





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def     lowerCamelCase_	(      lowerCAmelCase:	Dict				)->		List[str]:
  _snake_case   :				str														=	[
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(lowerCAmelCase    ,							lowerCAmelCase				)




def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Optional[Any]:
  _snake_case   :				Any														=	emb.weight.shape
  _snake_case   :				Union[str, Any]														=	nn.Linear(lowerCAmelCase    ,							lowerCAmelCase    ,							bias=lowerCAmelCase				)
  _snake_case   :				Tuple														=	emb.weight.data
  return lin_layer




def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		Any:
  _snake_case   :				Optional[Any]														=	torch.load(lowerCAmelCase    ,							map_location='cpu'				)
  _snake_case   :				Optional[int]														=	Namespace(**checkpoint['cfg']['model']				)
  _snake_case   :				Optional[Any]														=	checkpoint['model']
  remove_ignore_keys_(lowerCAmelCase				)
  _snake_case   :				str														=	state_dict['decoder.embed_tokens.weight'].shape[0]

  _snake_case   :				Union[str, Any]														=	{key.replace('decoder'    ,							'model'				): val for key, val in state_dict.items()}

  _snake_case   :				List[str]														=	XGLMConfig(
      vocab_size=lowerCAmelCase    ,							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    ,							)

  _snake_case   :				List[Any]														=	XGLMForCausalLM(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	model.load_state_dict(lowerCAmelCase    ,							strict=lowerCAmelCase				)
  print(lowerCAmelCase				)
  _snake_case   :				Tuple														=	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)











def     lowerCamelCase_	(      lowerCAmelCase:	bytes				)->		str:
  return "".join([hex(lowerCAmelCase				)[2:].zfill(2				).upper() for byte in list(lowerCAmelCase				)]				)


def     lowerCamelCase_	(      lowerCAmelCase:	str				)->		bytes:
  # Check data validity, following RFC3548
  # https://www.ietf.org/rfc/rfc3548.txt
  if (len(lowerCAmelCase				) % 2) != 0:
    raise ValueError(
        'Base16 encoded data is invalid:\nData does not have an even number of hex digits.'				)
  # Check the character set - the standard base16 alphabet
  # is uppercase according to RFC3548 section 6
  if not set(lowerCAmelCase				) <= set('0123456789ABCDEF'				):
    raise ValueError(
        'Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters.'				)
  # For every two hexadecimal digits (= a byte), turn it into an integer.
  # Then, string the result together into bytes, and return it.
  return bytes(int(data[i] + data[i + 1]    ,							16				) for i in range(0    ,							len(lowerCAmelCase				)    ,							2				)				)


if __name__ == "__main__":
      import doctest

      doctest.testmod()




