
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			[tensor.shape for tensor in tensor_list]
		return all(shape == shapes[0] for shape in shapes[1:]     )





class 			snake_case  (				__lowercase							,      __lowercase							,      __lowercase							,      unittest.TestCase ):
							UpperCAmelCase__      =   StableDiffusionLatentUpscalePipeline
							UpperCAmelCase__      =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
							    '''height''',
							    '''width''',
							    '''cross_attention_kwargs''',
							    '''negative_prompt_embeds''',
							    '''prompt_embeds''',
							}
							UpperCAmelCase__      =   PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
							UpperCAmelCase__      =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							UpperCAmelCase__      =   frozenset(
							    [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
							UpperCAmelCase__      =   frozenset([] )

							UpperCAmelCase__      =   True

							@property
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			1
									SCREAMING_SNAKE_CASE_    			=			4
									SCREAMING_SNAKE_CASE_    			=			(16, 16)

									SCREAMING_SNAKE_CASE_    			=			floats_tensor((batch_size, num_channels) + sizes							,							rng=random.Random(0     )     ).to(SCREAMING_SNAKE_CASE_     )
									return image

							def _lowercase       (self     ):

									"""simple docstring"""







									torch.manual_seed(0     )
									SCREAMING_SNAKE_CASE_    			=			UNetaDConditionModel(
									    act_fn='''gelu'''							,							attention_head_dim=8							,							norm_num_groups=SCREAMING_SNAKE_CASE_							,							block_out_channels=[32, 32, 64, 64]							,							time_cond_proj_dim=1_60							,							conv_in_kernel=1							,							conv_out_kernel=1							,							cross_attention_dim=32							,							down_block_types=(
									        '''KDownBlock2D''',
									        '''KCrossAttnDownBlock2D''',
									        '''KCrossAttnDownBlock2D''',
									        '''KCrossAttnDownBlock2D''',
									    )							,							in_channels=8							,							mid_block_type=SCREAMING_SNAKE_CASE_							,							only_cross_attention=SCREAMING_SNAKE_CASE_							,							out_channels=5							,							resnet_time_scale_shift='''scale_shift'''							,							time_embedding_type='''fourier'''							,							timestep_post_act='''gelu'''							,							up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')							,							)
									SCREAMING_SNAKE_CASE_    			=			AutoencoderKL(
									    block_out_channels=[32, 32, 64, 64]							,							in_channels=3							,							out_channels=3							,							down_block_types=[
									        '''DownEncoderBlock2D''',
									        '''DownEncoderBlock2D''',
									        '''DownEncoderBlock2D''',
									        '''DownEncoderBlock2D''',
									    ]							,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']							,							latent_channels=4							,							)
									SCREAMING_SNAKE_CASE_    			=			EulerDiscreteScheduler(prediction_type='''sample'''     )
									SCREAMING_SNAKE_CASE_    			=			CLIPTextConfig(
									    bos_token_id=0							,							eos_token_id=2							,							hidden_size=32							,							intermediate_size=37							,							layer_norm_eps=1e-0_5							,							num_attention_heads=4							,							num_hidden_layers=5							,							pad_token_id=1							,							vocab_size=10_00							,							hidden_act='''quick_gelu'''							,							projection_dim=5_12							,							)
									SCREAMING_SNAKE_CASE_    			=			CLIPTextModel(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''     )

									SCREAMING_SNAKE_CASE_    			=			{
									    '''unet''': model.eval(),
									    '''vae''': vae.eval(),
									    '''scheduler''': scheduler,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									}

									return components

							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_=0     ):

									"""simple docstring"""







									if str(SCREAMING_SNAKE_CASE_     ).startswith('''mps'''     ):
											SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(SCREAMING_SNAKE_CASE_     )
									else:
											SCREAMING_SNAKE_CASE_    			=			torch.Generator(device=SCREAMING_SNAKE_CASE_     ).manual_seed(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			{
									    '''prompt''': '''A painting of a squirrel eating a burger''',
									    '''image''': self.dummy_image.cpu(),
									    '''generator''': generator,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''numpy''',
									}
									return inputs

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			'''cpu'''

