


'''simple docstring'''

from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


lowerCamelCase							:   int  						=	logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase							:   Dict  						=	'\n    Examples:\n        ```py\n        >>> from PIL import Image\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif, load_image\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e-img2img"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 3.0\n        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"\n        >>> image = load_image(image_url).convert("RGB")\n\n        >>> images = pipe(\n        ...     image,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")\n        ```\n'
@dataclass
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Union[PIL.Image.Image, np.ndarray]






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Union[str, Any]   ,						UpperCamelCase    :    PriorTransformer   ,						UpperCamelCase    :    CLIPVisionModel   ,						UpperCamelCase    :    CLIPImageProcessor   ,						UpperCamelCase    :    HeunDiscreteScheduler   ,						UpperCamelCase    :    ShapERenderer   ,						):

             '''simple docstring'''







             super().__init__()

             self.register_modules(
                 prior=UpperCamelCase   ,						image_encoder=UpperCamelCase   ,						image_processor=UpperCamelCase   ,						scheduler=UpperCamelCase   ,						renderer=UpperCamelCase   ,						)



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Dict   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Any ):

             '''simple docstring'''







             if latents is None:
                    lowercase__      			= randn_tensor(UpperCamelCase   ,						generator=UpperCamelCase   ,						device=UpperCamelCase   ,						dtype=UpperCamelCase )
             else:
                    if latents.shape != shape:
                           raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
                    lowercase__      			= latents.to(UpperCamelCase )

             lowercase__      			= latents * scheduler.init_noise_sigma
             return latents



      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    Any=0 ):

             '''simple docstring'''







             if is_accelerate_available():
                    from accelerate import cpu_offload
             else:
                    raise ImportError('''Please install accelerate via `pip install accelerate`''' )

             lowercase__      			= torch.device(f"cuda:{gpu_id}" )

             lowercase__      			= [self.image_encoder, self.prior]
             for cpu_offloaded_model in models:
                    if cpu_offloaded_model is not None:
                           cpu_offload(UpperCamelCase   ,						UpperCamelCase )



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             if self.device != torch.device('''meta''' ) or not hasattr(self.image_encoder   ,						'''_hf_hook''' ):
                    return self.device
             for module in self.image_encoder.modules():
                    if (
                        hasattr(UpperCamelCase   ,						'''_hf_hook''' )
                        and hasattr(module._hf_hook   ,						'''execution_device''' )
                        and module._hf_hook.execution_device is not None
                    ):
                           return torch.device(module._hf_hook.execution_device )
             return self.device



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    str   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    Any   ,						):

             '''simple docstring'''







             if isinstance(UpperCamelCase   ,						UpperCamelCase ) and isinstance(image[0]   ,						torch.Tensor ):
                    lowercase__      			= torch.cat(UpperCamelCase   ,						axis=0 ) if image[0].ndim == 4 else torch.stack(UpperCamelCase   ,						axis=0 )

             if not isinstance(UpperCamelCase   ,						torch.Tensor ):
                    lowercase__      			= self.image_processor(UpperCamelCase   ,						return_tensors='''pt''' ).pixel_values[0].unsqueeze(0 )

             lowercase__      			= image.to(dtype=self.image_encoder.dtype   ,						device=UpperCamelCase )

             lowercase__      			= self.image_encoder(UpperCamelCase )['''last_hidden_state''']
             lowercase__      			= image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

             lowercase__      			= image_embeds.repeat_interleave(UpperCamelCase   ,						dim=0 )

             if do_classifier_free_guidance:
                    lowercase__      			= torch.zeros_like(UpperCamelCase )

                    # For classifier free guidance, we need to do two forward passes.
                    # Here we concatenate the unconditional and text embeddings into a single batch
                    # to avoid doing two forward passes
                    lowercase__      			= torch.cat([negative_image_embeds, image_embeds] )

             return image_embeds






      @torch.no_grad()
      @replace_example_docstring(UpperCamelCase )
      def __call__(self    :    str   ,						UpperCamelCase    :    Union[PIL.Image.Image, List[PIL.Image.Image]]   ,						UpperCamelCase    :    int = 1   ,						UpperCamelCase    :    int = 25   ,						UpperCamelCase    :    Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,						UpperCamelCase    :    Optional[torch.FloatTensor] = None   ,						UpperCamelCase    :    float = 4.0   ,						UpperCamelCase    :    int = 64   ,						UpperCamelCase    :    Optional[str] = "pil"   ,						UpperCamelCase    :    bool = True   ,						):

             '''simple docstring'''







             if isinstance(UpperCamelCase   ,						PIL.Image.Image ):
                    lowercase__      			= 1
             elif isinstance(UpperCamelCase   ,						torch.Tensor ):
                    lowercase__      			= image.shape[0]
             elif isinstance(UpperCamelCase   ,						UpperCamelCase ) and isinstance(image[0]   ,						(torch.Tensor, PIL.Image.Image) ):
                    lowercase__      			= len(UpperCamelCase )
             else:
                    raise ValueError(
                        f"`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(UpperCamelCase )}" )

             lowercase__      			= self._execution_device

             lowercase__      			= batch_size * num_images_per_prompt

             lowercase__      			= guidance_scale > 1.0
             lowercase__      			= self._encode_image(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )

             # prior

             self.scheduler.set_timesteps(UpperCamelCase   ,						device=UpperCamelCase )
             lowercase__      			= self.scheduler.timesteps

             lowercase__      			= self.prior.config.num_embeddings
             lowercase__      			= self.prior.config.embedding_dim

             lowercase__      			= self.prepare_latents(
                 (batch_size, num_embeddings * embedding_dim)   ,						image_embeds.dtype   ,						UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase   ,						self.scheduler   ,						)

             # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
             lowercase__      			= latents.reshape(latents.shape[0]   ,						UpperCamelCase   ,						UpperCamelCase )

             for i, t in enumerate(self.progress_bar(UpperCamelCase ) ):
                    # expand the latents if we are doing classifier free guidance
                    lowercase__      			= torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
                    lowercase__      			= self.scheduler.scale_model_input(UpperCamelCase   ,						UpperCamelCase )

                    lowercase__      			= self.prior(
                        UpperCamelCase   ,						timestep=UpperCamelCase   ,						proj_embedding=UpperCamelCase   ,						).predicted_image_embedding

                    # remove the variance
                    lowercase__			,lowercase__      			= noise_pred.split(
                        scaled_model_input.shape[2]   ,						dim=2 )  # batch_size, num_embeddings, embedding_dim

                    if do_classifier_free_guidance is not None:
                           lowercase__			,lowercase__      			= noise_pred.chunk(2 )
                           lowercase__      			= noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                    lowercase__      			= self.scheduler.step(
                        UpperCamelCase   ,						timestep=UpperCamelCase   ,						sample=UpperCamelCase   ,						).prev_sample

             if output_type == "latent":
                    return ShapEPipelineOutput(images=UpperCamelCase )

             lowercase__      			= []
             for i, latent in enumerate(UpperCamelCase ):
                    print()
                    lowercase__      			= self.renderer.decode(
                        latent[None, :]   ,						UpperCamelCase   ,						size=UpperCamelCase   ,						ray_batch_size=4096   ,						n_coarse_samples=64   ,						n_fine_samples=128   ,						)

                    images.append(UpperCamelCase )

             lowercase__      			= torch.stack(UpperCamelCase )

             if output_type not in ["np", "pil"]:
                    raise ValueError(f"Only the output types `pil` and `np` are supported not output_type={output_type}" )

             lowercase__      			= images.cpu().numpy()

             if output_type == "pil":
                    lowercase__      			= [self.numpy_to_pil(UpperCamelCase ) for image in images]

             # Offload last model to CPU
             if hasattr(self   ,						'''final_offload_hook''' ) and self.final_offload_hook is not None:
                    self.final_offload_hook.offload()

             if not return_dict:
                    return (images,)

             return ShapEPipelineOutput(images=UpperCamelCase )








'''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						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							ShapEImgaImgPipeline
      lowerCAmelCase__					:     List[str]  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							[
          """num_images_per_prompt""",
          """num_inference_steps""",
          """generator""",
          """latents""",
          """guidance_scale""",
          """frame_size""",
          """output_type""",
          """return_dict""",
      ]
      lowerCAmelCase__					:     Tuple  					=							False



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return self.time_input_dim * 4



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             return 8



      @property
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             torch.manual_seed(0 )
             lowercase__      			= 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__      			= CLIPVisionModel(UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= CLIPImageProcessor(
                 crop_size=224   ,						do_center_crop=UpperCamelCase   ,						do_normalize=UpperCamelCase   ,						do_resize=UpperCamelCase   ,						image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,						image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,						resample=3   ,						size=224   ,						)

             return image_processor



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''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__      			= PriorTransformer(**UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''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__      			= ShapERenderer(**UpperCamelCase )
             return model



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.dummy_prior
             lowercase__      			= self.dummy_image_encoder
             lowercase__      			= self.dummy_image_processor
             lowercase__      			= self.dummy_renderer

             lowercase__      			= HeunDiscreteScheduler(
                 beta_schedule='''exp'''   ,						num_train_timesteps=1024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase   ,						clip_sample=UpperCamelCase   ,						clip_sample_range=1.0   ,						)
             lowercase__      			= {
                 '''prior''': prior,
                 '''image_encoder''': image_encoder,
                 '''image_processor''': image_processor,
                 '''renderer''': renderer,
                 '''scheduler''': scheduler,
             }

             return components



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    str=0 ):

             '''simple docstring'''







             lowercase__      			= floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase ) ).to(UpperCamelCase )

             if str(UpperCamelCase ).startswith('''mps''' ):
                    lowercase__      			= torch.manual_seed(UpperCamelCase )
             else:
                    lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(UpperCamelCase )
             lowercase__      			= {
                 '''image''': input_image,
                 '''generator''': generator,
                 '''num_inference_steps''': 1,
                 '''frame_size''': 32,
                 '''output_type''': '''np''',
             }
             return inputs



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= '''cpu'''

             lowercase__      			= self.get_dummy_components()

             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )

             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= pipe(**self.get_dummy_inputs(UpperCamelCase ) )
             lowercase__      			= output.images[0]
             lowercase__      			= image[0, -3:, -3:, -1]

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

             lowercase__      			= np.array(
                 [
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                 ] )

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



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= torch_device == '''cpu'''
             lowercase__      			= True
             self._test_inference_batch_single_identical(
                 batch_size=2   ,						test_max_difference=UpperCamelCase   ,						relax_max_difference=UpperCamelCase   ,						)






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.get_dummy_components()
             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= 1
             lowercase__      			= 2

             lowercase__      			= self.get_dummy_inputs(UpperCamelCase )

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

             lowercase__      			= pipe(**UpperCamelCase   ,						num_images_per_prompt=UpperCamelCase )[0]

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






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





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
             lowercase__      			= load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/shap_e/test_shap_e_img2img_out.npy''' )
             lowercase__      			= ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(0 )

             lowercase__      			= 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 )




