





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_big_bird import BigBirdTokenizer
else:
   A__:  Tuple        =     None

A__:  Optional[int]        =     logging.get_logger(__name__)
A__:  str        =     {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A__:  List[Any]        =     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

A__:  Optional[int]        =     {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}


A__:  Optional[int]        =     '▁'




class     _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__   						=    VOCAB_FILES_NAMES
      UpperCamelCase__   						=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase__   						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase__   						=    BigBirdTokenizer
      UpperCamelCase__   						=    ["""input_ids""", """attention_mask"""]
      UpperCamelCase__   						=    []






      def __init__(						self:       List[str]     ,  __lowerCamelCase:       Optional[Any]=None     ,  __lowerCamelCase:       str=None     ,  __lowerCamelCase:       Optional[Any]="<unk>"     ,  __lowerCamelCase:       Any="<s>"     ,  __lowerCamelCase:       Optional[int]="</s>"     ,  __lowerCamelCase:       Tuple="<pad>"     ,  __lowerCamelCase:       List[Any]="[SEP]"     ,  __lowerCamelCase:       str="[MASK]"     ,  __lowerCamelCase:       int="[CLS]"     ,  **__lowerCamelCase:       Dict     ,  ):
          '''simple docstring'''


          UpperCamelCase__:     int     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else bos_token
          UpperCamelCase__:     int     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else eos_token
          UpperCamelCase__:     List[Any]     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else unk_token
          UpperCamelCase__:     str     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else pad_token
          UpperCamelCase__:     Tuple     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else cls_token
          UpperCamelCase__:     List[str]     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else sep_token

          # Mask token behave like a normal word, i.e. include the space before it
          UpperCamelCase__:     List[str]     							=			AddedToken(__lowerCamelCase     ,  lstrip=__lowerCamelCase     ,  rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase     ,  __lowerCamelCase ) else mask_token

          super().__init__(
              __lowerCamelCase     ,  tokenizer_file=__lowerCamelCase     ,  bos_token=__lowerCamelCase     ,  eos_token=__lowerCamelCase     ,  unk_token=__lowerCamelCase     ,  sep_token=__lowerCamelCase     ,  pad_token=__lowerCamelCase     ,  cls_token=__lowerCamelCase     ,  mask_token=__lowerCamelCase     ,  **__lowerCamelCase     ,  )

          UpperCamelCase__:     Dict     							=			vocab_file
          UpperCamelCase__:     Optional[int]     							=			False if not self.vocab_file else True






      def        UpperCAmelCase_    (						self:       int     ,  __lowerCamelCase:       List[str]     ,  __lowerCamelCase:       List[str] = None ):
          '''simple docstring'''


          UpperCamelCase__:     Union[str, Any]     							=			[self.sep_token_id]
          UpperCamelCase__:     Any     							=			[self.cls_token_id]
          if token_ids_a is None:
              return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep






      def        UpperCAmelCase_    (						self:       List[Any]     ,  __lowerCamelCase:       Any     ,  __lowerCamelCase:       List[str] = None     ,  __lowerCamelCase:       Dict = False ):
          '''simple docstring'''


          if already_has_special_tokens:
              if token_ids_a is not None:
                  raise ValueError(
                      "You should not supply a second sequence if the provided sequence of "
                      "ids is already formatted with special tokens for the model." )
              return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

          if token_ids_a is None:
              return [1] + ([0] * len(__lowerCamelCase )) + [1]
          return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]






      def        UpperCAmelCase_    (						self:       Tuple     ,  __lowerCamelCase:       int     ,  __lowerCamelCase:       str = None ):
          '''simple docstring'''


          UpperCamelCase__:     str     							=			[self.sep_token_id]
          UpperCamelCase__:     Optional[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 ) * [0] + len(token_ids_a + sep ) * [1]






      def        UpperCAmelCase_    (						self:       Tuple     ,  __lowerCamelCase:       Union[str, Any]     ,  __lowerCamelCase:       Optional[Any] = None ):
          '''simple docstring'''


          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer." )

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

          if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ):
              copyfile(self.vocab_file     ,  __lowerCamelCase )

          return (out_vocab_file,)






"""simple docstring"""
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase    :int     =					['small', 'medium', 'large']

_lowerCAmelCase    :int     =					'lm_head.decoder.weight'
_lowerCAmelCase    :Dict     =					'lm_head.weight'





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    d.pop(UpperCamelCase__							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    torch.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					argparse.ArgumentParser()
  parser.add_argument('--dialogpt_path', default='.', type=str)
  _lowerCAmelCase    :str     =					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase    :Tuple     =					os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
    _lowerCAmelCase    :int     =					f"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )


