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

import regex as re

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


__lowerCAmelCase			:     Tuple           =   logging.get_logger(__name__)


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

__lowerCAmelCase			:     int           =   {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

__lowerCAmelCase			:     Optional[int]           =   {'facebook/blenderbot_small-90M': 512}

def a__    (       A_					):






  '''simple docstring'''





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

  __magic_name__				     =       set(A_					)
  return pairs





class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		VOCAB_FILES_NAMES
  a__           =		PRETRAINED_VOCAB_FILES_MAP
  a__           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__           =		["""input_ids""", """attention_mask"""]
  def __init__( self			: Optional[Any]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: List[Any]			,							UpperCamelCase__			: List[Any]="__start__"			,							UpperCamelCase__			: int="__end__"			,							UpperCamelCase__			: List[str]="__unk__"			,							UpperCamelCase__			: str="__null__"			,							**UpperCamelCase__			: List[str]			,							)			->	Any:







    """simple docstring"""






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

    with open(UpperCamelCase__			,							encoding="""utf-8"""    ) as vocab_handle:
      __magic_name__				     =       json.load(UpperCamelCase__    )
    __magic_name__				     =       {v: k for k, v in self.encoder.items()}
    with open(UpperCamelCase__			,							encoding="""utf-8"""    ) as merges_handle:
      __magic_name__				     =       merges_handle.read().split("""\n"""    )[1:-1]
    __magic_name__				     =       [tuple(merge.split()    ) for merge in merges]
    __magic_name__				     =       dict(zip(UpperCamelCase__			,							range(len(UpperCamelCase__    )    )    )    )
    __magic_name__				     =       {}
  @property
  def 							_lowercase							( self			: Dict    )			->	int:







    """simple docstring"""






    return len(self.encoder    )
  def 							_lowercase							( self			: int    )			->	Dict:







    """simple docstring"""






    return dict(self.encoder			,							**self.added_tokens_encoder    )
  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: str    )			->	str:







    """simple docstring"""






    if token in self.cache:
      return self.cache[token]
    __magic_name__				     =       re.sub("""([.,!?()])"""			,							R""" \1"""			,							UpperCamelCase__    )
    __magic_name__				     =       re.sub("""(')"""			,							R""" \1 """			,							UpperCamelCase__    )
    __magic_name__				     =       re.sub(R"""\s{2,}"""			,							""" """			,							UpperCamelCase__    )
    if "\n" in token:
      __magic_name__				     =       token.replace("""\n"""			,							""" __newln__"""    )

    __magic_name__				     =       token.split(""" """    )
    __magic_name__				     =       []
    for token in tokens:
      if not len(UpperCamelCase__    ):
        continue

      __magic_name__				     =       token.lower()
      __magic_name__				     =       tuple(UpperCamelCase__    )
      __magic_name__				     =       tuple(list(word[:-1]    ) + [word[-1] + """</w>"""]    )
      __magic_name__				     =       get_pairs(UpperCamelCase__    )

      if not pairs:
        words.append(UpperCamelCase__    )
        continue

      while True:
        __magic_name__				     =       min(UpperCamelCase__			,							key=lambda UpperCamelCase__    : self.bpe_ranks.get(UpperCamelCase__			,							float("""inf"""    )    )    )
        if bigram not in self.bpe_ranks:
          break
        __magic_name__     ,    __magic_name__				     =       bigram
        __magic_name__				     =       []
        __magic_name__				     =       0

        while i < len(UpperCamelCase__    ):
          try:
            __magic_name__				     =       word.index(UpperCamelCase__			,							UpperCamelCase__    )
            new_word.extend(word[i:j]    )
            __magic_name__				     =       j
          except ValueError:
            new_word.extend(word[i:]    )
            break

          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
        __magic_name__				     =       tuple(UpperCamelCase__    )
        __magic_name__				     =       new_word
        if len(UpperCamelCase__    ) == 1:
          break
        else:
          __magic_name__				     =       get_pairs(UpperCamelCase__    )
      __magic_name__				     =       """@@ """.join(UpperCamelCase__    )
      __magic_name__				     =       word[:-4]

      __magic_name__				     =       word
      words.append(UpperCamelCase__    )
    return " ".join(UpperCamelCase__    )
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: str    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       []

    __magic_name__				     =       re.findall(R"""\S+\n?"""			,							UpperCamelCase__    )

    for token in words:
      split_tokens.extend(list(self.bpe(UpperCamelCase__    ).split(""" """    )    )    )
    return split_tokens
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: str    )			->	int:







    """simple docstring"""






    __magic_name__				     =       token.lower()
    return self.encoder.get(UpperCamelCase__			,							self.encoder.get(self.unk_token    )    )
  def 							_lowercase							( self			: List[str]			,							UpperCamelCase__			: int    )			->	str:







    """simple docstring"""






    return self.decoder.get(UpperCamelCase__			,							self.unk_token    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: List[str]    )			->	str:







    """simple docstring"""






    __magic_name__				     =       """ """.join(UpperCamelCase__    ).replace("""@@ """			,							""""""    ).strip()
    return out_string





  def 							_lowercase							( self			: 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
    __magic_name__				     =       os.path.join(
        UpperCamelCase__			,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )
    __magic_name__				     =       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"""    )

    __magic_name__				     =       0
    with open(UpperCamelCase__			,							"""w"""			,							encoding="""utf-8"""    ) as writer:
      writer.write("""#version: 0.2\n"""    )
      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 {merge_file}: BPE merge indices are not consecutive.'''
              """ Please check that the tokenizer is not corrupted!"""    )
          __magic_name__				     =       token_index
        writer.write(""" """.join(UpperCamelCase__    ) + """\n"""    )
        index += 1

    return vocab_file, merge_file



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			:     List[Any]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     int           =   {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		"""biogpt"""
  def __init__( self			: List[str]			,							UpperCamelCase__			: Optional[Any]=4_2384			,							UpperCamelCase__			: Union[str, Any]=1024			,							UpperCamelCase__			: Any=24			,							UpperCamelCase__			: Union[str, Any]=16			,							UpperCamelCase__			: Tuple=4096			,							UpperCamelCase__			: Tuple="gelu"			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: Dict=0.1			,							UpperCamelCase__			: str=1024			,							UpperCamelCase__			: Any=0.02			,							UpperCamelCase__			: List[str]=1E-12			,							UpperCamelCase__			: Dict=True			,							UpperCamelCase__			: Optional[int]=True			,							UpperCamelCase__			: Union[str, Any]=0.0			,							UpperCamelCase__			: Any=0.0			,							UpperCamelCase__			: List[str]=1			,							UpperCamelCase__			: Dict=0			,							UpperCamelCase__			: List[str]=2			,							**UpperCamelCase__			: Optional[int]			,							)			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       vocab_size
    __magic_name__				     =       max_position_embeddings
    __magic_name__				     =       hidden_size
    __magic_name__				     =       num_hidden_layers
    __magic_name__				     =       num_attention_heads
    __magic_name__				     =       intermediate_size
    __magic_name__				     =       hidden_act
    __magic_name__				     =       hidden_dropout_prob
    __magic_name__				     =       attention_probs_dropout_prob
    __magic_name__				     =       initializer_range
    __magic_name__				     =       layer_norm_eps
    __magic_name__				     =       scale_embedding
    __magic_name__				     =       use_cache
    __magic_name__				     =       layerdrop
    __magic_name__				     =       activation_dropout
    super().__init__(pad_token_id=UpperCamelCase__			,							bos_token_id=UpperCamelCase__			,							eos_token_id=UpperCamelCase__			,							**UpperCamelCase__    )

