



"""simple docstring"""
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def 			lowercase	(_snake_case			)       ->    Any:




  '''simple docstring'''







  if "model" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("model." ,""			)
  if "norm1" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("norm1" ,"attention.output.LayerNorm"			)
  if "norm2" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("norm2" ,"output.LayerNorm"			)
  if "norm" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("norm" ,"LayerNorm"			)
  if "transformer" in orig_key:
    __UpperCamelCase  						=							orig_key.split("."			)[0].split("_"			)[-1]
    __UpperCamelCase  						=							orig_key.replace(f"""transformer_{layer_num}""" ,f"""encoder.layer.{layer_num}"""			)
  if "mha.attn" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("mha.attn" ,"attention.self"			)
  if "mha" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("mha" ,"attention"			)
  if "W_q" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("W_q" ,"self.query"			)
  if "W_k" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("W_k" ,"self.key"			)
  if "W_v" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("W_v" ,"self.value"			)
  if "ff1" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("ff1" ,"intermediate.dense"			)
  if "ff2" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("ff2" ,"output.dense"			)
  if "ff" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("ff" ,"output.dense"			)
  if "mlm_class" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("mlm.mlm_class" ,"cls.predictions.decoder"			)
  if "mlm" in orig_key:
    __UpperCamelCase  						=							orig_key.replace("mlm" ,"cls.predictions.transform"			)
  if "cls" not in orig_key:
    __UpperCamelCase  						=							"yoso." + orig_key

  return orig_key



def 			lowercase	(_snake_case ,_snake_case			)       ->    List[Any]:




  '''simple docstring'''







  for key in orig_state_dict.copy().keys():
    __UpperCamelCase  						=							orig_state_dict.pop(_snake_case			)

    if ("pooler" in key) or ("sen_class" in key):
      continue
    else:
      __UpperCamelCase  						=							val

  __UpperCamelCase  						=							orig_state_dict["cls.predictions.decoder.bias"]
  __UpperCamelCase  						=							torch.arange(_snake_case			).expand((1, -1)			) + 2

  return orig_state_dict



def 			lowercase	(_snake_case ,_snake_case ,_snake_case			)       ->    int:




  '''simple docstring'''







  __UpperCamelCase  						=							torch.load(_snake_case ,map_location="cpu"			)["model_state_dict"]
  __UpperCamelCase  						=							YosoConfig.from_json_file(_snake_case			)
  __UpperCamelCase  						=							YosoForMaskedLM(_snake_case			)

  __UpperCamelCase  						=							convert_checkpoint_helper(config.max_position_embeddings ,_snake_case			)

  print(model.load_state_dict(_snake_case			)			)
  model.eval()
  model.save_pretrained(_snake_case			)

  print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""			)


if __name__ == "__main__":
 _A													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for YOSO model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 _A													=   parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''





from __future__ import annotations




def 							lowerCamelCase	(					_snake_case	:    list[int]							):





    '''simple docstring'''


    return len(set(_snake_case							)							) == len(_snake_case							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




