



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


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import ChineseCLIPImageProcessor


class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def __init__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase=7 ,					__UpperCAmelCase=3 ,					__UpperCAmelCase=18 ,					__UpperCAmelCase=30 ,					__UpperCAmelCase=400 ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=None ,					__UpperCAmelCase=True ,					__UpperCAmelCase=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3] ,					__UpperCAmelCase=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1] ,					__UpperCAmelCase=True ,					):







											"""simple docstring"""



											a__	:							Optional[int]      						= size if size is not None else {"height": 224, "width": 224}
											a__	:							Any      						= crop_size if crop_size is not None else {"height": 18, "width": 18}
											a__	:							List[Any]      						= parent
											a__	:							Optional[int]      						= batch_size
											a__	:							Dict      						= num_channels
											a__	:							Dict      						= image_size
											a__	:							Optional[int]      						= min_resolution
											a__	:							Optional[Any]      						= max_resolution
											a__	:							Union[str, Any]      						= do_resize
											a__	:							Optional[Any]      						= size
											a__	:							Any      						= do_center_crop
											a__	:							int      						= crop_size
											a__	:							int      						= do_normalize
											a__	:							Union[str, Any]      						= image_mean
											a__	:							str      						= image_std
											a__	:							Union[str, Any]      						= do_convert_rgb



							def 		_A	(							self				):







											"""simple docstring"""



											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_convert_rgb": self.do_convert_rgb,
											}




							def 		_A	(							self ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False ,					__UpperCAmelCase=False				):







											"""simple docstring"""



											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
															a__	:							Union[str, Any]      						= []
															for i in range(self.batch_size				):
																			image_inputs.append(
																			    np.random.randint(
																			        255 ,					size=(self.num_channels, self.max_resolution, self.max_resolution) ,					dtype=np.uinta				)				)
											else:
															a__	:							Tuple      						= []
															for i in range(self.batch_size				):
																			a__	:							Tuple      						= np.random.choice(np.arange(self.min_resolution ,					self.max_resolution				) ,					2				)
																			image_inputs.append(np.random.randint(255 ,					size=(self.num_channels, width, height) ,					dtype=np.uinta				)				)

											if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															a__	:							Optional[Any]      						= [Image.fromarray(np.moveaxis(__UpperCAmelCase ,					0 ,					-1				)				) for x in image_inputs]

											if torchify:
															a__	:							Any      						= [torch.from_numpy(__UpperCAmelCase				) for x in image_inputs]

											return image_inputs





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





							'''simple docstring'''

							A				:str =       ChineseCLIPImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[int]      						= ChineseCLIPImageProcessingTester(self ,					do_center_crop=__UpperCAmelCase				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							str      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_resize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"size"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_center_crop"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"center_crop"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_mean"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_std"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Tuple      						= self.image_processing_class.from_dict(self.image_processor_dict				)
											self.assertEqual(image_processor.size ,					{"height": 224, "width": 224}				)
											self.assertEqual(image_processor.crop_size ,					{"height": 18, "width": 18}				)

											a__	:							str      						= self.image_processing_class.from_dict(self.image_processor_dict ,					size=42 ,					crop_size=84				)
											self.assertEqual(image_processor.size ,					{"shortest_edge": 42}				)
											self.assertEqual(image_processor.crop_size ,					{"height": 84, "width": 84}				)



							def 		_A	(							self				):







											"""simple docstring"""



											pass



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Tuple      						= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    ) ,					)

											# Test batched
											a__	:							Any      						= image_processing(__UpperCAmelCase ,					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 		_A	(							self				):







											"""simple docstring"""



											a__	:							List[Any]      						= self.image_processing_class(**self.image_processor_dict				)
											# create random numpy tensors
											a__	:							List[str]      						= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase ,					numpify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					np.ndarray				)

											# Test not batched input
											a__	:							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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    ) ,					)

											# Test batched
											a__	:							List[str]      						= image_processing(__UpperCAmelCase ,					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 		_A	(							self				):







											"""simple docstring"""



											a__	:							int      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PyTorch tensors
											a__	:							Dict      						= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase ,					torchify=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					torch.Tensor				)

											# Test not batched input
											a__	:							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.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    ) ,					)

											# Test batched
											a__	:							Tuple      						= image_processing(__UpperCAmelCase ,					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"],
											    ) ,					)





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





							'''simple docstring'''

							A				:Dict =       ChineseCLIPImageProcessor if is_vision_available() else None



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Optional[Any]      						= ChineseCLIPImageProcessingTester(self ,					num_channels=4 ,					do_center_crop=__UpperCAmelCase				)
											a__	:							List[str]      						= 3



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()



							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.image_processing_class(**self.image_processor_dict				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_resize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"size"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_center_crop"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"center_crop"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_normalize"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_mean"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"image_std"				)				)
											self.assertTrue(hasattr(__UpperCAmelCase ,					"do_convert_rgb"				)				)



							def 		_A	(							self				):







											"""simple docstring"""



											pass




							def 		_A	(							self				):







											"""simple docstring"""



											a__	:							Dict      						= self.image_processing_class(**self.image_processor_dict				)
											# create random PIL images
											a__	:							Any      						= self.image_processor_tester.prepare_inputs(equal_resolution=__UpperCAmelCase				)
											for image in image_inputs:
															self.assertIsInstance(__UpperCAmelCase ,					Image.Image				)

											# Test not batched input
											a__	:							Tuple      						= image_processing(image_inputs[0] ,					return_tensors="pt"				).pixel_values
											self.assertEqual(
											    encoded_images.shape ,					(
											        1,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    ) ,					)

											# Test batched
											a__	:							str      						= image_processing(__UpperCAmelCase ,					return_tensors="pt"				).pixel_values
											self.assertEqual(
											    encoded_images.shape ,					(
											        self.image_processor_tester.batch_size,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size["height"],
											        self.image_processor_tester.crop_size["width"],
											    ) ,					)





"""simple docstring"""

def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						float:
 return 1_0 - x * x



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 # Bolzano theory in order to find if there is a root between a and b
 if equation(lowercase__   ) * equation(lowercase__   ) >= 0:
  raise ValueError("Wrong space!"   )

 lowerCAmelCase__   :				Union[str, Any] 			=				a
 while (b - a) >= 0.01:
  # Find middle point
  lowerCAmelCase__   :				int 			=				(a + b) / 2
  # Check if middle point is root
  if equation(lowercase__   ) == 0.0:
   break
  # Decide the side to repeat the steps
  if equation(lowercase__   ) * equation(lowercase__   ) < 0:
   lowerCAmelCase__   :				str 			=				c
  else:
   lowerCAmelCase__   :				List[str] 			=				c
 return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))






