





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

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


UpperCAmelCase	:	Tuple		     =       {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

UpperCAmelCase	:	Any		     =       {
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

UpperCAmelCase	:	Optional[Any]		     =       {"""facebook/blenderbot-3B""": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _A       (		):





  """simple docstring"""



  a__	:					Optional[int]										=(
      list(range(ord("!"						)       ,       ord("~"						) + 1						)						) + list(range(ord("¡"						)       ,       ord("¬"						) + 1						)						) + list(range(ord("®"						)       ,       ord("ÿ"						) + 1						)						)
  )
  a__	:					Tuple										=bs[:]
  a__	:					List[str]										=0
  for b in range(2**8						):
    if b not in bs:
      bs.append(SCREAMING_SNAKE_CASE__						)
      cs.append(2**8 + n						)
      n += 1
  a__	:					List[str]										=[chr(SCREAMING_SNAKE_CASE__						) for n in cs]
  return dict(zip(SCREAMING_SNAKE_CASE__       ,       SCREAMING_SNAKE_CASE__						)						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					List[str]										=set()
  a__	:					int										=word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)						)
    a__	:					List[str]										=char
  return pairs







class 				__lowerCAmelCase       (    A_):
      _lowercase				:					List[str]						=			VOCAB_FILES_NAMES
      _lowercase				:					List[Any]						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					Dict						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					Dict						=			["input_ids", "attention_mask"]







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__="replace"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="<mask>"		,      lowerCAmelCase__=False		,      **lowerCAmelCase__		,      )   ->						Union[str, Any]:



        '''simple docstring'''




        a__	:					Dict										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else bos_token
        a__	:					Tuple										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else eos_token
        a__	:					Optional[int]										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else sep_token
        a__	:					List[str]										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else cls_token
        a__	:					Optional[int]										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else unk_token
        a__	:					List[str]										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        a__	:					Dict										=AddedToken(A_		,      lstrip=A_		,      rstrip=A_							) if isinstance(A_		,      A_							) else mask_token

        super().__init__(
            errors=A_		,      bos_token=A_		,      eos_token=A_		,      unk_token=A_		,      sep_token=A_		,      cls_token=A_		,      pad_token=A_		,      mask_token=A_		,      add_prefix_space=A_		,      **A_		,      )

        with open(A_		,      encoding="utf-8"							) as vocab_handle:
          a__	:					int										=json.load(A_							)
        a__	:					Union[str, Any]										={v: k for k, v in self.encoder.items()}
        a__	:					List[Any]										=errors  # how to handle errors in decoding
        a__	:					List[str]										=bytes_to_unicode()
        a__	:					List[Any]										={v: k for k, v in self.byte_encoder.items()}
        with open(A_		,      encoding="utf-8"							) as merges_handle:
          a__	:					Dict										=merges_handle.read().split("\n"							)[1:-1]
        a__	:					Tuple										=[tuple(merge.split()							) for merge in bpe_merges]
        a__	:					Any										=dict(zip(A_		,      range(len(A_							)							)							)							)
        a__	:					Any										={}
        a__	:					List[Any]										=add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        a__	:					int										=re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)







      @property
      # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




        return len(self.encoder							)







      def    _lowercase  (    self							)   ->						Union[str, Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




        if token in self.cache:
          return self.cache[token]
        a__	:					Dict										=tuple(A_							)
        a__	:					Optional[Any]										=get_pairs(A_							)

        if not pairs:
          return token

        while True:
          a__	:					Union[str, Any]										=min(A_		,      key=lambda lowerCAmelCase__							: self.bpe_ranks.get(A_		,      float("inf"							)							)							)
          if bigram not in self.bpe_ranks:
            break
          a__					,							a__	:					Union[str, Any]										=bigram
          a__	:					int										=[]
          a__	:					Union[str, Any]										=0
          while i < len(A_							):
            try:
              a__	:					Optional[int]										=word.index(A_		,      A_							)
            except ValueError:
              new_word.extend(word[i:]							)
              break
            else:
              new_word.extend(word[i:j]							)
              a__	:					List[str]										=j

            if word[i] == first and i < len(A_							) - 1 and word[i + 1] == second:
              new_word.append(first + second							)
              i += 2
            else:
              new_word.append(word[i]							)
              i += 1
          a__	:					Optional[Any]										=tuple(A_							)
          a__	:					Tuple										=new_word
          if len(A_							) == 1:
            break
          else:
            a__	:					List[Any]										=get_pairs(A_							)
        a__	:					List[Any]										=" ".join(A_							)
        a__	:					int										=word
        return word







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[Any]										=[]
        for token in re.findall(self.pat		,      A_							):
          a__	:					Union[str, Any]										="".join(
              self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_							).split(" "							)							)
        return bpe_tokens







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        return self.decoder.get(A_							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					Dict										="".join(A_							)
        a__	:					Optional[Any]										=bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"		,      errors=self.errors							)
        return text







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						Tuple[str]:



        '''simple docstring'''




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

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

        a__	:					Tuple										=0
        with open(A_		,      "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 lowerCAmelCase__							: 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!"							)
              a__	:					Any										=token_index
            writer.write(" ".join(A_							) + "\n"							)
            index += 1

        return vocab_file, merge_file







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = False							)   ->						List[int]:



        '''simple docstring'''




        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A_		,      token_ids_a=A_		,      already_has_special_tokens=A_							)

        if token_ids_a is None:
          return [1] + ([0] * len(A_							)) + [1]
        return [1] + ([0] * len(A_							)) + [1, 1] + ([0] * len(A_							)) + [1]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						List[int]:



        '''simple docstring'''




        a__	:					Optional[int]										=[self.sep_token_id]
        a__	:					Union[str, Any]										=[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 + sep + token_ids_a + sep							) * [0]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=False		,      **lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					str										=kwargs.pop("add_prefix_space"		,      self.add_prefix_space							)
        if (is_split_into_words or add_prefix_space) and (len(A_							) > 0 and not text[0].isspace()):
          a__	:					Tuple										=" " + text
        return (text, kwargs)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						Tuple:



        '''simple docstring'''




        return token_ids_a + [self.eos_token_id]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						List[int]:



        '''simple docstring'''




        a__	:					str										=[]
        for is_user, text in conversation.iter_texts():
          if is_user:
            # We need to space prefix as it's being done within blenderbot
            inputs.append(" " + text							)
          else:
            # Generated responses should contain them already.
            inputs.append(A_							)

        a__	:					Optional[int]										="  ".join(A_							)
        a__	:					str										=self.encode(A_							)
        if len(A_							) > self.model_max_length:
          a__	:					str										=input_ids[-self.model_max_length :]
          logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''							)
        return input_ids










import warnings

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


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





