
"""simple docstring"""





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    _UpperCAmelCase(   unittest.TestCase     ):
		lowercase__      =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	Union[str, Any]:






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



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Union[str, Any]:






							'''simple docstring'''
							for example in examples:
												_UpperCamelCase										=  video_classifier(__a)

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



		@require_torch
		def 						UpperCAmelCase							( self)	->	str:






							'''simple docstring'''
							_UpperCamelCase										=  '''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
							_UpperCamelCase										=  VideoMAEFeatureExtractor(
							    size={'''shortest_edge''': 10}    ,		crop_size={'''height''': 10, '''width''': 10})
							_UpperCamelCase										=  pipeline(
							    '''video-classification'''    ,		model=__a    ,		feature_extractor=__a    ,		frame_sampling_rate=4)

							_UpperCamelCase										=  hf_hub_download(repo_id='''nateraw/video-demo'''    ,		filename='''archery.mp4'''    ,		repo_type='''dataset''')
							_UpperCamelCase										=  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'''}]    ,		)

							_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	Union[str, Any]:






							'''simple docstring'''
							pass








"""simple docstring"""





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a														=		namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict(
					    {
					        '''default''': DatasetInfo(
					            features=Features(
					                {
					                    '''tokens''': Sequence(Value('''string'''     )     ),
					                    '''ner_tags''': Sequence(
					                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']     )     ),
					                    '''langs''': Sequence(Value('''string'''     )     ),
					                    '''spans''': Sequence(Value('''string'''     )     ),
					                }     ),					splits=[
					                {
					                    '''name''': '''train''',
					                    '''num_bytes''': 2_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															for split in result:
																				assert result[split].name == expected[split].name
																				assert result[split].num_examples == expected[split].num_examples
																				assert is_apercent_close(result[split].num_bytes,					expected[split].num_bytes     )
										else:
															result == expected






