




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case__ :						Any     =			{
    '''configuration_unispeech_sat''': ['''UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechSatConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case__ :						Union[str, Any]     =			[
       '''UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechSatForAudioFrameClassification''',
       '''UniSpeechSatForCTC''',
       '''UniSpeechSatForPreTraining''',
       '''UniSpeechSatForSequenceClassification''',
       '''UniSpeechSatForXVector''',
       '''UniSpeechSatModel''',
       '''UniSpeechSatPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech_sat import (
          UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechSatForAudioFrameClassification,
          UniSpeechSatForCTC,
          UniSpeechSatForPreTraining,
          UniSpeechSatForSequenceClassification,
          UniSpeechSatForXVector,
          UniSpeechSatModel,
          UniSpeechSatPreTrainedModel,
      )

else:
   import sys

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










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


snake_case__ :						Union[str, Any]     =			get_tests_dir('''fixtures/test_sentencepiece.model''')





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


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	str			)					->str:
     '''simple docstring'''







     UpperCAmelCase__    						=   tempfile.mkdtemp()

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

     UpperCAmelCase__    						=   SpeechaTextTokenizer.from_pretrained(self.tmpdirname			)
     tokenizer.save_pretrained(self.tmpdirname			)

     UpperCAmelCase__    						=   {
         """feature_size""": 24,
         """num_mel_bins""": 24,
         """padding_value""": 0.0,
         """sampling_rate""": 1_6000,
         """return_attention_mask""": False,
         """do_normalize""": True,
     }
     save_json(lowerCamelCase_						,    save_dir / FEATURE_EXTRACTOR_NAME			)




   def 	UpperCAmelCase       (					self     :	Union[str, Any]						,    **lowerCamelCase_     :	Tuple			)					->Any:
     '''simple docstring'''







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




   def 	UpperCAmelCase       (					self     :	Dict						,    **lowerCamelCase_     :	str			)					->Optional[Any]:
     '''simple docstring'''







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




   def 	UpperCAmelCase       (					self     :	List[str]			)					->Dict:
     '''simple docstring'''







     shutil.rmtree(self.tmpdirname			)




   def 	UpperCAmelCase       (					self     :	str			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_tokenizer()
     UpperCAmelCase__    						=   self.get_feature_extractor()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     processor.save_pretrained(self.tmpdirname			)
     UpperCAmelCase__    						=   SpeechaTextProcessor.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     :	List[str]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   SpeechaTextProcessor(
         tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()			)
     processor.save_pretrained(self.tmpdirname			)

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

     UpperCAmelCase__    						=   SpeechaTextProcessor.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     :	Union[str, Any]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     UpperCAmelCase__    						=   floats_list((3, 1000)			)

     UpperCAmelCase__    						=   feature_extractor(lowerCamelCase_						,    return_tensors="""np"""			)
     UpperCAmelCase__    						=   processor(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     :	str			)					->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

     UpperCAmelCase__    						=   """This is a test string"""

     UpperCAmelCase__    						=   processor(text=lowerCamelCase_			)

     UpperCAmelCase__    						=   tokenizer(lowerCamelCase_			)

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




   def 	UpperCAmelCase       (					self     :	Optional[Any]			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(tokenizer=lowerCamelCase_						,    feature_extractor=lowerCamelCase_			)

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

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

     self.assertListEqual(lowerCamelCase_						,    lowerCamelCase_			)







   def 	UpperCAmelCase       (					self     :	Dict			)					->Tuple:
     '''simple docstring'''







     UpperCAmelCase__    						=   self.get_feature_extractor()
     UpperCAmelCase__    						=   self.get_tokenizer()

     UpperCAmelCase__    						=   SpeechaTextProcessor(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"""						,    )




