction by offering discounts, coupons, or loyalty programs. You can also handle complaints and refunds quickly and professionally.
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Abacre Restaurant Point of Sale supports various hardware devices that enhance your restaurant operations. You can use serial or kitchen matrix printers to print orders to the kitchen or bar. You can use poles (line and graphic displays) to show order information or promotional messages to your guests. You can use cash drawers to store cash payments securely. You can also use barcode scanners, scales, magnetic card readers, fingerprint scanners, or touch screen monitors.
-If you want to try Abacre Restaurant Point of Sale before buying it, you can download the full-featured 30-day trial version from the official website. The trial version is fully functional and has no limitations except the time limit. You can use the trial version on any number of computers or devices.
-To install the software on your computer or device, you need to run the setup wizard that guides you through the installation process. You need to agree to the end user license agreement (EULA), choose the installation folder and components, and create shortcuts. You can also choose to install the software on multiple computers using the network installation option.
-If you are not satisfied with Abacre Restaurant Point of Sale or if you want to remove it from your computer or device for any reason, you can uninstall it easily using the Windows Control Panel or the uninstaller program that comes with the software. You can also delete the installation folder and Here is the continuation of the article.
Abacre Restaurant Point of Sale offers three license types for different needs and budgets: Lite, Standard, and Professional. Each license type allows you to use the software on one workstation (computer or device). You can also buy additional licenses for more workstations at discounted prices. The main difference between the license types is the number of features and functions they include. You can compare the features and prices of each license type using the feature matrix.
-The Lite license is the most affordable option, but it has the least features and functions. It costs $149.99 for one workstation. It includes basic features such as taking orders, printing bills, accepting payments, and generating sales reports. It does not include advanced features such as inventory management, menu engineering, reservations, delivery, loyalty programs, gift cards, barcode scanners, fingerprint scanners, or touch screen monitors.
-The Standard license is the most popular option, as it has more features and functions than the Lite license. It costs $299.99 for one workstation. It includes all the features of the Lite license plus inventory management, menu engineering, reservations, delivery, loyalty programs, gift cards, barcode scanners, fingerprint scanners, and touch screen monitors. It does not include some features such as kitchen displays, kitchen printers, poles, cash drawers, scales, or magnetic card readers.
-The Professional license is the most comprehensive option, as it has all the features and functions of the software. It costs $449.99 for one workstation. It includes all the features of the Standard license plus kitchen displays, kitchen printers, poles, cash drawers, scales, and magnetic card readers. It also includes some exclusive features such as multi-location support, cloud backup and restore, web interface access, remote database access, and email notifications.
-If you have decided which license type you want to buy, you can order online using a secure payment system. You can pay by credit card, PayPal, check, bank wire transfer, purchase order, phone call, or fax over a secure web server. You can also choose the currency and language of your order. You can order online from the official website or from one of the resellers. After you place your order, you will receive a confirmation email with your registration key and instructions on how to enter it into the software. You will also receive free email support and updates for one year.
-Abacre Restaurant Point of Sale is a new generation of restaurant management software for Windows that offers a complete solution from taking orders from patrons to billing and tax reports. It has a user-friendly interface optimized for high speed input and error prevention. It has reliable and secure authorization levels and customizable guest bill layouts and currency, tax, and gratuity settings. It supports multiple payment methods and automatic tax calculations. It provides a rich set of reports for managers and owners. It standardizes restaurant management process and improves serving speed. It supports various hardware devices such as serial and kitchen matrix printers, poles, and cash drawers. You can download and install Abacre Restaurant Point of Sale easily from the official website. You can also buy Abacre Restaurant Point of Sale online using a secure payment system. You can choose from three license types: Lite, Standard, or Professional, depending on your needs and budget. You can also compare the features and prices of each license type using the feature matrix. Abacre Restaurant Point of Sale is a great software that can help you achieve full control over your restaurant business.
-Q: What are the system requirements for Abacre Restaurant Point of Sale?
-A: Abacre Restaurant Point of Sale works perfectly on Windows XP/2003/Vista/2008/Windows 7/8/10. It requires at least 512 MB of RAM and 100 MB of free disk space.
-Q: How can I learn how to use Abacre Restaurant Point of Sale?
-A: You can learn how to use Abacre Restaurant Point of Sale by viewing a 5-minute interactive flash movie that shows you the main features and functions of the software. You can also read the manual that provides detailed instructions on how to use the software. You can also watch video tutorials that demonstrate how to use the software in different scenarios.
-Q: How can I get technical support for Abacre Restaurant Point of Sale?
-A: You can get technical support for Abacre Restaurant Point of Sale by sending an email to support@abacre.com or by posting your questions on the discussion forums. You can also find answers to frequently asked questions on the support page. You can also contact Abacre Limited by phone or fax using the contact information on their website.
-Q: How can I update Abacre Restaurant Point of Sale?
-A: You can update Abacre Restaurant Point of Sale by downloading the latest version from the official website or by using the built-in update feature in the software. You can also subscribe to RSS feeds or free newsletter to get notified about new versions and updates.
-Q: How can I give feedback or suggestions for Abacre Restaurant Point of Sale?
-A: You can give feedback or suggestions for Abacre Restaurant Point of Sale by sending an email to feedback@abacre.com or by posting your comments on the feedback page. You can also rate and review Abacre Restaurant Point of Sale on various websites such as CNET Download.com, Softpedia.com, or Tucows.com.
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Geografía e Historia 1 ESO Santillana.pdf consists of eight chapters that cover different topics related to geography and history. Each chapter is divided into several sections that present the information in a clear and organized way. The book also has some features that make it more attractive and user-friendly:
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You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction | Github Repo
- """ - ) - - dark_button.click(fn=dark_inference, inputs=input_image, outputs=res_image) - exposure_button.click(fn=exposure_inference, inputs=input_image, outputs=res_image) - dark_example_images.click(fn=set_example_image, inputs=dark_example_images, outputs=dark_example_images.components) - exposure_example_images.click(fn=set_example_image, inputs=exposure_example_images, outputs=exposure_example_images.components) - -demo.launch(enable_queue=True) \ No newline at end of file diff --git a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/README.md b/spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/README.md deleted file mode 100644 index 2b0c6de8f2508ca3f1bdd3d4a203a71dfcffce3a..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/cascade_rpn/README.md +++ /dev/null @@ -1,29 +0,0 @@ -# Cascade RPN - -[ALGORITHM] - -We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720). - -``` -@inproceedings{vu2019cascade, - title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution}, - author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D}, - booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, - year={2019} -} -``` - -## Benchmark - -### Region proposal performance - -| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 | Download | -|:------:|:--------:|:-----:|:--------:|:-------------------:|:--------------:|:-------:|:--------------------------------------:| -| CRPN | R-50-FPN | caffe | - | - | - | 72.0 | [model](https://drive.google.com/file/d/1qxVdOnCgK-ee7_z0x6mvAir_glMu2Ihi/view?usp=sharing) | - -### Detection performance - -| Method | Proposal | Backbone | Style | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | -|:-------------:|:-----------:|:--------:|:-------:|:--------:|:--------:|:-------------------:|:--------------:|:------:|:--------------------------------------------:| -| Fast R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 39.9 | [model](https://drive.google.com/file/d/1NmbnuY5VHi8I9FE8xnp5uNvh2i-t-6_L/view?usp=sharing) | -| Faster R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 40.4 | [model](https://drive.google.com/file/d/1dS3Q66qXMJpcuuQgDNkLp669E5w1UMuZ/view?usp=sharing) | diff --git a/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py deleted file mode 100644 index e77a7fa8d6b8c1ad7fe293bc932d621464287e0c..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py +++ /dev/null @@ -1,5 +0,0 @@ -_base_ = [ - '../_base_/models/faster_rcnn_r50_fpn.py', - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' -] diff --git a/spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py b/spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py deleted file mode 100644 index 8830ef08481bae863bd1401223f4cbd14210e87f..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_detection/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py +++ /dev/null @@ -1,16 +0,0 @@ -_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' -model = dict( - pretrained='open-mmlab://regnetx_4.0gf', - backbone=dict( - type='RegNet', - arch='regnetx_4.0gf', - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, - style='pytorch'), - neck=dict( - type='FPN', - in_channels=[80, 240, 560, 1360], - out_channels=256, - num_outs=5)) diff --git a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py b/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py deleted file mode 100644 index af3f765b76e7269d22c8f362e1d41f03d1efaf93..0000000000000000000000000000000000000000 --- a/spaces/Andy1621/uniformer_image_segmentation/configs/fcn/fcn_d6_r101b-d16_512x1024_80k_cityscapes.py +++ /dev/null @@ -1,4 +0,0 @@ -_base_ = './fcn_d6_r50b-d16_512x1024_80k_cityscapes.py' -model = dict( - pretrained='torchvision://resnet101', - backbone=dict(type='ResNet', depth=101)) diff --git a/spaces/Anish13/fruit/app.py b/spaces/Anish13/fruit/app.py deleted file mode 100644 index ea6b61784d89366b1dc522dbc0590eb759c36b6e..0000000000000000000000000000000000000000 --- a/spaces/Anish13/fruit/app.py +++ /dev/null @@ -1,24 +0,0 @@ -import gradio as gr -from fastai.vision.all import * - -learn = load_learner('export.pkl') -categories = ('Lemon', 'Orange','dragon fruit', 'green apple', 'red apple', 'yellow apple') -def classify_image(img): - pred, idx, probs = learn.predict(img) - return dict(zip(categories, map(float, probs))) - -image = gr.inputs.Image(shape = (256, 256)) -label = gr.outputs.Label() -examples = ['apple.jpeg', 'dragon.jpeg', 'orange.jpeg', 'lemon.webp', 'green.jpeg', 'yellow.jpeg'] - -intf = gr.Interface(fn = classify_image, inputs=image, outputs=label, examples=examples) -intf.launch() - - - - -# def greet(name): -# return "Hello " + name + "!!" - -# iface = gr.Interface(fn=greet, inputs="text", outputs="text") -# iface.launch() diff --git a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/guided_diffusion/gaussian_diffusion.py b/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/guided_diffusion/gaussian_diffusion.py deleted file mode 100644 index b23ebf5a92f61a1a6b1490146768f22b6c96331b..0000000000000000000000000000000000000000 --- a/spaces/Anonymous-123/ImageNet-Editing/editing_diffusion/guided_diffusion/guided_diffusion/gaussian_diffusion.py +++ /dev/null @@ -1,922 +0,0 @@ -""" -This code started out as a PyTorch port of Ho et al's diffusion models: -https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py - -Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules. -""" - -import enum -import math - -import numpy as np -import torch as th - -from .nn import mean_flat -from .losses import normal_kl, discretized_gaussian_log_likelihood - -import pdb - - -def get_named_beta_schedule(schedule_name, num_diffusion_timesteps): - """ - Get a pre-defined beta schedule for the given name. - - The beta schedule library consists of beta schedules which remain similar - in the limit of num_diffusion_timesteps. - Beta schedules may be added, but should not be removed or changed once - they are committed to maintain backwards compatibility. - """ - if schedule_name == "linear": - # Linear schedule from Ho et al, extended to work for any number of - # diffusion steps. - scale = 1000 / num_diffusion_timesteps - beta_start = scale * 0.0001 - beta_end = scale * 0.02 - return np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64) - elif schedule_name == "cosine": - return betas_for_alpha_bar( - num_diffusion_timesteps, lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, - ) - else: - raise NotImplementedError(f"unknown beta schedule: {schedule_name}") - - -def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): - """ - Create a beta schedule that discretizes the given alpha_t_bar function, - which defines the cumulative product of (1-beta) over time from t = [0,1]. - - :param num_diffusion_timesteps: the number of betas to produce. - :param alpha_bar: a lambda that takes an argument t from 0 to 1 and - produces the cumulative product of (1-beta) up to that - part of the diffusion process. - :param max_beta: the maximum beta to use; use values lower than 1 to - prevent singularities. - """ - betas = [] - for i in range(num_diffusion_timesteps): - t1 = i / num_diffusion_timesteps - t2 = (i + 1) / num_diffusion_timesteps - betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) - return np.array(betas) - - -class ModelMeanType(enum.Enum): - """ - Which type of output the model predicts. - """ - - PREVIOUS_X = enum.auto() # the model predicts x_{t-1} - START_X = enum.auto() # the model predicts x_0 - EPSILON = enum.auto() # the model predicts epsilon - - -class ModelVarType(enum.Enum): - """ - What is used as the model's output variance. - - The LEARNED_RANGE option has been added to allow the model to predict - values between FIXED_SMALL and FIXED_LARGE, making its job easier. - """ - - LEARNED = enum.auto() - FIXED_SMALL = enum.auto() - FIXED_LARGE = enum.auto() - LEARNED_RANGE = enum.auto() - - -class LossType(enum.Enum): - MSE = enum.auto() # use raw MSE loss (and KL when learning variances) - RESCALED_MSE = enum.auto() # use raw MSE loss (with RESCALED_KL when learning variances) - KL = enum.auto() # use the variational lower-bound - RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB - - def is_vb(self): - return self == LossType.KL or self == LossType.RESCALED_KL - - -class GaussianDiffusion: - 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betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) - ) - # log calculation clipped because the posterior variance is 0 at the - # beginning of the diffusion chain. - self.posterior_log_variance_clipped = np.log( - np.append(self.posterior_variance[1], self.posterior_variance[1:]) - ) - self.posterior_mean_coef1 = ( - betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) - ) - self.posterior_mean_coef2 = ( - (1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod) - ) - - def q_mean_variance(self, x_start, t): - """ - Get the distribution q(x_t | x_0). - - :param x_start: the [N x C x ...] tensor of noiseless inputs. - :param t: the number of diffusion steps (minus 1). Here, 0 means one step. - :return: A tuple (mean, variance, log_variance), all of x_start's shape. - """ - mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start - variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape) - log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape) - return mean, variance, log_variance - - def q_sample(self, x_start, t, noise=None): - """ - Diffuse the data for a given number of diffusion steps. - - In other words, sample from q(x_t | x_0). - - :param x_start: the initial data batch. - :param t: the number of diffusion steps (minus 1). Here, 0 means one step. - :param noise: if specified, the split-out normal noise. - :return: A noisy version of x_start. - """ - if noise is None: - noise = th.randn_like(x_start) - assert noise.shape == x_start.shape - return ( - _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start - + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise - ) - - def q_posterior_mean_variance(self, x_start, x_t, t): - """ - Compute the mean and variance of the diffusion posterior: - - q(x_{t-1} | x_t, x_0) - - """ - assert x_start.shape == x_t.shape - posterior_mean = ( - _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start - + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t - ) - posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape) - posterior_log_variance_clipped = _extract_into_tensor( - self.posterior_log_variance_clipped, t, x_t.shape - ) - assert ( - posterior_mean.shape[0] - == posterior_variance.shape[0] - == posterior_log_variance_clipped.shape[0] - == x_start.shape[0] - ) - return posterior_mean, posterior_variance, posterior_log_variance_clipped - - def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None): - """ - Apply the model to get p(x_{t-1} | x_t), as well as a prediction of - the initial x, x_0. - - :param model: the model, which takes a signal and a batch of timesteps - as input. - :param x: the [N x C x ...] tensor at time t. - :param t: a 1-D Tensor of timesteps. - :param clip_denoised: if True, clip the denoised signal into [-1, 1]. - :param denoised_fn: if not None, a function which applies to the - x_start prediction before it is used to sample. Applies before - clip_denoised. - :param model_kwargs: if not None, a dict of extra keyword arguments to - pass to the model. This can be used for conditioning. - :return: a dict with the following keys: - - 'mean': the model mean output. - - 'variance': the model variance output. - - 'log_variance': the log of 'variance'. - - 'pred_xstart': the prediction for x_0. - """ - if model_kwargs is None: - model_kwargs = {} - - B, C = x.shape[:2] - assert t.shape == (B,) - model_output = model(x, self._scale_timesteps(t), **model_kwargs) - - if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]: - assert model_output.shape == (B, C * 2, *x.shape[2:]) - model_output, model_var_values = th.split(model_output, C, dim=1) - if self.model_var_type == ModelVarType.LEARNED: - model_log_variance = model_var_values - model_variance = th.exp(model_log_variance) - else: - min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape) - max_log = _extract_into_tensor(np.log(self.betas), t, x.shape) - # The model_var_values is [-1, 1] for [min_var, max_var]. - frac = (model_var_values + 1) / 2 - model_log_variance = frac * max_log + (1 - frac) * min_log - model_variance = th.exp(model_log_variance) - else: - model_variance, model_log_variance = { - # for fixedlarge, we set the initial (log-)variance like so - # to get a better decoder log likelihood. - ModelVarType.FIXED_LARGE: ( - np.append(self.posterior_variance[1], self.betas[1:]), - np.log(np.append(self.posterior_variance[1], self.betas[1:])), - ), - ModelVarType.FIXED_SMALL: ( - self.posterior_variance, - self.posterior_log_variance_clipped, - ), - }[self.model_var_type] - model_variance = _extract_into_tensor(model_variance, t, x.shape) - model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape) - - def process_xstart(x): - if denoised_fn is not None: - x = denoised_fn(x) - if clip_denoised: - return x.clamp(-1, 1) - return x - - if self.model_mean_type == ModelMeanType.PREVIOUS_X: - pred_xstart = process_xstart( - self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output) - ) - model_mean = model_output - elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]: - if self.model_mean_type == ModelMeanType.START_X: - pred_xstart = process_xstart(model_output) - else: - pred_xstart = process_xstart( - self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output) - ) - model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t) - else: - raise NotImplementedError(self.model_mean_type) - - assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape - return { - "mean": model_mean, - "variance": model_variance, - "log_variance": model_log_variance, - "pred_xstart": pred_xstart, - } - - def _predict_xstart_from_eps(self, x_t, t, eps): - assert x_t.shape == eps.shape - return ( - _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps - ) - - def _predict_xstart_from_xprev(self, x_t, t, xprev): - assert x_t.shape == xprev.shape - return ( # (xprev - coef2*x_t) / coef1 - _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev - - _extract_into_tensor( - self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape - ) - * x_t - ) - - def _predict_eps_from_xstart(self, x_t, t, pred_xstart): - return ( - _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart - ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) - - def _scale_timesteps(self, t): - if self.rescale_timesteps: - return t.float() * (1000.0 / self.num_timesteps) - return t - - def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None): - """ - Compute the mean for the previous step, given a function cond_fn that - computes the gradient of a conditional log probability with respect to - x. In particular, cond_fn computes grad(log(p(y|x))), and we want to - condition on y. - - This uses the conditioning strategy from Sohl-Dickstein et al. (2015). - """ - gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs) - new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float() - return new_mean - - def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None): - """ - Compute what the p_mean_variance output would have been, should the - model's score function be conditioned by cond_fn. - - See condition_mean() for details on cond_fn. - - Unlike condition_mean(), this instead uses the conditioning strategy - from Song et al (2020). - """ - alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape) - - eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"]) - eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, self._scale_timesteps(t), **model_kwargs) - - out = p_mean_var.copy() - out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps) - out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t) - return out - - def p_sample( - self, model, x, t, clip_denoised=True, denoised_fn=None, cond_fn=None, model_kwargs=None, - ): - """ - Sample x_{t-1} from the model at the given timestep. - - :param model: the model to sample from. - :param x: the current tensor at x_{t-1}. - :param t: the value of t, starting at 0 for the first diffusion step. - :param clip_denoised: if True, clip the x_start prediction to [-1, 1]. - :param denoised_fn: if not None, a function which applies to the - x_start prediction before it is used to sample. - :param cond_fn: if not None, this is a gradient function that acts - similarly to the model. - :param model_kwargs: if not None, a dict of extra keyword arguments to - pass to the model. This can be used for conditioning. - :return: a dict containing the following keys: - - 'sample': a random sample from the model. - - 'pred_xstart': a prediction of x_0. - """ - out = self.p_mean_variance( - model, - x, - t, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - model_kwargs=model_kwargs, - ) - noise = th.randn_like(x) - nonzero_mask = ( - (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) - ) # no noise when t == 0 - if cond_fn is not None: - out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs) - sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise - return {"sample": sample, "pred_xstart": out["pred_xstart"]} - - def p_sample_loop( - self, - model, - shape, - noise=None, - clip_denoised=True, - denoised_fn=None, - cond_fn=None, - model_kwargs=None, - device=None, - progress=False, - skip_timesteps=0, - init_image=None, - randomize_class=False, - ): - """ - Generate samples from the model. - - :param model: the model module. - :param shape: the shape of the samples, (N, C, H, W). - :param noise: if specified, the noise from the encoder to sample. - Should be of the same shape as `shape`. - :param clip_denoised: if True, clip x_start predictions to [-1, 1]. - :param denoised_fn: if not None, a function which applies to the - x_start prediction before it is used to sample. - :param cond_fn: if not None, this is a gradient function that acts - similarly to the model. - :param model_kwargs: if not None, a dict of extra keyword arguments to - pass to the model. This can be used for conditioning. - :param device: if specified, the device to create the samples on. - If not specified, use a model parameter's device. - :param progress: if True, show a tqdm progress bar. - :return: a non-differentiable batch of samples. - """ - final = None - for sample in self.p_sample_loop_progressive( - model, - shape, - noise=noise, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - cond_fn=cond_fn, - model_kwargs=model_kwargs, - device=device, - progress=progress, - skip_timesteps=skip_timesteps, - init_image=init_image, - randomize_class=randomize_class, - ): - final = sample - return final["sample"] - - def p_sample_loop_progressive( - self, - model, - shape, - noise=None, - clip_denoised=True, - denoised_fn=None, - cond_fn=None, - model_kwargs=None, - device=None, - progress=False, - skip_timesteps=0, - init_image=None, - postprocess_fn=None, - randomize_class=False, - ): - """ - Generate samples from the model and yield intermediate samples from - each timestep of diffusion. - - Arguments are the same as p_sample_loop(). - Returns a generator over dicts, where each dict is the return value of - p_sample(). - """ - # if device is None: - # device = next(model.parameters()).device - assert isinstance(shape, (tuple, list)) - if noise is not None: - img = noise - ''' - img_guidance = noise.to(device) - t_batch = th.tensor([int(t0*self.num_timesteps)-1]*len(img_guidance), device=device) - img = self.q_sample(img_guidance, t_batch) - indices = list(range(int(t0*self.num_timesteps)))[::-1] - ''' - else: - img = th.randn(*shape, device=device) - - # pdb.set_trace() - if skip_timesteps and init_image is None: - init_image = th.zeros_like(img) - - indices = list(range(self.num_timesteps - skip_timesteps))[::-1] - - batch_size = shape[0] - init_image_batch = th.tile(init_image, dims=(batch_size, 1, 1, 1)) - img = self.q_sample( - x_start=init_image_batch, - t=th.tensor(indices[0], dtype=th.long, device=device), - noise=img, - ) - - if progress: - # Lazy import so that we don't depend on tqdm. - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - t = th.tensor([i] * shape[0], device=device) - if randomize_class and "y" in model_kwargs: - model_kwargs["y"] = th.randint( - low=0, - high=model.num_classes, - size=model_kwargs["y"].shape, - device=model_kwargs["y"].device, - ) - with th.no_grad(): - out = self.p_sample( - model, - img, - t, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - cond_fn=cond_fn, - model_kwargs=model_kwargs, - ) - if postprocess_fn is not None: - out = postprocess_fn(out, t) - - yield out - img = out["sample"] - - def ddim_sample( - self, - model, - x, - t, - clip_denoised=True, - denoised_fn=None, - cond_fn=None, - model_kwargs=None, - eta=0.0, - ): - """ - Sample x_{t-1} from the model using DDIM. - - Same usage as p_sample(). - """ - out = self.p_mean_variance( - model, - x, - t, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - model_kwargs=model_kwargs, - ) - if cond_fn is not None: - out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs) - - # Usually our model outputs epsilon, but we re-derive it - # in case we used x_start or x_prev prediction. - eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"]) - - alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape) - alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape) - sigma = ( - eta - * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar)) - * th.sqrt(1 - alpha_bar / alpha_bar_prev) - ) - # Equation 12. - noise = th.randn_like(x) - mean_pred = ( - out["pred_xstart"] * th.sqrt(alpha_bar_prev) - + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps - ) - nonzero_mask = ( - (t != 0).float().view(-1, *([1] * (len(x.shape) - 1))) - ) # no noise when t == 0 - sample = mean_pred + nonzero_mask * sigma * noise - return {"sample": sample, "pred_xstart": out["pred_xstart"]} - - def ddim_reverse_sample( - self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None, eta=0.0, - ): - """ - Sample x_{t+1} from the model using DDIM reverse ODE. - """ - assert eta == 0.0, "Reverse ODE only for deterministic path" - out = self.p_mean_variance( - model, - x, - t, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - model_kwargs=model_kwargs, - ) - # Usually our model outputs epsilon, but we re-derive it - # in case we used x_start or x_prev prediction. - eps = ( - _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x - - out["pred_xstart"] - ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape) - alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape) - - # Equation 12. reversed - mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps - - return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]} - - def