





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_						(					_lowerCamelCase							,					unittest.TestCase   ):
					lowerCAmelCase_							=		ShapEImgaImgPipeline
					lowerCAmelCase_							=		['''image''']
					lowerCAmelCase_							=		['''image''']
					lowerCAmelCase_							=		[
					    '''num_images_per_prompt''',
					    '''num_inference_steps''',
					    '''generator''',
					    '''latents''',
					    '''guidance_scale''',
					    '''frame_size''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase_							=		False




					@property
					def 		lowerCAmelCase     (						self					)						->							List[str]:
										return 32




					@property
					def 		lowerCAmelCase     (						self					)						->							str:
										return 32




					@property
					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										return self.time_input_dim * 4




					@property
					def 		lowerCAmelCase     (						self					)						->							str:
										return 8




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										torch.manual_seed(0					)
										_snake_case													=			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			,  )

										_snake_case													=			CLIPVisionModel(lowerCAmelCase_					)
										return model




					@property
					def 		lowerCAmelCase     (						self					)						->							Any:
										_snake_case													=			CLIPImageProcessor(
										    crop_size=224			,  do_center_crop=lowerCAmelCase_			,  do_normalize=lowerCAmelCase_			,  do_resize=lowerCAmelCase_			,  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 		lowerCAmelCase     (						self					)						->							int:
										torch.manual_seed(0					)

										_snake_case													=			{
										    '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,
										}

										_snake_case													=			PriorTransformer(**lowerCAmelCase_					)
										return model




					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										torch.manual_seed(0					)

										_snake_case													=			{
										    '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,
										    ),
										}
										_snake_case													=			ShapERenderer(**lowerCAmelCase_					)
										return model




					def 		lowerCAmelCase     (						self					)						->							str:
										_snake_case													=			self.dummy_prior
										_snake_case													=			self.dummy_image_encoder
										_snake_case													=			self.dummy_image_processor
										_snake_case													=			self.dummy_renderer

										_snake_case													=			HeunDiscreteScheduler(
										    beta_schedule='exp'			,  num_train_timesteps=1024			,  prediction_type='sample'			,  use_karras_sigmas=lowerCAmelCase_			,  clip_sample=lowerCAmelCase_			,  clip_sample_range=1.0			,  )
										_snake_case													=			{
										    'prior': prior,
										    'image_encoder': image_encoder,
										    'image_processor': image_processor,
										    'renderer': renderer,
										    'scheduler': scheduler,
										}

										return components




					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_			,  lowerCAmelCase_=0					)						->							Union[str, Any]:
										_snake_case													=			floats_tensor((1, 3, 64, 64)			,  rng=random.Random(lowerCAmelCase_					)					).to(lowerCAmelCase_					)

										if str(lowerCAmelCase_					).startswith('mps'					):
															_snake_case													=			torch.manual_seed(lowerCAmelCase_					)
										else:
															_snake_case													=			torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
										_snake_case													=			{
										    'image': input_image,
										    'generator': generator,
										    'num_inference_steps': 1,
										    'frame_size': 32,
										    'output_type': 'np',
										}
										return inputs




					def 		lowerCAmelCase     (						self					)						->							Dict:
										_snake_case													=			'cpu'

										_snake_case													=			self.get_dummy_components()

										_snake_case													=			self.pipeline_class(**lowerCAmelCase_					)
										_snake_case													=			pipe.to(lowerCAmelCase_					)

										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

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

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

										_snake_case													=			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 		lowerCAmelCase     (						self					)						->							str:
										# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
										self._test_inference_batch_consistent(batch_sizes=[1, 2]					)




					def 		lowerCAmelCase     (						self					)						->							List[Any]:
										_snake_case													=			torch_device == 'cpu'
										_snake_case													=			True
										self._test_inference_batch_single_identical(
										    batch_size=2			,  test_max_difference=lowerCAmelCase_			,  relax_max_difference=lowerCAmelCase_			,  )






					def 		lowerCAmelCase     (						self					)						->							List[str]:
										_snake_case													=			self.get_dummy_components()
										_snake_case													=			self.pipeline_class(**lowerCAmelCase_					)
										_snake_case													=			pipe.to(lowerCAmelCase_					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

										_snake_case													=			1
										_snake_case													=			2

										_snake_case													=			self.get_dummy_inputs(lowerCAmelCase_					)

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

										_snake_case													=			pipe(**lowerCAmelCase_			,  num_images_per_prompt=lowerCAmelCase_					)[0]

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





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




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






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

										_snake_case													=			torch.Generator(device=lowerCAmelCase_					).manual_seed(0					)

										_snake_case													=			pipe(
										    lowerCAmelCase_			,  generator=lowerCAmelCase_			,  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(lowerCAmelCase_			,  lowerCAmelCase_					)










from cva import destroyAllWindows, imread, imshow, waitKey





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict  )     ->	Optional[Any]:



					'''simple docstring'''




					_snake_case							,       _snake_case													=			img.shape[0], img.shape[1]

					# converting each pixel's color to its negative
					for i in range(UpperCamelCase__  ):
										for j in range(UpperCamelCase__  ):
															_snake_case													=			[255, 255, 255] - img[i][j]

					return img


if __name__ == "__main__":
					# read original image
					UpperCAmelCase_ 							=       imread("""image_data/lena.jpg""", 1)

					# convert to its negative
					UpperCAmelCase_ 							=       convert_to_negative(img)

					# show result image
					imshow("""negative of original image""", img)
					waitKey(0)
					destroyAllWindows()



