


'''simple docstring'''

from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def __UpperCamelCase   (			__lowerCamelCase		: int      ,  __lowerCamelCase		: Dict				)      ->     int:







     '''simple docstring'''






     _a						=		k_size // 2
     _a			,			_a						=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     _a						=		1 / (2 * pi * sigma) * exp(-(square(__lowerCamelCase				) + square(__lowerCamelCase				)) / (2 * square(__lowerCamelCase				))				)
     return g






def __UpperCamelCase   (			__lowerCamelCase		: List[Any]      ,  __lowerCamelCase		: Optional[int]      ,  __lowerCamelCase		: int				)      ->     Any:







     '''simple docstring'''






     _a			,			_a						=		image.shape[0], image.shape[1]
     # dst image height and width
     _a						=		height - k_size + 1
     _a						=		width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     _a						=		zeros((dst_height * dst_width, k_size * k_size)				)
     _a						=		0
     for i, j in product(range(__lowerCamelCase				)      ,  range(__lowerCamelCase				)				):
          _a						=		ravel(image[i : i + k_size, j : j + k_size]				)
          _a						=		window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     _a						=		gen_gaussian_kernel(__lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		ravel(__lowerCamelCase				)

     # reshape and get the dst image
     _a						=		dot(__lowerCamelCase      ,  __lowerCamelCase				).reshape(__lowerCamelCase      ,  __lowerCamelCase				).astype(__lowerCamelCase				)

     return dst


if __name__ == "__main__":
   # read original image
   lowercase__				     =		imread(R"../image_data/lena.jpg")
   # turn image in gray scale value
   lowercase__				     =		cvtColor(img, COLOR_BGR2GRAY)

   # get values with two different mask size
   lowercase__				     =		gaussian_filter(gray, 3, sigma=1)
   lowercase__				     =		gaussian_filter(gray, 5, sigma=0.8)

   # show result images
   imshow("gaussian filter with 3x3 mask", gaussianaxa)
   imshow("gaussian filter with 5x5 mask", gaussianaxa)
   waitKey()





'''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,
   )