ddim_sample_loop( - self, - model, - shape, - noise=None, - clip_denoised=True, - denoised_fn=None, - cond_fn=None, - model_kwargs=None, - device=None, - progress=False, - eta=0.0, - skip_timesteps=0, - init_image=None, - randomize_class=False, - ): - """ - Generate samples from the model using DDIM. - - Same usage as p_sample_loop(). - """ - final = None - for sample in self.ddim_sample_loop_progressive( - model, - shape, - noise=noise, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - cond_fn=cond_fn, - model_kwargs=model_kwargs, - device=device, - progress=progress, - eta=eta, - skip_timesteps=skip_timesteps, - init_image=init_image, - randomize_class=randomize_class, - ): - final = sample - return final["sample"] - - def ddim_sample_loop_progressive( - self, - model, - shape, - noise=None, - clip_denoised=True, - denoised_fn=None, - cond_fn=None, - model_kwargs=None, - device=None, - progress=False, - eta=0.0, - skip_timesteps=0, - init_image=None, - postprocess_fn=None, - randomize_class=False, - ): - """ - Use DDIM to sample from the model and yield intermediate samples from - each timestep of DDIM. - - Same usage as p_sample_loop_progressive(). - """ - if device is None: - device = next(model.parameters()).device - assert isinstance(shape, (tuple, list)) - if noise is not None: - img = noise - else: - img = th.randn(*shape, device=device) - - if skip_timesteps and init_image is None: - init_image = th.zeros_like(img) - - indices = list(range(self.num_timesteps - skip_timesteps))[::-1] - - if init_image is not None: - my_t = th.ones([shape[0]], device=device, dtype=th.long) * indices[0] - batch_size = shape[0] - init_image_batch = th.tile(init_image, dims=(batch_size, 1, 1, 1)) - img = self.q_sample(init_image_batch, my_t, img) - - if progress: - # Lazy import so that we don't depend on tqdm. - from tqdm.auto import tqdm - - indices = tqdm(indices) - - for i in indices: - t = th.tensor([i] * shape[0], device=device) - if randomize_class and "y" in model_kwargs: - model_kwargs["y"] = th.randint( - low=0, - high=model.num_classes, - size=model_kwargs["y"].shape, - device=model_kwargs["y"].device, - ) - with th.no_grad(): - out = self.ddim_sample( - model, - img, - t, - clip_denoised=clip_denoised, - denoised_fn=denoised_fn, - cond_fn=cond_fn, - model_kwargs=model_kwargs, - eta=eta, - ) - - if postprocess_fn is not None: - out = postprocess_fn(out, t) - - yield out - img = out["sample"] - - def _vb_terms_bpd(self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None): - """ - Get a term for the variational lower-bound. - - The resulting units are bits (rather than nats, as one might expect). - This allows for comparison to other papers. - - :return: a dict with the following keys: - - 'output': a shape [N] tensor of NLLs or KLs. - - 'pred_xstart': the x_0 predictions. - """ - true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance( - x_start=x_start, x_t=x_t, t=t - ) - out = self.p_mean_variance( - model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs - ) - kl = normal_kl(true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]) - kl = mean_flat(kl) / np.log(2.0) - - decoder_nll = -discretized_gaussian_log_likelihood( - x_start, means=out["mean"], log_scales=0.5 * out["log_variance"] - ) - assert decoder_nll.shape == x_start.shape - decoder_nll = mean_flat(decoder_nll) / np.log(2.0) - - # At the first timestep return the decoder NLL, - # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t)) - output = th.where((t == 0), decoder_nll, kl) - return {"output": output, "pred_xstart": out["pred_xstart"]} - - def training_losses(self, model, x_start, t, model_kwargs=None, noise=None): - """ - Compute training losses for a single timestep. - - :param model: the model to evaluate loss on. - :param x_start: the [N x C x ...] tensor of inputs. - :param t: a batch of timestep indices. - :param model_kwargs: if not None, a dict of extra keyword arguments to - pass to the model. This can be used for conditioning. - :param noise: if specified, the specific Gaussian noise to try to remove. - :return: a dict with the key "loss" containing a tensor of shape [N]. - Some mean or variance settings may also have other keys. - """ - if model_kwargs is None: - model_kwargs = {} - if noise is None: - noise = th.randn_like(x_start) - x_t = self.q_sample(x_start, t, noise=noise) - - terms = {} - - if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL: - terms["loss"] = self._vb_terms_bpd( - model=model, - x_start=x_start, - x_t=x_t, - t=t, - clip_denoised=False, - model_kwargs=model_kwargs, - )["output"] - if self.loss_type == LossType.RESCALED_KL: - terms["loss"] *= self.num_timesteps - elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE: - model_output = model(x_t, self._scale_timesteps(t), **model_kwargs) - - if self.model_var_type in [ - ModelVarType.LEARNED, - ModelVarType.LEARNED_RANGE, - ]: - B, C = x_t.shape[:2] - assert model_output.shape == (B, C * 2, *x_t.shape[2:]) - model_output, model_var_values = th.split(model_output, C, dim=1) - # Learn the variance using the variational bound, but don't let - # it affect our mean prediction. - frozen_out = th.cat([model_output.detach(), model_var_values], dim=1) - terms["vb"] = self._vb_terms_bpd( - model=lambda *args, r=frozen_out: r, - x_start=x_start, - x_t=x_t, - t=t, - clip_denoised=False, - )["output"] - if self.loss_type == LossType.RESCALED_MSE: - # Divide by 1000 for equivalence with initial implementation. - # Without a factor of 1/1000, the VB term hurts the MSE term. - terms["vb"] *= self.num_timesteps / 1000.0 - - target = { - ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance( - x_start=x_start, x_t=x_t, t=t - )[0], - ModelMeanType.START_X: x_start, - ModelMeanType.EPSILON: noise, - }[self.model_mean_type] - assert model_output.shape == target.shape == x_start.shape - terms["mse"] = mean_flat((target - model_output) ** 2) - if "vb" in terms: - terms["loss"] = terms["mse"] + terms["vb"] - else: - terms["loss"] = terms["mse"] - else: - raise NotImplementedError(self.loss_type) - - return terms - - def _prior_bpd(self, x_start): - """ - Get the prior KL term for the variational lower-bound, measured in - bits-per-dim. - - This term can't be optimized, as it only depends on the encoder. - - :param x_start: the [N x C x ...] tensor of inputs. - :return: a batch of [N] KL values (in bits), one per batch element. - """ - batch_size = x_start.shape[0] - t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device) - qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t) - kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0) - return mean_flat(kl_prior) / np.log(2.0) - - def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None): - """ - Compute the entire variational lower-bound, measured in bits-per-dim, - as well as other related quantities. - - :param model: the model to evaluate loss on. - :param x_start: the [N x C x ...] tensor of inputs. - :param clip_denoised: if True, clip denoised samples. - :param model_kwargs: if not None, a dict of extra keyword arguments to - pass to the model. This can be used for conditioning. - - :return: a dict containing the following keys: - - total_bpd: the total variational lower-bound, per batch element. - - prior_bpd: the prior term in the lower-bound. - - vb: an [N x T] tensor of terms in the lower-bound. - - xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep. - - mse: an [N x T] tensor of epsilon MSEs for each timestep. - """ - device = x_start.device - batch_size = x_start.shape[0] - - vb = [] - xstart_mse = [] - mse = [] - for t in list(range(self.num_timesteps))[::-1]: - t_batch = th.tensor([t] * batch_size, device=device) - noise = th.randn_like(x_start) - x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise) - # Calculate VLB term at the current timestep - with th.no_grad(): - out = self._vb_terms_bpd( - model, - x_start=x_start, - x_t=x_t, - t=t_batch, - clip_denoised=clip_denoised, - model_kwargs=model_kwargs, - ) - vb.append(out["output"]) - xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2)) - eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"]) - mse.append(mean_flat((eps - noise) ** 2)) - - vb = th.stack(vb, dim=1) - xstart_mse = th.stack(xstart_mse, dim=1) - mse = th.stack(mse, dim=1) - - prior_bpd = self._prior_bpd(x_start) - total_bpd = vb.sum(dim=1) + prior_bpd - return { - "total_bpd": total_bpd, - "prior_bpd": prior_bpd, - "vb": vb, - "xstart_mse": xstart_mse, - "mse": mse, - } - - -def _extract_into_tensor(arr, timesteps, broadcast_shape): - """ - Extract values from a 1-D numpy array for a batch of indices. - - :param arr: the 1-D numpy array. - :param timesteps: a tensor of indices into the array to extract. - :param broadcast_shape: a larger shape of K dimensions with the batch - dimension equal to the length of timesteps. - :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims. - """ - res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float() - while len(res.shape) < len(broadcast_shape): - res = res[..., None] - return res.expand(broadcast_shape) diff --git a/spaces/Ashwanthram/myGenVoiceBot/README.md b/spaces/Ashwanthram/myGenVoiceBot/README.md deleted file mode 100644 index 4d0dcc9984911dfc6112d6d931389b8947a65f44..0000000000000000000000000000000000000000 --- a/spaces/Ashwanthram/myGenVoiceBot/README.md +++ /dev/null @@ -1,12 +0,0 @@ ---- -title: MyGenVoiceBot -emoji: 🌍 -colorFrom: yellow -colorTo: red -sdk: gradio -sdk_version: 3.39.0 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/spaces/Awesimo/jojogan/e4e/models/psp.py b/spaces/Awesimo/jojogan/e4e/models/psp.py deleted file mode 100644 index 36c0b2b7b3fdd28bc32272d0d8fcff24e4848355..0000000000000000000000000000000000000000 --- a/spaces/Awesimo/jojogan/e4e/models/psp.py +++ /dev/null @@ -1,99 +0,0 @@ -import matplotlib - -matplotlib.use('Agg') -import torch -from torch import nn -from e4e.models.encoders import psp_encoders -from e4e.models.stylegan2.model import Generator -from e4e.configs.paths_config import model_paths - - -def get_keys(d, name): - if 'state_dict' in d: - d = d['state_dict'] - d_filt = {k[len(name) + 1:]: v for k, v in d.items() if k[:len(name)] == name} - return d_filt - - -class pSp(nn.Module): - - def __init__(self, opts, device): - super(pSp, self).__init__() - self.opts = opts - self.device = device - # Define architecture - self.encoder = self.set_encoder() - self.decoder = Generator(opts.stylegan_size, 512, 8, channel_multiplier=2) - self.face_pool = torch.nn.AdaptiveAvgPool2d((256, 256)) - # Load weights if needed - self.load_weights() - - def set_encoder(self): - if self.opts.encoder_type == 'GradualStyleEncoder': - encoder = psp_encoders.GradualStyleEncoder(50, 'ir_se', self.opts) - elif self.opts.encoder_type == 'Encoder4Editing': - encoder = psp_encoders.Encoder4Editing(50, 'ir_se', self.opts) - else: - raise Exception('{} is not a valid encoders'.format(self.opts.encoder_type)) - return encoder - - def load_weights(self): - if self.opts.checkpoint_path is not None: - print('Loading e4e over the pSp framework from checkpoint: {}'.format(self.opts.checkpoint_path)) - ckpt = torch.load(self.opts.checkpoint_path, map_location='cpu') - self.encoder.load_state_dict(get_keys(ckpt, 'encoder'), strict=True) - self.decoder.load_state_dict(get_keys(ckpt, 'decoder'), strict=True) - self.__load_latent_avg(ckpt) - else: - print('Loading encoders weights from irse50!') - encoder_ckpt = torch.load(model_paths['ir_se50']) - self.encoder.load_state_dict(encoder_ckpt, strict=False) - print('Loading decoder weights from pretrained!') - ckpt = torch.load(self.opts.stylegan_weights) - self.decoder.load_state_dict(ckpt['g_ema'], strict=False) - self.__load_latent_avg(ckpt, repeat=self.encoder.style_count) - - def forward(self, x, resize=True, latent_mask=None, input_code=False, randomize_noise=True, - inject_latent=None, return_latents=False, alpha=None): - if input_code: - codes = x - else: - codes = self.encoder(x) - # normalize with respect to the center of an average face - if self.opts.start_from_latent_avg: - if codes.ndim == 2: - codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :] - else: - codes = codes + self.latent_avg.repeat(codes.shape[0], 1, 1) - - if latent_mask is not None: - for i in latent_mask: - if inject_latent is not None: - if alpha is not None: - codes[:, i] = alpha * inject_latent[:, i] + (1 - alpha) * codes[:, i] - else: - codes[:, i] = inject_latent[:, i] - else: - codes[:, i] = 0 - - input_is_latent = not input_code - images, result_latent = self.decoder([codes], - input_is_latent=input_is_latent, - randomize_noise=randomize_noise, - return_latents=return_latents) - - if resize: - images = self.face_pool(images) - - if return_latents: - return images, result_latent - else: - return images - - def __load_latent_avg(self, ckpt, repeat=None): - if 'latent_avg' in ckpt: - self.latent_avg = ckpt['latent_avg'].to(self.device) - if repeat is not None: - self.latent_avg = self.latent_avg.repeat(repeat, 1) - else: - self.latent_avg = None diff --git a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py b/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py deleted file mode 100644 index 22016be150df4abbe912700d7ca29f8b7b72554a..0000000000000000000000000000000000000000 --- a/spaces/Awiny/Image2Paragraph/models/grit_src/third_party/CenterNet2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_C4_1x.py +++ /dev/null @@ -1,8 +0,0 @@ -from ..common.train import train -from ..common.optim import SGD as optimizer -from ..common.coco_schedule import lr_multiplier_1x as lr_multiplier -from ..common.data.coco import dataloader -from ..common.models.mask_rcnn_c4 import model - -model.backbone.freeze_at = 2 -train.init_checkpoint = "detectron2://ImageNetPretrained/MSRA/R-50.pkl" diff --git a/spaces/Banbri/zcvzcv/src/components/ui/checkbox.tsx b/spaces/Banbri/zcvzcv/src/components/ui/checkbox.tsx deleted file mode 100644 index 5850485b9fecba303bdba1849e5a7b6329300af4..0000000000000000000000000000000000000000 --- a/spaces/Banbri/zcvzcv/src/components/ui/checkbox.tsx +++ /dev/null @@ -1,30 +0,0 @@ -"use client" - -import * as React from "react" -import * as CheckboxPrimitive from "@radix-ui/react-checkbox" -import { Check } from "lucide-react" - -import { cn } from "@/lib/utils" - -const Checkbox = React.forwardRef< - React.ElementRefSi eres un fan de los juegos de fútbol, es posible que hayas oído hablar de eFootball PES 2021, la última edición del popular juego de simulación de fútbol de Konami. PES 2021 ofrece gráficos realistas, jugabilidad y modos que te permiten disfrutar de la emoción del hermoso juego. Sin embargo, algunos usuarios pueden encontrar un error frustrante al intentar lanzar el juego en su PC con Windows. El código de error es 0xc00007b, y evita que el juego se inicie correctamente. En este artículo, explicaremos qué es este error, por qué ocurre y cómo solucionarlo. También te mostraremos cómo descargar 0xc00007b para PES 2021 en caso de que aún no lo tengas.
-Download ☆☆☆ https://bltlly.com/2v6M88
El error 0xc00007b es un error común de Windows que generalmente afecta a aplicaciones y juegos que usan bibliotecas de Microsoft Visual C++. El mensaje de error dice "La aplicación no pudo iniciarse correctamente (0xc000007b). Haga clic en Aceptar para cerrar la aplicación." Esto significa que hay algo mal con los archivos o la configuración de la aplicación o juego que está tratando de ejecutar.
