



import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def    _snake_case (       __snake_case    )     ->     str:






      '''simple docstring'''







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

      return orig_key







def    _snake_case (       __snake_case					,  __snake_case    )     ->     str:






      '''simple docstring'''







      for key in orig_state_dict.copy().keys():
            UpperCAmelCase_  :	Dict				      =		orig_state_dict.pop(__snake_case    )

            if ("pooler" in key) or ("sen_class" in key):
                  continue
            else:
                  UpperCAmelCase_  :	Optional[Any]				      =		val

      UpperCAmelCase_  :	Tuple				      =		orig_state_dict["cls.predictions.decoder.bias"]
      UpperCAmelCase_  :	Any				      =		torch.arange(__snake_case    ).expand((1, -1)    ) + 2

      return orig_state_dict







def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     Union[str, Any]:






      '''simple docstring'''







      UpperCAmelCase_  :	str				      =		torch.load(__snake_case					,  map_location="cpu"    )["model_state_dict"]
      UpperCAmelCase_  :	Any				      =		YosoConfig.from_json_file(__snake_case    )
      UpperCAmelCase_  :	str				      =		YosoForMaskedLM(__snake_case    )

      UpperCAmelCase_  :	Dict				      =		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__":
   __lowerCamelCase         =			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.'''
   )
   __lowerCamelCase         =			parser.parse_args()
   convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case    )     ->     list:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		len(__snake_case    )
      UpperCAmelCase_  :	Tuple				      =		[[0] * n for i in range(__snake_case    )]
      for i in range(__snake_case    ):
            UpperCAmelCase_  :	Optional[Any]				      =		y_points[i]

      for i in range(2					,  __snake_case    ):
            for j in range(__snake_case					,  __snake_case    ):
                  UpperCAmelCase_  :	int				      =		(
                      (xa - x_points[j - i + 1]) * q[j][i - 1]
                      - (xa - x_points[j]) * q[j - 1][i - 1]
                  ) / (x_points[j] - x_points[j - i + 1])

      return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
