

'''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 TvltImageProcessor


def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   Union[str, Any]       ,   SCREAMING_SNAKE_CASE      :   Any=10       ,   SCREAMING_SNAKE_CASE      :   Any=10       ,   SCREAMING_SNAKE_CASE      :   Tuple=False       ,   SCREAMING_SNAKE_CASE      :   Optional[Any]=False						):
  UpperCAmelCase													=	[]
  for i in range(image_processor_tester.num_frames						):
    video.append(np.random.randint(255       ,   size=(image_processor_tester.num_channels, width, height)       ,   dtype=np.uinta						)						)

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    UpperCAmelCase													=	[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE       ,   0       ,   -1						)						) for frame in video]

  if torchify:
    UpperCAmelCase													=	[torch.from_numpy(SCREAMING_SNAKE_CASE						) for frame in video]

  return video


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

  UpperCAmelCase													=	[]
  for i in range(image_processor_tester.batch_size						):
    if equal_resolution:
      UpperCAmelCase													=	UpperCAmelCase													=	image_processor_tester.max_resolution
    else:
      UpperCAmelCase							,      UpperCAmelCase													=	np.random.choice(
          np.arange(image_processor_tester.min_resolution       ,   image_processor_tester.max_resolution						)       ,   2						)
      UpperCAmelCase													=	prepare_video(
          image_processor_tester=SCREAMING_SNAKE_CASE       ,   width=SCREAMING_SNAKE_CASE       ,   height=SCREAMING_SNAKE_CASE       ,   numpify=SCREAMING_SNAKE_CASE       ,   torchify=SCREAMING_SNAKE_CASE       ,   )
    video_inputs.append(SCREAMING_SNAKE_CASE						)

  return video_inputs



class        lowercase_	(		unittest.TestCase ):

  '''simple docstring'''





  def __init__(     self   , a_   , a_=7   , a_=3   , a_=4   , a_=1_8   , a_=3_0   , a_=4_0_0   , a_=True   , a_=None   , a_=True   , a_=[0.5, 0.5, 0.5]   , a_=[0.5, 0.5, 0.5]   , a_=True   , a_=None   , )							->				List[str]:


    """simple docstring"""
    UpperCAmelCase													=	size if size is not None else {'shortest_edge': 1_8}
    UpperCAmelCase													=	crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

    UpperCAmelCase													=	parent
    UpperCAmelCase													=	batch_size
    UpperCAmelCase													=	num_channels
    UpperCAmelCase													=	num_frames
    UpperCAmelCase													=	image_size
    UpperCAmelCase													=	min_resolution
    UpperCAmelCase													=	max_resolution
    UpperCAmelCase													=	do_resize
    UpperCAmelCase													=	size
    UpperCAmelCase													=	do_normalize
    UpperCAmelCase													=	image_mean
    UpperCAmelCase													=	image_std
    UpperCAmelCase													=	do_center_crop
    UpperCAmelCase													=	crop_size





  def 						snake_case_			(     self     )							->				Optional[int]:


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




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

  '''simple docstring'''





  __lowerCAmelCase					: Any      =   TvltImageProcessor if is_vision_available() else None
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    UpperCAmelCase													=	TvltImageProcessorTester(self     )
  @property
  def 						snake_case_			(     self     )							->				List[Any]:


    """simple docstring"""
    return self.image_processor_tester.prepare_image_processor_dict()
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    self.assertTrue(hasattr(a_   , 'image_mean'     )     )
    self.assertTrue(hasattr(a_   , 'image_std'     )     )
    self.assertTrue(hasattr(a_   , 'do_normalize'     )     )
    self.assertTrue(hasattr(a_   , 'do_resize'     )     )
    self.assertTrue(hasattr(a_   , 'do_center_crop'     )     )
    self.assertTrue(hasattr(a_   , 'size'     )     )
  def 						snake_case_			(     self     )							->				Dict:


    """simple docstring"""
    # Initialize image_processor
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PIL videos
    UpperCAmelCase													=	prepare_video_inputs(self.image_processor_tester   , equal_resolution=a_     )
    for video in video_inputs:
      self.assertIsInstance(a_   , a_     )
      self.assertIsInstance(video[0]   , Image.Image     )

    # Test not batched input
    UpperCAmelCase													=	image_processor(video_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_videos.shape   , (
            1,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processor(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_videos.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )
  def 						snake_case_			(     self     )							->				int:


    """simple docstring"""
    # Initialize image_processor
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random numpy tensors
    UpperCAmelCase													=	prepare_video_inputs(self.image_processor_tester   , equal_resolution=a_   , numpify=a_     )
    for video in video_inputs:
      self.assertIsInstance(a_   , a_     )
      self.assertIsInstance(video[0]   , np.ndarray     )

    # Test not batched input
    UpperCAmelCase													=	image_processor(video_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_videos.shape   , (
            1,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processor(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_videos.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )





  def 						snake_case_			(     self     )							->				str:


    """simple docstring"""
    # Initialize image_processor
    UpperCAmelCase													=	self.image_processing_class(**self.image_processor_dict     )
    # create random PyTorch tensors
    UpperCAmelCase													=	prepare_video_inputs(self.image_processor_tester   , equal_resolution=a_   , torchify=a_     )
    for video in video_inputs:
      self.assertIsInstance(a_   , a_     )
      self.assertIsInstance(video[0]   , torch.Tensor     )

    # Test not batched input
    UpperCAmelCase													=	image_processor(video_inputs[0]   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_videos.shape   , (
            1,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )

    # Test batched
    UpperCAmelCase													=	image_processor(a_   , return_tensors='pt'     ).pixel_values
    self.assertEqual(
        encoded_videos.shape   , (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_frames,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['height'],
            self.image_processor_tester.crop_size['width'],
        )   , )





'''simple docstring'''




def       lowerCamelCase__		(						SCREAMING_SNAKE_CASE      :   list[list[int]]						):
  if len(SCREAMING_SNAKE_CASE						) == 0 or not all(len(SCREAMING_SNAKE_CASE						) == 2 for pair in coordinates						):
    raise ValueError('The program cannot work out a fitting polynomial.'						)

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

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

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

  UpperCAmelCase													=	len(SCREAMING_SNAKE_CASE						)

  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(SCREAMING_SNAKE_CASE						)
    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(SCREAMING_SNAKE_CASE						) > 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]]))


