





'''simple docstring'''






import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=torch.load(__SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''   )

  _UpperCamelCase       			=chkpt['''model''']

  # We have the base model one level deeper than the original XLM repository
  _UpperCamelCase       			={}
  for k, v in state_dict.items():
    if "pred_layer" in k:
      _UpperCamelCase       			=v
    else:
      _UpperCamelCase       			=v

  _UpperCamelCase       			=chkpt['''params''']
  _UpperCamelCase       			={n: v for n, v in config.items() if not isinstance(__SCREAMING_SNAKE_CASE  ,      (torch.FloatTensor, numpy.ndarray)   )}

  _UpperCamelCase       			=chkpt['''dico_word2id''']
  _UpperCamelCase       			={s + '''</w>''' if s.find('''@@'''   ) == -1 and i > 13 else s.replace('''@@'''  ,      ''''''   ): i for s, i in vocab.items()}

  # Save pytorch-model
  _UpperCamelCase       			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
  _UpperCamelCase       			=pytorch_dump_folder_path + '''/''' + CONFIG_NAME
  _UpperCamelCase       			=pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''   )
  torch.save(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  print(f'''Save configuration file to {pytorch_config_dump_path}'''   )
  with open(__SCREAMING_SNAKE_CASE  ,      '''w'''  ,      encoding='''utf-8'''   ) as f:
    f.write(json.dumps(__SCREAMING_SNAKE_CASE  ,      indent=2   ) + '''\n'''   )

  print(f'''Save vocab file to {pytorch_config_dump_path}'''   )
  with open(__SCREAMING_SNAKE_CASE  ,      '''w'''  ,      encoding='''utf-8'''   ) as f:
    f.write(json.dumps(__SCREAMING_SNAKE_CASE  ,      indent=2   ) + '''\n'''   )


if __name__ == "__main__":
      __lowerCamelCase		=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      __lowerCamelCase		=   parser.parse_args()
      convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)













'''simple docstring'''






from __future__ import annotations

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

__lowerCamelCase						: Optional[int]		=   TypeVar('T')







class 		UpperCAmelCase   (				Generic[T]):


  """simple docstring"""







  lowerCAmelCase_					=				42  # Cache store of keys
  lowerCAmelCase_					=				42  # References of the keys in cache
  lowerCAmelCase_					=				10  # Maximum capacity of cache


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


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

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


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




  def __repr__(				self    :					str  )     ->						str:
    return F'''LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store  )}'''


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      __lowerCamelCase						: LRUCache[str | int]		=   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]"






