





'''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 		lowerCamelCase  (      _A      ,   unittest.TestCase       ):
	snake_case_      =   ShapEImgaImgPipeline
	snake_case_      =   ["image"]
	snake_case_      =   ["image"]
	snake_case_      =   [
	    "num_images_per_prompt",
	    "num_inference_steps",
	    "generator",
	    "latents",
	    "guidance_scale",
	    "frame_size",
	    "output_type",
	    "return_dict",
	]
	snake_case_      =   False



	@property
	def   _lowerCamelCase    (   self	):
			return 32



	@property
	def   _lowerCamelCase    (   self	):
			return 32



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



	@property
	def   _lowerCamelCase    (   self	):
			return 8



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    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		,      )

			lowerCAmelCase     :    Dict						=   CLIPVisionModel(a_	)
			return model



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   CLIPImageProcessor(
			    crop_size=224		,      do_center_crop=a_		,      do_normalize=a_		,      do_resize=a_		,      image_mean=[0.48145466, 0.4578275, 0.40821073]		,      image_std=[0.26862954, 0.26130258, 0.27577711]		,      resample=3		,      size=224		,      )

			return image_processor



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

			lowerCAmelCase     :    Dict						=   {
			    "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,
			}

			lowerCAmelCase     :    str						=   PriorTransformer(**a_	)
			return model



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

			lowerCAmelCase     :    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,
			    ),
			}
			lowerCAmelCase     :    Optional[int]						=   ShapERenderer(**a_	)
			return model



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    str						=   self.dummy_prior
			lowerCAmelCase     :    Optional[Any]						=   self.dummy_image_encoder
			lowerCAmelCase     :    Union[str, Any]						=   self.dummy_image_processor
			lowerCAmelCase     :    Any						=   self.dummy_renderer

			lowerCAmelCase     :    int						=   HeunDiscreteScheduler(
			    beta_schedule="exp"		,      num_train_timesteps=1_024		,      prediction_type="sample"		,      use_karras_sigmas=a_		,      clip_sample=a_		,      clip_sample_range=1.0		,      )
			lowerCAmelCase     :    Optional[Any]						=   {
			    "prior": prior,
			    "image_encoder": image_encoder,
			    "image_processor": image_processor,
			    "renderer": renderer,
			    "scheduler": scheduler,
			}

			return components



	def   _lowerCamelCase    (   self		,      a_		,      a_=0	):
			lowerCAmelCase     :    Any						=   floats_tensor((1, 3, 64, 64)		,      rng=random.Random(a_	)	).to(a_	)

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   "cpu"

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

			lowerCAmelCase     :    Tuple						=   self.pipeline_class(**a_	)
			lowerCAmelCase     :    List[str]						=   pipe.to(a_	)

			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    List[str]						=   pipe(**self.get_dummy_inputs(a_	)	)
			lowerCAmelCase     :    int						=   output.images[0]
			lowerCAmelCase     :    str						=   image[0, -3:, -3:, -1]

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

			lowerCAmelCase     :    Optional[int]						=   np.array(
			    [
			        0.00039216,
			        0.00039216,
			        0.00039216,
			        0.00039216,
			        0.00039216,
			        0.00039216,
			        0.00039216,
			        0.00039216,
			        0.00039216,
			    ]	)

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



	def   _lowerCamelCase    (   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   torch_device == "cpu"
			lowerCAmelCase     :    Dict						=   True
			self._test_inference_batch_single_identical(
			    batch_size=2		,      test_max_difference=a_		,      relax_max_difference=a_		,      )





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   self.get_dummy_components()
			lowerCAmelCase     :    Optional[int]						=   self.pipeline_class(**a_	)
			lowerCAmelCase     :    Optional[Any]						=   pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Dict						=   1
			lowerCAmelCase     :    Union[str, Any]						=   2

			lowerCAmelCase     :    Union[str, Any]						=   self.get_dummy_inputs(a_	)

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

			lowerCAmelCase     :    Dict						=   pipe(**a_		,      num_images_per_prompt=a_	)[0]

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






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



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





	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   load_image(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/shap_e/corgi.png"	)
			lowerCAmelCase     :    Optional[Any]						=   load_numpy(
			    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
			    "/shap_e/test_shap_e_img2img_out.npy"	)
			lowerCAmelCase     :    Tuple						=   ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"	)
			lowerCAmelCase     :    Optional[int]						=   pipe.to(a_	)
			pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Union[str, Any]						=   torch.Generator(device=a_	).manual_seed(0	)

			lowerCAmelCase     :    str						=   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_	)










'''simple docstring'''






def      __A						(							a_						:  int	):
		if not isinstance(a_      ,a_	):
				lowerCAmelCase     :    Dict						=   f'''Input value of [number={number}] must be an integer'''
				raise TypeError(a_	)
		if number < 0:
				return False
		lowerCAmelCase     :    Dict						=   number * number
		while number > 0:
				if number % 1_0 != number_square % 1_0:
						return False
				number //= 1_0
				number_square //= 1_0
		return True


if __name__ == "__main__":
					import doctest

					doctest.testmod()



