




import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   Dict		=   ["small", "medium", "large"]

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Union[str, Any]						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







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


if __name__ == "__main__":
  _lowerCAmelCase						:   Tuple		=   argparse.ArgumentParser()
  parser.add_argument("--dialogpt_path", default=".", type=str)
  _lowerCAmelCase						:   str		=   parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase						:   str		=   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,
    )











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





