
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 ):



      """simple docstring"""







      def 						snake_case_     ( self						:    Union[str, Any]			):
          __lowercase						:     Any		       =    '''<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						:     str		       =    dict(zip(_snake_case			,    range(len(_snake_case			)			)			)			)

          __lowercase						:     Tuple		       =    {
              '''pad_token''': '''<pad>''',
              '''unk_token''': '''<unk>''',
              '''bos_token''': '''<s>''',
              '''eos_token''': '''</s>''',
          }
          __lowercase						:     List[str]		       =    {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 1_6000,
              '''return_attention_mask''': False,
              '''do_normalize''': True,
          }

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

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




      def 						snake_case_     ( self						:    Dict			,    **_snake_case						:    List[str]			):
          __lowercase						:     Dict		       =    self.add_kwargs_tokens_map.copy()
          kwargs.update(_snake_case			)
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,    **_snake_case			)




      def 						snake_case_     ( self						:    Optional[int]			,    **_snake_case						:    Any			):
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,    **_snake_case			)




      def 						snake_case_     ( self						:    Dict			):
          shutil.rmtree(self.tmpdirname			)




      def 						snake_case_     ( self						:    Any			):
          __lowercase						:     Tuple		       =    self.get_tokenizer()
          __lowercase						:     Tuple		       =    self.get_feature_extractor()

          __lowercase						:     str		       =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          processor.save_pretrained(self.tmpdirname			)
          __lowercase						:     List[Any]		       =    WavaVecaProcessor.from_pretrained(self.tmpdirname			)

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

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




      def 						snake_case_     ( self						:    List[Any]			):
          __lowercase						:     Union[str, Any]		       =    WavaVecaProcessor(tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor()			)
          processor.save_pretrained(self.tmpdirname			)

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

          __lowercase						:     Tuple		       =    WavaVecaProcessor.from_pretrained(
              self.tmpdirname			,    bos_token='''(BOS)'''			,    eos_token='''(EOS)'''			,    do_normalize=_snake_case			,    padding_value=1.0			)

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

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




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     Union[str, Any]		       =    self.get_feature_extractor()
          __lowercase						:     List[Any]		       =    self.get_tokenizer()

          __lowercase						:     Dict		       =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          __lowercase						:     Union[str, Any]		       =    floats_list((3, 1000)			)

          __lowercase						:     Dict		       =    feature_extractor(_snake_case			,    return_tensors='''np'''			)
          __lowercase						:     Union[str, Any]		       =    processor(_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						:    Tuple			):
          __lowercase						:     int		       =    self.get_feature_extractor()
          __lowercase						:     Optional[int]		       =    self.get_tokenizer()

          __lowercase						:     Optional[Any]		       =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          __lowercase						:     List[str]		       =    '''This is a test string'''

          __lowercase						:     Union[str, Any]		       =    processor(text=_snake_case			)

          __lowercase						:     Any		       =    tokenizer(_snake_case			)

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




      def 						snake_case_     ( self						:    Dict			):
          __lowercase						:     Union[str, Any]		       =    self.get_feature_extractor()
          __lowercase						:     List[str]		       =    self.get_tokenizer()

          __lowercase						:     Union[str, Any]		       =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          __lowercase						:     Union[str, Any]		       =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __lowercase						:     Tuple		       =    processor.batch_decode(_snake_case			)
          __lowercase						:     Union[str, Any]		       =    tokenizer.batch_decode(_snake_case			)

          self.assertListEqual(_snake_case			,    _snake_case			)




      def 						snake_case_     ( self						:    Optional[Any]			):
          __lowercase						:     List[Any]		       =    self.get_feature_extractor()
          __lowercase						:     Optional[int]		       =    self.get_tokenizer()

          __lowercase						:     int		       =    WavaVecaProcessor(tokenizer=_snake_case			,    feature_extractor=_snake_case			)

          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, is_vision_available


__lowerCAmelCase   :     Optional[int]      =						{"configuration_vit_hybrid": ["VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTHybridConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     List[Any]      =						[
     "VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTHybridForImageClassification",
     "ViTHybridModel",
     "ViTHybridPreTrainedModel",
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase   :     Tuple      =						["ViTHybridImageProcessor"]


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 __lowerCAmelCase   :     Tuple      =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


