code: 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 ViltImageProcessor
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							unittest.TestCase    ):
  
  
  
  
      def __init__(					self  :   List[str]      ,      lowerCAmelCase  :   Union[str, Any]      ,      lowerCAmelCase  :   Any=7      ,      lowerCAmelCase  :   Any=3      ,      lowerCAmelCase  :   Tuple=18      ,      lowerCAmelCase  :   Optional[int]=30      ,      lowerCAmelCase  :   Dict=400      ,      lowerCAmelCase  :   Optional[int]=True      ,      lowerCAmelCase  :   Dict=None      ,      lowerCAmelCase  :   Dict=2      ,      lowerCAmelCase  :   Dict=True      ,      lowerCAmelCase  :   Dict=[0.5, 0.5, 0.5]      ,      lowerCAmelCase  :   Any=[0.5, 0.5, 0.5]      ,      )	-> int:
        a__     		=     size if size is not None else {"shortest_edge": 30}
        a__     		=     parent
        a__     		=     batch_size
        a__     		=     num_channels
        a__     		=     image_size
        a__     		=     min_resolution
        a__     		=     max_resolution
        a__     		=     do_resize
        a__     		=     size
        a__     		=     size_divisor
        a__     		=     do_normalize
        a__     		=     image_mean
        a__     		=     image_std
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]   )	-> Optional[int]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   Optional[Any]      ,      lowerCAmelCase  :   Tuple=False   )	-> str:
        if not batched:
          a__     		=     self.size["shortest_edge"]
          a__     		=     image_inputs[0]
          if isinstance(lowerCAmelCase      ,      Image.Image   ):
            a__		,a__     		=     image.size
          else:
            a__		,a__     		=     image.shape[1], image.shape[2]
          a__     		=     size / min(lowerCAmelCase      ,      lowerCAmelCase   )
          if h < w:
            a__		,a__     		=     size, scale * w
          else:
            a__		,a__     		=     scale * h, size
  
          a__     		=     int((1333 / 800) * size   )
          if max(lowerCAmelCase      ,      lowerCAmelCase   ) > max_size:
            a__     		=     max_size / max(lowerCAmelCase      ,      lowerCAmelCase   )
            a__     		=     newh * scale
            a__     		=     neww * scale
  
          a__		,a__     		=     int(newh + 0.5   ), int(neww + 0.5   )
          a__		,a__     		=     (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )
  
        else:
          a__     		=     []
          for image in image_inputs:
            a__		,a__     		=     self.get_expected_values([image]   )
            expected_values.append((expected_height, expected_width)   )
          a__     		=     max(lowerCAmelCase      ,      key=lambda lowerCAmelCase   : item[0]   )[0]
          a__     		=     max(lowerCAmelCase      ,      key=lambda lowerCAmelCase   : item[1]   )[1]
  
        return expected_height, expected_width
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					UpperCAmelCase_				(							_a     ,     unittest.TestCase    ):
      UpperCAmelCase_			:       List[Any]						 =							ViltImageProcessor if is_vision_available() else None
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]   )	-> List[str]:
        a__     		=     ViltImageProcessingTester(self   )
  
  
  
  
      @property
      def 						__lowerCamelCase (					self  :   List[str]   )	-> List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> List[str]:
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "image_mean"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "image_std"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "do_normalize"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "do_resize"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "size"   )   )
        self.assertTrue(hasattr(lowerCAmelCase      ,      "size_divisor"   )   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]   )	-> Union[str, Any]:
        a__     		=     self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}   )
  
        a__     		=     self.image_processing_class.from_dict(self.image_processor_dict      ,      size=42   )
        self.assertEqual(image_processor.size      ,      {"shortest_edge": 42}   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Dict:
        pass
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> Optional[Any]:
        # Initialize image_processing
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        a__     		=     prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowerCAmelCase   )
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase      ,      Image.Image   )
  
        # Test not batched input
        a__     		=     image_processing(image_inputs[0]      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_values(lowerCAmelCase   )
        self.assertEqual(
            encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )
  
        # Test batched
        a__     		=     image_processing(lowerCAmelCase      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_values(lowerCAmelCase      ,      batched=lowerCAmelCase   )
        self.assertEqual(
            encoded_images.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Any   )	-> Union[str, Any]:
        # Initialize image_processing
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        a__     		=     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
        a__     		=     image_processing(image_inputs[0]      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_values(lowerCAmelCase   )
        self.assertEqual(
            encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )
  
        # Test batched
        a__     		=     image_processing(lowerCAmelCase      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_values(lowerCAmelCase      ,      batched=lowerCAmelCase   )
        self.assertEqual(
            encoded_images.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Tuple   )	-> Any:
        # Initialize image_processing
        a__     		=     self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        a__     		=     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
        a__     		=     image_processing(image_inputs[0]      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_values(lowerCAmelCase   )
        self.assertEqual(
            encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )
  
