
"""simple docstring"""


from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass







@dataclass
class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     int
						_lowerCamelCase:     int







class 					lowerCAmelCase_					:



						'''simple docstring'''


						def __init__(			self  :   Tuple	,A_  :   int   )  -> Union[str, Any]:
							A     = [[] for _ in range(A_   )]
							A     = size


						def __getitem__(			self  :   str	,A_  :   int   )  -> Iterator[Edge]:
							return iter(self._graph[vertex]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> Any:
							return self._size


						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   int	,A_  :   int	,A_  :   int   )  -> Tuple:
							if weight not in (0, 1):
								raise ValueError('Edge weight must be either 0 or 1.'   )

							if to_vertex < 0 or to_vertex >= self.size:
								raise ValueError('Vertex indexes must be in [0; size).'   )

							self._graph[from_vertex].append(Edge(A_	,A_   )   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   int	,A_  :   int   )  -> int | None:
							A     = deque([start_vertex]   )
							A     = [None] * self.size
							A     = 0

							while queue:
								A     = queue.popleft()
								A     = distances[current_vertex]
								if current_distance is None:
									continue

								for edge in self[current_vertex]:
									A     = current_distance + edge.weight
									A     = distances[edge.destination_vertex]
									if (
									    isinstance(A_	,A_   )
									    and new_distance >= dest_vertex_distance
									):
										continue
									A     = new_distance
									if edge.weight == 0:
										queue.appendleft(edge.destination_vertex   )
									else:
										queue.append(edge.destination_vertex   )

							if distances[finish_vertex] is None:
								raise ValueError('No path from start_vertex to finish_vertex.'   )

							return distances[finish_vertex]


if __name__ == "__main__":
		import doctest

		doctest.testmod()


"""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 LevitImageProcessor







class 							SCREAMING_SNAKE_CASE   (						unittest.TestCase			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,lowercase_     :							Dict  ,lowercase_     :							List[str]=7  ,lowercase_     :							Tuple=3  ,lowercase_     :							List[str]=1_8  ,lowercase_     :							Optional[Any]=3_0  ,lowercase_     :							List[Any]=4_0_0  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Any=None  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=None  ,lowercase_     :							List[Any]=True  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,lowercase_     :							Dict=[0.5, 0.5, 0.5]  ,):
						lowerCAmelCase__      :		Any    							=      size if size is not None else {'''shortest_edge''': 1_8}
						lowerCAmelCase__      :		Union[str, Any]    							=      crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
						lowerCAmelCase__      :		Dict    							=      parent
						lowerCAmelCase__      :		Dict    							=      batch_size
						lowerCAmelCase__      :		List[str]    							=      num_channels
						lowerCAmelCase__      :		Any    							=      image_size
						lowerCAmelCase__      :		Union[str, Any]    							=      min_resolution
						lowerCAmelCase__      :		Dict    							=      max_resolution
						lowerCAmelCase__      :		List[str]    							=      do_resize
						lowerCAmelCase__      :		Optional[Any]    							=      size
						lowerCAmelCase__      :		Tuple    							=      do_center_crop
						lowerCAmelCase__      :		Optional[int]    							=      crop_size
						lowerCAmelCase__      :		List[str]    							=      do_normalize
						lowerCAmelCase__      :		Tuple    							=      image_mean
						lowerCAmelCase__      :		int    							=      image_std







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "do_center_crop": self.do_center_crop,
						    "size": self.size,
						    "crop_size": self.crop_size,
						}







@require_torch
@require_vision
class 							SCREAMING_SNAKE_CASE   (						a_       ,				unittest.TestCase			):






					"""simple docstring"""



					lowercase__												=			LevitImageProcessor if is_vision_available() else None







					def 			__lowerCAmelCase					(			self     :							List[str]						):
						lowerCAmelCase__      :		Optional[Any]    							=      LevitImageProcessingTester(self						)







					@property
					def 			__lowerCAmelCase					(			self     :							Any						):
						return self.image_processor_tester.prepare_image_processor_dict()







					def 			__lowerCAmelCase					(			self     :							str						):
						lowerCAmelCase__      :		Optional[int]    							=      self.image_processing_class(**self.image_processor_dict						)
						self.assertTrue(hasattr(lowercase_  ,'''image_mean'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''image_std'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_normalize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_resize'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''do_center_crop'''						)						)
						self.assertTrue(hasattr(lowercase_  ,'''size'''						)						)







					def 			__lowerCAmelCase					(			self     :							Any						):
						lowerCAmelCase__      :		int    							=      self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size  ,{'''shortest_edge''': 1_8}						)
						self.assertEqual(image_processor.crop_size  ,{'''height''': 1_8, '''width''': 1_8}						)

						lowerCAmelCase__      :		List[str]    							=      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 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						pass







					def 			__lowerCAmelCase					(			self     :							Optional[int]						):
						# Initialize image_processing
						lowerCAmelCase__      :		List[str]    							=      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__      :		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__      :		Optional[int]    							=      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 			__lowerCAmelCase					(			self     :							Union[str, Any]						):
						# Initialize image_processing
						lowerCAmelCase__      :		str    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						lowerCAmelCase__      :		Optional[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__      :		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__      :		Union[str, Any]    							=      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 			__lowerCAmelCase					(			self     :							int						):
						# Initialize image_processing
						lowerCAmelCase__      :		Tuple    							=      self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						lowerCAmelCase__      :		Dict    							=      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__      :		Dict    							=      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__      :		Optional[int]    							=      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'''],
						    )  ,)




