import logging

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

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       np.ndarray	)							->					np.ndarray:







  """simple docstring"""





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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       int	)							->					np.ndarray:







  """simple docstring"""





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

  return covariance_sum / features.shape[1]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       int	)							->					np.ndarray:







  """simple docstring"""





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

  return covariance_sum / features.shape[1]



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       int	)							->					np.ndarray:







  """simple docstring"""





  if features.any():
    SCREAMING_SNAKE_CASE__										=      features.mean(1	)
    # Center the dataset
    SCREAMING_SNAKE_CASE__										=      features - np.reshape(__UpperCamelCase ,     (data_mean.size, 1)	)
    SCREAMING_SNAKE_CASE__										=      np.dot(__UpperCamelCase ,     centered_data.T	) / features.shape[1]
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      np.linalg.eigh(__UpperCamelCase	)
    # Take all the columns in the reverse order (-1), and then takes only the first
    SCREAMING_SNAKE_CASE__										=      eigenvectors[:, ::-1][:, 0:dimensions]
    # Project the database on the new space
    SCREAMING_SNAKE_CASE__										=      np.dot(filtered_eigenvectors.T ,     __UpperCamelCase	)
    logging.info("""Principal Component Analysis computed"""	)

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



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       np.ndarray ,     __UpperCamelCase    :       int ,     __UpperCamelCase    :       int	)							->					np.ndarray:







  """simple docstring"""





  assert classes > dimensions

  # Check if features have been already loaded
  if features.any:
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      eigh(
        covariance_between_classes(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	) ,     covariance_within_classes(__UpperCamelCase ,     __UpperCamelCase ,     __UpperCamelCase	) ,     )
    SCREAMING_SNAKE_CASE__										=      eigenvectors[:, ::-1][:, :dimensions]
    SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      np.linalg.svd(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__										=      svd_matrix[:, 0:dimensions]
    SCREAMING_SNAKE_CASE__										=      np.dot(filtered_svd_matrix.T ,     __UpperCamelCase	)
    logging.info("""Linear Discriminant Analysis computed"""	)

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



def 	__SCREAMING_SNAKE_CASE  (					)							->					None:







  """simple docstring"""





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

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



def 	__SCREAMING_SNAKE_CASE  (					)							->					None:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	)
  SCREAMING_SNAKE_CASE__										=      2
  SCREAMING_SNAKE_CASE__										=      np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]	)

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase			: Union[str, Any]							   =		logging.get_logger(__name__)







class __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["pixel_values"]




   def __init__(    self			:				Any       ,	_lowercase			:				bool = True       ,	_lowercase			:				int = 32       ,	_lowercase			:				Any=PILImageResampling.BILINEAR       ,	_lowercase			:				bool = True       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor
     SCREAMING_SNAKE_CASE__										=      resample
     super().__init__(**_lowercase      )




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      get_image_size(_lowercase      )
     # Rounds the height and width down to the closest multiple of size_divisor
     SCREAMING_SNAKE_CASE__										=      height // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      width // size_divisor * size_divisor
     SCREAMING_SNAKE_CASE__										=      resize(_lowercase       ,	(new_h, new_w)       ,	resample=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )
     return image




   def 	__a						(    self			:				int       ,	_lowercase			:				np.ndarray       ,	_lowercase			:				float       ,	_lowercase			:				Optional[ChannelDimension] = None       ,	**_lowercase			:				str      ):



     """simple docstring"""






     return rescale(image=_lowercase       ,	scale=_lowercase       ,	data_format=_lowercase       ,	**_lowercase      )




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int=None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				Optional[Union[TensorType, str]] = None       ,	_lowercase			:				ChannelDimension = ChannelDimension.FIRST       ,	**_lowercase			:				Optional[int]       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      do_resize if do_resize is not None else self.do_resize
     SCREAMING_SNAKE_CASE__										=      do_rescale if do_rescale is not None else self.do_rescale
     SCREAMING_SNAKE_CASE__										=      size_divisor if size_divisor is not None else self.size_divisor
     SCREAMING_SNAKE_CASE__										=      resample if resample is not None else self.resample

     if do_resize and size_divisor is None:
       raise ValueError("""size_divisor is required for resizing"""      )

     SCREAMING_SNAKE_CASE__										=      make_list_of_images(_lowercase      )

     if not valid_images(_lowercase      ):
       raise ValueError("""Invalid image(s)"""      )

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE__										=      [to_numpy_array(_lowercase      ) for img in images]

     if do_resize:
       SCREAMING_SNAKE_CASE__										=      [self.resize(_lowercase       ,	size_divisor=_lowercase       ,	resample=_lowercase      ) for image in images]

     if do_rescale:
       SCREAMING_SNAKE_CASE__										=      [self.rescale(_lowercase       ,	scale=1 / 2_55      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      [to_channel_dimension_format(_lowercase       ,	_lowercase      ) for image in images]

     SCREAMING_SNAKE_CASE__										=      {"""pixel_values""": images}
     return BatchFeature(data=_lowercase       ,	tensor_type=_lowercase      )
