

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 							A_				(	unittest.TestCase      ):


     def 						UpperCAmelCase						(							self    :	int      )							->			Any:
      __lowerCAmelCase:     str     			=   '<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(' '      )
      __lowerCAmelCase:     List[Any]     			=   dict(zip(UpperCAmelCase    ,						range(len(UpperCAmelCase      )      )      )      )

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

      __lowerCAmelCase:     Optional[Any]     			=   tempfile.mkdtemp()
      __lowerCAmelCase:     Union[str, Any]     			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES['vocab_file']      )
      __lowerCAmelCase:     Dict     			=   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 						UpperCAmelCase						(							self    :	int    ,						**UpperCAmelCase    :	Any      )							->			List[Any]:
      __lowerCAmelCase:     Optional[Any]     			=   self.add_kwargs_tokens_map.copy()
      kwargs.update(UpperCAmelCase      )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,						**UpperCAmelCase      )


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


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			Optional[Any]:
      shutil.rmtree(self.tmpdirname      )


     def 						UpperCAmelCase						(							self    :	Any      )							->			Optional[Any]:
      __lowerCAmelCase:     Optional[Any]     			=   self.get_tokenizer()
      __lowerCAmelCase:     Any     			=   self.get_feature_extractor()

      __lowerCAmelCase:     Tuple     			=   WavaVecaProcessor(tokenizer=UpperCAmelCase    ,						feature_extractor=UpperCAmelCase      )

      processor.save_pretrained(self.tmpdirname      )
      __lowerCAmelCase:     List[Any]     			=   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 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[Any]:
      __lowerCAmelCase:     Dict     			=   WavaVecaProcessor(tokenizer=self.get_tokenizer()    ,						feature_extractor=self.get_feature_extractor()      )
      processor.save_pretrained(self.tmpdirname      )

      __lowerCAmelCase:     Any     			=   self.get_tokenizer(bos_token='(BOS)'    ,						eos_token='(EOS)'      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.get_feature_extractor(do_normalize=UpperCAmelCase    ,						padding_value=1.0      )

      __lowerCAmelCase:     str     			=   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 						UpperCAmelCase						(							self    :	str      )							->			int:
      __lowerCAmelCase:     Dict     			=   self.get_feature_extractor()
      __lowerCAmelCase:     Dict     			=   self.get_tokenizer()

      __lowerCAmelCase:     Tuple     			=   WavaVecaProcessor(tokenizer=UpperCAmelCase    ,						feature_extractor=UpperCAmelCase      )

      __lowerCAmelCase:     List[Any]     			=   floats_list((3, 1_0_0_0)      )

      __lowerCAmelCase:     List[str]     			=   feature_extractor(UpperCAmelCase    ,						return_tensors='np'      )
      __lowerCAmelCase:     Any     			=   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 						UpperCAmelCase						(							self    :	Optional[int]      )							->			Tuple:
      __lowerCAmelCase:     List[str]     			=   self.get_feature_extractor()
      __lowerCAmelCase:     int     			=   self.get_tokenizer()

      __lowerCAmelCase:     Tuple     			=   WavaVecaProcessor(tokenizer=UpperCAmelCase    ,						feature_extractor=UpperCAmelCase      )

      __lowerCAmelCase:     Optional[Any]     			=   'This is a test string'

      __lowerCAmelCase:     Dict     			=   processor(text=UpperCAmelCase      )

      __lowerCAmelCase:     Dict     			=   tokenizer(UpperCAmelCase      )

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


     def 						UpperCAmelCase						(							self    :	int      )							->			Dict:
      __lowerCAmelCase:     str     			=   self.get_feature_extractor()
      __lowerCAmelCase:     Union[str, Any]     			=   self.get_tokenizer()

      __lowerCAmelCase:     int     			=   WavaVecaProcessor(tokenizer=UpperCAmelCase    ,						feature_extractor=UpperCAmelCase      )

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

      __lowerCAmelCase:     List[Any]     			=   processor.batch_decode(UpperCAmelCase      )
      __lowerCAmelCase:     str     			=   tokenizer.batch_decode(UpperCAmelCase      )

      self.assertListEqual(UpperCAmelCase    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Tuple      )							->			Optional[int]:
      __lowerCAmelCase:     int     			=   self.get_feature_extractor()
      __lowerCAmelCase:     int     			=   self.get_tokenizer()

      __lowerCAmelCase:     Union[str, Any]     			=   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_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a	 =     {
    '''configuration_gpt2''': ['''GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPT2Config''', '''GPT2OnnxConfig'''],
    '''tokenization_gpt2''': ['''GPT2Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''GPT2TokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''GPT2DoubleHeadsModel''',
      '''GPT2ForQuestionAnswering''',
      '''GPT2ForSequenceClassification''',
      '''GPT2ForTokenClassification''',
      '''GPT2LMHeadModel''',
      '''GPT2Model''',
      '''GPT2PreTrainedModel''',
      '''load_tf_weights_in_gpt2''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     [
      '''TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''TFGPT2DoubleHeadsModel''',
      '''TFGPT2ForSequenceClassification''',
      '''TFGPT2LMHeadModel''',
      '''TFGPT2MainLayer''',
      '''TFGPT2Model''',
      '''TFGPT2PreTrainedModel''',
  ]

try:
  if not is_keras_nlp_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''TFGPT2Tokenizer''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a	 =     ['''FlaxGPT2LMHeadModel''', '''FlaxGPT2Model''', '''FlaxGPT2PreTrainedModel''']

if TYPE_CHECKING:
  from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
  from .tokenization_gpta import GPTaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_fast import GPTaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gpta import (
        GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTaDoubleHeadsModel,
        GPTaForQuestionAnswering,
        GPTaForSequenceClassification,
        GPTaForTokenClassification,
        GPTaLMHeadModel,
        GPTaModel,
        GPTaPreTrainedModel,
        load_tf_weights_in_gpta,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_gpta import (
        TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFGPTaDoubleHeadsModel,
        TFGPTaForSequenceClassification,
        TFGPTaLMHeadModel,
        TFGPTaMainLayer,
        TFGPTaModel,
        TFGPTaPreTrainedModel,
    )

  try:
    if not is_keras_nlp_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_gpta_tf import TFGPTaTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
  import sys

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





