






'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

UpperCamelCase      					=logging.get_logger(__name__)


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

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




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    List[Any]			  =    VOCAB_FILES_NAMES
 __a      :    Optional[int]			  =    PRETRAINED_VOCAB_FILES_MAP
 __a      :    List[Any]			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a      :    List[Any]			  =    ['''input_ids''', '''attention_mask''']
 __a      :    Optional[int]			  =    BlenderbotTokenizer
 def __init__(				self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase="replace"      ,  __lowerCAmelCase="<s>"      ,  __lowerCAmelCase="</s>"      ,  __lowerCAmelCase="</s>"      ,  __lowerCAmelCase="<s>"      ,  __lowerCAmelCase="<unk>"      ,  __lowerCAmelCase="<pad>"      ,  __lowerCAmelCase="<mask>"      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=True      ,  **__lowerCAmelCase      ,  ):
       super().__init__(
           __lowerCAmelCase      ,  __lowerCAmelCase      ,  tokenizer_file=__lowerCAmelCase      ,  errors=__lowerCAmelCase      ,  bos_token=__lowerCAmelCase      ,  eos_token=__lowerCAmelCase      ,  sep_token=__lowerCAmelCase      ,  cls_token=__lowerCAmelCase      ,  unk_token=__lowerCAmelCase      ,  pad_token=__lowerCAmelCase      ,  mask_token=__lowerCAmelCase      ,  add_prefix_space=__lowerCAmelCase      ,  trim_offsets=__lowerCAmelCase      ,  **__lowerCAmelCase      ,  )

       UpperCamelCase_					: int            =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
       if pre_tok_state.get("""add_prefix_space"""      ,  __lowerCAmelCase			) != add_prefix_space:
             UpperCamelCase_					: Union[str, Any]            =				getattr(__lowerCAmelCase      ,  pre_tok_state.pop("""type"""			)			)
             UpperCamelCase_					: int            =				add_prefix_space
             UpperCamelCase_					: int            =				pre_tok_class(**__lowerCAmelCase			)

       UpperCamelCase_					: List[str]            =				add_prefix_space

       UpperCamelCase_					: Any            =				"""post_processor"""
       UpperCamelCase_					: Any            =				getattr(self.backend_tokenizer      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       if tokenizer_component_instance:
             UpperCamelCase_					: int            =				json.loads(tokenizer_component_instance.__getstate__()			)

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                   UpperCamelCase_					: str            =				tuple(state["""sep"""]			)
             if "cls" in state:
                   UpperCamelCase_					: Any            =				tuple(state["""cls"""]			)

             UpperCamelCase_					: int            =				False

             if state.get("""add_prefix_space"""      ,  __lowerCAmelCase			) != add_prefix_space:
                   UpperCamelCase_					: Optional[int]            =				add_prefix_space
                   UpperCamelCase_					: Dict            =				True

             if state.get("""trim_offsets"""      ,  __lowerCAmelCase			) != trim_offsets:
                   UpperCamelCase_					: List[str]            =				trim_offsets
                   UpperCamelCase_					: Optional[int]            =				True

             if changes_to_apply:
                   UpperCamelCase_					: List[Any]            =				getattr(__lowerCAmelCase      ,  state.pop("""type"""			)			)
                   UpperCamelCase_					: Any            =				component_class(**__lowerCAmelCase			)
                   setattr(self.backend_tokenizer      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def 	_UpperCAmelCase							(				self			):
       if self._mask_token is None:
             if self.verbose:
                   logger.error("""Using mask_token, but it is not set yet."""			)
             return None
       return str(self._mask_token			)
 @mask_token.setter
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[int]            =				AddedToken(__lowerCAmelCase      ,  lstrip=__lowerCAmelCase      ,  rstrip=__lowerCAmelCase			) if isinstance(__lowerCAmelCase      ,  __lowerCAmelCase			) else value
       UpperCamelCase_					: Optional[int]            =				value
 def 	_UpperCAmelCase							(				self      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: Union[str, Any]            =				kwargs.get("""is_split_into_words"""      ,  __lowerCAmelCase			)
       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*__lowerCAmelCase      ,  **__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: str            =				kwargs.get("""is_split_into_words"""      ,  __lowerCAmelCase			)

       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*__lowerCAmelCase      ,  **__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: str            =				self._tokenizer.model.save(__lowerCAmelCase      ,  name=__lowerCAmelCase			)
       return tuple(__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: Tuple            =				[self.sep_token_id]
       UpperCamelCase_					: int            =				[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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       return token_ids_a + [self.eos_token_id]





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: 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(__lowerCAmelCase			)

       UpperCamelCase_					: List[str]            =				"""  """.join(__lowerCAmelCase			)
       UpperCamelCase_					: Any            =				self.encode(__lowerCAmelCase			)
       if len(__lowerCAmelCase			) > self.model_max_length:
             UpperCamelCase_					: List[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'''

# Lint as: python3
import itertools
import os
import re


UpperCamelCase      					=re.compile(R"([A-Z]+)([A-Z][a-z])")
UpperCamelCase      					=re.compile(R"([a-z\d])([A-Z])")

UpperCamelCase      					=re.compile(R"(?<!_)_(?!_)")
UpperCamelCase      					=re.compile(R"(_{2,})")

UpperCamelCase      					=R"^\w+(\.\w+)*$"

UpperCamelCase      					=R"<>:/\|?*"



def 				snake_case       (  a_				:			Union[str, Any]						)							->					List[Any]:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				_uppercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      UpperCamelCase_					: Any            =				_lowercase_uppercase_re.sub(r"""\1_\2"""     , a_						)
      return name.lower()



def 				snake_case       (  a_				:			str						)							->					Optional[int]:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				_single_underscore_re.split(a_						)
      UpperCamelCase_					: Any            =				[_multiple_underscores_re.split(a_						) for n in name]
      return "".join(n.capitalize() for n in itertools.chain.from_iterable(a_						) if n != """"""						)



def 				snake_case       (  a_				:			str						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      return camelcase_to_snakecase(a_						)



def 				snake_case       (  a_				:			int     , a_				:			List[Any]						)							->					List[Any]:





      """simple docstring"""

      if os.path.basename(a_						) != name:
            raise ValueError(f"Should be a dataset name, not a path: {name}"						)
      if not re.match(_split_re     , a_						):
            raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
      return f"{filename_prefix_for_name(a_						)}-{split}"



def 				snake_case       (  a_				:			str     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			Optional[int]=None						)							->					Tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				filename_prefix_for_split(a_     , a_						)
      if filetype_suffix:
            prefix += f".{filetype_suffix}"
      UpperCamelCase_					: List[str]            =				os.path.join(a_     , a_						)
      return f"{filepath}*"



def 				snake_case       (  a_				:			List[Any]     , a_				:			Optional[int]     , a_				:			List[Any]     , a_				:			int=None     , a_				:			Dict=None						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				filename_prefix_for_split(a_     , a_						)
      UpperCamelCase_					: Union[str, Any]            =				os.path.join(a_     , a_						)

      if shard_lengths:
            UpperCamelCase_					: List[Any]            =				len(a_						)
            UpperCamelCase_					: Optional[int]            =				[f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(a_						)]
            if filetype_suffix:
                  UpperCamelCase_					: Tuple            =				[filename + f".{filetype_suffix}" for filename in filenames]
            return filenames
      else:
            UpperCamelCase_					: List[Any]            =				prefix
            if filetype_suffix:
                  filename += f".{filetype_suffix}"
            return [filename]






