




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 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			int:






         """simple docstring"""
         a__      =       """<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(""" """    )
         a__      =       dict(zip(A						,				range(len(A    )    )    )    )

         a__      =       {
             """pad_token""": """<pad>""",
             """unk_token""": """<unk>""",
             """bos_token""": """<s>""",
             """eos_token""": """</s>""",
         }
         a__      =       {
             """feature_size""": 1,
             """padding_value""": 0.0,
             """sampling_rate""": 1_60_00,
             """return_attention_mask""": False,
             """do_normalize""": True,
         }

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

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]						,				**A :       Any    )			->			Dict:






         """simple docstring"""
         a__      =       self.add_kwargs_tokens_map.copy()
         kwargs.update(A    )
         return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple						,				**A :       Union[str, Any]    )			->			Optional[Any]:






         """simple docstring"""
         return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname						,				**A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[Any]:






         """simple docstring"""
         shutil.rmtree(self.tmpdirname    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			List[str]:






         """simple docstring"""
         a__      =       self.get_tokenizer()
         a__      =       self.get_feature_extractor()

         a__      =       WavaVecaProcessor(tokenizer=A						,				feature_extractor=A    )

         processor.save_pretrained(self.tmpdirname    )
         a__      =       WavaVecaProcessor.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 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Any:






         """simple docstring"""
         a__      =       WavaVecaProcessor(tokenizer=self.get_tokenizer()						,				feature_extractor=self.get_feature_extractor()    )
         processor.save_pretrained(self.tmpdirname    )

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

         a__      =       WavaVecaProcessor.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 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Union[str, Any]:






         """simple docstring"""
         a__      =       self.get_feature_extractor()
         a__      =       self.get_tokenizer()

         a__      =       WavaVecaProcessor(tokenizer=A						,				feature_extractor=A    )

         a__      =       floats_list((3, 10_00)    )

         a__      =       feature_extractor(A						,				return_tensors="""np"""    )
         a__      =       processor(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 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       self.get_feature_extractor()
         a__      =       self.get_tokenizer()

         a__      =       WavaVecaProcessor(tokenizer=A						,				feature_extractor=A    )

         a__      =       """This is a test string"""

         a__      =       processor(text=A    )

         a__      =       tokenizer(A    )

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

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			Tuple:






         """simple docstring"""
         a__      =       self.get_feature_extractor()
         a__      =       self.get_tokenizer()

         a__      =       WavaVecaProcessor(tokenizer=A						,				feature_extractor=A    )

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

         a__      =       processor.batch_decode(A    )
         a__      =       tokenizer.batch_decode(A    )

         self.assertListEqual(A						,				A    )






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       self.get_feature_extractor()
         a__      =       self.get_tokenizer()

         a__      =       WavaVecaProcessor(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"""						,				)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_						:					str			   =       {
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       [
     '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Wav2Vec2ConformerForAudioFrameClassification''',
     '''Wav2Vec2ConformerForCTC''',
     '''Wav2Vec2ConformerForPreTraining''',
     '''Wav2Vec2ConformerForSequenceClassification''',
     '''Wav2Vec2ConformerForXVector''',
     '''Wav2Vec2ConformerModel''',
     '''Wav2Vec2ConformerPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_wavaveca_conformer import (
     WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     WavaVecaConformerConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_wavaveca_conformer import (
      WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WavaVecaConformerForAudioFrameClassification,
      WavaVecaConformerForCTC,
      WavaVecaConformerForPreTraining,
      WavaVecaConformerForSequenceClassification,
      WavaVecaConformerForXVector,
      WavaVecaConformerModel,
      WavaVecaConformerPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






