
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 		UpperCAmelCase     (      a_		)						->      Optional[Any]:







				"""simple docstring"""



				__A       =					[
				    "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(__UpperCamelCase						,  __UpperCamelCase		)


def 		UpperCAmelCase     (      a_		)						->      int:







				"""simple docstring"""



				__A     ,				__A       =					emb.weight.shape
				__A       =					nn.Linear(__UpperCamelCase						,  __UpperCamelCase						,  bias=__UpperCamelCase		)
				__A       =					emb.weight.data
				return lin_layer


def 		UpperCAmelCase     (      a_		)						->      Optional[int]:







				"""simple docstring"""



				__A       =					torch.load(__UpperCamelCase						,  map_location="cpu"		)
				__A       =					mam_aaa["args"] or mam_aaa["cfg"]["model"]
				__A       =					mam_aaa["model"]
				remove_ignore_keys_(__UpperCamelCase		)
				__A       =					state_dict["encoder.embed_tokens.weight"].shape[0]

				__A       =					MaMaaaConfig(
				    vocab_size=__UpperCamelCase						,  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       =					state_dict["decoder.embed_tokens.weight"]
				__A       =					MaMaaaForConditionalGeneration(__UpperCamelCase		)
				model.model.load_state_dict(__UpperCamelCase						,  strict=__UpperCamelCase		)
				__A       =					make_linear_from_emb(model.model.shared		)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE				: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.')
					SCREAMING_SNAKE_CASE				:Tuple									=							parser.parse_args()
					SCREAMING_SNAKE_CASE				:Optional[Any]									=							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)





def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:

			if exponent == 1:
						return base
			if exponent % 2 == 0:
						A__	  =     _modexpt(__UpperCamelCase			,						exponent // 2			,						__UpperCamelCase       ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(__UpperCamelCase			,						exponent - 1			,						__UpperCamelCase       )) % modulo_value


def A    (							__UpperCamelCase = 1_777			,						__UpperCamelCase = 1_855			,						__UpperCamelCase = 8       )    ->   int:
			A__	  =     base
			for _ in range(1			,						__UpperCamelCase       ):
						A__	  =     _modexpt(__UpperCamelCase			,						__UpperCamelCase			,						10**digits       )

			return result


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




