


import logging

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

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







def 						lowerCamelCase(   a__):
				return input_array.reshape((input_array.size, 1))







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =np.nan
				for i in range(a__):
								_SCREAMING_SNAKE_CASE			  =features[:, labels == i]
								_SCREAMING_SNAKE_CASE			  =data.mean(1)
								# Centralize the data of class i
								_SCREAMING_SNAKE_CASE			  =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)
												_SCREAMING_SNAKE_CASE			  =np.dot(a__	,centered_data.T)

				return covariance_sum / features.shape[1]







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =features.mean(1)
				_SCREAMING_SNAKE_CASE			  =np.nan
				for i in range(a__):
								_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(a__) - column_reshape(a__)	,(column_reshape(a__) - column_reshape(a__)).T	,)
								else:
												# If covariance_sum is np.nan (i.e. first loop)
												_SCREAMING_SNAKE_CASE			  =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__):

				# Check if the features have been loaded
				if features.any():
								_SCREAMING_SNAKE_CASE			  =features.mean(1)
								# Center the dataset
								_SCREAMING_SNAKE_CASE			  =features - np.reshape(a__	,(data_mean.size, 1))
								_SCREAMING_SNAKE_CASE			  =np.dot(a__	,centered_data.T) / features.shape[1]
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =np.linalg.eigh(a__)
								# 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	,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__):
				assert classes > dimensions

				# Check if features have been already loaded
				if features.any:
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =eigh(
								    covariance_between_classes(a__	,a__	,a__)	,covariance_within_classes(a__	,a__	,a__)	,)
								_SCREAMING_SNAKE_CASE			  =eigenvectors[:, ::-1][:, :dimensions]
								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =np.linalg.svd(a__)
								_SCREAMING_SNAKE_CASE			  =svd_matrix[:, 0:dimensions]
								_SCREAMING_SNAKE_CASE			  =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(   ):
				# Create dummy dataset with 2 classes and 3 features
				_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(a__) as error_info:
								_SCREAMING_SNAKE_CASE			  =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(   ):
				_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(a__) as error_info:
								_SCREAMING_SNAKE_CASE			  =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()



import unittest

import numpy as np
import requests

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

							from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
							snake_case_						:			Any													=							False

if is_vision_available():
							from PIL import Image

							from transformers import PixaStructImageProcessor

class 						A__							(		unittest.TestCase  ):




						def __init__(       self					:      Tuple				,    _a					:      str				,    _a					:      List[str]=7				,    _a					:      Union[str, Any]=3				,    _a					:      List[str]=18				,    _a					:      List[Any]=30				,    _a					:      Optional[Any]=400				,    _a					:      Dict=None				,    _a					:      Union[str, Any]=True				,    _a					:      List[str]=True				,    _a					:      Optional[Any]=None				,    )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =size if size is not None else {'''height''': 20, '''width''': 20}
										_SCREAMING_SNAKE_CASE			  =parent
										_SCREAMING_SNAKE_CASE			  =batch_size
										_SCREAMING_SNAKE_CASE			  =num_channels
										_SCREAMING_SNAKE_CASE			  =image_size
										_SCREAMING_SNAKE_CASE			  =min_resolution
										_SCREAMING_SNAKE_CASE			  =max_resolution
										_SCREAMING_SNAKE_CASE			  =size
										_SCREAMING_SNAKE_CASE			  =do_normalize
										_SCREAMING_SNAKE_CASE			  =do_convert_rgb
										_SCREAMING_SNAKE_CASE			  =[512, 1024, 2048, 4096]
										_SCREAMING_SNAKE_CASE			  =patch_size if patch_size is not None else {'''height''': 16, '''width''': 16}




						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




						def 							__UpperCamelCase				(       self					:      str  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'''
										_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(_a				,    stream=_a  ).raw  ).convert('''RGB'''  )
										return raw_image

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self  )




						@property
						def 							__UpperCamelCase				(       self					:      int  )							->     Any:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor_tester.prepare_dummy_image()

										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										_SCREAMING_SNAKE_CASE			  =2048

										_SCREAMING_SNAKE_CASE			  =image_processor(_a				,    return_tensors='''pt'''				,    max_patches=_a  )
										self.assertTrue(torch.allclose(inputs.flattened_patches.mean()				,    torch.tensor(0.06_06  )				,    atol=1E-3				,    rtol=1E-3  )  )




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     Optional[int]:


										"""simple docstring"""





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

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





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

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										_SCREAMING_SNAKE_CASE			  =True

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														with self.assertRaises(_a  ):
																		_SCREAMING_SNAKE_CASE			  =image_processor(
																		    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches

														_SCREAMING_SNAKE_CASE			  ='''Hello'''

														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a				,    header_text=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										# create random PyTorch tensors
										_SCREAMING_SNAKE_CASE			  =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
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * self.image_processor_tester.num_channels
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )

@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11   ,     reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."   ,     )
@require_torch
@require_vision
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						PixaStructImageProcessor if is_vision_available() else None




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =PixaStructImageProcessingTester(self				,    num_channels=4  )
										_SCREAMING_SNAKE_CASE			  =3




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     List[str]:


										"""simple docstring"""





										return self.image_processor_tester.prepare_image_processor_dict()




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processing_class(**self.image_processor_dict  )
										self.assertTrue(hasattr(_a				,    '''do_normalize'''  )  )
										self.assertTrue(hasattr(_a				,    '''do_convert_rgb'''  )  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





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

										# Test not batched input
										_SCREAMING_SNAKE_CASE			  =(
										    (self.image_processor_tester.patch_size['''height'''] * self.image_processor_tester.patch_size['''width'''])
										    * (self.image_processor_tester.num_channels - 1)
										) + 2

										for max_patch in self.image_processor_tester.max_patches:
														# Test not batched input
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    image_inputs[0]				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (1, max_patch, expected_hidden_dim)				,    )

														# Test batched
														_SCREAMING_SNAKE_CASE			  =image_processor(
														    _a				,    return_tensors='''pt'''				,    max_patches=_a  ).flattened_patches
														self.assertEqual(
														    encoded_images.shape				,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)				,    )