"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Optional[int]:
				SCREAMING_SNAKE_CASE__		  =				[
				    "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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:
				SCREAMING_SNAKE_CASE__		  =				emb.weight.shape
				SCREAMING_SNAKE_CASE__		  =				nn.Linear(__UpperCAmelCase  ,					__UpperCAmelCase  ,					bias=__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				emb.weight.data
				return lin_layer




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Dict:
				SCREAMING_SNAKE_CASE__		  =				torch.load(__UpperCAmelCase  ,					map_location="cpu"       )
				SCREAMING_SNAKE_CASE__		  =				mam_aaa["args"] or mam_aaa["cfg"]["model"]
				SCREAMING_SNAKE_CASE__		  =				mam_aaa["model"]
				remove_ignore_keys_(__UpperCAmelCase       )
				SCREAMING_SNAKE_CASE__		  =				state_dict["encoder.embed_tokens.weight"].shape[0]

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

				return model


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





"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

				if not isinstance(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								raise ValueError("multiplicative_persistence() only accepts integral values"       )
				if num < 0:
								raise ValueError("multiplicative_persistence() does not accept negative values"       )

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

								SCREAMING_SNAKE_CASE__		  =				1
								for i in range(0  ,					len(__UpperCAmelCase       )       ):
												total *= numbers[i]

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   int:

				if not isinstance(__UpperCAmelCase  ,					__UpperCAmelCase       ):
								raise ValueError("additive_persistence() only accepts integral values"       )
				if num < 0:
								raise ValueError("additive_persistence() does not accept negative values"       )

				SCREAMING_SNAKE_CASE__		  =				0
				SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

				while len(__UpperCAmelCase       ) != 1:
								SCREAMING_SNAKE_CASE__		  =				[int(__UpperCAmelCase       ) for i in num_string]

								SCREAMING_SNAKE_CASE__		  =				0
								for i in range(0  ,					len(__UpperCAmelCase       )       ):
												total += numbers[i]

								SCREAMING_SNAKE_CASE__		  =				str(__UpperCAmelCase       )

								steps += 1
				return steps


if __name__ == "__main__":
						import doctest

						doctest.testmod()




