


'''simple docstring'''




from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


_lowerCAmelCase  :		Tuple		     =	logging.get_logger(__name__)



@dataclass
class  lowerCAmelCase				(				a__							):
	_lowerCamelCase   :					Any			      = [
	    """no_inference""",
	    """no_cuda""",
	    """no_tpu""",
	    """no_speed""",
	    """no_memory""",
	    """no_env_print""",
	    """no_multi_process""",
	]



	def __init__(					self ,					**snake_case__    ):





		for deprecated_arg in self.deprecated_args:
			if deprecated_arg in kwargs:
				lowerCAmelCase					:    Optional[int]      							= deprecated_arg[3:]
				setattr(self ,					lowercase__ ,					not kwargs.pop(lowercase__    )    )
				logger.warning(
				    f"{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"
				    f" {positive_arg}={kwargs[positive_arg]}"    )

		lowerCAmelCase					:    Optional[Any]      							= kwargs.pop('torchscript' ,					self.torchscript    )
		lowerCAmelCase					:    Dict      							= kwargs.pop('torch_xla_tpu_print_metrics' ,					self.torch_xla_tpu_print_metrics    )
		lowerCAmelCase					:    Tuple      							= kwargs.pop('fp16_opt_level' ,					self.fpaa_opt_level    )
		super().__init__(**lowercase__    )

	_lowerCamelCase   :					bool			      = field(default=a__      ,				metadata={"""help""": """Trace the models using torchscript"""}							)
	_lowerCamelCase   :					bool			      = field(default=a__      ,				metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}							)
	_lowerCamelCase   :					str			      = field(
	    default="""O1"""      ,				metadata={
	        """help""": (
	            """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']. """
	            """See details at https://nvidia.github.io/apex/amp.html"""
	        )
	    }      ,				)



	@cached_property
	def 				lowercase     (					self    ):
		requires_backends(self ,					['torch']    )
		logger.info('PyTorch: setting up devices'    )
		if not self.cuda:
			lowerCAmelCase					:    List[str]      							= torch.device('cpu'    )
			lowerCAmelCase					:    int      							= 0
		elif is_torch_tpu_available():
			lowerCAmelCase					:    Tuple      							= xm.xla_device()
			lowerCAmelCase					:    int      							= 0
		else:
			lowerCAmelCase					:    Union[str, Any]      							= torch.device('cuda' if torch.cuda.is_available() else 'cpu'    )
			lowerCAmelCase					:    Optional[Any]      							= torch.cuda.device_count()
		return device, n_gpu



	@property
	def 				lowercase     (					self    ):
		return is_torch_tpu_available() and self.tpu



	@property
	def 				lowercase     (					self    ):
		requires_backends(self ,					['torch']    )
		# TODO(PVP): currently only single GPU is supported
		return torch.cuda.current_device()



	@property
	def 				lowercase     (					self    ):
		requires_backends(self ,					['torch']    )
		return self._setup_devices[0]



	@property
	def 				lowercase     (					self    ):
		requires_backends(self ,					['torch']    )
		return self._setup_devices[1]



	@property
	def 				lowercase     (					self    ):
		return self.n_gpu > 0




'''simple docstring'''




import math
import sys

import cva
import numpy as np

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




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= math.sqrt(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= 1 / (sigma * math.sqrt(2 * math.pi				))
	return cons * np.exp(-((img / sigma) ** 2) * 0.5				)

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




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				int					,   _A    :				float				)    ->      np.ndarray:




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				np.ndarray					,   _A    :				float					,   _A    :				float					,   _A    :				int					,   )    ->      np.ndarray:




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				list				)    ->      tuple:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= args[1] if args[1:] else '../image_data/lena.jpg'
	lowerCAmelCase					:    Any      							= 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					:    int      							= int(args[4]				)
		lowerCAmelCase					:    Optional[Any]      							= kernel_size + abs(kernel_size % 2 - 1				)
	else:
		lowerCAmelCase					:    Optional[int]      							= 5
	return filename, spatial_variance, intensity_variance, kernel_size


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

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