
'''simple docstring'''

import logging

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

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







def 	A  (   UpperCamelCase_       :  np.ndarray   )       ->							np.ndarray:







			'''simple docstring'''



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







def 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int   )       ->							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 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int   )       ->							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 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  int   )       ->							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 	A  (   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:
						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 	A  (   )       ->							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 	A  (   )       ->							None:







			'''simple docstring'''



			lowerCAmelCase__										=				np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]   )
			lowerCAmelCase__										=				2
			lowerCAmelCase__										=				np.array([[6.92_820_323, 8.66_025_404, 10.39_230_485], [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()










import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase	:   Any    =   False


class 	__snake_case   ( unittest.TestCase ):
		pass



@nightly
@require_torch_gpu
class 	__snake_case   ( unittest.TestCase ):
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()
		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)

						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a_						)
										lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained(a_    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  generator.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt='first prompt'    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=2    ,output_type='numpy'    ,).images

						assert np.abs(image - new_image						).sum() < 1e-5, "Models don't have the same forward pass"



		def 						SCREAMING_SNAKE_CASE_					(							self						):







						"""simple docstring"""
						lowerCAmelCase__		  =  VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'    ,torch_dtype=torch.floataa						)
						pipe.to(a_						)
						pipe.set_progress_bar_config(disable=a_						)

						lowerCAmelCase__		  =  'cyberpunk 2077'
						lowerCAmelCase__		  =  load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'						)
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.dual_guided(
						    prompt=a_    ,image=a_    ,text_to_image_strength=0.75    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'    ,).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1

						lowerCAmelCase__		  =  'A painting of a squirrel eating a burger '
						lowerCAmelCase__		  =  torch.manual_seed(0						)
						lowerCAmelCase__		  =  pipe.text_to_image(
						    prompt=a_    ,generator=a_    ,guidance_scale=7.5    ,num_inference_steps=50    ,output_type='numpy'						).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1

						lowerCAmelCase__		  =  pipe.image_variation(a_    ,generator=a_    ,output_type='numpy'						).images

						lowerCAmelCase__		  =  image[0, 253:256, 253:256, -1]

						assert image.shape == (1, 512, 512, 3)
						lowerCAmelCase__		  =  np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]						)

						assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-1
