






import logging

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

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






def   UpperCamelCase  (       snake_case__     :   np.ndarray  ):




     '''simple docstring'''






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






def   UpperCamelCase  (       snake_case__     :   np.ndarray	,snake_case__     :   np.ndarray	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						np.nan
     for i in range(snake_case__  ):
          __snake_case			:int          =						features[:, labels == i]
          __snake_case			:Union[str, Any]          =						data.mean(1  )
          # Centralize the data of class i
          __snake_case			:Tuple          =						data - column_reshape(snake_case__  )
          if i > 0:
               # If covariance_sum is not None
               covariance_sum += np.dot(snake_case__	,centered_data.T  )
          else:
               # If covariance_sum is np.nan (i.e. first loop)
               __snake_case			:List[Any]          =						np.dot(snake_case__	,centered_data.T  )

     return covariance_sum / features.shape[1]






def   UpperCamelCase  (       snake_case__     :   np.ndarray	,snake_case__     :   np.ndarray	,snake_case__     :   int  ):




     '''simple docstring'''






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

     return covariance_sum / features.shape[1]






def   UpperCamelCase  (       snake_case__     :   np.ndarray	,snake_case__     :   int  ):




     '''simple docstring'''






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

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






def   UpperCamelCase  (       snake_case__     :   np.ndarray	,snake_case__     :   np.ndarray	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     assert classes > dimensions

     # Check if features have been already loaded
     if features.any:
          __snake_case       , __snake_case			:Optional[Any]          =						eigh(
              covariance_between_classes(snake_case__	,snake_case__	,snake_case__  )	,covariance_within_classes(snake_case__	,snake_case__	,snake_case__  )	,)
          __snake_case			:Optional[int]          =						eigenvectors[:, ::-1][:, :dimensions]
          __snake_case       , __snake_case       , __snake_case			:Union[str, Any]          =						np.linalg.svd(snake_case__  )
          __snake_case			:Any          =						svd_matrix[:, 0:dimensions]
          __snake_case			:List[str]          =						np.dot(filtered_svd_matrix.T	,snake_case__  )
          logging.info("""Linear Discriminant Analysis computed"""  )

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






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:str          =						np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]  )
     __snake_case			:str          =						np.array([0, 0, 0, 1, 1]  )
     __snake_case			:List[str]          =						2
     __snake_case			:Tuple          =						2

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






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[Any]          =						np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]  )
     __snake_case			:List[Any]          =						2
     __snake_case			:Dict          =						np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]  )

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


if __name__ == "__main__":
     import doctest

     doctest.testmod()










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 GLPNImageProcessor




class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=7			,						a__=3			,						a__=18			,						a__=30			,						a__=4_00			,						a__=True			,						a__=32			,						a__=True			,						) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[Any]          =						parent
            __snake_case			:Dict          =						batch_size
            __snake_case			:Optional[Any]          =						num_channels
            __snake_case			:Dict          =						image_size
            __snake_case			:Dict          =						min_resolution
            __snake_case			:Dict          =						max_resolution
            __snake_case			:List[Any]          =						do_resize
            __snake_case			:Dict          =						size_divisor
            __snake_case			:Union[str, Any]          =						do_rescale



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return {
                "do_resize": self.do_resize,
                "size_divisor": self.size_divisor,
                "do_rescale": self.do_rescale,
            }




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

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= GLPNImageProcessor if is_vision_available() else None



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:Dict          =						GLPNImageProcessingTester(self    )



       @property
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            return self.image_processor_tester.prepare_image_processor_dict()



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.image_processing_class(**self.image_processor_dict    )
            self.assertTrue(hasattr(a__			,						"""do_resize"""    )    )
            self.assertTrue(hasattr(a__			,						"""size_divisor"""    )    )
            self.assertTrue(hasattr(a__			,						"""resample"""    )    )
            self.assertTrue(hasattr(a__			,						"""do_rescale"""    )    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PIL images
            __snake_case			:Optional[Any]          =						prepare_image_inputs(self.image_processor_tester			,						equal_resolution=a__    )
            for image in image_inputs:
                 self.assertIsInstance(a__			,						Image.Image    )

            # Test not batched input (GLPNImageProcessor doesn't support batching)
            __snake_case			:Union[str, Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int          =						self.image_processing_class(**self.image_processor_dict    )
            # create random numpy tensors
            __snake_case			:List[Any]          =						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 (GLPNImageProcessor doesn't support batching)
            __snake_case			:int          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.image_processing_class(**self.image_processor_dict    )
            # create random PyTorch tensors
            __snake_case			:Optional[int]          =						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 (GLPNImageProcessor doesn't support batching)
            __snake_case			:List[Any]          =						image_processing(image_inputs[0]			,						return_tensors="""pt"""    ).pixel_values
            self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
            self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )


