






from queue import PriorityQueue
from typing import Any

import numpy as np


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		)->  float | int:





						"""simple docstring"""



						for nxt, d in graph[v]:
												if nxt in visited_forward:
																		continue
												_UpperCAmelCase									=			cst_fwd.get(__lowerCAmelCase ,		np.inf	)
												_UpperCAmelCase									=			cst_fwd[v] + d
												if new_cost_f < old_cost_f:
																		queue.put((new_cost_f, nxt)	)
																		_UpperCAmelCase									=			new_cost_f
																		_UpperCAmelCase									=			v
												if nxt in visited_backward:
																		if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																								_UpperCAmelCase									=			cst_fwd[v] + d + cst_bwd[nxt]
						return shortest_distance


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  int:





						"""simple docstring"""



						_UpperCAmelCase									=			-1

						_UpperCAmelCase									=			set()
						_UpperCAmelCase									=			set()
						_UpperCAmelCase									=			{source: 0}
						_UpperCAmelCase									=			{destination: 0}
						_UpperCAmelCase									=			{source: None}
						_UpperCAmelCase									=			{destination: None}
						_UpperCAmelCase									=			PriorityQueue()
						_UpperCAmelCase									=			PriorityQueue()

						_UpperCAmelCase									=			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():
												_UpperCAmelCase					, _UpperCAmelCase									=			queue_forward.get()
												visited_forward.add(__lowerCAmelCase	)

												_UpperCAmelCase					, _UpperCAmelCase									=			queue_backward.get()
												visited_backward.add(__lowerCAmelCase	)

												_UpperCAmelCase									=			pass_and_relaxation(
												    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		)

												_UpperCAmelCase									=			pass_and_relaxation(
												    __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		)

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

						if shortest_distance != np.inf:
												_UpperCAmelCase									=			shortest_distance
						return shortest_path_distance


_a      	=  {
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}
_a      	=  {
    '''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()




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 MobileNetVaImageProcessor







class  snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						def __init__( self      ,  __lowercase      ,  __lowercase=7      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=None      ,  )    ->			List[Any]:
										lowerCAmelCase_       :					Optional[Any]			 = size if size is not None else {'''shortest_edge''': 2_0}
										lowerCAmelCase_       :					Any			 = crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
										lowerCAmelCase_       :					Any			 = parent
										lowerCAmelCase_       :					Any			 = batch_size
										lowerCAmelCase_       :					Optional[int]			 = num_channels
										lowerCAmelCase_       :					Tuple			 = image_size
										lowerCAmelCase_       :					List[str]			 = min_resolution
										lowerCAmelCase_       :					Dict			 = max_resolution
										lowerCAmelCase_       :					Tuple			 = do_resize
										lowerCAmelCase_       :					Optional[Any]			 = size
										lowerCAmelCase_       :					Union[str, Any]			 = do_center_crop
										lowerCAmelCase_       :					Optional[Any]			 = crop_size




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}







@require_torch
@require_vision
class  snake_case__(							UpperCAmelCase__,  unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Dict    =							MobileNetVaImageProcessor if is_vision_available() else None




						def   lowercase_       ( self							)    ->			List[str]:
										lowerCAmelCase_       :					Union[str, Any]			 = MobileNetVaImageProcessingTester(self							)




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




						def   lowercase_       ( self							)    ->			List[Any]:
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_resize'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''size'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''do_center_crop'''							)							)
										self.assertTrue(hasattr(__lowercase      ,  '''crop_size'''							)							)




						def   lowercase_       ( self							)    ->			int:
										lowerCAmelCase_       :					str			 = self.image_processing_class.from_dict(self.image_processor_dict							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 2_0}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 1_8, '''width''': 1_8}							)

										lowerCAmelCase_       :					Tuple			 = self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2      ,  crop_size=8_4							)
										self.assertEqual(image_processor.size      ,  {'''shortest_edge''': 4_2}							)
										self.assertEqual(image_processor.crop_size      ,  {'''height''': 8_4, '''width''': 8_4}							)




						def   lowercase_       ( self							)    ->			Tuple:
										pass




						def   lowercase_       ( self							)    ->			Union[str, Any]:
										# Initialize image_processing
										lowerCAmelCase_       :					Optional[int]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PIL images
										lowerCAmelCase_       :					Union[str, Any]			 = 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_       :					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
										lowerCAmelCase_       :					Tuple			 = 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							)    ->			Optional[int]:
										# Initialize image_processing
										lowerCAmelCase_       :					Tuple			 = self.image_processing_class(**self.image_processor_dict							)
										# create random numpy tensors
										lowerCAmelCase_       :					Any			 = 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_       :					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
										lowerCAmelCase_       :					Tuple			 = 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							)    ->			Any:
										# Initialize image_processing
										lowerCAmelCase_       :					Union[str, Any]			 = self.image_processing_class(**self.image_processor_dict							)
										# create random PyTorch tensors
										lowerCAmelCase_       :					int			 = 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_       :					List[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
										lowerCAmelCase_       :					Dict			 = 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'''],
										    )      ,  )