



"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "google/realm-cc-news-pretrained-embedder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-encoder": (
        "https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-scorer": (
        "https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json"
    ),
    "google/realm-cc-news-pretrained-openqa": (
        "https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json"
    ),
    "google/realm-orqa-nq-openqa": "https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json",
    "google/realm-orqa-nq-reader": "https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json",
    "google/realm-orqa-wq-openqa": "https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json",
    "google/realm-orqa-wq-reader": "https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json",
    # See all REALM models at https://huggingface.co/models?filter=realm
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"realm"



						def __init__(self						,     _lowercase=30522						,     _lowercase=768						,     _lowercase=128						,     _lowercase=12						,     _lowercase=12						,     _lowercase=8						,     _lowercase=3072						,     _lowercase="gelu_new"						,     _lowercase=0.1						,     _lowercase=0.1						,     _lowercase=512						,     _lowercase=2						,     _lowercase=0.02						,     _lowercase=1e-12						,     _lowercase=256						,     _lowercase=10						,     _lowercase=1e-3						,     _lowercase=5						,     _lowercase=320						,     _lowercase=13353718						,     _lowercase=5000						,     _lowercase=1						,     _lowercase=0						,     _lowercase=2						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(pad_token_id=_lowercase						,     bos_token_id=_lowercase						,     eos_token_id=_lowercase						,     **_lowercase							)

													# Common config
													__a		:		Any									=       vocab_size
													__a		:		Union[str, Any]									=       max_position_embeddings
													__a		:		int									=       hidden_size
													__a		:		Optional[int]									=       retriever_proj_size
													__a		:		int									=       num_hidden_layers
													__a		:		List[Any]									=       num_attention_heads
													__a		:		Union[str, Any]									=       num_candidates
													__a		:		int									=       intermediate_size
													__a		:		List[Any]									=       hidden_act
													__a		:		Dict									=       hidden_dropout_prob
													__a		:		int									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		str									=       type_vocab_size
													__a		:		List[Any]									=       layer_norm_eps

													# Reader config
													__a		:		Any									=       span_hidden_size
													__a		:		List[Any]									=       max_span_width
													__a		:		Tuple									=       reader_layer_norm_eps
													__a		:		Dict									=       reader_beam_size
													__a		:		List[Any]									=       reader_seq_len

													# Retrieval config
													__a		:		str									=       num_block_records
													__a		:		int									=       searcher_beam_size






"""simple docstring"""

import math
import sys

import cva
import numpy as np





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float					):
							# For applying gaussian function for each element in matrix.
							__a		:		int									=       math.sqrt(_lowerCamelCase					)
							__a		:		Any									=       1 / (sigma * math.sqrt(2 * math.pi					))
							return cons * np.exp(-((img / sigma) ** 2) * 0.5					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: int					):
							__a		:		Any									=       kernel_size // 2
							return img[x - half : x + half + 1, y - half : y + half + 1]





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: float					):
							# Creates a gaussian kernel of given dimension.
							__a		:		int									=       np.zeros((kernel_size, kernel_size)					)
							for i in range(0  ,       _lowerCamelCase					):
														for j in range(0  ,       _lowerCamelCase					):
																					__a		:		Any									=       math.sqrt(
																					    abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
							return vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: np.ndarray  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: float  ,       _lowerCamelCase				: int  ,       ):
							__a		:		Tuple									=       np.zeros(img.shape					)
							__a		:		Optional[int]									=       get_gauss_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a							,     __a		:		int									=       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		:		List[str]									=       get_slice(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       img_s - img_s[kernel_size // 2, kernel_size // 2]
																					__a		:		Optional[Any]									=       vec_gaussian(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Optional[Any]									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Any									=       np.multiply(_lowerCamelCase  ,       _lowerCamelCase					)
																					__a		:		Tuple									=       np.sum(_lowerCamelCase					) / np.sum(_lowerCamelCase					)
																					__a		:		Optional[Any]									=       val
							return imga





def       __magic_name__    (   _lowerCamelCase				: list					):
							__a		:		Optional[Any]									=       args[1] if args[1:] else """../image_data/lena.jpg"""
							__a		:		Union[str, Any]									=       float(args[2]					) if args[2:] else 1.0
							__a		:		Optional[int]									=       float(args[3]					) if args[3:] else 1.0
							if args[4:]:
														__a		:		Any									=       int(args[4]					)
														__a		:		Any									=       kernel_size + abs(kernel_size % 2 - 1					)
							else:
														__a		:		Optional[int]									=       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()

