


'''simple docstring'''




import argparse
from collections import defaultdict

import yaml


__snake_case    						="""docs/source/en/_toctree.yml"""





def 	a_					( lowerCamelCase	:     Any				):
   lowerCAmelCase     =			defaultdict(lowerCamelCase				)
   for doc in model_doc:
      counts[doc["local"]] += 1
   lowerCAmelCase     =			[key for key, value in counts.items() if value > 1]

   lowerCAmelCase     =			[]
   for duplicate_key in duplicates:
      lowerCAmelCase     =			list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
      if len(lowerCamelCase				) > 1:
         raise ValueError(
             f'''{duplicate_key} is present several times in the documentation table of content at '''
             '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
             'others.'				)
      # Only add this once
      new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

   # Add none duplicate-keys
   new_doc.extend([doc for doc in model_doc if counts[doc['local']] == 1]				)

   # Sort
   return sorted(lowerCamelCase	, key=lambda lowerCamelCase				: s["title"].lower()				)





def 	a_					( lowerCamelCase	:     List[Any]=False				):
   with open(lowerCamelCase	, encoding='utf-8'				) as f:
      lowerCAmelCase     =			yaml.safe_load(f.read()				)

   # Get to the API doc
   lowerCAmelCase     =			0
   while content[api_idx]["title"] != "API":
      api_idx += 1
   lowerCAmelCase     =			content[api_idx]['sections']

   # Then to the model doc
   lowerCAmelCase     =			0
   while api_doc[model_idx]["title"] != "Models":
      model_idx += 1

   lowerCAmelCase     =			api_doc[model_idx]['sections']

   lowerCAmelCase     =			[(idx, section) for idx, section in enumerate(lowerCamelCase				) if 'sections' in section]
   lowerCAmelCase     =			False
   for idx, modality_doc in modalities_docs:
      lowerCAmelCase     =			modality_doc['sections']
      lowerCAmelCase     =			clean_model_doc_toc(lowerCamelCase				)

      if old_modality_doc != new_modality_doc:
         lowerCAmelCase     =			True
         if overwrite:
            lowerCAmelCase     =			new_modality_doc

   if diff:
      if overwrite:
         lowerCAmelCase     =			model_doc
         lowerCAmelCase     =			api_doc
         with open(lowerCamelCase	, 'w'	, encoding='utf-8'				) as f:
            f.write(yaml.dump(lowerCamelCase	, allow_unicode=lowerCamelCase				)				)
      else:
         raise ValueError(
             'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()
       parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
       __snake_case    						=parser.parse_args()

       check_model_doc(args.fix_and_overwrite)












import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY




@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 __SCREAMING_SNAKE_CASE							       =	MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ):
        A__											=						hf_hub_download(
            repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						VideoClassificationPipeline(model=__lowerCamelCase,image_processor=__lowerCamelCase,top_k=2 )
        A__											=						[
            example_video_filepath,
            '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
        ]
        return video_classifier, examples



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        for example in examples:
               A__											=						video_classifier(__lowerCamelCase )

               self.assertEqual(
                   __lowerCamelCase,[
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                       {'''score''': ANY(__lowerCamelCase ), '''label''': ANY(__lowerCamelCase )},
                   ],)



 @require_torch
 def 				UpperCamelCase			(							self ):
        A__											=						'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
        A__											=						VideoMAEFeatureExtractor(
            size={'''shortest_edge''': 10},crop_size={'''height''': 10, '''width''': 10} )
        A__											=						pipeline(
            '''video-classification''',model=__lowerCamelCase,feature_extractor=__lowerCamelCase,frame_sampling_rate=4 )

        A__											=						hf_hub_download(repo_id='''nateraw/video-demo''',filename='''archery.mp4''',repo_type='''dataset''' )
        A__											=						video_classifier(__lowerCamelCase,top_k=2 )
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],)

        A__											=						video_classifier(
            [
                video_file_path,
                video_file_path,
            ],top_k=2,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4 ),[
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
                [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
            ],)




 @require_tf
 def 				UpperCamelCase			(							self ):
        pass