-Hay varias causas posibles de este error, como:
-La buena noticia es que hay varias maneras de corregir este error y hacer que su aplicación o juego funcione de nuevo.
-Dependiendo de la causa exacta del problema, hay diferentes métodos que puede tratar de solucionar el error 0xc00007b en su PC con Windows. Estos son algunos de los más efectivos:
-Otra posible razón para el error es que está utilizando una versión de Windows desactualizada o no soportada. Las versiones de software más antiguas a menudo tienen errores o problemas de compatibilidad que pueden causar errores. Para solucionar esto, debe actualizar su sistema Windows a la última versión disponible. Para hacer esto, siga estos pasos:
- -A veces, el error puede ocurrir porque no tiene suficientes permisos para ejecutar la aplicación o el juego. Para solucionar esto, debe ejecutar su aplicación con derechos de administrador. Esto le dará acceso completo a los recursos y archivos del sistema que necesita. Para hacer esto, siga estos pasos:
-Como mencionamos anteriormente, el error 0xc00007b a menudo está relacionado con las bibliotecas de Microsoft Visual C++ que son utilizadas por muchas aplicaciones y juegos. Si estas bibliotecas están dañadas o faltan, puede encontrar el error. Para solucionar esto, debe reinstalar los paquetes redistribuibles de Microsoft Visual C++ en su PC. Estos son los componentes de software que proporcionan el entorno de tiempo de ejecución para tus aplicaciones y juegos. Para ello, sigue estos pasos:
-Si ninguno de los métodos anteriores funciona, es posible que necesite desinstalar y reinstalar PES 2021 en su PC. Esto asegurará que usted tiene una instalación fresca y limpia del juego, sin ningún archivo corrupto o faltante que puede causar el error. Para hacer esto, siga estos pasos:
-El último método que puede intentar para solucionar el error 0xc00007b es arreglar cualquier archivo dañado o dañado en su sistema Windows. Estos archivos pueden interferir con el correcto funcionamiento de sus aplicaciones y juegos, y causar errores. Para solucionarlos, debe usar una herramienta integrada llamada System File Checker (SFC). Esta herramienta escaneará su sistema en busca de errores y los reparará automáticamente. Para hacer esto, siga estos pasos:
-El primer paso es visitar el sitio web oficial de PES 2021, que es . Aquí encontrarás toda la información sobre el juego, como sus características, modos, equipos, jugadores, etc. También encontrarás enlaces para descargar PES 2021 para diferentes plataformas, como PC, PlayStation, Xbox, etc.
-El siguiente paso es elegir su plataforma y edición preferida de PES 2021. El juego
El siguiente paso es elegir su plataforma y edición preferida de PES 2021. El juego está disponible para PC, PlayStation 4, PlayStation 5, Xbox One y Xbox Series X/S. También puede elegir entre la edición estándar y la edición de actualización de temporada. La edición estándar incluye el juego completo y algunos artículos de bonificación, como el modo UEFA Euro 2020, la Iconic Moment Series y las monedas myClub. La edición de actualización de temporada incluye el mismo contenido que la edición estándar, pero con listas actualizadas, kits y estadios para la temporada 2020/2021. Los precios de las ediciones varían dependiendo de su plataforma y región.
-El paso final es descargar e instalar PES 2021 en su PC o consola. Para hacer esto, necesita tener suficiente espacio de almacenamiento y una conexión a Internet estable. El tamaño de descarga de PES 2021 es de unos 40 GB para PC y 50 GB para consolas. El proceso de instalación puede tardar algún tiempo dependiendo de su sistema. Para descargar e instalar PES 2021, siga estos pasos:
-Aquí están algunas de las preguntas más frecuentes sobre PES 2021 y el error 0xc00007b:
-A: Según el sitio web oficial de PES 2021, estos son los requisitos mínimos del sistema para PES 2021 en PC:
-A: Para jugar PES 2021 online con otros jugadores, necesitas tener una conexión a Internet y una cuenta de Konami ID. Puedes crear una cuenta de Konami ID gratis visitando . Una vez que tenga una cuenta, puede acceder a varios modos en línea en PES 2021, como el modo eFootball, el modo myClub, el modo Matchday, el modo cooperativo en línea, etc.
-A: Para actualizar PES 2021 para obtener las últimas características y contenido, necesita tener una conexión a Internet y suficiente espacio de almacenamiento en su PC o consola. Puede comprobar las actualizaciones manualmente en Configuración > Sistema > Actualizaciones en PES 2021. Alternativamente, puede habilitar las actualizaciones automáticas yendo a Configuración > Sistema > Actualizaciones automáticas en PES
A: PES 2021 ofrece muchas opciones para personalizar tu experiencia de juego según tus preferencias y estilo. Puede cambiar varios ajustes, como el ángulo de la cámara, el nivel de dificultad, la velocidad del juego, los efectos de sonido, etc. También puede editar sus jugadores, equipos, kits, logotipos, etc. utilizando el modo de edición. También puedes descargar e instalar varios mods y parches de la comunidad PES que añaden más características y contenido al juego.
-A: Si tiene algún problema o pregunta con respecto a PES 2021, puede ponerse en contacto con el equipo de soporte de PES 2021 visitando . Aquí encontrarás una sección de preguntas frecuentes que responde a algunas de las preguntas más frecuentes sobre el juego. También puede enviar una solicitud de soporte llenando un formulario con sus detalles y problemas. El equipo de soporte le responderá lo antes posible.
64aa2da5cfGPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
" -examples = [ - ['Karl is writing some amazing software'], - ["Cubby is also working on software but it is not a lot of natural language processing"], - ["Now they are going to work together on NLP as osteopathic physicians combining their powers"] -] -gr.Interface.load("huggingface/EleutherAI/gpt-j-6B", inputs=gr.inputs.Textbox(lines=5, label="Input Text"),title=title,description=description,article=article, examples=examples,enable_queue=True).launch() \ No newline at end of file diff --git a/spaces/BilalSardar/Object-Color-Detection-in-Video/README.md b/spaces/BilalSardar/Object-Color-Detection-in-Video/README.md deleted file mode 100644 index 864efdeab10a571c4970725c97410f26302055b9..0000000000000000000000000000000000000000 --- a/spaces/BilalSardar/Object-Color-Detection-in-Video/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: Object & Color Detection In Video -emoji: 😏 -colorFrom: green -colorTo: purple -sdk: gradio -sdk_version: 3.11 -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/BramVanroy/opus-mt/utils.py b/spaces/BramVanroy/opus-mt/utils.py deleted file mode 100644 index 2af6857513b01bf322038ea5f28d3b5b57e14dd0..0000000000000000000000000000000000000000 --- a/spaces/BramVanroy/opus-mt/utils.py +++ /dev/null @@ -1,109 +0,0 @@ -from collections import defaultdict -from typing import List, Optional, Tuple, Union - -import stanza -import streamlit as st -from stanza import Pipeline -from torch.nn import Parameter -from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer - - -MODEL_MAP = { - "Bulgarian": "bg", - "Catalan": "en", - "Chinese": "zh", - "Danish": "da", - "Dutch": "nl", - "English": "en", - "Finnish": "fi", - "French": "fr", - "German": "de", - "Greek": "el", - "Hungarian": "hu", - "Italian": "it", - "Japanese": "ja", - "Lithuanian": "lt", - "Norwegian": "no", - "Polish": "pl", - "Portuguese": "pt", - "Romanian": "ro", - "Russian": "ru", - "Spanish": "es", - "Swedish": "sv", - "Turkish": "tr", - "Vietnamese": "vi" -} - -REV_MODEL_MAP = {v: k for k, v in MODEL_MAP.items()} - -@st.cache(show_spinner=False) -def get_all_src_lang_combos(): - from huggingface_hub import list_models - # Get helsinki-nlp Opus models - model_list = list_models() - prefix = "Helsinki-NLP/opus-mt-" - models = [tuple(x.modelId.replace(prefix, "").split("-", 1)) for x in model_list if x.modelId.startswith(prefix)] - src2tgts = defaultdict(list) - for model in models: - src2tgts[model[0]].append(model[1]) - - return {k: sorted(v) for k, v in src2tgts.items()} - - -SRC_TGT_MODELS = get_all_src_lang_combos() - - -@st.cache(show_spinner=False) -def get_tgt_langs_for_src(src_lang): - """Returns a list of full language names that are compatible with a given source language""" - try: - tgt_langs_abbrs = SRC_TGT_MODELS[src_lang] - return sorted([REV_MODEL_MAP[lang] for lang in tgt_langs_abbrs if lang in REV_MODEL_MAP]) - except KeyError: - return None - - -st_hash_funcs = {PreTrainedModel: lambda model: model.name_or_path, - PreTrainedTokenizer: lambda tokenizer: tokenizer.name_or_path, - Pipeline: lambda pipeline: pipeline.lang, - Parameter: lambda param: param.data} - - -@st.cache(show_spinner=False, hash_funcs=st_hash_funcs, allow_output_mutation=True) -def load_mt_pipeline(model_name: str) -> Optional[Tuple[PreTrainedModel, PreTrainedTokenizer]]: - """Load an opus-mt model, download it if it has not been installed yet.""" - try: - model = AutoModelForSeq2SeqLM.from_pretrained(model_name) - tokenizer = AutoTokenizer.from_pretrained(model_name) - return model, tokenizer - except: - return None - - -@st.cache(show_spinner=False, hash_funcs=st_hash_funcs, allow_output_mutation=True) -def load_stanza(lang: str) -> Optional[Pipeline]: - """Load an opus-mt model, download it if it has not been installed yet.""" - try: - stanza.download(lang, processors="tokenize", verbose=False) - return Pipeline(lang=lang, processors="tokenize", verbose=False) - except: - return None - - -@st.cache(show_spinner=False, hash_funcs=st_hash_funcs, allow_output_mutation=True) -def sentence_split(model: Pipeline, text) -> List[str]: - doc = model(text) - return [sentence.text for sentence in doc.sentences] - - -@st.cache(show_spinner=False, hash_funcs=st_hash_funcs, allow_output_mutation=True) -def translate(model: PreTrainedModel, tokenizer: PreTrainedTokenizer, text: Union[str, List[str]]) -> List[str]: - translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True)) - return [tokenizer.decode(tokens, skip_special_tokens=True) for tokens in translated] - - -def set_st_query_params(): - query_params = {"src_lang": st.session_state["src_lang"], - "tgt_lang": st.session_state["tgt_lang"], - "text": st.session_state["text"]} - st.experimental_set_query_params(**query_params) diff --git a/spaces/CALM/Dashboard/app.py b/spaces/CALM/Dashboard/app.py deleted file mode 100644 index 91f50e5f2d23583e6e1610d8546ab8c5e3e1a64a..0000000000000000000000000000000000000000 --- a/spaces/CALM/Dashboard/app.py +++ /dev/null @@ -1,156 +0,0 @@ -import pandas as pd -import streamlit as st -import wandb - -from dashboard_utils.bubbles import get_global_metrics, get_new_bubble_data, get_leaderboard -from dashboard_utils.main_metrics import get_main_metrics -from streamlit_observable import observable -import time -import requests - -import streamlit as st -from streamlit_lottie import st_lottie - - -def load_lottieurl(url: str): - r = requests.get(url) - if r.status_code != 200: - return None - return r.json() - - -# Only need to set these here as we are add controls outside of Hydralit, to customise a run Hydralit! -st.set_page_config(page_title="Dashboard", layout="wide") - -st.markdown("Awesome multilingual OCR toolkits based on PaddlePaddle （practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices） | Github Repo
" -examples = [['example.jpg','en']] -gr.Interface( - inference, - [gr.inputs.Image(type='file', label='Input'),gr.inputs.Dropdown(choices=['ch', 'en', 'fr', 'german', 'korean', 'japan'], type="value", default='en', label='language')], - gr.outputs.Image(type='file', label='Output'), - title=title, - description=description, - article=article, - examples=examples - ).launch() \ No newline at end of file diff --git a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py b/spaces/FridaZuley/RVC_HFKawaii/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py deleted file mode 100644 index 823b44fb64898e8dcbb12180ba45d1718f9b03f7..0000000000000000000000000000000000000000 --- a/spaces/FridaZuley/RVC_HFKawaii/infer/lib/uvr5_pack/lib_v5/nets_537238KB.py +++ /dev/null @@ -1,123 +0,0 @@ -import numpy as np -import torch -import torch.nn.functional as F -from torch import nn - -from . import layers_537238KB as layers - - -class BaseASPPNet(nn.Module): - def __init__(self, nin, ch, dilations=(4, 8, 16)): - super(BaseASPPNet, self).__init__() - self.enc1 = layers.Encoder(nin, ch, 3, 2, 1) - self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1) - self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1) - self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1) - - self.aspp = layers.ASPPModule(ch * 8, ch * 16, dilations) - - self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1) - self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1) - self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1) - self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1) - - def __call__(self, x): - h, e1 = self.enc1(x) - h, e2 = self.enc2(h) - h, e3 = self.enc3(h) - h, e4 = self.enc4(h) - - h = self.aspp(h) - - h = self.dec4(h, e4) - h = self.dec3(h, e3) - h = self.dec2(h, e2) - h = self.dec1(h, e1) - - return h - - -class CascadedASPPNet(nn.Module): - def __init__(self, n_fft): - super(CascadedASPPNet, self).