





from __future__ import annotations

from typing import Any



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Tuple							,	a				:     int							,	a				:     int							,	a				:     float = 0    )	->None:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					row, column
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[[default_value for c in range(a    )] for r in range(a    )]

							def __str__(      self				:     List[Any]    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					f"""Matrix consist of {self.row} rows and {self.column} columns\n"""

													# Make string identifier
													SCREAMING_SNAKE_CASE__     :				Tuple   =					0
													for row_vector in self.array:
																			for obj in row_vector:
																									SCREAMING_SNAKE_CASE__     :				int   =					max(a							,	len(str(a    )    )    )
													SCREAMING_SNAKE_CASE__     :				Dict   =					f"""%{max_element_length}s"""

													# Make string and return
													def single_line(a				:     list[float]    ) -> str:
																			nonlocal string_format_identifier
																			SCREAMING_SNAKE_CASE__     :				Dict   =					"["
																			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				:     int    )	->str:
													return str(self    )

							def      A_  (      self				:     str							,	a				:     tuple[int, int]    )	->bool:
													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				:     Tuple							,	a				:     tuple[int, int]    )	->Any:
													assert self.validate_indicies(a    )
													return self.array[loc[0]][loc[1]]

							def __setitem__(      self				:     Optional[int]							,	a				:     tuple[int, int]							,	a				:     float    )	->None:
													assert self.validate_indicies(a    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					value

							def __add__(      self				:     List[str]							,	a				:     Matrix    )	->Matrix:
													assert isinstance(a							,	a    )
													assert self.row == another.row and self.column == another.column

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

							def __neg__(      self				:     Tuple    )	->Matrix:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					Matrix(self.row							,	self.column    )
													for r in range(self.row    ):
																			for c in range(self.column    ):
																									SCREAMING_SNAKE_CASE__     :				Any   =					-self[r, c]
													return result

							def __sub__(      self				:     Dict							,	a				:     Matrix    )	->Matrix:
													return self + (-another)

							def __mul__(      self				:     Dict							,	a				:     int | float | Matrix    )	->Matrix:

													if isinstance(a							,	(int, float)    ):  # Scalar multiplication
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Matrix(self.row							,	self.column    )
																			for r in range(self.row    ):
																									for c in range(self.column    ):
																															SCREAMING_SNAKE_CASE__     :				Tuple   =					self[r, c] * another
																			return result
													elif isinstance(a							,	a    ):  # Matrix multiplication
																			assert self.column == another.row
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					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:
																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					f"""Unsupported type given for another ({type(a    )})"""
																			raise TypeError(a    )

							def      A_  (      self				:     Optional[int]    )	->Matrix:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Matrix(self.column							,	self.row    )
													for r in range(self.row    ):
																			for c in range(self.column    ):
																									SCREAMING_SNAKE_CASE__     :				Any   =					self[r, c]
													return result






							def      A_  (      self				:     int							,	a				:     Matrix							,	a				:     Matrix    )	->Any:
													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
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					v.transpose()
													SCREAMING_SNAKE_CASE__     :				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       UpperCAmelCase							(     ):





										'''simple docstring'''


										SCREAMING_SNAKE_CASE__     :				Dict   =					Matrix(3			,   3			,   0     )
										for i in range(3     ):
																SCREAMING_SNAKE_CASE__     :				int   =					1
										print(f"""a^(-1) is {ainv}"""     )
										# u, v
										SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					Matrix(3			,   1			,   0     )
										SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1, 2, -3
										SCREAMING_SNAKE_CASE__     :				List[Any]   =					Matrix(3			,   1			,   0     )
										SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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(_lowerCamelCase			,   _lowerCamelCase     )}"""     )







				def       UpperCAmelCase							(     ):





										'''simple docstring'''


										import doctest

										doctest.testmod()



				testa()






