


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

_A			=						'\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    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Union[PIL.Image.Image, np.ndarray]






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    def __init__(      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       )  ->	List[str]:
         super().__init__()

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






    def _a (      self ,       A_ ,       A_ ,       A_ ,       A_ ,       A_ ,       A_   )  ->	List[str]:
         if latents is None:
              __UpperCamelCase   							=randn_tensor(A_ ,       generator=A_ ,       device=A_ ,       dtype=A_   )
         else:
              if latents.shape != shape:
                   raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
              __UpperCamelCase   							=latents.to(A_   )

         __UpperCamelCase   							=latents * scheduler.init_noise_sigma
         return latents






    def _a (      self ,       A_=0   )  ->	Union[str, Any]:
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError('Please install accelerate via `pip install accelerate`'   )

         __UpperCamelCase   							=torch.device(f'cuda:{gpu_id}'   )

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






    @property
    def _a (      self   )  ->	Dict:
         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(A_ ,       '_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 ,       A_ ,       A_ ,       A_ ,       A_ ,       )  ->	Optional[int]:
         if isinstance(A_ ,       A_   ) and isinstance(image[0] ,       torch.Tensor   ):
              __UpperCamelCase   							=torch.cat(A_ ,       axis=0   ) if image[0].ndim == 4 else torch.stack(A_ ,       axis=0   )

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

         __UpperCamelCase   							=image.to(dtype=self.image_encoder.dtype ,       device=A_   )

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

         __UpperCamelCase   							=image_embeds.repeat_interleave(A_ ,       dim=0   )

         if do_classifier_free_guidance:
              __UpperCamelCase   							=torch.zeros_like(A_   )

              # 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
              __UpperCamelCase   							=torch.cat([negative_image_embeds, image_embeds]   )

         return image_embeds






    @torch.no_grad()
    @replace_example_docstring(A_   )
    def __call__(      self ,       A_ ,       A_ = 1 ,       A_ = 25 ,       A_ = None ,       A_ = None ,       A_ = 4.0 ,       A_ = 64 ,       A_ = "pil" ,       A_ = True ,       )  ->	int:

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

         __UpperCamelCase   							=self._execution_device

         __UpperCamelCase   							=batch_size * num_images_per_prompt

         __UpperCamelCase   							=guidance_scale > 1.0
         __UpperCamelCase   							=self._encode_image(A_ ,       A_ ,       A_ ,       A_   )

         # prior

         self.scheduler.set_timesteps(A_ ,       device=A_   )
         __UpperCamelCase   							=self.scheduler.timesteps

         __UpperCamelCase   							=self.prior.config.num_embeddings
         __UpperCamelCase   							=self.prior.config.embedding_dim

         __UpperCamelCase   							=self.prepare_latents(
             (batch_size, num_embeddings * embedding_dim) ,       image_embeds.dtype ,       A_ ,       A_ ,       A_ ,       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
         __UpperCamelCase   							=latents.reshape(latents.shape[0] ,       A_ ,       A_   )

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

              __UpperCamelCase   							=self.prior(
                  A_ ,       timestep=A_ ,       proj_embedding=A_ ,       ).predicted_image_embedding

              # remove the variance
              __UpperCamelCase							,				__UpperCamelCase   							=noise_pred.split(
                  scaled_model_input.shape[2] ,       dim=2   )  # batch_size, num_embeddings, embedding_dim

              if do_classifier_free_guidance is not None:
                   __UpperCamelCase							,				__UpperCamelCase   							=noise_pred.chunk(2   )
                   __UpperCamelCase   							=noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

              __UpperCamelCase   							=self.scheduler.step(
                  A_ ,       timestep=A_ ,       sample=A_ ,       ).prev_sample

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

         __UpperCamelCase   							=[]
         for i, latent in enumerate(A_   ):
              print()
              __UpperCamelCase   							=self.renderer.decode(
                  latent[None, :] ,       A_ ,       size=A_ ,       ray_batch_size=4096 ,       n_coarse_samples=64 ,       n_fine_samples=128 ,       )

              images.append(A_   )

         __UpperCamelCase   							=torch.stack(A_   )

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

         __UpperCamelCase   							=images.cpu().numpy()

         if output_type == "pil":
              __UpperCamelCase   							=[self.numpy_to_pil(A_   ) 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=A_   )









import pprint

import requests

_A			=						'https://zenquotes.io/api'

def 						_UpperCAmelCase    (      ):
     return requests.get(API_ENDPOINT_URL + '/today'    ).json()

def 						_UpperCAmelCase    (      ):
     return requests.get(API_ENDPOINT_URL + '/random'    ).json()


if __name__ == "__main__":
   _A			=						random_quotes()
   pprint.pprint(response)