__init__() - self.stg1_low_band_net = BaseASPPNet(2, 64) - self.stg1_high_band_net = BaseASPPNet(2, 64) - - self.stg2_bridge = layers.Conv2DBNActiv(66, 32, 1, 1, 0) - self.stg2_full_band_net = BaseASPPNet(32, 64) - - self.stg3_bridge = layers.Conv2DBNActiv(130, 64, 1, 1, 0) - self.stg3_full_band_net = BaseASPPNet(64, 128) - - self.out = nn.Conv2d(128, 2, 1, bias=False) - self.aux1_out = nn.Conv2d(64, 2, 1, bias=False) - self.aux2_out = nn.Conv2d(64, 2, 1, bias=False) - - self.max_bin = n_fft // 2 - self.output_bin = n_fft // 2 + 1 - - self.offset = 128 - - def forward(self, x, aggressiveness=None): - mix = x.detach() - x = x.clone() - - x = x[:, :, : self.max_bin] - - bandw = x.size()[2] // 2 - aux1 = torch.cat( - [ - self.stg1_low_band_net(x[:, :, :bandw]), - self.stg1_high_band_net(x[:, :, bandw:]), - ], - dim=2, - ) - - h = torch.cat([x, aux1], dim=1) - aux2 = self.stg2_full_band_net(self.stg2_bridge(h)) - - h = torch.cat([x, aux1, aux2], dim=1) - h = self.stg3_full_band_net(self.stg3_bridge(h)) - - mask = torch.sigmoid(self.out(h)) - mask = F.pad( - input=mask, - pad=(0, 0, 0, self.output_bin - mask.size()[2]), - mode="replicate", - ) - - if self.training: - aux1 = torch.sigmoid(self.aux1_out(aux1)) - aux1 = F.pad( - input=aux1, - pad=(0, 0, 0, self.output_bin - aux1.size()[2]), - mode="replicate", - ) - aux2 = torch.sigmoid(self.aux2_out(aux2)) - aux2 = F.pad( - input=aux2, - pad=(0, 0, 0, self.output_bin - aux2.size()[2]), - mode="replicate", - ) - return mask * mix, aux1 * mix, aux2 * mix - else: - if aggressiveness: - mask[:, :, : aggressiveness["split_bin"]] = torch.pow( - mask[:, :, : aggressiveness["split_bin"]], - 1 + aggressiveness["value"] / 3, - ) - mask[:, :, aggressiveness["split_bin"] :] = torch.pow( - mask[:, :, aggressiveness["split_bin"] :], - 1 + aggressiveness["value"], - ) - - return mask * mix - - def predict(self, x_mag, aggressiveness=None): - h = self.forward(x_mag, aggressiveness) - - if self.offset > 0: - h = h[:, :, :, self.offset : -self.offset] - assert h.size()[3] > 0 - - return h diff --git a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/model.py b/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/model.py deleted file mode 100644 index e050d3204d8f1becdf0f8b3133470708e5420cea..0000000000000000000000000000000000000000 --- a/spaces/GipAdonimus/Real-Time-Voice-Cloning/encoder/model.py +++ /dev/null @@ -1,135 +0,0 @@ -from encoder.params_model import * -from encoder.params_data import * -from scipy.interpolate import interp1d -from sklearn.metrics import roc_curve -from torch.nn.utils import clip_grad_norm_ -from scipy.optimize import brentq -from torch import nn -import numpy as np -import torch - - -class SpeakerEncoder(nn.Module): - def __init__(self, device, loss_device): - super().__init__() - self.loss_device = loss_device - - # Network defition - self.lstm = nn.LSTM(input_size=mel_n_channels, - hidden_size=model_hidden_size, - num_layers=model_num_layers, - batch_first=True).to(device) - self.linear = nn.Linear(in_features=model_hidden_size, - out_features=model_embedding_size).to(device) - self.relu = torch.nn.ReLU().to(device) - - # Cosine similarity scaling (with fixed initial parameter values) - self.similarity_weight = nn.Parameter(torch.tensor([10.])).to(loss_device) - self.similarity_bias = nn.Parameter(torch.tensor([-5.])).to(loss_device) - - # Loss - self.loss_fn = nn.CrossEntropyLoss().to(loss_device) - - def do_gradient_ops(self): - # Gradient scale - self.similarity_weight.grad *= 0.01 - self.similarity_bias.grad *= 0.01 - - # Gradient clipping - clip_grad_norm_(self.parameters(), 3, norm_type=2) - - def forward(self, utterances, hidden_init=None): - """ - Computes the embeddings of a batch of utterance spectrograms. - - :param utterances: batch of mel-scale filterbanks of same duration as a tensor of shape - (batch_size, n_frames, n_channels) - :param hidden_init: initial hidden state of the LSTM as a tensor of shape (num_layers, - batch_size, hidden_size). Will default to a tensor of zeros if None. - :return: the embeddings as a tensor of shape (batch_size, embedding_size) - """ - # Pass the input through the LSTM layers and retrieve all outputs, the final hidden state - # and the final cell state. - out, (hidden, cell) = self.lstm(utterances, hidden_init) - - # We take only the hidden state of the last layer - embeds_raw = self.relu(self.linear(hidden[-1])) - - # L2-normalize it - embeds = embeds_raw / (torch.norm(embeds_raw, dim=1, keepdim=True) + 1e-5) - - return embeds - - def similarity_matrix(self, embeds): - """ - Computes the similarity matrix according the section 2.1 of GE2E. - - :param embeds: the embeddings as a tensor of shape (speakers_per_batch, - utterances_per_speaker, embedding_size) - :return: the similarity matrix as a tensor of shape (speakers_per_batch, - utterances_per_speaker, speakers_per_batch) - """ - speakers_per_batch, utterances_per_speaker = embeds.shape[:2] - - # Inclusive centroids (1 per speaker). Cloning is needed for reverse differentiation - centroids_incl = torch.mean(embeds, dim=1, keepdim=True) - centroids_incl = centroids_incl.clone() / (torch.norm(centroids_incl, dim=2, keepdim=True) + 1e-5) - - # Exclusive centroids (1 per utterance) - centroids_excl = (torch.sum(embeds, dim=1, keepdim=True) - embeds) - centroids_excl /= (utterances_per_speaker - 1) - centroids_excl = centroids_excl.clone() / (torch.norm(centroids_excl, dim=2, keepdim=True) + 1e-5) - - # Similarity matrix. The cosine similarity of already 2-normed vectors is simply the dot - # product of these vectors (which is just an element-wise multiplication reduced by a sum). - # We vectorize the computation for efficiency. - sim_matrix = torch.zeros(speakers_per_batch, utterances_per_speaker, - speakers_per_batch).to(self.loss_device) - mask_matrix = 1 - np.eye(speakers_per_batch, dtype=np.int) - for j in range(speakers_per_batch): - mask = np.where(mask_matrix[j])[0] - sim_matrix[mask, :, j] = (embeds[mask] * centroids_incl[j]).sum(dim=2) - sim_matrix[j, :, j] = (embeds[j] * centroids_excl[j]).sum(dim=1) - - ## Even more vectorized version (slower maybe because of transpose) - # sim_matrix2 = torch.zeros(speakers_per_batch, speakers_per_batch, utterances_per_speaker - # ).to(self.loss_device) - # eye = np.eye(speakers_per_batch, dtype=np.int) - # mask = np.where(1 - eye) - # sim_matrix2[mask] = (embeds[mask[0]] * centroids_incl[mask[1]]).sum(dim=2) - # mask = np.where(eye) - # sim_matrix2[mask] = (embeds * centroids_excl).sum(dim=2) - # sim_matrix2 = sim_matrix2.transpose(1, 2) - - sim_matrix = sim_matrix * self.similarity_weight + self.similarity_bias - return sim_matrix - - def loss(self, embeds): - """ - Computes the softmax loss according the section 2.1 of GE2E. - - :param embeds: the embeddings as a tensor of shape (speakers_per_batch, - utterances_per_speaker, embedding_size) - :return: the loss and the EER for this batch of embeddings. - """ - speakers_per_batch, utterances_per_speaker = embeds.shape[:2] - - # Loss - sim_matrix = self.similarity_matrix(embeds) - sim_matrix = sim_matrix.reshape((speakers_per_batch * utterances_per_speaker, - speakers_per_batch)) - ground_truth = np.repeat(np.arange(speakers_per_batch), utterances_per_speaker) - target = torch.from_numpy(ground_truth).long().to(self.loss_device) - loss = self.loss_fn(sim_matrix, target) - - # EER (not backpropagated) - with torch.no_grad(): - inv_argmax = lambda i: np.eye(1, speakers_per_batch, i, dtype=np.int)[0] - labels = np.array([inv_argmax(i) for i in ground_truth]) - preds = sim_matrix.detach().cpu().numpy() - - # Snippet from https://yangcha.github.io/EER-ROC/ - fpr, tpr, thresholds = roc_curve(labels.flatten(), preds.flatten()) - eer = brentq(lambda x: 1. - x - interp1d(fpr, tpr)(x), 0., 1.) - - return loss, eer diff --git a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp b/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp deleted file mode 100644 index 36d06c8e9068570c3e7624895d474f33dbfe3d29..0000000000000000000000000000000000000000 --- a/spaces/Gmq-x/gpt-academic/crazy_functions/test_project/cpp/libJPG/jpgd.cpp +++ /dev/null @@ -1,3276 +0,0 @@ -// jpgd.cpp - C++ class for JPEG decompression. -// Public domain, Rich GeldreichDonut: OCR-free Document Understanding Transformer | Github Repo
" -examples = [["example_1.jpg", "select the menu icon right of cloud icon at the top", "", True], - ["example_1.jpg", "click on down arrow beside the entertainment", "", True], - ["example_1.jpg", "select the down arrow button beside lifestyle", "", True], - ["example_1.jpg", "click on the image beside the option traffic", "", True], - ["example_3.jpg", "select the third row first image", "", True], - ["example_3.jpg", "click the tick mark on the first image", "", True], - ["example_3.jpg", "select the ninth image", "", True], - ["example_3.jpg", "select the add icon", "", True], - ["example_3.jpg", "click the first image", "", True], - ["val-image-4.jpg", 'select 4153365454', "", True], - ['val-image-4.jpg', 'go to cell', "", True], - ['val-image-4.jpg', 'select number above cell', "", True], - ["val-image-1.jpg", "select calendar option", "", True], - ["val-image-1.jpg", "select photos&videos option", "", True], - ["val-image-2.jpg", "click on change store", "", True], - ["val-image-2.jpg", "click on shop menu at the bottom", "", True], - ["val-image-3.jpg", "click on image above short meow", "", True], - ["val-image-3.jpg", "go to cat sounds", "", True], - ["example_2.jpg", "click on green color button", "", True], - ["example_2.jpg", "click on text which is beside call now", "", True], - ["example_2.jpg", "click on more button", "", True], - ["example_2.jpg", "enter the text field next to the name", "", True], - ] - -demo = gr.Interface(fn=process_refexp, - inputs=[gr.Image(type="pil"), "text", "text", gr.Checkbox( - value=True, label="Return Annotated Image", visible=False)], - outputs=[gr.Image(type="pil"), "json"], - title=title, - description=description, - article=article, - examples=examples, - # caching examples inference takes too long to start space after app change commit - cache_examples=False - ) - -# share=True when running in a Jupyter Notebook -demo.launch(server_name="0.0.0.0") diff --git a/spaces/HGZeon/test_model_2/app.py b/spaces/HGZeon/test_model_2/app.py deleted file mode 100644 index dd24ed351a08a992006b3f3e0985fbab7e91bfc0..0000000000000000000000000000000000000000 --- a/spaces/HGZeon/test_model_2/app.py +++ /dev/null @@ -1,17 +0,0 @@ -import gradio as gr -from transformers import pipeline - -pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") - - -def predict(image): - predictions = pipeline(image) - return {p["label"]: p["score"] for p in predictions} - - -gr.Interface( - predict, - inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"), - outputs=gr.outputs.Label(num_top_classes=2), - title="Hot Dog? Or Not?", -).launch() diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/m2m_100/tok.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/m2m_100/tok.sh deleted file mode 100644 index ba2ec5a2f3f4794d2e528d3a6574bf05abe1d043..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/m2m_100/tok.sh +++ /dev/null @@ -1,83 +0,0 @@ -#!/usr/bin/env bash -# Copyright (c) 2019-present, Facebook, Inc. -# All rights reserved. -# -# This source code is licensed under the license found in the -# LICENSE file in the root directory of this source tree. -# - -set -e - -TOKENIZERS_SCRIPTS=tokenizers -INSTALL_PATH=$TOKENIZERS_SCRIPTS/thirdparty - -N_THREADS=8 - -lg=$1 - -MOSES=$INSTALL_PATH/mosesdecoder -REPLACE_UNICODE_PUNCT=$MOSES/scripts/tokenizer/replace-unicode-punctuation.perl -NORM_PUNC=$MOSES/scripts/tokenizer/normalize-punctuation.perl -REM_NON_PRINT_CHAR=$MOSES/scripts/tokenizer/remove-non-printing-char.perl -TOKENIZER=$MOSES/scripts/tokenizer/tokenizer.perl - -# special tokenization for Romanian -WMT16_SCRIPTS=$INSTALL_PATH/wmt16-scripts - -NORMALIZE_ROMANIAN=$WMT16_SCRIPTS/preprocess/normalise-romanian.py -REMOVE_DIACRITICS=$WMT16_SCRIPTS/preprocess/remove-diacritics.py - -# Burmese -MY_SEGMENT=$INSTALL_PATH/seg_my.py - -# Arabic -AR_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenizer_ar.sh - -# Korean -KO_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ko.sh - -# Japanese -JA_SEGMENT=$TOKENIZERS_SCRIPTS/seg_ja.sh - -# Indic -IN_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_indic.py -INDIC_RESOURCES_PATH=$INSTALL_PATH/indic_nlp_resources - -# Thai -THAI_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_thai.py - -# Chinese -CHINESE_TOKENIZER=$TOKENIZERS_SCRIPTS/tokenize_zh.py - -# Chinese -if [ "$lg" = "zh" ]; then - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | python $CHINESE_TOKENIZER -# Thai -elif [ "$lg" = "th" ]; then - cat - | python $THAI_TOKENIZER -# Japanese -elif [ "$lg" = "ja" ]; then - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | ${JA_SEGMENT} -# Korean -elif [ "$lg" = "ko" ]; then - cat - | $REM_NON_PRINT_CHAR | ${KO_SEGMENT} -# Romanian -elif [ "$lg" = "ro" ]; then - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $NORMALIZE_ROMANIAN | $REMOVE_DIACRITICS | $TOKENIZER -no-escape -threads $N_THREADS -l $lg -# Burmese -elif [ "$lg" = "my" ]; then - cat - | python ${MY_SEGMENT} -# Arabic -elif [ "$lg" = "ar" ]; then - cat - | ${AR_TOKENIZER} -# Indic -elif [ "$lg" = "ne" ]; then - cat - | python ${IN_TOKENIZER} $lg -elif [ "$lg" = "si" ]; then - cat - | python ${IN_TOKENIZER} $lg -elif [ "$lg" = "hi" ]; then - cat - | python ${IN_TOKENIZER} $lg -# other languages -else - cat - | $REPLACE_UNICODE_PUNCT | $NORM_PUNC -l $lg | $REM_NON_PRINT_CHAR | $TOKENIZER -no-escape -threads $N_THREADS -l $lg -fi diff --git a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh b/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh deleted file mode 100644 index cb5bbb7277bfb9f2d5440da0514bf7b16da8140d..0000000000000000000000000000000000000000 --- a/spaces/HarryLee/eCommerceImageCaptioning/fairseq/examples/wav2vec/unsupervised/kaldi_self_train/st/local/score.sh +++ /dev/null @@ -1,63 +0,0 @@ -#!/usr/bin/env bash -# Copyright 2012 Johns Hopkins University (Author: Daniel Povey) -# 2014 Guoguo Chen -# Apache 2.0 - -[ -f ./path.sh ] && . ./path.sh - -# begin configuration section. -cmd=run.pl -stage=0 -decode_mbr=true -word_ins_penalty=0.0,0.5,1.0 -min_lmwt=7 -max_lmwt=17 -iter=final -#end configuration section. - -[ -f ./path.sh ] && . ./path.sh -. parse_options.sh || exit 1; - -if [ $# -ne 3 ]; then - echo "Usage: local/score.sh [--cmd (run.pl|queue.pl...)]theme.push_to_hub().
