
import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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


enable_full_determinism()







class  UpperCamelCase__		(    unittest.TestCase   ):







	'''simple docstring'''





	def   __snake_case				(			self		):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	@property
	def   __snake_case				(			self		):
					A__  :	int         =							1
					A__  :	List[Any]         =							3
					A__  :	Optional[Any]         =							(32, 32)

					A__  :	Optional[Any]         =							floats_tensor((batch_size, num_channels) + sizes				,       rng=random.Random(0		)		).to(lowerCAmelCase_		)
					return image





	@property
	def   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	str         =							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   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	Optional[int]         =							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   __snake_case				(			self		):
					torch.manual_seed(0		)
					A__  :	Tuple         =							RobertaSeriesConfig(
					    hidden_size=32				,       project_dim=32				,       intermediate_size=37				,       layer_norm_eps=1e-05				,       num_attention_heads=4				,       num_hidden_layers=5				,       pad_token_id=1				,       vocab_size=5006				,       )
					return RobertaSeriesModelWithTransformation(lowerCAmelCase_		)





	@property
	def   __snake_case				(			self		):







					def extract(*UpperCamelCase__				,       **UpperCamelCase__		):







									class  UpperCamelCase__		:







										'''simple docstring'''





										def __init__(			self		):
														A__  :	Any         =							torch.ones([0]		)





										def   __snake_case				(			self				,       UpperCamelCase__		):
														self.pixel_values.to(lowerCAmelCase_		)
														return self




									return Out()

					return extract





	def   __snake_case				(			self		):
					A__  :	int         =							'''cpu'''  # ensure determinism for the device-dependent torch.Generator
					A__  :	List[str]         =							self.dummy_cond_unet
					A__  :	int         =							PNDMScheduler(skip_prk_steps=lowerCAmelCase_		)
					A__  :	Optional[int]         =							self.dummy_vae
					A__  :	List[str]         =							self.dummy_text_encoder
					A__  :	Any         =							XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
					A__  :	List[Any]         =							77

					A__  :	str         =							self.dummy_image.to(lowerCAmelCase_		)
					A__  :	Union[str, Any]         =							init_image / 2 + 0.5

					# make sure here that pndm scheduler skips prk
					A__  :	Tuple         =							AltDiffusionImgaImgPipeline(
					    unet=lowerCAmelCase_				,       scheduler=lowerCAmelCase_				,       vae=lowerCAmelCase_				,       text_encoder=lowerCAmelCase_				,       tokenizer=lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       feature_extractor=self.dummy_extractor				,       )
					A__  :	Union[str, Any]         =							VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,       do_normalize=lowerCAmelCase_		)
					A__  :	Optional[int]         =							alt_pipe.to(lowerCAmelCase_		)
					alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

					A__  :	int         =							'''A painting of a squirrel eating a burger'''
					A__  :	Optional[Any]         =							torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)
					A__  :	List[Any]         =							alt_pipe(
					    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='''np'''				,       image=lowerCAmelCase_				,       )

					A__  :	Union[str, Any]         =							output.images

					A__  :	Any         =							torch.Generator(device=lowerCAmelCase_		).manual_seed(0		)
					A__  :	Dict         =							alt_pipe(
					    [prompt]				,       generator=lowerCAmelCase_				,       guidance_scale=6.0				,       num_inference_steps=2				,       output_type='''np'''				,       image=lowerCAmelCase_				,       return_dict=lowerCAmelCase_				,       )[0]

					A__  :	Union[str, Any]         =							image[0, -3:, -3:, -1]
					A__  :	Tuple         =							image_from_tuple[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					A__  :	Union[str, Any]         =							np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9]		)

					assert np.abs(image_slice.flatten() - expected_slice		).max() < 5e-3
					assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 5e-3





	@unittest.skipIf(torch_device != '''cuda'''				,       '''This test requires a GPU'''		)
	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							self.dummy_cond_unet
					A__  :	Any         =							PNDMScheduler(skip_prk_steps=lowerCAmelCase_		)
					A__  :	Optional[int]         =							self.dummy_vae
					A__  :	str         =							self.dummy_text_encoder
					A__  :	Optional[Any]         =							XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
					A__  :	int         =							77

