





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

_lowerCamelCase :    str  							= '''
    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 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   42


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							def __init__( self     :					List[str],			_UpperCAmelCase     :					PriorTransformer,			_UpperCAmelCase     :					CLIPVisionModel,			_UpperCAmelCase     :					CLIPImageProcessor,			_UpperCAmelCase     :					HeunDiscreteScheduler,			_UpperCAmelCase     :					ShapERenderer,			)				->			Optional[int]:





														"""simple docstring"""
														super().__init__()

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






							def  A_     ( self     :					str,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Tuple						)				->			int:





														"""simple docstring"""
														if latents is None:
																					SCREAMING_SNAKE_CASE__       :      Any     =						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}'''						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						latents.to(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						latents * scheduler.init_noise_sigma
														return latents






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Optional[Any]=0						)				->			Dict:





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

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.device(F'''cuda:{gpu_id}'''						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						[self.image_encoder, self.prior]
														for cpu_offloaded_model in models:
																					if cpu_offloaded_model is not None:
																												cpu_offload(_UpperCAmelCase,			_UpperCAmelCase						)






							@property
							def  A_     ( self     :					Dict						)				->			Union[str, Any]:





														"""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  A_     ( self     :					int,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Optional[int],			)				->			List[str]:





														"""simple docstring"""
														if isinstance(_UpperCAmelCase,			_UpperCAmelCase						) and isinstance(image[0],			torch.Tensor						):
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.cat(_UpperCAmelCase,			axis=0						) if image[0].ndim == 4 else torch.stack(_UpperCAmelCase,			axis=0						)

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

														SCREAMING_SNAKE_CASE__       :      List[str]     =						image.to(dtype=self.image_encoder.dtype,			device=_UpperCAmelCase						)

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

														SCREAMING_SNAKE_CASE__       :      Tuple     =						image_embeds.repeat_interleave(_UpperCAmelCase,			dim=0						)

														if do_classifier_free_guidance:
																					SCREAMING_SNAKE_CASE__       :      Any     =						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
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.cat([negative_image_embeds, image_embeds]						)

														return image_embeds






							@torch.no_grad()
							@replace_example_docstring(_UpperCAmelCase						)
							def __call__( self     :					Dict,			_UpperCAmelCase     :					Union[PIL.Image.Image, List[PIL.Image.Image]],			_UpperCAmelCase     :					int = 1,			_UpperCAmelCase     :					int = 2_5,			_UpperCAmelCase     :					Optional[Union[torch.Generator, List[torch.Generator]]] = None,			_UpperCAmelCase     :					Optional[torch.FloatTensor] = None,			_UpperCAmelCase     :					float = 4.0,			_UpperCAmelCase     :					int = 6_4,			_UpperCAmelCase     :					Optional[str] = "pil",			_UpperCAmelCase     :					bool = True,			)				->			Optional[int]:





														"""simple docstring"""

														if isinstance(_UpperCAmelCase,			PIL.Image.Image						):
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						1
														elif isinstance(_UpperCAmelCase,			torch.Tensor						):
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						image.shape[0]
														elif isinstance(_UpperCAmelCase,			_UpperCAmelCase						) and isinstance(image[0],			(torch.Tensor, PIL.Image.Image)						):
																					SCREAMING_SNAKE_CASE__       :      str     =						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						)}'''						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						self._execution_device

														SCREAMING_SNAKE_CASE__       :      List[str]     =						batch_size * num_images_per_prompt

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						guidance_scale > 1.0
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self._encode_image(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)

														# prior

														self.scheduler.set_timesteps(_UpperCAmelCase,			device=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.scheduler.timesteps

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

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						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
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.scheduler.scale_model_input(_UpperCAmelCase,			_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.prior(
																					    _UpperCAmelCase,			timestep=_UpperCAmelCase,			proj_embedding=_UpperCAmelCase,			).predicted_image_embedding

																					# remove the variance
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      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:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						noise_pred.chunk(2						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.scheduler.step(
																					    _UpperCAmelCase,			timestep=_UpperCAmelCase,			sample=_UpperCAmelCase,			).prev_sample

