


"""simple docstring"""


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


_snake_case          =							logging.get_logger(__name__)  # pylint: disable=invalid-name

_snake_case          =							"\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e-img2img\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n        >>> image = load_image(image_url).convert(\"RGB\")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n        ```\n"



@dataclass
class   _a      (      SCREAMING_SNAKE_CASE_ ):
    a_    :				Union[PIL.Image.Image, np.ndarray]





class   _a      (      SCREAMING_SNAKE_CASE_ ):
    def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					PriorTransformer							,					SCREAMING_SNAKE_CASE__							:					CLIPVisionModel							,					SCREAMING_SNAKE_CASE__							:					CLIPImageProcessor							,					SCREAMING_SNAKE_CASE__							:					HeunDiscreteScheduler							,					SCREAMING_SNAKE_CASE__							:					ShapERenderer							,					):
          super().__init__()

          self.register_modules(
              prior=SCREAMING_SNAKE_CASE__							,					image_encoder=SCREAMING_SNAKE_CASE__							,					image_processor=SCREAMING_SNAKE_CASE__							,					scheduler=SCREAMING_SNAKE_CASE__							,					renderer=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					int							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					List[str]				):
          if latents is None:
                lowerCamelCase__           =		randn_tensor(SCREAMING_SNAKE_CASE__							,					generator=SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__							,					dtype=SCREAMING_SNAKE_CASE__				)
          else:
                if latents.shape != shape:
                      raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
                lowerCamelCase__           =		latents.to(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		latents * scheduler.init_noise_sigma
          return latents
    def      _UpperCamelCase       ( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					List[str]=0				):
          if is_accelerate_available():
                from accelerate import cpu_offload
          else:
                raise ImportError('Please install accelerate via `pip install accelerate`'				)

          lowerCamelCase__           =		torch.device(F'cuda:{gpu_id}'				)

          lowerCamelCase__           =		[self.image_encoder, self.prior]
          for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                      cpu_offload(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          if self.device != torch.device('meta'				) or not hasattr(self.image_encoder							,					'_hf_hook'				):
                return self.device
          for module in self.image_encoder.modules():
                if (
                    hasattr(SCREAMING_SNAKE_CASE__							,					'_hf_hook'				)
                    and hasattr(module._hf_hook							,					'execution_device'				)
                    and module._hf_hook.execution_device is not None
                ):
                      return torch.device(module._hf_hook.execution_device				)
          return self.device
    def      _UpperCamelCase       ( self							:					Optional[int]							,					SCREAMING_SNAKE_CASE__							:					List[str]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					int							,					):
          if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					torch.Tensor				):
                lowerCamelCase__           =		torch.cat(SCREAMING_SNAKE_CASE__							,					axis=0				) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE__							,					axis=0				)

          if not isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		self.image_processor(SCREAMING_SNAKE_CASE__							,					return_tensors='pt'				).pixel_values[0].unsqueeze(0				)

          lowerCamelCase__           =		image.to(dtype=self.image_encoder.dtype							,					device=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		self.image_encoder(SCREAMING_SNAKE_CASE__				)['last_hidden_state']
          lowerCamelCase__           =		image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

          lowerCamelCase__           =		image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE__							,					dim=0				)

          if do_classifier_free_guidance:
                lowerCamelCase__           =		torch.zeros_like(SCREAMING_SNAKE_CASE__				)

                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                lowerCamelCase__           =		torch.cat([negative_image_embeds, image_embeds]				)

          return image_embeds


    @torch.no_grad()
    @replace_example_docstring(SCREAMING_SNAKE_CASE__				)
    def __call__( self							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Union[PIL.Image.Image, List[PIL.Image.Image]]							,					SCREAMING_SNAKE_CASE__							:					int = 1							,					SCREAMING_SNAKE_CASE__							:					int = 25							,					SCREAMING_SNAKE_CASE__							:					Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					SCREAMING_SNAKE_CASE__							:					Optional[torch.FloatTensor] = None							,					SCREAMING_SNAKE_CASE__							:					float = 4.0							,					SCREAMING_SNAKE_CASE__							:					int = 64							,					SCREAMING_SNAKE_CASE__							:					Optional[str] = "pil"							,					SCREAMING_SNAKE_CASE__							:					bool = True							,					):

          if isinstance(SCREAMING_SNAKE_CASE__							,					PIL.Image.Image				):
                lowerCamelCase__           =		1
          elif isinstance(SCREAMING_SNAKE_CASE__							,					torch.Tensor				):
                lowerCamelCase__           =		image.shape[0]
          elif isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				) and isinstance(image[0]							,					(torch.Tensor, PIL.Image.Image)				):
                lowerCamelCase__           =		len(SCREAMING_SNAKE_CASE__				)
          else:
                raise ValueError(
                    F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(SCREAMING_SNAKE_CASE__				)}'				)

          lowerCamelCase__           =		self._execution_device

          lowerCamelCase__           =		batch_size * num_images_per_prompt

          lowerCamelCase__           =		guidance_scale > 1.0
          lowerCamelCase__           =		self._encode_image(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          # prior

          self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__							,					device=SCREAMING_SNAKE_CASE__				)
          lowerCamelCase__           =		self.scheduler.timesteps

          lowerCamelCase__           =		self.prior.config.num_embeddings
          lowerCamelCase__           =		self.prior.config.embedding_dim

          lowerCamelCase__           =		self.prepare_latents(
              (batch_size, num_embeddings * embedding_dim)							,					image_embeds.dtype							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					self.scheduler							,					)

          # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
          lowerCamelCase__           =		latents.reshape(latents.shape[0]							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

          for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE__				)				):
                # expand the latents if we are doing classifier free guidance
                lowerCamelCase__           =		torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
                lowerCamelCase__           =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)

                lowerCamelCase__           =		self.prior(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					proj_embedding=SCREAMING_SNAKE_CASE__							,					).predicted_image_embedding

                # remove the variance
                lowerCamelCase__    ,  lowerCamelCase__           =		noise_pred.split(
                    scaled_model_input.shape[2]							,					dim=2				)  # batch_size, num_embeddings, embedding_dim

                if do_classifier_free_guidance is not None:
                      lowerCamelCase__    ,  lowerCamelCase__           =		noise_pred.chunk(2				)
                      lowerCamelCase__           =		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                lowerCamelCase__           =		self.scheduler.step(
                    SCREAMING_SNAKE_CASE__							,					timestep=SCREAMING_SNAKE_CASE__							,					sample=SCREAMING_SNAKE_CASE__							,					).prev_sample

          if output_type == "latent":
                return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		[]
          for i, latent in enumerate(SCREAMING_SNAKE_CASE__				):
                print()
                lowerCamelCase__           =		self.renderer.decode(
                    latent[None, :]							,					SCREAMING_SNAKE_CASE__							,					size=SCREAMING_SNAKE_CASE__							,					ray_batch_size=40_96							,					n_coarse_samples=64							,					n_fine_samples=1_28							,					)

                images.append(SCREAMING_SNAKE_CASE__				)

          lowerCamelCase__           =		torch.stack(SCREAMING_SNAKE_CASE__				)

          if output_type not in ["np", "pil"]:
                raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'				)

          lowerCamelCase__           =		images.cpu().numpy()

          if output_type == "pil":
                lowerCamelCase__           =		[self.numpy_to_pil(SCREAMING_SNAKE_CASE__				) for image in images]

          # Offload last model to CPU
          if hasattr(self							,					'final_offload_hook'				) and self.final_offload_hook is not None:
                self.final_offload_hook.offload()

          if not return_dict:
                return (images,)

          return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE__				)







"""simple docstring"""


import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict

import requests
from slack_sdk import WebClient


_snake_case          =							WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])



def   snake_case						(				_a:     Any							)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		test_results.split(' '							)

      lowerCamelCase__           =		0
      lowerCamelCase__           =		0

      # When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
      # When it is too long, those signs are not present.
      lowerCamelCase__           =		expressions[-2] if '=' in expressions[-1] else expressions[-1]

      for i, expression in enumerate(_a							):
            if "failed" in expression:
                  failed += int(expressions[i - 1]							)
            if "passed" in expression:
                  success += int(expressions[i - 1]							)

      return failed, success, time_spent



def   snake_case						(				_a:     Optional[int]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		{}
      lowerCamelCase__           =		None
      lowerCamelCase__           =		False
      for line in failures_short_lines.split('\n'							):
            if re.search(R'_ \[doctest\]'		,   _a							):
                  lowerCamelCase__           =		True
                  lowerCamelCase__           =		line.split(' '							)[2]
            elif in_error and not line.split(' '							)[0].isdigit():
                  lowerCamelCase__           =		line
                  lowerCamelCase__           =		False

      return failures



class   _a      :
    def __init__( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					str							,					SCREAMING_SNAKE_CASE__							:					Dict				):
          lowerCamelCase__           =		title

          lowerCamelCase__           =		doc_test_results['time_spent'].split(','				)[0]
          lowerCamelCase__           =		doc_test_results['success']
          lowerCamelCase__           =		doc_test_results['failures']
          lowerCamelCase__           =		self.n_success + self.n_failures

          # Failures and success of the modeling tests
          lowerCamelCase__           =		doc_test_results
    @property
    def      _UpperCamelCase       ( self							:					List[str]				):
          lowerCamelCase__           =		[self._time_spent]
          lowerCamelCase__           =		0

          for time in time_spent:
                lowerCamelCase__           =		time.split(':'				)

                # Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
                if len(SCREAMING_SNAKE_CASE__				) == 1:
                      lowerCamelCase__           =		[0, 0, time_parts[0]]

                lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		int(time_parts[0]				), int(time_parts[1]				), float(time_parts[2]				)
                total_secs += hours * 36_00 + minutes * 60 + seconds

          lowerCamelCase__    ,  lowerCamelCase__    ,  lowerCamelCase__           =		total_secs // 36_00, (total_secs % 36_00) // 60, total_secs % 60
          return F'{int(SCREAMING_SNAKE_CASE__				)}h{int(SCREAMING_SNAKE_CASE__				)}m{int(SCREAMING_SNAKE_CASE__				)}s'
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
    @property
    def      _UpperCamelCase       ( self							:					Dict				):
          return {
              "type": "section",
              "text": {
                  "type": "plain_text",
                  "text": F'🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.',
                  "emoji": True,
              },
              "accessory": {
                  "type": "button",
                  "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                  "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					Any				):
          return {
              "type": "section",
              "text": {
                  "type": "plain_text",
                  "text": (
                      F'There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'
                      F' {self.time}.'
                  ),
                  "emoji": True,
              },
              "accessory": {
                  "type": "button",
                  "text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
                  "url": F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					Union[str, Any]				):
          lowerCamelCase__           =		40
          lowerCamelCase__           =		{k: v['failed'] for k, v in doc_test_results.items() if isinstance(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__				)}

          lowerCamelCase__           =		''
          for category, failures in category_failures.items():
                if len(SCREAMING_SNAKE_CASE__				) == 0:
                      continue

                if report != "":
                      report += "\n\n"

                report += F'*{category} failures*:'.ljust(line_length // 2				).rjust(line_length // 2				) + "\n"
                report += "`"
                report += "`\n`".join(SCREAMING_SNAKE_CASE__				)
                report += "`"

          return {
              "type": "section",
              "text": {
                  "type": "mrkdwn",
                  "text": F'The following examples had failures:\n\n\n{report}\n',
              },
          }
    @property
    def      _UpperCamelCase       ( self							:					str				):
          lowerCamelCase__           =		[self.header]

          if self.n_failures > 0:
                blocks.append(self.failures				)

          if self.n_failures > 0:
                blocks.extend([self.category_failures]				)

          if self.n_failures == 0:
                blocks.append(self.no_failures				)

          return json.dumps(SCREAMING_SNAKE_CASE__				)
    @staticmethod
    def      _UpperCamelCase       ( ):
          lowerCamelCase__           =		[
              {
                  'type': 'section',
                  'text': {
                      'type': 'plain_text',
                      'text': 'There was an issue running the tests.',
                  },
                  'accessory': {
                      'type': 'button',
                      'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
                      'url': F'https://github.com/huggingface/transformers/actions/runs/{os.environ["GITHUB_RUN_ID"]}',
                  },
              }
          ]

          print('Sending the following payload'				)
          print(json.dumps({'blocks': json.loads(SCREAMING_SNAKE_CASE__				)}				)				)

          client.chat_postMessage(
              channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					text='There was an issue running the tests.'							,					blocks=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					Optional[int]				):
          print('Sending the following payload'				)
          print(json.dumps({'blocks': json.loads(self.payload				)}				)				)

          lowerCamelCase__           =		F'{self.n_failures} failures out of {self.n_tests} tests,' if self.n_failures else 'All tests passed.'

          lowerCamelCase__           =		client.chat_postMessage(
              channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					blocks=self.payload							,					text=SCREAMING_SNAKE_CASE__							,					)
    def      _UpperCamelCase       ( self							:					Optional[Any]							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					List[Any]							,					SCREAMING_SNAKE_CASE__							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					Tuple				):
          lowerCamelCase__           =		''
          for key, value in failures.items():
                lowerCamelCase__           =		value[:2_00] + ' [Truncated]' if len(SCREAMING_SNAKE_CASE__				) > 2_50 else value
                failures_text += F'*{key}*\n_{value}_\n\n'

          lowerCamelCase__           =		job_name
          lowerCamelCase__           =		{'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}

          if job_link is not None:
                lowerCamelCase__           =		{
                    'type': 'button',
                    'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
                    'url': job_link,
                }

          return [
              {"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
              content,
              {"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
          ]


    def      _UpperCamelCase       ( self							:					Optional[int]				):
          if self.thread_ts is None:
                raise ValueError('Can only post reply if a post has been made.'				)

          lowerCamelCase__           =		self.doc_test_results.pop('job_link'				)
          self.doc_test_results.pop('failures'				)
          self.doc_test_results.pop('success'				)
          self.doc_test_results.pop('time_spent'				)

