
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

lowerCamelCase  =logging.get_logger(__name__)


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

lowerCamelCase  ={
    "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"
    },
}

lowerCamelCase  ={"facebook/blenderbot-3B": 1_2_8}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		 =	['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE_		 =	BlenderbotTokenizer






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE="replace"    ,    __SCREAMING_SNAKE_CASE="<s>"    ,    __SCREAMING_SNAKE_CASE="</s>"    ,    __SCREAMING_SNAKE_CASE="</s>"    ,    __SCREAMING_SNAKE_CASE="<s>"    ,    __SCREAMING_SNAKE_CASE="<unk>"    ,    __SCREAMING_SNAKE_CASE="<pad>"    ,    __SCREAMING_SNAKE_CASE="<mask>"    ,    __SCREAMING_SNAKE_CASE=False    ,    __SCREAMING_SNAKE_CASE=True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> int:




           """simple docstring"""







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

           UpperCamelCase__     : str											=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
           if pre_tok_state.get('''add_prefix_space'''    ,    __SCREAMING_SNAKE_CASE	) != add_prefix_space:
                 UpperCamelCase__     : int											=					getattr(__SCREAMING_SNAKE_CASE    ,    pre_tok_state.pop('''type'''	)	)
                 UpperCamelCase__     : List[Any]											=					add_prefix_space
                 UpperCamelCase__     : List[str]											=					pre_tok_class(**__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : str											=					add_prefix_space

           UpperCamelCase__     : Dict											=					'''post_processor'''
           UpperCamelCase__     : Union[str, Any]											=					getattr(self.backend_tokenizer    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)
           if tokenizer_component_instance:
                 UpperCamelCase__     : Any											=					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__     : List[Any]											=					tuple(state['''cls''']	)

                 UpperCamelCase__     : List[Any]											=					False

                 if state.get('''add_prefix_space'''    ,    __SCREAMING_SNAKE_CASE	) != add_prefix_space:
                       UpperCamelCase__     : Union[str, Any]											=					add_prefix_space
                       UpperCamelCase__     : List[str]											=					True

                 if state.get('''trim_offsets'''    ,    __SCREAMING_SNAKE_CASE	) != trim_offsets:
                       UpperCamelCase__     : List[Any]											=					trim_offsets
                       UpperCamelCase__     : Optional[int]											=					True

                 if changes_to_apply:
                       UpperCamelCase__     : Tuple											=					getattr(__SCREAMING_SNAKE_CASE    ,    state.pop('''type'''	)	)
                       UpperCamelCase__     : Optional[Any]											=					component_class(**__SCREAMING_SNAKE_CASE	)
                       setattr(self.backend_tokenizer    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	)






     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> str:




           """simple docstring"""







           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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[str]:




           """simple docstring"""







           UpperCamelCase__     : Union[str, Any]											=					AddedToken(__SCREAMING_SNAKE_CASE    ,    lstrip=__SCREAMING_SNAKE_CASE    ,    rstrip=__SCREAMING_SNAKE_CASE	) if isinstance(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) else value
           UpperCamelCase__     : Any											=					value






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> BatchEncoding:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					kwargs.get('''is_split_into_words'''    ,    __SCREAMING_SNAKE_CASE	)
           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(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    *__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)	-> BatchEncoding:




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					kwargs.get('''is_split_into_words'''    ,    __SCREAMING_SNAKE_CASE	)

           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(*__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : Optional[Any]											=					self._tokenizer.model.save(__SCREAMING_SNAKE_CASE    ,    name=__SCREAMING_SNAKE_CASE	)
           return tuple(__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           UpperCamelCase__     : int											=					[self.sep_token_id]
           UpperCamelCase__     : 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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> Union[str, Any]:




           """simple docstring"""







           return token_ids_a + [self.eos_token_id]






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




           """simple docstring"""







           UpperCamelCase__     : int											=					[]
           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(__SCREAMING_SNAKE_CASE	)

