"""simple docstring"""




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

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


_a      :		Any           =   logging.get_logger(__name__)

_a      :		Dict           =   {'vocab_file': 'vocab.txt'}

_a      :		str           =   {
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

_a      :		List[str]           =   {
    'openbmb/cpm-ant-10b': 1_024,
}





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Tuple:
  _lowerCAmelCase						:       List[str]  			=     collections.OrderedDict()
  with open(_lowerCamelCase			,"""r"""			,encoding="""utf-8"""	) as reader:
    _lowerCAmelCase						:       Tuple  			=     reader.readlines()
  for index, token in enumerate(_lowerCamelCase	):
    _lowerCAmelCase						:       str  			=     token.rstrip("""\n"""	)
    _lowerCAmelCase						:       str  			=     index
  return vocab
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__="<unk>"				,     a__=200  ):
       _lowerCAmelCase						:       Optional[Any]  			=     vocab
       _lowerCAmelCase						:       Any  			=     unk_token
       _lowerCAmelCase						:       int  			=     max_input_chars_per_word






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Any  			=     list(a__  )
       if len(a__  ) > self.max_input_chars_per_word:
         return [self.unk_token]

       _lowerCAmelCase						:       Union[str, Any]  			=     0
       _lowerCAmelCase						:       Dict  			=     []
       while start < len(a__  ):
         _lowerCAmelCase						:       Tuple  			=     len(a__  )
         _lowerCAmelCase						:       int  			=     None
         while start < end:
           _lowerCAmelCase						:       str  			=     """""".join(chars[start:end]  )
           if substr in self.vocab:
             _lowerCAmelCase						:       int  			=     substr
             break
           end -= 1
         if cur_substr is None:
           sub_tokens.append(self.unk_token  )
           start += 1
         else:
           sub_tokens.append(a__  )
           _lowerCAmelCase						:       Union[str, Any]  			=     end

       return sub_tokens







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Optional[int]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[str]							=		["input_ids", "attention_mask"]
     _UpperCamelCase							:			str							=		False






     def __init__(		self				,     a__				,     a__="<d>"				,     a__="</d>"				,     a__="<s>"				,     a__="</s>"				,     a__="<pad>"				,     a__="<unk>"				,     a__="</n>"				,     a__="</_>"				,     a__="left"				,     **a__				,     ):
       requires_backends(self				,     ["""jieba"""]  )
       super().__init__(
           bod_token=a__				,     eod_token=a__				,     bos_token=a__				,     eos_token=a__				,     pad_token=a__				,     unk_token=a__				,     line_token=a__				,     space_token=a__				,     padding_side=a__				,     **a__				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     bod_token
       _lowerCAmelCase						:       List[Any]  			=     eod_token
       _lowerCAmelCase						:       List[str]  			=     load_vocab(a__  )
       _lowerCAmelCase						:       Tuple  			=     self.encoder[space_token]
       _lowerCAmelCase						:       Optional[Any]  			=     self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       _lowerCAmelCase						:       Any  			=     collections.OrderedDict(sorted(self.encoder.items()				,     key=lambda a__  : x[1]  )  )
       _lowerCAmelCase						:       int  			=     {v: k for k, v in self.encoder.items()}

       _lowerCAmelCase						:       Optional[int]  			=     WordpieceTokenizer(vocab=self.encoder				,     unk_token=self.unk_token  )






     @property
     def     __A  (		self  ):
       return self.encoder[self.bod_token]






     @property
     def     __A  (		self  ):
       return self.encoder[self.eod_token]






     @property
     def     __A  (		self  ):
       return self.encoder["\n"]






