



'''simple docstring'''
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


snake_case_			:     List[str]  	=  logging.get_logger(__name__)






class 							lowercase__     (							lowercase       ):
	lowercase__      =      ['pixel_values']





	def __init__(			self :    List[str]			,lowerCamelCase__ :    List[Any] = True			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    List[Any] = 0.9			,lowerCamelCase__ :    List[Any] = PILImageResampling.BICUBIC			,lowerCamelCase__ :    Any = True			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    int = 1 / 255			,lowerCamelCase__ :    List[str] = True			,lowerCamelCase__ :    Dict = True			,lowerCamelCase__ :    str = None			,lowerCamelCase__ :    Union[str, Any] = None			,**lowerCamelCase__ :    Optional[int]			,):







							'''simple docstring'''
							super().__init__(**__lowerCAmelCase			)
							_UpperCamelCase :		Tuple			    =       size if size is not None else {'shortest_edge': 224}
							_UpperCamelCase :		Dict			    =       get_size_dict(__lowerCAmelCase			,default_to_square=__lowerCAmelCase			)
							_UpperCamelCase :		int			    =       crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase :		List[str]			    =       get_size_dict(__lowerCAmelCase			,param_name='crop_size'			)

							_UpperCamelCase :		int			    =       do_resize
							_UpperCamelCase :		Any			    =       size
							_UpperCamelCase :		Dict			    =       crop_pct
							_UpperCamelCase :		Union[str, Any]			    =       resample
							_UpperCamelCase :		Optional[int]			    =       do_center_crop
							_UpperCamelCase :		Tuple			    =       crop_size
							_UpperCamelCase :		List[str]			    =       do_rescale
							_UpperCamelCase :		Dict			    =       rescale_factor
							_UpperCamelCase :		List[str]			    =       do_normalize
							_UpperCamelCase :		Optional[int]			    =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							_UpperCamelCase :		List[str]			    =       image_std if image_std is not None else IMAGENET_DEFAULT_STD





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[Any] = None			,lowerCamelCase__ :    List[str] = PILImageResampling.BICUBIC			,lowerCamelCase__ :    Optional[Any] = None			,**lowerCamelCase__ :    Any			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       get_size_dict(__lowerCAmelCase			,default_to_square=__lowerCAmelCase			)
							if "shortest_edge" not in size and ("height" not in size or "width" not in size):
													raise ValueError(F'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'			)
							if crop_pct is not None:
													if "shortest_edge" in size:
																			_UpperCamelCase :		Optional[int]			    =       int(size['shortest_edge'] / crop_pct			)
													elif "height" in size and "width" in size:
																			if size["height"] == size["width"]:
																									_UpperCamelCase :		List[Any]			    =       int(size['height'] / crop_pct			)
																			else:
																									_UpperCamelCase :		Optional[int]			    =       (int(size['height'] / crop_pct			), int(size['width'] / crop_pct			))
													else:
																			raise ValueError('Invalid size for resize: {}'.format(__lowerCAmelCase			)			)

													_UpperCamelCase :		int			    =       get_resize_output_image_size(__lowerCAmelCase			,size=__lowerCAmelCase			,default_to_square=__lowerCAmelCase			)
							else:
													if "shortest_edge" in size:
																			_UpperCamelCase :		Union[str, Any]			    =       get_resize_output_image_size(__lowerCAmelCase			,size=size['shortest_edge']			,default_to_square=__lowerCAmelCase			)
													elif "height" in size and "width" in size:
																			_UpperCamelCase :		Union[str, Any]			    =       (size['height'], size['width'])
													else:
																			raise ValueError('Invalid size for resize: {}'.format(__lowerCAmelCase			)			)

							return resize(__lowerCAmelCase			,size=__lowerCAmelCase			,resample=__lowerCAmelCase			,data_format=__lowerCAmelCase			,**__lowerCAmelCase			)





	def      UpperCamelCase_  (			self :    Optional[Any]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Optional[Any] = None			,**lowerCamelCase__ :    Optional[int]			,):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       get_size_dict(__lowerCAmelCase			)
							if "height" not in size or "width" not in size:
													raise ValueError(F'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'			)
							return center_crop(__lowerCAmelCase			,size=(size['height'], size['width'])			,data_format=__lowerCAmelCase			,**__lowerCAmelCase			)





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Any			,lowerCamelCase__ :    List[str] = None			,**lowerCamelCase__ :    Any			,):







							'''simple docstring'''
							return rescale(__lowerCAmelCase			,scale=__lowerCAmelCase			,data_format=__lowerCAmelCase			,**__lowerCAmelCase			)





	def      UpperCamelCase_  (			self :    Optional[int]			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Union[str, Any]			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    List[Any] = None			,**lowerCamelCase__ :    Any			,):







