




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_						=			logging.get_logger(__name__)

lowerCAmelCase_						=			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 						_A (  _lowerCamelCase				):
  _UpperCamelCase       :      str			=      '''xglm'''
  _UpperCamelCase       :      Optional[int]			=      ['''past_key_values''']

  _UpperCamelCase       :      Optional[int]			=      {
      '''num_attention_heads''': '''attention_heads''',
      '''hidden_size''': '''d_model''',
      '''num_hidden_layers''': '''num_layers''',
  }






  def __init__(  self		:     Any				,						_A		:     List[str]=256_008				,						_A		:     Union[str, Any]=2_048				,						_A		:     List[Any]=1_024				,						_A		:     Dict=4_096				,						_A		:     List[str]=24				,						_A		:     str=16				,						_A		:     Optional[Any]="gelu"				,						_A		:     Optional[int]=0.1				,						_A		:     int=0.1				,						_A		:     Dict=0.0				,						_A		:     str=0.0				,						_A		:     Optional[Any]=0.02				,						_A		:     Union[str, Any]=True				,						_A		:     Optional[Any]=True				,						_A		:     Dict=2				,						_A		:     Tuple=1				,						_A		:     Any=0				,						_A		:     Any=2				,						**_A		:     Optional[int]				,						)		->   Dict:






       """simple docstring"""







       lowercase :       int       =			vocab_size
       lowercase :       Union[str, Any]       =			max_position_embeddings
       lowercase :       str       =			d_model
       lowercase :       Tuple       =			ffn_dim
       lowercase :       Tuple       =			num_layers
       lowercase :       str       =			attention_heads
       lowercase :       Any       =			activation_function
       lowercase :       List[str]       =			dropout
       lowercase :       List[Any]       =			attention_dropout
       lowercase :       Any       =			activation_dropout
       lowercase :       Any       =			layerdrop
       lowercase :       Dict       =			init_std
       lowercase :       List[str]       =			scale_embedding  # scale factor will be sqrt(d_model) if True
       lowercase :       Tuple       =			use_cache

       super().__init__(
           pad_token_id=_A				,						bos_token_id=_A				,						eos_token_id=_A				,						decoder_start_token_id=_A				,						**_A				,						)





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


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