


"""simple docstring"""







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def  a__      (    SCREAMING_SNAKE_CASE   :   List[Any]    ):


  '''simple docstring'''







  lowerCAmelCase					:					List[Any]								=							[
      "decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    )
def  a__      (    SCREAMING_SNAKE_CASE   :   Optional[Any]    ):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase					:					int								=							emb.weight.shape
  lowerCAmelCase					:					Union[str, Any]								=							nn.Linear(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				bias=SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					Union[str, Any]								=							emb.weight.data
  return lin_layer
def  a__      (    SCREAMING_SNAKE_CASE   :   Tuple    ):


  '''simple docstring'''







  lowerCAmelCase					:					Any								=							torch.load(SCREAMING_SNAKE_CASE    ,				map_location="cpu"    )
  lowerCAmelCase					:					List[str]								=							Namespace(**checkpoint["cfg"]["model"]    )
  lowerCAmelCase					:					Any								=							checkpoint["model"]
  remove_ignore_keys_(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					int								=							state_dict["decoder.embed_tokens.weight"].shape[0]

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

  lowerCAmelCase					:					Tuple								=							XGLMConfig(
      vocab_size=SCREAMING_SNAKE_CASE    ,				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    ,				)

  lowerCAmelCase					:					List[str]								=							XGLMForCausalLM(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					int								=							model.load_state_dict(SCREAMING_SNAKE_CASE    ,				strict=SCREAMING_SNAKE_CASE    )
  print(SCREAMING_SNAKE_CASE    )
  lowerCAmelCase					:					str								=							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"""







import baseaa
def  a__      (    SCREAMING_SNAKE_CASE   :   str    ):


  '''simple docstring'''







  return baseaa.baaencode(string.encode("utf-8"    )    )
def  a__      (    SCREAMING_SNAKE_CASE   :   bytes    ):


  '''simple docstring'''







  return baseaa.baadecode(SCREAMING_SNAKE_CASE    ).decode("utf-8"    )


if __name__ == "__main__":
     lowerCAmelCase__				    =       '''Hello World!'''
     lowerCAmelCase__				    =       baseaa_encode(test)
     print(encoded)

     lowerCAmelCase__				    =       baseaa_decode(encoded)
     print(decoded)





