


"""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


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

__SCREAMING_SNAKE_CASE							:    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      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Union[PIL.Image.Image, np.ndarray]





class      __A     (snake_case__):





							'''simple docstring'''

							def __init__(				self							:      Tuple		,       UpperCAmelCase_							:      PriorTransformer		,       UpperCAmelCase_							:      CLIPVisionModel		,       UpperCAmelCase_							:      CLIPImageProcessor		,       UpperCAmelCase_							:      HeunDiscreteScheduler		,       UpperCAmelCase_							:      ShapERenderer		,       )					->int:



														"""simple docstring"""



														super().__init__()

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



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Optional[int]		,       UpperCAmelCase_							:      Dict		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      int		,       UpperCAmelCase_							:      Dict       )					->Union[str, Any]:



														"""simple docstring"""



														if latents is None:
																					snake_case_           =		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}"""       )
																					snake_case_           =		latents.to(UpperCAmelCase_       )

														snake_case_           =		latents * scheduler.init_noise_sigma
														return latents



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      List[str]=0       )					->Optional[int]:



														"""simple docstring"""



														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(UpperCAmelCase_		,       UpperCAmelCase_       )



							@property
							def 		lowerCAmelCase       (				self							:      List[str]       )					->Optional[int]:



														"""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							:      str		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      List[str]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Tuple		,       )					->Optional[int]:



														"""simple docstring"""



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

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

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

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

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

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

														return image_embeds



							@torch.no_grad()
							@replace_example_docstring(UpperCAmelCase_       )
							def __call__(				self							:      int		,       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		,       )					->Any:



														"""simple docstring"""



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

														snake_case_           =		self._execution_device

														snake_case_           =		batch_size * num_images_per_prompt

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

														# prior

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

																					snake_case_           =		self.prior(
																					    UpperCAmelCase_		,       timestep=UpperCAmelCase_		,       proj_embedding=UpperCAmelCase_		,       ).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(
																					    UpperCAmelCase_		,       timestep=UpperCAmelCase_		,       sample=UpperCAmelCase_		,       ).prev_sample

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

														snake_case_           =		[]
														for i, latent in enumerate(UpperCAmelCase_       ):
																					print()
																					snake_case_           =		self.renderer.decode(
																					    latent[None, :]		,       UpperCAmelCase_		,       size=UpperCAmelCase_		,       ray_batch_size=4_096		,       n_coarse_samples=64		,       n_fine_samples=128		,       )

																					images.append(UpperCAmelCase_       )

														snake_case_           =		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}"""       )

														snake_case_           =		images.cpu().numpy()

														if output_type == "pil":
																					snake_case_           =		[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"""
def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# bit count represents no. of bits in the gray code
							if bit_count < 0:
														raise ValueError("""The given input must be positive"""				)

							# get the generated string sequence
							snake_case_           =		gray_code_sequence_string(_SCREAMING_SNAKE_CASE				)
							#
							# convert them to integers
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														snake_case_           =		int(sequence[i]				, 2				)

							return sequence







def 						_a				(      _SCREAMING_SNAKE_CASE				)    ->				list:

							# The approach is a recursive one
							# Base case achieved when either n = 0 or n=1
							if bit_count == 0:
														return ["0"]

							if bit_count == 1:
														return ["0", "1"]

							snake_case_           =		1 << bit_count  # defines the length of the sequence
							# 1<< n is equivalent to 2^n

							# recursive answer will generate answer for n-1 bits
							snake_case_           =		gray_code_sequence_string(bit_count - 1				)

							snake_case_           =		[]

							# append 0 to first half of the smaller sequence generated
							for i in range(seq_len // 2				):
														snake_case_           =		"""0""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							# append 1 to second half ... start from the end of the list
							for i in reversed(range(seq_len // 2				)				):
														snake_case_           =		"""1""" + smaller_sequence[i]
														sequence.append(_SCREAMING_SNAKE_CASE				)

							return sequence


if __name__ == "__main__":
				import doctest

				doctest.testmod()



