


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Tuple	   =       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int     ,		_a:str     ,		_a:Optional[Any]   ):
       snake_case__						= hf_hub_download(
           repo_id='''nateraw/video-demo'''     ,		filename='''archery.mp4'''     ,		repo_type='''dataset'''   )
       snake_case__						= VideoClassificationPipeline(model=_a     ,		image_processor=_a     ,		top_k=2   )
       snake_case__						= [
           example_video_filepath,
           '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
       ]
       return video_classifier, examples

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]     ,		_a:Optional[Any]     ,		_a:Optional[Any]   ):
       for example in examples:
         snake_case__						= video_classifier(_a   )

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

     @require_torch
     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
       snake_case__						= VideoMAEFeatureExtractor(
           size={'''shortest_edge''': 10}     ,		crop_size={'''height''': 10, '''width''': 10}   )
       snake_case__						= pipeline(
           '''video-classification'''     ,		model=_a     ,		feature_extractor=_a     ,		frame_sampling_rate=4   )

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

       snake_case__						= video_classifier(
           [
               video_file_path,
               video_file_path,
           ]     ,		top_k=2     ,		)
       self.assertEqual(
           nested_simplify(_a     ,		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 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       pass


import argparse
from collections import defaultdict

import yaml


_lowercase:	List[Any]       			=		'''docs/source/en/_toctree.yml'''






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							defaultdict(snake_case  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							[]
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({'local': doc['local'], 'title': doc['title']}  )
														else:
																					new_doc_list.append(snake_case  )

							_lowerCAmelCase							=							new_doc_list
							_lowerCAmelCase							=							[key for key, value in counts.items() if value > 1]

							_lowerCAmelCase							=							[]
							for duplicate_key in duplicates:
														_lowerCAmelCase							=							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}  )
														if len(snake_case  ) > 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 doc_list if 'local' not in counts or counts[doc['local']] == 1]  )
							_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(snake_case  ) > 1:
														raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'  )

							overview_doc.extend(snake_case  )

							# Sort
							return overview_doc






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , 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[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase							=							api_doc[scheduler_idx]['sections']
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							_lowerCAmelCase							=							False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_scheduler_doc

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






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , 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[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase							=							False
							_lowerCAmelCase							=							api_doc[pipeline_idx]['sections']
							_lowerCAmelCase							=							[]

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase							=							pipeline_doc['section']
																					_lowerCAmelCase							=							clean_doc_toc(snake_case  )
																					if overwrite:
																												_lowerCAmelCase							=							new_sub_pipeline_doc
														new_pipeline_docs.append(snake_case  )

							# sort overall pipeline doc
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														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__":
				_lowercase:	Tuple       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	Optional[Any]       			=		parser.parse_args()

				check_scheduler_doc(args.fix_and_overwrite)
				check_pipeline_doc(args.fix_and_overwrite)






