






'''simple docstring'''




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 					__lowercase						(			__lowercase       )       ->						Optional[Any]:

      '''simple docstring'''


      _A			     =	[
          "decoder.version",
          "decoder.output_projection.weight",
          "_float_tensor",
          "decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(__lowercase   ,  __lowercase       )

def 					__lowercase						(			__lowercase       )       ->						Any:

      '''simple docstring'''


      _A     ,	_A			     =	emb.weight.shape
      _A			     =	nn.Linear(__lowercase   ,  __lowercase   ,  bias=__lowercase       )
      _A			     =	emb.weight.data
      return lin_layer

def 					__lowercase						(			__lowercase       )       ->						Optional[int]:

      '''simple docstring'''


      _A			     =	torch.load(__lowercase   ,  map_location="cpu"       )
      _A			     =	Namespace(**checkpoint["cfg"]["model"]       )
      _A			     =	checkpoint["model"]
      remove_ignore_keys_(__lowercase       )
      _A			     =	state_dict["decoder.embed_tokens.weight"].shape[0]

      _A			     =	{key.replace("decoder"   ,  "model"       ): val for key, val in state_dict.items()}

      _A			     =	XGLMConfig(
          vocab_size=__lowercase   ,  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   ,  )

      _A			     =	XGLMForCausalLM(__lowercase       )
      _A			     =	model.load_state_dict(__lowercase   ,  strict=__lowercase       )
      print(__lowercase       )
      _A			     =	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      lowercase			(				A_  ,   A_       )->      float:
  '''simple docstring'''







  if mass < 0:
    raise ValueError("The mass of a body cannot be negative"       )
  return 0.5 * mass * abs(A_       ) * abs(A_       )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)






