

"""simple docstring"""




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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					def __init__(			self   ,	A__   ,	A__=7   ,	A__=3   ,	A__=18   ,	A__=30   ,	A__=400   ,	A__=True   ,	A__=None   ,	A__=True   ,	A__=None   ,	A__=True   ,	A__=[0.5, 0.5, 0.5]   ,	A__=[0.5, 0.5, 0.5]   ,	A__=False   ,	):
						A__							:   int					       =		size if size is not None else {'height': 20, 'width': 20}
						A__							:   int					       =		crop_size if crop_size is not None else {'height': 18, 'width': 18}
						A__							:   Union[str, Any]					       =		parent
						A__							:   Any					       =		batch_size
						A__							:   List[str]					       =		num_channels
						A__							:   List[str]					       =		image_size
						A__							:   Optional[Any]					       =		min_resolution
						A__							:   Optional[int]					       =		max_resolution
						A__							:   Union[str, Any]					       =		do_resize
						A__							:   Any					       =		size
						A__							:   int					       =		do_center_crop
						A__							:   int					       =		crop_size
						A__							:   Union[str, Any]					       =		do_normalize
						A__							:   Optional[int]					       =		image_mean
						A__							:   int					       =		image_std
						A__							:   Tuple					       =		do_reduce_labels






					def   __A      (			self      ):
						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 						UpperCamelCase ()					->				List[Any]:
	A__							:   Union[str, Any]					       =		load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,				split="""test""" )

	A__							:   Union[str, Any]					       =		Image.open(dataset[0]["""file"""] )
	A__							:   Any					       =		Image.open(dataset[1]["""file"""] )

	return image, map

def 						UpperCamelCase ()					->				Union[str, Any]:
	A__							:   int					       =		load_dataset("""hf-internal-testing/fixtures_ade20k"""   ,				split="""test""" )

	A__							:   str					       =		Image.open(ds[0]["""file"""] )
	A__							:   List[str]					       =		Image.open(ds[1]["""file"""] )
	A__							:   int					       =		Image.open(ds[2]["""file"""] )
	A__							:   Dict					       =		Image.open(ds[3]["""file"""] )

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



@require_torch
@require_vision
class    _a						(SCREAMING_SNAKE_CASE_		,       unittest.TestCase    ):


					'''simple docstring'''






					UpperCAmelCase__:       Union[str, Any]  							=	BeitImageProcessor if is_vision_available() else None






					def   __A      (			self      ):
						A__							:   str					       =		BeitImageProcessingTester(self      )






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






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.image_processing_class(**self.image_processor_dict      )
						self.assertTrue(hasattr(snake_case__   ,	"""do_resize"""      )      )
						self.assertTrue(hasattr(snake_case__   ,	"""size"""      )      )
						self.assertTrue(hasattr(snake_case__   ,	"""do_center_crop"""      )      )
						self.assertTrue(hasattr(snake_case__   ,	"""center_crop"""      )      )
						self.assertTrue(hasattr(snake_case__   ,	"""do_normalize"""      )      )
						self.assertTrue(hasattr(snake_case__   ,	"""image_mean"""      )      )
						self.assertTrue(hasattr(snake_case__   ,	"""image_std"""      )      )






					def   __A      (			self      ):
						A__							:   List[Any]					       =		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   ,	snake_case__      )

						A__							:   List[str]					       =		self.image_processing_class.from_dict(
						    self.image_processor_dict   ,	size=42   ,	crop_size=84   ,	reduce_labels=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   ,	snake_case__      )






					def   __A      (			self      ):
						pass






					def   __A      (			self      ):
						A__							:   Optional[int]					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PIL images
						A__							:   int					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=snake_case__      )
						for image in image_inputs:
							self.assertIsInstance(snake_case__   ,	Image.Image      )

						# Test not batched input
						A__							:   Tuple					       =		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
						A__							:   Tuple					       =		image_processing(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      ):
						A__							:   List[str]					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random numpy tensors
						A__							:   Tuple					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=snake_case__   ,	numpify=snake_case__      )
						for image in image_inputs:
							self.assertIsInstance(snake_case__   ,	np.ndarray      )

						# Test not batched input
						A__							:   str					       =		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
						A__							:   Union[str, Any]					       =		image_processing(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      ):
						A__							:   Tuple					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						A__							:   str					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=snake_case__   ,	torchify=snake_case__      )
						for image in image_inputs:
							self.assertIsInstance(snake_case__   ,	torch.Tensor      )

						# Test not batched input
						A__							:   Union[str, Any]					       =		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
						A__							:   Union[str, Any]					       =		image_processing(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      ):
						A__							:   List[Any]					       =		self.image_processing_class(**self.image_processor_dict      )
						# create random PyTorch tensors
						A__							:   Tuple					       =		prepare_image_inputs(self.image_processor_tester   ,	equal_resolution=snake_case__   ,	torchify=snake_case__      )
						A__							:   List[Any]					       =		[]
						for image in image_inputs:
							self.assertIsInstance(snake_case__   ,	torch.Tensor      )
							maps.append(torch.zeros(image.shape[-2:]      ).long()      )

						# Test not batched input
						A__							:   List[Any]					       =		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() <= 255      )

						# Test batched
						A__							:   List[Any]					       =		image_processing(snake_case__   ,	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() <= 255      )

						# Test not batched input (PIL images)
						A__							:   List[Any]					       =		prepare_semantic_single_inputs()

						A__							:   Any					       =		image_processing(snake_case__   ,	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() <= 255      )

						# Test batched input (PIL images)
						A__							:   Optional[int]					       =		prepare_semantic_batch_inputs()

						A__							:   Dict					       =		image_processing(snake_case__   ,	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() <= 255      )






					def   __A      (			self      ):
						A__							:   str					       =		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
						A__							:   Optional[int]					       =		prepare_semantic_single_inputs()
						A__							:   int					       =		image_processing(snake_case__   ,	snake_case__   ,	return_tensors="""pt"""      )
						self.assertTrue(encoding["""labels"""].min().item() >= 0      )
						self.assertTrue(encoding["""labels"""].max().item() <= 150      )

						A__							:   Union[str, Any]					       =		True
						A__							:   Any					       =		image_processing(snake_case__   ,	snake_case__   ,	return_tensors="""pt"""      )
						self.assertTrue(encoding["""labels"""].min().item() >= 0      )
						self.assertTrue(encoding["""labels"""].max().item() <= 255      )






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



