






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


__lowerCamelCase						= get_tests_dir('fixtures/test_sentencepiece.model')

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




						def      snake_case__(    self       )			->						Tuple:
													_a  :    List[str]			=						tempfile.mkdtemp()

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

													_a  :    Any			=						SpeechaTextTokenizer.from_pretrained(self.tmpdirname       )
													tokenizer.save_pretrained(self.tmpdirname       )

													_a  :    int			=						{
													    'feature_size': 24,
													    'num_mel_bins': 24,
													    'padding_value': 0.0,
													    'sampling_rate': 16_000,
													    'return_attention_mask': False,
													    'do_normalize': True,
													}
													save_json(__SCREAMING_SNAKE_CASE		,	save_dir / FEATURE_EXTRACTOR_NAME       )




						def      snake_case__(    self		,	**lowercase       )			->						Tuple:
													return SpeechaTextTokenizer.from_pretrained(self.tmpdirname		,	**__SCREAMING_SNAKE_CASE       )




						def      snake_case__(    self		,	**lowercase       )			->						Optional[Any]:
													return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname		,	**__SCREAMING_SNAKE_CASE       )




						def      snake_case__(    self       )			->						Optional[int]:
													shutil.rmtree(self.tmpdirname       )




						def      snake_case__(    self       )			->						str:
													_a  :    Union[str, Any]			=						self.get_tokenizer()
													_a  :    int			=						self.get_feature_extractor()

													_a  :    int			=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,	feature_extractor=__SCREAMING_SNAKE_CASE       )

													processor.save_pretrained(self.tmpdirname       )
													_a  :    List[str]			=						SpeechaTextProcessor.from_pretrained(self.tmpdirname       )

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

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




						def      snake_case__(    self       )			->						Optional[int]:
													_a  :    Optional[Any]			=						SpeechaTextProcessor(
													    tokenizer=self.get_tokenizer()		,	feature_extractor=self.get_feature_extractor()       )
													processor.save_pretrained(self.tmpdirname       )

													_a  :    Optional[Any]			=						self.get_tokenizer(bos_token='''(BOS)'''		,	eos_token='''(EOS)'''       )
													_a  :    Optional[int]			=						self.get_feature_extractor(do_normalize=__SCREAMING_SNAKE_CASE		,	padding_value=1.0       )

													_a  :    Dict			=						SpeechaTextProcessor.from_pretrained(
													    self.tmpdirname		,	bos_token='''(BOS)'''		,	eos_token='''(EOS)'''		,	do_normalize=__SCREAMING_SNAKE_CASE		,	padding_value=1.0       )

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

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




						def      snake_case__(    self       )			->						Any:
													_a  :    List[str]			=						self.get_feature_extractor()
													_a  :    Optional[Any]			=						self.get_tokenizer()

													_a  :    Any			=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,	feature_extractor=__SCREAMING_SNAKE_CASE       )

													_a  :    Dict			=						floats_list((3, 1_000)       )

													_a  :    int			=						feature_extractor(__SCREAMING_SNAKE_CASE		,	return_tensors='''np'''       )
													_a  :    int			=						processor(__SCREAMING_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       )			->						str:
													_a  :    List[str]			=						self.get_feature_extractor()
													_a  :    List[str]			=						self.get_tokenizer()

													_a  :    str			=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,	feature_extractor=__SCREAMING_SNAKE_CASE       )

													_a  :    Any			=						'This is a test string'

													_a  :    Optional[Any]			=						processor(text=__SCREAMING_SNAKE_CASE       )

													_a  :    Any			=						tokenizer(__SCREAMING_SNAKE_CASE       )

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




						def      snake_case__(    self       )			->						Optional[Any]:
													_a  :    int			=						self.get_feature_extractor()
													_a  :    Optional[Any]			=						self.get_tokenizer()

													_a  :    List[str]			=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,	feature_extractor=__SCREAMING_SNAKE_CASE       )

													_a  :    Optional[int]			=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_a  :    Tuple			=						processor.batch_decode(__SCREAMING_SNAKE_CASE       )
													_a  :    Any			=						tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )

													self.assertListEqual(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE       )







						def      snake_case__(    self       )			->						List[Any]:
													_a  :    int			=						self.get_feature_extractor()
													_a  :    List[Any]			=						self.get_tokenizer()

													_a  :    Optional[Any]			=						SpeechaTextProcessor(tokenizer=__SCREAMING_SNAKE_CASE		,	feature_extractor=__SCREAMING_SNAKE_CASE       )

													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_torch_available


__UpperCAmelCase												=   {'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase												=   [
       'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWDForCTC',
       'SEWDForSequenceClassification',
       'SEWDModel',
       'SEWDPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase												=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


