


"""simple docstring"""



from functools import reduce

UpperCAmelCase      		=			(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)






def       lowerCamelCase					(a_						:str = N)     ->							str:
       return max(
           # mypy cannot properly interpret reduce
           int(reduce(lambda a_				,				a_: str(int(a_) * int(a_))				,				n[i : i + 13]))
           for i in range(len(a_) - 12))


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





"""simple docstring"""



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       lowerCamelCase					(a_						:Dict)     ->							Dict:
       lowercase		:Tuple       							=				[
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(a_				,				a_)






def       lowerCamelCase					(a_						:Union[str, Any])     ->							str:
       lowercase							,  lowercase		:Tuple       							=				emb.weight.shape
       lowercase		:List[str]       							=				nn.Linear(a_				,				a_				,				bias=a_)
       lowercase		:List[str]       							=				emb.weight.data
       return lin_layer






def       lowerCamelCase					(a_						:int				,				a_						:Union[str, Any]="facebook/mbart-large-en-ro"				,				a_						:Union[str, Any]=False				,				a_						:List[Any]=False)     ->							List[Any]:
       lowercase		:List[Any]       							=				torch.load(a_				,				map_location='''cpu''')['''model''']
       remove_ignore_keys_(a_)
       lowercase		:Dict       							=				state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase		:Tuple       							=				MBartConfig.from_pretrained(a_				,				vocab_size=a_)
       if mbart_aa and finetuned:
              lowercase		:List[Any]       							=				'''relu'''

       lowercase		:Optional[int]       							=				state_dict['''decoder.embed_tokens.weight''']
       lowercase		:Union[str, Any]       							=				MBartForConditionalGeneration(a_)
       model.model.load_state_dict(a_)

       if finetuned:
              lowercase		:Dict       							=				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='''bart.large, bart.large.cnn or a 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.''')
 parser.add_argument(
     '''--hf_config''',
     default='''facebook/mbart-large-cc25''',
     type=str,
     help='''Which huggingface architecture to use: mbart-large''',
 )
 parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
 parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
 UpperCAmelCase      		=			parser.parse_args()
 UpperCAmelCase      		=			convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)

