






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


_UpperCamelCase   :						Tuple     						= get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")






@require_torch
class __a   (   unittest.TestCase				):



  def 						A_				(self  :		Optional[int]   )		->							Optional[Any]:
   __A	:       str       =	tempfile.mkdtemp()

   __A	:       Optional[int]       =	SpeechTaTokenizer(lowercase_   )
   tokenizer.save_pretrained(self.tmpdirname   )

   __A	:       Union[str, Any]       =	{
       'feature_size': 1,
       'padding_value': 0.0,
       'sampling_rate': 16_000,
       'do_normalize': False,
       'num_mel_bins': 80,
       'hop_length': 16,
       'win_length': 64,
       'win_function': 'hann_window',
       'fmin': 80,
       'fmax': 7_600,
       'mel_floor': 1E-10,
       'reduction_factor': 2,
       'return_attention_mask': True,
   }

   __A	:       List[str]       =	os.path.join(self.tmpdirname				,			lowercase_   )
   with open(self.feature_extraction_file				,			'w'				,			encoding='utf-8'   ) as fp:
    fp.write(json.dumps(lowercase_   ) + '\n'   )



  def 						A_				(self  :		int				,			**lowercase_  :		str   )		->							Union[str, Any]:
   return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,			**lowercase_   )



  def 						A_				(self  :		int				,			**lowercase_  :		str   )		->							Any:
   return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,			**lowercase_   )



  def 						A_				(self  :		Optional[Any]   )		->							List[str]:
   shutil.rmtree(self.tmpdirname   )



  def 						A_				(self  :		Tuple   )		->							Any:
   __A	:       int       =	self.get_tokenizer()
   __A	:       Optional[int]       =	self.get_feature_extractor()

   __A	:       Union[str, Any]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   processor.save_pretrained(self.tmpdirname   )
   __A	:       int       =	SpeechTaProcessor.from_pretrained(self.tmpdirname   )

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

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



  def 						A_				(self  :		Union[str, Any]   )		->							Dict:
   __A	:       int       =	SpeechTaProcessor(tokenizer=self.get_tokenizer()				,			feature_extractor=self.get_feature_extractor()   )
   processor.save_pretrained(self.tmpdirname   )

   __A	:       Any       =	self.get_tokenizer(bos_token='(BOS)'				,			eos_token='(EOS)'   )
   __A	:       Tuple       =	self.get_feature_extractor(do_normalize=lowercase_				,			padding_value=1.0   )

   __A	:       List[Any]       =	SpeechTaProcessor.from_pretrained(
       self.tmpdirname				,			bos_token='(BOS)'				,			eos_token='(EOS)'				,			do_normalize=lowercase_				,			padding_value=1.0   )

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

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



  def 						A_				(self  :		List[str]   )		->							str:
   __A	:       Optional[int]       =	self.get_feature_extractor()
   __A	:       Dict       =	self.get_tokenizer()

   __A	:       List[Any]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Optional[Any]       =	floats_list((3, 1_000)   )

   __A	:       List[Any]       =	feature_extractor(audio=lowercase_				,			return_tensors='np'   )
   __A	:       Optional[Any]       =	processor(audio=lowercase_				,			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 						A_				(self  :		str   )		->							Optional[int]:
   __A	:       Dict       =	self.get_feature_extractor()
   __A	:       Optional[Any]       =	self.get_tokenizer()

   __A	:       str       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Tuple       =	floats_list((3, 1_000)   )

   __A	:       str       =	feature_extractor(audio_target=lowercase_				,			return_tensors='np'   )
   __A	:       str       =	processor(audio_target=lowercase_				,			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 						A_				(self  :		Union[str, Any]   )		->							Any:
   __A	:       Optional[int]       =	self.get_feature_extractor()
   __A	:       Any       =	self.get_tokenizer()

   __A	:       Optional[Any]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Any       =	'This is a test string'

   __A	:       Any       =	processor(text=lowercase_   )
   __A	:       Union[str, Any]       =	tokenizer(lowercase_   )

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



  def 						A_				(self  :		List[Any]   )		->							List[Any]:
   __A	:       Dict       =	self.get_feature_extractor()
   __A	:       List[str]       =	self.get_tokenizer()

   __A	:       Dict       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       List[str]       =	'This is a test string'

   __A	:       List[Any]       =	processor(text_target=lowercase_   )
   __A	:       str       =	tokenizer(lowercase_   )

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



  def 						A_				(self  :		Optional[Any]   )		->							int:
   __A	:       str       =	self.get_feature_extractor()
   __A	:       Tuple       =	self.get_tokenizer()

   __A	:       List[str]       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

   __A	:       Optional[Any]       =	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

   __A	:       Union[str, Any]       =	processor.batch_decode(lowercase_   )
   __A	:       Optional[Any]       =	tokenizer.batch_decode(lowercase_   )

   self.assertListEqual(lowercase_				,			lowercase_   )



  def 						A_				(self  :		Optional[Any]   )		->							Optional[Any]:
   __A	:       Union[str, Any]       =	self.get_feature_extractor()
   __A	:       Optional[int]       =	self.get_tokenizer()

   __A	:       str       =	SpeechTaProcessor(tokenizer=lowercase_				,			feature_extractor=lowercase_   )

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













"""simple docstring"""


import math




def 				a (     UpperCamelCase_							:				int		)  ->		list[int]:
 if not isinstance(UpperCamelCase_     ,	UpperCamelCase_		):
  __A	:       Any       =	F"Input value of [number={number}] must be an integer"
  raise TypeError(UpperCamelCase_		)
 if number < 1:
  __A	:       int       =	F"Input value of [number={number}] must be a positive integer"
  raise ValueError(UpperCamelCase_		)
 __A	:       Optional[Any]       =	[number]
 while number != 1:
  if number % 2 == 0:
   __A	:       Optional[int]       =	math.floor(math.sqrt(UpperCamelCase_		)		)
  else:
   __A	:       List[str]       =	math.floor(
       math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		) * math.sqrt(UpperCamelCase_		)		)
  sequence.append(UpperCamelCase_		)
 return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()





