






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference






class 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       Optional[int]     =   ShapEImgaImgPipeline
		__snake_case      :       int     =   ["image"]
		__snake_case      :       List[Any]     =   ["image"]
		__snake_case      :       Optional[int]     =   [
		    "num_images_per_prompt",
		    "num_inference_steps",
		    "generator",
		    "latents",
		    "guidance_scale",
		    "frame_size",
		    "output_type",
		    "return_dict",
		]
		__snake_case      :       Union[str, Any]     =   False







		@property
		def  A__      (					self		):
						return 32







		@property
		def  A__      (					self		):
						return 32







		@property
		def  A__      (					self		):
						return self.time_input_dim * 4







		@property
		def  A__      (					self		):
						return 8







		@property
		def  A__      (					self		):
						torch.manual_seed(0		)
						_A	:				int   =      CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size       ,image_size=64       ,projection_dim=self.text_embedder_hidden_size       ,intermediate_size=37       ,num_attention_heads=4       ,num_channels=3       ,num_hidden_layers=5       ,patch_size=1       ,)

						_A	:				List[Any]   =      CLIPVisionModel(A__		)
						return model







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      CLIPImageProcessor(
						    crop_size=224       ,do_center_crop=A__       ,do_normalize=A__       ,do_resize=A__       ,image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,resample=3       ,size=224       ,)

						return image_processor







		@property
		def  A__      (					self		):
						torch.manual_seed(0		)

						_A	:				Any   =      {
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 16,
						    '''embedding_dim''': self.time_input_dim,
						    '''num_embeddings''': 32,
						    '''embedding_proj_dim''': self.text_embedder_hidden_size,
						    '''time_embed_dim''': self.time_embed_dim,
						    '''num_layers''': 1,
						    '''clip_embed_dim''': self.time_input_dim * 2,
						    '''additional_embeddings''': 0,
						    '''time_embed_act_fn''': '''gelu''',
						    '''norm_in_type''': '''layer''',
						    '''embedding_proj_norm_type''': '''layer''',
						    '''encoder_hid_proj_type''': None,
						    '''added_emb_type''': None,
						}

						_A	:				Union[str, Any]   =      PriorTransformer(**A__		)
						return model







		@property
		def  A__      (					self		):
						torch.manual_seed(0		)

						_A	:				Optional[int]   =      {
						    '''param_shapes''': (
						        (self.renderer_dim, 93),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						        (self.renderer_dim, 8),
						    ),
						    '''d_latent''': self.time_input_dim,
						    '''d_hidden''': self.renderer_dim,
						    '''n_output''': 12,
						    '''background''': (
						        0.1,
						        0.1,
						        0.1,
						    ),
						}
						_A	:				str   =      ShapERenderer(**A__		)
						return model







		def  A__      (					self		):
						_A	:				Dict   =      self.dummy_prior
						_A	:				Optional[Any]   =      self.dummy_image_encoder
						_A	:				Tuple   =      self.dummy_image_processor
						_A	:				Union[str, Any]   =      self.dummy_renderer

						_A	:				Optional[Any]   =      HeunDiscreteScheduler(
						    beta_schedule='''exp'''       ,num_train_timesteps=1024       ,prediction_type='''sample'''       ,use_karras_sigmas=A__       ,clip_sample=A__       ,clip_sample_range=1.0       ,)
						_A	:				Any   =      {
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''image_processor''': image_processor,
						    '''renderer''': renderer,
						    '''scheduler''': scheduler,
						}

						return components







		def  A__      (					self       ,A__       ,A__=0		):
						_A	:				Tuple   =      floats_tensor((1, 3, 64, 64)       ,rng=random.Random(A__		)		).to(A__		)

						if str(A__		).startswith('''mps'''		):
										_A	:				int   =      torch.manual_seed(A__		)
						else:
										_A	:				List[Any]   =      torch.Generator(device=A__		).manual_seed(A__		)
						_A	:				Tuple   =      {
						    '''image''': input_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 1,
						    '''frame_size''': 32,
						    '''output_type''': '''np''',
						}
						return inputs







		def  A__      (					self		):
						_A	:				Tuple   =      '''cpu'''

						_A	:				Optional[Any]   =      self.get_dummy_components()

						_A	:				str   =      self.pipeline_class(**A__		)
						_A	:				List[Any]   =      pipe.to(A__		)

						pipe.set_progress_bar_config(disable=A__		)

						_A	:				Union[str, Any]   =      pipe(**self.get_dummy_inputs(A__		)		)
						_A	:				List[str]   =      output.images[0]
						_A	:				List[str]   =      image[0, -3:, -3:, -1]

						assert image.shape == (20, 32, 32, 3)

						_A	:				Optional[int]   =      np.array(
						    [
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						        0.00_03_92_16,
						    ]		)

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







		def  A__      (					self		):
						# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
						self._test_inference_batch_consistent(batch_sizes=[1, 2]		)







		def  A__      (					self		):
						_A	:				Union[str, Any]   =      torch_device == '''cpu'''
						_A	:				Dict   =      True
						self._test_inference_batch_single_identical(
						    batch_size=2       ,test_max_difference=A__       ,relax_max_difference=A__       ,)







		def  A__      (					self		):
						_A	:				List[str]   =      self.get_dummy_components()
						_A	:				Dict   =      self.pipeline_class(**A__		)
						_A	:				List[str]   =      pipe.to(A__		)
						pipe.set_progress_bar_config(disable=A__		)

						_A	:				Dict   =      1
						_A	:				int   =      2

						_A	:				str   =      self.get_dummy_inputs(A__		)

						for key in inputs.keys():
										if key in self.batch_params:
														_A	:				List[Any]   =      batch_size * [inputs[key]]

						_A	:				int   =      pipe(**A__       ,num_images_per_prompt=A__		)[0]

						assert images.shape[0] == batch_size * num_images_per_prompt






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







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







		def  A__      (					self		):
						_A	:				Union[str, Any]   =      load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''		)
						_A	:				List[str]   =      load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/shap_e/test_shap_e_img2img_out.npy'''		)
						_A	:				Dict   =      ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''		)
						_A	:				Union[str, Any]   =      pipe.to(A__		)
						pipe.set_progress_bar_config(disable=A__		)

						_A	:				Any   =      torch.Generator(device=A__		).manual_seed(0		)

						_A	:				Optional[int]   =      pipe(
						    A__       ,generator=A__       ,guidance_scale=3.0       ,num_inference_steps=64       ,frame_size=64       ,output_type='''np'''       ,).images[0]

						assert images.shape == (20, 64, 64, 3)

						assert_mean_pixel_difference(A__       ,A__		)











from PIL import Image



def  a__ (__lowercase							:Image					,  __lowercase							:float   )		-> Image:

				def brightness(__lowercase							:int   ) -> float:
								return 128 + level + (c - 128)

				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
		_UpperCamelCase			:	Any								=change_brightness(img, 100)
		brigt_img.save('image_data/lena_brightness.png', format='png')



