






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 ViTImageProcessor
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				def __init__(  self		:					int,						lowerCamelCase		:					Any,						lowerCamelCase		:					Optional[int]=13,						lowerCamelCase		:					int=3,						lowerCamelCase		:					Optional[Any]=224,						lowerCamelCase		:					List[str]=30,						lowerCamelCase		:					List[Any]=400,						lowerCamelCase		:					List[str]=True,						lowerCamelCase		:					Tuple=None,						lowerCamelCase		:					int=True,						lowerCamelCase		:					Union[str, Any]=[0.5, 0.5, 0.5],						lowerCamelCase		:					Any=[0.5, 0.5, 0.5],						):

								'''simple docstring'''
								lowercase__     				=    size if size is not None else {'''height''': 18, '''width''': 18}
								lowercase__     				=    parent
								lowercase__     				=    batch_size
								lowercase__     				=    num_channels
								lowercase__     				=    image_size
								lowercase__     				=    min_resolution
								lowercase__     				=    max_resolution
								lowercase__     				=    do_resize
								lowercase__     				=    size
								lowercase__     				=    do_normalize
								lowercase__     				=    image_mean
								lowercase__     				=    image_std




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}



@require_torch
@require_vision
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= ViTImageProcessor if is_vision_available() else None




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    EfficientFormerImageProcessorTester(self						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self.image_proc_tester.prepare_image_processor_dict()




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								self.assertTrue(hasattr(lowerCamelCase,						'''image_mean'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''image_std'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_normalize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''do_resize'''						)						)
								self.assertTrue(hasattr(lowerCamelCase,						'''size'''						)						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								pass




				def        lowercase__     (  self		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PIL images
								lowercase__     				=    prepare_image_inputs(self.image_proc_tester,						equal_resolution=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						Image.Image						)

								# Test not batched input
								lowercase__     				=    image_processor(image_inputs[0],						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random numpy tensors
								lowercase__     				=    prepare_image_inputs(self.image_proc_tester,						equal_resolution=lowerCamelCase,						numpify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						np.ndarray						)

								# Test not batched input
								lowercase__     				=    image_processor(image_inputs[0],						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    ),						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.image_processing_class(**self.image_processor_dict						)
								# create random PyTorch tensors
								lowercase__     				=    prepare_image_inputs(self.image_proc_tester,						equal_resolution=lowerCamelCase,						torchify=lowerCamelCase						)
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,						torch.Tensor						)

								# Test not batched input
								lowercase__     				=    image_processor(image_inputs[0],						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        1,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    ),						)

								# Test batched
								lowercase__     				=    image_processor(lowerCamelCase,						return_tensors='''pt'''						).pixel_values
								self.assertEqual(
								    encoded_images.shape,						(
								        self.image_proc_tester.batch_size,
								        self.image_proc_tester.num_channels,
								        self.image_proc_tester.size['''height'''],
								        self.image_proc_tester.size['''width'''],
								    ),						)







from __future__ import annotations

from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42



class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					str,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    [[] for _ in range(lowerCamelCase						)]
								lowercase__     				=    size




				def __getitem__(  self		:					Optional[Any],						lowerCamelCase		:					int						):

								'''simple docstring'''
								return iter(self._graph[vertex]						)




				@property
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								return self._size




				def        lowercase__     (  self		:					Union[str, Any],						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								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(lowerCamelCase,						lowerCamelCase						)						)




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					int						):

								'''simple docstring'''
								lowercase__     				=    deque([start_vertex]						)
								lowercase__     				=    [None] * self.size
								lowercase__     				=    0

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

												for edge in self[current_vertex]:
																lowercase__     				=    current_distance + edge.weight
																lowercase__     				=    distances[edge.destination_vertex]
																if (
																    isinstance(lowerCamelCase,						lowerCamelCase						)
																    and new_distance >= dest_vertex_distance
																):
																				continue
																lowercase__     				=    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()




