


'''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 ):
	A_           =   MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a							):

						'''simple docstring'''





						for example in examples:
											__a      :	Union[str, Any]    =  video_classifier(__a							)

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




	@require_torch
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
						__a      :	List[Any]    =  VideoMAEFeatureExtractor(
						    size={'shortest_edge': 10}						,    crop_size={'height': 10, 'width': 10}							)
						__a      :	List[str]    =  pipeline(
						    'video-classification'						,    model=__a						,    feature_extractor=__a						,    frame_sampling_rate=4							)

						__a      :	List[Any]    =  hf_hub_download(repo_id='nateraw/video-demo'						,    filename='archery.mp4'						,    repo_type='dataset'							)
						__a      :	str    =  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'}]						,    )

						__a      :	Any    =  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							):

						'''simple docstring'''





						pass







'''simple docstring'''



import re

from filelock import FileLock


try:
	import nltk

	__lowercase     :						Optional[Any]	=     True
except (ImportError, ModuleNotFoundError):
	__lowercase     :						Dict	=     False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					re.sub('<n>'						,				''						,				_SCREAMING_SNAKE_CASE       )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE       )       )