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import BridgeTowerImageProcessor



class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     Any							,	a				:     bool = True							,	a				:     Dict[str, int] = None							,	a				:     int = 32							,	a				:     bool = True							,	a				:     Union[int, float] = 1 / 2_55							,	a				:     bool = True							,	a				:     bool = True							,	a				:     Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]							,	a				:     Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]							,	a				:     bool = True							,	a				:     Any=7							,	a				:     str=30							,	a				:     Dict=4_00							,	a				:     Optional[int]=3							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				int   =					parent
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_resize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					size if size is not None else {"shortest_edge": 2_88}
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					size_divisor
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					do_rescale
													SCREAMING_SNAKE_CASE__     :				Tuple   =					rescale_factor
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					do_normalize
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					do_center_crop
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					image_mean
													SCREAMING_SNAKE_CASE__     :				Dict   =					image_std
													SCREAMING_SNAKE_CASE__     :				List[str]   =					do_pad
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					min_resolution
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					max_resolution

							def      A_  (      self				:     List[str]    )	->Tuple:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "size_divisor": self.size_divisor,
													}






							def      A_  (      self				:     int							,	a				:     Optional[int]							,	a				:     Union[str, Any]=False    )	->Optional[Any]:
													if not batched:
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.size["shortest_edge"]
																			SCREAMING_SNAKE_CASE__     :				Dict   =					image_inputs[0]
																			if isinstance(a							,	Image.Image    ):
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Dict   =					image.size
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					image.shape[1], image.shape[2]
																			SCREAMING_SNAKE_CASE__     :				Any   =					size / min(a							,	a    )
																			if h < w:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					size, scale * w
																			else:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[str]   =					scale * h, size

																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					int((13_33 / 8_00) * size    )
																			if max(a							,	a    ) > max_size:
																									SCREAMING_SNAKE_CASE__     :				List[Any]   =					max_size / max(a							,	a    )
																									SCREAMING_SNAKE_CASE__     :				int   =					newh * scale
																									SCREAMING_SNAKE_CASE__     :				Optional[int]   =					neww * scale

																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					int(newh + 0.5    ), int(neww + 0.5    )
																			SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					(
																			    newh // self.size_divisor * self.size_divisor,
																			    neww // self.size_divisor * self.size_divisor,
																			)

													else:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[]
																			for image in image_inputs:
																									SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.get_expected_values([image]    )
																									expected_values.append((expected_height, expected_width)    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[0]    )[0]
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					max(a							,	key=lambda a    : item[1]    )[1]

													return expected_height, expected_width







@require_torch
@require_vision
class    _a     (				lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						BridgeTowerImageProcessor if is_vision_available() else None

							def      A_  (      self				:     List[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					BridgeTowerImageProcessingTester(self    )

							@property
							def      A_  (      self				:     Optional[int]    )	->Optional[Any]:
													return self.image_processor_tester.prepare_image_processor_dict()

							def      A_  (      self				:     Tuple    )	->str:
													SCREAMING_SNAKE_CASE__     :				int   =					self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(a							,	"image_mean"    )    )
													self.assertTrue(hasattr(a							,	"image_std"    )    )
													self.assertTrue(hasattr(a							,	"do_normalize"    )    )
													self.assertTrue(hasattr(a							,	"do_resize"    )    )
													self.assertTrue(hasattr(a							,	"size"    )    )
													self.assertTrue(hasattr(a							,	"size_divisor"    )    )

							def      A_  (      self				:     List[Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     Tuple    )	->Optional[Any]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	Image.Image    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				int   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Any   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)

							def      A_  (      self				:     Optional[int]    )	->Any:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				str   =					self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													SCREAMING_SNAKE_CASE__     :				str   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	numpify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	np.ndarray    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)






							def      A_  (      self				:     str    )	->Optional[int]:
													# Initialize image processor
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					prepare_image_inputs(self.image_processor_tester							,	equal_resolution=a							,	torchify=a    )
													for image in image_inputs:
																			self.assertIsInstance(a							,	torch.Tensor    )

													# Test not batched input
													SCREAMING_SNAKE_CASE__     :				Tuple   =					image_processing(image_inputs[0]							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Tuple   =					self.image_processor_tester.get_expected_values(a    )
													self.assertEqual(
													    encoded_images.shape							,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,	)

													# Test batched
													SCREAMING_SNAKE_CASE__     :				Any   =					image_processing(a							,	return_tensors="pt"    ).pixel_values

													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.image_processor_tester.get_expected_values(a							,	batched=a    )
													self.assertEqual(
													    encoded_images.shape							,	(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        expected_height,
													        expected_width,
													    )							,	)