




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   A(						unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

 def 				a__ (				self					:  Dict						,							A_					:  Any						,							A_					:  Tuple						,							A_					:  Tuple  )   ->	List[Any]:

       """simple docstring"""






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

 def 				a__ (				self					:  Any						,							A_					:  List[Any]						,							A_					:  Tuple  )   ->	Optional[int]:

       """simple docstring"""






       for example in examples:
             lowerCamelCase_		     =    video_classifier(A_  )

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

 @require_torch
 def 				a__ (				self					:  str  )   ->	Optional[Any]:

       """simple docstring"""






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

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

       lowerCamelCase_		     =    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 				a__ (				self					:  Union[str, Any]  )   ->	Tuple:

       """simple docstring"""






       pass





from random import randint
from tempfile import TemporaryFile

import numpy as np


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  str:
		'''simple docstring'''

		UpperCamelCase		=     0
		if start < end:
				UpperCamelCase		=     randint(lowercase    ,       lowercase )
				UpperCamelCase		=     a[end]
				UpperCamelCase		=     a[pivot]
				UpperCamelCase		=     temp

				UpperCamelCase	,  UpperCamelCase		=     _in_place_partition(lowercase    ,       lowercase    ,       lowercase )
				count += _in_place_quick_sort(lowercase    ,       lowercase    ,       p - 1 )
				count += _in_place_quick_sort(lowercase    ,       p + 1    ,       lowercase )
		return count


def       A       (					lowercase    ,       lowercase    ,       lowercase )   ->  int:
		'''simple docstring'''

		UpperCamelCase		=     0
		UpperCamelCase		=     randint(lowercase    ,       lowercase )
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     a[pivot]
		UpperCamelCase		=     temp
		UpperCamelCase		=     start - 1
		for index in range(lowercase    ,       lowercase ):
				count += 1
				if a[index] < a[end]:  # check if current val is less than pivot value
						UpperCamelCase		=     new_pivot_index + 1
						UpperCamelCase		=     a[new_pivot_index]
						UpperCamelCase		=     a[index]
						UpperCamelCase		=     temp

		UpperCamelCase		=     a[new_pivot_index + 1]
		UpperCamelCase		=     a[end]
		UpperCamelCase		=     temp
		return new_pivot_index + 1, count


_UpperCAmelCase					: Union[str, Any]      =       TemporaryFile()
_UpperCAmelCase					: List[Any]      =       100  # 1000 elements are to be sorted


_UpperCAmelCase   ,_UpperCAmelCase					: Any      =       0, 1  # mean and standard deviation
_UpperCAmelCase					: Any      =       np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)


outfile.seek(0)  # using the same array
_UpperCAmelCase					: Any      =       np.load(outfile)
_UpperCAmelCase					: str      =       len(M) - 1
_UpperCAmelCase					: List[str]      =       _in_place_quick_sort(M, 0, r)

print(
    "No of Comparisons for 100 elements selected from a standard normal distribution"
    "is :"
)


print(z)
