



'''simple docstring'''





import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="%(message)s")




def lowerCAmelCase						(__A):



     """simple docstring"""



     return input_array.reshape((input_array.size, 1))




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						np.nan
     for i in range(__A):
          _a											=						features[:, labels == i]
          _a											=						data.mean(1)
          # Centralize the data of class i
          _a											=						data - column_reshape(__A)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += np.dot(__A						,			centered_data.T)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a											=						np.dot(__A						,			centered_data.T)

     return covariance_sum / features.shape[1]




def lowerCAmelCase						(__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						features.mean(1)
     _a											=						np.nan
     for i in range(__A):
          _a											=						features[:, labels == i]
          _a											=						data.shape[1]
          _a											=						data.mean(1)
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += device_data * np.dot(
                   column_reshape(__A) - column_reshape(__A)						,			(column_reshape(__A) - column_reshape(__A)).T						,			)
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               _a											=						device_data * np.dot(
                   column_reshape(__A) - column_reshape(__A)						,			(column_reshape(__A) - column_reshape(__A)).T						,			)

     return covariance_sum / features.shape[1]




def lowerCAmelCase						(__A						,			__A):



     """simple docstring"""



     if features.any():
          _a											=						features.mean(1)
          # Center the dataset
          _a											=						features - np.reshape(__A						,			(data_mean.size, 1))
          _a											=						np.dot(__A						,			centered_data.T) / features.shape[1]
          _a						,					_a											=						np.linalg.eigh(__A)
          # Take all the columns in the reverse order (-1), and then takes only the first
          _a											=						eigenvectors[:, ::-1][:, 0:dimensions]
          # Project the database on the new space
          _a											=						np.dot(filtered_eigenvectors.T						,			__A)
          logging.info('''Principal Component Analysis computed''')

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR						,			format='''%(message)s'''						,			force=__A)
          logging.error('''Dataset empty''')
          raise AssertionError




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          _a						,					_a											=						eigh(
              covariance_between_classes(__A						,			__A						,			__A)						,			covariance_within_classes(__A						,			__A						,			__A)						,			)
          _a											=						eigenvectors[:, ::-1][:, :dimensions]
          _a						,					_a						,					_a											=						np.linalg.svd(__A)
          _a											=						svd_matrix[:, 0:dimensions]
          _a											=						np.dot(filtered_svd_matrix.T						,			__A)
          logging.info('''Linear Discriminant Analysis computed''')

          return projected_data
     else:
          logging.basicConfig(level=logging.ERROR						,			format='''%(message)s'''						,			force=__A)
          logging.error('''Dataset empty''')
          raise AssertionError




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]])
     _a											=						np.array([0, 0, 0, 1, 1])
     _a											=						2
     _a											=						2

     # Assert that the function raises an AssertionError if dimensions > classes
     with pytest.raises(__A) as error_info:
          _a											=						linear_discriminant_analysis(
              __A						,			__A						,			__A						,			__A)
          if isinstance(__A						,			np.ndarray):
               raise AssertionError(
                   '''Did not raise AssertionError for dimensions > classes''')
          assert error_info.type is AssertionError




def lowerCAmelCase						():



     """simple docstring"""



     _a											=						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
     _a											=						2
     _a											=						np.array([[6.92_82_03_23, 8.66_02_54_04, 10.39_23_04_85], [3.0, 3.0, 3.0]])

     with pytest.raises(__A) as error_info:
          _a											=						principal_component_analysis(__A						,			__A)
          if not np.allclose(__A						,			__A):
               raise AssertionError
          assert error_info.type is AssertionError


if __name__ == "__main__":
  import doctest

  doctest.testmod()








'''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 MobileNetVaImageProcessor






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def __init__(self				,     A				,     A=7				,     A=3				,     A=18				,     A=30				,     A=400				,     A=True				,     A=None				,     A=True				,     A=None				,     )					->  Any:


      """simple docstring"""



      _a											=						size if size is not None else {'''shortest_edge''': 20}
      _a											=						crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
      _a											=						parent
      _a											=						batch_size
      _a											=						num_channels
      _a											=						image_size
      _a											=						min_resolution
      _a											=						max_resolution
      _a											=						do_resize
      _a											=						size
      _a											=						do_center_crop
      _a											=						crop_size



 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return {
          "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 __A    (						A     ,     unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase					: Tuple    = MobileNetVaImageProcessor if is_vision_available() else None



 def       a__						(self )					->  int:


      """simple docstring"""



      _a											=						MobileNetVaImageProcessingTester(self )



 @property
 def       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						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				,     '''crop_size''' ) )



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


      """simple docstring"""



      _a											=						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} )

      _a											=						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       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      pass



 def       a__						(self )					->  Any:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PIL images
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     Image.Image )

      # Test not batched input
      _a											=						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
      _a											=						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       a__						(self )					->  Optional[int]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     numpify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     np.ndarray )

      # Test not batched input
      _a											=						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
      _a											=						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       a__						(self )					->  Optional[Any]:


      """simple docstring"""



      _a											=						self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a											=						prepare_image_inputs(self.image_processor_tester				,     equal_resolution=A				,     torchify=A )
      for image in image_inputs:
           self.assertIsInstance(A				,     torch.Tensor )

      # Test not batched input
      _a											=						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
      _a											=						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'''],
          )				,     )



