



"""simple docstring"""

import os
import sys

lowercase__					:						Any												=			os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase__					:						Optional[Any]												=			[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]




@add_start_docstrings(AutoConfig.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoConfig.from_pretrained(*_a	,    **_a					)




@add_start_docstrings(AutoTokenizer.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoTokenizer.from_pretrained(*_a	,    **_a					)




@add_start_docstrings(AutoModel.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoModel.from_pretrained(*_a	,    **_a					)




@add_start_docstrings(AutoModelForCausalLM.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoModelForCausalLM.from_pretrained(*_a	,    **_a					)




@add_start_docstrings(AutoModelForMaskedLM.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoModelForMaskedLM.from_pretrained(*_a	,    **_a					)




@add_start_docstrings(AutoModelForSequenceClassification.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoModelForSequenceClassification.from_pretrained(*_a	,    **_a					)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__					)
def __lowercase    (							*_a	,    **_a					):
    return AutoModelForQuestionAnswering.from_pretrained(*_a	,    **_a					)










"""simple docstring"""

import math
import sys

import cva
import numpy as np




def __lowercase    (							_a	,    _a					):
    # For applying gaussian function for each element in matrix.
    snake_case_				:  Dict       =			math.sqrt(_a					)
    snake_case_				:  Tuple       =			1 / (sigma * math.sqrt(2 * math.pi					))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5					)




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Optional[int]       =			kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def __lowercase    (							_a	,    _a					):
    # Creates a gaussian kernel of given dimension.
    snake_case_				:  Union[str, Any]       =			np.zeros((kernel_size, kernel_size)					)
    for i in range(0	,    _a					):
        for j in range(0	,    _a					):
            snake_case_				:  Union[str, Any]       =			math.sqrt(
                abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
    return vec_gaussian(_a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    ):
    snake_case_				:  int       =			np.zeros(img.shape					)
    snake_case_				:  str       =			get_gauss_kernel(_a	,    _a					)
    snake_case_,			snake_case_				:  Any       =			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					):
            snake_case_				:  List[Any]       =			get_slice(_a	,    _a	,    _a	,    _a					)
            snake_case_				:  Union[str, Any]       =			img_s - img_s[kernel_size // 2, kernel_size // 2]
            snake_case_				:  Optional[Any]       =			vec_gaussian(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  List[Any]       =			np.sum(_a					) / np.sum(_a					)
            snake_case_				:  Dict       =			val
    return imga




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			args[1] if args[1:] else '''../image_data/lena.jpg'''
    snake_case_				:  int       =			float(args[2]					) if args[2:] else 1.0
    snake_case_				:  Dict       =			float(args[3]					) if args[3:] else 1.0
    if args[4:]:
        snake_case_				:  Optional[Any]       =			int(args[4]					)
        snake_case_				:  Union[str, Any]       =			kernel_size + abs(kernel_size % 2 - 1					)
    else:
        snake_case_				:  Union[str, Any]       =			5
    return filename, spatial_variance, intensity_variance, kernel_size


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

     lowercase__					:						Tuple												=			img / 2_55
     lowercase__					:						Optional[int]												=			out.astype('''float32''')
     lowercase__					:						Union[str, Any]												=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowercase__					:						List[str]												=			out * 2_55
     lowercase__					:						List[str]												=			np.uinta(out)
     cva.imshow('''output image''', out)
     cva.waitKey(0)
     cva.destroyAllWindows()