-Towards Real-World Blind Face Restoration with Generative Facial Prior | Github Repo
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With the hacked Hay Day APK, you don't have to worry about coins and diamonds anymore. You can have unlimited coins and diamonds in your account without spending any real money or doing any work. You can use them to buy anything you want in the game without any limitations or restrictions. You can also use them to speed up anything you want without any waiting time or cost. You can also use them to expand your land as much as you want without any requirements or obstacles. You can also use them to unlock all the special items that are otherwise only available for premium users or high-level players.
-Resources and products are the two main outputs of your farm in Hay Day. You need resources like crops, eggs, milk, wool, honey, etc. to produce products like bread, cheese, cake, popcorn, etc. You also need products to fulfill orders, complete tasks, participate in events, etc. However, resources and products are not easy to come by in Hay Day. You have to wait for crops to grow, animals to produce, machines to work, etc. You also have to deal with limited storage space, pests, visitors, etc. Sometimes, you might run out of resources or products or space or time or all of them at once.
-With the hacked Hay Day APK, you don't have to worry about resources and products anymore. You can have unlimited resources and products in your inventory without waiting or working for them. You can use them to produce anything you want in the game without any limitations or restrictions. You can also use them to fulfill any order, complete any task, participate in any event, etc. without any difficulty or delay. You can also use them to trade with other players for more coins or diamonds or items without any loss or risk.
-Buildings and decorations are the two main ways to customize your farm in Hay Day. You need buildings like barns, silos, shops, factories, etc. to store, process, and sell your resources and products. You need decorations like fences, paths, trees, flowers, etc. to beautify your farm and make it more attractive and unique. However, buildings and decorations are not easy to access in Hay Day. You need coins and diamonds to buy them, and you need to reach certain levels to unlock them. Some of them are also exclusive to premium users or seasonal events. Sometimes, you might not have enough coins or diamonds or levels or access to get the buildings and decorations you want.
-With the hacked Hay Day APK, you don't have to worry about buildings and decorations anymore. You can have unlimited access to all the buildings and decorations in the game without spending any coins or diamonds or reaching any levels. You can use them to customize your farm however you like without any limitations or restrictions. You can also use them to enhance your farm's productivity, efficiency, and profitability without any trade-offs or drawbacks. You can also use them to express your creativity, style, and personality without any boundaries or rules.
-Customization and personalization are the two main ways to make your farm your own in Hay Day. You can customize your farm by choosing the layout, design, color, theme, etc. of your buildings, decorations, crops, animals, machines, etc. You can also personalize your farm by naming it, adding a profile picture, writing a status message, joining a neighborhood, making friends, etc. However, customization and personalization are not easy to achieve in Hay Day. You need coins and diamonds to buy new options, and you need to unlock them by playing the game. Some of them are also limited by time or availability. Sometimes, you might not have enough coins or diamonds or options or freedom to customize and personalize your farm the way you want.
-With the hacked Hay Day APK, you don't have to worry about customization and personalization anymore. You can have unlimited customization and personalization options in the game without spending any coins or diamonds or playing the game. You can use them to make your farm your own without any limitations or restrictions. You can also use them to show off your farm to other players without any fear or shame. You can also use them to have fun with your farm without any boredom or monotony.
-Managing your farm efficiently is one of the keys to success in Hay Day. Even with the hack, you still need to plan ahead, prioritize tasks, balance resources, and optimize processes. Here are some tips and tricks for managing your farm efficiently:
-Trading and selling your goods for more profit is another key to success in Hay Day. Even with the hack, you still need to know how to price your goods, when to sell them, where to sell them, and who to sell them to. Here are some tips and tricks for trading and selling your goods for more profit:
-Interacting with your neighbors and friends is another key to success in Hay Day. Even with the hack, you still need to socialize, cooperate, compete, and have fun with other players in the game. Here are some tips and tricks for interacting with your neighbors and friends:
-Enjoying the Valley and other events is another key to success in Hay Day. Even with the hack, you still need to explore, discover, and have fun with the different features and activities that the game offers. Here are some tips and tricks for enjoying the Valley and other events:
-In conclusion, hacking Hay Day is a great way to get unlimited everything in your favorite farm game. By downloading and installing the hacked Hay Day APK, you can have unlimited coins and diamonds, unlimited resources and products, unlimited access to all buildings and decorations, and unlimited customization and personalization options in the game. You can also use some features, tips, and tricks that will help you manage your farm efficiently, trade and sell your goods for more profit, interact with your neighbors and friends, and enjoy the Valley and other events.
-If you are interested in hacking Hay Day, you can follow the steps we provided above to download and install the hacked Hay Day APK. However, please note that hacking Hay Day is not endorsed or supported by Supercell or any official sources. Hacking Hay Day may also violate the terms of service and privacy policy of the game. Hacking Hay Day may also expose your device to viruses or malware that may harm your device or data. Therefore, hacking Hay Day is at your own risk and responsibility. We are not liable for any damage or harm that may occur from hacking Hay Day.
-If you enjoyed this article, please share it with your friends who also love playing Hay Day. Also, feel free to leave a comment below if you have any questions or feedback about hacking Hay Day. Thank you for reading!
-A: Hacking Hay Day is not safe because it is not authorized or approved by Supercell or any official sources. Hacking Hay Day may also violate the terms of service and privacy policy of the game. Hacking Hay Day may also expose your device to viruses or malware that may harm your device or data.
-A: Hacking Hay Day is not legal because it is not compliant with the rules and regulations of the game. Hacking Hay Day may also infringe on the intellectual property rights of Supercell or other parties. Hacking Hay Day may also result in legal actions or consequences from Supercell or other authorities.
-A: Hacking Hay Day is not ethical because it is not fair or respectful to Supercell or other players. Hacking Hay Day may also ruin the fun and challenge of the game. Hacking Hay Day may also affect the balance and quality of the game.
-A: Hacking Hay Day is not permanent because it is not compatible with the updates and patches of the game. Hacking Hay Day may also be detected and banned by Supercell or other security measures. Hacking Hay Day may also be removed or overwritten by reinstalling or resetting the game.
-A: Hacking Hay Day is not worth it because it is not safe, legal, ethical, or permanent. Hacking Hay Day may also cause more problems than benefits for you or your device or data. Hacking Hay Day may also spoil your enjoyment of the game.
401be4b1e0If you are a Java developer, you might have encountered the need to perform various operations on files, streams, readers, writers, and other IO-related tasks. However, the standard Java IO API can be cumbersome, verbose, and error-prone. That's why many developers prefer to use a third-party library that simplifies and enhances the IO functionality in Java.
-One of the most popular and widely used libraries for IO operations in Java is Commons-IO-2.6.jar, which is part of the Apache Commons project. In this article, we will show you how to download and use this library in your Java projects, and what are the benefits of doing so.
-Download File ⚹ https://urlca.com/2uOaWX
The Apache Commons IO library is a collection of utilities that assist with developing IO functionality in Java. It provides various classes, methods, and interfaces that make working with files, streams, readers, writers, comparators, filters, monitors, serialization, and more much easier and faster.
-The library is divided into six main packages:
-io: This package defines utility classes for working with streams, readers, writers, and files.comparator: This package provides various Comparator implementations for Files.file: This package provides extensions in the realm of java.nio.file.filefilter: This package defines an interface (IOFileFilter) that combines both FileFilter and FilenameFilter.function: This package defines IO-only related functional interfaces for lambda expressions and method references.input: This package provides implementations of input classes, such as InputStream and Reader.input.buffer: This package provides implementations of buffered input classes, such as CircularBufferInputStream and PeekableInputStream.monitor: This package provides a component for monitoring file system events (directory and file create, update, and delete events).output: This package provides implementations of output classes, such as OutputStream and Writer.serialization: This package provides a framework for controlling the deserialization of classes.By using Commons-IO-2.6.jar in your Java projects, you can enjoy the following benefits:
-The official source of the Commons-IO-2.6.jar library is the Apache Commons IO website, where you can find the latest release, the source code, the documentation, the issue tracker, and the mailing lists. You can download the library as a JAR file or a ZIP file that contains the JAR file, the source code, the test code, and the documentation.
-The Commons-IO-2.6.jar library has no external dependencies, except for the Java Runtime Environment (JRE) version 1.7 or higher. However, if you want to use some optional features of the library, such as serialization or monitoring, you may need to add some additional dependencies to your project. These dependencies are listed on the website and in the pom.xml file of the library.
How to download commons-io-2.6.jar file from Apache Commons IO
-Download commons-io-2.6.jar file with Maven dependency
-Download commons-io-2.6.jar file with Gradle dependency
-Download commons-io-2.6.jar file with Sbt dependency
-Download commons-io-2.6.jar file with Ivy dependency
-Download commons-io-2.6.jar file with Grape dependency
-Download commons-io-2.6.jar file with Buildr dependency
-Download commons-io-2.6-javadoc.jar file for documentation
-Download commons-io-2.6-sources.jar file for source code
-Download commons-io-2.6.pom file for project metadata
-What is commons-io-2.6.jar file and what does it do
-How to use commons-io-2.6.jar file in Java projects
-How to verify the integrity of downloaded commons-io-2.6.jar file
-How to update commons-io-2.6.jar file to the latest version
-How to fix errors or issues with commons-io-2.6.jar file
-How to uninstall or remove commons-io-2.6.jar file from the project
-How to compare commons-io-2.6.jar file with other versions of commons-io
-How to find the license and terms of use for commons-io-2.6.jar file
-How to access the utility classes and methods in commons-io-2.6.jar file
-How to work with streams, readers, writers and files using commons-io-2.6.jar file
-How to use the comparator implementations for files in commons-io-2.6.jar file
-How to use the endian transformation classes in commons-io-2.6.jar file
-How to use the filters and filter-based classes in commons-io-2.6.jar file
-How to use the input and output stream implementations in commons-io-2.6.jar file
-How to use the monitor package for monitoring directories and files in commons-io-2.6.jar file
If you cannot access the official website or you prefer to use a different source, you can also find the Commons-IO-2.6.jar library on various online repositories and mirrors. However, you should always verify the integrity of the downloaded files before using them in your project. You can do this by checking the SHA-1 or MD5 checksums of the files against the ones provided on the official website. You can also use digital signatures to verify that the files have not been tampered with or corrupted.
-If you are using a dependency management tool such as Maven, Gradle, Sbt, Ivy, Grape, or Buildr in your project, you can easily add the Commons-IO-2.6.jar library to your project by using the following scripts:
-| Tool | Script |
|---|---|
| Maven | |
| Gradle | |
| Sbt | |
| Ivy | |
| Grape | |
| Buildr | |
Alternatively, you can manually add the JAR file to your project's classpath or modulepath.
