
import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							str=7							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=3							,			SCREAMING_SNAKE_CASE__		:							List[Any]=18							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=30							,			SCREAMING_SNAKE_CASE__		:							Dict=4_00							,			SCREAMING_SNAKE_CASE__		:							Dict=True							,			SCREAMING_SNAKE_CASE__		:							List[Any]=None							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=[0.5, 0.5, 0.5]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=[0.5, 0.5, 0.5]							,			SCREAMING_SNAKE_CASE__		:							str=False							,			)     ->							Dict:
						__lowerCamelCase    					=	size if size is not None else {'''height''': 20, '''width''': 20}
						__lowerCamelCase    					=	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	min_resolution
						__lowerCamelCase    					=	max_resolution
						__lowerCamelCase    					=	do_resize
						__lowerCamelCase    					=	size
						__lowerCamelCase    					=	do_center_crop
						__lowerCamelCase    					=	crop_size
						__lowerCamelCase    					=	do_normalize
						__lowerCamelCase    					=	image_mean
						__lowerCamelCase    					=	image_std
						__lowerCamelCase    					=	do_reduce_labels







		def  __A       (		self		:							Tuple  )     ->							Union[str, Any]:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_reduce_labels": self.do_reduce_labels,
						}





def  __magic_name__ (   )			->   Any:
				__lowerCamelCase    					=	load_dataset('''hf-internal-testing/fixtures_ade20k'''		,  split='''test'''							)

				__lowerCamelCase    					=	Image.open(dataset[0]['''file''']							)
				__lowerCamelCase    					=	Image.open(dataset[1]['''file''']							)

				return image, map




def  __magic_name__ (   )			->   Tuple:
				__lowerCamelCase    					=	load_dataset('''hf-internal-testing/fixtures_ade20k'''		,  split='''test'''							)

				__lowerCamelCase    					=	Image.open(ds[0]['''file''']							)
				__lowerCamelCase    					=	Image.open(ds[1]['''file''']							)
				__lowerCamelCase    					=	Image.open(ds[2]['''file''']							)
				__lowerCamelCase    					=	Image.open(ds[3]['''file''']							)

				return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						int        =		BeitImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Tuple  )     ->							int:
						__lowerCamelCase    					=	BeitImageProcessingTester(self  )






		@property
		def  __A       (		self		:							Dict  )     ->							Any:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							int  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_resize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''size'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_center_crop'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''center_crop'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''image_mean'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''image_std'''  )  )






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.image_processing_class.from_dict(self.image_processor_dict  )
						self.assertEqual(image_processor.size							,			{'''height''': 20, '''width''': 20}  )
						self.assertEqual(image_processor.crop_size							,			{'''height''': 18, '''width''': 18}  )
						self.assertEqual(image_processor.do_reduce_labels							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.image_processing_class.from_dict(
						    self.image_processor_dict							,			size=42							,			crop_size=84							,			reduce_labels=SCREAMING_SNAKE_CASE__  )
						self.assertEqual(image_processor.size							,			{'''height''': 42, '''width''': 42}  )
						self.assertEqual(image_processor.crop_size							,			{'''height''': 84, '''width''': 84}  )
						self.assertEqual(image_processor.do_reduce_labels							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]  )     ->							Dict:
						pass






		def  __A       (		self		:							List[Any]  )     ->							Tuple:
						# Initialize image_processing
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PIL images
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			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(SCREAMING_SNAKE_CASE__							,			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  __A       (		self		:							int  )     ->							Dict:
						# Initialize image_processing
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random numpy tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			numpify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			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(SCREAMING_SNAKE_CASE__							,			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  __A       (		self		:							int  )     ->							str:
						# Initialize image_processing
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			torchify=SCREAMING_SNAKE_CASE__  )
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			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(SCREAMING_SNAKE_CASE__							,			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  __A       (		self		:							str  )     ->							Optional[int]:
						# Initialize image_processing
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						# create random PyTorch tensors
						__lowerCamelCase    					=	prepare_image_inputs(self.image_processor_tester							,			equal_resolution=SCREAMING_SNAKE_CASE__							,			torchify=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	[]
						for image in image_inputs:
										self.assertIsInstance(SCREAMING_SNAKE_CASE__							,			torch.Tensor  )
										maps.append(torch.zeros(image.shape[-2:]  ).long()  )

