
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ):
 __a			:  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(lowerCAmelCase__  ,       lowerCAmelCase__  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    Dict  ):
 __a			:  List[str]		    =  emb.weight.shape
 __a			:  str		    =  nn.Linear(lowerCAmelCase__  ,       lowerCAmelCase__  ,       bias=lowerCAmelCase__  )
 __a			:  Any		    =  emb.weight.data
 return lin_layer




def        __UpperCamelCase					(    lowerCAmelCase__							:    List[Any]  ):
 __a			:  Dict		    =  torch.load(lowerCAmelCase__  ,       map_location='''cpu'''  )
 __a			:  Tuple		    =  mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
 __a			:  Dict		    =  mam_aaa['''model''']
 remove_ignore_keys_(lowerCAmelCase__  )
 __a			:  Dict		    =  state_dict['''encoder.embed_tokens.weight'''].shape[0]

 __a			:  Dict		    =  MaMaaaConfig(
     vocab_size=lowerCAmelCase__  ,       max_position_embeddings=1_0_2_4  ,       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'''  ,       )

 __a			:  Tuple		    =  state_dict['''decoder.embed_tokens.weight''']
 __a			:  int		    =  MaMaaaForConditionalGeneration(lowerCAmelCase__  )
 model.model.load_state_dict(lowerCAmelCase__  ,       strict=lowerCAmelCase__  )
 __a			:  Optional[Any]		    =  make_linear_from_emb(model.model.shared  )

 return model


if __name__ == "__main__":
      lowercase__             =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__             =parser.parse_args()
      lowercase__             =convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
      model.save_pretrained(args.pytorch_dump_folder_path)


def        __UpperCamelCase					(    lowerCAmelCase__							:    int = 1_0_0_0  ):
 __a			:  int		    =  -1
 __a			:  Any		    =  0
 for a in range(1  ,       n // 3  ):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  __a			:  List[Any]		    =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  __a			:  Union[str, Any]		    =  n - a - b
  if c * c == (a * a + b * b):
   __a			:  Union[str, Any]		    =  a * b * c
   if candidate >= product:
    __a			:  List[Any]		    =  candidate
 return product


if __name__ == "__main__":
      print(F"""{solution() = }""")
