
from __future__ import annotations

from typing import Any





class       __A:

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     float = 0   ):
												lowerCamelCase_  ,   lowerCamelCase_							  =					row, column
												lowerCamelCase_							  =					[[default_value for c in range(__A   )] for r in range(__A   )]

					def __str__(	self					:     int   ):
												lowerCamelCase_							  =					F'''Matrix consist of {self.row} rows and {self.column} columns\n'''

												# Make string identifier
												lowerCamelCase_							  =					0
												for row_vector in self.array:
																			for obj in row_vector:
																										lowerCamelCase_							  =					max(__A   ,					len(str(__A   )   )   )
												lowerCamelCase_							  =					F'''%{max_element_length}s'''

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

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

					def __repr__(	self					:     List[str]   ):
												return str(self   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     tuple[int, int]   ):
												if not (isinstance(__A   ,					(list, tuple)   ) and len(__A   ) == 2):
																			return False
												elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																			return False
												else:
																			return True

					def __getitem__(	self					:     int   ,					__UpperCamelCase					:     tuple[int, int]   ):
												assert self.validate_indicies(__A   )
												return self.array[loc[0]][loc[1]]

					def __setitem__(	self					:     Any   ,					__UpperCamelCase					:     tuple[int, int]   ,					__UpperCamelCase					:     float   ):
												assert self.validate_indicies(__A   )
												lowerCamelCase_							  =					value

					def __add__(	self					:     int   ,					__UpperCamelCase					:     Matrix   ):
												assert isinstance(__A   ,					__A   )
												assert self.row == another.row and self.column == another.column

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

					def __neg__(	self					:     Dict   ):
												lowerCamelCase_							  =					Matrix(self.row   ,					self.column   )
												for r in range(self.row   ):
																			for c in range(self.column   ):
																										lowerCamelCase_							  =					-self[r, c]
												return result

					def __sub__(	self					:     List[Any]   ,					__UpperCamelCase					:     Matrix   ):
												return self + (-another)

					def __mul__(	self					:     List[str]   ,					__UpperCamelCase					:     int | float | Matrix   ):

												if isinstance(__A   ,					(int, float)   ):  # Scalar multiplication
																			lowerCamelCase_							  =					Matrix(self.row   ,					self.column   )
																			for r in range(self.row   ):
																										for c in range(self.column   ):
																																	lowerCamelCase_							  =					self[r, c] * another
																			return result
												elif isinstance(__A   ,					__A   ):  # Matrix multiplication
																			assert self.column == another.row
																			lowerCamelCase_							  =					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:
																			lowerCamelCase_							  =					F'''Unsupported type given for another ({type(__A   )})'''
																			raise TypeError(__A   )

					def    lowercase__      (	self					:     int   ):
												lowerCamelCase_							  =					Matrix(self.column   ,					self.row   )
												for r in range(self.row   ):
																			for c in range(self.column   ):
																										lowerCamelCase_							  =					self[r, c]
												return result



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Matrix   ,					__UpperCamelCase					:     Matrix   ):
												assert isinstance(__A   ,					__A   ) and isinstance(__A   ,					__A   )
												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
												lowerCamelCase_							  =					v.transpose()
												lowerCamelCase_							  =					(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						(							)  ->    Dict:
									lowerCamelCase_							  =					Matrix(3			,				3			,				0       )
									for i in range(3       ):
																lowerCamelCase_							  =					1
									print(F'''a^(-1) is {ainv}'''       )
									# u, v
									lowerCamelCase_							  =					Matrix(3			,				1			,				0       )
									lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					1, 2, -3
									lowerCamelCase_							  =					Matrix(3			,				1			,				0       )
									lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					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(_lowercase			,				_lowercase       )}'''       )



		def      __lowerCAmelCase						(							)  ->    str:
									import doctest

									doctest.testmod()


		testa()







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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					do_resize
												lowerCamelCase_							  =					size
												# Default value set here for backwards compatibility where the value in config is None
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												lowerCamelCase_							  =					resample
												lowerCamelCase_							  =					do_rescale
												lowerCamelCase_							  =					rescale_factor
												lowerCamelCase_							  =					do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												lowerCamelCase_							  =					image_std if image_std is not None else IMAGENET_STANDARD_STD

					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												if "shortest_edge" not in size:
																			raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
												lowerCamelCase_							  =					size["""shortest_edge"""]

												if shortest_edge < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[int, float]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     int   ,					):
												return rescale(__UpperCamelCase   ,					scale=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Union[float, List[float]]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     List[str]   ,					):
												return normalize(__UpperCamelCase   ,					mean=__UpperCamelCase   ,					std=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     ChannelDimension = ChannelDimension.FIRST   ,					**__UpperCamelCase					:     List[str]   ,					):
												lowerCamelCase_							  =					do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else self.crop_pct
												lowerCamelCase_							  =					resample if resample is not None else self.resample
												lowerCamelCase_							  =					do_rescale if do_rescale is not None else self.do_rescale
												lowerCamelCase_							  =					rescale_factor if rescale_factor is not None else self.rescale_factor
												lowerCamelCase_							  =					do_normalize if do_normalize is not None else self.do_normalize
												lowerCamelCase_							  =					image_mean if image_mean is not None else self.image_mean
												lowerCamelCase_							  =					image_std if image_std is not None else self.image_std

												lowerCamelCase_							  =					size if size is not None else self.size
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					make_list_of_images(__UpperCamelCase   )

												if not valid_images(__UpperCamelCase   ):
																			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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""   )

												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.
												lowerCamelCase_							  =					[to_numpy_array(__UpperCamelCase   ) for image in images]

												if do_resize:
																			lowerCamelCase_							  =					[self.resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					crop_pct=__UpperCamelCase   ,					resample=__UpperCamelCase   ) for image in images]

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

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

												lowerCamelCase_							  =					[to_channel_dimension_format(__UpperCamelCase   ,					__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





