



'''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 ChineseCLIPImageProcessor






class 							lowercase__     (							unittest.TestCase       ):





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Optional[Any]=7			,lowerCamelCase__ :    Optional[int]=3			,lowerCamelCase__ :    Union[str, Any]=18			,lowerCamelCase__ :    Tuple=30			,lowerCamelCase__ :    Optional[Any]=400			,lowerCamelCase__ :    List[Any]=True			,lowerCamelCase__ :    List[Any]=None			,lowerCamelCase__ :    Tuple=True			,lowerCamelCase__ :    Tuple=None			,lowerCamelCase__ :    Tuple=True			,lowerCamelCase__ :    List[Any]=[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]			,lowerCamelCase__ :    Dict=[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]			,lowerCamelCase__ :    Optional[int]=True			,):







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





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''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      UpperCamelCase_  (			self :    List[Any]			,lowerCamelCase__ :    Optional[int]=False			,lowerCamelCase__ :    List[Any]=False			,lowerCamelCase__ :    Union[str, Any]=False			):







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

							if equal_resolution:
													_UpperCamelCase :		List[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:
													_UpperCamelCase :		int			    =       []
													for i in range(self.batch_size			):
																			_UpperCamelCase :		List[str]			    =       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
													_UpperCamelCase :		Optional[int]			    =       [Image.fromarray(np.moveaxis(snake_case__			,0			,-1			)			) for x in image_inputs]

							if torchify:
													_UpperCamelCase :		Optional[Any]			    =       [torch.from_numpy(snake_case__			) for x in image_inputs]

							return image_inputs






@require_torch
@require_vision
class 							lowercase__     (							A_						, unittest.TestCase       ):
	lowercase__      =      ChineseCLIPImageProcessor if is_vision_available() else None





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       ChineseCLIPImageProcessingTester(self			,do_center_crop=snake_case__			)





	@property
	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(snake_case__			,'do_resize'			)			)
							self.assertTrue(hasattr(snake_case__			,'size'			)			)
							self.assertTrue(hasattr(snake_case__			,'do_center_crop'			)			)
							self.assertTrue(hasattr(snake_case__			,'center_crop'			)			)
							self.assertTrue(hasattr(snake_case__			,'do_normalize'			)			)
							self.assertTrue(hasattr(snake_case__			,'image_mean'			)			)
							self.assertTrue(hasattr(snake_case__			,'image_std'			)			)
							self.assertTrue(hasattr(snake_case__			,'do_convert_rgb'			)			)





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       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}			)

							_UpperCamelCase :		Optional[int]			    =       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      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_UpperCamelCase :		Union[str, Any]			    =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
							for image in image_inputs:
													self.assertIsInstance(snake_case__			,Image.Image			)

							# Test not batched input
							_UpperCamelCase :		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
							_UpperCamelCase :		Optional[Any]			    =       image_processing(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      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random numpy tensors
							_UpperCamelCase :		Optional[Any]			    =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			,numpify=snake_case__			)
							for image in image_inputs:
													self.assertIsInstance(snake_case__			,np.ndarray			)

							# Test not batched input
							_UpperCamelCase :		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
							_UpperCamelCase :		int			    =       image_processing(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      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							_UpperCamelCase :		int			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PyTorch tensors
							_UpperCamelCase :		Optional[Any]			    =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			,torchify=snake_case__			)
							for image in image_inputs:
													self.assertIsInstance(snake_case__			,torch.Tensor			)

							# Test not batched input
							_UpperCamelCase :		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
							_UpperCamelCase :		List[Any]			    =       image_processing(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 							lowercase__     (							A_						, unittest.TestCase       ):
	lowercase__      =      ChineseCLIPImageProcessor if is_vision_available() else None





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       ChineseCLIPImageProcessingTester(self			,num_channels=4			,do_center_crop=snake_case__			)
							_UpperCamelCase :		Tuple			    =       3





	@property
	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							return self.image_processor_tester.prepare_image_processor_dict()





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       self.image_processing_class(**self.image_processor_dict			)
							self.assertTrue(hasattr(snake_case__			,'do_resize'			)			)
							self.assertTrue(hasattr(snake_case__			,'size'			)			)
							self.assertTrue(hasattr(snake_case__			,'do_center_crop'			)			)
							self.assertTrue(hasattr(snake_case__			,'center_crop'			)			)
							self.assertTrue(hasattr(snake_case__			,'do_normalize'			)			)
							self.assertTrue(hasattr(snake_case__			,'image_mean'			)			)
							self.assertTrue(hasattr(snake_case__			,'image_std'			)			)
							self.assertTrue(hasattr(snake_case__			,'do_convert_rgb'			)			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			):







							'''simple docstring'''
							pass





	def      UpperCamelCase_  (			self :    int			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       self.image_processing_class(**self.image_processor_dict			)
							# create random PIL images
							_UpperCamelCase :		Any			    =       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
							for image in image_inputs:
													self.assertIsInstance(snake_case__			,Image.Image			)

							# Test not batched input
							_UpperCamelCase :		str			    =       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
							_UpperCamelCase :		Tuple			    =       image_processing(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'],
							    )			,)










'''simple docstring'''
from __future__ import annotations

import numpy as np






def A__   (					UpperCAmelCase_							):
						_UpperCamelCase    ,      _UpperCamelCase :		Optional[int]			    =       np.shape(UpperCAmelCase_							)
						if rows != columns:
												_UpperCamelCase :		Union[str, Any]			    =       (
												    '\'table\' has to be of square shaped array but got a '
												    f'{rows}x{columns} array:\n{table}'
												)
												raise ValueError(UpperCAmelCase_							)

						_UpperCamelCase :		Optional[Any]			    =       np.zeros((rows, columns)							)
						_UpperCamelCase :		Tuple			    =       np.zeros((rows, columns)							)
						for i in range(UpperCAmelCase_							):
												for j in range(UpperCAmelCase_							):
																		_UpperCamelCase :		Optional[Any]			    =       sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase_							)							)
																		if upper[j][j] == 0:
																								raise ArithmeticError('No LU decomposition exists'							)
																		_UpperCamelCase :		Optional[Any]			    =       (table[i][j] - total) / upper[j][j]
												_UpperCamelCase :		int			    =       1
												for j in range(UpperCAmelCase_  ,  UpperCAmelCase_							):
																		_UpperCamelCase :		Optional[int]			    =       sum(lower[i][k] * upper[k][j] for k in range(UpperCAmelCase_							)							)
																		_UpperCamelCase :		Tuple			    =       table[i][j] - total
						return lower, upper


if __name__ == "__main__":
					import doctest

					doctest.testmod()


