





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def A_      (						A__							)    ->						List[str]:

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

       return orig_key



def A_      (						A__						, A__							)    ->						Tuple:

       for key in orig_state_dict.copy().keys():
              a__		:      Optional[int]					=							orig_state_dict.pop(lowercase_							)

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

       a__		:      List[str]					=							orig_state_dict["cls.predictions.decoder.bias"]
       a__		:      Any					=							torch.arange(lowercase_							).expand((1, -1)							) + 2

       return orig_state_dict



def A_      (						A__						, A__						, A__							)    ->						Optional[Any]:
       a__		:      List[Any]					=							torch.load(lowercase_						, map_location='cpu'							)["model_state_dict"]
       a__		:      Union[str, Any]					=							YosoConfig.from_json_file(lowercase_							)
       a__		:      Any					=							YosoForMaskedLM(lowercase_							)

       a__		:      Optional[Any]					=							convert_checkpoint_helper(config.max_position_embeddings						, lowercase_							)

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

       print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'							)


if __name__ == "__main__":
    lowercase :			Tuple     		=    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."""
    )
    lowercase :			Union[str, Any]     		=    parser.parse_args()
    convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""


from collections.abc import Callable

import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_		,lowercase_      )       ->		np.ndarray:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							int(np.ceil((x_end - xa) / step_size      )      )
      _UpperCamelCase   :						Tuple    =							np.zeros((n + 1,)      )
      _UpperCamelCase   :						List[str]    =							ya
      _UpperCamelCase   :						List[Any]    =							xa

      for k in range(lowercase_      ):
            _UpperCamelCase   :						List[str]    =							y[k] + step_size * ode_func(lowercase_		,y[k]      )
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()
