
'''simple docstring'''






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 CLIPImageProcessor

class 							_snake_case     (  unittest.TestCase ):
	"""simple docstring"""





	def __init__(						self				: int					,						UpperCamelCase_				: Any					,						UpperCamelCase_				: Optional[int]=7					,						UpperCamelCase_				: List[Any]=3					,						UpperCamelCase_				: int=18					,						UpperCamelCase_				: Tuple=30					,						UpperCamelCase_				: Optional[Any]=400					,						UpperCamelCase_				: Dict=True					,						UpperCamelCase_				: int=None					,						UpperCamelCase_				: Tuple=True					,						UpperCamelCase_				: Any=None					,						UpperCamelCase_				: Dict=True					,						UpperCamelCase_				: List[str]=[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_				: int=[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_				: Union[str, Any]=True					,						):
			lowerCAmelCase_   :       Union[str, Any]    							=size if size is not None else {'''shortest_edge''': 20}
			lowerCAmelCase_   :       List[str]    							=crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
			lowerCAmelCase_   :       Optional[Any]    							=parent
			lowerCAmelCase_   :       Union[str, Any]    							=batch_size
			lowerCAmelCase_   :       List[str]    							=num_channels
			lowerCAmelCase_   :       Optional[int]    							=image_size
			lowerCAmelCase_   :       List[Any]    							=min_resolution
			lowerCAmelCase_   :       Tuple    							=max_resolution
			lowerCAmelCase_   :       Optional[int]    							=do_resize
			lowerCAmelCase_   :       Optional[Any]    							=size
			lowerCAmelCase_   :       Optional[int]    							=do_center_crop
			lowerCAmelCase_   :       List[str]    							=crop_size
			lowerCAmelCase_   :       Optional[Any]    							=do_normalize
			lowerCAmelCase_   :       Optional[Any]    							=image_mean
			lowerCAmelCase_   :       str    							=image_std
			lowerCAmelCase_   :       Dict    							=do_convert_rgb




	def 	__A      (						self				: List[str]      ):
			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				: Optional[int]					,						UpperCamelCase_				: str=False					,						UpperCamelCase_				: List[str]=False					,						UpperCamelCase_				: Optional[Any]=False      ):
			assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

			if equal_resolution:
					lowerCAmelCase_   :       int    							=[]
					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:
					lowerCAmelCase_   :       Dict    							=[]
					for i in range(self.batch_size      ):
							lowerCAmelCase_							,		lowerCAmelCase_   :       int    							=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
					lowerCAmelCase_   :       str    							=[Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE					,						0					,						-1      )      ) for x in image_inputs]

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

			return image_inputs







@require_torch
@require_vision
class 							_snake_case     (  snake_case__						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  Union[str, Any]								=		CLIPImageProcessor if is_vision_available() else None




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       List[Any]    							=CLIPImageProcessingTester(self      )




	@property
	def 	__A      (						self				: Optional[int]      ):
			return self.image_processor_tester.prepare_image_processor_dict()




	def 	__A      (						self				: Optional[int]      ):
			lowerCAmelCase_   :       int    							=self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_resize'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''size'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_center_crop'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''center_crop'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_normalize'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''image_mean'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''image_std'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_convert_rgb'''      )      )




	def 	__A      (						self				: Optional[Any]      ):
			lowerCAmelCase_   :       str    							=self.image_processing_class.from_dict(self.image_processor_dict      )
			self.assertEqual(image_processor.size					,						{'''shortest_edge''': 20}      )
			self.assertEqual(image_processor.crop_size					,						{'''height''': 18, '''width''': 18}      )

			lowerCAmelCase_   :       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				: Any      ):
			pass




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			lowerCAmelCase_   :       Optional[Any]    							=self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE      )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,						Image.Image      )

