



'''simple docstring'''

import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list


@require_torchaudio
@require_sentencepiece
class 	a__     (				unittest.TestCase	):


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''laion/clap-htsat-unfused'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= tempfile.mkdtemp()


	def 				lowercase__  (self : Any,					**__UpperCAmelCase : Any      )     ->			Dict:

					"""simple docstring"""

					return RobertaTokenizer.from_pretrained(self.checkpoint,					**__UpperCAmelCase      )


	def 				lowercase__  (self : str,					**__UpperCAmelCase : Optional[int]      )     ->			Union[str, Any]:

					"""simple docstring"""

					return ClapFeatureExtractor.from_pretrained(self.checkpoint,					**__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any]      )     ->			str:

					"""simple docstring"""

					shutil.rmtree(self.tmpdirname      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_tokenizer()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_feature_extractor()

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ClapProcessor(tokenizer=__UpperCAmelCase,					feature_extractor=__UpperCAmelCase      )

					processor.save_pretrained(self.tmpdirname      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ClapProcessor.from_pretrained(self.tmpdirname      )

					self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer.get_vocab()      )
					self.assertIsInstance(processor.tokenizer,					__UpperCAmelCase      )

					self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor.to_json_string()      )
					self.assertIsInstance(processor.feature_extractor,					__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= ClapProcessor(tokenizer=self.get_tokenizer(),					feature_extractor=self.get_feature_extractor()      )
					processor.save_pretrained(self.tmpdirname      )

					SCREAMING_SNAKE_CASE  :						int      					= self.get_tokenizer(bos_token='''(BOS)''',					eos_token='''(EOS)'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= self.get_feature_extractor(do_normalize=__UpperCAmelCase,					padding_value=1.0      )

					SCREAMING_SNAKE_CASE  :						int      					= ClapProcessor.from_pretrained(
					    self.tmpdirname,					bos_token='''(BOS)''',					eos_token='''(EOS)''',					do_normalize=__UpperCAmelCase,					padding_value=1.0      )

					self.assertEqual(processor.tokenizer.get_vocab(),					tokenizer_add_kwargs.get_vocab()      )
					self.assertIsInstance(processor.tokenizer,					__UpperCAmelCase      )

					self.assertEqual(processor.feature_extractor.to_json_string(),					feature_extractor_add_kwargs.to_json_string()      )
					self.assertIsInstance(processor.feature_extractor,					__UpperCAmelCase      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.get_feature_extractor()
					SCREAMING_SNAKE_CASE  :						str      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						str      					= ClapProcessor(tokenizer=__UpperCAmelCase,					feature_extractor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= floats_list((3, 1000)      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= feature_extractor(__UpperCAmelCase,					return_tensors='''np'''      )
					SCREAMING_SNAKE_CASE  :						str      					= processor(audios=__UpperCAmelCase,					return_tensors='''np'''      )

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


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_feature_extractor()
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Any      					= ClapProcessor(tokenizer=__UpperCAmelCase,					feature_extractor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= '''This is a test string'''

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= processor(text=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer(__UpperCAmelCase      )

					for key in encoded_tok.keys():
									self.assertListEqual(encoded_tok[key],					encoded_processor[key]      )


	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_feature_extractor()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						Any      					= ClapProcessor(tokenizer=__UpperCAmelCase,					feature_extractor=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					SCREAMING_SNAKE_CASE  :						str      					= processor.batch_decode(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						int      					= tokenizer.batch_decode(__UpperCAmelCase      )

					self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )






	def 				lowercase__  (self : str      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_feature_extractor()
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.get_tokenizer()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= ClapProcessor(tokenizer=__UpperCAmelCase,					feature_extractor=__UpperCAmelCase      )

					self.assertListEqual(
					    processor.model_input_names[2:],					feature_extractor.model_input_names,					msg='''`processor` and `feature_extractor` model input names do not match''',					)









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0
				if start < end:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[end]
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[pivot]
								SCREAMING_SNAKE_CASE  :						Dict      					= temp

								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= _in_place_partition(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	p - 1       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	p + 1    ,	_SCREAMING_SNAKE_CASE       )
				return count






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						Tuple      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[end]
				SCREAMING_SNAKE_CASE  :						int      					= a[pivot]
				SCREAMING_SNAKE_CASE  :						Tuple      					= temp
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= start - 1
				for index in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
								count += 1
								if a[index] < a[end]:  # check if current val is less than pivot value
												SCREAMING_SNAKE_CASE  :						Tuple      					= new_pivot_index + 1
												SCREAMING_SNAKE_CASE  :						Dict      					= a[new_pivot_index]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[index]
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= temp

				SCREAMING_SNAKE_CASE  :						List[Any]      					= a[new_pivot_index + 1]
				SCREAMING_SNAKE_CASE  :						Any      					= a[end]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= temp
				return new_pivot_index + 1, count


snake_case_			=     TemporaryFile()
snake_case_			=     100  # 1000 elements are to be sorted


snake_case_			,				snake_case_			=     0, 1  # mean and standard deviation
snake_case_			=     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_			=     np.load(outfile)
snake_case_			=     len(M) - 1
snake_case_			=     _in_place_quick_sort(M, 0, r)

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




print(z)




