




import copy
import random

from transformers import CLIPTokenizer
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def __init__(			self   :Dict ,      *lowerCamelCase   :int ,      **lowerCamelCase   :int      )     ->			List[str]:
       super().__init__(*lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						{}
      def 	UpperCAmelCase_						(			self   :Tuple ,      lowerCamelCase   :Dict ,      *lowerCamelCase   :List[str] ,      **lowerCamelCase   :Optional[int]      )     ->			Optional[int]:
       UpperCAmelCase__							 =						super().add_tokens(lowerCamelCase ,      *lowerCamelCase ,      **lowerCamelCase      )
       if num_added_tokens == 0:
        raise ValueError(
            f'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
            " `placeholder_token` that is not already in the tokenizer."      )
      def 	UpperCAmelCase_						(			self   :int ,      lowerCamelCase   :List[str] ,      *lowerCamelCase   :List[str] ,      lowerCamelCase   :List[str]=1 ,      **lowerCamelCase   :Union[str, Any]      )     ->			Dict:
       UpperCAmelCase__							 =						[]
       if num_vec_per_token == 1:
        self.try_adding_tokens(lowerCamelCase ,      *lowerCamelCase ,      **lowerCamelCase      )
        output.append(lowerCamelCase      )
       else:
        UpperCAmelCase__							 =						[]
        for i in range(lowerCamelCase      ):
         UpperCAmelCase__							 =						placeholder_token + f'''_{i}'''
         self.try_adding_tokens(lowerCamelCase ,      *lowerCamelCase ,      **lowerCamelCase      )
         output.append(lowerCamelCase      )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
       for token in self.token_map:
        if token in placeholder_token:
         raise ValueError(
             f'''The tokenizer already has placeholder token {token} that can get confused with'''
             f''' {placeholder_token}keep placeholder tokens independent'''      )
       UpperCAmelCase__							 =						output
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Union[str, Any] ,      lowerCamelCase   :int=False ,      lowerCamelCase   :Dict=1.0      )     ->			Any:
       if isinstance(lowerCamelCase ,      lowerCamelCase      ):
        UpperCAmelCase__							 =						[]
        for i in range(len(lowerCamelCase      )      ):
         output.append(self.replace_placeholder_tokens_in_text(text[i] ,      vector_shuffle=lowerCamelCase      )      )
        return output
       for placeholder_token in self.token_map:
        if placeholder_token in text:
         UpperCAmelCase__							 =						self.token_map[placeholder_token]
         UpperCAmelCase__							 =						tokens[: 1 + int(len(lowerCamelCase      ) * prop_tokens_to_load      )]
         if vector_shuffle:
          UpperCAmelCase__							 =						copy.copy(lowerCamelCase      )
          random.shuffle(lowerCamelCase      )
         UpperCAmelCase__							 =						text.replace(lowerCamelCase ,      " ".join(lowerCamelCase      )      )
       return text
      def __call__(			self   :int ,      lowerCamelCase   :str ,      *lowerCamelCase   :int ,      lowerCamelCase   :Tuple=False ,      lowerCamelCase   :Tuple=1.0 ,      **lowerCamelCase   :Any      )     ->			int:
       return super().__call__(
           self.replace_placeholder_tokens_in_text(
               lowerCamelCase ,      vector_shuffle=lowerCamelCase ,      prop_tokens_to_load=lowerCamelCase      ) ,      *lowerCamelCase ,      **lowerCamelCase ,      )







      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :int ,      *lowerCamelCase   :Tuple ,      lowerCamelCase   :int=False ,      lowerCamelCase   :List[str]=1.0 ,      **lowerCamelCase   :Dict      )     ->			List[Any]:
       return super().encode(
           self.replace_placeholder_tokens_in_text(
               lowerCamelCase ,      vector_shuffle=lowerCamelCase ,      prop_tokens_to_load=lowerCamelCase      ) ,      *lowerCamelCase ,      **lowerCamelCase ,      )











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


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





