




"""simple docstring"""







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor




class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





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



				def       snake_case    (     self					:	str						):
						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       lowerCAmelCase__       (			lowerCAmelCase_  ,			unittest.TestCase    ):


				"""simple docstring"""





				__UpperCAmelCase :     int       =      DPTImageProcessor if is_vision_available() else None



				def       snake_case    (     self					:	Tuple						):
						__lowercase				:		Dict												=				DPTImageProcessingTester(self						)



				@property
				def       snake_case    (     self					:	Dict						):
						return self.image_processor_tester.prepare_image_processor_dict()



				def       snake_case    (     self					:	Union[str, Any]						):
						__lowercase				:		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__    ,   "size"						)						)



				def       snake_case    (     self					:	int						):
						__lowercase				:		Optional[int]												=				self.image_processing_class.from_dict(self.image_processor_dict						)
						self.assertEqual(image_processor.size    ,   {"height": 1_8, "width": 1_8}						)

						__lowercase				:		List[str]												=				self.image_processing_class.from_dict(self.image_processor_dict    ,   size=4_2						)
						self.assertEqual(image_processor.size    ,   {"height": 4_2, "width": 4_2}						)



				def       snake_case    (     self					:	Optional[Any]						):
						# Initialize image_processing
						__lowercase				:		Any												=				self.image_processing_class(**self.image_processor_dict						)
						# create random PIL images
						__lowercase				:		Optional[int]												=				prepare_image_inputs(self.image_processor_tester    ,   equal_resolution=lowercase__						)
						for image in image_inputs:
								self.assertIsInstance(lowercase__    ,   Image.Image						)

						# Test not batched input
						__lowercase				:		List[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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,   )

						# Test batched
						__lowercase				:		List[str]												=				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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,   )



				def       snake_case    (     self					:	List[str]						):
						# Initialize image_processing
						__lowercase				:		str												=				self.image_processing_class(**self.image_processor_dict						)
						# create random numpy tensors
						__lowercase				:		Dict												=				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
						__lowercase				:		Optional[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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,   )

						# Test batched
						__lowercase				:		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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,   )



				def       snake_case    (     self					:	Union[str, Any]						):
						# Initialize image_processing
						__lowercase				:		str												=				self.image_processing_class(**self.image_processor_dict						)
						# create random PyTorch tensors
						__lowercase				:		Tuple												=				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
						__lowercase				:		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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,   )

						# Test batched
						__lowercase				:		Optional[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.size["height"],
						        self.image_processor_tester.size["width"],
						    )    ,   )






"""simple docstring"""







from collections import deque

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		__lowercase				:		Any												=				len(_lowerCamelCase				)
		__lowercase				:		str												=				deque()
		__lowercase				:		Tuple												=				[False for _ in range(_lowerCamelCase				)]
		__lowercase				:		Optional[int]												=				[-1 for _ in range(_lowerCamelCase				)]
		__lowercase				:		Dict												=				index_of[:]

		def strong_connect(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		List[str]												=				index  # the number when this node is seen
				__lowercase				:		List[str]												=				index  # lowest rank node reachable from here
				index += 1
				stack.append(_lowerCamelCase				)
				__lowercase				:		List[Any]												=				True

				for w in g[v]:
						if index_of[w] == -1:
								__lowercase				:		Optional[int]												=				strong_connect(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)
								__lowercase				:		List[Any]												=				(
								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
								)
						elif on_stack[w]:
								__lowercase				:		List[Any]												=				(
								    lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
								)

				if lowlink_of[v] == index_of[v]:
						__lowercase				:		Any												=				[]
						__lowercase				:		List[Any]												=				stack.pop()
						__lowercase				:		Any												=				False
						component.append(_lowerCamelCase				)
						while w != v:
								__lowercase				:		List[str]												=				stack.pop()
								__lowercase				:		Dict												=				False
								component.append(_lowerCamelCase				)
						components.append(_lowerCamelCase				)
				return index

		__lowercase				:		Tuple												=				[]
		for v in range(_lowerCamelCase				):
				if index_of[v] == -1:
						strong_connect(_lowerCamelCase,  0,  _lowerCamelCase				)

		return components

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->Tuple:






		"""simple docstring"""


		__lowercase				:		int												=				[[] for _ in range(_lowerCamelCase				)]
		for u, v in edges:
				g[u].append(_lowerCamelCase				)
		return g


if __name__ == "__main__":
						# Test
						__A   :  int					=      7
						__A   :  int					=      [0, 0, 1, 2, 3, 3, 4, 4, 6]
						__A   :  List[Any]					=      [1, 3, 2, 0, 1, 4, 5, 6, 5]
						__A   :  List[str]					=      [(u, v) for u, v in zip(source, target)]
						__A   :  List[str]					=      create_graph(n_vertices, edges)

						assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)
