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        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       Union[str, Any]:



   """simple docstring"""







   lowercase							=		torch.load(SCREAMING_SNAKE_CASE__,		map_location='''cpu'''       )

   lowercase							=		chkpt["""model"""]

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

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

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

   # Save pytorch-model
   lowercase							=		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
   lowercase							=		pytorch_dump_folder_path + """/""" + CONFIG_NAME
   lowercase							=		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__":
      A_      =      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."
      )
      A_      =      parser.parse_args()
      convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		img.shape
			for i in range(kernel_size // 2,		size_x - kernel_size // 2       ):
						for j in range(kernel_size // 2,		size_y - kernel_size // 2       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						A_    ,    A_    ,    A_    ,    A_      =      parse_args(sys.argv)
						A_      =      cva.imread(filename, 0)
						cva.imshow("input image", img)

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()





