

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







			@property
			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								return 32







			@property
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								return 32







			@property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								return self.time_input_dim * 4







			@property
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								return 8







			@property
			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								torch.manual_seed(0  )
								UpperCAmelCase			:   List[str]      =				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			,  )

								UpperCAmelCase			:   Optional[Any]      =				CLIPVisionModel(lowercase  )
								return model







			@property
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				CLIPImageProcessor(
								    crop_size=2_24			,  do_center_crop=lowercase			,  do_normalize=lowercase			,  do_resize=lowercase			,  image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]			,  image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]			,  resample=3			,  size=2_24			,  )

								return image_processor







			@property
			def 			__lowerCAmelCase				(			self			: List[str]  ):





								'''simple docstring'''



								torch.manual_seed(0  )

								UpperCAmelCase			:   Optional[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,
								}

								UpperCAmelCase			:   Optional[int]      =				PriorTransformer(**lowercase  )
								return model







			@property
			def 			__lowerCAmelCase				(			self			: str  ):





								'''simple docstring'''



								torch.manual_seed(0  )

								UpperCAmelCase			:   List[Any]      =				{
								    "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,
								    ),
								}
								UpperCAmelCase			:   Optional[Any]      =				ShapERenderer(**lowercase  )
								return model







			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Any      =				self.dummy_prior
								UpperCAmelCase			:   str      =				self.dummy_image_encoder
								UpperCAmelCase			:   Tuple      =				self.dummy_image_processor
								UpperCAmelCase			:   List[str]      =				self.dummy_renderer

								UpperCAmelCase			:   List[str]      =				HeunDiscreteScheduler(
								    beta_schedule="exp"			,  num_train_timesteps=10_24			,  prediction_type="sample"			,  use_karras_sigmas=lowercase			,  clip_sample=lowercase			,  clip_sample_range=1.0			,  )
								UpperCAmelCase			:   Any      =				{
								    "prior": prior,
								    "image_encoder": image_encoder,
								    "image_processor": image_processor,
								    "renderer": renderer,
								    "scheduler": scheduler,
								}

								return components







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: Union[str, Any]			,  lowercase			: Union[str, Any]=0  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				floats_tensor((1, 3, 64, 64)			,  rng=random.Random(lowercase  )  ).to(lowercase  )

								if str(lowercase  ).startswith("mps"  ):
													UpperCAmelCase			:   int      =				torch.manual_seed(lowercase  )
								else:
													UpperCAmelCase			:   Dict      =				torch.Generator(device=lowercase  ).manual_seed(lowercase  )
								UpperCAmelCase			:   Dict      =				{
								    "image": input_image,
								    "generator": generator,
								    "num_inference_steps": 1,
								    "frame_size": 32,
								    "output_type": "np",
								}
								return inputs







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				"cpu"

								UpperCAmelCase			:   List[str]      =				self.get_dummy_components()

								UpperCAmelCase			:   Dict      =				self.pipeline_class(**lowercase  )
								UpperCAmelCase			:   List[Any]      =				pipe.to(lowercase  )

								pipe.set_progress_bar_config(disable=lowercase  )

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

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

								UpperCAmelCase			:   Optional[Any]      =				np.array(
								    [
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								        0.0_0_0_3_9_2_1_6,
								    ]  )

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







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								self._test_inference_batch_consistent(batch_sizes=[1, 2]  )







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				torch_device == "cpu"
								UpperCAmelCase			:   List[Any]      =				True
								self._test_inference_batch_single_identical(
								    batch_size=2			,  test_max_difference=lowercase			,  relax_max_difference=lowercase			,  )







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self.get_dummy_components()
								UpperCAmelCase			:   Union[str, Any]      =				self.pipeline_class(**lowercase  )
								UpperCAmelCase			:   Optional[Any]      =				pipe.to(lowercase  )
								pipe.set_progress_bar_config(disable=lowercase  )

								UpperCAmelCase			:   Tuple      =				1
								UpperCAmelCase			:   List[Any]      =				2

								UpperCAmelCase			:   str      =				self.get_dummy_inputs(lowercase  )

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

								UpperCAmelCase			:   Optional[Any]      =				pipe(**lowercase			,  num_images_per_prompt=lowercase  )[0]

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







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







			def 			__lowerCAmelCase				(			self			: int  ):





								'''simple docstring'''



								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



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

								UpperCAmelCase			:   str      =				torch.Generator(device=lowercase  ).manual_seed(0  )

								UpperCAmelCase			:   Optional[int]      =				pipe(
								    lowercase			,  generator=lowercase			,  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(lowercase			,  lowercase  )





"""simple docstring"""






import os



def   lowercase_	(    ):




					'''simple docstring'''






					UpperCAmelCase			:   Union[str, Any]      =				os.path.join(os.path.dirname(_lowercase	)							,      "num.txt"	)
					with open(_lowercase	) as file_hand:
										return str(sum(int(_lowercase	) for line in file_hand	)	)[:10]


if __name__ == "__main__":
	print(solution())


