
'''simple docstring'''



import unittest

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 PoolFormerImageProcessor




class   UpperCAmelCase_					(		unittest.TestCase			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=7   ,       _lowercase=3   ,       _lowercase=30   ,       _lowercase=400   ,       _lowercase=True   ,       _lowercase=None   ,       _lowercase=0.9   ,       _lowercase=None   ,       _lowercase=True   ,       _lowercase=[0.5, 0.5, 0.5]   ,       _lowercase=[0.5, 0.5, 0.5]   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			size if size is not None else {"""shortest_edge""": 30}
												_lowerCAmelCase  						=			crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			min_resolution
												_lowerCAmelCase  						=			max_resolution
												_lowerCAmelCase  						=			do_resize_and_center_crop
												_lowerCAmelCase  						=			size
												_lowerCAmelCase  						=			crop_pct
												_lowerCAmelCase  						=			crop_size
												_lowerCAmelCase  						=			do_normalize
												_lowerCAmelCase  						=			image_mean
												_lowerCAmelCase  						=			image_std




						def  _lowercase     ( self				):

												"""simple docstring"""



												return {
												    "size": self.size,
												    "do_resize_and_center_crop": self.do_resize_and_center_crop,
												    "crop_pct": self.crop_pct,
												    "crop_size": self.crop_size,
												    "do_normalize": self.do_normalize,
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												}






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






						'''simple docstring'''






						_lowercase		:				Union[str, Any]									=       PoolFormerImageProcessor if is_vision_available() else None




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			PoolFormerImageProcessingTester(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   ,       """do_resize_and_center_crop"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """size"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """crop_pct"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """do_normalize"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """image_mean"""				)				)
												self.assertTrue(hasattr(_lowercase   ,       """image_std"""				)				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.image_processing_class.from_dict(self.image_processor_dict				)
												self.assertEqual(image_processor.size   ,       {"""shortest_edge""": 30}				)
												self.assertEqual(image_processor.crop_size   ,       {"""height""": 30, """width""": 30}				)

												_lowerCAmelCase  						=			self.image_processing_class.from_dict(self.image_processor_dict   ,       size=42   ,       crop_size=84				)
												self.assertEqual(image_processor.size   ,       {"""shortest_edge""": 42}				)
												self.assertEqual(image_processor.crop_size   ,       {"""height""": 84, """width""": 84}				)




						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

												self.assertEqual(
												    encoded_images.shape   ,       (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,       )

												# Test batched
												_lowerCAmelCase  						=			image_processing(_lowercase   ,       return_tensors="""pt"""				).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,       (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""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
												self.assertEqual(
												    encoded_images.shape   ,       (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,       )

												# Test batched
												_lowerCAmelCase  						=			image_processing(_lowercase   ,       return_tensors="""pt"""				).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,       (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""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
												self.assertEqual(
												    encoded_images.shape   ,       (
												        1,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""width"""],
												    )   ,       )

												# Test batched
												_lowerCAmelCase  						=			image_processing(_lowercase   ,       return_tensors="""pt"""				).pixel_values
												self.assertEqual(
												    encoded_images.shape   ,       (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        self.image_processor_tester.crop_size["""height"""],
												        self.image_processor_tester.crop_size["""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()
