
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

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


lowercase_       :						Any     					=	logging.get_logger(__name__)


lowercase_       :						Union[str, Any]     					=	{
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

lowercase_       :						List[str]     					=	{
    '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'
        ),
    },
}

lowercase_       :						int     					=	'</w>'
lowercase_       :						List[Any]     					=	'@@ '





def    _lowerCAmelCase    (						lowerCamelCase__							:							Tuple				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = set()
  _SCREAMING_SNAKE_CASE   :				Any							    = word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)				)
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = char
  return pairs


# Speech2Text2 has no max input length
lowercase_       :						Dict     					=	{'facebook/s2t-wav2vec2-large-en-de': 10_24}





class 			UpperCamelCase      (							snake_case__  ):
     A__      =			VOCAB_FILES_NAMES
     A__      =			PRETRAINED_VOCAB_FILES_MAP
     A__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A__      =			["""input_ids""", """attention_mask"""]
     def __init__(     self  ,     snake_case__  ,     snake_case__="<s>"  ,     snake_case__="<pad>"  ,     snake_case__="</s>"  ,     snake_case__="<unk>"  ,     snake_case__=False  ,     snake_case__=None  ,     **snake_case__  ,     ):




       """simple docstring"""


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

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = do_lower_case

       with open(UpperCAmelCase_  ,     encoding="utf-8"				) as vocab_handle:
         _SCREAMING_SNAKE_CASE   :				int							    = json.load(UpperCAmelCase_				)
       _SCREAMING_SNAKE_CASE   :				List[str]							    = {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.'''				)

         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = None
         _SCREAMING_SNAKE_CASE   :				str							    = None
       else:
         with open(UpperCAmelCase_  ,     encoding="utf-8"				) as merges_handle:
           _SCREAMING_SNAKE_CASE   :				str							    = merges_handle.read().split("\n"				)[:-1]

         _SCREAMING_SNAKE_CASE   :				Dict							    = [tuple(merge.split()[:2]				) for merge in merges]
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = dict(zip(UpperCAmelCase_  ,     range(len(UpperCAmelCase_				)				)				)				)
         _SCREAMING_SNAKE_CASE   :				Dict							    = {}
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return len(self.decoder				)
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       return dict(self.encoder  ,     **self.added_tokens_encoder				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = tuple(token[:-1]				) + (token[-1] + BPE_TOKEN_MERGES,)
       if token in self.cache:
         return self.cache[token]
       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = get_pairs(UpperCAmelCase_				)

       if not pairs:
         return token

       while True:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = min(UpperCAmelCase_  ,     key=lambda snake_case__				: self.bpe_ranks.get(UpperCAmelCase_  ,     float("inf"				)				)				)
         if bigram not in self.bpe_ranks:
           break
         _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   :				Tuple							    = bigram
         _SCREAMING_SNAKE_CASE   :				int							    = []
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = 0
         while i < len(UpperCAmelCase_				):
           try:
             _SCREAMING_SNAKE_CASE   :				str							    = word.index(UpperCAmelCase_  ,     UpperCAmelCase_				)
           except ValueError:
             new_word.extend(word[i:]				)
             break
           else:
             new_word.extend(word[i:j]				)
             _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = tuple(UpperCAmelCase_				)
         _SCREAMING_SNAKE_CASE   :				Optional[int]							    = new_word
         if len(UpperCAmelCase_				) == 1:
           break
         else:
           _SCREAMING_SNAKE_CASE   :				Tuple							    = get_pairs(UpperCAmelCase_				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = " ".join(UpperCAmelCase_				)
       if word == "\n  " + BPE_TOKEN_MERGES:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "\n" + BPE_TOKEN_MERGES

       if word.endswith(UpperCAmelCase_				):
         _SCREAMING_SNAKE_CASE   :				Tuple							    = word.replace(UpperCAmelCase_  ,     ""				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = word.replace(" "  ,     UpperCAmelCase_				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = word
       return word
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """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:
         _SCREAMING_SNAKE_CASE   :				List[str]							    = text.lower()

       _SCREAMING_SNAKE_CASE   :				int							    = text.split()

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = []
       for token in text:
         if token:
           split_tokens.extend(list(self.bpe(UpperCAmelCase_				).split(" "				)				)				)

       return split_tokens
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       return self.encoder.get(UpperCAmelCase_  ,     self.encoder.get(self.unk_token				)				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Tuple							    = self.decoder.get(UpperCAmelCase_  ,     self.unk_token				)
       return result
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = " ".join(UpperCAmelCase_				)

       # make sure @@ tokens are concatenated
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = "".join(string.split(UpperCAmelCase_				)				)

       return string




     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__ = None				):




