




"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def 						snake_case__       (					_lowerCamelCase				)			->str:






		"""simple docstring"""


		__lowercase				:		Optional[Any]												=				[
		    "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 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase						,__lowercase				:		Optional[int]												=				emb.weight.shape
		__lowercase				:		int												=				nn.Linear(_lowerCamelCase,  _lowerCamelCase,  bias=_lowerCamelCase				)
		__lowercase				:		str												=				emb.weight.data
		return lin_layer

def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		__lowercase				:		List[str]												=				torch.load(_lowerCamelCase,  map_location="cpu"				)
		__lowercase				:		Union[str, Any]												=				mam_aaa["args"] or mam_aaa["cfg"]["model"]
		__lowercase				:		List[str]												=				mam_aaa["model"]
		remove_ignore_keys_(_lowerCamelCase				)
		__lowercase				:		Any												=				state_dict["encoder.embed_tokens.weight"].shape[0]

		__lowercase				:		List[Any]												=				MaMaaaConfig(
		    vocab_size=_lowerCamelCase,  max_position_embeddings=10_24,  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				:		Any												=				state_dict["decoder.embed_tokens.weight"]
		__lowercase				:		List[str]												=				MaMaaaForConditionalGeneration(_lowerCamelCase				)
		model.model.load_state_dict(_lowerCamelCase,  strict=_lowerCamelCase				)
		__lowercase				:		Tuple												=				make_linear_from_emb(model.model.shared				)

		return model


if __name__ == "__main__":
						__A   :  List[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.')
						__A   :  Optional[int]					=      parser.parse_args()
						__A   :  str					=      convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if a < 0:
				raise ValueError("Input value must be a positive integer"				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				raise TypeError("Input value must be a 'int' type"				)
		return bin(_lowerCamelCase				).count("1"				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
