


from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 							__lowercase   ( __lowerCAmelCase							:				NDArray[floataa]					,    __lowerCAmelCase							:				NDArray[floataa]					,    __lowerCAmelCase							:				list[int]					,    __lowerCAmelCase							:				int					,    ):
							a__      ,					a__             =     coefficient_matrix.shape
							a__      ,					a__             =     constant_matrix.shape

							if rowsa != colsa:
														a__             =     F'Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'
														raise ValueError(__lowerCAmelCase   )

							if colsa != 1:
														a__             =     F'Constant matrix must be nx1 but received {rowsa}x{colsa}'
														raise ValueError(__lowerCAmelCase   )

							if rowsa != rowsa:
														a__             =     (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'received {rowsa}x{colsa} and {rowsa}x{colsa}'
														)
														raise ValueError(__lowerCAmelCase   )

							if len(__lowerCAmelCase   ) != rowsa:
														a__             =     (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'matrix but received {len(__lowerCAmelCase   )} and {rowsa}'
														)
														raise ValueError(__lowerCAmelCase   )

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'   )

							a__             =     np.concatenate(
							    (coefficient_matrix, constant_matrix)					,    axis=1   )

							a__      ,					a__             =     table.shape

							strictly_diagonally_dominant(__lowerCAmelCase   )

							# Iterates the whole matrix for given number of times
							for _ in range(__lowerCAmelCase   ):
														a__             =     []
														for row in range(__lowerCAmelCase   ):
																					a__             =     0
																					for col in range(__lowerCAmelCase   ):
																												if col == row:
																																			a__             =     table[row][col]
																												elif col == cols - 1:
																																			a__             =     table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					a__             =     (temp + val) / denom
																					new_val.append(__lowerCAmelCase   )
														a__             =     new_val

							return [float(__lowerCAmelCase   ) for i in new_val]






def 							__lowercase   ( __lowerCAmelCase							:				NDArray[floataa]   ):
							a__      ,					a__             =     table.shape

							a__             =     True

							for i in range(0					,    __lowerCAmelCase   ):
														a__             =     0
														for j in range(0					,    cols - 1   ):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'   )

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
						import doctest

						doctest.testmod()








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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case							:       Union[str, Any]     			=      logging.get_logger(__name__)







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    List[Any]          =			['''pixel_values''']
					def __init__(      self   :Optional[int]			,__snake_case   :bool = True			,__snake_case   :Optional[Dict[str, int]] = None			,__snake_case   :PILImageResampling = PILImageResampling.BICUBIC			,__snake_case   :bool = True			,__snake_case   :bool = True			,__snake_case   :Union[int, float] = 1 / 2_55			,__snake_case   :Dict[str, int] = None			,__snake_case   :bool = True			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,**__snake_case   :Dict			,)					->    None:
												super().__init__(**__snake_case      )
												a__             =     size if size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case      )
												a__             =     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
												a__             =     get_size_dict(__snake_case			,default_to_square=__snake_case			,param_name='crop_size'      )

												a__             =     do_resize
												a__             =     do_rescale
												a__             =     do_normalize
												a__             =     do_center_crop
												a__             =     crop_size
												a__             =     size
												a__             =     resample
												a__             =     rescale_factor
												a__             =     image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												a__             =     image_std if image_std is not None else IMAGENET_DEFAULT_STD
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :PILImageResampling = PILImageResampling.BILINEAR			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :List[Any]			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )

												if "shortest_edge" in size:
																			a__             =     get_resize_output_image_size(__snake_case			,size=size['shortest_edge']			,default_to_square=__snake_case      )
																			# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
												elif "height" in size and "width" in size:
																			a__             =     (size['height'], size['width'])
												else:
																			raise ValueError(F'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
												return resize(__snake_case			,size=__snake_case			,resample=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :np.ndarray			,__snake_case   :Dict[str, int]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												a__             =     get_size_dict(__snake_case      )
												if "height" not in size or "width" not in size:
																			raise ValueError(F'The `size` parameter must contain the keys (height, width). Got {size.keys()}'      )
												return center_crop(__snake_case			,size=(size['height'], size['width'])			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :List[Any]			,__snake_case   :np.ndarray			,__snake_case   :float			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :int      )					->    np.ndarray:
												return rescale(__snake_case			,scale=__snake_case			,data_format=__snake_case			,**__snake_case      )
					def  lowerCamelCase__(      self   :Tuple			,__snake_case   :np.ndarray			,__snake_case   :Union[float, List[float]]			,__snake_case   :Union[float, List[float]]			,__snake_case   :Optional[Union[str, ChannelDimension]] = None			,**__snake_case   :Any			,)					->    np.ndarray:
												return normalize(__snake_case			,mean=__snake_case			,std=__snake_case			,data_format=__snake_case			,**__snake_case      )



					def  lowerCamelCase__(      self   :Any			,__snake_case   :ImageInput			,__snake_case   :Optional[bool] = None			,__snake_case   :Dict[str, int] = None			,__snake_case   :PILImageResampling = None			,__snake_case   :bool = None			,__snake_case   :int = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[float] = None			,__snake_case   :Optional[bool] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[float, List[float]]] = None			,__snake_case   :Optional[Union[str, TensorType]] = None			,__snake_case   :Union[str, ChannelDimension] = ChannelDimension.FIRST			,**__snake_case   :Optional[int]			,)					->    BatchFeature:
												a__             =     do_resize if do_resize is not None else self.do_resize
												a__             =     do_rescale if do_rescale is not None else self.do_rescale
												a__             =     do_normalize if do_normalize is not None else self.do_normalize
												a__             =     do_center_crop if do_center_crop is not None else self.do_center_crop
												a__             =     crop_size if crop_size is not None else self.crop_size
												a__             =     get_size_dict(__snake_case			,param_name='crop_size'			,default_to_square=__snake_case      )
												a__             =     resample if resample is not None else self.resample
												a__             =     rescale_factor if rescale_factor is not None else self.rescale_factor
												a__             =     image_mean if image_mean is not None else self.image_mean
												a__             =     image_std if image_std is not None else self.image_std

												a__             =     size if size is not None else self.size
												a__             =     get_size_dict(__snake_case      )

												if not is_batched(__snake_case      ):
																			a__             =     [images]

												if not valid_images(__snake_case      ):
																			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:
																			raise ValueError('Size must be specified if do_resize is True.'      )

												if do_center_crop and crop_size is None:
																			raise ValueError('Crop size 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.'      )

												# All transformations expect numpy arrays.
												a__             =     [to_numpy_array(__snake_case      ) for image in images]

												if do_resize:
																			a__             =     [self.resize(image=__snake_case			,size=__snake_case			,resample=__snake_case      ) for image in images]

												if do_center_crop:
																			a__             =     [self.center_crop(image=__snake_case			,size=__snake_case      ) for image in images]

												if do_rescale:
																			a__             =     [self.rescale(image=__snake_case			,scale=__snake_case      ) for image in images]

												if do_normalize:
																			a__             =     [self.normalize(image=__snake_case			,mean=__snake_case			,std=__snake_case      ) for image in images]

												a__             =     [to_channel_dimension_format(__snake_case			,__snake_case      ) for image in images]

												a__             =     {'pixel_values': images}
												return BatchFeature(data=__snake_case			,tensor_type=__snake_case      )