			# Test not batched input
			lowerCAmelCase_   :       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
			lowerCAmelCase_   :       List[str]    							=image_processing(_SCREAMING_SNAKE_CASE					,						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				: int      ):
			lowerCAmelCase_   :       str    							=self.image_processing_class(**self.image_processor_dict      )
			# create random numpy tensors
			lowerCAmelCase_   :       Tuple    							=self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE					,						numpify=_SCREAMING_SNAKE_CASE      )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,						np.ndarray      )

			# Test not batched input
			lowerCAmelCase_   :       Tuple    							=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
			lowerCAmelCase_   :       List[str]    							=image_processing(_SCREAMING_SNAKE_CASE					,						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				: str      ):
			lowerCAmelCase_   :       Optional[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PyTorch tensors
			lowerCAmelCase_   :       Tuple    							=self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE					,						torchify=_SCREAMING_SNAKE_CASE      )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,						torch.Tensor      )

			# Test not batched input
			lowerCAmelCase_   :       Union[str, 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.crop_size['''height'''],
			        self.image_processor_tester.crop_size['''width'''],
			    )					,						)

			# Test batched
			lowerCAmelCase_   :       Optional[Any]    							=image_processing(_SCREAMING_SNAKE_CASE					,						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 							_snake_case     (  snake_case__						,    unittest.TestCase ):
	"""simple docstring"""





	_UpperCamelCase							:  List[Any]								=		CLIPImageProcessor if is_vision_available() else None




	def 	__A      (						self				: Tuple      ):
			lowerCAmelCase_   :       Optional[int]    							=CLIPImageProcessingTester(self					,						num_channels=4      )
			lowerCAmelCase_   :       Optional[Any]    							=3




	@property
	def 	__A      (						self				: str      ):
			return self.image_processor_tester.prepare_image_processor_dict()




	def 	__A      (						self				: Any      ):
			lowerCAmelCase_   :       Optional[Any]    							=self.image_processing_class(**self.image_processor_dict      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_resize'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''size'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_center_crop'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''center_crop'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_normalize'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''image_mean'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''image_std'''      )      )
			self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,						'''do_convert_rgb'''      )      )




	def 	__A      (						self				: Any      ):
			pass





	def 	__A      (						self				: Union[str, Any]      ):
			lowerCAmelCase_   :       Any    							=self.image_processing_class(**self.image_processor_dict      )
			# create random PIL images
			lowerCAmelCase_   :       List[Any]    							=self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE      )
			for image in image_inputs:
					self.assertIsInstance(_SCREAMING_SNAKE_CASE					,						Image.Image      )

			# Test not batched input
			lowerCAmelCase_   :       Optional[int]    							=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
			lowerCAmelCase_   :       str    							=image_processing(_SCREAMING_SNAKE_CASE					,						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'''],
			    )					,						)














def   snake_case		(				UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							str ):
				# "Simpson Rule"
				# int(f) = delta_x/2 * (b-a)/3*(f1 + 4f2 + 2f_3 + ... + fn)
				A									=   (boundary[1] - boundary[0]) / steps
				A									=   boundary[0]
				A									=   boundary[1]
				A									=   make_points(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				A									=   0.0
				y += (h / 3.0) * f(UpperCAmelCase )
				A									=   2
				for i in x_i:
								y += (h / 3) * (4 - 2 * (cnt % 2)) * f(UpperCAmelCase )
								cnt += 1
				y += (h / 3.0) * f(UpperCAmelCase )
				return y







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int,    UpperCAmelCase						:							List[Any] ):
				A									=   a + h
				while x < (b - h):
								yield x
								A									=   x + h







def   snake_case		(				UpperCAmelCase						:							str ):  # enter your function here
				A									=   (x - 0) * (x - 0)
				return y







def   snake_case		(				):
				A									=   0.0  # Lower bound of integration
				A									=   1.0  # Upper bound of integration
				A									=   10.0  # define number of steps or resolution
				A									=   [a, b]  # define boundary of integration
				A									=   method_a(UpperCAmelCase,    UpperCAmelCase )
				print(f'y = {y}' )


if __name__ == "__main__":
					main()





