




import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


lowerCAmelCase_		      = logging.get_logger(__name__)






def 					snake_case(				__magic_name__ ,		__magic_name__    ) ->							Union[str, Any]:





     '''simple docstring'''


     lowercase					:   Union[str, Any]								= set()
     lowercase					:   List[str]								= []

     def parse_line(__magic_name__    ):
          for line in fp:
               if isinstance(__A ,		__A    ):
                    lowercase					:   Optional[Any]								= line.decode('''UTF-8'''    )
               if "warnings summary (final)" in line:
                    continue
               # This means we are outside the body of a warning
               elif not line.startswith(''' '''    ):
                    # process a single warning and move it to `selected_warnings`.
                    if len(__A    ) > 0:
                         lowercase					:   Dict								= '''\n'''.join(__A    )
                         # Only keep the warnings specified in `targets`
                         if any(F""": {x}: """ in warning for x in targets    ):
                              selected_warnings.add(__A    )
                         buffer.clear()
                    continue
               else:
                    lowercase					:   Any								= line.strip()
                    buffer.append(__A    )

     if from_gh:
          for filename in os.listdir(__A    ):
               lowercase					:   str								= os.path.join(__A ,		__A    )
               if not os.path.isdir(__A    ):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(__A    ) as fp:
                         parse_line(__A    )
     else:
          try:
               with zipfile.ZipFile(__A    ) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(__A    ):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(__A    ) as fp:
                                   parse_line(__A    )
          except Exception:
               logger.warning(
                   F"""{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."""    )

     return selected_warnings






def 					snake_case(				__magic_name__ ,		__magic_name__    ) ->							Optional[int]:





     '''simple docstring'''


     lowercase					:   int								= set()

     lowercase					:   int								= [os.path.join(__A ,		__A    ) for p in os.listdir(__A    ) if (p.endswith('''.zip'''    ) or from_gh)]
     for p in paths:
          selected_warnings.update(extract_warnings_from_single_artifact(__A ,		__A    )    )

     return selected_warnings


if __name__ == "__main__":






 def 					snake_case(				__magic_name__    ) ->							List[str]:





      '''simple docstring'''


      return values.split(''','''    )

 lowerCAmelCase_		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
 parser.add_argument(
     '--output_dir',
     type=str,
     required=True,
     help='Where to store the downloaded artifacts and other result files.',
 )
 parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
 # optional parameters
 parser.add_argument(
     '--targets',
     default='DeprecationWarning,UserWarning,FutureWarning',
     type=list_str,
     help='Comma-separated list of target warning(s) which we want to extract.',
 )
 parser.add_argument(
     '--from_gh',
     action='store_true',
     help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
 )

 lowerCAmelCase_		      = parser.parse_args()

 lowerCAmelCase_		      = args.from_gh
 if from_gh:
  # The artifacts have to be downloaded using `actions/download-artifact@v3`
  pass
 else:
  os.makedirs(args.output_dir, exist_ok=True)

  # get download links
  lowerCAmelCase_		      = get_artifacts_links(args.workflow_run_id, token=args.token)
  with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
   json.dump(artifacts, fp, ensure_ascii=False, indent=4)

  # download artifacts
  for idx, (name, url) in enumerate(artifacts.items()):
   print(name)
   print(url)
   print('=' * 80)
   download_artifact(name, url, args.output_dir, args.token)
   # Be gentle to GitHub
   time.sleep(1)

    # extract warnings from artifacts
 lowerCAmelCase_		      = extract_warnings(args.output_dir, args.targets)
 lowerCAmelCase_		      = sorted(selected_warnings)
 with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
  json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)

'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


lowercase    :							Optional[Any]      =					False


class 						__UpperCAmelCase      (					unittest.TestCase						):
							pass


@nightly
@require_torch_gpu
class 						__UpperCAmelCase      (					unittest.TestCase						):


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained('shi-labs/versatile-diffusion'  )
								# remove text_unet
								pipe.remove_unused_weights()
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(lowerCAmelCase_  )
									_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCAmelCase_  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		generator.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=2    ,   output_type='numpy'  ).images

								assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VersatileDiffusionTextToImagePipeline.from_pretrained(
								    'shi-labs/versatile-diffusion'    ,   torch_dtype=torch.floataa  )
								pipe.to(lowerCAmelCase_  )
								pipe.set_progress_bar_config(disable=lowerCAmelCase_  )

								_snake_case   						=		'A painting of a squirrel eating a burger '
								_snake_case   						=		torch.manual_seed(0  )
								_snake_case   						=		pipe(
								    prompt=lowerCAmelCase_    ,   generator=lowerCAmelCase_    ,   guidance_scale=7.5    ,   num_inference_steps=50    ,   output_type='numpy'  ).images

								_snake_case   						=		image[0, 2_53:2_56, 2_53:2_56, -1]

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case   						=		np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2

