






"""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, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import OwlViTImageProcessor

class 			a	(  unittest.TestCase      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=7				,						_lowerCamelCase=3				,						_lowerCamelCase=1_8				,						_lowerCamelCase=3_0				,						_lowerCamelCase=4_0_0				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=[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=[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=True				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			num_channels
      lowercase				       =			image_size
      lowercase				       =			min_resolution
      lowercase				       =			max_resolution
      lowercase				       =			do_resize
      lowercase				       =			size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase				       =			do_center_crop
      lowercase				       =			crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
      lowercase				       =			do_normalize
      lowercase				       =			image_mean
      lowercase				       =			image_std
      lowercase				       =			do_convert_rgb







    def      UpperCamelCase_ (     self			):
      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,
      }


@require_torch
@require_vision
class 			a	(  a_,							unittest.TestCase      ):
    UpperCAmelCase_					:	Tuple      	=OwlViTImageProcessor if is_vision_available() else None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			OwlViTImageProcessingTester(self			)







    @property
    def      UpperCamelCase_ (     self			):
      return self.image_processor_tester.prepare_image_processor_dict()







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_resize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'size'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'center_crop'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_normalize'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_mean'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'image_std'			)			)
      self.assertTrue(hasattr(_lowerCamelCase				,						'do_convert_rgb'			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size				,						{'height': 1_8, 'width': 1_8}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 1_8, 'width': 1_8}			)

      lowercase				       =			self.image_processing_class.from_dict(self.image_processor_dict				,						size=4_2				,						crop_size=8_4			)
      self.assertEqual(image_processor.size				,						{'height': 4_2, 'width': 4_2}			)
      self.assertEqual(image_processor.crop_size				,						{'height': 8_4, 'width': 8_4}			)







    def      UpperCamelCase_ (     self			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      lowercase				       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_lowerCamelCase			)

      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase				,						Image.Image			)

      # Test not batched input
      lowercase				       =			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
      lowercase				       =			image_processing(_lowerCamelCase				,						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			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      lowercase				       =			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
      lowercase				       =			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
      lowercase				       =			image_processing(_lowerCamelCase				,						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			):
      # Initialize image_processing
      lowercase				       =			self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      lowercase				       =			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
      lowercase				       =			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
      lowercase				       =			image_processing(_lowerCamelCase				,						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 		lowercase__ (		__A:						list[list[int]]					):





					'''simple docstring'''






					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.'''					)

					__magic_name__				:						Union[str, Any]     				=				{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.'''					)

					__magic_name__				:						List[Any]     				=				len(__A					)

					__magic_name__				:						int     				=				0
					__magic_name__				:						list[list[float]]     				=				[]
					# put the x and x to the power values in a matrix
					while count_of_line < x:
										__magic_name__				:						List[Any]     				=				0
										__magic_name__				:						List[str]     				=				coordinates[count_of_line][0]
										__magic_name__				:						list[float]     				=				[]
										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

					__magic_name__				:						Union[str, Any]     				=				0
					# put the y values into a vector
					__magic_name__				:						list[float]     				=				[]
					while count_of_line < x:
										vector.append(coordinates[count_of_line][1]					)
										count_of_line += 1

					__magic_name__				:						int     				=				0

					while count < x:
										__magic_name__				:						int     				=				0
										while zahlen < x:
															if count == zahlen:
																				zahlen += 1
															if zahlen == x:
																				break
															__magic_name__				:						Tuple     				=				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

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

					__magic_name__				:						List[Any]     				=				0
					__magic_name__				:						Union[str, Any]     				=				'''f(x)='''

					while count < x:
										__magic_name__				:						list[str]     				=				solution[count].split('''E'''					)
										if len(__A					) > 1:
															__magic_name__				:						Union[str, Any]     				=				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]]))



