


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def     __lowerCAmelCase						(							snake_case	:    Tuple )       ->							List[str]:
   __lowerCamelCase:				Tuple     =	[
       """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(snake_case						,      snake_case )



def     __lowerCAmelCase						(							snake_case	:    Any )       ->							Any:
   __lowerCamelCase   ,     __lowerCamelCase:				str     =	emb.weight.shape
   __lowerCamelCase:				Union[str, Any]     =	nn.Linear(snake_case						,      snake_case						,      bias=snake_case )
   __lowerCamelCase:				Tuple     =	emb.weight.data
   return lin_layer



def     __lowerCAmelCase						(							snake_case	:    Dict )       ->							Union[str, Any]:
   __lowerCamelCase:				Optional[Any]     =	torch.load(snake_case						,      map_location="""cpu""" )
   __lowerCamelCase:				List[Any]     =	mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
   __lowerCamelCase:				List[str]     =	mam_aaa["""model"""]
   remove_ignore_keys_(snake_case )
   __lowerCamelCase:				Optional[int]     =	state_dict["""encoder.embed_tokens.weight"""].shape[0]

   __lowerCamelCase:				Any     =	MaMaaaConfig(
       vocab_size=snake_case						,      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"""						,      )

   __lowerCamelCase:				str     =	state_dict["""decoder.embed_tokens.weight"""]
   __lowerCamelCase:				List[Any]     =	MaMaaaForConditionalGeneration(snake_case )
   model.model.load_state_dict(snake_case						,      strict=snake_case )
   __lowerCamelCase:				Any     =	make_linear_from_emb(model.model.shared )

   return model


if __name__ == "__main__":
   _A		:				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.''')
   _A		:				int									=       parser.parse_args()
   _A		:				List[Any]									=       convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
   model.save_pretrained(args.pytorch_dump_folder_path)






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

   __lowerCamelCase:				Any     =	[float(snake_case ) for n in range(limit + 1 )]

   for p in primes:
      for n in range(snake_case						,      limit + 1						,      snake_case ):
         phi[n] *= 1 - 1 / p

   return int(sum(phi[2:] ) )


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