           UpperCamelCase__     : Any											=					'''  '''.join(__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					self.encode(__SCREAMING_SNAKE_CASE	)
           if len(__SCREAMING_SNAKE_CASE	) > self.model_max_length:
                 UpperCamelCase__     : Any											=					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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


lowerCamelCase  =logging.get_logger(__name__)


lowerCamelCase  ="▁"

lowerCamelCase  ={"vocab_file": "spiece.model"}

lowerCamelCase  ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowerCamelCase  ={
    "google/reformer-crime-and-punishment": 5_2_4_2_8_8,
}




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_		 =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_		 =	['''input_ids''', '''attention_mask''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE="</s>"    ,    __SCREAMING_SNAKE_CASE="<unk>"    ,    __SCREAMING_SNAKE_CASE=[]    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : int											=					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=__SCREAMING_SNAKE_CASE    ,    unk_token=__SCREAMING_SNAKE_CASE    ,    additional_special_tokens=__SCREAMING_SNAKE_CASE    ,    sp_model_kwargs=self.sp_model_kwargs    ,    **__SCREAMING_SNAKE_CASE    ,    )

           UpperCamelCase__     : Dict											=					vocab_file
           UpperCamelCase__     : Any											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE	)






     @property
     def  __SCREAMING_SNAKE_CASE      (				self	)	-> List[Any]:




           """simple docstring"""







           return self.sp_model.get_piece_size()






     def  __SCREAMING_SNAKE_CASE      (				self	)	-> Dict[str, int]:




           """simple docstring"""







           UpperCamelCase__     : Tuple											=					{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE	): i for i in range(self.vocab_size	)}
           vocab.update(self.added_tokens_encoder	)
           return vocab






     def __getstate__(				self	)	-> Any:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					self.__dict__.copy()
           UpperCamelCase__     : int											=					None
           return state






     def __setstate__(				self    ,    __SCREAMING_SNAKE_CASE	)	-> str:




           """simple docstring"""







           UpperCamelCase__     : int											=					d

           # for backward compatibility
           if not hasattr(self    ,    '''sp_model_kwargs'''	):
                 UpperCamelCase__     : int											=					{}

           UpperCamelCase__     : Optional[Any]											=					spm.SentencePieceProcessor(**self.sp_model_kwargs	)
           self.sp_model.Load(self.vocab_file	)






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




           """simple docstring"""







           return self.sp_model.encode(__SCREAMING_SNAKE_CASE    ,    out_type=__SCREAMING_SNAKE_CASE	)






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




           """simple docstring"""







           return self.sp_model.piece_to_id(__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> List[Any]:




           """simple docstring"""







           if index < self.sp_model.get_piece_size():
                 UpperCamelCase__     : str											=					self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE	)
           return token






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE	)	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : List[str]											=					[]
           UpperCamelCase__     : Union[str, Any]											=					''''''
           for token in tokens:
                 # make sure that special tokens are not decoded using sentencepiece model
                 if token in self.all_special_tokens:
                       out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	) + token
                       UpperCamelCase__     : str											=					[]
                 else:
                       current_sub_tokens.append(__SCREAMING_SNAKE_CASE	)
           out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE	)
           return out_string.strip()






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




           """simple docstring"""







           if not os.path.isdir(__SCREAMING_SNAKE_CASE	):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
                 return
           UpperCamelCase__     : Optional[int]											=					os.path.join(
               __SCREAMING_SNAKE_CASE    ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

           if os.path.abspath(self.vocab_file	) != os.path.abspath(__SCREAMING_SNAKE_CASE	) and os.path.isfile(self.vocab_file	):
                 copyfile(self.vocab_file    ,    __SCREAMING_SNAKE_CASE	)
           elif not os.path.isfile(self.vocab_file	):
                 with open(__SCREAMING_SNAKE_CASE    ,    '''wb'''	) as fi:
                       UpperCamelCase__     : int											=					self.sp_model.serialized_model_proto()
                       fi.write(__SCREAMING_SNAKE_CASE	)

           return (out_vocab_file,)


