






'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      SCREAMING_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(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				str									=emb.weight.shape
      SCREAMING_SNAKE_CASE__     :				int									=nn.Linear(UpperCamelCase__,	UpperCamelCase__,	bias=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=emb.weight.data
      return lin_layer





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






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

      SCREAMING_SNAKE_CASE__     :				List[str]									=MaMaaaConfig(
          vocab_size=UpperCamelCase__,	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''',	)

      SCREAMING_SNAKE_CASE__     :				Tuple									=state_dict['''decoder.embed_tokens.weight''']
      SCREAMING_SNAKE_CASE__     :				str									=MaMaaaForConditionalGeneration(UpperCamelCase__							)
      model.model.load_state_dict(UpperCamelCase__,	strict=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Dict									=make_linear_from_emb(model.model.shared							)

      return model


if __name__ == "__main__":
  a_					= 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.')
  a_					= parser.parse_args()
  a_					= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
  model.save_pretrained(args.pytorch_dump_folder_path)







'''simple docstring'''
from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

a_					= TypeVar('T')






class      __SCREAMING_SNAKE_CASE      (			Generic[T]				):
 snake_case_  							=							42  # Cache store of keys
 snake_case_  							=							42  # References of the keys in cache
 snake_case_  							=							10  # Maximum capacity of cache




 def __init__(      self	:							Dict					,      __lowercase	:							int      )	->						None:
       SCREAMING_SNAKE_CASE__     :				Any									=deque()
       SCREAMING_SNAKE_CASE__     :				str									=set()
       if not n:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=sys.maxsize
       elif n < 0:
             raise ValueError('''n should be an integer greater than 0.'''      )
       else:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									=n




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							T      )	->						None:
       if x not in self.key_reference:
             if len(self.dq_store      ) == LRUCache._MAX_CAPACITY:
                   SCREAMING_SNAKE_CASE__     :				int									=self.dq_store.pop()
                   self.key_reference.remove(__lowercase      )
       else:
             self.dq_store.remove(__lowercase      )

       self.dq_store.appendleft(__lowercase      )
       self.key_reference.add(__lowercase      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						None:
       for k in self.dq_store:
             print(__lowercase      )




 def __repr__(      self	:							List[Any]      )	->						str:
       return F"LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store      )}"


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a_					= LRUCache(4)
  lru_cache.refer('A')
  lru_cache.refer(2)
  lru_cache.refer(3)
  lru_cache.refer('A')
  lru_cache.refer(4)
  lru_cache.refer(5)
  lru_cache.display()

  print(lru_cache)
  assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"