



"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						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      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						int			  =    emb.weight.shape
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    nn.Linear(lowerCamelCase_     ,	lowerCamelCase_     ,	bias=lowerCamelCase_  )
							_SCREAMING_SNAKE_CASE		:						int			  =    emb.weight.data
							return lin_layer



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							_SCREAMING_SNAKE_CASE		:						str			  =    torch.load(lowerCamelCase_     ,	map_location="""cpu"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    mam_aaa['args'] or mam_aaa['cfg']['model']
							_SCREAMING_SNAKE_CASE		:						Dict			  =    mam_aaa['model']
							remove_ignore_keys_(lowerCamelCase_  )
							_SCREAMING_SNAKE_CASE		:						str			  =    state_dict['encoder.embed_tokens.weight'].shape[0]

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    MaMaaaConfig(
							    vocab_size=lowerCamelCase_     ,	max_position_embeddings=1_024     ,	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"""     ,	)

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    state_dict['decoder.embed_tokens.weight']
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    MaMaaaForConditionalGeneration(lowerCamelCase_  )
							model.model.load_state_dict(lowerCamelCase_     ,	strict=lowerCamelCase_  )
							_SCREAMING_SNAKE_CASE		:						int			  =    make_linear_from_emb(model.model.shared  )

							return model


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



"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:

							if not isinstance(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ):
														raise TypeError("""only integers accepted as input"""  )
							else:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    str(abs(__SCREAMING_SNAKE_CASE  )  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    [list(__SCREAMING_SNAKE_CASE  ) for char in range(len(__SCREAMING_SNAKE_CASE  )  )]
														for index in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					num_transpositions[index].pop(__SCREAMING_SNAKE_CASE  )
														return max(
														    int("""""".join(list(__SCREAMING_SNAKE_CASE  )  )  ) for transposition in num_transpositions  )


if __name__ == "__main__":
			__import__('''doctest''').testmod()


