
import unittest

import numpy as np
import requests

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

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					SCREAMING_SNAKE_CASE__   :			str												=    False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor

class    lowerCAmelCase__     (	unittest.TestCase			):






		def __init__(		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]=7							,			SCREAMING_SNAKE_CASE__		:							List[str]=3							,			SCREAMING_SNAKE_CASE__		:							Tuple=18							,			SCREAMING_SNAKE_CASE__		:							int=30							,			SCREAMING_SNAKE_CASE__		:							Any=4_00							,			SCREAMING_SNAKE_CASE__		:							Any=None							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			)     ->							Union[str, Any]:
						__lowerCamelCase    					=	size if size is not None else {'''height''': 20, '''width''': 20}
						__lowerCamelCase    					=	parent
						__lowerCamelCase    					=	batch_size
						__lowerCamelCase    					=	num_channels
						__lowerCamelCase    					=	image_size
						__lowerCamelCase    					=	min_resolution
						__lowerCamelCase    					=	max_resolution
						__lowerCamelCase    					=	size
						__lowerCamelCase    					=	do_normalize
						__lowerCamelCase    					=	do_convert_rgb
						__lowerCamelCase    					=	[5_12, 10_24, 20_48, 40_96]
						__lowerCamelCase    					=	patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}






		def  __A       (		self		:							Union[str, Any]  )     ->							Union[str, Any]:
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







		def  __A       (		self		:							int  )     ->							Any:
						__lowerCamelCase    					=	'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
						__lowerCamelCase    					=	Image.open(requests.get(SCREAMING_SNAKE_CASE__							,			stream=SCREAMING_SNAKE_CASE__  ).raw  ).convert('''RGB'''  )
						return raw_image





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Dict        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Optional[Any]  )     ->							List[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self  )






		@property
		def  __A       (		self		:							Optional[int]  )     ->							Optional[int]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )






		def  __A       (		self		:							int  )     ->							str:
						__lowerCamelCase    					=	self.image_processor_tester.prepare_dummy_image()

						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						__lowerCamelCase    					=	20_48

						__lowerCamelCase    					=	image_processor(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  )
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,			torch.tensor(0.0606  )							,			atol=1e-3							,			rtol=1e-3  )  )






		def  __A       (		self		:							Union[str, Any]  )     ->							Dict:
						# Initialize image_processor
						__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    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							Dict  )     ->							str:
						# Initialize image_processor
						__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    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						__lowerCamelCase    					=	True

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
														__lowerCamelCase    					=	image_processor(
														    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches

										__lowerCamelCase    					=	'''Hello'''

										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__							,			header_text=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)






		def  __A       (		self		:							List[str]  )     ->							Any:
						# Initialize image_processor
						__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  )

						__lowerCamelCase    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)







		def  __A       (		self		:							Tuple  )     ->							List[str]:
						# Initialize image_processor
						__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    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,	reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""  ,	)
@require_torch
@require_vision
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Any        =		PixaStructImageProcessor if is_vision_available() else None






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	PixaStructImageProcessingTester(self							,			num_channels=4  )
						__lowerCamelCase    					=	3






		@property
		def  __A       (		self		:							List[Any]  )     ->							Optional[Any]:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Union[str, Any]  )     ->							List[str]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_convert_rgb'''  )  )







		def  __A       (		self		:							Optional[int]  )     ->							str:
						# Initialize image_processor
						__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    					=	(
						    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
										# Test not batched input
										__lowerCamelCase    					=	image_processor(
										    image_inputs[0]							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(1, max_patch, expected_hidden_dim)							,			)

										# Test batched
										__lowerCamelCase    					=	image_processor(
										    SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''							,			max_patches=SCREAMING_SNAKE_CASE__  ).flattened_patches
										self.assertEqual(
										    encoded_images.shape							,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,			)





from queue import PriorityQueue
from typing import Any

import numpy as np




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




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict							)			->   int:
				__lowerCamelCase    					=	-1

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

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

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

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								__lowerCamelCase    					=	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:
								__lowerCamelCase    					=	shortest_distance
				return shortest_path_distance


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
SCREAMING_SNAKE_CASE__   :			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()



