
"""simple docstring"""



import math
import sys

import cva
import numpy as np


def  _UpperCamelCase	(						_A					,      _A      )    ->  np.ndarray:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A      )    ->  np.ndarray:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A      )    ->  np.ndarray:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A					,      _A					,      _A					,      _A					,      )    ->  np.ndarray:





    """simple docstring"""

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


def  _UpperCamelCase	(						_A      )    ->  tuple:





    """simple docstring"""

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


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

 a					:	str							=		img / 2_5_5
 a					:	Union[str, Any]							=		out.astype('''float32''')
 a					:	str							=		bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
 a					:	List[str]							=		out * 2_5_5
 a					:	Any							=		np.uinta(out)
 cva.imshow('''output image''', out)
 cva.waitKey(0)
 cva.destroyAllWindows()

"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 		a_						( _UpperCAmelCase	):
    a      :				Any     =				['image_processor', 'tokenizer']
    a      :				Optional[int]     =				'AutoImageProcessor'
    a      :				Any     =				'AutoTokenizer'







    def __init__(			self			:       List[str]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       List[str]=None     ,					**__UpperCamelCase			:       List[Any]     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   None
        if "feature_extractor" in kwargs:
            warnings.warn(
                """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                """ instead."""     ,					__UpperCamelCase     ,					)
            _UpperCAmelCase          =   kwargs.pop("""feature_extractor"""     )

        _UpperCAmelCase          =   image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
            raise ValueError("""You need to specify an `image_processor`."""     )
        if tokenizer is None:
            raise ValueError("""You need to specify a `tokenizer`."""     )

        super().__init__(__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def __call__(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->List[str]:






        '''simple docstring'''






        if self._in_target_context_manager:
            return self.current_processor(*__UpperCamelCase     ,					**__UpperCamelCase     )

        _UpperCAmelCase          =   kwargs.pop("""images"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   kwargs.pop("""text"""     ,					__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            _UpperCAmelCase          =   args[0]
            _UpperCAmelCase          =   args[1:]

        if images is None and text is None:
            raise ValueError("""You need to specify either an `images` or `text` input to process."""     )

        if images is not None:
            _UpperCAmelCase          =   self.image_processor(__UpperCamelCase     ,					*__UpperCamelCase     ,					**__UpperCamelCase     )
        if text is not None:
            _UpperCAmelCase          =   self.tokenizer(__UpperCamelCase     ,					**__UpperCamelCase     )

        if text is None:
            return inputs
        elif images is None:
            return encodings
        else:
            _UpperCAmelCase          =   encodings["""input_ids"""]
            return inputs







    def      _snake_case					(			self			:       Union[str, Any]     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Tuple     )		->Tuple:






        '''simple docstring'''






        return self.tokenizer.batch_decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       int     ,					**__UpperCamelCase			:       Union[str, Any]     )		->int:






        '''simple docstring'''






        return self.tokenizer.decode(*__UpperCamelCase     ,					**__UpperCamelCase     )







    @contextmanager
    def      _snake_case					(			self			:       Tuple     )		->Union[str, Any]:






        '''simple docstring'''






        warnings.warn(
            """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
            """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
            """your images inputs, or in a separate call."""     )
        _UpperCAmelCase          =   True
        _UpperCAmelCase          =   self.tokenizer
        yield
        _UpperCAmelCase          =   self.image_processor
        _UpperCAmelCase          =   False







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]=False     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->List[str]:






        '''simple docstring'''






        if added_vocab is None:
            _UpperCAmelCase          =   self.tokenizer.get_added_vocab()

        _UpperCAmelCase          =   {}

        while tokens:
            _UpperCAmelCase          =   re.search(r"""<s_(.*?)>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            if start_token is None:
                break
            _UpperCAmelCase          =   start_token.group(1     )
            _UpperCAmelCase          =   re.search(rf"""</s_{key}>"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
            _UpperCAmelCase          =   start_token.group()
            if end_token is None:
                _UpperCAmelCase          =   tokens.replace(__UpperCamelCase     ,					""""""     )
            else:
                _UpperCAmelCase          =   end_token.group()
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.escape(__UpperCamelCase     )
                _UpperCAmelCase          =   re.search(f"""{start_token_escaped}(.*?){end_token_escaped}"""     ,					__UpperCamelCase     ,					re.IGNORECASE     )
                if content is not None:
                    _UpperCAmelCase          =   content.group(1     ).strip()
                    if r"<s_" in content and r"</s_" in content:  # non-leaf node
                        _UpperCAmelCase          =   self.tokenajson(__UpperCamelCase     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )
                        if value:
                            if len(__UpperCamelCase     ) == 1:
                                _UpperCAmelCase          =   value[0]
                            _UpperCAmelCase          =   value
                    else:  # leaf nodes
                        _UpperCAmelCase          =   []
                        for leaf in content.split(r"""<sep/>"""     ):
                            _UpperCAmelCase          =   leaf.strip()
                            if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                _UpperCAmelCase          =   leaf[1:-2]  # for categorical special tokens
                            output[key].append(__UpperCamelCase     )
                        if len(output[key]     ) == 1:
                            _UpperCAmelCase          =   output[key][0]

                _UpperCAmelCase          =   tokens[tokens.find(__UpperCamelCase     ) + len(__UpperCamelCase     ) :].strip()
                if tokens[:6] == r"<sep/>":  # non-leaf nodes
                    return [output] + self.tokenajson(tokens[6:]     ,					is_inner_value=__UpperCamelCase     ,					added_vocab=__UpperCamelCase     )

        if len(__UpperCamelCase     ):
            return [output] if is_inner_value else output
        else:
            return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def      _snake_case					(			self			:       Tuple     )		->Optional[int]:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor_class







    @property
    def      _snake_case					(			self			:       List[str]     )		->Dict:






        '''simple docstring'''






        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,					__UpperCamelCase     ,					)
        return self.image_processor