          lowerCamelCase__           =		sorted(self.doc_test_results.items()							,					key=lambda SCREAMING_SNAKE_CASE__				: t[0]				)
          for job, job_result in sorted_dict:
                if len(job_result['failures']				):
                      lowerCamelCase__           =		F'*Num failures* :{len(job_result["failed"]				)} \n'
                      lowerCamelCase__           =		job_result['failures']
                      lowerCamelCase__           =		self.get_reply_blocks(SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__							,					text=SCREAMING_SNAKE_CASE__				)

                      print('Sending the following reply'				)
                      print(json.dumps({'blocks': blocks}				)				)

                      client.chat_postMessage(
                          channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY']							,					text=F'Results for {job}'							,					blocks=SCREAMING_SNAKE_CASE__							,					thread_ts=self.thread_ts['ts']							,					)

                      time.sleep(1				)





def   snake_case						(				)->		Optional[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		os.environ['GITHUB_RUN_ID']
      lowerCamelCase__           =		F'https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'
      lowerCamelCase__           =		requests.get(_a							).json()
      lowerCamelCase__           =		{}

      try:
            jobs.update({job['name']: job['html_url'] for job in result['jobs']}							)
            lowerCamelCase__           =		math.ceil((result['total_count'] - 100) / 100							)

            for i in range(_a							):
                  lowerCamelCase__           =		requests.get(url + F'&page={i + 2}'							).json()
                  jobs.update({job['name']: job['html_url'] for job in result['jobs']}							)

            return jobs
      except Exception as e:
            print('Unknown error, could not fetch links.'		,   _a							)

      return {}



def   snake_case						(				_a:     str							)->		Dict:
      '''simple docstring'''


      lowerCamelCase__           =		{}

      if os.path.exists(_a							):
            lowerCamelCase__           =		os.listdir(_a							)
            for file in files:
                  try:
                        with open(os.path.join(_a		,   _a							)		,   encoding='utf-8'							) as f:
                              lowerCamelCase__           =		f.read()
                  except UnicodeDecodeError as e:
                        raise ValueError(F'Could not open {os.path.join(_a		,   _a							)}.'							) from e

      return _artifact



def   snake_case						(				)->		Optional[int]:
      '''simple docstring'''


      class   _a      :
          def __init__( self							:					Tuple							,					SCREAMING_SNAKE_CASE__							:					str				):
                lowerCamelCase__           =		name
                lowerCamelCase__           =		[]
          def __str__( self							:					Dict				):
                return self.name


          def      _UpperCamelCase       ( self							:					Union[str, Any]							,					SCREAMING_SNAKE_CASE__							:					str				):
                self.paths.append({'name': self.name, 'path': path}				)





      lowerCamelCase__           =		{}

      lowerCamelCase__           =		filter(os.path.isdir		,   os.listdir()							)
      for directory in directories:
            lowerCamelCase__           =		directory
            if artifact_name not in _available_artifacts:
                  lowerCamelCase__           =		Artifact(_a							)

                  _available_artifacts[artifact_name].add_path(_a							)

      return _available_artifacts


if __name__ == "__main__":
   _snake_case          =							get_job_links()
   _snake_case          =							retrieve_available_artifacts()

   _snake_case          =							collections.OrderedDict(
       [
           ("*.py", "API Examples"),
           ("*.md", "MD Examples"),
       ]
   )

   # This dict will contain all the information relative to each doc test category:
   # - failed: list of failed tests
   # - failures: dict in the format 'test': 'error_message'
   _snake_case          =							{
       v: {
           "failed": [],
           "failures": {},
       }
       for v in docs.values()
   }

   # Link to the GitHub Action job
   _snake_case          =							github_actions_job_links.get("run_doctests")

   _snake_case          =							available_artifacts["doc_tests_gpu_test_reports"].paths[0]
   _snake_case          =							retrieve_artifact(artifact_path["name"])
   if "stats" in artifact:
      _snake_case      ,					_snake_case      ,					_snake_case          =							handle_test_results(artifact["stats"])
      _snake_case          =							failed
      _snake_case          =							success
      _snake_case          =							time_spent[1:-1] + ", "

      _snake_case          =							extract_first_line_failure(artifact["failures_short"])
      for line in artifact["summary_short"].split("\n"):
         if re.search("FAILED", line):
            _snake_case          =							line.replace("FAILED ", "")
            _snake_case          =							line.split()[0].replace("\n", "")

            if "::" in line:
               _snake_case      ,					_snake_case          =							line.split("::")
            else:
               _snake_case      ,					_snake_case          =							line, line

            for file_regex in docs.keys():
               if fnmatch(file_path, file_regex):
                  _snake_case          =							docs[file_regex]
                  doc_test_results[category]["failed"].append(test)

                  _snake_case          =							all_failures[test] if test in all_failures else "N/A"
                  _snake_case          =							failure
                  break

   _snake_case          =							Message("🤗 Results of the doc tests.", doc_test_results)
   message.post()
   message.post_reply()