					A__  :	int         =							self.dummy_image.to(lowerCAmelCase_		)

					# put models in fp16
					A__  :	Any         =							unet.half()
					A__  :	Optional[Any]         =							vae.half()
					A__  :	List[str]         =							bert.half()

					# make sure here that pndm scheduler skips prk
					A__  :	str         =							AltDiffusionImgaImgPipeline(
					    unet=lowerCAmelCase_				,       scheduler=lowerCAmelCase_				,       vae=lowerCAmelCase_				,       text_encoder=lowerCAmelCase_				,       tokenizer=lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       feature_extractor=self.dummy_extractor				,       )
					A__  :	List[Any]         =							VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor				,       do_normalize=lowerCAmelCase_		)
					A__  :	Dict         =							alt_pipe.to(lowerCAmelCase_		)
					alt_pipe.set_progress_bar_config(disable=lowerCAmelCase_		)

					A__  :	str         =							'''A painting of a squirrel eating a burger'''
					A__  :	Tuple         =							torch.manual_seed(0		)
					A__  :	Union[str, Any]         =							alt_pipe(
					    [prompt]				,       generator=lowerCAmelCase_				,       num_inference_steps=2				,       output_type='''np'''				,       image=lowerCAmelCase_				,       ).images

					assert image.shape == (1, 32, 32, 3)





	@unittest.skipIf(torch_device != '''cuda'''				,       '''This test requires a GPU'''		)
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''		)
					# resize to resolution that is divisible by 8 but not 16 or 32
					A__  :	Optional[int]         =							init_image.resize((760, 504)		)

					A__  :	str         =							'''BAAI/AltDiffusion'''
					A__  :	Tuple         =							AltDiffusionImgaImgPipeline.from_pretrained(
					    lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       )
					pipe.to(lowerCAmelCase_		)
					pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
					pipe.enable_attention_slicing()

					A__  :	List[str]         =							'''A fantasy landscape, trending on artstation'''

					A__  :	Any         =							torch.manual_seed(0		)
					A__  :	List[str]         =							pipe(
					    prompt=lowerCAmelCase_				,       image=lowerCAmelCase_				,       strength=0.7_5				,       guidance_scale=7.5				,       generator=lowerCAmelCase_				,       output_type='''np'''				,       )
					A__  :	int         =							output.images[0]

					A__  :	List[str]         =							image[255:258, 383:386, -1]

					assert image.shape == (504, 760, 3)
					A__  :	Any         =							np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0]		)

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







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







	'''simple docstring'''





	def   __snake_case				(			self		):
					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()





	def   __snake_case				(			self		):
					A__  :	Dict         =							load_image(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/img2img/sketch-mountains-input.jpg'''		)
					A__  :	Any         =							init_image.resize((768, 512)		)
					A__  :	Dict         =							load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''		)

					A__  :	Optional[Any]         =							'''BAAI/AltDiffusion'''
					A__  :	str         =							AltDiffusionImgaImgPipeline.from_pretrained(
					    lowerCAmelCase_				,       safety_checker=lowerCAmelCase_				,       )
					pipe.to(lowerCAmelCase_		)
					pipe.set_progress_bar_config(disable=lowerCAmelCase_		)
					pipe.enable_attention_slicing()

					A__  :	str         =							'''A fantasy landscape, trending on artstation'''

					A__  :	str         =							torch.manual_seed(0		)
					A__  :	List[Any]         =							pipe(
					    prompt=lowerCAmelCase_				,       image=lowerCAmelCase_				,       strength=0.7_5				,       guidance_scale=7.5				,       generator=lowerCAmelCase_				,       output_type='''np'''				,       )
					A__  :	int         =							output.images[0]

					assert image.shape == (512, 768, 3)
					# img2img is flaky across GPUs even in fp32, so using MAE here
					assert np.abs(expected_image - image		).max() < 1e-2



from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')