import math

import flax.linen as nn
import jax.numpy as jnp






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase = 1,		UpperCAmelCase = 1,		UpperCAmelCase = 1.0e4,		UpperCAmelCase = False,		UpperCAmelCase = 1.0,		)->       jnp.ndarray:



			"""simple docstring"""







			assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
			assert embedding_dim % 2 == 0, f'Embedding dimension {embedding_dim} should be even'
			lowercase							=		float(embedding_dim // 2       )
			lowercase							=		math.log(max_timescale / min_timescale       ) / (num_timescales - freq_shift)
			lowercase							=		min_timescale * jnp.exp(jnp.arange(UpperCAmelCase,		dtype=jnp.floataa       ) * -log_timescale_increment       )
			lowercase							=		jnp.expand_dims(UpperCAmelCase,		1       ) * jnp.expand_dims(UpperCAmelCase,		0       )

			# scale embeddings
			lowercase							=		scale * emb

			if flip_sin_to_cos:
						lowercase							=		jnp.concatenate([jnp.cos(UpperCAmelCase       ), jnp.sin(UpperCAmelCase       )],		axis=1       )
			else:
						lowercase							=		jnp.concatenate([jnp.sin(UpperCAmelCase       ), jnp.cos(UpperCAmelCase       )],		axis=1       )
			lowercase							=		jnp.reshape(UpperCAmelCase,		[jnp.shape(UpperCAmelCase       )[0], embedding_dim]       )
			return signal






class      __lowercase					(    nn.Module    ):
						lowercase					 =     32
						lowercase					 =     jnp.floataa

						@nn.compact
						def __call__(	self				:      str							,							__lowerCamelCase				:      str   )							->		int:


									'''simple docstring'''

									lowercase							=		nn.Dense(self.time_embed_dim							,							dtype=self.dtype							,							name='''linear_1'''   )(__lowerCamelCase   )
									lowercase							=		nn.silu(__lowerCamelCase   )
									lowercase							=		nn.Dense(self.time_embed_dim							,							dtype=self.dtype							,							name='''linear_2'''   )(__lowerCamelCase   )
									return temb






class      __lowercase					(    nn.Module    ):
						lowercase					 =     32
						lowercase					 =     False
						lowercase					 =     1

						@nn.compact
						def __call__(	self				:      List[Any]							,							__lowerCamelCase				:      Dict   )							->		Dict:


									'''simple docstring'''

									return get_sinusoidal_embeddings(
									    __lowerCamelCase							,							embedding_dim=self.dim							,							flip_sin_to_cos=self.flip_sin_to_cos							,							freq_shift=self.freq_shift   )






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







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






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







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


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

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





