





'''simple docstring'''







from __future__ import annotations

from typing import Any


class 					__a      :

							def __init__(							self							:     str       ,     __magic_name__							:     int       ,     __magic_name__							:     int       ,     __magic_name__							:     float = 0   )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[Any]     						=  row, column
													UpperCAmelCase_			:      Any     						=  [[default_value for c in range(__magic_name__   )] for r in range(__magic_name__   )]

							def __str__(							self							:     Any   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

													# Make string identifier
													UpperCAmelCase_			:      Tuple     						=  0
													for row_vector in self.array:
																			for obj in row_vector:
																									UpperCAmelCase_			:      Optional[Any]     						=  max(__magic_name__       ,     len(str(__magic_name__   )   )   )
													UpperCAmelCase_			:      Union[str, Any]     						=  F"""%{max_element_length}s"""

													# Make string and return
													def single_line(__magic_name__							:     list[float]   ) -> str:
																			nonlocal string_format_identifier
																			UpperCAmelCase_			:      Dict     						=  '''['''
																			line += ", ".join(string_format_identifier % (obj,) for obj in row_vector   )
																			line += "]"
																			return line

													s += "\n".join(single_line(__magic_name__   ) for row_vector in self.array   )
													return s

							def __repr__(							self							:     Tuple   )					->       str:







													"""simple docstring"""
													return str(self   )

							def     UpperCAmelCase__	(							self							:     Tuple       ,     __magic_name__							:     tuple[int, int]   )					->       bool:







													"""simple docstring"""
													if not (isinstance(__magic_name__       ,     (list, tuple)   ) and len(__magic_name__   ) == 2):
																			return False
													elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																			return False
													else:
																			return True

							def __getitem__(							self							:     Optional[Any]       ,     __magic_name__							:     tuple[int, int]   )					->       Any:







													"""simple docstring"""
													assert self.validate_indicies(__magic_name__   )
													return self.array[loc[0]][loc[1]]

							def __setitem__(							self							:     str       ,     __magic_name__							:     tuple[int, int]       ,     __magic_name__							:     float   )					->       None:







													"""simple docstring"""
													assert self.validate_indicies(__magic_name__   )
													UpperCAmelCase_			:      Optional[int]     						=  value

							def __add__(							self							:     Any       ,     __magic_name__							:     Matrix   )					->       Matrix:







													"""simple docstring"""
													assert isinstance(__magic_name__       ,     __magic_name__   )
													assert self.row == another.row and self.column == another.column

													# Add
													UpperCAmelCase_			:      Optional[Any]     						=  Matrix(self.row       ,     self.column   )
													for r in range(self.row   ):
																			for c in range(self.column   ):
																									UpperCAmelCase_			:      str     						=  self[r, c] + another[r, c]
													return result

							def __neg__(							self							:     Optional[Any]   )					->       Matrix:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  Matrix(self.row       ,     self.column   )
													for r in range(self.row   ):
																			for c in range(self.column   ):
																									UpperCAmelCase_			:      str     						=  -self[r, c]
													return result

							def __sub__(							self							:     Dict       ,     __magic_name__							:     Matrix   )					->       Matrix:







													"""simple docstring"""
													return self + (-another)

							def __mul__(							self							:     Tuple       ,     __magic_name__							:     int | float | Matrix   )					->       Matrix:







													"""simple docstring"""

													if isinstance(__magic_name__       ,     (int, float)   ):  # Scalar multiplication
																			UpperCAmelCase_			:      Tuple     						=  Matrix(self.row       ,     self.column   )
																			for r in range(self.row   ):
																									for c in range(self.column   ):
																															UpperCAmelCase_			:      List[str]     						=  self[r, c] * another
																			return result
													elif isinstance(__magic_name__       ,     __magic_name__   ):  # Matrix multiplication
																			assert self.column == another.row
																			UpperCAmelCase_			:      Dict     						=  Matrix(self.row       ,     another.column   )
																			for r in range(self.row   ):
																									for c in range(another.column   ):
																															for i in range(self.column   ):
																																					result[r, c] += self[r, i] * another[i, c]
																			return result
													else:
																			UpperCAmelCase_			:      List[str]     						=  F"""Unsupported type given for another ({type(__magic_name__   )})"""
																			raise TypeError(__magic_name__   )

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Matrix:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  Matrix(self.column       ,     self.row   )
													for r in range(self.row   ):
																			for c in range(self.column   ):
																									UpperCAmelCase_			:      List[Any]     						=  self[r, c]
													return result



							def     UpperCAmelCase__	(							self							:     Any       ,     __magic_name__							:     Matrix       ,     __magic_name__							:     Matrix   )					->       Any:







													"""simple docstring"""
													assert isinstance(__magic_name__       ,     __magic_name__   ) and isinstance(__magic_name__       ,     __magic_name__   )
													assert self.row == self.column == u.row == v.row  # u, v should be column vector
													assert u.column == v.column == 1  # u, v should be column vector

