






'''simple docstring'''

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 							snake_case					(     a_							: Optional[Any]	)       ->				Optional[Any]:





      """simple docstring"""

      UpperCamelCase_				:   Optional[Any]					    =							[
          """decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(a_    ,   a_	)



def 							snake_case					(     a_							: int	)       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_    ,			UpperCamelCase_				:   List[str]					    =							emb.weight.shape
      UpperCamelCase_				:   Optional[Any]					    =							nn.Linear(a_    ,   a_    ,   bias=a_	)
      UpperCamelCase_				:   Union[str, Any]					    =							emb.weight.data
      return lin_layer



def 							snake_case					(     a_							: Union[str, Any]	)       ->				Union[str, Any]:





      """simple docstring"""

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

      UpperCamelCase_				:   Optional[int]					    =							{key.replace("""decoder"""    ,   """model"""	): val for key, val in state_dict.items()}

      UpperCamelCase_				:   List[Any]					    =							XGLMConfig(
          vocab_size=a_    ,   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    ,   )

      UpperCamelCase_				:   int					    =							XGLMForCausalLM(a_	)
      UpperCamelCase_				:   Tuple					    =							model.load_state_dict(a_    ,   strict=a_	)
      print(a_	)
      UpperCamelCase_				:   str					    =							make_linear_from_emb(model.model.embed_tokens	)

      return model


if __name__ == "__main__":
      UpperCamelCase							=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.")
      UpperCamelCase							=parser.parse_args()
      UpperCamelCase							=convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
      model.save_pretrained(args.pytorch_dump_folder_path)














'''simple docstring'''

def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      assert (
          isinstance(a_    ,   a_	) and number_of_steps > 0
      ), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
      if number_of_steps == 1:
            return 1
      UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							1, 1
      for _ in range(number_of_steps - 1	):
            UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							current + previous, current
      return current


if __name__ == "__main__":
      import doctest

      doctest.testmod()






