


import json
import os
import shutil
import tempfile
import unittest

from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor, WavaVecaProcessor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_wavaveca import floats_list







class  lowerCamelCase__  (			unittest.TestCase):





     def 	__A   (self			)	->  List[Any]:
      _lowercase				 ='''<pad> <s> </s> <unk> | E T A O N I H S R D L U M W C F G Y P B V K \' X J Q Z'''.split(''' '''			)
      _lowercase				 =dict(zip(UpperCAmelCase						,	range(len(UpperCAmelCase			)			)			)			)

      _lowercase				 ={
          '''pad_token''': '''<pad>''',
          '''unk_token''': '''<unk>''',
          '''bos_token''': '''<s>''',
          '''eos_token''': '''</s>''',
      }
      _lowercase				 ={
          '''feature_size''': 1,
          '''padding_value''': 0.0,
          '''sampling_rate''': 1_6_0_0_0,
          '''return_attention_mask''': False,
          '''do_normalize''': True,
      }

      _lowercase				 =tempfile.mkdtemp()
      _lowercase				 =os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['''vocab_file''']			)
      _lowercase				 =os.path.join(self.tmpdirname						,	UpperCAmelCase			)
      with open(self.vocab_file						,	'''w'''						,	encoding='''utf-8'''			) as fp:
       fp.write(json.dumps(UpperCAmelCase			) + '''\n'''			)

      with open(self.feature_extraction_file						,	'''w'''						,	encoding='''utf-8'''			) as fp:
       fp.write(json.dumps(UpperCAmelCase			) + '''\n'''			)





     def 	__A   (self						,	**UpperCAmelCase			)	->  Any:
      _lowercase				 =self.add_kwargs_tokens_map.copy()
      kwargs.update(UpperCAmelCase			)
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,	**UpperCAmelCase			)





     def 	__A   (self						,	**UpperCAmelCase			)	->  List[Any]:
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,	**UpperCAmelCase			)





     def 	__A   (self			)	->  Any:
      shutil.rmtree(self.tmpdirname			)





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

      _lowercase				 =WavaVecaProcessor(tokenizer=UpperCAmelCase						,	feature_extractor=UpperCAmelCase			)

      processor.save_pretrained(self.tmpdirname			)
      _lowercase				 =WavaVecaProcessor.from_pretrained(self.tmpdirname			)

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

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





     def 	__A   (self			)	->  Optional[Any]:
      _lowercase				 =WavaVecaProcessor(tokenizer=self.get_tokenizer()						,	feature_extractor=self.get_feature_extractor()			)
      processor.save_pretrained(self.tmpdirname			)

      _lowercase				 =self.get_tokenizer(bos_token='''(BOS)'''						,	eos_token='''(EOS)'''			)
      _lowercase				 =self.get_feature_extractor(do_normalize=UpperCAmelCase						,	padding_value=1.0			)

      _lowercase				 =WavaVecaProcessor.from_pretrained(
          self.tmpdirname						,	bos_token='''(BOS)'''						,	eos_token='''(EOS)'''						,	do_normalize=UpperCAmelCase						,	padding_value=1.0			)

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

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





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

      _lowercase				 =WavaVecaProcessor(tokenizer=UpperCAmelCase						,	feature_extractor=UpperCAmelCase			)

      _lowercase				 =floats_list((3, 1_0_0_0)			)

      _lowercase				 =feature_extractor(UpperCAmelCase						,	return_tensors='''np'''			)
      _lowercase				 =processor(UpperCAmelCase						,	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:
      _lowercase				 =self.get_feature_extractor()
      _lowercase				 =self.get_tokenizer()

      _lowercase				 =WavaVecaProcessor(tokenizer=UpperCAmelCase						,	feature_extractor=UpperCAmelCase			)

      _lowercase				 ='''This is a test string'''

      _lowercase				 =processor(text=UpperCAmelCase			)

      _lowercase				 =tokenizer(UpperCAmelCase			)

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





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

      _lowercase				 =WavaVecaProcessor(tokenizer=UpperCAmelCase						,	feature_extractor=UpperCAmelCase			)

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

      _lowercase				 =processor.batch_decode(UpperCAmelCase			)
      _lowercase				 =tokenizer.batch_decode(UpperCAmelCase			)

      self.assertListEqual(UpperCAmelCase						,	UpperCAmelCase			)





     def 	__A   (self			)	->  Any:
      _lowercase				 =self.get_feature_extractor()
      _lowercase				 =self.get_tokenizer()

      _lowercase				 =WavaVecaProcessor(tokenizer=UpperCAmelCase						,	feature_extractor=UpperCAmelCase			)

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__        =				{
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''FlaubertForMultipleChoice''',
      '''FlaubertForQuestionAnswering''',
      '''FlaubertForQuestionAnsweringSimple''',
      '''FlaubertForSequenceClassification''',
      '''FlaubertForTokenClassification''',
      '''FlaubertModel''',
      '''FlaubertWithLMHeadModel''',
      '''FlaubertPreTrainedModel''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__        =				[
      '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFFlaubertForMultipleChoice''',
      '''TFFlaubertForQuestionAnsweringSimple''',
      '''TFFlaubertForSequenceClassification''',
      '''TFFlaubertForTokenClassification''',
      '''TFFlaubertModel''',
      '''TFFlaubertPreTrainedModel''',
      '''TFFlaubertWithLMHeadModel''',
  ]


if TYPE_CHECKING:
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  from .tokenization_flaubert import FlaubertTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )

else:
  import sys

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