						# Test not batched input
						__lowerCamelCase    					=	image_processing(image_inputs[0]							,			maps[0]							,			return_tensors='''pt'''  )
						self.assertEqual(
						    encoding['''pixel_values'''].shape							,			(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(
						    encoding['''labels'''].shape							,			(
						        1,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(encoding['''labels'''].dtype							,			torch.long  )
						self.assertTrue(encoding['''labels'''].min().item() >= 0  )
						self.assertTrue(encoding['''labels'''].max().item() <= 2_55  )

						# Test batched
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )
						self.assertEqual(
						    encoding['''pixel_values'''].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'''],
						    )							,			)
						self.assertEqual(
						    encoding['''labels'''].shape							,			(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(encoding['''labels'''].dtype							,			torch.long  )
						self.assertTrue(encoding['''labels'''].min().item() >= 0  )
						self.assertTrue(encoding['''labels'''].max().item() <= 2_55  )

						# Test not batched input (PIL images)
						__lowerCamelCase					,    __lowerCamelCase    					=	prepare_semantic_single_inputs()

						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )
						self.assertEqual(
						    encoding['''pixel_values'''].shape							,			(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(
						    encoding['''labels'''].shape							,			(
						        1,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(encoding['''labels'''].dtype							,			torch.long  )
						self.assertTrue(encoding['''labels'''].min().item() >= 0  )
						self.assertTrue(encoding['''labels'''].max().item() <= 2_55  )

						# Test batched input (PIL images)
						__lowerCamelCase					,    __lowerCamelCase    					=	prepare_semantic_batch_inputs()

						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )
						self.assertEqual(
						    encoding['''pixel_values'''].shape							,			(
						        2,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(
						    encoding['''labels'''].shape							,			(
						        2,
						        self.image_processor_tester.crop_size['''height'''],
						        self.image_processor_tester.crop_size['''width'''],
						    )							,			)
						self.assertEqual(encoding['''labels'''].dtype							,			torch.long  )
						self.assertTrue(encoding['''labels'''].min().item() >= 0  )
						self.assertTrue(encoding['''labels'''].max().item() <= 2_55  )







		def  __A       (		self		:							str  )     ->							int:
						# Initialize image_processing
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )

						# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
						__lowerCamelCase					,    __lowerCamelCase    					=	prepare_semantic_single_inputs()
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )
						self.assertTrue(encoding['''labels'''].min().item() >= 0  )
						self.assertTrue(encoding['''labels'''].max().item() <= 1_50  )

						__lowerCamelCase    					=	True
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )
						self.assertTrue(encoding['''labels'''].min().item() >= 0  )
						self.assertTrue(encoding['''labels'''].max().item() <= 2_55  )





from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE__   :			List[Any]												=    TypeVar("T")




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (position - 1) // 2




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 1




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				return (2 * position) + 2

class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							List[str]  )     ->							None:
						__lowerCamelCase    					=	[]
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __len__(		self		:							Optional[int]  )     ->							int:
						return self.elements






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.heap  )






		def  __A       (		self		:							Union[str, Any]  )     ->							bool:
						# Check if the priority queue is empty
						return self.elements == 0






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an element with given priority to the queue
						self.heap.append((elem, weight)  )
						__lowerCamelCase    					=	self.elements
						self.elements += 1
						self._bubble_up(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Any  )     ->							T:
						# Remove and return the element with lowest weight (highest priority)
						if self.elements > 1:
										self._swap_nodes(0							,			self.elements - 1  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap.pop()
						del self.position_map[elem]
						self.elements -= 1
						if self.elements > 0:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[0]
										self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return elem