-The Commons-IO-2.6.jar library provides several utility classes that offer static methods for various IO tasks. Some of the most commonly used utility classes are:
-IOUtils: This class provides methods for copying, closing, reading, writing, and converting streams, readers, writers, and byte arrays.FileUtils: This class provides methods for working with files and directories, such as copying, moving, deleting, reading, writing, comparing, listing, filtering, and monitoring.FilenameUtils: This class provides methods for manipulating file names and paths, such as getting the extension, base name, full path, normalized path, and relative path.FileSystemUtils: This class provides methods for accessing the file system information, such as the free space and the type of the file system.EndianUtils: This class provides methods for swapping the endianness of bytes and primitive types.HexDump: This class provides methods for dumping and loading hexadecimal data to and from streams and files.Charsets: This class provides constants and methods for handling character sets and encodings.The Commons-IO-2.6.jar library also provides various implementations of input and output classes that extend or wrap the standard Java IO classes. These classes offer additional functionality or convenience for working with streams, readers, writers, and files. Some of the most commonly used input and output classes are:
-TeeInputStream: This class is a proxy stream that copies the input to another output stream as well as to the original output stream.TeeOutputStream: This class is a proxy stream that copies the output to another output stream as well as to the original output stream.CountingInputStream: This class is a proxy stream that counts the number of bytes that have passed through the stream.CountingOutputStream: This class is a proxy stream that counts the number of bytes that have been written to the stream.NullInputStream: This class is an input stream that always returns EOF (-1).NullOutputStream: This class is an output stream that discards all data.ClosedInputStream: This class is an input stream that always throws an IOException when read.ClosedOutputStream: This class is an output stream that always throws an IOException when written.BoundedInputStream: This class is an input stream that limits the number of bytes that can be read from another input stream.BoundedOutputStream: This class is an output stream that limits the number of bytes that can be written to another output stream.ChunkedInputStream: This class is an input stream that reads data in chunks from another input stream.ChunkedOutputStream: This class is an output stream that writes data in chunks to another output stream.ReversedLinesFileReader: This class is a reader that reads lines from a file in reverse order.Tailer: This class is a reader that continuously reads from a file as it grows (similar to the Unix "tail" command).LockableFileWriter: This class is a writer that locks the file while writing to prevent other processes from writing to it.SwappedDataInputStream: This class is a data input stream that reads data in a different endianness than the native one.SwappedDataOutputStream: This class is a data output stream that writes data in a different endianness than the native one.To use these classes in your code, you need to import them from their respective packages and create instances of them using constructors or factory methods. For example:
-// Import the classes import org.apache.commons.io.input.TeeInputStream; import org.apache.commons.io.output.TeeOutputStream; // Create input streams FileInputStream fis = new FileInputStream("input.txt"); FileOutputStream fos1 = new FileOutputStream("output1.txt"); FileOutputStream fos2 = new FileOutputStream("output2.txt"); // Create a TeeInputStream that copies the input to fos1 TeeInputStream tis = new TeeInputStream(fis, fos1); // Create a TeeOutputStream that copies the output to fos2 TeeOutputStream tos = new TeeOutputStream(fos1, fos2); // Read and write data using the tee streams byte[] buffer = new byte[1024]; int len; while ((len = tis.read(buffer)) != -1) tos.write(buffer, 0, len); // Close the streams tis.close(); tos.close(); The Commons-IO-2.6.jar library also provides various implementations of file filters that can be used to filter files and directories based on various criteria, such as name, size, date, type, content, and more. These filters implement the IOFileFilter interface, which extends both FileFilter and FilenameFilter interfaces. Some of the most commonly used file filters are:
NameFileFilter: This filter accepts files that match a specified name or a list of names.PrefixFileFilter: This filter accepts files that start with a specified prefix or a list of prefixes.SuffixFileFilter: This filter accepts files that end with a specified suffix or a list of suffixes.WildcardFileFilter: This filter accepts files that match a specified wildcard or a list of wildcards.RegexFileFilter: This filter accepts files that match a specified regular expression or a list of regular expressions.SizeFileFilter: This filter accepts files that are equal to, greater than, or less than a specified size.AgeFileFilter: This filter accepts files that are newer than, older than, or equal to a specified date.TypeFileFilter: This filter accepts files that are either directories or files.HiddenFileFilter: This filter accepts files that are either hidden or visible.EmptyFileFilter: This filter accepts files that are either empty or not empty.CanReadFileFilter: This filter accepts files that can be read.CanWriteFileFilter: This filter accepts files that can be written.CanExecuteFileFilter: This filter accepts files that can be executed.MagicNumberFileFilter: This filter accepts files that have a specific magic number (a sequence of bytes) at the beginning of the file.CRC32ChecksumFileFilter: This filter accepts files that have a specific CRC32 checksum value.TrueFileFilter: This filter always accepts files.FalseFileFilter: This filter always rejects files.NotFileFilter: This filter negates another file filter.AndFileFilter: This filter performs a logical AND operation on two or more file filters.OrFileFilter: This filter performs a logical OR operation on two or more file filters.XorFileFilter: This filter performs a logical XOR operation on two file filters.ConditionalFileFilter: This filter applies one file filter if another file filter matches, otherwise applies another file filter.To use these filters in your code, you need to import them from their respective packages and create instances of them using constructors or factory methods. You can then use them with methods that accept FileFilter or FilenameFilter parameters, such as listFiles(), walkFileTree(),
-// Import the filters import org.apache.commons.io.filefilter.AgeFileFilter; import org.apache.commons.io.filefilter.HiddenFileFilter; import org.apache.commons.io.filefilter.OrFileFilter; // Create a directory File dir = new File("C:/temp"); // Create an age file filter that accepts files older than one hour long oneHourAgo = System.currentTimeMillis() - 60 * 60 * 1000; AgeFileFilter aff = new AgeFileFilter(oneHourAgo); // Create a hidden file filter that accepts hidden files HiddenFileFilter hff = HiddenFileFilter.HIDDEN; // Create an OR file filter that combines the age and hidden filters OrFile Filter off = new OrFileFilter(aff, hff); // List the files in the directory that match the OR file filter File[] files = dir.listFiles(off); // Print the file names for (File file : files) System.out.println(file.getName());
In this article, we have learned how to download and use Commons-IO-2.6.jar in Java. We have seen what is Commons-IO-2.6.jar, what are the benefits of using it, how to download it from various sources and verify its integrity, how to add it to our project using dependency management tools or manually, and how to use its utility classes, input and output classes, and file filters. We have also provided some code examples to demonstrate the usage of the library.
-If you are looking for a simple, consistent, performant, compatible, and reliable library for IO operations in Java, you should definitely give Commons-IO-2.6.jar a try. It will save you a lot of time and effort, and make your code more readable and maintainable. You can find more information about the library on its official website, where you can also download the latest release, browse the source code, read the documentation, report issues, and join the community.
-The minimum Java version required for Commons-IO-2.6.jar is Java 7. However, some features of the library may require higher Java versions, such as Java 8 or Java 9. You can check the compatibility matrix on the website for more details.
-To update Commons-IO-2.6.jar to the latest version, you can either download the new JAR file from the website or use your dependency management tool to update the version number in your script. You should also check the release notes and the migration guide on the website for any changes or breaking changes that may affect your code.
-To run an application or an applet packaged as a JAR file using Commons-IO-2.6.jar, you need to include the library in your classpath or modulepath when launching the JAR file. For example:
-java -cp commons-io-2.6.jar:myapp.jar com.example.MyApp When using Commons-IO-2.6.jar, you may encounter various exceptions and errors that indicate something went wrong with the IO operations. Some of the common exceptions and errors are:
-IOException: This is a general exception that indicates an IO failure or interruption.FileNotFoundException: This is a subclass of IOException that indicates that a file or directory does not exist or cannot be accessed.EOFException: This is a subclass of IOException that indicates that the end of a stream has been reached unexpectedly.UnsupportedEncodingException: This is a subclass of IOException that indicates that a character encoding is not supported.MalformedInputException: This is a subclass of IOException that indicates that an input stream does not conform to an expected format.CRC32ChecksumException: This is a subclass of IOException that indicates that a CRC32 checksum does not match.Error: This is a general error that indicates a serious problem that cannot be handled by the application.NoClassDefFoundError: This is a subclass of To handle these exceptions and errors, you should use appropriate try-catch-finally blocks in your code, and perform necessary actions such as logging, retrying, recovering, or notifying the user. You should also follow the best practices for exception handling in Java, such as: - Use specific exception classes for different types of errors . - Catch exceptions at the appropriate level of abstraction . - Log and handle exceptions in a consistent and informative manner . - Avoid empty catch blocks and swallowing exceptions . - Propagate exceptions up the call stack when appropriate . - Use finally blocks for cleanup and resource management . - Use a global exception handler . - Don't close resources manually . - Don't log and rethrow. - Check for suppressed exceptions. - Explicitly define exceptions in the throws clause. - Throw early and handle exceptions late. By following these best practices, you can improve the quality, reliability, and maintainability of your code, and avoid common pitfalls and bugs when dealing with exceptions in Java.
401be4b1e0Download File ··· https://gohhs.com/2uFVpW
DISCO Project Page | Github Repo
- """) - -if RUN_MODE == "local": - demo.launch(server_name='9.134.253.83',server_port=7788) -else: - demo.queue(default_enabled=True, status_update_rate=5) - demo.launch() \ No newline at end of file diff --git a/spaces/dongsiqie/Code-Interpreter/app.py b/spaces/dongsiqie/Code-Interpreter/app.py deleted file mode 100644 index 500c50c1745dc3bf7d3ecea1b1f363fadc366fb8..0000000000000000000000000000000000000000 --- a/spaces/dongsiqie/Code-Interpreter/app.py +++ /dev/null @@ -1,197 +0,0 @@ -from response_parser import * -import gradio as gr - - -def initialization(state_dict: Dict) -> None: - if not os.path.exists('cache'): - os.mkdir('cache') - if state_dict["bot_backend"] is None: - state_dict["bot_backend"] = BotBackend() - if 'OPENAI_API_KEY' in os.environ: - del os.environ['OPENAI_API_KEY'] - - -def get_bot_backend(state_dict: Dict) -> BotBackend: - return state_dict["bot_backend"] - - -def switch_to_gpt4(state_dict: Dict, whether_switch: bool) -> None: - bot_backend = get_bot_backend(state_dict) - if whether_switch: - bot_backend.update_gpt_model_choice("GPT-4") - else: - bot_backend.update_gpt_model_choice("GPT-3.5") - - -def add_text(state_dict: Dict, history: List, text: str) -> Tuple[List, Dict]: - bot_backend = get_bot_backend(state_dict) - bot_backend.add_text_message(user_text=text) - - history = history + [(text, None)] - - return history, gr.update(value="", interactive=False) - - -def add_file(state_dict: Dict, history: List, file) -> List: - bot_backend = get_bot_backend(state_dict) - path = file.name - filename = os.path.basename(path) - - bot_msg = [f'📁[{filename}]', None] - history.append(bot_msg) - - bot_backend.add_file_message(path=path, bot_msg=bot_msg) - - return history - - -def undo_upload_file(state_dict: Dict, history: List) -> Tuple[List, Dict]: - bot_backend = get_bot_backend(state_dict) - bot_msg = bot_backend.revoke_file() - - if bot_msg is None: - return history, gr.Button.update(interactive=False) - - else: - assert history[-1] == bot_msg - del history[-1] - if bot_backend.revocable_files: - return history, gr.Button.update(interactive=True) - else: - return history, gr.Button.update(interactive=False) - - -def refresh_file_display(state_dict: Dict) -> List[str]: - bot_backend = get_bot_backend(state_dict) - work_dir = bot_backend.jupyter_work_dir - filenames = os.listdir(work_dir) - paths = [] - for filename in filenames: - paths.append( - os.path.join(work_dir, filename) - ) - return paths - - -def restart_ui(history: List) -> Tuple[List, Dict, Dict, Dict, Dict]: - history.clear() - return ( - history, - gr.Textbox.update(value="", interactive=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False), - gr.Button.update(interactive=False) - ) - - -def restart_bot_backend(state_dict: Dict) -> None: - bot_backend = get_bot_backend(state_dict) - bot_backend.restart() - - -def bot(state_dict: Dict, history: List) -> List: - bot_backend = get_bot_backend(state_dict) - - while bot_backend.finish_reason in ('new_input', 'function_call'): - if history[-1][0] is None: - history.append( - [None, ""] - ) - else: - history[-1][1] = "" - - response = chat_completion(bot_backend=bot_backend) - for chunk in response: - history, weather_exit = parse_response( - chunk=chunk, - history=history, - bot_backend=bot_backend - ) - yield history - if weather_exit: - exit(-1) - - yield history - - -if __name__ == '__main__': - config = get_config() - with gr.Blocks(theme=gr.themes.Base()) as block: - """ - Reference: https://www.gradio.app/guides/creating-a-chatbot-fast - """ - # UI components - state = gr.State(value={"bot_backend": None}) - with gr.Tab("Chat"): - chatbot = gr.Chatbot([], elem_id="chatbot", label="Local Code Interpreter", height=480) - with gr.Row(): - with gr.Column(scale=0.85): - text_box = gr.Textbox( - show_label=False, - placeholder="Enter text and press enter, or upload a file", - container=False - ) - with gr.Column(scale=0.15, min_width=0): - file_upload_button = gr.UploadButton("📁", file_types=['file']) - with gr.Row(equal_height=True): - with gr.Column(scale=0.7): - check_box = gr.Checkbox(label="Use GPT-4", interactive=config['model']['GPT-4']['available']) - check_box.change(fn=switch_to_gpt4, inputs=[state, check_box]) - with gr.Column(scale=0.15, min_width=0): - restart_button = gr.Button(value='🔄 Restart') - with gr.Column(scale=0.15, min_width=0): - undo_file_button = gr.Button(value="↩️Undo upload file", interactive=False) - with gr.Tab("Files"): - file_output = gr.Files() - gr.Markdown( - ''' -
- - A demo for fairseq speech-to-speech translation models. It supports S2UT and UnitY models for bidirectional Hokkien and English translation. Please select the model and record the input to submit. -
-i have tried your tutorial for downloading odin and i have flashed the kernal v10s with no success. one more thing is i have been able to down load the GT s2 edge v2 firmware with your suggested method but the phone is not even recognizing my wifi. Im also trying to flash the S2 on at&t network but i just want to make sure i am doing it correct as i want to avoid any issues. please help. thanks alot
-Download ○ https://urlca.com/2uDdSM
Hi Chaladi, my android galaxy s plus GT i9500 G has been bricked while working on it.I tried updating my jellybean and 2.2.2 version then again updating to 2.3.3 but it didnt work for me and i also tried to restart the device and also tried installing the whole galaxy s plus firmware using odin but it didnt work either. Then i downloaded the odin 4.38 euhm multi downloader and flashed the necessary omap 4.1 kernel with it and it worked for me.
