


"""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


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

UpperCAmelCase    :     Optional[int]   						=		'\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      lowerCamelCase__   (     A				):







					"""simple docstring"""


					__a	       =							42






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : Dict ,       UpperCamelCase    : PriorTransformer ,       UpperCamelCase    : CLIPVisionModel ,       UpperCamelCase    : CLIPImageProcessor ,       UpperCamelCase    : HeunDiscreteScheduler ,       UpperCamelCase    : ShapERenderer ,       ):
												'''simple docstring'''






												super().__init__()

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






					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Union[str, Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												if latents is None:
																			__UpperCAmelCase   :						str      							=					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}'''  )
																			__UpperCAmelCase   :						Any      							=					latents.to(UpperCamelCase  )

												__UpperCAmelCase   :						Optional[int]      							=					latents * scheduler.init_noise_sigma
												return latents






					def lowerCamelCase__							(       self    : Optional[int] ,       UpperCamelCase    : int=0  ):
												'''simple docstring'''






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

												__UpperCAmelCase   :						Optional[int]      							=					torch.device(f'''cuda:{gpu_id}'''  )

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






					@property
					def lowerCamelCase__							(       self    : Tuple  ):
												'''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(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    : Any ,       UpperCamelCase    : str ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : List[Any] ,       UpperCamelCase    : Optional[int] ,       ):
												'''simple docstring'''






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

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

												__UpperCAmelCase   :						str      							=					image.to(dtype=self.image_encoder.dtype ,       device=UpperCamelCase  )

												__UpperCAmelCase   :						List[Any]      							=					self.image_encoder(UpperCamelCase  )["""last_hidden_state"""]
												__UpperCAmelCase   :						int      							=					image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

												__UpperCAmelCase   :						Any      							=					image_embeds.repeat_interleave(UpperCamelCase ,       dim=0  )

												if do_classifier_free_guidance:
																			__UpperCAmelCase   :						Optional[int]      							=					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
																			__UpperCAmelCase   :						Union[str, Any]      							=					torch.cat([negative_image_embeds, image_embeds]  )

												return image_embeds






					@torch.no_grad()
					@replace_example_docstring(UpperCamelCase  )
					def __call__(       self    : Union[str, Any] ,       UpperCamelCase    : Union[PIL.Image.Image, List[PIL.Image.Image]] ,       UpperCamelCase    : int = 1 ,       UpperCamelCase    : int = 25 ,       UpperCamelCase    : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       UpperCamelCase    : Optional[torch.FloatTensor] = None ,       UpperCamelCase    : float = 4.0 ,       UpperCamelCase    : int = 64 ,       UpperCamelCase    : Optional[str] = "pil" ,       UpperCamelCase    : bool = True ,       ):
												'''simple docstring'''






												if isinstance(UpperCamelCase ,       PIL.Image.Image  ):
																			__UpperCAmelCase   :						Any      							=					1
												elif isinstance(UpperCamelCase ,       torch.Tensor  ):
																			__UpperCAmelCase   :						Optional[Any]      							=					image.shape[0]
												elif isinstance(UpperCamelCase ,       UpperCamelCase  ) and isinstance(image[0] ,       (torch.Tensor, PIL.Image.Image)  ):
																			__UpperCAmelCase   :						List[Any]      							=					len(UpperCamelCase  )
												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(UpperCamelCase  )}'''  )

												__UpperCAmelCase   :						List[Any]      							=					self._execution_device

												__UpperCAmelCase   :						List[str]      							=					batch_size * num_images_per_prompt

												__UpperCAmelCase   :						int      							=					guidance_scale > 1.0
												__UpperCAmelCase   :						Optional[int]      							=					self._encode_image(UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  )

												# prior

												self.scheduler.set_timesteps(UpperCamelCase ,       device=UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					self.scheduler.timesteps

												__UpperCAmelCase   :						List[str]      							=					self.prior.config.num_embeddings
												__UpperCAmelCase   :						Optional[int]      							=					self.prior.config.embedding_dim

												__UpperCAmelCase   :						Dict      							=					self.prepare_latents(
												    (batch_size, num_embeddings * embedding_dim) ,       image_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
												__UpperCAmelCase   :						Tuple      							=					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
																			__UpperCAmelCase   :						Dict      							=					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
																			__UpperCAmelCase   :						Dict      							=					self.scheduler.scale_model_input(UpperCamelCase ,       UpperCamelCase  )

																			__UpperCAmelCase   :						Tuple      							=					self.prior(
																			    UpperCamelCase ,       timestep=UpperCamelCase ,       proj_embedding=UpperCamelCase ,       ).predicted_image_embedding

																			# remove the variance
																			__UpperCAmelCase							,__UpperCAmelCase   :						List[Any]      							=					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   :						Union[str, Any]      							=					noise_pred.chunk(2  )
																										__UpperCAmelCase   :						List[Any]      							=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

																			__UpperCAmelCase   :						List[str]      							=					self.scheduler.step(
																			    UpperCamelCase ,       timestep=UpperCamelCase ,       sample=UpperCamelCase ,       ).prev_sample

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

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

																			images.append(UpperCamelCase  )

												__UpperCAmelCase   :						Optional[int]      							=					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}'''  )

												__UpperCAmelCase   :						List[Any]      							=					images.cpu().numpy()

												if output_type == "pil":
																			__UpperCAmelCase   :						Union[str, Any]      							=					[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  )




"""simple docstring"""






import math
def      lowerCamelCase       (     _UpperCamelCase		:      int							)		->       list[int]:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					[]
							__UpperCAmelCase   :						Dict      							=					2
							__UpperCAmelCase   :						Union[str, Any]      							=					int(math.sqrt(_UpperCamelCase							)							)  # Size of every segment
							__UpperCAmelCase   :						Tuple      							=					[True] * (end + 1)
							__UpperCAmelCase   :						int      							=					[]

							while start <= end:
														if temp[start] is True:
																					in_prime.append(_UpperCamelCase							)
																					for i in range(start * start       ,			end + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Dict      							=					False
														start += 1
							prime += in_prime

							__UpperCAmelCase   :						Optional[int]      							=					end + 1
							__UpperCAmelCase   :						Dict      							=					min(2 * end       ,			_UpperCamelCase							)

							while low <= n:
														__UpperCAmelCase   :						Union[str, Any]      							=					[True] * (high - low + 1)
														for each in in_prime:
																					__UpperCAmelCase   :						Dict      							=					math.floor(low / each							) * each
																					if t < low:
																												t += each

																					for j in range(_UpperCamelCase       ,			high + 1       ,			_UpperCamelCase							):
																												__UpperCAmelCase   :						Tuple      							=					False

														for j in range(len(_UpperCamelCase							)							):
																					if temp[j] is True:
																												prime.append(j + low							)

														__UpperCAmelCase   :						Tuple      							=					high + 1
														__UpperCAmelCase   :						Optional[int]      							=					min(high + end       ,			_UpperCamelCase							)

							return prime
print(sieve(10**6))
