"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def        a_  (   _lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       lowercase__    :	str     					=			[
           '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						:				Optional[Any]  ):





       '''simple docstring'''

       lowercase__					,   lowercase__    :	Optional[Any]     					=			emb.weight.shape
       lowercase__    :	Optional[int]     					=			nn.Linear(_lowerCAmelCase     ,				_lowerCAmelCase     ,				bias=_lowerCAmelCase  )
       lowercase__    :	Dict     					=			emb.weight.data
       return lin_layer





def        a_  (   _lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			torch.load(_lowerCAmelCase     ,				map_location='cpu'  )
       lowercase__    :	Optional[int]     					=			mam_aaa['args'] or mam_aaa['cfg']['model']
       lowercase__    :	Union[str, Any]     					=			mam_aaa['model']
       remove_ignore_keys_(_lowerCAmelCase  )
       lowercase__    :	Tuple     					=			state_dict['encoder.embed_tokens.weight'].shape[0]

       lowercase__    :	Optional[int]     					=			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'     ,				)

       lowercase__    :	List[Any]     					=			state_dict['decoder.embed_tokens.weight']
       lowercase__    :	List[str]     					=			MaMaaaForConditionalGeneration(_lowerCAmelCase  )
       model.model.load_state_dict(_lowerCAmelCase     ,				strict=_lowerCAmelCase  )
       lowercase__    :	Optional[int]     					=			make_linear_from_emb(model.model.shared  )

       return model


if __name__ == "__main__":
       _UpperCamelCase     :		List[str]											=					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     :		List[Any]											=					parser.parse_args()
       _UpperCamelCase     :		Tuple											=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
       model.save_pretrained(args.pytorch_dump_folder_path)


"""simple docstring"""





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			[0] * len(_lowerCAmelCase  )

       for i in range(1     ,				len(_lowerCAmelCase  )  ):
              # use last results for better performance - dynamic programming
              lowercase__    :	List[str]     					=			prefix_result[i - 1]
              while j > 0 and input_string[i] != input_string[j]:
                     lowercase__    :	Dict     					=			prefix_result[j - 1]

              if input_string[i] == input_string[j]:
                     j += 1
              lowercase__    :	Union[str, Any]     					=			j

       return prefix_result





def        a_  (   _lowerCAmelCase						:				str  ):





       '''simple docstring'''

       return max(prefix_function(_lowerCAmelCase  )  )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

