



"""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


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

SCREAMING_SNAKE_CASE_				= """
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e-img2img\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"
        >>> image = load_image(image_url).convert(\"RGB\")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")
        ```
"""




@dataclass
class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					42




class 			snake_case_						(						a_				):






							def __init__(   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     ):
									super().__init__()

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






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     a_			,     a_      ):
									if latents is None:
											a_     :      Optional[Any] 						=     randn_tensor(a_			,     generator=a_			,     device=a_			,     dtype=a_      )
									else:
											if latents.shape != shape:
													raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""      )
											a_     :      Optional[int] 						=     latents.to(a_      )

									a_     :      Union[str, Any] 						=     latents * scheduler.init_noise_sigma
									return latents






							def snake_case_  (   self			,     a_=0      ):
									if is_accelerate_available():
											from accelerate import cpu_offload
									else:
											raise ImportError("Please install accelerate via `pip install accelerate`"      )

									a_     :      str 						=     torch.device(F"""cuda:{gpu_id}"""      )

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






							@property
							def snake_case_  (   self      ):
									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(a_			,     "_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 snake_case_  (   self			,     a_			,     a_			,     a_			,     a_			,     ):
									if isinstance(a_			,     a_      ) and isinstance(image[0]			,     torch.Tensor      ):
											a_     :      Tuple 						=     torch.cat(a_			,     axis=0      ) if image[0].ndim == 4 else torch.stack(a_			,     axis=0      )

									if not isinstance(a_			,     torch.Tensor      ):
											a_     :      str 						=     self.image_processor(a_			,     return_tensors="pt"      ).pixel_values[0].unsqueeze(0      )

									a_     :      Optional[Any] 						=     image.to(dtype=self.image_encoder.dtype			,     device=a_      )

									a_     :      List[Any] 						=     self.image_encoder(a_      )["last_hidden_state"]
									a_     :      List[str] 						=     image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

									a_     :      Union[str, Any] 						=     image_embeds.repeat_interleave(a_			,     dim=0      )

									if do_classifier_free_guidance:
											a_     :      Tuple 						=     torch.zeros_like(a_      )

											# 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
											a_     :      Optional[int] 						=     torch.cat([negative_image_embeds, image_embeds]      )

									return image_embeds






							@torch.no_grad()
							@replace_example_docstring(a_      )
							def __call__(   self			,     a_			,     a_ = 1			,     a_ = 2_5			,     a_ = None			,     a_ = None			,     a_ = 4.0			,     a_ = 6_4			,     a_ = "pil"			,     a_ = True			,     ):

									if isinstance(a_			,     PIL.Image.Image      ):
											a_     :      Union[str, Any] 						=     1
									elif isinstance(a_			,     torch.Tensor      ):
											a_     :      int 						=     image.shape[0]
									elif isinstance(a_			,     a_      ) and isinstance(image[0]			,     (torch.Tensor, PIL.Image.Image)      ):
											a_     :      Optional[int] 						=     len(a_      )
									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(a_      )}"""      )

									a_     :      List[Any] 						=     self._execution_device

									a_     :      Optional[int] 						=     batch_size * num_images_per_prompt

									a_     :      Tuple 						=     guidance_scale > 1.0
									a_     :      Any 						=     self._encode_image(a_			,     a_			,     a_			,     a_      )

									# prior

									self.scheduler.set_timesteps(a_			,     device=a_      )
									a_     :      List[str] 						=     self.scheduler.timesteps

									a_     :      List[Any] 						=     self.prior.config.num_embeddings
									a_     :      Union[str, Any] 						=     self.prior.config.embedding_dim

									a_     :      str 						=     self.prepare_latents(
									    (batch_size, num_embeddings * embedding_dim)			,     image_embeds.dtype			,     a_			,     a_			,     a_			,     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
									a_     :      List[Any] 						=     latents.reshape(latents.shape[0]			,     a_			,     a_      )

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

											a_     :      Union[str, Any] 						=     self.prior(
											    a_			,     timestep=a_			,     proj_embedding=a_			,     ).predicted_image_embedding

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

											if do_classifier_free_guidance is not None:
													a_						,				a_     :      Optional[int] 						=     noise_pred.chunk(2      )
													a_     :      str 						=     noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

											a_     :      Any 						=     self.scheduler.step(
											    a_			,     timestep=a_			,     sample=a_			,     ).prev_sample

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

									a_     :      Dict 						=     []
									for i, latent in enumerate(a_      ):
											print()
											a_     :      Optional[int] 						=     self.renderer.decode(
											    latent[None, :]			,     a_			,     size=a_			,     ray_batch_size=4_0_9_6			,     n_coarse_samples=6_4			,     n_fine_samples=1_2_8			,     )

											images.append(a_      )

									a_     :      str 						=     torch.stack(a_      )

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

									a_     :      str 						=     images.cpu().numpy()

									if output_type == "pil":
											a_     :      List[Any] 						=     [self.numpy_to_pil(a_      ) 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=a_      )




"""simple docstring"""
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


SCREAMING_SNAKE_CASE_				= False




class 			snake_case_						(						unittest.TestCase				):
							pass




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






							def snake_case_  (   self      ):
									a_     :      Tuple 						=     VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"      )
									pipe.to(a_      )
									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Optional[Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"      )
									a_     :      Optional[Any] 						=     torch.manual_seed(0      )
									a_     :      Dict 						=     pipe(
									    image=a_			,     generator=a_			,     guidance_scale=7.5			,     num_inference_steps=5_0			,     output_type="numpy"			,     ).images

									a_     :      List[Any] 						=     image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									assert image.shape == (1, 5_1_2, 5_1_2, 3)
									a_     :      Any 						=     np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]      )

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