

"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					(		A_    ,							unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       ShapEImgaImgPipeline
					lowercase__				 =       ['''image''']
					lowercase__				 =       ['''image''']
					lowercase__				 =       [
					    '''num_images_per_prompt''',
					    '''num_inference_steps''',
					    '''generator''',
					    '''latents''',
					    '''guidance_scale''',
					    '''frame_size''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowercase__				 =       False







					@property
					def        __lowerCAmelCase (       self			:				Dict		):
						return 3_2







					@property
					def        __lowerCAmelCase (       self			:				Tuple		):
						return 3_2







					@property
					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						return self.time_input_dim * 4







					@property
					def        __lowerCAmelCase (       self			:				Tuple		):
						return 8







					@property
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						Tuple  				=	CLIPVisionConfig(
						    hidden_size=self.text_embedder_hidden_size   ,image_size=6_4   ,projection_dim=self.text_embedder_hidden_size   ,intermediate_size=3_7   ,num_attention_heads=4   ,num_channels=3   ,num_hidden_layers=5   ,patch_size=1   ,)

						lowerCAmelCase__	:						Tuple  				=	CLIPVisionModel(lowercase_		)
						return model







					@property
					def        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						Tuple  				=	CLIPImageProcessor(
						    crop_size=2_2_4   ,do_center_crop=lowercase_   ,do_normalize=lowercase_   ,do_resize=lowercase_   ,image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]   ,image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]   ,resample=3   ,size=2_2_4   ,)

						return image_processor







					@property
					def        __lowerCAmelCase (       self			:				int		):
						torch.manual_seed(0		)

						lowerCAmelCase__	:						int  				=	{
						    '''num_attention_heads''': 2,
						    '''attention_head_dim''': 1_6,
						    '''embedding_dim''': self.time_input_dim,
						    '''num_embeddings''': 3_2,
						    '''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,
						}

						lowerCAmelCase__	:						Tuple  				=	PriorTransformer(**lowercase_		)
						return model







					@property
					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						torch.manual_seed(0		)

						lowerCAmelCase__	:						Tuple  				=	{
						    '''param_shapes''': (
						        (self.renderer_dim, 9_3),
						        (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''': 1_2,
						    '''background''': (
						        0.1,
						        0.1,
						        0.1,
						    ),
						}
						lowerCAmelCase__	:						List[Any]  				=	ShapERenderer(**lowercase_		)
						return model







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						int  				=	self.dummy_prior
						lowerCAmelCase__	:						str  				=	self.dummy_image_encoder
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dummy_image_processor
						lowerCAmelCase__	:						str  				=	self.dummy_renderer

						lowerCAmelCase__	:						int  				=	HeunDiscreteScheduler(
						    beta_schedule='''exp'''   ,num_train_timesteps=1_0_2_4   ,prediction_type='''sample'''   ,use_karras_sigmas=lowercase_   ,clip_sample=lowercase_   ,clip_sample_range=1.0   ,)
						lowerCAmelCase__	:						Optional[int]  				=	{
						    '''prior''': prior,
						    '''image_encoder''': image_encoder,
						    '''image_processor''': image_processor,
						    '''renderer''': renderer,
						    '''scheduler''': scheduler,
						}

						return components







					def        __lowerCAmelCase (       self			:				List[str]   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[int]=0		):
						lowerCAmelCase__	:						int  				=	floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(lowercase_		)		).to(lowercase_		)

						if str(lowercase_		).startswith('''mps'''		):
							lowerCAmelCase__	:						Tuple  				=	torch.manual_seed(lowercase_		)
						else:
							lowerCAmelCase__	:						int  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						str  				=	{
						    '''image''': input_image,
						    '''generator''': generator,
						    '''num_inference_steps''': 1,
						    '''frame_size''': 3_2,
						    '''output_type''': '''np''',
						}
						return inputs







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[Any]  				=	'''cpu'''

						lowerCAmelCase__	:						str  				=	self.get_dummy_components()

						lowerCAmelCase__	:						Optional[Any]  				=	self.pipeline_class(**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	pipe.to(lowercase_		)

						pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						Tuple  				=	pipe(**self.get_dummy_inputs(lowercase_		)		)
						lowerCAmelCase__	:						Any  				=	output.images[0]
						lowerCAmelCase__	:						str  				=	image[0, -3:, -3:, -1]

						assert image.shape == (2_0, 3_2, 3_2, 3)

						lowerCAmelCase__	:						Tuple  				=	np.array(
						    [
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						        0.0003_9216,
						    ]		)

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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						# 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			:				int		):
						lowerCAmelCase__	:						Optional[Any]  				=	torch_device == '''cpu'''
						lowerCAmelCase__	:						Dict  				=	True
						self._test_inference_batch_single_identical(
						    batch_size=2   ,test_max_difference=lowercase_   ,relax_max_difference=lowercase_   ,)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						Any  				=	self.get_dummy_components()
						lowerCAmelCase__	:						List[Any]  				=	self.pipeline_class(**lowercase_		)
						lowerCAmelCase__	:						Any  				=	pipe.to(lowercase_		)
						pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						int  				=	1
						lowerCAmelCase__	:						str  				=	2

						lowerCAmelCase__	:						str  				=	self.get_dummy_inputs(lowercase_		)

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

						lowerCAmelCase__	:						List[str]  				=	pipe(**lowercase_   ,num_images_per_prompt=lowercase_		)[0]

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







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






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				List[str]		):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







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

						lowerCAmelCase__	:						int  				=	torch.Generator(device=lowercase_		).manual_seed(0		)

						lowerCAmelCase__	:						Any  				=	pipe(
						    lowercase_   ,generator=lowercase_   ,guidance_scale=3.0   ,num_inference_steps=6_4   ,frame_size=6_4   ,output_type='''np'''   ,).images[0]

						assert images.shape == (2_0, 6_4, 6_4, 3)

						assert_mean_pixel_difference(lowercase_   ,lowercase_		)








from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			2
		__lowercase=			[]
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.append(lowercase__	)
		if n > 1:
				factors.append(lowercase__	)
		return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()






