


'''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 	a				(		unittest.TestCase       ):


     """simple docstring"""






     def __init__(				self     :		str							, snake_case     :		List[Any]							, snake_case     :		List[str]=7							, snake_case     :		List[str]=3							, snake_case     :		Dict=18							, snake_case     :		Optional[int]=30							, snake_case     :		Dict=400							, snake_case     :		Tuple=True							, snake_case     :		int=None							, snake_case     :		Optional[int]=True							, snake_case     :		int=None							, snake_case     :		Tuple=True							, snake_case     :		Tuple=[0.48_145_466, 0.4_578_275, 0.40_821_073]							, snake_case     :		Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711]							, snake_case     :		Union[str, Any]=True							, )						->							List[Any]:
          __UpperCAmelCase	: Any  							=			size if size is not None else {'''shortest_edge''': 20}
          __UpperCAmelCase	: Optional[Any]  							=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
          __UpperCAmelCase	: Dict  							=			parent
          __UpperCAmelCase	: List[str]  							=			batch_size
          __UpperCAmelCase	: Optional[int]  							=			num_channels
          __UpperCAmelCase	: List[str]  							=			image_size
          __UpperCAmelCase	: Tuple  							=			min_resolution
          __UpperCAmelCase	: Optional[Any]  							=			max_resolution
          __UpperCAmelCase	: Optional[Any]  							=			do_resize
          __UpperCAmelCase	: Optional[int]  							=			size
          __UpperCAmelCase	: Tuple  							=			do_center_crop
          __UpperCAmelCase	: str  							=			crop_size
          __UpperCAmelCase	: Union[str, Any]  							=			do_normalize
          __UpperCAmelCase	: Dict  							=			image_mean
          __UpperCAmelCase	: Optional[int]  							=			image_std
          __UpperCAmelCase	: Any  							=			do_convert_rgb

     def  lowerCamelCase__ (				self     :		List[str]			)						->							Dict:
          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  lowerCamelCase__ (				self     :		Union[str, Any]							, snake_case     :		Tuple=False							, snake_case     :		Union[str, Any]=False							, snake_case     :		Optional[int]=False			)						->							Optional[int]:
          assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

          if equal_resolution:
               __UpperCAmelCase	: Optional[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	: Tuple  							=			[]
               for i in range(self.batch_size			):
                    __UpperCAmelCase							,   __UpperCAmelCase	: 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
               __UpperCAmelCase	: Optional[Any]  							=			[Image.fromarray(np.moveaxis(A_							, 0							, -1			)			) for x in image_inputs]

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

          return image_inputs



@require_torch
@require_vision
class 	a				(		snake_case_	,  unittest.TestCase       ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE       : str							   =   CLIPImageProcessor if is_vision_available() else None

     def  lowerCamelCase__ (				self     :		List[Any]			)						->							Dict:
          __UpperCAmelCase	: Optional[Any]  							=			CLIPImageProcessingTester(self			)

     @property
     def  lowerCamelCase__ (				self     :		Union[str, Any]			)						->							List[str]:
          return self.image_processor_tester.prepare_image_processor_dict()

     def  lowerCamelCase__ (				self     :		List[str]			)						->							List[str]:
          __UpperCAmelCase	: Optional[Any]  							=			self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(A_							, '''do_resize'''			)			)
          self.assertTrue(hasattr(A_							, '''size'''			)			)
          self.assertTrue(hasattr(A_							, '''do_center_crop'''			)			)
          self.assertTrue(hasattr(A_							, '''center_crop'''			)			)
          self.assertTrue(hasattr(A_							, '''do_normalize'''			)			)
          self.assertTrue(hasattr(A_							, '''image_mean'''			)			)
          self.assertTrue(hasattr(A_							, '''image_std'''			)			)
          self.assertTrue(hasattr(A_							, '''do_convert_rgb'''			)			)

     def  lowerCamelCase__ (				self     :		Any			)						->							List[str]:
          __UpperCAmelCase	: Any  							=			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}			)

          __UpperCAmelCase	: List[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  lowerCamelCase__ (				self     :		Optional[int]			)						->							Dict:
          pass

     def  lowerCamelCase__ (				self     :		Optional[Any]			)						->							Union[str, Any]:
          __UpperCAmelCase	: int  							=			self.image_processing_class(**self.image_processor_dict			)
          # create random PIL images
          __UpperCAmelCase	: List[str]  							=			self.image_processor_tester.prepare_inputs(equal_resolution=A_			)
          for image in image_inputs:
               self.assertIsInstance(A_							, Image.Image			)

          # Test not batched input
          __UpperCAmelCase	: List[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
          __UpperCAmelCase	: Optional[Any]  							=			image_processing(A_							, 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  lowerCamelCase__ (				self     :		Tuple			)						->							Union[str, Any]:
          __UpperCAmelCase	: int  							=			self.image_processing_class(**self.image_processor_dict			)
          # create random numpy tensors
          __UpperCAmelCase	: int  							=			self.image_processor_tester.prepare_inputs(equal_resolution=A_							, numpify=A_			)
          for image in image_inputs:
               self.assertIsInstance(A_							, np.ndarray			)

