





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





   @property
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      return 32





   @property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return 32





   @property
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      return self.time_input_dim * 4





   @property
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      return 8





   @property
   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      torch.manual_seed(0      )
      a__	:						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					,			)

      a__	:						Optional[Any]      		=  CLIPVisionModel(snake_case      )
      return model





   @property
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						str      		=  CLIPImageProcessor(
          crop_size=224					,			do_center_crop=snake_case					,			do_normalize=snake_case					,			do_resize=snake_case					,			image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,			image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,			resample=3					,			size=224					,			)

      return image_processor





   @property
   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      torch.manual_seed(0      )

      a__	:						List[str]      		=  {
          "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__	:						Optional[int]      		=  PriorTransformer(**snake_case      )
      return model





   @property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      torch.manual_seed(0      )

      a__	:						List[str]      		=  {
          "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__	:						Optional[Any]      		=  ShapERenderer(**snake_case      )
      return model





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Optional[Any]      		=  self.dummy_prior
      a__	:						List[Any]      		=  self.dummy_image_encoder
      a__	:						Union[str, Any]      		=  self.dummy_image_processor
      a__	:						Optional[int]      		=  self.dummy_renderer

      a__	:						Any      		=  HeunDiscreteScheduler(
          beta_schedule="exp"					,			num_train_timesteps=1_024					,			prediction_type="sample"					,			use_karras_sigmas=snake_case					,			clip_sample=snake_case					,			clip_sample_range=1.0					,			)
      a__	:						str      		=  {
          "prior": prior,
          "image_encoder": image_encoder,
          "image_processor": image_processor,
          "renderer": renderer,
          "scheduler": scheduler,
      }

      return components





   def 	_snake_case ( self					,			snake_case					,			snake_case=0      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						str      		=  floats_tensor((1, 3, 64, 64)					,			rng=random.Random(snake_case      )      ).to(snake_case      )

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





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Optional[int]      		=  "cpu"

      a__	:						str      		=  self.get_dummy_components()

      a__	:						Tuple      		=  self.pipeline_class(**snake_case      )
      a__	:						Any      		=  pipe.to(snake_case      )

      pipe.set_progress_bar_config(disable=snake_case      )

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

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

      a__	:						Optional[Any]      		=  np.array(
          [
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
              0.00_039_216,
          ]      )

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





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      a__	:						int      		=  torch_device == "cpu"
      a__	:						Optional[int]      		=  True
      self._test_inference_batch_single_identical(
          batch_size=2					,			test_max_difference=snake_case					,			relax_max_difference=snake_case					,			)





   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





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

      a__	:						Union[str, Any]      		=  1
      a__	:						Optional[Any]      		=  2

      a__	:						Optional[Any]      		=  self.get_dummy_inputs(snake_case      )

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

      a__	:						Optional[Any]      		=  pipe(**snake_case					,			num_images_per_prompt=snake_case      )[0]

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







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





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[int]      		=  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__	:						Union[str, Any]      		=  ShapEImgaImgPipeline.from_pretrained("openai/shap-e-img2img"      )
      a__	:						Optional[Any]      		=  pipe.to(snake_case      )
      pipe.set_progress_bar_config(disable=snake_case      )

      a__	:						int      		=  torch.Generator(device=snake_case      ).manual_seed(0      )

      a__	:						List[Any]      		=  pipe(
          snake_case					,			generator=snake_case					,			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(snake_case					,			snake_case      )








import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
  SCREAMING_SNAKE_CASE__       :			int			      =						{
      """linear""": PIL.Image.Resampling.BILINEAR,
      """bilinear""": PIL.Image.Resampling.BILINEAR,
      """bicubic""": PIL.Image.Resampling.BICUBIC,
      """lanczos""": PIL.Image.Resampling.LANCZOS,
      """nearest""": PIL.Image.Resampling.NEAREST,
  }
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

