





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


__UpperCamelCase:	List[str] = logging.get_logger(__name__)  # pylint: disable=invalid-name

__UpperCamelCase:	Dict = """
    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					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				42


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def __init__(						self:						Optional[Any],					lowerCamelCase_:						PriorTransformer,					lowerCamelCase_:						CLIPVisionModel,					lowerCamelCase_:						CLIPImageProcessor,					lowerCamelCase_:						HeunDiscreteScheduler,					lowerCamelCase_:						ShapERenderer,					):
			super().__init__()

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

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

			lowercase__					:      Optional[Any]      =		latents * scheduler.init_noise_sigma
			return latents

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						str=0			):
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'			)

			lowercase__					:      Optional[int]      =		torch.device(F"""cuda:{gpu_id}"""			)

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

	@property
	def    snake_case__(						self:						List[str]			):
			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(lowerCamelCase_,					'_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:						List[str],					lowerCamelCase_:						List[str],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						str,					lowerCamelCase_:						Any,					):
			if isinstance(lowerCamelCase_,					lowerCamelCase_			) and isinstance(image[0],					torch.Tensor			):
					lowercase__					:      Union[str, Any]      =		torch.cat(lowerCamelCase_,					axis=0			) if image[0].ndim == 4 else torch.stack(lowerCamelCase_,					axis=0			)

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

			lowercase__					:      Any      =		image.to(dtype=self.image_encoder.dtype,					device=lowerCamelCase_			)

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

			lowercase__					:      Optional[Any]      =		image_embeds.repeat_interleave(lowerCamelCase_,					dim=0			)

			if do_classifier_free_guidance:
					lowercase__					:      str      =		torch.zeros_like(lowerCamelCase_			)

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

			return image_embeds





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

			if isinstance(lowerCamelCase_,					PIL.Image.Image			):
					lowercase__					:      Any      =		1
			elif isinstance(lowerCamelCase_,					torch.Tensor			):
					lowercase__					:      Any      =		image.shape[0]
			elif isinstance(lowerCamelCase_,					lowerCamelCase_			) and isinstance(image[0],					(torch.Tensor, PIL.Image.Image)			):
					lowercase__					:      Tuple      =		len(lowerCamelCase_			)
			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(lowerCamelCase_			)}"""			)

			lowercase__					:      List[Any]      =		self._execution_device

			lowercase__					:      Optional[int]      =		batch_size * num_images_per_prompt

			lowercase__					:      Optional[int]      =		guidance_scale > 1.0
			lowercase__					:      List[Any]      =		self._encode_image(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_			)

			# prior

			self.scheduler.set_timesteps(lowerCamelCase_,					device=lowerCamelCase_			)
			lowercase__					:      Dict      =		self.scheduler.timesteps

			lowercase__					:      Dict      =		self.prior.config.num_embeddings
			lowercase__					:      int      =		self.prior.config.embedding_dim

			lowercase__					:      Dict      =		self.prepare_latents(
			    (batch_size, num_embeddings * embedding_dim),					image_embeds.dtype,					lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					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__					:      List[str]      =		latents.reshape(latents.shape[0],					lowerCamelCase_,					lowerCamelCase_			)

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

					lowercase__					:      Any      =		self.prior(
					    lowerCamelCase_,					timestep=lowerCamelCase_,					proj_embedding=lowerCamelCase_,					).predicted_image_embedding

					# remove the variance
					lowercase__   ,				lowercase__					:      int      =		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__					:      Dict      =		noise_pred.chunk(2			)
							lowercase__					:      Any      =		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

					lowercase__					:      Union[str, Any]      =		self.scheduler.step(
					    lowerCamelCase_,					timestep=lowerCamelCase_,					sample=lowerCamelCase_,					).prev_sample

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

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

					images.append(lowerCamelCase_			)

			lowercase__					:      Optional[Any]      =		torch.stack(lowerCamelCase_			)

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

			if output_type == "pil":
					lowercase__					:      Optional[Any]      =		[self.numpy_to_pil(lowerCamelCase_			) 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=lowerCamelCase_			)









from ..utils import DummyObject, requires_backends


class       __lowerCAmelCase					(       metaclass=_UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["note_seq"]

	def __init__(						self:						int,					*lowerCamelCase_:						Optional[int],					**lowerCamelCase_:						Tuple			):
			requires_backends(self,					['note_seq']			)

	@classmethod
	def    snake_case__(						cls:						Optional[Any],					*lowerCamelCase_:						Tuple,					**lowerCamelCase_:						Optional[Any]			):
			requires_backends(cls,					['note_seq']			)





	@classmethod
	def    snake_case__(						cls:						Any,					*lowerCamelCase_:						Dict,					**lowerCamelCase_:						str			):
			requires_backends(cls,					['note_seq']			)


