'''simple docstring'''




import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Dict:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[int]							      =    [
           "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(_UpperCAmelCase			,	_UpperCAmelCase    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      List[Any]    )    ->    Union[str, Any]:


       """simple docstring"""







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

def 	UpperCamelCase_							(					_UpperCAmelCase  :      Any    )    ->    int:


       """simple docstring"""







       _UpperCAmelCase     :  List[Any]							      =    torch.load(_UpperCAmelCase			,	map_location="cpu"    )
       _UpperCAmelCase     :  Any							      =    mam_aaa["args"] or mam_aaa["cfg"]["model"]
       _UpperCAmelCase     :  List[Any]							      =    mam_aaa["model"]
       remove_ignore_keys_(_UpperCAmelCase    )
       _UpperCAmelCase     :  int							      =    state_dict["encoder.embed_tokens.weight"].shape[0]

       _UpperCAmelCase     :  Tuple							      =    MaMaaaConfig(
           vocab_size=_UpperCAmelCase			,	max_position_embeddings=1_024			,	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     :  Union[str, Any]							      =    state_dict["decoder.embed_tokens.weight"]
       _UpperCAmelCase     :  Union[str, Any]							      =    MaMaaaForConditionalGeneration(_UpperCAmelCase    )
       model.model.load_state_dict(_UpperCAmelCase			,	strict=_UpperCAmelCase    )
       _UpperCAmelCase     :  Optional[Any]							      =    make_linear_from_emb(model.model.shared    )

       return model


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  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.""")
  __SCREAMING_SNAKE_CASE       :     int     =  parser.parse_args()
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)





'''simple docstring'''

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







       if not isinstance(A  , A       ):
              raise TypeError('''only integers accepted as input'''       )
       else:
              lowercase__      			= str(abs(A       )       )
              lowercase__      			= [list(A       ) for char in range(len(A       )       )]
              for index in range(len(A       )       ):
                     num_transpositions[index].pop(A       )
              return max(
                  int(''''''.join(list(A       )       )       ) for transposition in num_transpositions       )


if __name__ == "__main__":
 __import__('doctest').testmod()




