import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor



class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_ = True				,				A_ = None				,				A_ = 32				,				A_ = True				,				A_ = 1 / 2_55				,				A_ = True				,				A_ = True				,				A_ = [0.48145466, 0.4578275, 0.40821073]				,				A_ = [0.26862954, 0.26130258, 0.27577711]				,				A_ = True				,				A_=7				,				A_=30				,				A_=4_00				,				A_=3				,				) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				parent
           _lowerCamelCase			    =				do_resize
           _lowerCamelCase			    =				size if size is not None else {'''shortest_edge''': 2_88}
           _lowerCamelCase			    =				size_divisor
           _lowerCamelCase			    =				do_rescale
           _lowerCamelCase			    =				rescale_factor
           _lowerCamelCase			    =				do_normalize
           _lowerCamelCase			    =				do_center_crop
           _lowerCamelCase			    =				image_mean
           _lowerCamelCase			    =				image_std
           _lowerCamelCase			    =				do_pad
           _lowerCamelCase			    =				batch_size
           _lowerCamelCase			    =				num_channels
           _lowerCamelCase			    =				min_resolution
           _lowerCamelCase			    =				max_resolution



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






           """simple docstring"""
           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 		UpperCamelCase_						(					self				,				A_				,				A_=False						) ->				Union[str, Any]:






           """simple docstring"""
           if not batched:
                 _lowerCamelCase			    =				self.size['''shortest_edge''']
                 _lowerCamelCase			    =				image_inputs[0]
                 if isinstance(A_				,				Image.Image						):
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.size
                 else:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				image.shape[1], image.shape[2]
                 _lowerCamelCase			    =				size / min(A_				,				A_						)
                 if h < w:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				size, scale * w
                 else:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				scale * h, size

                 _lowerCamelCase			    =				int((13_33 / 8_00) * size						)
                 if max(A_				,				A_						) > max_size:
                       _lowerCamelCase			    =				max_size / max(A_				,				A_						)
                       _lowerCamelCase			    =				newh * scale
                       _lowerCamelCase			    =				neww * scale

                 _lowerCamelCase    ,					_lowerCamelCase			    =				int(newh + 0.5						), int(neww + 0.5						)
                 _lowerCamelCase    ,					_lowerCamelCase			    =				(
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 _lowerCamelCase			    =				[]
                 for image in image_inputs:
                       _lowerCamelCase    ,					_lowerCamelCase			    =				self.get_expected_values([image]						)
                       expected_values.append((expected_height, expected_width)						)
                 _lowerCamelCase			    =				max(A_				,				key=lambda A_						: item[0]						)[0]
                 _lowerCamelCase			    =				max(A_				,				key=lambda A_						: item[1]						)[1]

           return expected_height, expected_width





@require_torch
@require_vision
class  UpperCamelCase				( __lowercase   ,  unittest.TestCase ):

     '''simple docstring'''

     A_										=  BridgeTowerImageProcessor if is_vision_available() else None



     def 		UpperCamelCase_						(					self						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				BridgeTowerImageProcessingTester(self						)



     @property
     def 		UpperCamelCase_						(					self						) ->				List[str]:






           """simple docstring"""
           return self.image_processor_tester.prepare_image_processor_dict()



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           self.assertTrue(hasattr(A_				,				'''image_mean'''						)						)
           self.assertTrue(hasattr(A_				,				'''image_std'''						)						)
           self.assertTrue(hasattr(A_				,				'''do_normalize'''						)						)
           self.assertTrue(hasattr(A_				,				'''do_resize'''						)						)
           self.assertTrue(hasattr(A_				,				'''size'''						)						)
           self.assertTrue(hasattr(A_				,				'''size_divisor'''						)						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           pass



     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           # Initialize image processor
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PIL images
           _lowerCamelCase			    =				prepare_image_inputs(self.image_processor_tester				,				equal_resolution=A_						)
           for image in image_inputs:
                 self.assertIsInstance(A_				,				Image.Image						)

           # Test not batched input
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)
           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)
           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)



     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           # Initialize image processor
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random numpy tensors
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)
           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)
           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)





     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           # Initialize image processor
           _lowerCamelCase			    =				self.image_processing_class(**self.image_processor_dict						)
           # create random PyTorch tensors
           _lowerCamelCase			    =				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
           _lowerCamelCase			    =				image_processing(image_inputs[0]				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_						)
           self.assertEqual(
               encoded_images.shape				,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,				)

           # Test batched
           _lowerCamelCase			    =				image_processing(A_				,				return_tensors='''pt'''						).pixel_values

           _lowerCamelCase    ,					_lowerCamelCase			    =				self.image_processor_tester.get_expected_values(A_				,				batched=A_						)
           self.assertEqual(
               encoded_images.shape				,				(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )				,				)
import logging

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

logging.basicConfig(level=logging.INFO, format='%(message)s')
def       __magic_name__(  __UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      return input_array.reshape((input_array.size, 1)		)
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      _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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      _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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		)						->    np.ndarray:



      '''simple docstring'''

      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       __magic_name__(  )						->    None:



      '''simple docstring'''

      _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       __magic_name__(  )						->    None:



      '''simple docstring'''

      _lowerCamelCase			    =				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]		)
      _lowerCamelCase			    =				2
      _lowerCamelCase			    =				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(__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()