


# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


lowerCAmelCase       			=						re.compile(R'''^(?P<major>\d+)''' R'''\.(?P<minor>\d+)''' R'''\.(?P<patch>\d+)$''')





@total_ordering
@dataclass
class  A       :
							UpperCamelCase_  :				Union[str, Any]			=42
							UpperCamelCase_  :				Any			=None
							UpperCamelCase_  :				Any			=None
							UpperCamelCase_  :				int			=None
							UpperCamelCase_  :				Any			=None


							def      _A			(self   ):
									__lowercase=     _str_to_version_tuple(self.version_str   )


							def __repr__(self   ):
									return f'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'


							@property
							def      _A			(self   ):
									return self.major, self.minor, self.patch


							def      _A			(self ,							lowerCAmelCase   ):
									if isinstance(__lowerCAmelCase ,							__lowerCAmelCase   ):
											return Version(__lowerCAmelCase   )
									elif isinstance(__lowerCAmelCase ,							__lowerCAmelCase   ):
											return other
									raise TypeError(f'{other} (type {type(__lowerCAmelCase   )}) cannot be compared to version.'   )


							def __eq__(self ,							lowerCAmelCase   ):
									try:
											__lowercase=     self._validate_operand(__lowerCAmelCase   )
									except (TypeError, ValueError):
											return False
									else:
											return self.tuple == other.tuple


							def __lt__(self ,							lowerCAmelCase   ):
									__lowercase=     self._validate_operand(__lowerCAmelCase   )
									return self.tuple < other.tuple


							def __hash__(self   ):
									return hash(_version_tuple_to_str(self.tuple   )   )


							@classmethod
							def      _A			(cls ,							lowerCAmelCase   ):
									__lowercase=     {f.name for f in dataclasses.fields(cls   )}
									return cls(**{k: v for k, v in dic.items() if k in field_names}   )







							def      _A			(self   ):
									return self.version_str







def 			_lowerCamelCase(      lowercase__      )		->      Dict:

		'''simple docstring'''







		__lowercase=     _VERSION_REG.match(lowercase__      )
		if not res:
				raise ValueError(F'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'      )
		return tuple(int(lowercase__      ) for v in [res.group('major'      ), res.group('minor'      ), res.group('patch'      )]      )







def 			_lowerCamelCase(      lowercase__      )		->      int:

		'''simple docstring'''







		return ".".join(str(lowercase__      ) for v in version_tuple      )














import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		: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(snake_case,	snake_case			)

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__  ,					__magic_name__		:Tuple   =   emb.weight.shape
				__magic_name__		:int   =   nn.Linear(snake_case,	snake_case,	bias=snake_case			)
				__magic_name__		:str   =   emb.weight.data
				return lin_layer

def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   torch.load(snake_case,	map_location='''cpu'''			)
				__magic_name__		:Optional[Any]   =   mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
				__magic_name__		:List[Any]   =   mam_aaa['''model''']
				remove_ignore_keys_(snake_case			)
				__magic_name__		:Tuple   =   state_dict['''encoder.embed_tokens.weight'''].shape[0]

				__magic_name__		:List[str]   =   MaMaaaConfig(
				    vocab_size=snake_case,	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''',	)

				__magic_name__		:int   =   state_dict['''decoder.embed_tokens.weight''']
				__magic_name__		:List[str]   =   MaMaaaForConditionalGeneration(snake_case			)
				model.model.load_state_dict(snake_case,	strict=snake_case			)
				__magic_name__		:List[str]   =   make_linear_from_emb(model.model.shared			)

				return model


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Optional[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__   :      int				=		parser.parse_args()
					SCREAMING_SNAKE_CASE__   :      Any				=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
					model.save_pretrained(args.pytorch_dump_folder_path)






