






import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

_UpperCAmelCase					:						List[Any]         =     '\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\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], "shark_3d.gif")\n        ```\n'


@dataclass
class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]




class 		lowerCAmelCase_				(  UpperCamelCase ):





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, )		->				Optional[int]:
        super().__init__()

        self.register_modules(
            prior=_UpperCAmelCase							, text_encoder=_UpperCAmelCase							, tokenizer=_UpperCAmelCase							, scheduler=_UpperCAmelCase							, renderer=_UpperCAmelCase							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Union[str, Any]:
        if latents is None:
           lowerCamelCase									=randn_tensor(_UpperCAmelCase							, generator=_UpperCAmelCase							, device=_UpperCAmelCase							, dtype=_UpperCAmelCase  )
        else:
           if latents.shape != shape:
              raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''  )
           lowerCamelCase									=latents.to(_UpperCAmelCase  )

        lowerCamelCase									=latents * scheduler.init_noise_sigma
        return latents





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=0  )		->				List[Any]:
        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.text_encoder, self.prior]
        for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
              cpu_offload(_UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase=0  )		->				Optional[Any]:
        if is_accelerate_available() and is_accelerate_version(""">="""							, """0.17.0.dev0"""  ):
           from accelerate import cpu_offload_with_hook
        else:
           raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )

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

        if self.device.type != "cpu":
           self.to("""cpu"""							, silence_dtype_warnings=_UpperCAmelCase  )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        lowerCamelCase									=None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
           lowerCamelCase				,       lowerCamelCase									=cpu_offload_with_hook(_UpperCAmelCase							, _UpperCAmelCase							, prev_module_hook=_UpperCAmelCase  )

        if self.safety_checker is not None:
           lowerCamelCase				,       lowerCamelCase									=cpu_offload_with_hook(self.safety_checker							, _UpperCAmelCase							, prev_module_hook=_UpperCAmelCase  )

        # We'll offload the last model manually.
        lowerCamelCase									=hook





     @property
     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        if self.device != torch.device("""meta"""  ) or not hasattr(self.text_encoder							, """_hf_hook"""  ):
           return self.device
        for module in self.text_encoder.modules():
           if (
               hasattr(_UpperCAmelCase							, """_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							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, )		->				List[str]:
        len(_UpperCAmelCase  ) if isinstance(_UpperCAmelCase							, _UpperCAmelCase  ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        lowerCamelCase									=0
        # get prompt text embeddings
        lowerCamelCase									=self.tokenizer(
            _UpperCAmelCase							, padding="""max_length"""							, max_length=self.tokenizer.model_max_length							, truncation=_UpperCAmelCase							, return_tensors="""pt"""							, )
        lowerCamelCase									=text_inputs.input_ids
        lowerCamelCase									=self.tokenizer(_UpperCAmelCase							, padding="""longest"""							, return_tensors="""pt"""  ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_UpperCAmelCase							, _UpperCAmelCase  ):
           lowerCamelCase									=self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
           logger.warning(
               """The following part of your input was truncated because CLIP can only handle sequences up to"""
               F''' {self.tokenizer.model_max_length} tokens: {removed_text}'''  )

        lowerCamelCase									=self.text_encoder(text_input_ids.to(_UpperCAmelCase  )  )
        lowerCamelCase									=text_encoder_output.text_embeds

        lowerCamelCase									=prompt_embeds.repeat_interleave(_UpperCAmelCase							, dim=0  )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        lowerCamelCase									=prompt_embeds / torch.linalg.norm(_UpperCAmelCase							, dim=-1							, keepdim=_UpperCAmelCase  )

        if do_classifier_free_guidance:
           lowerCamelCase									=torch.zeros_like(_UpperCAmelCase  )

           # 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_prompt_embeds, prompt_embeds]  )

        # Rescale the features to have unit variance
        lowerCamelCase									=math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds

        return prompt_embeds





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

        if isinstance(_UpperCAmelCase							, _UpperCAmelCase  ):
           lowerCamelCase									=1
        elif isinstance(_UpperCAmelCase							, _UpperCAmelCase  ):
           lowerCamelCase									=len(_UpperCAmelCase  )
        else:
           raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase  )}'''  )

        lowerCamelCase									=self._execution_device

        lowerCamelCase									=batch_size * num_images_per_prompt

        lowerCamelCase									=guidance_scale > 1.0
        lowerCamelCase									=self._encode_prompt(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )

        # prior

        self.scheduler.set_timesteps(_UpperCAmelCase							, device=_UpperCAmelCase  )
        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)							, prompt_embeds.dtype							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, 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]							, _UpperCAmelCase							, _UpperCAmelCase  )

        for i, t in enumerate(self.progress_bar(_UpperCAmelCase  )  ):
           # 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(_UpperCAmelCase							, _UpperCAmelCase  )

           lowerCamelCase									=self.prior(
               _UpperCAmelCase							, timestep=_UpperCAmelCase							, proj_embedding=_UpperCAmelCase							, ).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(
               _UpperCAmelCase							, timestep=_UpperCAmelCase							, sample=_UpperCAmelCase							, ).prev_sample

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

        lowerCamelCase									=[]
        for i, latent in enumerate(_UpperCAmelCase  ):
           lowerCamelCase									=self.renderer.decode(
               latent[None, :]							, _UpperCAmelCase							, size=_UpperCAmelCase							, ray_batch_size=4096							, n_coarse_samples=64							, n_fine_samples=128							, )
           images.append(_UpperCAmelCase  )

        lowerCamelCase									=torch.stack(_UpperCAmelCase  )

        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(_UpperCAmelCase  ) 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=_UpperCAmelCase  )







def  UpperCAmelCase__						(    lowerCamelCase	:			list    ):
   lowerCamelCase									=1.3
   lowerCamelCase									=len(lowerCamelCase    )
   lowerCamelCase									=False

   while not completed:
      # Update the gap value for a next comb
      lowerCamelCase									=int(gap / shrink_factor    )
      if gap <= 1:
         lowerCamelCase									=True

      lowerCamelCase									=0
      while index + gap < len(lowerCamelCase    ):
         if data[index] > data[index + gap]:
            # Swap values
            lowerCamelCase				,       lowerCamelCase									=data[index + gap], data[index]
            lowerCamelCase									=False
         index += 1

   return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _UpperCAmelCase					:						List[Any]         =     input('Enter numbers separated by a comma:\n').strip()
   _UpperCAmelCase					:						Optional[Any]         =     [int(item) for item in user_input.split(',')]
   print(comb_sort(unsorted))