





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

lowerCAmelCase       		=      """
    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       ):
	snake_case_      =   42






class 		lowerCamelCase  (      _UpperCAmelCase       ):



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

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



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_	):
			if latents is None:
					lowerCAmelCase     :    Any						=   randn_tensor(lowercase__		,      generator=lowercase__		,      device=lowercase__		,      dtype=lowercase__	)
			else:
					if latents.shape != shape:
							raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''	)
					lowerCAmelCase     :    Tuple						=   latents.to(lowercase__	)

			lowerCAmelCase     :    Any						=   latents * scheduler.init_noise_sigma
			return latents



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

			lowerCAmelCase     :    List[Any]						=   torch.device(F'''cuda:{gpu_id}'''	)

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



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

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

			lowerCAmelCase     :    List[Any]						=   image.to(dtype=self.image_encoder.dtype		,      device=lowercase__	)

			lowerCAmelCase     :    int						=   self.image_encoder(lowercase__	)["""last_hidden_state"""]
			lowerCAmelCase     :    Any						=   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

			lowerCAmelCase     :    List[str]						=   image_embeds.repeat_interleave(lowercase__		,      dim=0	)

			if do_classifier_free_guidance:
					lowerCAmelCase     :    Any						=   torch.zeros_like(lowercase__	)

					# 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
					lowerCAmelCase     :    List[str]						=   torch.cat([negative_image_embeds, image_embeds]	)

			return image_embeds





	@torch.no_grad()
	@replace_example_docstring(lowercase__	)
	def __call__(   self		,      a_		,      a_ = 1		,      a_ = 25		,      a_ = None		,      a_ = None		,      a_ = 4.0		,      a_ = 64		,      a_ = "pil"		,      a_ = True		,      ):

			if isinstance(lowercase__		,      PIL.Image.Image	):
					lowerCAmelCase     :    Dict						=   1
			elif isinstance(lowercase__		,      torch.Tensor	):
					lowerCAmelCase     :    str						=   image.shape[0]
			elif isinstance(lowercase__		,      lowercase__	) and isinstance(image[0]		,      (torch.Tensor, PIL.Image.Image)	):
					lowerCAmelCase     :    Union[str, Any]						=   len(lowercase__	)
			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(lowercase__	)}'''	)

			lowerCAmelCase     :    Tuple						=   self._execution_device

			lowerCAmelCase     :    int						=   batch_size * num_images_per_prompt

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

			# prior

			self.scheduler.set_timesteps(lowercase__		,      device=lowercase__	)
			lowerCAmelCase     :    Any						=   self.scheduler.timesteps

			lowerCAmelCase     :    int						=   self.prior.config.num_embeddings
			lowerCAmelCase     :    Optional[int]						=   self.prior.config.embedding_dim

			lowerCAmelCase     :    Tuple						=   self.prepare_latents(
			    (batch_size, num_embeddings * embedding_dim)		,      image_embeds.dtype		,      lowercase__		,      lowercase__		,      lowercase__		,      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
			lowerCAmelCase     :    Optional[int]						=   latents.reshape(latents.shape[0]		,      lowercase__		,      lowercase__	)

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

					lowerCAmelCase     :    List[Any]						=   self.prior(
					    lowercase__		,      timestep=lowercase__		,      proj_embedding=lowercase__		,      ).predicted_image_embedding

					# remove the variance
					lowerCAmelCase     :    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:
							lowerCAmelCase     :    List[Any]						=   noise_pred.chunk(2	)
							lowerCAmelCase     :    Tuple						=   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

					lowerCAmelCase     :    int						=   self.scheduler.step(
					    lowercase__		,      timestep=lowercase__		,      sample=lowercase__		,      ).prev_sample

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

			lowerCAmelCase     :    List[str]						=   []
			for i, latent in enumerate(lowercase__	):
					print()
					lowerCAmelCase     :    int						=   self.renderer.decode(
					    latent[None, :]		,      lowercase__		,      size=lowercase__		,      ray_batch_size=4_096		,      n_coarse_samples=64		,      n_fine_samples=128		,      )

					images.append(lowercase__	)

			lowerCAmelCase     :    Any						=   torch.stack(lowercase__	)

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

			lowerCAmelCase     :    Optional[Any]						=   images.cpu().numpy()

			if output_type == "pil":
					lowerCAmelCase     :    List[str]						=   [self.numpy_to_pil(lowercase__	) 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=lowercase__	)










'''simple docstring'''






import numpy as np






def      __A						(							a_						:  np.array	):
		return 1 / (1 + np.exp(-vector	))


if __name__ == "__main__":
					import doctest

					doctest.testmod()



