'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 			A__      (	snake_case_ :		Tuple      ):
 SCREAMING_SNAKE_CASE__:   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 			A__      (	snake_case_ :		Dict      ):
 SCREAMING_SNAKE_CASE__:   int=      emb.weight.shape
 SCREAMING_SNAKE_CASE__:   Any=      nn.Linear(UpperCAmelCase__       ,				UpperCAmelCase__       ,				bias=UpperCAmelCase__      )
 SCREAMING_SNAKE_CASE__:   Dict=      emb.weight.data
 return lin_layer







def 			A__      (	snake_case_ :		List[Any]      ):
 SCREAMING_SNAKE_CASE__:   int=      torch.load(UpperCAmelCase__       ,				map_location='''cpu'''      )
 SCREAMING_SNAKE_CASE__:   str=      mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 SCREAMING_SNAKE_CASE__:   List[Any]=      mam_aaa["""model"""]
 remove_ignore_keys_(UpperCAmelCase__      )
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      state_dict["""encoder.embed_tokens.weight"""].shape[0]

 SCREAMING_SNAKE_CASE__:   int=      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'''       ,				)

 SCREAMING_SNAKE_CASE__:   List[str]=      state_dict["""decoder.embed_tokens.weight"""]
 SCREAMING_SNAKE_CASE__:   Optional[Any]=      MaMaaaForConditionalGeneration(UpperCAmelCase__      )
 model.model.load_state_dict(UpperCAmelCase__       ,				strict=UpperCAmelCase__      )
 SCREAMING_SNAKE_CASE__:   int=      make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
      lowercase_     :   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.')
      lowercase_     :   Any 				=				parser.parse_args()
      lowercase_     :   Dict 				=				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
      model.save_pretrained(args.pytorch_dump_folder_path)


import re







def 			A__      (	snake_case_ :		str      ):

 if len(re.findall('''[ATCG]'''       ,				snake_case_      )      ) != len(snake_case_      ):
  raise ValueError('''Invalid Strand'''      )

 return dna.translate(dna.maketrans('''ATCG'''       ,				'''TAGC'''      )      )


if __name__ == "__main__":
      import doctest

      doctest.testmod()

