
'''simple docstring'''





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


UpperCAmelCase    					=   "▁"

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

UpperCAmelCase    					=   {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

UpperCAmelCase    					=   {
    "google/pegasus-xsum": 512,
}


UpperCAmelCase    					=   logging.get_logger(__name__)

class 						a   (				__magic_name__    ):
    _snake_case		    = VOCAB_FILES_NAMES

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




    def __init__(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Tuple="<pad>",    SCREAMING_SNAKE_CASE_	:				int="</s>",    SCREAMING_SNAKE_CASE_	:				List[str]="<unk>",    SCREAMING_SNAKE_CASE_	:				List[str]="<mask_2>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<mask_1>",    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Any=1_03,    SCREAMING_SNAKE_CASE_	:				Optional[Dict[str, Any]] = None,    **SCREAMING_SNAKE_CASE_	:				str,    ):
          snake_case       :     List[str] =    offset
          if additional_special_tokens is not None:
                if not isinstance(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      ):
                      raise TypeError(
                          F"""additional_special_tokens should be of type {type(SCREAMING_SNAKE_CASE_      )}, but is"""
                          F""" {type(SCREAMING_SNAKE_CASE_      )}"""      )

                snake_case       :     int =    (
                    ([mask_token_sent] + additional_special_tokens)
                    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                    else additional_special_tokens
                )
                # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                additional_special_tokens_extended += [
                    F"""<unk_{i}>""" for i in range(len(SCREAMING_SNAKE_CASE_      ),    self.offset - 1      )
                ]

                if len(set(SCREAMING_SNAKE_CASE_      )      ) != len(SCREAMING_SNAKE_CASE_      ):
                      raise ValueError(
                          '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                          F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""      )
                snake_case       :     Any =    additional_special_tokens_extended
          else:
                snake_case       :     Union[str, Any] =    [mask_token_sent] if mask_token_sent is not None else []
                additional_special_tokens += [F"""<unk_{i}>""" for i in range(2,    self.offset      )]

          snake_case       :     Union[str, Any] =    {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    mask_token=SCREAMING_SNAKE_CASE_,    pad_token=SCREAMING_SNAKE_CASE_,    mask_token_sent=SCREAMING_SNAKE_CASE_,    offset=SCREAMING_SNAKE_CASE_,    additional_special_tokens=SCREAMING_SNAKE_CASE_,    sp_model_kwargs=self.sp_model_kwargs,    **SCREAMING_SNAKE_CASE_,    )
          snake_case       :     Union[str, Any] =    mask_token_sent
          snake_case       :     List[str] =    vocab_file
          snake_case       :     Tuple =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(SCREAMING_SNAKE_CASE_      )

          # add special tokens to encoder dict
          snake_case       :     Dict[int, str] =    {
              0: self.pad_token,
              1: self.eos_token,
          }

          if self.mask_token_sent is not None:
                self.encoder.update(
                    {
                        2: self.mask_token_sent,
                        3: self.mask_token,
                    }      )

          if self.offset > 0:
                # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                # mask_token_sent is already added to list -> so start at 1
                self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1,    self.offset - 1      )}      )

          snake_case       :     Dict[str, int] =    {v: k for k, v in self.encoder.items()}




    @property
    def 	__snake_case							(							self	:				Optional[Any]      ):
          return len(self.sp_model      ) + self.offset




    def 	__snake_case							(							self	:				Optional[int]      ):
          snake_case       :     int =    {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      ):
          snake_case       :     Tuple =    self.__dict__.copy()
          snake_case       :     List[str] =    None
          return state




    def __setstate__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				Tuple      ):
          snake_case       :     Dict =    d

          # for backward compatibility
          if not hasattr(self,    '''sp_model_kwargs'''      ):
                snake_case       :     Union[str, Any] =    {}

          snake_case       :     str =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(self.vocab_file      )




    def 	__snake_case							(							self	:				Any,    SCREAMING_SNAKE_CASE_	:				str      ):
          return self.sp_model.encode(SCREAMING_SNAKE_CASE_,    out_type=SCREAMING_SNAKE_CASE_      )




    def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
          if token in self.decoder:
                return self.decoder[token]
          elif token in self.added_tokens_decoder:
                return self.added_tokens_decoder[token]
          snake_case       :     Optional[Any] =    self.sp_model.piece_to_id(SCREAMING_SNAKE_CASE_      )
          return sp_id + self.offset




    def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				int      ):
          if index in self.encoder:
                return self.encoder[index]
          elif index in self.added_tokens_encoder:
                return self.added_tokens_encoder[index]
          else:
                snake_case       :     Any =    self.sp_model.IdToPiece(index - self.offset      )
          return token




    def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
          snake_case       :     Dict =    []
          snake_case       :     List[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
                      snake_case       :     int =    []
                else:
                      current_sub_tokens.append(SCREAMING_SNAKE_CASE_      )
          out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_      )
          return out_string.strip()




    def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Optional[Any]=False      ):
          return 1




    def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Any      ):
          snake_case       :     str =    set(self.all_special_ids      )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

          return [1 if x in all_special_ids else 0 for x in seq]




    def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List,    SCREAMING_SNAKE_CASE_	:				Optional[List] = None,    SCREAMING_SNAKE_CASE_	:				bool = False      ):
          if already_has_special_tokens:
                return self._special_token_mask(SCREAMING_SNAKE_CASE_      )
          elif token_ids_a is None:
                return self._special_token_mask(SCREAMING_SNAKE_CASE_      ) + [1]
          else:
                return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




    def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Tuple=None      ):
          if token_ids_a is None:
                return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]




    def 	__snake_case							(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
          if not os.path.isdir(SCREAMING_SNAKE_CASE_      ):
                logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
                return
          snake_case       :     List[Any] =    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:
                      snake_case       :     Optional[int] =    self.sp_model.serialized_model_proto()
                      fi.write(SCREAMING_SNAKE_CASE_      )

          return (out_vocab_file,)



'''simple docstring'''





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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase    					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    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 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    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 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids

