



"""simple docstring"""






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





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

       return orig_key






def 	snake_case_  (				A_ :		Optional[Any],    A_ :		Optional[int]						):


       '''simple docstring'''





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

              if ("pooler" in key) or ("sen_class" in key):
                     continue
              else:
                     _lowerCamelCase						:    List[str]         =			val

       _lowerCamelCase						:    Tuple         =			orig_state_dict['''cls.predictions.decoder.bias''']
       _lowerCamelCase						:    str         =			torch.arange(A_						).expand((1, -1)						) + 2

       return orig_state_dict






def 	snake_case_  (				A_ :		int,    A_ :		int,    A_ :		List[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:    Any         =			torch.load(A_,    map_location='''cpu'''						)['''model_state_dict''']
       _lowerCamelCase						:    int         =			YosoConfig.from_json_file(A_						)
       _lowerCamelCase						:    Optional[int]         =			YosoForMaskedLM(A_						)

       _lowerCamelCase						:    Union[str, 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__      =    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)







"""simple docstring"""






import math






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       return math.sqrt(A_						) * math.sqrt(A_						) == num






def 	snake_case_  (				A_ :		int						):


       '''simple docstring'''





       _lowerCamelCase						:    Dict         =			0
       _lowerCamelCase						:    Tuple         =			n
       while left <= right:
              _lowerCamelCase						:    List[str]         =			(left + right) // 2
              if mid**2 == n:
                     return True
              elif mid**2 > n:
                     _lowerCamelCase						:    int         =			mid - 1
              else:
                     _lowerCamelCase						:    Optional[Any]         =			mid + 1
       return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()