        # Test batched
        a__     		=     image_processing(lowerCAmelCase      ,      return_tensors="pt"   ).pixel_values
  
        a__		,a__     		=     self.image_processor_tester.get_expected_values(lowerCAmelCase      ,      batched=lowerCAmelCase   )
        self.assertEqual(
            encoded_images.shape      ,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,      )
  
  
style_context: import random
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				:
  
  
  
  
      def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
        a__     		=     sample
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        a__     		=     target
        if len(self.target   ) == 0:
          raise ValueError("Target data can not be empty"   )
        if len(self.sample   ) != len(self.target   ):
          raise ValueError("Sample data and Target data do not have matching lengths"   )
        a__     		=     learning_rate
        a__     		=     epoch_number
        a__     		=     bias
        a__     		=     len(lowerCAmelCase   )
        a__     		=     len(sample[0]   )  # number of columns in dataset
        a__     		=     []
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
        for sample in self.sample:
          sample.insert(0      ,      self.bias   )
  
        for _ in range(self.col_sample   ):
          self.weight.append(random.random()   )
  
        self.weight.insert(0      ,      self.bias   )
  
        a__     		=     0
  
        while True:
          a__     		=     False
          for i in range(self.number_sample   ):
            a__     		=     0
            for j in range(self.col_sample + 1   ):
              a__     		=     u + self.weight[j] * self.sample[i][j]
            a__     		=     self.sign(lowerCAmelCase   )
            if y != self.target[i]:
              for j in range(self.col_sample + 1   ):
                a__     		=     (
                    self.weight[j]
                    + self.learning_rate
                    * (self.target[i] - y)
                    * self.sample[i][j]
                )
              a__     		=     True
              # print('Epoch: \n',epoch_count)
          a__     		=     epoch_count + 1
          # if you want control the epoch or just by error
          if not has_misclassified:
            print(("\nEpoch:\n", epoch_count)   )
            print("------------------------\n"   )
            # if epoch_count > self.epoch_number or not error:
            break
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
        if len(self.sample   ) == 0:
          raise ValueError("Sample data can not be empty"   )
        sample.insert(0      ,      self.bias   )
        a__     		=     0
        for i in range(self.col_sample + 1   ):
          a__     		=     u + self.weight[i] * sample[i]
  
        a__     		=     self.sign(lowerCAmelCase   )
  
        if y == -1:
          print(("Sample: ", sample)   )
          print("classification: P1"   )
        else:
          print(("Sample: ", sample)   )
          print("classification: P2"   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
        return 1 if u >= 0 else -1
  
  _SCREAMING_SNAKE_CASE   						=    [
      [-0.6508, 0.1097, 4.0009],
      [-1.4492, 0.8896, 4.4005],
      [2.0850, 0.6876, 12.0710],
      [0.2626, 1.1476, 7.7985],
      [0.6418, 1.0234, 7.0427],
      [0.2569, 0.6730, 8.3265],
      [1.1155, 0.6043, 7.4446],
      [0.0914, 0.3399, 7.0677],
      [0.0121, 0.5256, 4.6316],
      [-0.0429, 0.4660, 5.4323],
      [0.4340, 0.6870, 8.2287],
      [0.2735, 1.0287, 7.1934],
      [0.4839, 0.4851, 7.4850],
      [0.4089, -0.1267, 5.5019],
      [1.4391, 0.1614, 8.5843],
      [-0.9115, -0.1973, 2.1962],
      [0.3654, 1.0475, 7.4858],
      [0.2144, 0.7515, 7.1699],
      [0.2013, 1.0014, 6.5489],
      [0.6483, 0.2183, 5.8991],
      [-0.1147, 0.2242, 7.2435],
      [-0.7970, 0.8795, 3.8762],
      [-1.0625, 0.6366, 2.4707],
      [0.5307, 0.1285, 5.6883],
      [-1.2200, 0.7777, 1.7252],
      [0.3957, 0.1076, 5.6623],
      [-0.1013, 0.5989, 7.1812],
      [2.4482, 0.9455, 11.2095],
      [2.0149, 0.6192, 10.9263],
      [0.2012, 0.2611, 5.4631],
  ]
  
  _SCREAMING_SNAKE_CASE   						=    [
      -1,
      -1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      1,
      1,
      -1,
      1,
      -1,
      -1,
      -1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      1,
      1,
      1,
      1,
      -1,
      -1,
      1,
      -1,
      1,
  ]
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
       _SCREAMING_SNAKE_CASE   						=    Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print("""Finished training perceptron""")
       print("""Enter values to predict or q to exit""")
       while True:
            _SCREAMING_SNAKE_CASE   						=    []
            for i in range(len(samples[0])):
                 _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
                 if user_input == "q":
                      break
                 _SCREAMING_SNAKE_CASE   						=    float(user_input)
                 sample.insert(i, observation)
            network.sort(sample)
  
  