



"""simple docstring"""
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				,       lowerCamelCase__				,       lowerCamelCase__ = True				,       lowerCamelCase__ = None				,       lowerCamelCase__ = 3_2				,       lowerCamelCase__ = True				,       lowerCamelCase__ = 1 / 2_5_5				,       lowerCamelCase__ = True				,       lowerCamelCase__ = True				,       lowerCamelCase__ = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]				,       lowerCamelCase__ = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]				,       lowerCamelCase__ = True				,       lowerCamelCase__=7				,       lowerCamelCase__=3_0				,       lowerCamelCase__=4_0_0				,       lowerCamelCase__=3				,       ):
									_lowerCamelCase        =				parent
									_lowerCamelCase        =				do_resize
									_lowerCamelCase        =				size if size is not None else {'''shortest_edge''': 2_8_8}
									_lowerCamelCase        =				size_divisor
									_lowerCamelCase        =				do_rescale
									_lowerCamelCase        =				rescale_factor
									_lowerCamelCase        =				do_normalize
									_lowerCamelCase        =				do_center_crop
									_lowerCamelCase        =				image_mean
									_lowerCamelCase        =				image_std
									_lowerCamelCase        =				do_pad
									_lowerCamelCase        =				batch_size
									_lowerCamelCase        =				num_channels
									_lowerCamelCase        =				min_resolution
									_lowerCamelCase        =				max_resolution

					def 			snake_case__      (    self   ):
									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 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=False   ):
									if not batched:
													_lowerCamelCase        =				self.size['''shortest_edge''']
													_lowerCamelCase        =				image_inputs[0]
													if isinstance(lowerCamelCase__				,       Image.Image   ):
																	_lowerCamelCase  ,     _lowerCamelCase        =				image.size
													else:
																	_lowerCamelCase  ,     _lowerCamelCase        =				image.shape[1], image.shape[2]
													_lowerCamelCase        =				size / min(lowerCamelCase__				,       lowerCamelCase__   )
													if h < w:
																	_lowerCamelCase  ,     _lowerCamelCase        =				size, scale * w
													else:
																	_lowerCamelCase  ,     _lowerCamelCase        =				scale * h, size

													_lowerCamelCase        =				int((1_3_3_3 / 8_0_0) * size   )
													if max(lowerCamelCase__				,       lowerCamelCase__   ) > max_size:
																	_lowerCamelCase        =				max_size / max(lowerCamelCase__				,       lowerCamelCase__   )
																	_lowerCamelCase        =				newh * scale
																	_lowerCamelCase        =				neww * scale

													_lowerCamelCase  ,     _lowerCamelCase        =				int(newh + 0.5   ), int(neww + 0.5   )
													_lowerCamelCase  ,     _lowerCamelCase        =				(
													    newh // self.size_divisor * self.size_divisor,
													    neww // self.size_divisor * self.size_divisor,
													)

									else:
													_lowerCamelCase        =				[]
													for image in image_inputs:
																	_lowerCamelCase  ,     _lowerCamelCase        =				self.get_expected_values([image]   )
																	expected_values.append((expected_height, expected_width)   )
													_lowerCamelCase        =				max(lowerCamelCase__				,       key=lambda lowerCamelCase__   : item[0]   )[0]
													_lowerCamelCase        =				max(lowerCamelCase__				,       key=lambda lowerCamelCase__   : item[1]   )[1]

									return expected_height, expected_width


@require_torch
@require_vision
class 							lowerCamelCase_(   A__,				unittest.TestCase    ):





					'''simple docstring'''



					lowercase__   :       str										= BridgeTowerImageProcessor if is_vision_available() else None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				BridgeTowerImageProcessingTester(self   )

					@property
					def 			snake_case__      (    self   ):
									return self.image_processor_tester.prepare_image_processor_dict()

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''image_mean'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''image_std'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''do_normalize'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''do_resize'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''size'''   )   )
									self.assertTrue(hasattr(lowerCamelCase__				,       '''size_divisor'''   )   )

					def 			snake_case__      (    self   ):
									pass

					def 			snake_case__      (    self   ):
									# Initialize image processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PIL images
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       Image.Image   )

									# Test not batched input
									_lowerCamelCase        =				image_processing(image_inputs[0]				,       return_tensors='''pt'''   ).pixel_values

									_lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor_tester.get_expected_values(lowerCamelCase__   )
									self.assertEqual(
									    encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

									# Test batched
									_lowerCamelCase        =				image_processing(lowerCamelCase__				,       return_tensors='''pt'''   ).pixel_values

									_lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor_tester.get_expected_values(lowerCamelCase__				,       batched=lowerCamelCase__   )
									self.assertEqual(
									    encoded_images.shape				,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,       )

					def 			snake_case__      (    self   ):
									# Initialize image processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__				,       numpify=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       np.ndarray   )

									# Test not batched input
									_lowerCamelCase        =				image_processing(image_inputs[0]				,       return_tensors='''pt'''   ).pixel_values

									_lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor_tester.get_expected_values(lowerCamelCase__   )
									self.assertEqual(
									    encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

									# Test batched
									_lowerCamelCase        =				image_processing(lowerCamelCase__				,       return_tensors='''pt'''   ).pixel_values

									_lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor_tester.get_expected_values(lowerCamelCase__				,       batched=lowerCamelCase__   )
									self.assertEqual(
									    encoded_images.shape				,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,       )





					def 			snake_case__      (    self   ):
									# Initialize image processor
									_lowerCamelCase        =				self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									_lowerCamelCase        =				prepare_image_inputs(self.image_processor_tester				,       equal_resolution=lowerCamelCase__				,       torchify=lowerCamelCase__   )
									for image in image_inputs:
													self.assertIsInstance(lowerCamelCase__				,       torch.Tensor   )

									# Test not batched input
									_lowerCamelCase        =				image_processing(image_inputs[0]				,       return_tensors='''pt'''   ).pixel_values

									_lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor_tester.get_expected_values(lowerCamelCase__   )
									self.assertEqual(
									    encoded_images.shape				,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)				,       )

									# Test batched
									_lowerCamelCase        =				image_processing(lowerCamelCase__				,       return_tensors='''pt'''   ).pixel_values

									_lowerCamelCase  ,     _lowerCamelCase        =				self.image_processor_tester.get_expected_values(lowerCamelCase__				,       batched=lowerCamelCase__   )
									self.assertEqual(
									    encoded_images.shape				,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )				,       )









"""simple docstring"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
