



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


_lowercase:					str      =						get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")



@require_torch
class       _lowercase				(  unittest.TestCase      ):





	"""simple docstring"""




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  tempfile.mkdtemp()

						a   	=  SpeechTaTokenizer(lowerCamelCase_						)
						tokenizer.save_pretrained(self.tmpdirname						)

						a   	=  {
						    '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-1_0,
						    'reduction_factor': 2,
						    'return_attention_mask': True,
						}

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




	def 	UpperCamelCase_			(self						,			**lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						,			**lowerCamelCase_						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						shutil.rmtree(self.tmpdirname						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_tokenizer()
						a   	=  self.get_feature_extractor()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

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

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

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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

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

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

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

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_feature_extractor()
						a   	=  self.get_tokenizer()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

						a   	=  floats_list((3, 1000)						)

						a   	=  feature_extractor(audio=lowerCamelCase_						,			return_tensors="np"						)
						a   	=  processor(audio=lowerCamelCase_						,			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   	=  self.get_feature_extractor()
						a   	=  self.get_tokenizer()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

						a   	=  floats_list((3, 1000)						)

						a   	=  feature_extractor(audio_target=lowerCamelCase_						,			return_tensors="np"						)
						a   	=  processor(audio_target=lowerCamelCase_						,			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   	=  self.get_feature_extractor()
						a   	=  self.get_tokenizer()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

						a   	=  'This is a test string'

						a   	=  processor(text=lowerCamelCase_						)
						a   	=  tokenizer(lowerCamelCase_						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_feature_extractor()
						a   	=  self.get_tokenizer()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

						a   	=  'This is a test string'

						a   	=  processor(text_target=lowerCamelCase_						)
						a   	=  tokenizer(lowerCamelCase_						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_feature_extractor()
						a   	=  self.get_tokenizer()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

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

						a   	=  processor.batch_decode(lowerCamelCase_						)
						a   	=  tokenizer.batch_decode(lowerCamelCase_						)

						self.assertListEqual(lowerCamelCase_						,			lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.get_feature_extractor()
						a   	=  self.get_tokenizer()

						a   	=  SpeechTaProcessor(tokenizer=lowerCamelCase_						,			feature_extractor=lowerCamelCase_						)

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








"""simple docstring"""


from doctest import testmod
from math import sqrt




def      snake_case    (A_ :int ):







  '''simple docstring'''

  a	:     list[int]							=  []
  if num < 1:
    return facs
  facs.append(1 )
  if num == 1:
    return facs
  facs.append(A_ )
  for i in range(2      ,     int(sqrt(A_ ) ) + 1 ):
    if num % i == 0:  # If i is a factor of num
      facs.append(A_ )
      a	:     Optional[Any]							=  num // i  # num//i is the other factor of num
      if d != i:  # If d and i are distinct
        facs.append(A_ )  # we have found another factor
  facs.sort()
  return facs


if __name__ == "__main__":
   testmod(name='factors_of_a_number', verbose=True)




