


import math
import sys

import cva
import numpy as np






def __UpperCamelCase   (			__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: float				)      ->     Dict:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int      ,  __lowerCamelCase		: int				)      ->     Tuple:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: float				)      ->     List[str]:







     '''simple docstring'''






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






def __UpperCamelCase   (			__lowerCamelCase		: np.ndarray      ,  __lowerCamelCase		: float      ,  __lowerCamelCase		: float      ,  __lowerCamelCase		: int      ,  )      ->     Union[str, Any]:







     '''simple docstring'''






     _a						=		np.zeros(img.shape				)
     _a						=		get_gauss_kernel(_lowerCamelCase      ,  _lowerCamelCase				)
     _a						=		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				):
               _a						=		get_slice(_lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase      ,  _lowerCamelCase				)
               _a						=		img_s - img_s[kernel_size // 2, kernel_size // 2]
               _a						=		vec_gaussian(_lowerCamelCase      ,  _lowerCamelCase				)
               _a						=		np.multiply(_lowerCamelCase      ,  _lowerCamelCase				)
               _a						=		np.multiply(_lowerCamelCase      ,  _lowerCamelCase				)
               _a						=		np.sum(_lowerCamelCase				) / np.sum(_lowerCamelCase				)
               _a						=		val
     return imga






def __UpperCamelCase   (			__lowerCamelCase		: list				)      ->     Optional[Any]:







     '''simple docstring'''






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


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

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





'''simple docstring'''

import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
lowercase__				     =		data_utils.TransfoXLTokenizer
lowercase__				     =		data_utils.TransfoXLCorpus
lowercase__				     =		data_utils
lowercase__				     =		data_utils






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Tuple      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: Tuple				)      ->     Dict:







     '''simple docstring'''






     if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(__lowerCamelCase      ,  "rb"				) as fp:
               _a						=		pickle.load(__lowerCamelCase      ,  encoding="latin1"				)
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          _a						=		pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
          print(F"Save vocabulary to {pytorch_vocab_dump_path}"				)
          _a						=		corpus.vocab.__dict__
          torch.save(__lowerCamelCase      ,  __lowerCamelCase				)

          _a						=		corpus.__dict__
          corpus_dict_no_vocab.pop("vocab"      ,  __lowerCamelCase				)
          _a						=		pytorch_dump_folder_path + "/" + CORPUS_NAME
          print(F"Save dataset to {pytorch_dataset_dump_path}"				)
          torch.save(__lowerCamelCase      ,  __lowerCamelCase				)

     if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          _a						=		os.path.abspath(__lowerCamelCase				)
          _a						=		os.path.abspath(__lowerCamelCase				)

          print(F"Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."				)
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
               _a						=		TransfoXLConfig()
          else:
               _a						=		TransfoXLConfig.from_json_file(__lowerCamelCase				)
          print(F"Building PyTorch model from configuration: {config}"				)
          _a						=		TransfoXLLMHeadModel(__lowerCamelCase				)

          _a						=		load_tf_weights_in_transfo_xl(__lowerCamelCase      ,  __lowerCamelCase      ,  __lowerCamelCase				)
          # Save pytorch-model
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          _a						=		os.path.join(__lowerCamelCase      ,  __lowerCamelCase				)
          print(F"Save PyTorch model to {os.path.abspath(__lowerCamelCase				)}"				)
          torch.save(model.state_dict()      ,  __lowerCamelCase				)
          print(F"Save configuration file to {os.path.abspath(__lowerCamelCase				)}"				)
          with open(__lowerCamelCase      ,  "w"      ,  encoding="utf-8"				) as f:
               f.write(config.to_json_string()				)


if __name__ == "__main__":
   lowercase__				     =		argparse.ArgumentParser()
   parser.add_argument(
       "--pytorch_dump_folder_path",
       default=None,
       type=str,
       required=True,
       help="Path to the folder to store the PyTorch model or dataset/vocab.",
   )
   parser.add_argument(
       "--tf_checkpoint_path",
       default="",
       type=str,
       help="An optional path to a TensorFlow checkpoint path to be converted.",
   )
   parser.add_argument(
       "--transfo_xl_config_file",
       default="",
       type=str,
       help=(
           "An optional config json file corresponding to the pre-trained BERT model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--transfo_xl_dataset_file",
       default="",
       type=str,
       help="An optional dataset file to be converted in a vocabulary.",
   )
   lowercase__				     =		parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )

