






'''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 UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




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






      @property
      def 					A__   (			self      :		Any		)	->	List[Any]:


       '''simple docstring'''

       return 32






      @property
      def 					A__   (			self      :		Optional[int]		)	->	Any:


       '''simple docstring'''

       return 32






      @property
      def 					A__   (			self      :		str		)	->	Tuple:


       '''simple docstring'''

       return self.time_input_dim * 4






      @property
      def 					A__   (			self      :		Any		)	->	List[str]:


       '''simple docstring'''

       return 8






      @property
      def 					A__   (			self      :		Union[str, Any]		)	->	Tuple:


       '''simple docstring'''

       torch.manual_seed(0		)
       lowercase       : List[Any]						=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  ,   )

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






      @property
      def 					A__   (			self      :		Dict		)	->	int:


       '''simple docstring'''

       lowercase       : Dict						=CLIPImageProcessor(
           crop_size=224  ,   do_center_crop=UpperCAmelCase  ,   do_normalize=UpperCAmelCase  ,   do_resize=UpperCAmelCase  ,   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=224  ,   )

       return image_processor






      @property
      def 					A__   (			self      :		str		)	->	List[str]:


       '''simple docstring'''

       torch.manual_seed(0		)

       lowercase       : Union[str, 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,
       }

       lowercase       : Any						=PriorTransformer(**UpperCAmelCase		)
       return model






      @property
      def 					A__   (			self      :		Optional[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       torch.manual_seed(0		)

       lowercase       : 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,
           ),
       }
       lowercase       : Optional[int]						=ShapERenderer(**UpperCAmelCase		)
       return model






      def 					A__   (			self      :		Union[str, Any]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : str						=self.dummy_prior
       lowercase       : List[str]						=self.dummy_image_encoder
       lowercase       : str						=self.dummy_image_processor
       lowercase       : Dict						=self.dummy_renderer

       lowercase       : Optional[Any]						=HeunDiscreteScheduler(
           beta_schedule='''exp'''  ,   num_train_timesteps=1024  ,   prediction_type='''sample'''  ,   use_karras_sigmas=UpperCAmelCase  ,   clip_sample=UpperCAmelCase  ,   clip_sample_range=1.0  ,   )
       lowercase       : Any						={
           '''prior''': prior,
           '''image_encoder''': image_encoder,
           '''image_processor''': image_processor,
           '''renderer''': renderer,
           '''scheduler''': scheduler,
       }

       return components






      def 					A__   (			self      :		Optional[Any]  ,   UpperCAmelCase      :		Union[str, Any]  ,   UpperCAmelCase      :		List[str]=0		)	->	List[str]:


       '''simple docstring'''

       lowercase       : List[str]						=floats_tensor((1, 3, 64, 64)  ,   rng=random.Random(UpperCAmelCase		)		).to(UpperCAmelCase		)

       if str(UpperCAmelCase		).startswith('''mps'''		):
        lowercase       : str						=torch.manual_seed(UpperCAmelCase		)
       else:
        lowercase       : Tuple						=torch.Generator(device=UpperCAmelCase		).manual_seed(UpperCAmelCase		)
       lowercase       : List[str]						={
           '''image''': input_image,
           '''generator''': generator,
           '''num_inference_steps''': 1,
           '''frame_size''': 32,
           '''output_type''': '''np''',
       }
       return inputs






      def 					A__   (			self      :		Dict		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : List[str]						='''cpu'''

       lowercase       : Optional[int]						=self.get_dummy_components()

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

       pipe.set_progress_bar_config(disable=UpperCAmelCase		)

       lowercase       : Tuple						=pipe(**self.get_dummy_inputs(UpperCAmelCase		)		)
       lowercase       : Tuple						=output.images[0]
       lowercase       : int						=image[0, -3:, -3:, -1]

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

       lowercase       : Tuple						=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 					A__   (			self      :		Tuple		)	->	List[Any]:


       '''simple docstring'''

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






      def 					A__   (			self      :		Dict		)	->	str:


       '''simple docstring'''

       lowercase       : Tuple						=torch_device == '''cpu'''
       lowercase       : List[str]						=True
       self._test_inference_batch_single_identical(
           batch_size=2  ,   test_max_difference=UpperCAmelCase  ,   relax_max_difference=UpperCAmelCase  ,   )






      def 					A__   (			self      :		List[str]		)	->	Optional[Any]:


       '''simple docstring'''

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

       lowercase       : Optional[Any]						=1
       lowercase       : Optional[int]						=2

       lowercase       : Tuple						=self.get_dummy_inputs(UpperCAmelCase		)

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

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

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





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




      """simple docstring"""




      def 					A__   (			self      :		List[Any]		)	->	List[str]:


       '''simple docstring'''

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






      def 					A__   (			self      :		str		)	->	List[str]:


       '''simple docstring'''

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

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

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




"""simple docstring"""






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase       :			Optional[Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Optional[Any]:
      '''simple docstring'''


      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""   ,						lowerCAmelCase__   ,						)
      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return image
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [image]

      if isinstance(image[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      image[0].size
            lowercase				,				lowercase								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowercase								=      [np.array(i.resize((w, h)   ,						resample=PIL_INTERPOLATION["""lanczos"""]						)						)[None, :] for i in image]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      np.array(lowerCAmelCase__						).astype(np.floataa						) / 255.0
            lowercase								=      image.transpose(0   ,						3   ,						1   ,						2						)
            lowercase								=      2.0 * image - 1.0
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(image[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Dict:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return mask
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [mask]

      if isinstance(mask[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      mask[0].size
            lowercase				,				lowercase								=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            lowercase								=      [np.array(m.convert("""L"""						).resize((w, h)   ,						resample=PIL_INTERPOLATION["""nearest"""]						)						)[None, :] for m in mask]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      mask.astype(np.floataa						) / 255.0
            lowercase								=      0
            lowercase								=      1
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(mask[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return mask






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  UNetaDModel
      snake_case__     :  RePaintScheduler

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 250						,			__lowerCAmelCase = 0.0						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      image

            lowercase								=      _preprocess_image(__lowerCAmelCase		)
            lowercase								=      original_image.to(device=self.device						,			dtype=self.unet.dtype		)
            lowercase								=      _preprocess_mask(__lowerCAmelCase		)
            lowercase								=      mask_image.to(device=self.device						,			dtype=self.unet.dtype		)

            lowercase								=      original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) and len(__lowerCAmelCase		) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase		)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

            lowercase								=      original_image.shape
            lowercase								=      randn_tensor(__lowerCAmelCase						,			generator=__lowerCAmelCase						,			device=self.device						,			dtype=self.unet.dtype		)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			self.device		)
            lowercase								=      eta

            lowercase								=      self.scheduler.timesteps[0] + 1
            lowercase								=      generator[0] if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
                  if t < t_last:
                        # predict the noise residual
                        lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample
                        # compute previous image: x_t -> x_t-1
                        lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

                  else:
                        # compute the reverse: x_t-1 -> x_t
                        lowercase								=      self.scheduler.undo_step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      t

            lowercase								=      (image / 2 + 0.5).clamp(0						,			1		)
            lowercase								=      image.cpu().permute(0						,			2						,			3						,			1		).numpy()
            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



