






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        lowerCAmelCase_				(      unittest.TestCase	):







				"""simple docstring"""




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







				def 				__a   ( self     :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     :Optional[Any]  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :str=False     ):






					if not batched:
						UpperCamelCase__ :Tuple						=      self.size["""shortest_edge"""]
						UpperCamelCase__ :List[Any]						=      image_inputs[0]
						if isinstance(__lowercase  ,     Image.Image     ):
							UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      image.size
						else:
							UpperCamelCase__				,		UpperCamelCase__ :Dict						=      image.shape[1], image.shape[2]
						UpperCamelCase__ :Optional[int]						=      size / min(__lowercase  ,     __lowercase     )
						if h < w:
							UpperCamelCase__				,		UpperCamelCase__ :Dict						=      size, scale * w
						else:
							UpperCamelCase__				,		UpperCamelCase__ :List[str]						=      scale * h, size

						UpperCamelCase__ :str						=      int((13_33 / 8_00) * size     )
						if max(__lowercase  ,     __lowercase     ) > max_size:
							UpperCamelCase__ :Any						=      max_size / max(__lowercase  ,     __lowercase     )
							UpperCamelCase__ :Any						=      newh * scale
							UpperCamelCase__ :Any						=      neww * scale

						UpperCamelCase__				,		UpperCamelCase__ :Dict						=      int(newh + 0.5     ), int(neww + 0.5     )
						UpperCamelCase__				,		UpperCamelCase__ :Union[str, Any]						=      (
						    newh // self.size_divisor * self.size_divisor,
						    neww // self.size_divisor * self.size_divisor,
						)

					else:
						UpperCamelCase__ :Optional[Any]						=      []
						for image in image_inputs:
							UpperCamelCase__				,		UpperCamelCase__ :Any						=      self.get_expected_values([image]     )
							expected_values.append((expected_height, expected_width)     )
						UpperCamelCase__ :str						=      max(__lowercase  ,     key=lambda lowerCamelCase__     : item[0]     )[0]
						UpperCamelCase__ :Dict						=      max(__lowercase  ,     key=lambda lowerCamelCase__     : item[1]     )[1]

					return expected_height, expected_width

@require_torch
@require_vision
class        lowerCAmelCase_				(      UpperCAmelCase__						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			BridgeTowerImageProcessor if is_vision_available() else None







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Any						=      BridgeTowerImageProcessingTester(self     )







				@property
				def 				__a   ( self     :Any     ):
					return self.image_processor_tester.prepare_image_processor_dict()







				def 				__a   ( self     :Optional[int]     ):
					UpperCamelCase__ :Any						=      self.image_processing_class(**self.image_processor_dict     )
					self.assertTrue(hasattr(__lowercase  ,     """image_mean"""     )     )
					self.assertTrue(hasattr(__lowercase  ,     """image_std"""     )     )
					self.assertTrue(hasattr(__lowercase  ,     """do_normalize"""     )     )
					self.assertTrue(hasattr(__lowercase  ,     """do_resize"""     )     )
					self.assertTrue(hasattr(__lowercase  ,     """size"""     )     )
					self.assertTrue(hasattr(__lowercase  ,     """size_divisor"""     )     )







				def 				__a   ( self     :List[str]     ):
					pass







				def 				__a   ( self     :Any     ):
					UpperCamelCase__ :int						=      self.image_processing_class(**self.image_processor_dict     )
					# create random PIL images
					UpperCamelCase__ :int						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__lowercase     )
					for image in image_inputs:
						self.assertIsInstance(__lowercase  ,     Image.Image     )

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

					UpperCamelCase__				,		UpperCamelCase__ :int						=      self.image_processor_tester.get_expected_values(__lowercase     )
					self.assertEqual(
					    encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

					# Test batched
					UpperCamelCase__ :Dict						=      image_processing(__lowercase  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      self.image_processor_tester.get_expected_values(__lowercase  ,     batched=__lowercase     )
					self.assertEqual(
					    encoded_images.shape  ,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,     )







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Tuple						=      self.image_processing_class(**self.image_processor_dict     )
					# create random numpy tensors
					UpperCamelCase__ :List[Any]						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__lowercase  ,     numpify=__lowercase     )
					for image in image_inputs:
						self.assertIsInstance(__lowercase  ,     np.ndarray     )

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

					UpperCamelCase__				,		UpperCamelCase__ :Optional[int]						=      self.image_processor_tester.get_expected_values(__lowercase     )
					self.assertEqual(
					    encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

					# Test batched
					UpperCamelCase__ :Optional[int]						=      image_processing(__lowercase  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      self.image_processor_tester.get_expected_values(__lowercase  ,     batched=__lowercase     )
					self.assertEqual(
					    encoded_images.shape  ,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,     )







				def 				__a   ( self     :List[str]     ):
					UpperCamelCase__ :Optional[Any]						=      self.image_processing_class(**self.image_processor_dict     )
					# create random PyTorch tensors
					UpperCamelCase__ :Optional[int]						=      prepare_image_inputs(self.image_processor_tester  ,     equal_resolution=__lowercase  ,     torchify=__lowercase     )
					for image in image_inputs:
						self.assertIsInstance(__lowercase  ,     torch.Tensor     )

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

					UpperCamelCase__				,		UpperCamelCase__ :Dict						=      self.image_processor_tester.get_expected_values(__lowercase     )
					self.assertEqual(
					    encoded_images.shape  ,     (1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,     )

					# Test batched
					UpperCamelCase__ :Tuple						=      image_processing(__lowercase  ,     return_tensors="""pt"""     ).pixel_values

					UpperCamelCase__				,		UpperCamelCase__ :List[Any]						=      self.image_processor_tester.get_expected_values(__lowercase  ,     batched=__lowercase     )
					self.assertEqual(
					    encoded_images.shape  ,     (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )  ,     )





from __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

	snake_case_						       =	0
	snake_case_						       =	0
	while row < size and col < size:
		# pivoting
		snake_case_						       =	max((abs(augmented[rowa][col]				), rowa) for rowa in range(_A	,      _A				)				)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_       , snake_case_						       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	0
			for cola in range(col + 1	,      size + 1				):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1	,      _A				):
		for row in range(_A				):
			snake_case_						       =	augmented[row][col] / augmented[col][col]
			for cola in range(_A	,      size + 1				):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]	,      10				)] for row in range(_A				)
	]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for x_val, y_val in enumerate(_A				):
		for col in range(_A				):
			snake_case_						       =	(x_val + 1) ** (size - col - 1)
		snake_case_						       =	y_val

	snake_case_						       =	solve(_A	,      _A				)

	def interpolated_func(_A				) -> int:
		return sum(
		    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
		    for x_val in range(_A				)				)

	return interpolated_func


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)


def 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

	snake_case_						       =	[
	    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,      order + 1				)
	]

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


if __name__ == "__main__":
		print(f'''{solution() = }''')
