






"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def   a    (     __UpperCAmelCase      :    str  )    ->							Dict:
						__magic_name__:	int							=       [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(__UpperCAmelCase      ,   __UpperCAmelCase  )



def   a    (     __UpperCAmelCase      :    int  )    ->							Optional[Any]:
						__magic_name__,			__magic_name__:	List[Any]							=       emb.weight.shape
						__magic_name__:	Tuple							=       nn.Linear(__UpperCAmelCase      ,   __UpperCAmelCase      ,   bias=__UpperCAmelCase  )
						__magic_name__:	Optional[Any]							=       emb.weight.data
						return lin_layer



def   a    (     __UpperCAmelCase      :    Tuple  )    ->							str:
						__magic_name__:	List[str]							=       torch.load(__UpperCAmelCase      ,   map_location="""cpu"""  )
						__magic_name__:	int							=       Namespace(**checkpoint["""cfg"""]["""model"""]  )
						__magic_name__:	str							=       checkpoint["""model"""]
						remove_ignore_keys_(__UpperCAmelCase  )
						__magic_name__:	List[str]							=       state_dict["""decoder.embed_tokens.weight"""].shape[0]

						__magic_name__:	List[Any]							=       {key.replace("""decoder"""      ,   """model"""  ): val for key, val in state_dict.items()}

						__magic_name__:	Dict							=       XGLMConfig(
						    vocab_size=__UpperCAmelCase      ,   max_position_embeddings=args.max_target_positions      ,   num_layers=args.decoder_layers      ,   attention_heads=args.decoder_attention_heads      ,   ffn_dim=args.decoder_ffn_embed_dim      ,   d_model=args.decoder_embed_dim      ,   layerdrop=args.decoder_layerdrop      ,   dropout=args.dropout      ,   attention_dropout=args.attention_dropout      ,   activation_dropout=args.activation_dropout      ,   activation_function="""gelu"""      ,   scale_embedding=not args.no_scale_embedding      ,   tie_word_embeddings=args.share_decoder_input_output_embed      ,   )

						__magic_name__:	Any							=       XGLMForCausalLM(__UpperCAmelCase  )
						__magic_name__:	List[Any]							=       model.load_state_dict(__UpperCAmelCase      ,   strict=__UpperCAmelCase  )
						print(__UpperCAmelCase  )
						__magic_name__:	Optional[int]							=       make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
							__lowerCamelCase							=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
							parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							__lowerCamelCase							=  parser.parse_args()
							__lowerCamelCase							=  convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)











"""simple docstring"""





def   a    (     __UpperCAmelCase      :    int = 1_0_0  )    ->							int:
						__magic_name__:	str							=       0
						__magic_name__:	Any							=       0
						for i in range(1      ,   n + 1  ):
												sum_of_squares += i**2
												sum_of_ints += i
						return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
							print(f'''{solution() = }''')



