






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 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  torch.load(lowerCamelCase				,  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(lowerCamelCase				,  (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(lowerCamelCase				,  lowerCamelCase					)

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

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


if __name__ == "__main__":
						__UpperCamelCase      :      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."""
						)
						__UpperCamelCase      :      Optional[int]     						=						parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)









import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	if (ksize % 2) == 0:
		__lowercase             =  ksize + 1
	__lowercase             =  np.zeros((ksize, ksize)				,  dtype=np.floataa					)

	# each value
	for y in range(lowerCamelCase					):
		for x in range(lowerCamelCase					):
			# distance from center
			__lowercase             =  x - ksize // 2
			__lowercase             =  y - ksize // 2

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

			# get kernel x
			__lowercase             =  cos_theta * px + sin_theta * py

			# get kernel y
			__lowercase             =  -sin_theta * px + cos_theta * py

			# fill kernel
			__lowercase             =  np.exp(
			    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

	return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						__UpperCamelCase      :      Union[str, Any]     						=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												__UpperCamelCase      :      Tuple     						=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						__UpperCamelCase      :      List[str]     						=						out / out.max() * 255
						__UpperCamelCase      :      List[str]     						=						out.astype(np.uinta)

						imshow("""Original""", gray)
						imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

						waitKey(0)

