
'''simple docstring'''






import math
import sys

import cva
import numpy as np







def 		A_							(						_lowerCAmelCase :     np.ndarray					,						_lowerCAmelCase :     float				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = math.sqrt(_lowerCAmelCase				)
      _lowerCamelCase					: Optional[int]		    = 1 / (sigma * math.sqrt(2 * math.pi				))
      return cons * np.exp(-((img / sigma) ** 2) * 0.5				)







def 		A_							(						_lowerCAmelCase :     np.ndarray					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     int					,						_lowerCAmelCase :     int				):



      """simple docstring"""




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







def 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     float				):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = np.zeros((kernel_size, kernel_size)				)
      for i in range(0					,						_lowerCAmelCase				):
            for j in range(0					,						_lowerCAmelCase				):
                  _lowerCamelCase					: Optional[int]		    = math.sqrt(
                      abs(i - kernel_size // 2				) ** 2 + abs(j - kernel_size // 2				) ** 2				)
      return vec_gaussian(_lowerCAmelCase					,						_lowerCAmelCase				)







def 		A_							(						_lowerCAmelCase :     np.ndarray					,						_lowerCAmelCase :     float					,						_lowerCAmelCase :     float					,						_lowerCAmelCase :     int					,						):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = np.zeros(img.shape				)
      _lowerCamelCase					: Optional[Any]		    = get_gauss_kernel(_lowerCAmelCase					,						_lowerCAmelCase				)
      _lowerCamelCase    ,		_lowerCamelCase					: Any		    = 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				):
                  _lowerCamelCase					: Optional[Any]		    = get_slice(_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase					,						_lowerCAmelCase				)
                  _lowerCamelCase					: Optional[int]		    = img_s - img_s[kernel_size // 2, kernel_size // 2]
                  _lowerCamelCase					: Any		    = vec_gaussian(_lowerCAmelCase					,						_lowerCAmelCase				)
                  _lowerCamelCase					: List[Any]		    = np.multiply(_lowerCAmelCase					,						_lowerCAmelCase				)
                  _lowerCamelCase					: Optional[int]		    = np.multiply(_lowerCAmelCase					,						_lowerCAmelCase				)
                  _lowerCamelCase					: Tuple		    = np.sum(_lowerCAmelCase				) / np.sum(_lowerCAmelCase				)
                  _lowerCamelCase					: Optional[int]		    = val
      return imga







def 		A_							(						_lowerCAmelCase :     list				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = args[1] if args[1:] else "../image_data/lena.jpg"
      _lowerCamelCase					: List[str]		    = float(args[2]				) if args[2:] else 1.0
      _lowerCamelCase					: Union[str, Any]		    = float(args[3]				) if args[3:] else 1.0
      if args[4:]:
            _lowerCamelCase					: List[str]		    = int(args[4]				)
            _lowerCamelCase					: Union[str, Any]		    = kernel_size + abs(kernel_size % 2 - 1				)
      else:
            _lowerCamelCase					: Optional[Any]		    = 5
      return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
 UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_, UpperCAmelCase_							: str =							parse_args(sys.argv)
 UpperCAmelCase_							: List[Any] =							cva.imread(filename, 0)
 cva.imshow('input image', img)

 UpperCAmelCase_							: Tuple =							img / 255
 UpperCAmelCase_							: Optional[Any] =							out.astype('float32')
 UpperCAmelCase_							: str =							bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
 UpperCAmelCase_							: Optional[int] =							out * 255
 UpperCAmelCase_							: List[str] =							np.uinta(out)
 cva.imshow('output image', out)
 cva.waitKey(0)
 cva.destroyAllWindows()
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		(      _A				,					_A       ):



 """simple docstring"""


 __a							=	torch.load(_A				,					map_location="cpu"       )

 __a							=	chkpt["model"]

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

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

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

 # Save pytorch-model
 __a							=	pytorch_dump_folder_path + "/" + WEIGHTS_NAME
 __a							=	pytorch_dump_folder_path + "/" + CONFIG_NAME
 __a							=	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__":
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		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."""
  )
  SCREAMING_SNAKE_CASE			:						Any				  =		parser.parse_args()
  convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)


