





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class        _a(    unittest.TestCase    ):







		def __init__(    self ,						__snake_case ,						__snake_case = True ,						__snake_case = None ,						__snake_case = 3_2 ,						__snake_case = True ,						__snake_case = 1 / 2_5_5 ,						__snake_case = True ,						__snake_case = True ,						__snake_case = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] ,						__snake_case = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] ,						__snake_case = True ,						__snake_case=7 ,						__snake_case=3_0 ,						__snake_case=4_0_0 ,						__snake_case=3 ,						)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   parent
							_snake_case       :					Any				       =   do_resize
							_snake_case       :					str				       =   size if size is not None else {"shortest_edge": 2_8_8}
							_snake_case       :					Any				       =   size_divisor
							_snake_case       :					str				       =   do_rescale
							_snake_case       :					Optional[int]				       =   rescale_factor
							_snake_case       :					int				       =   do_normalize
							_snake_case       :					Optional[int]				       =   do_center_crop
							_snake_case       :					str				       =   image_mean
							_snake_case       :					Optional[Any]				       =   image_std
							_snake_case       :					List[str]				       =   do_pad
							_snake_case       :					str				       =   batch_size
							_snake_case       :					Union[str, Any]				       =   num_channels
							_snake_case       :					List[str]				       =   min_resolution
							_snake_case       :					Optional[int]				       =   max_resolution







		def        lowercase       (    self				)      ->      str:


							'''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,
							    "size_divisor": self.size_divisor,
							}







		def        lowercase       (    self ,						__snake_case ,						__snake_case=False				)      ->      int:


							'''simple docstring'''

							if not batched:
												_snake_case       :					Optional[int]				       =   self.size["shortest_edge"]
												_snake_case       :					str				       =   image_inputs[0]
												if isinstance(lowerCAmelCase__ ,						Image.Image				):
																	_snake_case       , _snake_case       :					Optional[Any]				       =   image.size
												else:
																	_snake_case       , _snake_case       :					Dict				       =   image.shape[1], image.shape[2]
												_snake_case       :					str				       =   size / min(lowerCAmelCase__ ,						lowerCAmelCase__				)
												if h < w:
																	_snake_case       , _snake_case       :					Dict				       =   size, scale * w
												else:
																	_snake_case       , _snake_case       :					Tuple				       =   scale * h, size

												_snake_case       :					Any				       =   int((1_3_3_3 / 8_0_0) * size				)
												if max(lowerCAmelCase__ ,						lowerCAmelCase__				) > max_size:
																	_snake_case       :					str				       =   max_size / max(lowerCAmelCase__ ,						lowerCAmelCase__				)
																	_snake_case       :					List[str]				       =   newh * scale
																	_snake_case       :					Union[str, Any]				       =   neww * scale

												_snake_case       , _snake_case       :					Optional[int]				       =   int(newh + 0.5				), int(neww + 0.5				)
												_snake_case       , _snake_case       :					Union[str, Any]				       =   (
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

							else:
												_snake_case       :					Any				       =   []
												for image in image_inputs:
																	_snake_case       , _snake_case       :					List[str]				       =   self.get_expected_values([image]				)
																	expected_values.append((expected_height, expected_width)				)
												_snake_case       :					Optional[int]				       =   max(lowerCAmelCase__ ,						key=lambda __snake_case				: item[0]				)[0]
												_snake_case       :					List[Any]				       =   max(lowerCAmelCase__ ,						key=lambda __snake_case				: item[1]				)[1]

							return expected_height, expected_width






@require_torch
@require_vision
class        _a(    _snake_case	,   unittest.TestCase    ):
		lowerCamelCase__     :List[str]   				=		BridgeTowerImageProcessor if is_vision_available() else None







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   BridgeTowerImageProcessingTester(self				)







		@property
		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							return self.image_processor_tester.prepare_image_processor_dict()







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   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"				)				)
							self.assertTrue(hasattr(lowerCAmelCase__ ,						"size_divisor"				)				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							pass







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[int]				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PIL images
							_snake_case       :					Tuple				       =   prepare_image_inputs(self.image_processor_tester ,						equal_resolution=lowerCAmelCase__				)
							for image in image_inputs:
												self.assertIsInstance(lowerCAmelCase__ ,						Image.Image				)

							# Test not batched input
							_snake_case       :					Tuple				       =   image_processing(image_inputs[0] ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					Union[str, Any]				       =   self.image_processor_tester.get_expected_values(lowerCAmelCase__				)
							self.assertEqual(
							    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

							# Test batched
							_snake_case       :					Optional[int]				       =   image_processing(lowerCAmelCase__ ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					List[str]				       =   self.image_processor_tester.get_expected_values(lowerCAmelCase__ ,						batched=lowerCAmelCase__				)
							self.assertEqual(
							    encoded_images.shape ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,						)







		def        lowercase       (    self				)      ->      Union[str, Any]:


							'''simple docstring'''

							_snake_case       :					str				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random numpy tensors
							_snake_case       :					Union[str, 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
							_snake_case       :					List[Any]				       =   image_processing(image_inputs[0] ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					List[Any]				       =   self.image_processor_tester.get_expected_values(lowerCAmelCase__				)
							self.assertEqual(
							    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

							# Test batched
							_snake_case       :					Optional[int]				       =   image_processing(lowerCAmelCase__ ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					List[Any]				       =   self.image_processor_tester.get_expected_values(lowerCAmelCase__ ,						batched=lowerCAmelCase__				)
							self.assertEqual(
							    encoded_images.shape ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,						)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					int				       =   self.image_processing_class(**self.image_processor_dict				)
							# create random PyTorch tensors
							_snake_case       :					Optional[int]				       =   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
							_snake_case       :					Union[str, Any]				       =   image_processing(image_inputs[0] ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					int				       =   self.image_processor_tester.get_expected_values(lowerCAmelCase__				)
							self.assertEqual(
							    encoded_images.shape ,						(1, self.image_processor_tester.num_channels, expected_height, expected_width) ,						)

							# Test batched
							_snake_case       :					Tuple				       =   image_processing(lowerCAmelCase__ ,						return_tensors="pt"				).pixel_values

							_snake_case       , _snake_case       :					Tuple				       =   self.image_processor_tester.get_expected_values(lowerCAmelCase__ ,						batched=lowerCAmelCase__				)
							self.assertEqual(
							    encoded_images.shape ,						(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    ) ,						)






import sys
import turtle







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2







def    A				(							UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  ):
					my_pen.up()
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.down()
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)
					my_pen.goto(vertexa[0]  ,  vertexa[1]				)

					if depth == 0:
										return

					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)
					triangle(UpperCAmelCase  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  get_mid(UpperCAmelCase  ,  UpperCAmelCase				)  ,  depth - 1				)


if __name__ == "__main__":
							if len(sys.argv) != 2:
														raise ValueError(
														    'Correct format for using this script: '
														    'python fractals.py <int:depth_for_fractal>'
														)
							__lowerCAmelCase :Any     =    turtle.Turtle()
							my_pen.ht()
							my_pen.speed(5)
							my_pen.pencolor('red')

							__lowerCAmelCase :Optional[int]     =    [(-1_75, -1_25), (0, 1_75), (1_75, -1_25)]  # vertices of triangle
							triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))