


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def lowerCAmelCase_      (				UpperCamelCase__	: str ):






 """simple docstring"""







 __lowercase									=	[
     """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 lowerCAmelCase_      (				UpperCamelCase__	: Tuple ):






 """simple docstring"""







 __lowercase							,		__lowercase									=	emb.weight.shape
 __lowercase									=	nn.Linear(UpperCamelCase__				,   UpperCamelCase__				,   bias=UpperCamelCase__ )
 __lowercase									=	emb.weight.data
 return lin_layer


def lowerCAmelCase_      (				UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	torch.load(UpperCamelCase__				,   map_location="""cpu""" )
 __lowercase									=	mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowercase									=	mam_aaa["""model"""]
 remove_ignore_keys_(UpperCamelCase__ )
 __lowercase									=	state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowercase									=	MaMaaaConfig(
     vocab_size=UpperCamelCase__				,   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									=	state_dict["""decoder.embed_tokens.weight"""]
 __lowercase									=	MaMaaaForConditionalGeneration(UpperCamelCase__ )
 model.model.load_state_dict(UpperCamelCase__				,   strict=UpperCamelCase__ )
 __lowercase									=	make_linear_from_emb(model.model.shared )

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




"""simple docstring"""

def lowerCAmelCase_      (				UpperCamelCase__	: str				,   UpperCamelCase__	: str ):






 """simple docstring"""







 assert x is not None
 assert y is not None

 __lowercase									=	len(UpperCamelCase__ )
 __lowercase									=	len(UpperCamelCase__ )

 # declaring the array for storing the dp values
 __lowercase									=	[[0] * (n + 1) for _ in range(m + 1 )]  # noqa: E741

 for i in range(1				,   m + 1 ):
  for j in range(1				,   n + 1 ):
   __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

   __lowercase									=	max(l[i - 1][j]				,   l[i][j - 1]				,   l[i - 1][j - 1] + match )

 __lowercase									=	""""""
 __lowercase							,		__lowercase									=	m, n
 while i > 0 and j > 0:
  __lowercase									=	1 if x[i - 1] == y[j - 1] else 0

  if l[i][j] == l[i - 1][j - 1] + match:
   if match == 1:
    __lowercase									=	x[i - 1] + seq
   i -= 1
   j -= 1
  elif l[i][j] == l[i - 1][j]:
   i -= 1
  else:
   j -= 1

 return l[m][n], seq


if __name__ == "__main__":
       UpperCAmelCase__  							="AGGTAB"
       UpperCAmelCase__  							="GXTXAYB"
       UpperCAmelCase__  							=4
       UpperCAmelCase__  							="GTAB"

       UpperCAmelCase__							,      UpperCAmelCase__  							=longest_common_subsequence(a, b)
       print("len =", ln, ", sub-sequence =", subseq)
       import doctest

       doctest.testmod()