									SCREAMING_SNAKE_CASE_    			=			self.get_dummy_components()
									SCREAMING_SNAKE_CASE_    			=			self.pipeline_class(**SCREAMING_SNAKE_CASE_     )
									pipe.to(SCREAMING_SNAKE_CASE_     )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			pipe(**SCREAMING_SNAKE_CASE_     ).images
									SCREAMING_SNAKE_CASE_    			=			image[0, -3:, -3:, -1]

									self.assertEqual(image.shape							,							(1, 2_56, 2_56, 3)     )
									SCREAMING_SNAKE_CASE_    			=			np.array(
									    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]     )
									SCREAMING_SNAKE_CASE_    			=			np.abs(image_slice.flatten() - expected_slice     ).max()
									self.assertLessEqual(SCREAMING_SNAKE_CASE_							,							1e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_attention_slicing_forward_pass(expected_max_diff=7e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_cpu_offload_forward_pass(expected_max_diff=3e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_inference_batch_single_identical(expected_max_diff=7e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_save_load_local(expected_max_difference=3e-3     )

							def _lowercase       (self     ):

									"""simple docstring"""







									super().test_save_load_optional_components(expected_max_difference=3e-3     )







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			[
									    '''DDIMScheduler''',
									    '''DDPMScheduler''',
									    '''PNDMScheduler''',
									    '''HeunDiscreteScheduler''',
									    '''EulerAncestralDiscreteScheduler''',
									    '''KDPM2DiscreteScheduler''',
									    '''KDPM2AncestralDiscreteScheduler''',
									    '''DPMSolverSDEScheduler''',
									]
									SCREAMING_SNAKE_CASE_    			=			self.get_dummy_components()
									SCREAMING_SNAKE_CASE_    			=			self.pipeline_class(**SCREAMING_SNAKE_CASE_     )

									# make sure that PNDM does not need warm-up
									pipe.scheduler.register_to_config(skip_prk_steps=SCREAMING_SNAKE_CASE_     )

									pipe.to(SCREAMING_SNAKE_CASE_     )
									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			self.get_dummy_inputs(SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			2

									SCREAMING_SNAKE_CASE_    			=			[]
									for scheduler_enum in KarrasDiffusionSchedulers:
											if scheduler_enum.name in skip_schedulers:
													# no sigma schedulers are not supported
													# no schedulers
													continue

											SCREAMING_SNAKE_CASE_    			=			getattr(SCREAMING_SNAKE_CASE_							,							scheduler_enum.name     )
											SCREAMING_SNAKE_CASE_    			=			scheduler_cls.from_config(pipe.scheduler.config     )
											SCREAMING_SNAKE_CASE_    			=			pipe(**SCREAMING_SNAKE_CASE_     )[0]
											outputs.append(SCREAMING_SNAKE_CASE_     )

									assert check_same_shape(SCREAMING_SNAKE_CASE_     )







@require_torch_gpu
@slow
class 			snake_case  (				unittest.TestCase ):

							def _lowercase       (self     ):

									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(33     )

									SCREAMING_SNAKE_CASE_    			=			StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''							,							torch_dtype=torch.floataa     )
									pipe.to('''cuda'''     )

									SCREAMING_SNAKE_CASE_    			=			StableDiffusionLatentUpscalePipeline.from_pretrained(
									    '''stabilityai/sd-x2-latent-upscaler'''							,							torch_dtype=torch.floataa     )
									upscaler.to('''cuda'''     )

									SCREAMING_SNAKE_CASE_    			=			'''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

									SCREAMING_SNAKE_CASE_    			=			pipe(SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_							,							output_type='''latent'''     ).images

									SCREAMING_SNAKE_CASE_    			=			upscaler(
									    prompt=SCREAMING_SNAKE_CASE_							,							image=SCREAMING_SNAKE_CASE_							,							num_inference_steps=20							,							guidance_scale=0							,							generator=SCREAMING_SNAKE_CASE_							,							output_type='''np'''							,							).images[0]

									SCREAMING_SNAKE_CASE_    			=			load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'''     )
									assert np.abs((expected_image - image).mean()     ) < 5e-2







							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(33     )

									SCREAMING_SNAKE_CASE_    			=			StableDiffusionLatentUpscalePipeline.from_pretrained(
									    '''stabilityai/sd-x2-latent-upscaler'''							,							torch_dtype=torch.floataa     )
									upscaler.to('''cuda'''     )

									SCREAMING_SNAKE_CASE_    			=			'''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

									SCREAMING_SNAKE_CASE_    			=			load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'''     )

									SCREAMING_SNAKE_CASE_    			=			upscaler(
									    prompt=SCREAMING_SNAKE_CASE_							,							image=SCREAMING_SNAKE_CASE_							,							num_inference_steps=20							,							guidance_scale=0							,							generator=SCREAMING_SNAKE_CASE_							,							output_type='''np'''							,							).images[0]

									SCREAMING_SNAKE_CASE_    			=			load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'''     )
									assert np.abs((expected_image - image).max()     ) < 5e-2

"""simple docstring"""




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([[6.9_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(__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()