
'''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   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase = True   ,       _lowercase = None   ,       _lowercase = 32   ,       _lowercase = True   ,       _lowercase = 1 / 255   ,       _lowercase = True   ,       _lowercase = True   ,       _lowercase = [0.4814_5466, 0.457_8275, 0.4082_1073]   ,       _lowercase = [0.2686_2954, 0.2613_0258, 0.2757_7711]   ,       _lowercase = True   ,       _lowercase=7   ,       _lowercase=30   ,       _lowercase=400   ,       _lowercase=3   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			do_resize
												_lowerCAmelCase  						=			size if size is not None else {"""shortest_edge""": 288}
												_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  _lowercase     ( self				):

												"""simple docstring"""



												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  _lowercase     ( self   ,       _lowercase   ,       _lowercase=False				):

												"""simple docstring"""



												if not batched:
																		_lowerCAmelCase  						=			self.size["""shortest_edge"""]
																		_lowerCAmelCase  						=			image_inputs[0]
																		if isinstance(_lowercase   ,       Image.Image				):
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			image.size
																		else:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			image.shape[1], image.shape[2]
																		_lowerCAmelCase  						=			size / min(_lowercase   ,       _lowercase				)
																		if h < w:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			size, scale * w
																		else:
																								_lowerCAmelCase						,   _lowerCAmelCase  						=			scale * h, size

																		_lowerCAmelCase  						=			int((1_333 / 800) * size				)
																		if max(_lowercase   ,       _lowercase				) > max_size:
																								_lowerCAmelCase  						=			max_size / max(_lowercase   ,       _lowercase				)
																								_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(_lowercase   ,       key=lambda _lowercase				: item[0]				)[0]
																		_lowerCAmelCase  						=			max(_lowercase   ,       key=lambda _lowercase				: item[1]				)[1]

												return expected_height, expected_width






@require_torch
@require_vision
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BridgeTowerImageProcessingTester(self				)




						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# create random PIL images
												_lowerCAmelCase  						=			prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=_lowercase				)
												for image in image_inputs:
																		self.assertIsInstance(_lowercase   ,       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(_lowercase				)
												self.assertEqual(
												    encoded_images.shape   ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,       )

												# Test batched
												_lowerCAmelCase  						=			image_processing(_lowercase   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# create random numpy tensors
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			image_processing(image_inputs[0]   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			image_processing(_lowercase   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class(**self.image_processor_dict				)
												# create random PyTorch tensors
												_lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			image_processing(image_inputs[0]   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			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
												_lowerCAmelCase  						=			image_processing(_lowercase   ,       return_tensors="""pt"""				).pixel_values

												_lowerCAmelCase						,   _lowerCAmelCase  						=			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,
												    )   ,       )


'''simple docstring'''



import heapq as hq
import math
from collections.abc import Iterator




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			str(id_				)
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			None
												_lowerCAmelCase  						=			[]
												_lowerCAmelCase  						=			{}  # {vertex:distance}




						def __lt__( self   ,       _lowercase				):

												"""simple docstring"""



												return self.key < other.key




						def __repr__( self				):

												"""simple docstring"""



												return self.id




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												self.neighbors.append(_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			weight






def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Dict			,     __lowerCamelCase				:Optional[int]		):
						# add the neighbors:
						graph[a - 1].add_neighbor(graph[b - 1]		)
						graph[b - 1].add_neighbor(graph[a - 1]		)
						# add the edges:
						graph[a - 1].add_edge(graph[b - 1]			,     __lowerCamelCase		)
						graph[b - 1].add_edge(graph[a - 1]			,     __lowerCamelCase		)


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:Vertex		):
						_lowerCAmelCase  						=			[]
						for u in graph:
												_lowerCAmelCase  						=			math.inf
												_lowerCAmelCase  						=			None
						_lowerCAmelCase  						=			0
						_lowerCAmelCase  						=			graph[:]
						while q:
												_lowerCAmelCase  						=			min(__lowerCamelCase		)
												q.remove(__lowerCamelCase		)
												for v in u.neighbors:
																		if (v in q) and (u.edges[v.id] < v.key):
																								_lowerCAmelCase  						=			u
																								_lowerCAmelCase  						=			u.edges[v.id]
						for i in range(1			,     len(__lowerCamelCase		)		):
												a.append((int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)		)
						return a


def A				(__lowerCamelCase				:list			,     __lowerCamelCase				:Vertex		):
						for u in graph:
												_lowerCAmelCase  						=			math.inf
												_lowerCAmelCase  						=			None
						_lowerCAmelCase  						=			0

						_lowerCAmelCase  						=			list(__lowerCamelCase		)
						hq.heapify(__lowerCamelCase		)

						while h:
												_lowerCAmelCase  						=			hq.heappop(__lowerCamelCase		)
												for v in u.neighbors:
																		if (v in h) and (u.edges[v.id] < v.key):
																								_lowerCAmelCase  						=			u
																								_lowerCAmelCase  						=			u.edges[v.id]
																								hq.heapify(__lowerCamelCase		)

						for i in range(1			,     len(__lowerCamelCase		)		):
												yield (int(graph[i].id		) + 1, int(graph[i].pi.id		) + 1)


def A				():
						pass


if __name__ == "__main__":
						import doctest

						doctest.testmod()
