






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def 			A__							(      _a  :					str	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =[
     """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 			A__							(      _a  :					Optional[int]	):





 '''simple docstring'''







 snake_case__       ,   snake_case__  :    Union[str, Any]							       =emb.weight.shape
 snake_case__  :    Optional[Any]							       =nn.Linear(UpperCAmelCase__		,  UpperCAmelCase__		,  bias=UpperCAmelCase__	)
 snake_case__  :    Tuple							       =emb.weight.data
 return lin_layer







def 			A__							(      _a  :					int	):





 '''simple docstring'''







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

 snake_case__  :    int							       =MaMaaaConfig(
     vocab_size=UpperCAmelCase__		,  max_position_embeddings=1024		,  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"""		,  )

 snake_case__  :    Dict							       =state_dict["""decoder.embed_tokens.weight"""]
 snake_case__  :    List[Any]							       =MaMaaaForConditionalGeneration(UpperCAmelCase__	)
 model.model.load_state_dict(UpperCAmelCase__		,  strict=UpperCAmelCase__	)
 snake_case__  :    str							       =make_linear_from_emb(model.model.shared	)

 return model


if __name__ == "__main__":
     __lowerCamelCase	:		int										=   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	:		Dict										=   parser.parse_args()
     __lowerCamelCase	:		Optional[int]										=   convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)













class  _lowercase   :
      def __init__(     self				,			a   ):
       snake_case__  :    Optional[int]							       =size
       snake_case__  :    List[Any]							       =[0] * size
       snake_case__  :    List[Any]							       =[0] * size
      @staticmethod
      def 						lowercase__					(     a   ):
       return index | (index + 1)
      @staticmethod
      def 						lowercase__					(     a   ):
       return (index & (index + 1)) - 1
      def 						lowercase__					(     self				,			a				,			a   ):
       snake_case__  :    Optional[Any]							       =value
       while index < self.size:
        snake_case__  :    str							       =self.get_prev(a   ) + 1
        if current_left_border == index:
         snake_case__  :    Dict							       =value
        else:
         snake_case__  :    str							       =max(a				,			a				,			a   )
        snake_case__  :    Dict							       =self.get_next(a   )



      def 						lowercase__					(     self				,			a				,			a   ):
       right -= 1  # Because of right is exclusive
       snake_case__  :    Union[str, Any]							       =0
       while left <= right:
        snake_case__  :    List[str]							       =self.get_prev(a   )
        if left <= current_left:
         snake_case__  :    Optional[Any]							       =max(a				,			self.tree[right]   )
         snake_case__  :    Optional[Any]							       =current_left
        else:
         snake_case__  :    Tuple							       =max(a				,			self.arr[right]   )
         right -= 1
       return result


if __name__ == "__main__":
       import doctest

       doctest.testmod()





