


"""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    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







  lowerCAmelCase     :     int								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)

  lowerCAmelCase     :     Optional[Any]								=							chkpt["model"]

  # We have the base model one level deeper than the original XLM repository
  lowerCAmelCase     :     int								=							{}
  for k, v in state_dict.items():
    if "pred_layer" in k:
      lowerCAmelCase     :     Union[str, Any]								=							v
    else:
      lowerCAmelCase     :     int								=							v

  lowerCAmelCase     :     List[Any]								=							chkpt["params"]
  lowerCAmelCase     :     Tuple								=							{n: v for n, v in config.items() if not isinstance(SCREAMING_SNAKE_CASE				,      (torch.FloatTensor, numpy.ndarray)				)}

  lowerCAmelCase     :     List[str]								=							chkpt["dico_word2id"]
  lowerCAmelCase     :     int								=							{s + "</w>" if s.find("@@"				) == -1 and i > 1_3 else s.replace("@@"				,      ""				): i for s, i in vocab.items()}

  # Save pytorch-model
  lowerCAmelCase     :     List[Any]								=							pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  lowerCAmelCase     :     List[Any]								=							pytorch_dump_folder_path + "/" + CONFIG_NAME
  lowerCAmelCase     :     Dict								=							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

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				int
       a  :				TreeNode | None   =None
       a  :				TreeNode | None   =None

lowerCAmelCase__      				=   namedtuple('''CoinsDistribResult''', '''moves excess''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    TreeNode | None				):


  '''simple docstring'''







  if root is None:
    return 0

  # Validation
  def count_nodes(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_nodes(node.left				) + count_nodes(node.right				) + 1

  def count_coins(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_coins(node.left				) + count_coins(node.right				) + node.data

  if count_nodes(SCREAMING_SNAKE_CASE				) != count_coins(SCREAMING_SNAKE_CASE				):
    raise ValueError("The nodes number should be same as the number of coins"				)

  # Main calculation
  def get_distrib(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> CoinsDistribResult:

    if node is None:
      return CoinsDistribResult(0				,      1				)

    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.left				)
    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.right				)

    lowerCAmelCase     :     Union[str, Any]								=							1 - left_distrib_excess
    lowerCAmelCase     :     List[Any]								=							1 - right_distrib_excess

    lowerCAmelCase     :     int								=							(
        left_distrib_moves
        + right_distrib_moves
        + abs(SCREAMING_SNAKE_CASE				)
        + abs(SCREAMING_SNAKE_CASE				)
    )
    lowerCAmelCase     :     int								=							node.data - coins_to_left - coins_to_right

    return CoinsDistribResult(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  return get_distrib(SCREAMING_SNAKE_CASE				)[0]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





