


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

a : Optional[Any]		=				'''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

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

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

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

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''
@dataclass
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       )   ->			List[Any]:







								'''simple docstring'''

								super().__init__()

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






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								if latents is None:
											__lowercase 					=   randn_tensor(snake_case_ ,       generator=snake_case_ ,       device=snake_case_ ,       dtype=snake_case_						)
								else:
											if latents.shape != shape:
														raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
											__lowercase 					=   latents.to(snake_case_						)

								__lowercase 					=   latents * scheduler.init_noise_sigma
								return latents






					def A      (				self ,       snake_case_=0						)   ->			Union[str, 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(snake_case_ ,       snake_case_						)






					@property
					def A      (				self						)   ->			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(snake_case_ ,       '''_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 ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       )   ->			Any:







								'''simple docstring'''

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

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

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

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

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

								if do_classifier_free_guidance:
											__lowercase 					=   torch.zeros_like(snake_case_						)

											# 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(snake_case_						)
					def __call__(				self ,       snake_case_ ,       snake_case_ = 1 ,       snake_case_ = 2_5 ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = 4.0 ,       snake_case_ = 6_4 ,       snake_case_ = "pil" ,       snake_case_ = True ,       )   ->			Dict:







								'''simple docstring'''

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

								__lowercase 					=   self._execution_device

								__lowercase 					=   batch_size * num_images_per_prompt

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

								# prior

								self.scheduler.set_timesteps(snake_case_ ,       device=snake_case_						)
								__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 ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       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] ,       snake_case_ ,       snake_case_						)

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

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

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

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

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

								__lowercase 					=   []
								for i, latent in enumerate(snake_case_						):
											print()
											__lowercase 					=   self.renderer.decode(
											    latent[None, :] ,       snake_case_ ,       size=snake_case_ ,       ray_batch_size=4_0_9_6 ,       n_coarse_samples=6_4 ,       n_fine_samples=1_2_8 ,       )

											images.append(snake_case_						)

								__lowercase 					=   torch.stack(snake_case_						)

								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(snake_case_						) 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=snake_case_						)






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   len(_UpperCamelCase  )
			__lowercase 					=   len(matrix[0]  )
			__lowercase 					=   min(_UpperCamelCase       ,			_UpperCamelCase  )

			for row in range(_UpperCamelCase  ):
						# Check if diagonal element is not zero
						if matrix[row][row] != 0:
									# Eliminate all the elements below the diagonal
									for col in range(row + 1       ,			_UpperCamelCase  ):
												__lowercase 					=   matrix[col][row] / matrix[row][row]
												for i in range(_UpperCamelCase       ,			_UpperCamelCase  ):
															matrix[col][i] -= multiplier * matrix[row][i]
						else:
									# Find a non-zero diagonal element to swap rows
									__lowercase 					=   True
									for i in range(row + 1       ,			_UpperCamelCase  ):
												if matrix[i][row] != 0:
															__lowercase					, __lowercase 					=   matrix[i], matrix[row]
															__lowercase 					=   False
															break
									if reduce:
												rank -= 1
												for i in range(_UpperCamelCase  ):
															__lowercase 					=   matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
									row -= 1

			return rank


if __name__ == "__main__":
	import doctest

	doctest.testmod()


