



'''simple docstring'''






import argparse

import torch

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

  return orig_key
def    lowerCamelCase__ (		_A				,    _A       ):
  for key in orig_state_dict.copy().keys():
    a		:      List[str]       =  orig_state_dict.pop(_A       )

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

  a		:      Optional[int]       =  orig_state_dict['cls.predictions.decoder.bias']
  a		:      Optional[int]       =  torch.arange(_A       ).expand((1, -1)       ) + 2

  return orig_state_dict
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[int]       =  torch.load(_A				,    map_location='cpu'       )['model_state_dict']
  a		:      List[str]       =  YosoConfig.from_json_file(_A       )
  a		:      Dict       =  YosoForMaskedLM(_A       )

  a		:      Optional[Any]       =  convert_checkpoint_helper(config.max_position_embeddings				,    _A       )

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

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


if __name__ == "__main__":
     lowerCAmelCase:      Optional[int]		 =		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:      List[Any]		 =		parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''






from pathlib import Path

import fire
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  Path(_A       )
  a		:      Tuple       =  Path(_A       )
  dest_dir.mkdir(exist_ok=_A       )
  for path in src_dir.iterdir():
    a		:      Tuple       =  [x.rstrip() for x in list(path.open().readlines()       )][:n]
    a		:      List[str]       =  dest_dir.joinpath(path.name       )
    print(_A       )
    dest_path.open('w'       ).write('\n'.join(_A       )       )


if __name__ == "__main__":
     fire.Fire(minify)