


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 ConvNextImageProcessor







class   __lowercase						(unittest.TestCase     ):





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__=7 ,	lowerCAmelCase__=3 ,	lowerCAmelCase__=1_8 ,	lowerCAmelCase__=3_0 ,	lowerCAmelCase__=4_0_0 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=None ,	lowerCAmelCase__=0.875 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=[0.5, 0.5, 0.5] ,	lowerCAmelCase__=[0.5, 0.5, 0.5] ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				size if size is not None else {'shortest_edge': 2_0}
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				parent
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				batch_size
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				num_channels
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				image_size
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				min_resolution
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				max_resolution
										SCREAMING_SNAKE_CASE_		:    Any 		=				do_resize
										SCREAMING_SNAKE_CASE_		:    str 		=				size
										SCREAMING_SNAKE_CASE_		:    Dict 		=				crop_pct
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				do_normalize
										SCREAMING_SNAKE_CASE_		:    str 		=				image_mean
										SCREAMING_SNAKE_CASE_		:    Any 		=				image_std






							def 				UpperCamelCase__    (			self					):





										"""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,
										    "crop_pct": self.crop_pct,
										}







@require_torch
@require_vision
class   __lowercase						(__SCREAMING_SNAKE_CASE  ,					unittest.TestCase     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	ConvNextImageProcessor if is_vision_available() else None






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    str 		=				ConvNextImageProcessingTester(self					)






							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return self.image_processor_tester.prepare_image_processor_dict()






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowerCAmelCase__ ,	'do_resize'					)					)
										self.assertTrue(hasattr(lowerCAmelCase__ ,	'size'					)					)
										self.assertTrue(hasattr(lowerCAmelCase__ ,	'crop_pct'					)					)
										self.assertTrue(hasattr(lowerCAmelCase__ ,	'do_normalize'					)					)
										self.assertTrue(hasattr(lowerCAmelCase__ ,	'image_mean'					)					)
										self.assertTrue(hasattr(lowerCAmelCase__ ,	'image_std'					)					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Any 		=				self.image_processing_class.from_dict(self.image_processor_dict					)
										self.assertEqual(image_processor.size ,	{'shortest_edge': 2_0}					)

										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.image_processing_class.from_dict(self.image_processor_dict ,	size=4_2					)
										self.assertEqual(image_processor.size ,	{'shortest_edge': 4_2}					)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										pass






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    int 		=				self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										SCREAMING_SNAKE_CASE_		:    str 		=				prepare_image_inputs(self.image_processor_tester ,	equal_resolution=lowerCAmelCase__					)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__ ,	Image.Image					)

										# Test not batched input
										SCREAMING_SNAKE_CASE_		:    int 		=				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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    ) ,	)

										# Test batched
										SCREAMING_SNAKE_CASE_		:    int 		=				image_processing(lowerCAmelCase__ ,	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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    ) ,	)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				prepare_image_inputs(self.image_processor_tester ,	equal_resolution=lowerCAmelCase__ ,	numpify=lowerCAmelCase__					)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__ ,	np.ndarray					)

										# Test not batched input
										SCREAMING_SNAKE_CASE_		:    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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    ) ,	)

										# Test batched
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				image_processing(lowerCAmelCase__ ,	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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    ) ,	)






							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				prepare_image_inputs(self.image_processor_tester ,	equal_resolution=lowerCAmelCase__ ,	torchify=lowerCAmelCase__					)
										for image in image_inputs:
													self.assertIsInstance(lowerCAmelCase__ ,	torch.Tensor					)

										# Test not batched input
										SCREAMING_SNAKE_CASE_		:    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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    ) ,	)

										# Test batched
										SCREAMING_SNAKE_CASE_		:    int 		=				image_processing(lowerCAmelCase__ ,	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['shortest_edge'],
										        self.image_processor_tester.size['shortest_edge'],
										    ) ,	)









from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

lowerCAmelCase__ :			str         ={
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}






def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				{start}
			SCREAMING_SNAKE_CASE_		:    int 		=				[start]
			SCREAMING_SNAKE_CASE_		:    Queue 		=				Queue()
			queue.put(A__							)
			while not queue.empty():
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				queue.get()
						for w in graph[v]:
									if w not in explored:
												explored.add(A__							)
												result.append(A__							)
												queue.put(A__							)
			return result






def 	a__ (							A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    Any 		=				{start}
			SCREAMING_SNAKE_CASE_		:    str 		=				[start]
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				deque([start]							)
			while queue:
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				queue.popleft()
						for child in graph[v]:
									if child not in visited:
												visited.add(A__							)
												result.append(A__							)
												queue.append(A__							)
			return result






def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				F'''from __main__ import G, {name}'''
			SCREAMING_SNAKE_CASE_		:    Tuple 		=				1_0_0_0_0
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				timeit(F'''{name}(G, \'A\')''',			setup=A__,			number=A__							)
			print(F'''{name:<35} finished {number} runs in {res:.5f} seconds'''							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				benchmark_function('breadth_first_search')
				benchmark_function('breadth_first_search_with_deque')
				# breadth_first_search                finished 10000 runs in 0.20999 seconds
				# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