		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Update the weight of the given key
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase    					=	(elem, weight)
						if position > 0:
										__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
										if parent_weight > weight:
														self._bubble_up(SCREAMING_SNAKE_CASE__  )
										else:
														self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										self._bubble_down(SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (upward movement) [to be used internally
						# only]
						__lowerCamelCase    					=	self.position_map[elem]
						if curr_pos == 0:
										return None
						__lowerCamelCase    					=	get_parent_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[parent_position]
						if parent_weight > weight:
										self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										return self._bubble_up(SCREAMING_SNAKE_CASE__  )
						return None






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Place a node at the proper position (downward movement) [to be used
						# internally only]
						__lowerCamelCase    					=	self.position_map[elem]
						__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[curr_pos]
						__lowerCamelCase    					=	get_child_left_position(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	get_child_right_position(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements and child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < child_left_weight and child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						if child_left_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_left_position]
										if child_left_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						else:
										return None
						if child_right_position < self.elements:
										__lowerCamelCase					,    __lowerCamelCase    					=	self.heap[child_right_position]
										if child_right_weight < weight:
														self._swap_nodes(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
														return self._bubble_down(SCREAMING_SNAKE_CASE__  )
						return None







		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Swap the nodes at the given positions
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase    					=	self.heap[nodea_pos][0]
						__lowerCamelCase					,    __lowerCamelCase    					=	(
						    self.heap[nodea_pos],
						    self.heap[nodea_pos],
						)
						__lowerCamelCase    					=	nodea_pos
						__lowerCamelCase    					=	nodea_pos





class    lowerCAmelCase__     (	Generic[T]			):






		def __init__(		self		:							Tuple  )     ->							None:
						__lowerCamelCase    					=	{}
						__lowerCamelCase    					=	0






		def __repr__(		self		:							Optional[int]  )     ->							str:
						return str(self.connections  )






		def __len__(		self		:							List[str]  )     ->							int:
						return self.nodes






		def  __A       (		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							T  )     ->							None:
						# Add a node in the graph if it is not in the graph
						if node not in self.connections:
										__lowerCamelCase    					=	{}
										self.nodes += 1







		def  __A       (		self		:							Dict							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							T							,			SCREAMING_SNAKE_CASE__		:							int  )     ->							None:
						# Add an edge between 2 nodes in the graph
						self.add_node(SCREAMING_SNAKE_CASE__  )
						self.add_node(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	weight
						__lowerCamelCase    					=	weight





def  __magic_name__ (   __lowerCAmelCase	:						GraphUndirectedWeighted[T]		,  )			->   tuple[dict[T, int], dict[T, T | None]]:
				__lowerCamelCase    					=	{node: maxsize for node in graph.connections}
				__lowerCamelCase    					=	{node: None for node in graph.connections}

				__lowerCamelCase    					=	MinPriorityQueue()
				for node, weight in dist.items():
								priority_queue.push(__lowerCAmelCase		,  __lowerCAmelCase							)

				if priority_queue.is_empty():
								return dist, parent

				# initialization
				__lowerCamelCase    					=	priority_queue.extract_min()
				__lowerCamelCase    					=	0
				for neighbour in graph.connections[node]:
								if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
												__lowerCamelCase    					=	dist[node] + graph.connections[node][neighbour]
												priority_queue.update_key(__lowerCAmelCase		,  dist[neighbour]							)
												__lowerCamelCase    					=	node

    # running prim's algorithm
				while not priority_queue.is_empty():
								__lowerCamelCase    					=	priority_queue.extract_min()
								for neighbour in graph.connections[node]:
												if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
																__lowerCamelCase    					=	dist[node] + graph.connections[node][neighbour]
																priority_queue.update_key(__lowerCAmelCase		,  dist[neighbour]							)
																__lowerCamelCase    					=	node
				return dist, parent



