
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      _lowerCAmelCase(	__A   ,       __A			):
  # Load checkpoint
  UpperCAmelCase					      =  torch.load(__A   ,       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(__A   ,       (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(__A   ,       __A			)

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

  print(F"Save vocab file to {pytorch_config_dump_path}"			)
  with open(__A   ,       "w"   ,       encoding="utf-8"			) as f:
    f.write(json.dumps(__A   ,       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)






import unittest

import numpy as np
def      _lowerCAmelCase(	__A   ,       __A   ,       __A   ,       __A = None   ,       ):
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)
  UpperCAmelCase					      =  np.shape(__A			)

  if shape_a[0] != shape_b[0]:
    UpperCAmelCase					      =  (
        "Expected the same number of rows for A and B. "
        F"Instead found A of size {shape_a} and B of size {shape_b}"
    )
    raise ValueError(__A			)

  if shape_b[1] != shape_c[1]:
    UpperCAmelCase					      =  (
        "Expected the same number of columns for B and C. "
        F"Instead found B of size {shape_b} and C of size {shape_c}"
    )
    raise ValueError(__A			)

  UpperCAmelCase					      =  pseudo_inv
  if a_inv is None:
    try:
      UpperCAmelCase					      =  np.linalg.inv(__A			)
    except np.linalg.LinAlgError:
      raise ValueError(
          "Input matrix A is not invertible. Cannot compute Schur complement."			)

  return mat_c - mat_b.T @ a_inv @ mat_b






class 						__magic_name__    (       unittest.TestCase    ):


 def _UpperCamelCase	(						self		: List[str]    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   UpperCAmelCase					      =  schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )

   UpperCAmelCase					      =  np.block([[a, b], [b.T, c]]    )

   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )
   UpperCAmelCase					      =  np.linalg.det(lowerCAmelCase__    )

   self.assertAlmostEqual(lowerCAmelCase__			,						det_a * det_s    )


 def _UpperCamelCase	(						self		: str    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1], [6, 3]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Dict    )    ->      None:
   UpperCAmelCase					      =  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]    )
   UpperCAmelCase					      =  np.array([[0, 3], [3, 0], [2, 3]]    )
   UpperCAmelCase					      =  np.array([[2, 1, 3], [6, 3, 5]]    )

   with self.assertRaises(lowerCAmelCase__    ):
     schur_complement(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__    )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      unittest.main()



