


'''simple docstring'''



def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float						,				):
					__a      :	Dict    =  [redshift, radiation_density, matter_density, dark_energy]
					if any(p < 0 for p in parameters       ):
										raise ValueError('All input parameters must be positive'       )

					if any(p > 1 for p in parameters[1:4]       ):
										raise ValueError('Relative densities cannot be greater than one'       )
					else:
										__a      :	Optional[int]    =  1 - (matter_density + radiation_density + dark_energy)

										__a      :	str    =  (
										    radiation_density * (redshift + 1) ** 4
										    + matter_density * (redshift + 1) ** 3
										    + curvature * (redshift + 1) ** 2
										    + dark_energy
										)

										__a      :	Union[str, Any]    =  hubble_constant * e_a ** (1 / 2)
										return hubble


if __name__ == "__main__":
	import doctest

	# run doctest
	doctest.testmod()

	# demo LCDM approximation
	__lowercase     :						Any	=     0.3

	print(
	    hubble_parameter(
	        hubble_constant=68.3,
	        radiation_density=1E-4,
	        matter_density=matter_density,
	        dark_energy=1 - matter_density,
	        redshift=0,
	    )
	)







'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						shutil.rmtree(self.tmpdirname							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Tuple    =  ClapProcessor.from_pretrained(self.tmpdirname							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()							)
						processor.save_pretrained(self.tmpdirname							)

						__a      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)
						__a      :	List[str]    =  self.get_feature_extractor(do_normalize=__a						,    padding_value=1.0							)

						__a      :	Tuple    =  ClapProcessor.from_pretrained(
						    self.tmpdirname						,    bos_token='(BOS)'						,    eos_token='(EOS)'						,    do_normalize=__a						,    padding_value=1.0							)

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	str    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	int    =  floats_list((3, 1000)							)

						__a      :	str    =  feature_extractor(__a						,    return_tensors='np'							)
						__a      :	int    =  processor(audios=__a						,    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 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.get_feature_extractor()
						__a      :	Any    =  self.get_tokenizer()

						__a      :	Any    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Union[str, Any]    =  'This is a test string'

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

						__a      :	Tuple    =  tokenizer(__a							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	str    =  self.get_feature_extractor()
						__a      :	str    =  self.get_tokenizer()

						__a      :	List[str]    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

						__a      :	Dict    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__a      :	Optional[int]    =  processor.batch_decode(__a							)
						__a      :	Optional[Any]    =  tokenizer.batch_decode(__a							)

						self.assertListEqual(__a						,    __a							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	int    =  ClapProcessor(tokenizer=__a						,    feature_extractor=__a							)

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