       """simple docstring"""


       if not os.path.isdir(UpperCAmelCase_				):
         logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''				)
         return
       _SCREAMING_SNAKE_CASE   :				int							    = os.path.join(
           UpperCAmelCase_  ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)
       _SCREAMING_SNAKE_CASE   :				List[Any]							    = 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"				)

       _SCREAMING_SNAKE_CASE   :				int							    = 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 snake_case__				: 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!"				)
             _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = token_index
           writer.write(" ".join(UpperCAmelCase_				) + "\n"				)
           index += 1

       return (vocab_file, merges_file)





"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""image_processor""", """tokenizer"""]
     A__      =			"""AutoImageProcessor"""
     A__      =			"""AutoTokenizer"""
     def __init__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				int							    = None
       if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."  ,     snake_case__  ,     )
         _SCREAMING_SNAKE_CASE   :				Any							    = kwargs.pop("feature_extractor"				)

       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
       if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

       super().__init__(snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Any							    = self.image_processor
       _SCREAMING_SNAKE_CASE   :				Any							    = False
     def __call__(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       if self._in_target_context_manager:
         return self.current_processor(*snake_case__  ,     **snake_case__				)

       _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = kwargs.pop("images"  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = kwargs.pop("text"  ,     snake_case__				)
       if len(snake_case__				) > 0:
         _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = args[0]
         _SCREAMING_SNAKE_CASE   :				List[str]							    = args[1:]

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

       if images is not None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.image_processor(snake_case__  ,     *snake_case__  ,     **snake_case__				)
       if text is not None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.tokenizer(snake_case__  ,     **snake_case__				)

       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = encodings["input_ids"]
         return inputs
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.batch_decode(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.decode(*snake_case__  ,     **snake_case__				)
     @contextmanager
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       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 images inputs, or in a separate call."				)
       _SCREAMING_SNAKE_CASE   :				Dict							    = True
       _SCREAMING_SNAKE_CASE   :				str							    = self.tokenizer
       yield
       _SCREAMING_SNAKE_CASE   :				str							    = self.image_processor
       _SCREAMING_SNAKE_CASE   :				int							    = False
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__=False  ,     snake_case__=None				):




       """simple docstring"""


       if added_vocab is None:
         _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.tokenizer.get_added_vocab()

       _SCREAMING_SNAKE_CASE   :				int							    = {}

       while tokens:
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = re.search(r"<s_(.*?)>"  ,     snake_case__  ,     re.IGNORECASE				)
         if start_token is None:
           break
         _SCREAMING_SNAKE_CASE   :				List[Any]							    = start_token.group(1				)
         _SCREAMING_SNAKE_CASE   :				Any							    = re.search(rF'''</s_{key}>'''  ,     snake_case__  ,     re.IGNORECASE				)
         _SCREAMING_SNAKE_CASE   :				Tuple							    = start_token.group()
         if end_token is None:
           _SCREAMING_SNAKE_CASE   :				Optional[int]							    = tokens.replace(snake_case__  ,     ""				)
         else:
           _SCREAMING_SNAKE_CASE   :				Any							    = end_token.group()
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = re.escape(snake_case__				)
           _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = re.escape(snake_case__				)
           _SCREAMING_SNAKE_CASE   :				str							    = re.search(F'''{start_token_escaped}(.*?){end_token_escaped}'''  ,     snake_case__  ,     re.IGNORECASE				)
           if content is not None:
             _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = content.group(1				).strip()
             if r"<s_" in content and r"</s_" in content:  # non-leaf node
               _SCREAMING_SNAKE_CASE   :				Dict							    = self.tokenajson(snake_case__  ,     is_inner_value=snake_case__  ,     added_vocab=snake_case__				)
               if value:
                 if len(snake_case__				) == 1:
                   _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = value[0]
                 _SCREAMING_SNAKE_CASE   :				Dict							    = value
             else:  # leaf nodes
               _SCREAMING_SNAKE_CASE   :				Dict							    = []
               for leaf in content.split(r"<sep/>"				):
                 _SCREAMING_SNAKE_CASE   :				int							    = leaf.strip()
                 if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                   _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = leaf[1:-2]  # for categorical special tokens
                 output[key].append(snake_case__				)
               if len(output[key]				) == 1:
                 _SCREAMING_SNAKE_CASE   :				Dict							    = output[key][0]

           _SCREAMING_SNAKE_CASE   :				List[Any]							    = tokens[tokens.find(snake_case__				) + len(snake_case__				) :].strip()
           if tokens[:6] == r"<sep/>":  # non-leaf nodes
             return [output] + self.tokenajson(tokens[6:]  ,     is_inner_value=snake_case__  ,     added_vocab=snake_case__				)

       if len(snake_case__				):
         return [output] if is_inner_value else output
       else:
         return [] if is_inner_value else {"text_sequence": tokens}
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,     snake_case__  ,     )
       return self.image_processor_class




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,     snake_case__  ,     )
       return self.image_processor



