


'''simple docstring'''

import logging

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

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





def __snake_case      (   UpperCAmelCase_		:		np.ndarray	):
	return input_array.reshape((input_array.size, 1)	)





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		int	):
	lowerCamelCase_  				=						np.nan
	for i in range(UpperCAmelCase_	):
		lowerCamelCase_  				=						features[:, labels == i]
		lowerCamelCase_  				=						data.mean(1	)
		# Centralize the data of class i
		lowerCamelCase_  				=						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)
			lowerCamelCase_  				=						np.dot(UpperCAmelCase_      ,       centered_data.T	)

	return covariance_sum / features.shape[1]





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		int	):
	lowerCamelCase_  				=						features.mean(1	)
	lowerCamelCase_  				=						np.nan
	for i in range(UpperCAmelCase_	):
		lowerCamelCase_  				=						features[:, labels == i]
		lowerCamelCase_  				=						data.shape[1]
		lowerCamelCase_  				=						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)
			lowerCamelCase_  				=						device_data * np.dot(
			    column_reshape(UpperCAmelCase_	) - column_reshape(UpperCAmelCase_	)      ,       (column_reshape(UpperCAmelCase_	) - column_reshape(UpperCAmelCase_	)).T      ,       )

	return covariance_sum / features.shape[1]





def __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		int	):

	# Check if the features have been loaded
	if features.any():
		lowerCamelCase_  				=						features.mean(1	)
		# Center the dataset
		lowerCamelCase_  				=						features - np.reshape(UpperCAmelCase_      ,       (data_mean.size, 1)	)
		lowerCamelCase_  				=						np.dot(UpperCAmelCase_      ,       centered_data.T	) / features.shape[1]
		lowerCamelCase_				,lowerCamelCase_  				=						np.linalg.eigh(UpperCAmelCase_	)
		# Take all the columns in the reverse order (-1), and then takes only the first
		lowerCamelCase_  				=						eigenvectors[:, ::-1][:, 0:dimensions]
		# Project the database on the new space
		lowerCamelCase_  				=						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 __snake_case      (   UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		np.ndarray      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		int	):
	assert classes > dimensions

	# Check if features have been already loaded
	if features.any:
		lowerCamelCase_				,lowerCamelCase_  				=						eigh(
		    covariance_between_classes(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)      ,       covariance_within_classes(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_	)      ,       )
		lowerCamelCase_  				=						eigenvectors[:, ::-1][:, :dimensions]
		lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=						np.linalg.svd(UpperCAmelCase_	)
		lowerCamelCase_  				=						svd_matrix[:, 0:dimensions]
		lowerCamelCase_  				=						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 __snake_case      (   ):
	# Create dummy dataset with 2 classes and 3 features
	lowerCamelCase_  				=						np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]	)
	lowerCamelCase_  				=						np.array([0, 0, 0, 1, 1]	)
	lowerCamelCase_  				=						2
	lowerCamelCase_  				=						2

	# Assert that the function raises an AssertionError if dimensions > classes
	with pytest.raises(UpperCAmelCase_	) as error_info:
		lowerCamelCase_  				=						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 __snake_case      (   ):
	lowerCamelCase_  				=						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]	)
	lowerCamelCase_  				=						2
	lowerCamelCase_  				=						np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]]	)

	with pytest.raises(UpperCAmelCase_	) as error_info:
		lowerCamelCase_  				=						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()







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_						:       List[Any]      			=				{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       int      			=				["""OwlViTFeatureExtractor"""]
     a_						:       Any      			=				["""OwlViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       Tuple      			=				[
         """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OwlViTModel""",
         """OwlViTPreTrainedModel""",
         """OwlViTTextModel""",
         """OwlViTVisionModel""",
         """OwlViTForObjectDetection""",
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     a_						:       Tuple      			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