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

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

																					images.append(_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      str     =						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}'''						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						images.cpu().numpy()

														if output_type == "pil":
																					SCREAMING_SNAKE_CASE__       :      int     =						[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						)













import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any]=sys.maxsize						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bilinear"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						short_edge_length






							def __call__( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
														for img in imgs:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						img.shape[:2]
																					# later: provide list and randomly choose index for resize
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						np.random.randint(self.short_edge_length[0],			self.short_edge_length[1] + 1						)
																					if size == 0:
																												return img
																					SCREAMING_SNAKE_CASE__       :      int     =						size * 1.0 / min(_UpperCAmelCase,			_UpperCAmelCase						)
																					if h < w:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size, scale * w
																					else:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						scale * h, size
																					if max(_UpperCAmelCase,			_UpperCAmelCase						) > self.max_size:
																												SCREAMING_SNAKE_CASE__       :      str     =						self.max_size * 1.0 / max(_UpperCAmelCase,			_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Any     =						newh * scale
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						neww * scale
																					SCREAMING_SNAKE_CASE__       :      Any     =						int(neww + 0.5						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						int(newh + 0.5						)

																					if img.dtype == np.uinta:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						Image.fromarray(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pil_image.resize((neww, newh),			PILImageResampling.BILINEAR						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						np.asarray(_UpperCAmelCase						)
																					else:
																												SCREAMING_SNAKE_CASE__       :      str     =						img.permute(2,			0,			1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						nn.functional.interpolate(
																												    _UpperCAmelCase,			(newh, neww),			mode=self.interp_method,			align_corners=_UpperCAmelCase						).squeeze(0						)
																					img_augs.append(_UpperCAmelCase						)

														return img_augs


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],			cfg.INPUT.MAX_SIZE_TEST						)
														SCREAMING_SNAKE_CASE__       :      Any     =						cfg.INPUT.FORMAT
														SCREAMING_SNAKE_CASE__       :      List[str]     =						cfg.SIZE_DIVISIBILITY
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						cfg.PAD_VALUE
														SCREAMING_SNAKE_CASE__       :      Dict     =						cfg.INPUT.MAX_SIZE_TEST
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cfg.MODEL.DEVICE
														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						lambda _UpperCAmelCase						: (x - self.pixel_mean) / self.pixel_std






							def  A_     ( self     :					str,			_UpperCAmelCase     :					Union[str, Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tuple(max(_UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[im.shape[-2:] for im in images]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    nn.functional.pad(
														        _UpperCAmelCase,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
														    for size, im in zip(_UpperCAmelCase,			_UpperCAmelCase						)
														]

														return torch.stack(_UpperCAmelCase						), torch.tensor(_UpperCAmelCase						)






							def __call__( self     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[str]=False						)				->			Optional[Any]:





														"""simple docstring"""
														with torch.no_grad():
																					if not isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      str     =						[images]
																					if single_image:
																												assert len(_UpperCAmelCase						) == 1
																					for i in range(len(_UpperCAmelCase						)						):
																												if isinstance(images[i],			torch.Tensor						):
																																			images.insert(_UpperCAmelCase,			images.pop(_UpperCAmelCase						).to(self.device						).float()						)
																												elif not isinstance(images[i],			torch.Tensor						):
																																			images.insert(
																																			    _UpperCAmelCase,			torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase						),			input_format=self.input_format						)						)
																																			    .to(self.device						)
																																			    .float(),			)
            # resize smallest edge
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor([im.shape[:2] for im in images]						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.aug(_UpperCAmelCase						)
																					# transpose images and convert to torch tensors
																					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																					# now normalize before pad to avoid useless arithmetic
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						[self.normalizer(_UpperCAmelCase						) for x in images]
																					# now pad them to do the following operations
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(_UpperCAmelCase						)
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.true_divide(_UpperCAmelCase,			_UpperCAmelCase						)
																					if single_image:
																												return images[0], sizes[0], scales_yx[0]
																					else:
																												return images, sizes, scales_yx




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Union[str, Any]:

							'''simple docstring'''






							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple[int, int]       )	->    List[Any]:

							'''simple docstring'''






							assert torch.isfinite(SCREAMING_SNAKE_CASE__       ).all(), "Box tensor contains infinite or NaN!"
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						box_size
							tensor[:, 0].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 1].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 2].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 3].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )






