import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__											=					logging.get_logger(__name__)


UpperCamelCase__											=					{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

UpperCamelCase__											=					{
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__											=					'</w>'
UpperCamelCase__											=					'@@ '




def    lowerCAmelCase_ (     __A							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  set()
      UpperCAmelCase__								=  word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            UpperCAmelCase__								=  char
      return pairs


# Speech2Text2 has no max input length
UpperCamelCase__											=					{'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}







class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			VOCAB_FILES_NAMES
   __UpperCAmelCase : str      =			PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase : Union[str, Any]      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase : Dict      =			['input_ids', 'attention_mask']


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Dict="<s>"       ,  __UpperCAmelCase     :						Tuple="<pad>"       ,  __UpperCAmelCase     :						str="</s>"       ,  __UpperCAmelCase     :						int="<unk>"       ,  __UpperCAmelCase     :						List[str]=False       ,  __UpperCAmelCase     :						str=None       ,  **__UpperCAmelCase     :						Optional[Any]       ,  )						->				Tuple:
         """simple docstring"""



         super().__init__(
             unk_token=__UpperCAmelCase       ,  bos_token=__UpperCAmelCase       ,  eos_token=__UpperCAmelCase       ,  pad_token=__UpperCAmelCase       ,  do_lower_case=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )

         UpperCAmelCase__								=  do_lower_case

         with open(__UpperCAmelCase       ,  encoding="utf-8"		) as vocab_handle:
               UpperCAmelCase__								=  json.load(__UpperCAmelCase		)
         UpperCAmelCase__								=  {v: k for k, v in self.encoder.items()}

         if merges_file is None:
               logger.info(f"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""		)

               UpperCAmelCase__								=  None
               UpperCAmelCase__								=  None
         else:
               with open(__UpperCAmelCase       ,  encoding="utf-8"		) as merges_handle:
                     UpperCAmelCase__								=  merges_handle.read().split("\n"		)[:-1]

               UpperCAmelCase__								=  [tuple(merge.split()[:2]		) for merge in merges]
               UpperCAmelCase__								=  dict(zip(__UpperCAmelCase       ,  range(len(__UpperCAmelCase		)		)		)		)
               UpperCAmelCase__								=  {}


   @property
   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         return len(self.decoder		)


   def 			lowercase_      (self     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         return dict(self.encoder       ,  **self.added_tokens_encoder		)


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
         if token in self.cache:
               return self.cache[token]
         UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)

         if not pairs:
               return token

         while True:
               UpperCAmelCase__								=  min(__UpperCAmelCase       ,  key=lambda __UpperCAmelCase		: self.bpe_ranks.get(__UpperCAmelCase       ,  float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                     break
               UpperCAmelCase__		,      UpperCAmelCase__								=  bigram
               UpperCAmelCase__								=  []
               UpperCAmelCase__								=  0
               while i < len(__UpperCAmelCase		):
                     try:
                           UpperCAmelCase__								=  word.index(__UpperCAmelCase       ,  __UpperCAmelCase		)
                     except ValueError:
                           new_word.extend(word[i:]		)
                           break
                     else:
                           new_word.extend(word[i:j]		)
                           UpperCAmelCase__								=  j

                     if word[i] == first and i < len(__UpperCAmelCase		) - 1 and word[i + 1] == second:
                           new_word.append(first + second		)
                           i += 2
                     else:
                           new_word.append(word[i]		)
                           i += 1
               UpperCAmelCase__								=  tuple(__UpperCAmelCase		)
               UpperCAmelCase__								=  new_word
               if len(__UpperCAmelCase		) == 1:
                     break
               else:
                     UpperCAmelCase__								=  get_pairs(__UpperCAmelCase		)
         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)
         if word == "\n  " + BPE_TOKEN_MERGES:
               UpperCAmelCase__								=  "\n" + BPE_TOKEN_MERGES

         if word.endswith(__UpperCAmelCase		):
               UpperCAmelCase__								=  word.replace(__UpperCAmelCase       ,  ""		)

