


'''simple docstring'''



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class      a    (		__UpperCamelCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       "Speech2TextFeatureExtractor"
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       "Speech2TextTokenizer"

     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[Any]							,			snake_case					:		List[Any]      )						->							str:
          super().__init__(snake_case							,			snake_case      )
          __UpperCAmelCase     :			Tuple  							=      self.feature_extractor
          __UpperCAmelCase     :			str  							=      False

     def __call__(    self					:		Tuple							,			*snake_case					:		int							,			**snake_case					:		Optional[Any]      )						->							Tuple:
          # For backward compatibility
          if self._in_target_context_manager:
               return self.current_processor(*snake_case							,			**snake_case      )

          if "raw_speech" in kwargs:
               warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''      )
               __UpperCAmelCase     :			int  							=      kwargs.pop('''raw_speech'''      )
          else:
               __UpperCAmelCase     :			Any  							=      kwargs.pop('''audio'''							,			snake_case      )
          __UpperCAmelCase     :			int  							=      kwargs.pop('''sampling_rate'''							,			snake_case      )
          __UpperCAmelCase     :			List[str]  							=      kwargs.pop('''text'''							,			snake_case      )
          if len(snake_case      ) > 0:
               __UpperCAmelCase     :			List[str]  							=      args[0]
               __UpperCAmelCase     :			str  							=      args[1:]

          if audio is None and text is None:
               raise ValueError('''You need to specify either an `audio` or `text` input to process.'''      )

          if audio is not None:
               __UpperCAmelCase     :			int  							=      self.feature_extractor(snake_case							,			*snake_case							,			sampling_rate=snake_case							,			**snake_case      )
          if text is not None:
               __UpperCAmelCase     :			List[str]  							=      self.tokenizer(snake_case							,			**snake_case      )

          if text is None:
               return inputs
          elif audio is None:
               return encodings
          else:
               __UpperCAmelCase     :			str  							=      encodings['input_ids']
               return inputs

     def 			lowerCamelCase__			(    self					:		int							,			*snake_case					:		List[str]							,			**snake_case					:		Any      )						->							Dict:
          return self.tokenizer.batch_decode(*snake_case							,			**snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict							,			*snake_case					:		List[Any]							,			**snake_case					:		List[str]      )						->							List[Any]:
          return self.tokenizer.decode(*snake_case							,			**snake_case      )






     @contextmanager
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							int:
          warnings.warn(
              '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
              '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
              '''your audio inputs, or in a separate call.'''      )
          __UpperCAmelCase     :			Dict  							=      True
          __UpperCAmelCase     :			Dict  							=      self.tokenizer
          yield
          __UpperCAmelCase     :			Union[str, Any]  							=      self.feature_extractor
          __UpperCAmelCase     :			List[str]  							=      False




'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)

__UpperCAmelCase      :str			    =       {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase      :Optional[int]			    =       {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase      :Union[str, Any]			    =       {
    "bert-base-uncased": 5_1_2,
    "bert-large-uncased": 5_1_2,
    "bert-base-cased": 5_1_2,
    "bert-large-cased": 5_1_2,
    "bert-base-multilingual-uncased": 5_1_2,
    "bert-base-multilingual-cased": 5_1_2,
    "bert-base-chinese": 5_1_2,
    "bert-base-german-cased": 5_1_2,
    "bert-large-uncased-whole-word-masking": 5_1_2,
    "bert-large-cased-whole-word-masking": 5_1_2,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_1_2,
    "bert-base-cased-finetuned-mrpc": 5_1_2,
    "bert-base-german-dbmdz-cased": 5_1_2,
    "bert-base-german-dbmdz-uncased": 5_1_2,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_1_2,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_1_2,
    "wietsedv/bert-base-dutch-cased": 5_1_2,
}

__UpperCAmelCase      :str			    =       {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE	:			Tuple							       =       PRETRAINED_INIT_CONFIGURATION
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       BertTokenizer

     def __init__(    self					:		Optional[int]							,			snake_case					:		Union[str, Any]=None							,			snake_case					:		str=None							,			snake_case					:		Any=True							,			snake_case					:		Tuple="[UNK]"							,			snake_case					:		int="[SEP]"							,			snake_case					:		Optional[Any]="[PAD]"							,			snake_case					:		int="[CLS]"							,			snake_case					:		Optional[Any]="[MASK]"							,			snake_case					:		Union[str, Any]=True							,			snake_case					:		List[Any]=None							,			**snake_case					:		int							,			)						->							str:
          super().__init__(
              snake_case							,			tokenizer_file=snake_case							,			do_lower_case=snake_case							,			unk_token=snake_case							,			sep_token=snake_case							,			pad_token=snake_case							,			cls_token=snake_case							,			mask_token=snake_case							,			tokenize_chinese_chars=snake_case							,			strip_accents=snake_case							,			**snake_case							,			)

          __UpperCAmelCase     :			List[str]  							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              normalizer_state.get('''lowercase'''							,			snake_case      ) != do_lower_case
              or normalizer_state.get('''strip_accents'''							,			snake_case      ) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''							,			snake_case      ) != tokenize_chinese_chars
          ):
               __UpperCAmelCase     :			List[Any]  							=      getattr(snake_case							,			normalizer_state.pop('''type'''      )      )
               __UpperCAmelCase     :			List[Any]  							=      do_lower_case
               __UpperCAmelCase     :			List[Any]  							=      strip_accents
               __UpperCAmelCase     :			str  							=      tokenize_chinese_chars
               __UpperCAmelCase     :			List[str]  							=      normalizer_class(**snake_case      )

          __UpperCAmelCase     :			Optional[int]  							=      do_lower_case

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[str]=None      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

          return output

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[int]							,			snake_case					:		Optional[List[int]] = None      )						->							List[int]:
          __UpperCAmelCase     :			List[str]  							=      [self.sep_token_id]
          __UpperCAmelCase     :			Tuple  							=      [self.cls_token_id]
          if token_ids_a is None:
               return len(cls + token_ids_a + sep      ) * [0]
          return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]






     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Optional[str] = None      )						->							Tuple[str]:
          __UpperCAmelCase     :			Optional[int]  							=      self._tokenizer.model.save(snake_case							,			name=snake_case      )
          return tuple(snake_case      )