

"""simple docstring"""




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class       lowerCamelCase  (A__	,unittest.TestCase	):
			lowerCamelCase__      :						str    						=							DiTPipeline
			lowerCamelCase__      :						Any    						=							CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
			lowerCamelCase__      :						str    						=							PipelineTesterMixin.required_optional_params - {
			    'latents',
			    'num_images_per_prompt',
			    'callback',
			    'callback_steps',
			}
			lowerCamelCase__      :						int    						=							CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
			lowerCamelCase__      :						Tuple    						=							False

			def     SCREAMING_SNAKE_CASE (    self :  int				)      ->			Optional[int]:
						torch.manual_seed(0				)
						SCREAMING_SNAKE_CASE__									=					TransformeraDModel(
						    sample_size=1_6			, num_layers=2			, patch_size=4			, attention_head_dim=8			, num_attention_heads=2			, in_channels=4			, out_channels=8			, attention_bias=__UpperCAmelCase			, activation_fn="""gelu-approximate"""			, num_embeds_ada_norm=1_0_0_0			, norm_type="""ada_norm_zero"""			, norm_elementwise_affine=__UpperCAmelCase			, )
						SCREAMING_SNAKE_CASE__									=					AutoencoderKL()
						SCREAMING_SNAKE_CASE__									=					DDIMScheduler()
						SCREAMING_SNAKE_CASE__									=					{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
						return components

			def     SCREAMING_SNAKE_CASE (    self :  Dict			, __UpperCAmelCase :  int			, __UpperCAmelCase :  List[Any]=0				)      ->			str:
						if str(__UpperCAmelCase				).startswith("""mps"""				):
									SCREAMING_SNAKE_CASE__									=					torch.manual_seed(__UpperCAmelCase				)
						else:
									SCREAMING_SNAKE_CASE__									=					torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)
						SCREAMING_SNAKE_CASE__									=					{
						    """class_labels""": [1],
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """output_type""": """numpy""",
						}
						return inputs

			def     SCREAMING_SNAKE_CASE (    self :  Optional[int]				)      ->			Dict:
						SCREAMING_SNAKE_CASE__									=					"""cpu"""

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

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

						self.assertEqual(image.shape			, (1, 1_6, 1_6, 3)				)
						SCREAMING_SNAKE_CASE__									=					np.array([0.2_946, 0.6_601, 0.4_329, 0.3_296, 0.4_144, 0.5_319, 0.7_273, 0.5_013, 0.4_457]				)
						SCREAMING_SNAKE_CASE__									=					np.abs(image_slice.flatten() - expected_slice				).max()
						self.assertLessEqual(__UpperCAmelCase			, 1e-3				)

			def     SCREAMING_SNAKE_CASE (    self :  Union[str, Any]				)      ->			List[str]:
						self._test_inference_batch_single_identical(relax_max_difference=__UpperCAmelCase			, expected_max_diff=1e-3				)





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()			, reason="""XFormers attention is only available with CUDA and `xformers` installed"""			, )
			def     SCREAMING_SNAKE_CASE (    self :  List[Any]				)      ->			Union[str, Any]:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)





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

			def     SCREAMING_SNAKE_CASE (    self :  Optional[int]				)      ->			Union[str, Any]:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def     SCREAMING_SNAKE_CASE (    self :  Any				)      ->			List[Any]:
						SCREAMING_SNAKE_CASE__									=					torch.manual_seed(0				)

						SCREAMING_SNAKE_CASE__									=					DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256"""				)
						pipe.to("""cuda"""				)

						SCREAMING_SNAKE_CASE__									=					["""vase""", """umbrella""", """white shark""", """white wolf"""]
						SCREAMING_SNAKE_CASE__									=					pipe.get_label_ids(__UpperCAmelCase				)

						SCREAMING_SNAKE_CASE__									=					pipe(__UpperCAmelCase			, generator=__UpperCAmelCase			, num_inference_steps=4_0			, output_type="""np"""				).images

						for word, image in zip(__UpperCAmelCase			, __UpperCAmelCase				):
									SCREAMING_SNAKE_CASE__									=					load_numpy(
									    F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""				)
									assert np.abs((expected_image - image).max()				) < 1e-2





			def     SCREAMING_SNAKE_CASE (    self :  List[Any]				)      ->			Tuple:
						SCREAMING_SNAKE_CASE__									=					DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512"""				)
						SCREAMING_SNAKE_CASE__									=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
						pipe.to("""cuda"""				)

						SCREAMING_SNAKE_CASE__									=					["""vase""", """umbrella"""]
						SCREAMING_SNAKE_CASE__									=					pipe.get_label_ids(__UpperCAmelCase				)

						SCREAMING_SNAKE_CASE__									=					torch.manual_seed(0				)
						SCREAMING_SNAKE_CASE__									=					pipe(__UpperCAmelCase			, generator=__UpperCAmelCase			, num_inference_steps=2_5			, output_type="""np"""				).images

						for word, image in zip(__UpperCAmelCase			, __UpperCAmelCase				):
									SCREAMING_SNAKE_CASE__									=					load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    F"""/dit/{word}_512.npy"""				)

									assert np.abs((expected_image - image).max()				) < 1e-1







'''simple docstring'''




from PIL import Image



def       __lowerCamelCase  (					_lowercase	,				_lowercase		)      ->			Image:

							def brightness(_lowercase		) -> float:
														return 1_2_8 + level + (c - 1_2_8)

							if not -255.0 <= level <= 255.0:
														raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""		)
							return img.point(_lowercase		)


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

