




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD


def    A						(__A   :    List[str]       )   ->      Tuple:







     """simple docstring"""


     def wrapper(*__A   :    Any   ,				**__A   :    Union[str, Any]       ):
          UpperCAmelCase_          =							timeit.default_timer()
          UpperCAmelCase_          =							func(*__A   ,				**__A       )
          UpperCAmelCase_          =							timeit.default_timer() - starttime
          return delta

     UpperCAmelCase_          =							func.__name__

     return wrapper


def    A						(__A   :    dict   ,				__A   :    str=100   ,				__A   :    Dict=None       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							[]
     UpperCAmelCase_          =							seq_shapes or {}
     for i in range(__A       ):
          UpperCAmelCase_          =							{}
          for col_id, (k, v) in enumerate(features.items()       ):
               if isinstance(__A   ,				_ArrayXD       ):
                    UpperCAmelCase_          =							np.random.rand(*v.shape       ).astype(v.dtype       )
               elif isinstance(__A   ,				datasets.Value       ):
                    if v.dtype == "string":
                         UpperCAmelCase_          =							'''The small grey turtle was surprisingly fast when challenged.'''
                    else:
                         UpperCAmelCase_          =							np.random.randint(10   ,				size=1       ).astype(v.dtype       ).item()
               elif isinstance(__A   ,				datasets.Sequence       ):
                    while isinstance(__A   ,				datasets.Sequence       ):
                         UpperCAmelCase_          =							v.feature
                    UpperCAmelCase_          =							seq_shapes[k]
                    UpperCAmelCase_          =							np.random.rand(*__A       ).astype(v.dtype       )
               UpperCAmelCase_          =							data

          dummy_data.append((i, example)       )

     return dummy_data


def    A						(__A   :    Optional[Any]   ,				__A   :    List[Any]   ,				__A   :    Dict=100   ,				__A   :    str=None       )   ->      Optional[Any]:







     """simple docstring"""


     UpperCAmelCase_          =							generate_examples(__A   ,				num_examples=__A   ,				seq_shapes=__A       )

     with ArrowWriter(features=__A   ,				path=__A       ) as writer:
          for key, record in dummy_data:
               UpperCAmelCase_          =							features.encode_example(__A       )
               writer.write(__A       )

          UpperCAmelCase_     ,       UpperCAmelCase_          =							writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""       )

     UpperCAmelCase_          =							datasets.Dataset.from_file(filename=__A   ,				info=datasets.DatasetInfo(features=__A       )       )

     return dataset









"""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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







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







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				Dict		):
						for example in examples:
							lowerCAmelCase__	:						Dict  				=	video_classifier(lowercase_		)

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







					@require_torch
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
						lowerCAmelCase__	:						List[str]  				=	VideoMAEFeatureExtractor(
						    size={'''shortest_edge''': 1_0}   ,crop_size={'''height''': 1_0, '''width''': 1_0}		)
						lowerCAmelCase__	:						Optional[Any]  				=	pipeline(
						    '''video-classification'''   ,model=lowercase_   ,feature_extractor=lowercase_   ,frame_sampling_rate=4		)

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

						lowerCAmelCase__	:						Dict  				=	video_classifier(
						    [
						        video_file_path,
						        video_file_path,
						    ]   ,top_k=2   ,)
						self.assertEqual(
						    nested_simplify(lowercase_   ,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        __lowerCAmelCase (       self			:				int		):
						pass




