





'''simple docstring'''







import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
					from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


snake_case_  :							Optional[Any]            =	get_tests_dir("fixtures/test_sentencepiece.model")


@require_torch
@require_torchaudio
@require_sentencepiece
class        __a   (unittest.TestCase  ):

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







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	tempfile.mkdtemp()

													UpperCAmelCase_      :   Union[str, Any]      =	['''<s>''', '''<pad>''', '''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']
													UpperCAmelCase_      :   Optional[Any]      =	dict(zip(__magic_name__							,  range(len(__magic_name__ ) ) ) )
													UpperCAmelCase_      :   Optional[int]      =	Path(self.tmpdirname )
													save_json(__magic_name__							,  save_dir / VOCAB_FILES_NAMES['''vocab_file'''] )
													if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
																			copyfile(__magic_name__							,  save_dir / VOCAB_FILES_NAMES['''spm_file'''] )

													UpperCAmelCase_      :   Dict      =	SpeechaTextTokenizer.from_pretrained(self.tmpdirname )
													tokenizer.save_pretrained(self.tmpdirname )

													UpperCAmelCase_      :   Optional[int]      =	{
													    '''feature_size''': 24,
													    '''num_mel_bins''': 24,
													    '''padding_value''': 0.0,
													    '''sampling_rate''': 1_60_00,
													    '''return_attention_mask''': False,
													    '''do_normalize''': True,
													}
													save_json(__magic_name__							,  save_dir / FEATURE_EXTRACTOR_NAME )

							def      UpperCAmelCase__						(				self				:  Optional[Any]							,  **__magic_name__				:  Tuple )       ->							str:







													"""simple docstring"""
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  **__magic_name__				:  str )       ->							Optional[int]:







													"""simple docstring"""
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname							,  **__magic_name__ )

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







													"""simple docstring"""
													shutil.rmtree(self.tmpdirname )

							def      UpperCAmelCase__						(				self				:  Optional[Any] )       ->							List[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	self.get_tokenizer()
													UpperCAmelCase_      :   Any      =	self.get_feature_extractor()

													UpperCAmelCase_      :   Optional[Any]      =	SpeechaTextProcessor(tokenizer=__magic_name__							,  feature_extractor=__magic_name__ )

													processor.save_pretrained(self.tmpdirname )
													UpperCAmelCase_      :   Tuple      =	SpeechaTextProcessor.from_pretrained(self.tmpdirname )

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

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

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







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[int]      =	SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()							,  feature_extractor=self.get_feature_extractor() )
													processor.save_pretrained(self.tmpdirname )

													UpperCAmelCase_      :   List[Any]      =	self.get_tokenizer(bos_token='''(BOS)'''							,  eos_token='''(EOS)''' )
													UpperCAmelCase_      :   Any      =	self.get_feature_extractor(do_normalize=__magic_name__							,  padding_value=1.0 )

													UpperCAmelCase_      :   Union[str, Any]      =	SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname							,  bos_token='''(BOS)'''							,  eos_token='''(EOS)'''							,  do_normalize=__magic_name__							,  padding_value=1.0 )

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

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

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	self.get_feature_extractor()
													UpperCAmelCase_      :   Optional[int]      =	self.get_tokenizer()

													UpperCAmelCase_      :   Union[str, Any]      =	SpeechaTextProcessor(tokenizer=__magic_name__							,  feature_extractor=__magic_name__ )

													UpperCAmelCase_      :   Tuple      =	floats_list((3, 10_00) )

													UpperCAmelCase_      :   Optional[Any]      =	feature_extractor(__magic_name__							,  return_tensors='''np''' )
													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__							,  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				:  str )       ->							Optional[int]:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	self.get_feature_extractor()
													UpperCAmelCase_      :   List[str]      =	self.get_tokenizer()

													UpperCAmelCase_      :   Optional[int]      =	SpeechaTextProcessor(tokenizer=__magic_name__							,  feature_extractor=__magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	'''This is a test string'''

													UpperCAmelCase_      :   List[Any]      =	processor(text=__magic_name__ )

													UpperCAmelCase_      :   int      =	tokenizer(__magic_name__ )

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

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







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	self.get_feature_extractor()
													UpperCAmelCase_      :   Optional[Any]      =	self.get_tokenizer()

													UpperCAmelCase_      :   str      =	SpeechaTextProcessor(tokenizer=__magic_name__							,  feature_extractor=__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													UpperCAmelCase_      :   Any      =	processor.batch_decode(__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	tokenizer.batch_decode(__magic_name__ )

													self.assertListEqual(__magic_name__							,  __magic_name__ )



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







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	self.get_feature_extractor()
													UpperCAmelCase_      :   str      =	self.get_tokenizer()

													UpperCAmelCase_      :   List[Any]      =	SpeechaTextProcessor(tokenizer=__magic_name__							,  feature_extractor=__magic_name__ )

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






from __future__ import annotations

# Define the Gravitational Constant G and the function
_SCREAMING_SNAKE_CASE:    int     						=							6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   mass_a * mass_a

			if (force, mass_a, mass_a, distance).count(0	) != 1:
						raise ValueError("One and only one argument must be 0"	)
			if force < 0:
						raise ValueError("Gravitational force can not be negative"	)
			if distance < 0:
						raise ValueError("Distance can not be negative"	)
			if mass_a < 0 or mass_a < 0:
						raise ValueError("Mass can not be negative"	)
			if force == 0:
						_snake_case														=   GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
						return {"force": force}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_1": mass_a}
			elif mass_a == 0:
						_snake_case														=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
						return {"mass_2": mass_a}
			elif distance == 0:
						_snake_case														=   (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
						return {"distance": distance}
			raise ValueError("One and only one argument must be 0"	)


# Run doctest
if __name__ == "__main__":
			import doctest

			doctest.testmod()

