




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


__SCREAMING_SNAKE_CASE      :				Optional[int]				      =logging.get_logger(__name__)  # pylint: disable=invalid-name

__SCREAMING_SNAKE_CASE      :				int				      ="""
    Examples:
        ```py
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif

        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")

        >>> repo = \"openai/shap-e\"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 15.0
        >>> prompt = \"a shark\"

        >>> images = pipe(
        ...     prompt,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")
        ```
"""






@dataclass
class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]]






class      A     (						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(      self					:				Union[str, Any]		,					A__					:				PriorTransformer		,					A__					:				CLIPTextModelWithProjection		,					A__					:				CLIPTokenizer		,					A__					:				HeunDiscreteScheduler		,					A__					:				ShapERenderer		,					)			->    List[Any]:
           super().__init__()

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




     def 	__snake_case				(      self					:				Union[str, Any]		,					A__					:				Dict		,					A__					:				Any		,					A__					:				List[Any]		,					A__					:				List[str]		,					A__					:				Any		,					A__					:				Optional[int]     )			->    Optional[int]:
           if latents is None:
                 lowerCamelCase__ 					=			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}"""     )
                 lowerCamelCase__ 					=			latents.to(A__     )

           lowerCamelCase__ 					=			latents * scheduler.init_noise_sigma
           return latents




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				int=0     )			->    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(A__		,					A__     )




     def 	__snake_case				(      self					:				Optional[int]		,					A__					:				Optional[Any]=0     )			->    Optional[int]:
           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=A__     )
                 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(A__		,					A__		,					prev_module_hook=A__     )

           if self.safety_checker is not None:
                 lowerCamelCase__					,						lowerCamelCase__ 					=			cpu_offload_with_hook(self.safety_checker		,					A__		,					prev_module_hook=A__     )

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




     @property
     def 	__snake_case				(      self					:				Tuple     )			->    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(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 	__snake_case				(      self					:				List[str]		,					A__					:				Dict		,					A__					:				List[str]		,					A__					:				Union[str, Any]		,					A__					:				Optional[int]		,					)			->    Dict:
           len(A__     ) if isinstance(A__		,					A__     ) 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(
               A__		,					padding='max_length'		,					max_length=self.tokenizer.model_max_length		,					truncation=A__		,					return_tensors='pt'		,					)
           lowerCamelCase__ 					=			text_inputs.input_ids
           lowerCamelCase__ 					=			self.tokenizer(A__		,					padding='longest'		,					return_tensors='pt'     ).input_ids

           if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A__		,					A__     ):
                 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(A__     )     )
           lowerCamelCase__ 					=			text_encoder_output.text_embeds

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

           if do_classifier_free_guidance:
                 lowerCamelCase__ 					=			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
                 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(A__     )
     def __call__(      self					:				Optional[int]		,					A__					:				str		,					A__					:				int = 1		,					A__					:				int = 2_5		,					A__					:				Optional[Union[torch.Generator, List[torch.Generator]]] = None		,					A__					:				Optional[torch.FloatTensor] = None		,					A__					:				float = 4.0		,					A__					:				int = 6_4		,					A__					:				Optional[str] = "pil"		,					A__					:				bool = True		,					)			->    Dict:

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

           lowerCamelCase__ 					=			self._execution_device

           lowerCamelCase__ 					=			batch_size * num_images_per_prompt

           lowerCamelCase__ 					=			guidance_scale > 1.0
           lowerCamelCase__ 					=			self._encode_prompt(A__		,					A__		,					A__		,					A__     )

           # prior

           self.scheduler.set_timesteps(A__		,					device=A__     )
           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		,					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
           lowerCamelCase__ 					=			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
                 lowerCamelCase__ 					=			torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents
                 lowerCamelCase__ 					=			self.scheduler.scale_model_input(A__		,					A__     )

                 lowerCamelCase__ 					=			self.prior(
                     A__		,					timestep=A__		,					proj_embedding=A__		,					).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(
                     A__		,					timestep=A__		,					sample=A__		,					).prev_sample

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

           lowerCamelCase__ 					=			[]
           for i, latent in enumerate(A__     ):
                 lowerCamelCase__ 					=			self.renderer.decode(
                     latent[None, :]		,					A__		,					size=A__		,					ray_batch_size=4_0_9_6		,					n_coarse_samples=6_4		,					n_fine_samples=1_2_8		,					)
                 images.append(A__     )

           lowerCamelCase__ 					=			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}"""     )

           lowerCamelCase__ 					=			images.cpu().numpy()

           if output_type == "pil":
                 lowerCamelCase__ 					=			[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__     )





def      lowerCAmelCase_     (					lowerCamelCase:    Optional[int]  )   ->     Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__ 					=			[7_01, 3_01, 1_32, 57, 23, 10, 4, 1]
      for gap in gaps:
            for i in range(lowerCamelCase       ,			len(lowerCamelCase  )  ):
                  lowerCamelCase__ 					=			collection[i]
                  lowerCamelCase__ 					=			i
                  while j >= gap and collection[j - gap] > insert_value:
                        lowerCamelCase__ 					=			collection[j - gap]
                        j -= gap
                  if j != i:
                        lowerCamelCase__ 					=			insert_value
      return collection


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      __SCREAMING_SNAKE_CASE      :				List[Any]				      =input("""Enter numbers separated by a comma:\n""").strip()
      __SCREAMING_SNAKE_CASE      :				Dict				      =[int(item) for item in user_input.split(""",""")]
      print(shell_sort(unsorted))