'''simple docstring'''

import math



def 				UpperCamelCase__      (      a__   ):


						'''simple docstring'''






						if number == 0:
												_lowerCAmelCase					    ='Zero'
												return words
						else:
												_lowerCAmelCase					    =math.logaa(a__   )
												_lowerCAmelCase					    =digits + 1
												_lowerCAmelCase					    ={}
												_lowerCAmelCase					    =''
												_lowerCAmelCase					    ='One'
												_lowerCAmelCase					    ='Two'
												_lowerCAmelCase					    ='Three'
												_lowerCAmelCase					    ='Four'
												_lowerCAmelCase					    ='Five'
												_lowerCAmelCase					    ='Six'
												_lowerCAmelCase					    ='Seven'
												_lowerCAmelCase					    ='Eight'
												_lowerCAmelCase					    ='Nine'

												_lowerCAmelCase					    ={}
												_lowerCAmelCase					    =''
												_lowerCAmelCase					    ='Twenty'
												_lowerCAmelCase					    ='Thirty'
												_lowerCAmelCase					    ='Forty'
												_lowerCAmelCase					    ='Fifty'
												_lowerCAmelCase					    ='Sixty'
												_lowerCAmelCase					    ='Seventy'
												_lowerCAmelCase					    ='Eighty'
												_lowerCAmelCase					    ='Ninety'

												_lowerCAmelCase					    ={}
												_lowerCAmelCase					    ='Ten'
												_lowerCAmelCase					    ='Eleven'
												_lowerCAmelCase					    ='Twelve'
												_lowerCAmelCase					    ='Thirteen'
												_lowerCAmelCase					    ='Fourteen'
												_lowerCAmelCase					    ='Fifteen'
												_lowerCAmelCase					    ='Sixteen'
												_lowerCAmelCase					    ='Seventeen'
												_lowerCAmelCase					    ='Eighteen'
												_lowerCAmelCase					    ='Nineteen'

												_lowerCAmelCase					    ={}
												_lowerCAmelCase					    ='Hundred,'
												_lowerCAmelCase					    ='Thousand,'
												_lowerCAmelCase					    ='Lakh,'
												_lowerCAmelCase					    ='Crore,'

												_lowerCAmelCase					    =number
												_lowerCAmelCase					    =''
												_lowerCAmelCase					    =0
												_lowerCAmelCase					    =int(a__   )
												while counter < digits:
																		_lowerCAmelCase					    =temp_num % 1_0
																		if counter % 2 == 0:
																								_lowerCAmelCase					    =''
																								if counter in placevalue and current != 0:
																														_lowerCAmelCase					    =placevalue[counter]
																								if counter == 2:
																														_lowerCAmelCase					    =singles[current] + addition + words
																								elif counter == 0:
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				_lowerCAmelCase					    =teens[current] + addition + words
																																				_lowerCAmelCase					    =temp_num // 1_0
																																				counter += 1
																														else:
																																				_lowerCAmelCase					    =singles[current] + addition + words

																								else:
																														_lowerCAmelCase					    =doubles[current] + addition + words

																		else:
																								if counter == 1:
																														if current == 1:
																																				_lowerCAmelCase					    =teens[number % 1_0] + words
																														else:
																																				_lowerCAmelCase					    =''
																																				if counter in placevalue:
																																										_lowerCAmelCase					    =placevalue[counter]
																																				_lowerCAmelCase					    =doubles[current] + addition + words
																								else:
																														_lowerCAmelCase					    =''
																														if counter in placevalue:
																																				if current == 0 and ((temp_num % 1_0_0) // 1_0) == 0:
																																										_lowerCAmelCase					    =''
																																				else:
																																										_lowerCAmelCase					    =placevalue[counter]
																														if ((temp_num % 1_0_0) // 1_0) == 1:
																																				_lowerCAmelCase					    =teens[current] + addition + words
																																				_lowerCAmelCase					    =temp_num // 1_0
																																				counter += 1
																														else:
																																				_lowerCAmelCase					    =singles[current] + addition + words
																		counter += 1
																		_lowerCAmelCase					    =temp_num // 1_0
						return words


if __name__ == "__main__":
							import doctest

							doctest.testmod()










import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


A__       =			get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")





@require_torch
class    __lowerCAmelCase					(	unittest.TestCase			):




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       tempfile.mkdtemp()

									_lowerCAmelCase         =       SpeechTaTokenizer(_snake_case						)
									tokenizer.save_pretrained(self.tmpdirname						)

									_lowerCAmelCase         =       {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16000,
									    """do_normalize""": False,
									    """num_mel_bins""": 80,
									    """hop_length""": 16,
									    """win_length""": 64,
									    """win_function""": """hann_window""",
									    """fmin""": 80,
									    """fmax""": 7600,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									_lowerCAmelCase         =       os.path.join(self.tmpdirname		,							_snake_case						)
									with open(self.feature_extraction_file		,							"""w"""		,							encoding="""utf-8"""						) as fp:
														fp.write(json.dumps(_snake_case						) + """\n"""						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									shutil.rmtree(self.tmpdirname						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_tokenizer()
									_lowerCAmelCase         =       self.get_feature_extractor()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									processor.save_pretrained(self.tmpdirname						)
									_lowerCAmelCase         =       SpeechTaProcessor.from_pretrained(self.tmpdirname						)

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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=self.get_tokenizer()		,							feature_extractor=self.get_feature_extractor()						)
									processor.save_pretrained(self.tmpdirname						)

									_lowerCAmelCase         =       self.get_tokenizer(bos_token="""(BOS)"""		,							eos_token="""(EOS)"""						)
									_lowerCAmelCase         =       self.get_feature_extractor(do_normalize=_snake_case		,							padding_value=1.0						)

									_lowerCAmelCase         =       SpeechTaProcessor.from_pretrained(
									    self.tmpdirname		,							bos_token="""(BOS)"""		,							eos_token="""(EOS)"""		,							do_normalize=_snake_case		,							padding_value=1.0						)

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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio=_snake_case		,							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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio_target=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio_target=_snake_case		,							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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text_target=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									_lowerCAmelCase         =       processor.batch_decode(_snake_case						)
									_lowerCAmelCase         =       tokenizer.batch_decode(_snake_case						)

									self.assertListEqual(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

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



