



"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "decoder.output_projection.weight",
          "_float_tensor",
          "encoder.embed_positions._float_tensor",
          "decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(lowerCAmelCase__     ,      lowerCAmelCase__				)
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_			,   UpperCAmelCase_      			=							emb.weight.shape
      UpperCAmelCase_      			=							nn.Linear(lowerCAmelCase__     ,      lowerCAmelCase__     ,      bias=lowerCAmelCase__				)
      UpperCAmelCase_      			=							emb.weight.data
      return lin_layer
def        a__     (				lowerCAmelCase__				):
      UpperCAmelCase_      			=							torch.load(lowerCAmelCase__     ,      map_location="cpu"				)
      UpperCAmelCase_      			=							mam_aaa["args"] or mam_aaa["cfg"]["model"]
      UpperCAmelCase_      			=							mam_aaa["model"]
      remove_ignore_keys_(lowerCAmelCase__				)
      UpperCAmelCase_      			=							state_dict["encoder.embed_tokens.weight"].shape[0]

      UpperCAmelCase_      			=							MaMaaaConfig(
          vocab_size=lowerCAmelCase__     ,      max_position_embeddings=1024     ,      encoder_layers=args.encoder_layers     ,      decoder_layers=args.decoder_layers     ,      encoder_attention_heads=args.encoder_attention_heads     ,      decoder_attention_heads=args.decoder_attention_heads     ,      encoder_ffn_dim=args.encoder_ffn_embed_dim     ,      decoder_ffn_dim=args.decoder_ffn_embed_dim     ,      d_model=args.encoder_embed_dim     ,      encoder_layerdrop=args.encoder_layerdrop     ,      decoder_layerdrop=args.decoder_layerdrop     ,      dropout=args.dropout     ,      attention_dropout=args.attention_dropout     ,      activation_dropout=args.activation_dropout     ,      activation_function="relu"     ,      )

      UpperCAmelCase_      			=							state_dict["decoder.embed_tokens.weight"]
      UpperCAmelCase_      			=							MaMaaaForConditionalGeneration(lowerCAmelCase__				)
      model.model.load_state_dict(lowerCAmelCase__     ,      strict=lowerCAmelCase__				)
      UpperCAmelCase_      			=							make_linear_from_emb(model.model.shared				)

      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_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)









"""simple docstring"""





from collections.abc import Iterable
from typing import Generic, TypeVar

lowerCamelCase       	=						TypeVar("""_T""")
class 				lowercase__       (   Generic[_T]				):







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  Iterable[_T] | None = None   )			->						None:


             '''simple docstring'''




             UpperCAmelCase_      			=							list(iterable or []   )
             UpperCAmelCase_      			=							[]



       def __len__(							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             return len(self._stacka   ) + len(self._stacka   )



       def __repr__(							self      :  Optional[Any]   )			->						str:


             '''simple docstring'''




             return F"""Queue({tuple(self._stacka[::-1] + self._stacka   )})"""



       def 					lowercase__    (							self      :  Optional[Any]		,    _UpperCAmelCase      :  _T   )			->						None:


             '''simple docstring'''




             self._stacka.append(_UpperCAmelCase   )







       def 					lowercase__    (							self      :  Dict   )			->						_T:


             '''simple docstring'''




             UpperCAmelCase_      			=							self._stacka.pop
             UpperCAmelCase_      			=							self._stacka.append

             if not self._stacka:
                   while self._stacka:
                         stacka_append(stacka_pop()   )

             if not self._stacka:
                   raise IndexError("Queue is empty"   )
             return self._stacka.pop()


if __name__ == "__main__":
  from doctest import testmod

  testmod()




