




'''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


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

a    :							List[Any]										=    '\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 		a							(  _lowerCamelCase	):
				snake_case_           =						42







class 		a							(  _lowerCamelCase	):


				def __init__(						self       :   Union[str, Any]			,							lowercase_       :   PriorTransformer			,							lowercase_       :   CLIPVisionModel			,							lowercase_       :   CLIPImageProcessor			,							lowercase_       :   HeunDiscreteScheduler			,							lowercase_       :   ShapERenderer			,							):
									super().__init__()

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


				def     A_					(						self       :   Optional[Any]			,							lowercase_       :   int			,							lowercase_       :   Any			,							lowercase_       :   str			,							lowercase_       :   int			,							lowercase_       :   Any			,							lowercase_       :   Optional[Any]   ):
									if latents is None:
														snake_case_		     =							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}"   )
														snake_case_		     =							latents.to(lowercase_   )

									snake_case_		     =							latents * scheduler.init_noise_sigma
									return latents


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

									snake_case_		     =							torch.device(F"cuda:{gpu_id}"   )

									snake_case_		     =							[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       :   Any   ):
									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       :   List[str]			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   Dict			,							lowercase_       :   Any			,							):
									if isinstance(lowercase_			,							lowercase_   ) and isinstance(image[0]			,							torch.Tensor   ):
														snake_case_		     =							torch.cat(lowercase_			,							axis=0   ) if image[0].ndim == 4 else torch.stack(lowercase_			,							axis=0   )

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

									snake_case_		     =							image.to(dtype=self.image_encoder.dtype			,							device=lowercase_   )

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

									snake_case_		     =							image_embeds.repeat_interleave(lowercase_			,							dim=0   )

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

									return image_embeds


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

									if isinstance(lowercase_			,							PIL.Image.Image   ):
														snake_case_		     =							1
									elif isinstance(lowercase_			,							torch.Tensor   ):
														snake_case_		     =							image.shape[0]
									elif isinstance(lowercase_			,							lowercase_   ) and isinstance(image[0]			,							(torch.Tensor, PIL.Image.Image)   ):
														snake_case_		     =							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_   )}"   )

									snake_case_		     =							self._execution_device

									snake_case_		     =							batch_size * num_images_per_prompt

									snake_case_		     =							guidance_scale > 1.0
									snake_case_		     =							self._encode_image(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_   )

									# prior

									self.scheduler.set_timesteps(lowercase_			,							device=lowercase_   )
									snake_case_		     =							self.scheduler.timesteps

									snake_case_		     =							self.prior.config.num_embeddings
									snake_case_		     =							self.prior.config.embedding_dim

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

														snake_case_		     =							self.prior(
														    lowercase_			,							timestep=lowercase_			,							proj_embedding=lowercase_			,							).predicted_image_embedding

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

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

														snake_case_		     =							self.scheduler.step(
														    lowercase_			,							timestep=lowercase_			,							sample=lowercase_			,							).prev_sample

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

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

														images.append(lowercase_   )

									snake_case_		     =							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}"   )

									snake_case_		     =							images.cpu().numpy()

									if output_type == "pil":
														snake_case_		     =							[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 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   int:

					'''simple docstring'''

					return int((input_a, input_a).count(0      ) == 0      )


def 						__magic_name__       (      )						->   None:

					'''simple docstring'''

					assert and_gate(0, 0      ) == 0
					assert and_gate(0, 1      ) == 0
					assert and_gate(1, 0      ) == 0
					assert and_gate(1, 1      ) == 1


if __name__ == "__main__":
							test_and_gate()
							print(and_gate(1, 0))
							print(and_gate(0, 0))
							print(and_gate(0, 1))
							print(and_gate(1, 1))