													# Calculate
													UpperCAmelCase_			:      str     						=  v.transpose()
													UpperCAmelCase_			:      Dict     						=  (v_t * self * u)[0, 0] + 1
													if numerator_factor == 0:
																			return None  # It's not invertable
													return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":





					def        lowerCamelCase_      (  ) ->	None:
											# a^(-1)
											UpperCAmelCase_			:      Any     						=  Matrix(3,			3,			0   )
											for i in range(3   ):
																	UpperCAmelCase_			:      List[str]     						=  1
											print(F"""a^(-1) is {ainv}"""   )
											# u, v
											UpperCAmelCase_			:      List[Any]     						=  Matrix(3,			1,			0   )
											UpperCAmelCase_			:      Tuple     						=  1, 2, -3
											UpperCAmelCase_			:      Optional[Any]     						=  Matrix(3,			1,			0   )
											UpperCAmelCase_			:      Tuple     						=  4, -2, 5
											print(F"""u is {u}"""   )
											print(F"""v is {v}"""   )
											print(F"""uv^T is {u * v.transpose()}"""   )
											# Sherman Morrison
											print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(SCREAMING_SNAKE_CASE__,			SCREAMING_SNAKE_CASE__   )}"""   )





					def        lowerCamelCase_      (  ) ->	None:
											import doctest

											doctest.testmod()
					testa()












'''simple docstring'''







from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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


class 					__a      (lowerCamelCase     ):
							__a					:   Tuple									=       ["pixel_values"]

							def __init__(							self							:     List[Any]       ,     __magic_name__							:     bool = True       ,     __magic_name__							:     int = 32       ,     __magic_name__							:     Union[str, Any]=PILImageResampling.BILINEAR       ,     __magic_name__							:     bool = True       ,     **__magic_name__							:     List[str]       ,     )					->       None:







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  do_resize
													UpperCAmelCase_			:      Tuple     						=  do_rescale
													UpperCAmelCase_			:      List[Any]     						=  size_divisor
													UpperCAmelCase_			:      Any     						=  resample
													super().__init__(**__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[Any]       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     int       ,     __magic_name__							:     str       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Tuple   )					->       np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_						,       UpperCAmelCase_			:      List[str]     						=  get_image_size(__magic_name__   )
													# Rounds the height and width down to the closest multiple of size_divisor
													UpperCAmelCase_			:      Dict     						=  height // size_divisor * size_divisor
													UpperCAmelCase_			:      Dict     						=  width // size_divisor * size_divisor
													UpperCAmelCase_			:      Any     						=  resize(__magic_name__       ,     (new_h, new_w)       ,     resample=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )
													return image

							def     UpperCAmelCase__	(							self							:     int       ,     __magic_name__							:     np.ndarray       ,     __magic_name__							:     float       ,     __magic_name__							:     Optional[ChannelDimension] = None       ,     **__magic_name__							:     Optional[Any]   )					->       np.ndarray:







													"""simple docstring"""
													return rescale(image=__magic_name__       ,     scale=__magic_name__       ,     data_format=__magic_name__       ,     **__magic_name__   )



							def     UpperCAmelCase__	(							self							:     str       ,     __magic_name__							:     Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[int] = None       ,     __magic_name__							:     Any=None       ,     __magic_name__							:     Optional[bool] = None       ,     __magic_name__							:     Optional[Union[TensorType, str]] = None       ,     __magic_name__							:     ChannelDimension = ChannelDimension.FIRST       ,     **__magic_name__							:     Tuple       ,     )					->       BatchFeature:







													"""simple docstring"""
													UpperCAmelCase_			:      Dict     						=  do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_			:      str     						=  do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_			:      Any     						=  size_divisor if size_divisor is not None else self.size_divisor
													UpperCAmelCase_			:      Dict     						=  resample if resample is not None else self.resample

													if do_resize and size_divisor is None:
																			raise ValueError('''size_divisor is required for resizing'''   )

													UpperCAmelCase_			:      Optional[int]     						=  make_list_of_images(__magic_name__   )

													if not valid_images(__magic_name__   ):
																			raise ValueError('''Invalid image(s)'''   )

													# All transformations expect numpy arrays.
													UpperCAmelCase_			:      List[str]     						=  [to_numpy_array(__magic_name__   ) for img in images]

													if do_resize:
																			UpperCAmelCase_			:      str     						=  [self.resize(__magic_name__       ,     size_divisor=__magic_name__       ,     resample=__magic_name__   ) for image in images]

													if do_rescale:
																			UpperCAmelCase_			:      Tuple     						=  [self.rescale(__magic_name__       ,     scale=1 / 2_55   ) for image in images]

													UpperCAmelCase_			:      Union[str, Any]     						=  [to_channel_dimension_format(__magic_name__       ,     __magic_name__   ) for image in images]

													UpperCAmelCase_			:      int     						=  {'''pixel_values''': images}
													return BatchFeature(data=__magic_name__       ,     tensor_type=__magic_name__   )





