'''simple docstring'''

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      :				int 				=    get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')







@require_torch
class     _lowercase					(    unittest.TestCase      ):


 '''simple docstring'''






 def a (				self       :     List[str]							)  ->   str:
  __lowerCAmelCase    							=						tempfile.mkdtemp()

  __lowerCAmelCase    							=						SpeechTaTokenizer(__A							)
  tokenizer.save_pretrained(self.tmpdirname							)

  __lowerCAmelCase    							=						{
      "feature_size": 1,
      "padding_value": 0.0,
      "sampling_rate": 1_60_00,
      "do_normalize": False,
      "num_mel_bins": 80,
      "hop_length": 16,
      "win_length": 64,
      "win_function": "hann_window",
      "fmin": 80,
      "fmax": 76_00,
      "mel_floor": 1e-1_0,
      "reduction_factor": 2,
      "return_attention_mask": True,
  }

  __lowerCAmelCase    							=						os.path.join(self.tmpdirname				,	__A							)
  with open(self.feature_extraction_file				,	"""w"""				,	encoding="""utf-8"""							) as fp:
   fp.write(json.dumps(__A							) + """\n"""							)
 def a (				self       :     List[Any]				,	**SCREAMING_SNAKE_CASE__       :     Optional[int]							)  ->   str:
  return SpeechTaTokenizer.from_pretrained(self.tmpdirname				,	**__A							)
 def a (				self       :     str				,	**SCREAMING_SNAKE_CASE__       :     int							)  ->   Any:
  return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname				,	**__A							)
 def a (				self       :     List[str]							)  ->   Union[str, Any]:
  shutil.rmtree(self.tmpdirname							)
 def a (				self       :     Any							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self.get_tokenizer()
  __lowerCAmelCase    							=						self.get_feature_extractor()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

  processor.save_pretrained(self.tmpdirname							)
  __lowerCAmelCase    							=						SpeechTaProcessor.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 a (				self       :     List[str]							)  ->   Optional[int]:
  __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=__A				,	padding_value=1.0							)

  __lowerCAmelCase    							=						SpeechTaProcessor.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 a (				self       :     Any							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

  __lowerCAmelCase    							=						floats_list((3, 10_00)							)

  __lowerCAmelCase    							=						feature_extractor(audio=__A				,	return_tensors="""np"""							)
  __lowerCAmelCase    							=						processor(audio=__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 a (				self       :     Optional[int]							)  ->   Union[str, Any]:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

  __lowerCAmelCase    							=						floats_list((3, 10_00)							)

  __lowerCAmelCase    							=						feature_extractor(audio_target=__A				,	return_tensors="""np"""							)
  __lowerCAmelCase    							=						processor(audio_target=__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 a (				self       :     int							)  ->   int:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

  __lowerCAmelCase    							=						"This is a test string"

  __lowerCAmelCase    							=						processor(text=__A							)
  __lowerCAmelCase    							=						tokenizer(__A							)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]				,	encoded_processor[key]							)
 def a (				self       :     int							)  ->   Any:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

  __lowerCAmelCase    							=						"This is a test string"

  __lowerCAmelCase    							=						processor(text_target=__A							)
  __lowerCAmelCase    							=						tokenizer(__A							)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]				,	encoded_processor[key]							)
 def a (				self       :     List[str]							)  ->   Any:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

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

  __lowerCAmelCase    							=						processor.batch_decode(__A							)
  __lowerCAmelCase    							=						tokenizer.batch_decode(__A							)

  self.assertListEqual(__A				,	__A							)





 def a (				self       :     List[Any]							)  ->   Dict:
  __lowerCAmelCase    							=						self.get_feature_extractor()
  __lowerCAmelCase    							=						self.get_tokenizer()

  __lowerCAmelCase    							=						SpeechTaProcessor(tokenizer=__A				,	feature_extractor=__A							)

  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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_A      :				Optional[int] 				=    {
    '''configuration_speech_to_text_2''': ['''SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2Text2Config'''],
    '''processing_speech_to_text_2''': ['''Speech2Text2Processor'''],
    '''tokenization_speech_to_text_2''': ['''Speech2Text2Tokenizer'''],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A      :				Any 				=    [
     '''SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Speech2Text2ForCausalLM''',
     '''Speech2Text2PreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
 from .processing_speech_to_text_a import SpeechaTextaProcessor
 from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_to_text_a import (
      SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      SpeechaTextaForCausalLM,
      SpeechaTextaPreTrainedModel,
  )

else:
 import sys

 _A      :				Tuple 				=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






