
from random import randint
from tempfile import TemporaryFile

import numpy as np






def     lowerCAmelCase_		(   _snake_case :    Tuple ,  _snake_case :    Optional[int] ,  _snake_case :    List[str]  )							->						Optional[Any]:







	'''simple docstring'''


	__magic_name__						:						int        =	0
	if start < end:
		__magic_name__						:						int        =	randint(UpperCamelCase__ ,  UpperCamelCase__  )
		__magic_name__						:						Optional[Any]        =	a[end]
		__magic_name__						:						List[Any]        =	a[pivot]
		__magic_name__						:						Union[str, Any]        =	temp

		__magic_name__						:						Union[str, Any]        =	_in_place_partition(UpperCamelCase__ ,  UpperCamelCase__ ,  UpperCamelCase__  )
		count += _in_place_quick_sort(UpperCamelCase__ ,  UpperCamelCase__ ,  p - 1  )
		count += _in_place_quick_sort(UpperCamelCase__ ,  p + 1 ,  UpperCamelCase__  )
	return count






def     lowerCAmelCase_		(   _snake_case :    int ,  _snake_case :    str ,  _snake_case :    List[str]  )							->						List[Any]:







	'''simple docstring'''


	__magic_name__						:						Optional[Any]        =	0
	__magic_name__						:						Optional[Any]        =	randint(UpperCamelCase__ ,  UpperCamelCase__  )
	__magic_name__						:						List[Any]        =	a[end]
	__magic_name__						:						Optional[Any]        =	a[pivot]
	__magic_name__						:						int        =	temp
	__magic_name__						:						int        =	start - 1
	for index in range(UpperCamelCase__ ,  UpperCamelCase__  ):
		count += 1
		if a[index] < a[end]:  # check if current val is less than pivot value
			__magic_name__						:						Dict        =	new_pivot_index + 1
			__magic_name__						:						List[Any]        =	a[new_pivot_index]
			__magic_name__						:						Optional[int]        =	a[index]
			__magic_name__						:						Any        =	temp

	__magic_name__						:						int        =	a[new_pivot_index + 1]
	__magic_name__						:						int        =	a[end]
	__magic_name__						:						Any        =	temp
	return new_pivot_index + 1, count


snake_case					:						Dict				 =    TemporaryFile()
snake_case					:						Tuple				 =    100  # 1000 elements are to be sorted


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


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

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





print(z)


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
    from transformers import TvltImageProcessor

if is_speech_available():
    from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class 					_lowerCamelCase			(   unittest.TestCase			):





     """simple docstring"""







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




           """simple docstring"""







           UpperCamelCase__     : int											=					'''ZinengTang/tvlt-base'''
           UpperCamelCase__     : Optional[Any]											=					tempfile.mkdtemp()






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> Tuple:




           """simple docstring"""







           return TvltImageProcessor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    **__SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           shutil.rmtree(self.tmpdirname	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)
           processor.save_pretrained(self.tmpdirname	)
           UpperCamelCase__     : Optional[Any]											=					TvltProcessor.from_pretrained(self.tmpdirname	)

           self.assertIsInstance(processor.feature_extractor    ,    __SCREAMING_SNAKE_CASE	)
           self.assertIsInstance(processor.image_processor    ,    __SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					self.get_image_processor()
           UpperCamelCase__     : int											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					np.ones([1_2_0_0_0]	)

           UpperCamelCase__     : Dict											=					feature_extractor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Dict											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in audio_dict.keys():
                 self.assertAlmostEqual(audio_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Optional[int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					self.get_image_processor()
           UpperCamelCase__     : Tuple											=					self.get_feature_extractor()

           UpperCamelCase__     : str											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : List[Any]											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : Optional[int]											=					image_processor(__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)
           UpperCamelCase__     : Tuple											=					processor(images=__SCREAMING_SNAKE_CASE    ,    return_tensors='''np'''	)

           for key in image_dict.keys():
                 self.assertAlmostEqual(image_dict[key].sum()    ,    input_processor[key].sum()    ,    delta=1e-2	)






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					self.get_image_processor()
           UpperCamelCase__     : Optional[Any]											=					self.get_feature_extractor()

           UpperCamelCase__     : Union[str, Any]											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Dict											=					np.ones([1_2_0_0_0]	)
           UpperCamelCase__     : str											=					np.ones([3, 2_2_4, 2_2_4]	)

           UpperCamelCase__     : List[Any]											=					processor(audio=__SCREAMING_SNAKE_CASE    ,    images=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(list(inputs.keys()	)    ,    ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']	)

           # test if it raises when no input is passed
           with pytest.raises(__SCREAMING_SNAKE_CASE	):
                 processor()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           UpperCamelCase__     : int											=					self.get_image_processor()
           UpperCamelCase__     : str											=					self.get_feature_extractor()

           UpperCamelCase__     : Dict											=					TvltProcessor(image_processor=__SCREAMING_SNAKE_CASE    ,    feature_extractor=__SCREAMING_SNAKE_CASE	)

           self.assertListEqual(
               processor.model_input_names    ,    image_processor.model_input_names + feature_extractor.model_input_names    ,    msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''    ,    )


