



'''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


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

SCREAMING_SNAKE_CASE__      					=     "\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 (_UpperCAmelCase   ):
       lowerCAmelCase__  :Union[PIL.Image.Image, np.ndarray]





class 				snake_case (_UpperCAmelCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							int:

           '''simple docstring'''





           super().__init__()

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



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[Any]:

           '''simple docstring'''





           if latents is None:
               lowercase__		       =						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}'''      )
               lowercase__		       =						latents.to(lowercase__      )

           lowercase__		       =						latents * scheduler.init_noise_sigma
           return latents



       def 							_a      (				self     ,UpperCAmelCase_=0      )  ->							List[Any]:

           '''simple docstring'''





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

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

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



       @property
       def 							_a      (				self      )  ->							Optional[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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,)  ->							Union[str, Any]:

           '''simple docstring'''





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

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

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

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

           lowercase__		       =						image_embeds.repeat_interleave(lowercase__     ,dim=0      )

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

           return image_embeds







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

           '''simple docstring'''





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

           lowercase__		       =						self._execution_device

           lowercase__		       =						batch_size * num_images_per_prompt

           lowercase__		       =						guidance_scale > 1.0
           lowercase__		       =						self._encode_image(lowercase__     ,lowercase__     ,lowercase__     ,lowercase__      )

           # prior

           self.scheduler.set_timesteps(lowercase__     ,device=lowercase__      )
           lowercase__		       =						self.scheduler.timesteps

           lowercase__		       =						self.prior.config.num_embeddings
           lowercase__		       =						self.prior.config.embedding_dim

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

               lowercase__		       =						self.prior(
                   lowercase__     ,timestep=lowercase__     ,proj_embedding=lowercase__     ,).predicted_image_embedding

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

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

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

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

               images.append(lowercase__      )

           lowercase__		       =						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}'''      )

           lowercase__		       =						images.cpu().numpy()

           if output_type == "pil":
               lowercase__		       =						[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        lowerCamelCase  (				_snake_case  :						list[int]     ,_snake_case  :						list[int]       ):





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    print("The following activities are selected:"       )

    # The first activity is always selected
    lowercase__		       =						0
    print(_snake_case     ,end=","       )

    # Consider rest of the activities
    for j in range(_snake_case       ):
        # If this activity has start time greater than
        # or equal to the finish time of previously
        # selected activity, then select it
        if start[j] >= finish[i]:
            print(_snake_case     ,end=","       )
            lowercase__		       =						j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE__      					=     [1, 3, 0, 5, 8, 5]
 SCREAMING_SNAKE_CASE__      					=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)




