






"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def      UpperCamelCase       ( _A )     ->       Union[str, Any]:
				lowercase    :      List[str]       = [
				    """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(_A  ,				_A )


def      UpperCamelCase       ( _A )     ->       Optional[int]:
				lowercase      ,      lowercase    :      Any       = emb.weight.shape
				lowercase    :      List[str]       = nn.Linear(_A  ,				_A  ,				bias=_A )
				lowercase    :      int       = emb.weight.data
				return lin_layer


def      UpperCamelCase       ( _A )     ->       Dict:
				lowercase    :      Tuple       = torch.load(_A  ,				map_location="""cpu""" )
				lowercase    :      Optional[int]       = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
				lowercase    :      List[str]       = mam_aaa["""model"""]
				remove_ignore_keys_(_A )
				lowercase    :      str       = state_dict["""encoder.embed_tokens.weight"""].shape[0]

				lowercase    :      Any       = MaMaaaConfig(
				    vocab_size=_A  ,				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"""  ,				)

				lowercase    :      str       = state_dict["""decoder.embed_tokens.weight"""]
				lowercase    :      Tuple       = MaMaaaForConditionalGeneration(_A )
				model.model.load_state_dict(_A  ,				strict=_A )
				lowercase    :      List[Any]       = 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      UpperCamelCase       ( _A )     ->       int:
				lowercase    :      Dict       = 0
				while num > 0:
								digit_sum += num % 10
								num //= 10
				return digit_sum


def      UpperCamelCase       ( _A = 100 )     ->       int:
				lowercase    :      Union[str, Any]       = 1
				lowercase    :      Tuple       = 2

				for i in range(2  ,				max_n + 1 ):
								lowercase    :      Any       = pre_numerator
								lowercase    :      Dict       = 2 * i // 3 if i % 3 == 0 else 1
								lowercase    :      Optional[Any]       = cur_numerator
								lowercase    :      str       = e_cont * pre_numerator + temp

				return sum_digits(_A )


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