


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def       _lowerCAmelCase    (				lowerCAmelCase_				:Tuple				)->Any:
   '''simple docstring'''






   snake_case_            =       [
       "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       _lowerCAmelCase    (				lowerCAmelCase_				:int				)->Tuple:
   '''simple docstring'''






   snake_case_     ,     snake_case_            =       emb.weight.shape
   snake_case_            =       nn.Linear(lowerCAmelCase_		,    lowerCAmelCase_		,    bias=lowerCAmelCase_				)
   snake_case_            =       emb.weight.data
   return lin_layer



def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[int]				)->List[str]:
   '''simple docstring'''






   snake_case_            =       torch.load(lowerCAmelCase_		,    map_location="cpu"				)
   snake_case_            =       mam_aaa["args"] or mam_aaa["cfg"]["model"]
   snake_case_            =       mam_aaa["model"]
   remove_ignore_keys_(lowerCAmelCase_				)
   snake_case_            =       state_dict["encoder.embed_tokens.weight"].shape[0]

   snake_case_            =       MaMaaaConfig(
       vocab_size=lowerCAmelCase_		,    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"		,    )

   snake_case_            =       state_dict["decoder.embed_tokens.weight"]
   snake_case_            =       MaMaaaForConditionalGeneration(lowerCAmelCase_				)
   model.model.load_state_dict(lowerCAmelCase_		,    strict=lowerCAmelCase_				)
   snake_case_            =       make_linear_from_emb(model.model.shared				)

   return model


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:Union[str, 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						:Union[str, Any]			=  parser.parse_args()
   SCREAMING_SNAKE_CASE						:Optional[Any]			=  convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
   model.save_pretrained(args.pytorch_dump_folder_path)








from collections import deque

from .hash_table import HashTable


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  def __init__(  self							:							int     ,					*_lowerCAmelCase							:							List[Any]     ,					**_lowerCAmelCase							:							Any	) ->			Union[str, Any]:




     """simple docstring"""
     super().__init__(*_lowerCAmelCase     ,					**_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Tuple     ,					_lowerCAmelCase							:							Optional[int]     ,					_lowerCAmelCase							:							List[Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       deque([]	) if self.values[key] is None else self.values[key]
     self.values[key].appendleft(_lowerCAmelCase	)
     snake_case_            =       self.values[key]



  def        lowerCAmelCase__	(  self							:							List[str]	) ->			List[Any]:




     """simple docstring"""
     return (
         sum(self.charge_factor - len(_lowerCAmelCase	) for slot in self.values	)
         / self.size_table
         * self.charge_factor
     )



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							List[str]=None	) ->			int:




     """simple docstring"""
     if not (
         len(self.values[key]	) == self.charge_factor and self.values.count(_lowerCAmelCase	) == 0
     ):
        return key
     return super()._collision_resolution(_lowerCAmelCase     ,					_lowerCAmelCase	)




