


'''simple docstring'''

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

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


a_  :				Tuple						= logging.get_logger(__name__)


a_  :				List[Any]						= {
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

a_  :				Optional[Any]						= {
    """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"""
        ),
    },
}

a_  :				List[str]						= """</w>"""
a_  :				List[str]						= """@@ """





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  set()
 lowerCamelCase_      =  word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)		)
  lowerCamelCase_      =  char
 return pairs


# Speech2Text2 has no max input length
a_  :				Any						= {"""facebook/s2t-wav2vec2-large-en-de""": 1024}







class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  VOCAB_FILES_NAMES
  _lowerCamelCase		    =  PRETRAINED_VOCAB_FILES_MAP
  _lowerCamelCase		    =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowerCamelCase		    =  ["input_ids", "attention_mask"]






  def __init__(    self			,  UpperCamelCase			,  UpperCamelCase="<s>"			,  UpperCamelCase="<pad>"			,  UpperCamelCase="</s>"			,  UpperCamelCase="<unk>"			,  UpperCamelCase=False			,  UpperCamelCase=None			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(
       unk_token=UpperCamelCase			,  bos_token=UpperCamelCase			,  eos_token=UpperCamelCase			,  pad_token=UpperCamelCase			,  do_lower_case=UpperCamelCase			,  **UpperCamelCase			,  )

   lowerCamelCase_      =  do_lower_case

   with open(UpperCamelCase			,  encoding="utf-8"     ) as vocab_handle:
    lowerCamelCase_      =  json.load(UpperCamelCase     )
   lowerCamelCase_      =  {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.'''     )

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

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






  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   return len(self.decoder     )






  def   snake_case		(    self     ):







   """simple docstring"""
   return dict(self.encoder			,  **self.added_tokens_encoder     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  tuple(token[:-1]     ) + (token[-1] + BPE_TOKEN_MERGES,)
   if token in self.cache:
    return self.cache[token]
   lowerCamelCase_      =  get_pairs(UpperCamelCase     )

   if not pairs:
    return token

   while True:
    lowerCamelCase_      =  min(UpperCamelCase			,  key=lambda UpperCamelCase     : self.bpe_ranks.get(UpperCamelCase			,  float("inf"     )     )     )
    if bigram not in self.bpe_ranks:
     break
    lowerCamelCase_ ,lowerCamelCase_      =  bigram
    lowerCamelCase_      =  []
    lowerCamelCase_      =  0
    while i < len(UpperCamelCase     ):
     try:
      lowerCamelCase_      =  word.index(UpperCamelCase			,  UpperCamelCase     )
     except ValueError:
      new_word.extend(word[i:]     )
      break
     else:
      new_word.extend(word[i:j]     )
      lowerCamelCase_      =  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
    lowerCamelCase_      =  tuple(UpperCamelCase     )
    lowerCamelCase_      =  new_word
    if len(UpperCamelCase     ) == 1:
     break
    else:
     lowerCamelCase_      =  get_pairs(UpperCamelCase     )
   lowerCamelCase_      =  " ".join(UpperCamelCase     )
   if word == "\n  " + BPE_TOKEN_MERGES:
    lowerCamelCase_      =  "\n" + BPE_TOKEN_MERGES

   if word.endswith(UpperCamelCase     ):
    lowerCamelCase_      =  word.replace(UpperCamelCase			,  ""     )

   lowerCamelCase_      =  word.replace(" "			,  UpperCamelCase     )
   lowerCamelCase_      =  word
   return word






  def   snake_case		(    self			,  UpperCamelCase     ):







   """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:
    lowerCamelCase_      =  text.lower()

   lowerCamelCase_      =  text.split()

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

   return split_tokens






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   return self.encoder.get(UpperCamelCase			,  self.encoder.get(self.unk_token     )     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.decoder.get(UpperCamelCase			,  self.unk_token     )
   return result






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  " ".join(UpperCamelCase     )

   # make sure @@ tokens are concatenated
   lowerCamelCase_      =  "".join(string.split(UpperCamelCase     )     )

   return string







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







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

   lowerCamelCase_      =  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!"     )
      lowerCamelCase_      =  token_index
     writer.write(" ".join(UpperCamelCase     ) + "\n"     )
     index += 1

   return (vocab_file, merges_file)








'''simple docstring'''





import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     if isinstance(__A  ,	__A):
          _a      =  np.full((len(__A), sequence_length, 2)  ,	__A)
     else:
          _a      =  np.full((len(__A), sequence_length)  ,	__A)

     for i, tensor in enumerate(__A):
          if padding_side == "right":
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]
          else:
               if isinstance(__A  ,	__A):
                    _a      =  tensor[:sequence_length]
               else:
                    _a      =  tensor[:sequence_length]

     return out_tensor.tolist()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a      =  ord(__A)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
          return True
     _a      =  unicodedata.category(__A)
     if cat.startswith('''P'''):
          return True
     return False






@dataclass
class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       PreTrainedTokenizerBase
 __lowerCamelCase    :       Union[bool, str, PaddingStrategy]          =       True
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       Optional[int]          =       None
 __lowerCamelCase    :       int          =       -100
 __lowerCamelCase    :       str          =       "pt"



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      import torch

      _a      =  '''label''' if '''label''' in features[0].keys() else '''labels'''
      _a      =  [feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =  self.tokenizer.pad(
          A ,   padding=self.padding ,   max_length=self.max_length ,   pad_to_multiple_of=self.pad_to_multiple_of ,   return_tensors='''pt''' if labels is None else None ,   )

      if labels is None:
           return batch

      _a      =  torch.tensor(batch['''entity_ids''']       ).shape[1]
      _a      =  self.tokenizer.padding_side
      if padding_side == "right":
           _a      =  [
               list(A       ) + [self.label_pad_token_id] * (sequence_length - len(A       )) for label in labels
           ]
      else:
           _a      =  [
               [self.label_pad_token_id] * (sequence_length - len(A       )) + list(A       ) for label in labels
           ]

      _a      =  [feature['''ner_tags'''] for feature in features]
      _a      =  padding_tensor(A ,   -1 ,   A ,   A       )
      _a      =  [feature['''original_entity_spans'''] for feature in features]
      _a      =  padding_tensor(A ,   (-1, -1) ,   A ,   A       )
      _a      =  {k: torch.tensor(A ,   dtype=torch.intaa       ) for k, v in batch.items()}

      return batch



