





"""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


A_	: Union[str, Any]      				=logging.get_logger(__name__)  # pylint: disable=invalid-name

A_	: Optional[Any]      				='\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       __a  (    _SCREAMING_SNAKE_CASE						):
     SCREAMING_SNAKE_CASE__					: Any									=     42







class       __a  (    _SCREAMING_SNAKE_CASE						):



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

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



     def 		snake_case_     (			self   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__   ,	a__  ):
            if latents is None:
                   _lowerCamelCase             =   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}'  )
                   _lowerCamelCase             =   latents.to(_lowerCAmelCase  )

            _lowerCamelCase             =   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`'  )

            _lowerCamelCase             =   torch.device(F'cuda:{gpu_id}'  )

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

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

            _lowerCamelCase             =   image.to(dtype=self.image_encoder.dtype   ,	device=_lowerCAmelCase  )

            _lowerCamelCase             =   self.image_encoder(_lowerCAmelCase  )['last_hidden_state']
            _lowerCamelCase             =   image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

            _lowerCamelCase             =   image_embeds.repeat_interleave(_lowerCAmelCase   ,	dim=0  )

            if do_classifier_free_guidance:
                   _lowerCamelCase             =   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
                   _lowerCamelCase             =   torch.cat([negative_image_embeds, image_embeds]  )

            return image_embeds



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

            if isinstance(_lowerCAmelCase   ,	PIL.Image.Image  ):
                   _lowerCamelCase             =   1
            elif isinstance(_lowerCAmelCase   ,	torch.Tensor  ):
                   _lowerCamelCase             =   image.shape[0]
            elif isinstance(_lowerCAmelCase   ,	_lowerCAmelCase  ) and isinstance(image[0]   ,	(torch.Tensor, PIL.Image.Image)  ):
                   _lowerCamelCase             =   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  )}'  )

            _lowerCamelCase             =   self._execution_device

            _lowerCamelCase             =   batch_size * num_images_per_prompt

            _lowerCamelCase             =   guidance_scale > 1.0
            _lowerCamelCase             =   self._encode_image(_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase   ,	_lowerCAmelCase  )

            # prior

            self.scheduler.set_timesteps(_lowerCAmelCase   ,	device=_lowerCAmelCase  )
            _lowerCamelCase             =   self.scheduler.timesteps

            _lowerCamelCase             =   self.prior.config.num_embeddings
            _lowerCamelCase             =   self.prior.config.embedding_dim

            _lowerCamelCase             =   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
            _lowerCamelCase             =   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
                   _lowerCamelCase             =   torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
                   _lowerCamelCase             =   self.scheduler.scale_model_input(_lowerCAmelCase   ,	_lowerCAmelCase  )

                   _lowerCamelCase             =   self.prior(
                       _lowerCAmelCase   ,	timestep=_lowerCAmelCase   ,	proj_embedding=_lowerCAmelCase   ,	).predicted_image_embedding

                   # remove the variance
                   _lowerCamelCase      ,      _lowerCamelCase             =   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      ,      _lowerCamelCase             =   noise_pred.chunk(2  )
                          _lowerCamelCase             =   noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

                   _lowerCamelCase             =   self.scheduler.step(
                       _lowerCAmelCase   ,	timestep=_lowerCAmelCase   ,	sample=_lowerCAmelCase   ,	).prev_sample

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

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

                   images.append(_lowerCAmelCase  )

            _lowerCamelCase             =   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}'  )

            _lowerCamelCase             =   images.cpu().numpy()

            if output_type == "pil":
                   _lowerCamelCase             =   [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  )












def    UpperCAmelCase_				( __UpperCAmelCase		:      int				)   ->					int:
      assert isinstance(__UpperCAmelCase     ,     __UpperCAmelCase				), f"The input value of [n={number}] is not an integer"

      if number == 1:
            return 2
      elif number < 1:
            SCREAMING_SNAKE_CASE_      		=     f"The input value of [n={number}] has to be > 0"
            raise ValueError(__UpperCAmelCase				)
      else:
            SCREAMING_SNAKE_CASE_      		=     sylvester(number - 1				)
            SCREAMING_SNAKE_CASE_      		=     num - 1
            SCREAMING_SNAKE_CASE_      		=     num
            return lower * upper + 1


if __name__ == "__main__":
   print(f'''The 8th number in Sylvester\'s sequence: {sylvester(8)}''')