-How can i update i found the latest version which is 4.38 but how can I update the phone to that version.. One thing I want to clarify is I can boot the phone in download mode with odin 4.38 as mentioned above
-hi,
i have a gt s5300 and the latest version i got through odin multi downloader is 4.38, but i don't know how to update this, and which kernel to use. the are two available for me, ex samsung kernel and ralink kernel.
I use odin 4.38 multi downloader, i have tried to download.tar file from your site but it kept asking me to open with odin 4.38, so, is it possible to update this one? please let me know how to make this happen
hey mah! hopefully I'm not too late in contributing anything, since you are already doing great work. I just have a question which I think should be simple. I'm gonna be flashing a stock rom and I'm looking to get all my contacts back, but I noticed that I lost them when I started the recovery mode. I checked to see what those were using "find my phone" and I found it on my computer, but when I try to restore it in the recovery it says unable to restore. But when I look for it on my computer in the downloader it says successfully transferred. I could do a fresh install and start from scratch but I would like to avoid that as much as possible.
Is there any way I can restore that? You've been such a help that I really appreciate the time and effort you've put into this. Thanks mah.
Chess is one of the oldest and most popular board games in the world. It is a game of strategy, logic, and creativity that can challenge your mind and improve your cognitive abilities. Whether you are a beginner or a master, practicing chess regularly can help you sharpen your skills and enjoy the game more.
-Practicing chess can have many benefits for your brain and your life. Some of the advantages of playing chess are:
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To practice chess, you don't need much. All you need is a chess board, a set of chess pieces, and an opponent. However, if you don't have these things handy, or if you want to practice alone or online, you can also use some free and easy tools that can simulate a chess board and provide you with various features to enhance your learning experience. In this article, we will introduce you to three of these tools that you can download or access for free.
-Free Chess is a downloadable simulator of the classic board game, chess. It is a simple and lightweight program that runs on Windows devices. It allows you to play alone against the computer, or go head to head with a local friend. You can choose from ten levels of difficulty, from beginner to impossible. You can also switch between 3D and 2D graphics, undo your moves, get hints, copy the game notation, and toggle the sound effects. Free Chess is ad-free and does not require an internet connection.
-To download Free Chess, you can visit this link and click on the green "Free Download" button. You will be redirected to another page where you can choose a mirror site to download the file. Once the file is downloaded, you can open it and follow the instructions to install the program on your device. After the installation is complete, you can launch the program and start playing.
-Chess.com is the most popular chess app and website in the world. It has over 50 million users from all over the globe. It offers a variety of features for players of all levels and interests. Some of these features are:
-To download Chess.com app, you can visit this link and choose the version that suits your device. You can download the app for Android, iOS, Windows, Mac, Linux, Chromebook, Kindle, or Apple TV. Once the app is downloaded, you can open it and create a free account or log in with your existing account. You can also use your Facebook or Google account to sign up or log in. After that, you can access all the features of Chess.com and start playing.
-Lichess.org is a free and open-source chess platform that runs on any web browser. It is a non-profit project that is supported by donations and volunteers. It has over 10 million users from around the world. It offers a range of features for chess enthusiasts of all kinds. Some of these features are:
-To access Lichess.org editor and practice modes, you can visit this link and click on the "Tools" menu on the top right corner of the page. You will see two options: "Board editor" and "Practice". The board editor allows you to set up any position on the board and play it against the computer or share it with others. The practice mode allows you to practice specific chess skills such as checkmates, tactics, endgames, openings, and coordinates. You don't need to create an account or log in to use these features.
-In this article, we have shown you how to download chess board for practice using three free and easy tools: Free Chess, Chess.com app, and Lichess.org editor and practice modes. These tools can help you improve your chess skills with various features such as playing games, joining tournaments, watching videos, learning lessons, analyzing games, creating content, and more. You can choose the tool that best suits your preferences and needs.
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Now that you know how to download chess board for practice, what are you waiting for? Download one of these tools today and start practicing chess like a pro. You will be amazed by how much fun and rewarding chess can be. Happy playing!
- FAQs Q: How much does it cost to download chess board for practice? A: All the tools we have mentioned in this article are free to download and use. You don't need to pay anything to practice chess with these tools. Q: Which tool is the best for beginners? A: All the tools we have mentioned in this article are suitable for beginners as well as advanced players. However, if you are looking for a simple and easy tool to start with, we recommend Free Chess as it has a user-friendly interface and ten levels of difficulty. Q: Which tool is the best for online play? A: All the tools we have mentioned in this article allow you to play online with other players or with computer opponents. However, if you are looking for a tool that has a large and active online community, we recommend Chess.com app as it has over 50 million users and offers a variety of features for online play such as tournaments, clubs, teams, events, chat, and more. Q: Which tool is the best for offline play? A: All the tools we have mentioned in this article allow you to play offline with computer opponents or with a local friend. However, if you are looking for a tool that does not require an internet connection or a web browser, we recommend Free Chess as it is a downloadable program that runs on Windows devices. Q: Which tool is the best for learning chess? A: All the tools we have mentioned in this article offer some features for learning chess such as lessons, puzzles, drills, courses, articles, and more. However, if you are looking for a tool that has a comprehensive and interactive curriculum for learning chess from scratch or improving your skills, we recommend Chess.com app as it has over 1000 lessons, 50,000 puzzles, 400 courses, and 10,000 articles created by top players and coaches. Q: Which tool is the best for creating chess content? A: All the tools we have mentioned in this article offer some features for creating chess content such as games, variants, puzzles, studies, and tournaments. However, if you are looking for a tool that has a free and open-source platform for creating and sharing chess content with others, we recommend Lichess.org as it allows you to create your own games, variants, puzzles, studies, and tournaments with ease and flexibility. 401be4b1e0If you are a fan of the Angry Birds franchise, you might want to check out Angry Birds Epic APK, a free turn-based role-playing game that sends you on a sprawling adventure across the tropical beaches, frosty mountains, and deep dungeons of Piggy Island. In this game, you can play as heroic knight, mighty wizard, or helpful druid, and assemble the perfect party of birds to defeat the evil pigs and their boss villains. You can also craft hundreds of weapons and magical potions, level up your birds, and challenge real players from around the world in the arena. In this article, we will tell you everything you need to know about Angry Birds Epic APK, including what it is, how to download and install it, and why you should play it.
-Angry Birds Epic APK is an Android game that is developed by Rovio Entertainment Corporation, the same company that created the original Angry Birds game. It is a spin-off of the Angry Birds series that features a different genre and style. Instead of slinging birds at pigs using a catapult, you can control your birds in turn-based battles using skills, weapons, and magic. You can also explore a vast world of Piggy Island, collect loot, craft items, and customize your birds. The game has more than 85 million players around the world and is rated 4.4 out of 5 stars on Google Play Store.
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The story of Angry Birds Epic APK follows the classic rivalry between the birds and the pigs. The pigs have stolen the eggs of the birds and are planning to use them for their evil schemes. The birds must embark on an epic quest to rescue their eggs and stop the pigs from destroying their world. Along the way, they will encounter many enemies, allies, and surprises.
-The gameplay of Angry Birds Epic APK is similar to other turn-based RPGs. You can choose from three classes of birds: knight, wizard, or druid. Each class has its own strengths, weaknesses, and abilities. You can also switch between different birds during battle to adapt to different situations. You can use skills, weapons, and magic to attack, defend, heal, or buff your birds. You can also use special items like chili peppers, golden ducks, or friendship essences to unleash powerful effects. The battles are divided into rounds, and you can win by defeating all the enemies or fulfilling certain objectives.
-Angry Birds Epic APK has many features and benefits that make it an enjoyable and addictive game. Some of them are:
-If you want to play Angry Birds Epic APK on your Android device, you need to
download and install it on your device. There are two ways to do this: either from the official Google Play Store or from a third-party website that offers the APK file. Here are the steps for both methods:
-To download Angry Birds Epic APK from Google Play Store, you need to have an Android device that runs on Android 4.4 or higher, and a stable internet connection. You also need to have enough storage space on your device to install the game. Here are the steps for downloading Angry Birds Epic APK from Google Play Store:
-To download Angry Birds Epic APK from a third-party website, you need to have an Android device that runs on Android 3.2 or higher, and a stable internet connection. You also need to have enough storage space on your device to install the game. You also need to enable the "Unknown sources" option on your device settings to allow the installation of apps from sources other than Google Play Store. Here are the steps for downloading Angry Birds Epic APK from a third-party website:
-Angry Birds Epic APK is a fun and addictive game, but it can also be challenging and frustrating at times. To help you enjoy the game more and overcome its difficulties, here are some tips and tricks for playing Angry Birds Epic APK:
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Angry Birds Epic APK is a game that offers a lot of fun and entertainment for players of all ages and preferences. Whether you are a fan of Angry Birds, RPGs, or both, you will find something to enjoy in this game. Here are some reasons why you should play Angry Birds Epic APK:
-Like any other game, Angry Birds Epic APK has its pros and cons that may affect your gaming experience. Here are some of them:
-| Pros | Cons | |
|---|---|---|
| - It has a rich and colorful graphics that capture the charm and humor of the Angry Birds universe. | - It has some bugs and glitches that may affect the gameplay and performance. | |
| - It has a fun and engaging story that takes you to various locations and scenarios. | - It has some ads and in-app purchases that may be annoying or expensive. | |
| - It has a variety of characters, enemies, weapons, items, and magic that offer a lot of diversity and customization. | - It has some levels and enemies that may be too hard or unfair. | |
| - It has a challenging and rewarding gameplay that requires strategy and skill. | - It has some features and content that may be locked or limited unless you pay or play online. | |
| - It has a social and competitive aspect that allows you to join clans, chat with friends, and compete with other players in the arena. | - It has some issues with the server and the connection that may cause lag or errors. | |
| - It has a regular updates and events that add new content and features to the game. | - It has some compatibility problems with some devices or operating systems. |
Angry Birds Epic APK has received mostly positive reviews and ratings from players and critics alike. Here are some of the comments from the users who have played the game:
---"This game is awesome! I love the graphics, the story, the gameplay, everything! It's so fun and addictive, I can't stop playing it. The best Angry Birds game ever!"
-"I really enjoy this game. It's a great RPG with a lot of humor and action. The characters are cute and funny, the weapons are cool and creative, the battles are exciting and strategic. I recommend it to anyone who likes RPGs or Angry Birds."
-"This game is good, but it could be better. It has some problems with the ads, the in-app purchases, the difficulty, and the connection. Sometimes it crashes or freezes, sometimes it's too hard or too easy, sometimes it's too expensive or too limited. I hope they fix these issues soon."
-
Angry Birds Epic APK is a free turn-based RPG adventure that sends you on a sprawling adventure across the tropical beaches, frosty mountains, and deep dungeons of Piggy Island. You can play as heroic knight, mighty wizard, or helpful druid, and assemble the perfect party of birds to defeat the evil pigs and their boss villains. You can also craft hundreds of weapons and magical potions, level up your birds, and challenge real players from around the world in the arena. The game has many features and benefits that make it an enjoyable and addictive game, but it also has some drawbacks and challenges that may affect your gaming experience. If you want to play Angry Birds Epic APK on your Android device, you can download and install it from Google Play Store or from a third-party website. You can also use some tips and tricks to help you enjoy the game more and overcome its difficulties. Angry Birds Epic APK is a game that offers a lot of fun and entertainment for players of all ages and preferences. Whether you are a fan of Angry Birds, RPGs, or both, you will find something to enjoy in this game. So what are you waiting for? Download Angry Birds Epic APK now and join the epic adventure!
-If you liked this article, please share it with your friends and family who might be interested in playing Angry Birds Epic APK. You can also leave a comment below to let us know what you think about the game or ask any questions you might have. We would love to hear from you!
-Angry Birds Epic APK is the file format of Angry Birds Epic, which is the name of the game. APK stands for Android Package Kit, which is a file format used by Android devices to distribute and install applications. You can download Angry Birds Epic APK from Google Play Store or from a third-party website.
-Angry Birds Epic APK is safe to download if you download it from Google Play Store or from a reputable website that offers the APK file. However, you should be careful when downloading any APK file from unknown sources, as they may contain viruses or malware that can harm your device or steal your data.
-There are several ways to get more coins, gems, lucky coins, essence of friendship, and other rewards in Angry Birds Epic APK. Some of them are:
-To update Angry Birds Epic APK, you need to follow the same steps as downloading and installing it. If you downloaded it from Google Play Store, you can check for updates on the app page and tap on the "Update" button. If you downloaded it from a third-party website, you need to download the latest version of the APK file and install it on your device.
-If you have any questions, feedback, or issues regarding Angry Birds Epic APK, you can contact the developers of the game by using one of the following methods:
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