         UpperCAmelCase__								=  word.replace(" "       ,  __UpperCAmelCase		)
         UpperCAmelCase__								=  word
         return word


   def 			lowercase_      (self     :						Tuple       ,  __UpperCAmelCase     :						int		)						->				Optional[int]:
         """simple docstring"""



         if self.bpe_ranks is None:
               raise ValueError(
                   "This tokenizer was instantiated without a `merges.txt` file, so"
                   " that it can only be used for decoding, not for encoding."
                   "Make sure to provide `merges.txt` file at instantiation to enable "
                   "encoding."		)

         if self.do_lower_case:
               UpperCAmelCase__								=  text.lower()

         UpperCAmelCase__								=  text.split()

         UpperCAmelCase__								=  []
         for token in text:
               if token:
                     split_tokens.extend(list(self.bpe(__UpperCAmelCase		).split(" "		)		)		)

         return split_tokens


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				int:
         """simple docstring"""



         return self.encoder.get(__UpperCAmelCase       ,  self.encoder.get(self.unk_token		)		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						int		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.decoder.get(__UpperCAmelCase       ,  self.unk_token		)
         return result


   def 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						List[str]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  " ".join(__UpperCAmelCase		)

         # make sure @@ tokens are concatenated
         UpperCAmelCase__								=  "".join(string.split(__UpperCAmelCase		)		)

         return string





   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[str] = None		)						->				Tuple[str]:
         """simple docstring"""



         if not os.path.isdir(__UpperCAmelCase		):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
               return
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
         UpperCAmelCase__								=  os.path.join(
             __UpperCAmelCase       ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder       ,  indent=2       ,  sort_keys=__UpperCAmelCase       ,  ensure_ascii=__UpperCAmelCase		) + "\n"		)

         UpperCAmelCase__								=  0
         if self.bpe_ranks is None:
               return (vocab_file,)

         with open(__UpperCAmelCase       ,  "w"       ,  encoding="utf-8"		) as writer:
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       ,  key=lambda __UpperCAmelCase		: kv[1]		):
                     if index != token_index:
                           logger.warning(
                               f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                               " Please check that the tokenizer is not corrupted!"		)
                           UpperCAmelCase__								=  token_index
                     writer.write(" ".join(__UpperCAmelCase		) + "\n"		)
                     index += 1

         return (vocab_file, merges_file)





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Union[str, Any]				       ="""Wav2Vec2FeatureExtractor"""
	A__    :   Any				       ="""AutoTokenizer"""
	def __init__(			self							:   List[Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   str							):
						super().__init__(UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False
	@classmethod
	def      A_				(			cls							:   Union[str, Any]       ,   UpperCAmelCase_							:   Any       ,   **UpperCAmelCase_							:   Optional[Any]							):
						try:
											return super().from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						except OSError:
											warnings.warn(
											    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '       ,   UpperCAmelCase_       ,   )

											SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)

											return cls(feature_extractor=UpperCAmelCase_       ,   tokenizer=UpperCAmelCase_							)
	def __call__(			self							:   Any       ,   *UpperCAmelCase_							:   Dict       ,   **UpperCAmelCase_							:   Any							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
											SCREAMING_SNAKE_CASE__													= kwargs.pop('raw_speech'							)
						else:
											SCREAMING_SNAKE_CASE__													= kwargs.pop('audio'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('sampling_rate'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('text'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= 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:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   sampling_rate=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if text is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											SCREAMING_SNAKE_CASE__													= encodings['input_ids']
											return inputs
	def      A_				(			self							:   List[str]       ,   *UpperCAmelCase_							:   List[Any]       ,   **UpperCAmelCase_							:   Optional[int]							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor.pad(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= kwargs.pop('input_features'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('labels'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if input_features is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor.pad(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if labels is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.pad(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											SCREAMING_SNAKE_CASE__													= labels['input_ids']
											return input_features
	def      A_				(			self							:   List[Any]       ,   *UpperCAmelCase_							:   List[str]       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.batch_decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Tuple       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)





	@contextmanager
	def      A_				(			self							:   str							):
						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.'							)
						SCREAMING_SNAKE_CASE__													= True
						SCREAMING_SNAKE_CASE__													= self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False





