




'''simple docstring'''


import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				str:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a      	=							1
														_a      	=							3
														_a      	=							(32, 32)

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







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


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							UNetaDConditionModel(
														    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,     cross_attention_dim=32				,     )
														return model







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


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							AutoencoderKL(
														    block_out_channels=[32, 64]				,     in_channels=3				,     out_channels=3				,     down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,     up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,     latent_channels=4				,     )
														return model







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


														"""simple docstring"""


														torch.manual_seed(0   )
														_a      	=							CLIPTextConfig(
														    bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1e-05				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=10_00				,     )
														return CLIPTextModel(lowerCAmelCase_   )







							@property
							def   __lowerCAmelCase     (    self			:    Dict   )     ->				str:


														"""simple docstring"""


														def extract(*lowerCAmelCase_			:    str				,     **lowerCAmelCase_			:    List[Any]   ):

																					class      A       :







																												def __init__(    self			:    Dict   )     ->				int:


																																			"""simple docstring"""


																																			_a      	=							torch.ones([0]   )







																												def   __lowerCAmelCase     (    self			:    List[str]				,     lowerCAmelCase_			:    List[Any]   )     ->				Optional[Any]:


																																			"""simple docstring"""


																																			self.pixel_values.to(lowerCAmelCase_   )
																																			return self



																					return Out()

														return extract







							def   __lowerCAmelCase     (    self			:    str   )     ->				Tuple:


														"""simple docstring"""


														_a      	=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														_a      	=							self.dummy_cond_unet
														_a      	=							DDIMScheduler(
														    beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule='''scaled_linear'''				,     clip_sample=lowerCAmelCase_				,     set_alpha_to_one=lowerCAmelCase_				,     )

														_a      	=							self.dummy_vae
														_a      	=							self.dummy_text_encoder
														_a      	=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

														# make sure here that pndm scheduler skips prk
														_a      	=							StableDiffusionPipeline(
														    unet=lowerCAmelCase_				,     scheduler=lowerCAmelCase_				,     vae=lowerCAmelCase_				,     text_encoder=lowerCAmelCase_				,     tokenizer=lowerCAmelCase_				,     safety_checker=lowerCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							'''A painting of a squirrel eating a burger'''

														_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type='''np'''   )
														_a      	=							output.images

														_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type='''np'''				,     return_dict=lowerCAmelCase_				,     )[0]

														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														_a      	=							np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
														_a      	=							self.dummy_cond_unet
														_a      	=							PNDMScheduler(skip_prk_steps=lowerCAmelCase_   )
														_a      	=							self.dummy_vae
														_a      	=							self.dummy_text_encoder
														_a      	=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

														# make sure here that pndm scheduler skips prk
														_a      	=							StableDiffusionPipeline(
														    unet=lowerCAmelCase_				,     scheduler=lowerCAmelCase_				,     vae=lowerCAmelCase_				,     text_encoder=lowerCAmelCase_				,     tokenizer=lowerCAmelCase_				,     safety_checker=lowerCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(0   )
														_a      	=							sd_pipe([prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type='''np'''   )

														_a      	=							output.images

														_a      	=							torch.Generator(device=lowerCAmelCase_   ).manual_seed(0   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=6.0				,     num_inference_steps=2				,     output_type='''np'''				,     return_dict=lowerCAmelCase_				,     )[0]

														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							image_from_tuple[0, -3:, -3:, -1]

														assert image.shape == (1, 64, 64, 3)
														_a      	=							np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]   )

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
														assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2







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


														"""simple docstring"""


														_a      	=							StableDiffusionPipeline.from_pretrained(
														    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''				,     safety_checker=lowerCAmelCase_   )
														assert isinstance(lowerCAmelCase_				,     lowerCAmelCase_   )
														assert isinstance(pipe.scheduler				,     lowerCAmelCase_   )
														assert pipe.safety_checker is None

														_a      	=							pipe('''example prompt'''				,     num_inference_steps=2   ).images[0]
														assert image is not None

														# check that there's no error when saving a pipeline with one of the models being None
														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(lowerCAmelCase_   )
																					_a      	=							StableDiffusionPipeline.from_pretrained(lowerCAmelCase_   )

														# sanity check that the pipeline still works
														assert pipe.safety_checker is None
														_a      	=							pipe('''example prompt'''				,     num_inference_steps=2   ).images[0]
														assert image is not None







							@unittest.skipIf(torch_device != '''cuda'''				,     '''This test requires a GPU'''   )
							def   __lowerCAmelCase     (    self			:    int   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							self.dummy_cond_unet
														_a      	=							PNDMScheduler(skip_prk_steps=lowerCAmelCase_   )
														_a      	=							self.dummy_vae
														_a      	=							self.dummy_text_encoder
														_a      	=							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

														# put models in fp16
														_a      	=							unet.half()
														_a      	=							vae.half()
														_a      	=							bert.half()

