

import sys

lowercase__	:Any										=					(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		1
					for digit in s:
										product *= int(_lowercase	)
					return product



def  UpperCamelCase					(							lowerCAmelCase__ = N	):
					'''simple docstring'''
					lowercase					=		-sys.maxsize - 1
					lowercase					=		n[:13]
					lowercase					=		13
					while cur_index < len(_lowercase	) - 13:
										if int(n[cur_index]	) >= int(substr[0]	):
															lowercase					=		substr[1:] + n[cur_index]
															cur_index += 1
										else:
															lowercase					=		max(_lowercase				,    str_eval(_lowercase	)	)
															lowercase					=		n[cur_index : cur_index + 13]
															cur_index += 13
					return largest_product


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






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''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(lowerCAmelCase__				,    lowerCAmelCase__	)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		emb.weight.shape
					lowercase					=		nn.Linear(lowerCAmelCase__				,    lowerCAmelCase__				,    bias=lowerCAmelCase__	)
					lowercase					=		emb.weight.data
					return lin_layer



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		torch.load(lowerCAmelCase__				,    map_location='''cpu'''	)
					lowercase					=		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
					lowercase					=		mam_aaa['''model''']
					remove_ignore_keys_(lowerCAmelCase__	)
					lowercase					=		state_dict['''encoder.embed_tokens.weight'''].shape[0]

					lowercase					=		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					=		state_dict['''decoder.embed_tokens.weight''']
					lowercase					=		MaMaaaForConditionalGeneration(lowerCAmelCase__	)
					model.model.load_state_dict(lowerCAmelCase__				,    strict=lowerCAmelCase__	)
					lowercase					=		make_linear_from_emb(model.model.shared	)

					return model


if __name__ == "__main__":
							lowercase__	:Optional[int]										=					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__	:Tuple										=					parser.parse_args()
							lowercase__	:int										=					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)