							'''simple docstring'''
							return normalize(__lowerCAmelCase			,mean=__lowerCAmelCase			,std=__lowerCAmelCase			,data_format=__lowerCAmelCase			,**__lowerCAmelCase			)





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Dict			,lowerCamelCase__ :    Optional[Any] = None			,lowerCamelCase__ :    Any = None			,lowerCamelCase__ :    List[str] = None			,lowerCamelCase__ :    str = None			,lowerCamelCase__ :    Union[str, Any] = None			,lowerCamelCase__ :    Union[str, Any] = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    List[Any] = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Tuple = None			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Tuple = ChannelDimension.FIRST			,**lowerCamelCase__ :    int			,):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase :		str			    =       crop_pct if crop_pct is not None else self.crop_pct
							_UpperCamelCase :		Any			    =       resample if resample is not None else self.resample
							_UpperCamelCase :		int			    =       do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase :		Optional[int]			    =       do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase :		List[str]			    =       rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase :		str			    =       do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase :		List[str]			    =       image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase :		Dict			    =       image_std if image_std is not None else self.image_std

							_UpperCamelCase :		str			    =       size if size is not None else self.size
							_UpperCamelCase :		Dict			    =       get_size_dict(__lowerCAmelCase			,default_to_square=__lowerCAmelCase			)
							_UpperCamelCase :		Dict			    =       crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase :		Optional[int]			    =       get_size_dict(__lowerCAmelCase			,param_name='crop_size'			)

							_UpperCamelCase :		Tuple			    =       make_list_of_images(__lowerCAmelCase			)

							if not valid_images(__lowerCAmelCase			):
													raise ValueError(
													    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
													    'torch.Tensor, tf.Tensor or jax.ndarray.'			)

							if do_resize and size is None or resample is None:
													raise ValueError('Size and resample must be specified if do_resize is True.'			)

							if do_center_crop and crop_pct is None:
													raise ValueError('Crop_pct must be specified if do_center_crop is True.'			)

							if do_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'			)

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('Image mean and std must be specified if do_normalize is True.'			)

							# All transformations expect numpy arrays.
							_UpperCamelCase :		Any			    =       [to_numpy_array(__lowerCAmelCase			) for image in images]

							if do_resize:
													_UpperCamelCase :		Any			    =       [self.resize(image=__lowerCAmelCase			,size=__lowerCAmelCase			,crop_pct=__lowerCAmelCase			,resample=__lowerCAmelCase			) for image in images]

							if do_center_crop:
													_UpperCamelCase :		List[Any]			    =       [self.center_crop(image=__lowerCAmelCase			,size=__lowerCAmelCase			) for image in images]

							if do_rescale:
													_UpperCamelCase :		str			    =       [self.rescale(image=__lowerCAmelCase			,scale=__lowerCAmelCase			) for image in images]

							if do_normalize:
													_UpperCamelCase :		Any			    =       [self.normalize(image=__lowerCAmelCase			,mean=__lowerCAmelCase			,std=__lowerCAmelCase			) for image in images]

							_UpperCamelCase :		Dict			    =       [to_channel_dimension_format(__lowerCAmelCase			,__lowerCAmelCase			) for image in images]

							_UpperCamelCase :		Dict			    =       {'pixel_values': images}
							return BatchFeature(data=__lowerCAmelCase			,tensor_type=__lowerCAmelCase			)










'''simple docstring'''
from __future__ import annotations

import numpy as np






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[int]			    =       np.shape(UpperCAmelCase_							)
						if rows != columns:
												_UpperCamelCase :		Union[str, Any]			    =       (
												    '\'table\' has to be of square shaped array but got a '
												    f'{rows}x{columns} array:\n{table}'
												)
												raise ValueError(UpperCAmelCase_							)

						_UpperCamelCase :		Optional[Any]			    =       np.zeros((rows, columns)							)
						_UpperCamelCase :		Tuple			    =       np.zeros((rows, columns)							)
						for i in range(UpperCAmelCase_							):
												for j in range(UpperCAmelCase_							):
																		_UpperCamelCase :		Optional[Any]			    =       sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase_							)							)
																		if upper[j][j] == 0:
																								raise ArithmeticError('No LU decomposition exists'							)
																		_UpperCamelCase :		Optional[Any]			    =       (table[i][j] - total) / upper[j][j]
												_UpperCamelCase :		int			    =       1
												for j in range(UpperCAmelCase_  ,  UpperCAmelCase_							):
																		_UpperCamelCase :		Optional[int]			    =       sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase_							)							)
																		_UpperCamelCase :		Tuple			    =       table[i][j] - total
						return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()