														# make sure here that pndm scheduler skips prk
														_a      	=							StableDiffusionPipeline(
														    unet=lowerCAmelCase_				,     scheduler=lowerCAmelCase_				,     vae=lowerCAmelCase_				,     text_encoder=lowerCAmelCase_				,     tokenizer=lowerCAmelCase_				,     safety_checker=lowerCAmelCase_				,     feature_extractor=self.dummy_extractor				,     )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							'''A painting of a squirrel eating a burger'''
														_a      	=							sd_pipe([prompt]				,     num_inference_steps=2				,     output_type='''np'''   ).images

														assert image.shape == (1, 64, 64, 3)



@nightly
@require_torch_gpu
class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Tuple:


														"""simple docstring"""


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







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


														"""simple docstring"""


														_a      	=							StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				,     safety_checker=lowerCAmelCase_   )
														_a      	=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							(
														    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
														    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
														    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
														    ''' children from bahnhof zoo, detailed '''
														)
														_a      	=							40_03_66_03_46
														_a      	=							7

														# without safety guidance (sld_guidance_scale = 0)
														_a      	=							torch.manual_seed(lowerCAmelCase_   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=lowerCAmelCase_				,     num_inference_steps=50				,     output_type='''np'''				,     width=5_12				,     height=5_12				,     sld_guidance_scale=0				,     )

														_a      	=							output.images
														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

														assert image.shape == (1, 5_12, 5_12, 3)

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

														# without safety guidance (strong configuration)
														_a      	=							torch.manual_seed(lowerCAmelCase_   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=lowerCAmelCase_				,     num_inference_steps=50				,     output_type='''np'''				,     width=5_12				,     height=5_12				,     sld_guidance_scale=20_00				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

														_a      	=							output.images
														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

														assert image.shape == (1, 5_12, 5_12, 3)

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







							def   __lowerCAmelCase     (    self			:    int   )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				,     safety_checker=lowerCAmelCase_   )
														_a      	=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							'''padme amidala taking a bath artwork, safe for work, no nudity'''
														_a      	=							27_34_97_17_55
														_a      	=							7

														_a      	=							torch.manual_seed(lowerCAmelCase_   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=lowerCAmelCase_				,     num_inference_steps=50				,     output_type='''np'''				,     width=5_12				,     height=5_12				,     sld_guidance_scale=0				,     )

														_a      	=							output.images
														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

														assert image.shape == (1, 5_12, 5_12, 3)

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2

														_a      	=							torch.manual_seed(lowerCAmelCase_   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=lowerCAmelCase_				,     num_inference_steps=50				,     output_type='''np'''				,     width=5_12				,     height=5_12				,     sld_guidance_scale=20_00				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

														_a      	=							output.images
														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

														assert image.shape == (1, 5_12, 5_12, 3)

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







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


														"""simple docstring"""


														_a      	=							StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
														_a      	=							sd_pipe.to(lowerCAmelCase_   )
														sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_   )

														_a      	=							(
														    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
														    ''' leyendecker'''
														)
														_a      	=							10_44_35_52_34
														_a      	=							12

														_a      	=							torch.manual_seed(lowerCAmelCase_   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=lowerCAmelCase_				,     num_inference_steps=50				,     output_type='''np'''				,     width=5_12				,     height=5_12				,     sld_guidance_scale=0				,     )

														_a      	=							output.images
														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]   )

														assert image.shape == (1, 5_12, 5_12, 3)

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-7

														_a      	=							torch.manual_seed(lowerCAmelCase_   )
														_a      	=							sd_pipe(
														    [prompt]				,     generator=lowerCAmelCase_				,     guidance_scale=lowerCAmelCase_				,     num_inference_steps=50				,     output_type='''np'''				,     width=5_12				,     height=5_12				,     sld_guidance_scale=20_00				,     sld_warmup_steps=7				,     sld_threshold=0.0_2_5				,     sld_momentum_scale=0.5				,     sld_mom_beta=0.7				,     )

														_a      	=							output.images
														_a      	=							image[0, -3:, -3:, -1]
														_a      	=							np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]   )
														assert image.shape == (1, 5_12, 5_12, 3)

														assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def 						snake_case_       (UpperCamelCase			:    dict ):





							'''simple docstring'''


							return (data["data"], data["target"])
def 						snake_case_       (UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray					,		UpperCamelCase			:    np.ndarray ):





							'''simple docstring'''


							_a      	=							XGBRegressor(verbosity=0					,		random_state=42 )
							xgb.fit(UpperCamelCase					,		UpperCamelCase )
							# Predict target for test data
							_a      	=							xgb.predict(UpperCamelCase )
							_a      	=							predictions.reshape(len(UpperCamelCase )					,		1 )
							return predictions
def 						snake_case_       ():





							'''simple docstring'''


							_a      	=							fetch_california_housing()
							_a	,				_a      	=							data_handling(UpperCamelCase )
							_a	,				_a	,				_a	,				_a      	=							train_test_split(
							    UpperCamelCase					,		UpperCamelCase					,		test_size=0.25					,		random_state=1 )
							_a      	=							xgboost(UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase )
							# Error printing
							print(f'Mean Absolute Error : {mean_absolute_error(UpperCamelCase					,		UpperCamelCase )}' )
							print(f'Mean Square Error  : {mean_squared_error(UpperCamelCase					,		UpperCamelCase )}' )


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)
				main()