          # Test not batched input
          __UpperCAmelCase	: Dict  							=			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(A_							, 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  lowerCamelCase__ (				self     :		Any			)						->							List[str]:
          __UpperCAmelCase	: List[str]  							=			self.image_processing_class(**self.image_processor_dict			)
          # create random PyTorch tensors
          __UpperCAmelCase	: Any  							=			self.image_processor_tester.prepare_inputs(equal_resolution=A_							, torchify=A_			)
          for image in image_inputs:
               self.assertIsInstance(A_							, 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	: Union[str, Any]  							=			image_processing(A_							, 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				(		snake_case_	,  unittest.TestCase       ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE       : Tuple							   =   CLIPImageProcessor if is_vision_available() else None

     def  lowerCamelCase__ (				self     :		Dict			)						->							int:
          __UpperCAmelCase	: str  							=			CLIPImageProcessingTester(self							, num_channels=4			)
          __UpperCAmelCase	: Dict  							=			3

     @property
     def  lowerCamelCase__ (				self     :		Optional[int]			)						->							Tuple:
          return self.image_processor_tester.prepare_image_processor_dict()

     def  lowerCamelCase__ (				self     :		int			)						->							List[str]:
          __UpperCAmelCase	: Optional[int]  							=			self.image_processing_class(**self.image_processor_dict			)
          self.assertTrue(hasattr(A_							, '''do_resize'''			)			)
          self.assertTrue(hasattr(A_							, '''size'''			)			)
          self.assertTrue(hasattr(A_							, '''do_center_crop'''			)			)
          self.assertTrue(hasattr(A_							, '''center_crop'''			)			)
          self.assertTrue(hasattr(A_							, '''do_normalize'''			)			)
          self.assertTrue(hasattr(A_							, '''image_mean'''			)			)
          self.assertTrue(hasattr(A_							, '''image_std'''			)			)
          self.assertTrue(hasattr(A_							, '''do_convert_rgb'''			)			)

     def  lowerCamelCase__ (				self     :		int			)						->							Union[str, Any]:
          pass






     def  lowerCamelCase__ (				self     :		List[Any]			)						->							Dict:
          __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=A_			)
          for image in image_inputs:
               self.assertIsInstance(A_							, Image.Image			)

          # Test not batched input
          __UpperCAmelCase	: 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
          __UpperCAmelCase	: str  							=			image_processing(A_							, 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 						A_(			A		:						list[list[int]]):
							if len(A) == 0 or not all(len(A) == 2 for pair in coordinates):
														raise ValueError('The program cannot work out a fitting polynomial.')

							if len({tuple(A) for pair in coordinates}) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			{x for x, _ in coordinates}
							if len(A) == 1:
														return f'''x={coordinates[0][0]}'''

							if len(A) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			len(A)

							UpperCamelCase					       =			0
							UpperCamelCase					       =			[]
							# put the x and x to the power values in a matrix
							while count_of_line < x:
														UpperCamelCase					       =			0
														UpperCamelCase					       =			coordinates[count_of_line][0]
														UpperCamelCase					       =			[]
														while count_in_line < x:
																					count_line.append(a ** (x - (count_in_line + 1)))
																					count_in_line += 1
														matrix.append(A)
														count_of_line += 1

							UpperCamelCase					       =			0
							# put the y values into a vector
							UpperCamelCase					       =			[]
							while count_of_line < x:
														vector.append(coordinates[count_of_line][1])
														count_of_line += 1

							UpperCamelCase					       =			0

							while count < x:
														UpperCamelCase					       =			0
														while zahlen < x:
																					if count == zahlen:
																												zahlen += 1
																					if zahlen == x:
																												break
																					UpperCamelCase					       =			matrix[zahlen][count] / matrix[count][count]
																					for counting_columns, item in enumerate(matrix[count]):
																												# manipulating all the values in the matrix
																												matrix[zahlen][counting_columns] -= item * bruch
																					# manipulating the values in the vector
																					vector[zahlen] -= vector[count] * bruch
																					zahlen += 1
														count += 1

							UpperCamelCase					       =			0
							# make solutions
							UpperCamelCase					       =			[]
							while count < x:
														solution.append(str(vector[count] / matrix[count][count]))
														count += 1

							UpperCamelCase					       =			0
							UpperCamelCase					       =			'f(x)='

							while count < x:
														UpperCamelCase					       =			solution[count].split('E')
														if len(A) > 1:
																					UpperCamelCase					       =			f'''{remove_e[0]}*10^{remove_e[1]}'''
														solved += f'''x^{x - (count + 1)}*{solution[count]}'''
														if count + 1 != x:
																					solved += "+"
														count += 1

							return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))




