






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)












'''simple docstring'''


from ..utils import DummyObject, requires_backends







class      SCREAMING_SNAKE_CASE    (						metaclass=snake_case_				):
      __magic_name__  :       Dict   							=					['''transformers''', '''torch''', '''note_seq''']






      def __init__( self   :  List[str]						,						*lowercase__   :  Dict						,						**lowercase__   :  int       ):


           '''simple docstring'''

           requires_backends(self						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  Dict						,						*lowercase__   :  List[str]						,						**lowercase__   :  str       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )






      @classmethod
      def 							lowercase_						( cls   :  str						,						*lowercase__   :  Optional[int]						,						**lowercase__   :  Any       ):


           '''simple docstring'''

           requires_backends(cls						,						["""transformers""", """torch""", """note_seq"""]       )
