




'''simple docstring'''







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

lowercase      :   Dict      =       logging.get_logger(__name__)


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

lowercase      :   int      =       {
    """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"""
    },
}

lowercase      :   Any      =       {"""facebook/blenderbot-3B""": 128}



@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def        lowerCamelCase__				(      ):
     snake_case			:      Any        =			(
         list(range(ord("""!"""				)     ,   ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				)     ,   ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				)     ,   ord("""ÿ"""				) + 1				)				)
     )
     snake_case			:      Union[str, Any]        =			bs[:]
     snake_case			:      Tuple        =			0
     for b in range(2**8				):
          if b not in bs:
               bs.append(__lowercase				)
               cs.append(2**8 + n				)
               n += 1
     snake_case			:      Union[str, Any]        =			[chr(__lowercase				) for n in cs]
     return dict(zip(__lowercase     ,   __lowercase				)				)



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      int        =			set()
     snake_case			:      List[Any]        =			word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          snake_case			:      Dict        =			char
     return pairs







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     int          =					VOCAB_FILES_NAMES
     lowerCAmelCase_  :     List[str]          =					PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_  :     Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_  :     List[Any]          =					["""input_ids""", """attention_mask"""]







     def __init__(				self							,__a							,__a							,__a="replace"							,__a="<s>"							,__a="</s>"							,__a="</s>"							,__a="<s>"							,__a="<unk>"							,__a="<pad>"							,__a="<mask>"							,__a=False							,**__a							,)      ->				str:
          snake_case			:      List[Any]        =			AddedToken(__a							,lstrip=__a							,rstrip=__a							) if isinstance(__a							,__a							) else bos_token
          snake_case			:      Tuple        =			AddedToken(__a							,lstrip=__a							,rstrip=__a							) if isinstance(__a							,__a							) else eos_token
          snake_case			:      Dict        =			AddedToken(__a							,lstrip=__a							,rstrip=__a							) if isinstance(__a							,__a							) else sep_token
          snake_case			:      str        =			AddedToken(__a							,lstrip=__a							,rstrip=__a							) if isinstance(__a							,__a							) else cls_token
          snake_case			:      List[str]        =			AddedToken(__a							,lstrip=__a							,rstrip=__a							) if isinstance(__a							,__a							) else unk_token
          snake_case			:      Union[str, Any]        =			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
          snake_case			:      List[str]        =			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:
               snake_case			:      Dict        =			json.load(__a							)
          snake_case			:      Tuple        =			{v: k for k, v in self.encoder.items()}
          snake_case			:      int        =			errors  # how to handle errors in decoding
          snake_case			:      Tuple        =			bytes_to_unicode()
          snake_case			:      Tuple        =			{v: k for k, v in self.byte_encoder.items()}
          with open(__a							,encoding="""utf-8"""							) as merges_handle:
               snake_case			:      Any        =			merges_handle.read().split("""\n"""							)[1:-1]
          snake_case			:      List[Any]        =			[tuple(merge.split()							) for merge in bpe_merges]
          snake_case			:      Optional[Any]        =			dict(zip(__a							,range(len(__a							)							)							)							)
          snake_case			:      int        =			{}
          snake_case			:      List[str]        =			add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          snake_case			:      List[str]        =			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 			snake_case_      (				self							)      ->				Optional[int]:
          return len(self.encoder							)







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          return dict(self.encoder							,**self.added_tokens_encoder							)







     def 			snake_case_      (				self							,__a							)      ->				Dict:
          if token in self.cache:
               return self.cache[token]
          snake_case			:      Dict        =			tuple(__a							)
          snake_case			:      Tuple        =			get_pairs(__a							)

          if not pairs:
               return token

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







     def 			snake_case_      (				self							,__a							)      ->				List[Any]:
          snake_case			:      int        =			[]
          for token in re.findall(self.pat							,__a							):
               snake_case			:      Tuple        =			"""""".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 			snake_case_      (				self							,__a							)      ->				List[Any]:
          return self.encoder.get(__a							,self.encoder.get(self.unk_token							)							)







     def 			snake_case_      (				self							,__a							)      ->				Union[str, Any]:
          return self.decoder.get(__a							)







     def 			snake_case_      (				self							,__a							)      ->				List[str]:
          snake_case			:      List[str]        =			"""""".join(__a							)
          snake_case			:      Optional[int]        =			bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""							,errors=self.errors							)
          return text







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				Tuple[str]:
          if not os.path.isdir(__a							):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
               return
          snake_case			:      int        =			os.path.join(
              __a							,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
          snake_case			:      List[str]        =			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"""							)

          snake_case			:      str        =			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 __a							: 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!"""							)
                         snake_case			:      Tuple        =			token_index
                    writer.write(""" """.join(__a							) + """\n"""							)
                    index += 1

          return vocab_file, merge_file







     def 			snake_case_      (				self							,__a							,__a = None							,__a = False							)      ->				List[int]:
          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 			snake_case_      (				self							,__a							,__a = None							)      ->				List[int]:
          snake_case			:      Union[str, Any]        =			[self.sep_token_id]
          snake_case			:      Tuple        =			[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 			snake_case_      (				self							,__a							,__a=False							,**__a							)      ->				int:
          snake_case			:      List[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()):
               snake_case			:      Any        =			""" """ + text
          return (text, kwargs)







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				Union[str, Any]:
          return token_ids_a + [self.eos_token_id]







     def 			snake_case_      (				self							,__a							)      ->				List[int]:
          snake_case			:      List[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							)

          snake_case			:      Optional[Any]        =			"""  """.join(__a							)
          snake_case			:      List[str]        =			self.encode(__a							)
          if len(__a							) > self.model_max_length:
               snake_case			:      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









'''simple docstring'''







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase      :   int      =       logging.getLogger(__name__)







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     Union[str, Any]          =					"""masked_bert"""







     def __init__(				self							,__a=30_522							,__a=768							,__a=12							,__a=12							,__a=3_072							,__a="gelu"							,__a=0.1							,__a=0.1							,__a=512							,__a=2							,__a=0.02							,__a=1E-12							,__a=0							,__a="topK"							,__a="constant"							,__a=0.0							,**__a							,)      ->				List[str]:
          super().__init__(pad_token_id=__a							,**__a							)

          snake_case			:      Dict        =			vocab_size
          snake_case			:      Optional[Any]        =			hidden_size
          snake_case			:      Dict        =			num_hidden_layers
          snake_case			:      List[Any]        =			num_attention_heads
          snake_case			:      Dict        =			hidden_act
          snake_case			:      Any        =			intermediate_size
          snake_case			:      Optional[int]        =			hidden_dropout_prob
          snake_case			:      Optional[Any]        =			attention_probs_dropout_prob
          snake_case			:      List[Any]        =			max_position_embeddings
          snake_case			:      int        =			type_vocab_size
          snake_case			:      int        =			initializer_range
          snake_case			:      int        =			layer_norm_eps
          snake_case			:      Any        =			pruning_method
          snake_case			:      Union[str, Any]        =			mask_init
          snake_case			:      int        =			mask_scale