     @property
     def     __A  (		self  ):
       return len(self.encoder  )






     def     __A  (		self  ):
       return dict(self.encoder				,     **self.added_tokens_encoder  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     []
       for x in jieba.cut(a__				,     cut_all=a__  ):
         output_tokens.extend(self.wordpiece_tokenizer.tokenize(a__  )  )
       return output_tokens






     def     __A  (		self				,     a__				,     **a__  ):
       _lowerCAmelCase						:       Any  			=     [i for i in token_ids if i >= 0]
       _lowerCAmelCase						:       str  			=     [
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(a__				,     **a__  )






     def     __A  (		self				,     a__  ):
       return token in self.encoder






     def     __A  (		self				,     a__  ):
       return "".join(a__  )






     def     __A  (		self				,     a__  ):
       return self.encoder.get(a__				,     self.encoder.get(self.unk_token  )  )






     def     __A  (		self				,     a__  ):
       return self.decoder.get(a__				,     self.unk_token  )






     def     __A  (		self				,     a__				,     a__ = None  ):
       if os.path.isdir(a__  ):
         _lowerCAmelCase						:       int  			=     os.path.join(
             a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       else:
         _lowerCAmelCase						:       List[Any]  			=     (filename_prefix + """-""" if filename_prefix else """""") + save_directory
       _lowerCAmelCase						:       Any  			=     0
       if " " in self.encoder:
         _lowerCAmelCase						:       int  			=     self.encoder[""" """]
         del self.encoder[" "]
       if "\n" in self.encoder:
         _lowerCAmelCase						:       int  			=     self.encoder["""\n"""]
         del self.encoder["\n"]
       _lowerCAmelCase						:       List[Any]  			=     collections.OrderedDict(sorted(self.encoder.items()				,     key=lambda a__  : x[1]  )  )
       with open(a__				,     """w"""				,     encoding="""utf-8"""  ) as writer:
         for token, token_index in self.encoder.items():
           if index != token_index:
             logger.warning(
                 F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                 """ Please check that the vocabulary is not corrupted!"""  )
             _lowerCAmelCase						:       List[str]  			=     token_index
           writer.write(token + """\n"""  )
           index += 1
       return (vocab_file,)






     def     __A  (		self				,     a__				,     a__ = None  ):
       if token_ids_a is None:
         return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = False  ):

       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 not None:
         return [1] + ([0] * len(a__  )) + [1] + ([0] * len(a__  ))
       return [1] + ([0] * len(a__  ))







"""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

__A						   =			logging.get_logger(__name__)


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

__A						   =			{
    "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"
    },
}

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





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      str         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  BlenderbotTokenizer

				def __init__(	self							:				Any     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				Tuple=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				Optional[Any]="replace"     ,  UpperCamelCase__							:				int="<s>"     ,  UpperCamelCase__							:				str="</s>"     ,  UpperCamelCase__							:				List[Any]="</s>"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="<pad>"     ,  UpperCamelCase__							:				List[Any]="<mask>"     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				int=True     ,  **UpperCamelCase__							:				Union[str, Any]     ,  )->   int:



						'''simple docstring'''


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

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

						__lowerCAmelCase:       List[Any]          =			add_prefix_space

						__lowerCAmelCase:       Union[str, Any]          =			"post_processor"
						__lowerCAmelCase:       Tuple          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       List[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:
										__lowerCAmelCase:       str          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       Any          =			tuple(state["cls"])

								__lowerCAmelCase:       int          =			False

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

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Dict          =			trim_offsets
										__lowerCAmelCase:       Tuple          =			True

								if changes_to_apply:
										__lowerCAmelCase:       Dict          =			getattr(UpperCamelCase__     ,  state.pop("type"))
										__lowerCAmelCase:       str          =			component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)

				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							lowercase_     (	self							:				Optional[Any])->   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 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       Any          =			value

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[int])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						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(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

						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(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			[self.sep_token_id]
						__lowerCAmelCase:       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							:				Union[str, Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[Any]:



						'''simple docstring'''


						return token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				"Conversation")->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       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(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"  ".join(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.encode(UpperCamelCase__)
						if len(UpperCamelCase__) > self.model_max_length:
								__lowerCAmelCase:       int          =			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


