"""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


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

lowercase__					       =	'\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    __snake_case       (     __lowerCAmelCase				):
      a__        =		42







class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			Optional[Any]:





        '''simple docstring'''





        super().__init__()

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



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase)	->			Union[str, Any]:





        '''simple docstring'''





        if latents is None:
          a__:     Optional[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}')
          a__:     List[Any]										=	latents.to(lowercase)

        a__:     Any										=	latents * scheduler.init_noise_sigma
        return latents



      def   lowerCamelCase_   (						self    ,			lowercase=0)	->			List[Any]:





        '''simple docstring'''





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

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

        a__:     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)	->			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(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    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			str:





        '''simple docstring'''





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

        if not isinstance(lowercase    ,			torch.Tensor):
          a__:     List[Any]										=	self.image_processor(lowercase    ,			return_tensors='pt').pixel_values[0].unsqueeze(0)

        a__:     str										=	image.to(dtype=self.image_encoder.dtype    ,			device=lowercase)

        a__:     str										=	self.image_encoder(lowercase)['last_hidden_state']
        a__:     List[str]										=	image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

        a__:     List[str]										=	image_embeds.repeat_interleave(lowercase    ,			dim=0)

        if do_classifier_free_guidance:
          a__:     Optional[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
          a__:     Optional[int]										=	torch.cat([negative_image_embeds, image_embeds])

        return image_embeds



      @torch.no_grad()
      @replace_example_docstring(lowercase)
      def __call__(						self    ,			lowercase    ,			lowercase = 1    ,			lowercase = 25    ,			lowercase = None    ,			lowercase = None    ,			lowercase = 4.0    ,			lowercase = 64    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        if isinstance(lowercase    ,			PIL.Image.Image):
          a__:     Any										=	1
        elif isinstance(lowercase    ,			torch.Tensor):
          a__:     Tuple										=	image.shape[0]
        elif isinstance(lowercase    ,			lowercase) and isinstance(image[0]    ,			(torch.Tensor, PIL.Image.Image)):
          a__:     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)}')

        a__:     Any										=	self._execution_device

        a__:     Optional[Any]										=	batch_size * num_images_per_prompt

        a__:     Optional[Any]										=	guidance_scale > 1.0
        a__:     Dict										=	self._encode_image(lowercase    ,			lowercase    ,			lowercase    ,			lowercase)

        # prior

        self.scheduler.set_timesteps(lowercase    ,			device=lowercase)
        a__:     Dict										=	self.scheduler.timesteps

        a__:     List[Any]										=	self.prior.config.num_embeddings
        a__:     List[str]										=	self.prior.config.embedding_dim

        a__:     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
        a__:     List[Any]										=	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
          a__:     Dict										=	torch.cat([latents] * 2) if do_classifier_free_guidance else latents
          a__:     Tuple										=	self.scheduler.scale_model_input(lowercase    ,			lowercase)

          a__:     Any										=	self.prior(
              lowercase    ,			timestep=lowercase    ,			proj_embedding=lowercase    ,			).predicted_image_embedding

          # remove the variance
          a__							,    a__:     List[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__:     str										=	noise_pred.chunk(2)
            a__:     Tuple										=	noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

          a__:     str										=	self.scheduler.step(
              lowercase    ,			timestep=lowercase    ,			sample=lowercase    ,			).prev_sample

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

        a__:     Any										=	[]
        for i, latent in enumerate(lowercase):
          print()
          a__:     int										=	self.renderer.decode(
              latent[None, :]    ,			lowercase    ,			size=lowercase    ,			ray_batch_size=40_96    ,			n_coarse_samples=64    ,			n_fine_samples=1_28    ,			)

          images.append(lowercase)

        a__:     int										=	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}')

        a__:     List[str]										=	images.cpu().numpy()

        if output_type == "pil":
          a__:     Optional[int]										=	[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"""
def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  while a != 0:
    a__							,    a__:     List[str]										=	b % a, a
  return b







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->int:
  if gcd(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) != 1:
    a__:     Dict										=	F'mod inverse of {a!r} and {m!r} does not exist'
    raise ValueError(_SCREAMING_SNAKE_CASE   )
  a__							,    a__							,    a__:     Union[str, Any]										=	1, 0, a
  a__							,    a__							,    a__:     Any										=	0, 1, m
  while va != 0:
    a__:     int										=	ua // va
    a__							,    a__							,    a__							,    a__							,    a__							,    a__:     Any										=	(ua - q * va), (ua - q * va), (ua - q * va), va, va, va
  return ua % m


