



'''simple docstring'''




from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


UpperCamelCase__	:     Any    =			logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		torch.FloatTensor






class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		Any         =		1

			@register_to_config
			def __init__(							self					:			Any							,					lowerCAmelCase__					:			int = 4_0							,					lowerCAmelCase__					:			float = 0.0_02							,					lowerCAmelCase__					:			float = 80.0							,					lowerCAmelCase__					:			float = 0.5							,					lowerCAmelCase__					:			float = 1.0							,					lowerCAmelCase__					:			float = 7.0							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		sigma_max

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		np.linspace(0							,					1							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self._convert_to_karras(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.sigma_to_t(lowerCAmelCase__						)

					# setable values
					__SCREAMING_SNAKE_CASE	:		Any   	=		None
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.from_numpy(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		torch.from_numpy(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		False
					__SCREAMING_SNAKE_CASE	:		int   	=		False

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Tuple=None						):





					"""simple docstring"""






					if schedule_timesteps is None:
							__SCREAMING_SNAKE_CASE	:		int   	=		self.timesteps

					__SCREAMING_SNAKE_CASE	:		Any   	=		(schedule_timesteps == timestep).nonzero()
					return indices.item()

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			Union[float, torch.FloatTensor]						):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,					torch.Tensor						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		timestep.to(self.timesteps.device						)
					__SCREAMING_SNAKE_CASE	:		str   	=		self.index_for_timestep(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		str   	=		self.sigmas[step_idx]

					__SCREAMING_SNAKE_CASE	:		str   	=		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True
					return sample

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			Union[float, np.ndarray]						):





					"""simple docstring"""






					if not isinstance(lowerCAmelCase__							,					np.ndarray						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.array(lowerCAmelCase__							,					dtype=np.floataa						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44						)

					return timesteps

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Optional[int] = None							,					lowerCAmelCase__					:			Union[str, torch.device] = None							,					lowerCAmelCase__					:			Optional[List[int]] = None							,					):





					"""simple docstring"""






					if num_inference_steps is None and timesteps is None:
							raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""						)

					if num_inference_steps is not None and timesteps is not None:
							raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""						)

					# Follow DDPMScheduler custom timesteps logic
					if timesteps is not None:
							for i in range(1							,					len(lowerCAmelCase__						)						):
									if timesteps[i] >= timesteps[i - 1]:
											raise ValueError("""`timesteps` must be in descending order."""						)

							if timesteps[0] >= self.config.num_train_timesteps:
									raise ValueError(
									    F"`timesteps` must start before `self.config.train_timesteps`:"
									    F" {self.config.num_train_timesteps}."						)

							__SCREAMING_SNAKE_CASE	:		int   	=		np.array(lowerCAmelCase__							,					dtype=np.intaa						)
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		True
					else:
							if num_inference_steps > self.config.num_train_timesteps:
									raise ValueError(
									    F"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
									    F" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
									    F" maximal {self.config.num_train_timesteps} timesteps."						)

							__SCREAMING_SNAKE_CASE	:		Tuple   	=		num_inference_steps

							__SCREAMING_SNAKE_CASE	:		int   	=		self.config.num_train_timesteps // self.num_inference_steps
							__SCREAMING_SNAKE_CASE	:		str   	=		(np.arange(0							,					lowerCAmelCase__						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		False

					# Map timesteps to Karras sigmas directly for multistep sampling
					# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.config.num_train_timesteps
					__SCREAMING_SNAKE_CASE	:		Any   	=		timesteps[::-1].copy()
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		ramp / (num_train_timesteps - 1)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self._convert_to_karras(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.sigma_to_t(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		np.concatenate([sigmas, [self.sigma_min]]						).astype(np.floataa						)
					__SCREAMING_SNAKE_CASE	:		int   	=		torch.from_numpy(lowerCAmelCase__						).to(device=lowerCAmelCase__						)

					if str(lowerCAmelCase__						).startswith("""mps"""						):
							# mps does not support float64
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		torch.from_numpy(lowerCAmelCase__						).to(lowerCAmelCase__							,					dtype=torch.floataa						)
					else:
							__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.from_numpy(lowerCAmelCase__						).to(device=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		float   	=		self.config.sigma_min
					__SCREAMING_SNAKE_CASE	:		float   	=		self.config.sigma_max

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.config.rho
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sigma_min ** (1 / rho)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		sigma_max ** (1 / rho)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
					return sigmas

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.config.sigma_data

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sigma_data**2 / (sigma**2 + sigma_data**2)
					__SCREAMING_SNAKE_CASE	:		str   	=		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out

			def UpperCamelCase__ (							self					:			Union[str, Any]							,					lowerCAmelCase__					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		self.config.sigma_min
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.config.sigma_data

					__SCREAMING_SNAKE_CASE	:		int   	=		sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
					__SCREAMING_SNAKE_CASE	:		Any   	=		(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
					return c_skip, c_out

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			Union[float, torch.FloatTensor]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			Optional[torch.Generator] = None							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					if (
					    isinstance(lowerCAmelCase__							,					lowerCAmelCase__						)
					    or isinstance(lowerCAmelCase__							,					torch.IntTensor						)
					    or isinstance(lowerCAmelCase__							,					torch.LongTensor						)
					):
							raise ValueError(
							    (
							        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
							        F" `{self.__class__}.step()` is not supported. Make sure to pass"
							        """ one of the `scheduler.timesteps` as a timestep."""
							    )							,					)

					if not self.is_scale_input_called:
							logger.warning(
							    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
							    """See `StableDiffusionPipeline` for a usage example."""						)

					if isinstance(lowerCAmelCase__							,					torch.Tensor						):
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		timestep.to(self.timesteps.device						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.config.sigma_min
					__SCREAMING_SNAKE_CASE	:		int   	=		self.config.sigma_max

					__SCREAMING_SNAKE_CASE	:		int   	=		self.index_for_timestep(lowerCAmelCase__						)

					# sigma_next corresponds to next_t in original implementation
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.sigmas[step_index]
					if step_index + 1 < self.config.num_train_timesteps:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.sigmas[step_index + 1]
					else:
							# Set sigma_next to sigma_min
							__SCREAMING_SNAKE_CASE	:		Dict   	=		self.sigmas[-1]

					# Get scalings for boundary conditions
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		str   	=		self.get_scalings_for_boundary_condition(lowerCAmelCase__						)

					# 1. Denoise model output using boundary conditions
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		c_out * model_output + c_skip * sample
					if self.config.clip_denoised:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		denoised.clamp(-1							,					1						)

					# 2. Sample z ~ N(0, s_noise^2 * I)
					# Noise is not used for onestep sampling.
					if len(self.timesteps						) > 1:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		randn_tensor(
							    model_output.shape							,					dtype=model_output.dtype							,					device=model_output.device							,					generator=lowerCAmelCase__						)
					else:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.zeros_like(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		noise * self.config.s_noise

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		sigma_next.clamp(min=lowerCAmelCase__							,					max=lowerCAmelCase__						)

					# 3. Return noisy sample
					# tau = sigma_hat, eps = sigma_min
					__SCREAMING_SNAKE_CASE	:		Any   	=		denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

					if not return_dict:
							return (prev_sample,)

					return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			List[str]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			torch.FloatTensor							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		self.sigmas.to(device=original_samples.device							,					dtype=original_samples.dtype						)
					if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase__						):
							# mps does not support float64
							__SCREAMING_SNAKE_CASE	:		str   	=		self.timesteps.to(original_samples.device							,					dtype=torch.floataa						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		timesteps.to(original_samples.device							,					dtype=torch.floataa						)
					else:
							__SCREAMING_SNAKE_CASE	:		str   	=		self.timesteps.to(original_samples.device						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		timesteps.to(original_samples.device						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

					__SCREAMING_SNAKE_CASE	:		int   	=		sigmas[step_indices].flatten()
					while len(sigma.shape						) < len(original_samples.shape						):
							__SCREAMING_SNAKE_CASE	:		Any   	=		sigma.unsqueeze(-1						)

					__SCREAMING_SNAKE_CASE	:		str   	=		original_samples + noise * sigma
					return noisy_samples



			def __len__(							self					:			Optional[Any]						):





					"""simple docstring"""






					return self.config.num_train_timesteps




'''simple docstring'''




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


UpperCamelCase__	:     List[str]    =			logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__	:     Tuple    =			'''
    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 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

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






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			List[str]							,					lowerCAmelCase__					:			PriorTransformer							,					lowerCAmelCase__					:			CLIPTextModelWithProjection							,					lowerCAmelCase__					:			CLIPTokenizer							,					lowerCAmelCase__					:			HeunDiscreteScheduler							,					lowerCAmelCase__					:			ShapERenderer							,					):





					"""simple docstring"""






					super().__init__()

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

			def UpperCamelCase__ (							self					:			Tuple							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					if latents is None:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		randn_tensor(lowerCAmelCase__							,					generator=lowerCAmelCase__							,					device=lowerCAmelCase__							,					dtype=lowerCAmelCase__						)
					else:
							if latents.shape != shape:
									raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
							__SCREAMING_SNAKE_CASE	:		Any   	=		latents.to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		latents * scheduler.init_noise_sigma
					return latents

			def UpperCamelCase__ (							self					:			str							,					lowerCAmelCase__					:			int=0						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.device(F"cuda:{gpu_id}"						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		[self.text_encoder, self.prior]
					for cpu_offloaded_model in models:
							if cpu_offloaded_model is not None:
									cpu_offload(lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int							,					lowerCAmelCase__					:			str=0						):





					"""simple docstring"""






					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."""						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.device(F"cuda:{gpu_id}"						)

					if self.device.type != "cpu":
							self.to("""cpu"""							,					silence_dtype_warnings=lowerCAmelCase__						)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		cpu_offload_with_hook(lowerCAmelCase__							,					lowerCAmelCase__							,					prev_module_hook=lowerCAmelCase__						)

					if self.safety_checker is not None:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		cpu_offload_with_hook(self.safety_checker							,					lowerCAmelCase__							,					prev_module_hook=lowerCAmelCase__						)

					# We'll offload the last model manually.
					__SCREAMING_SNAKE_CASE	:		str   	=		hook

			@property
			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					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(lowerCAmelCase__							,					"""_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 UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Tuple							,					):





					"""simple docstring"""






					len(lowerCAmelCase__						) if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						) else 1

					# YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		0
					# get prompt text embeddings
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.tokenizer(
					    lowerCAmelCase__							,					padding="""max_length"""							,					max_length=self.tokenizer.model_max_length							,					truncation=lowerCAmelCase__							,					return_tensors="""pt"""							,					)
					__SCREAMING_SNAKE_CASE	:		str   	=		text_inputs.input_ids
					__SCREAMING_SNAKE_CASE	:		Dict   	=		self.tokenizer(lowerCAmelCase__							,					padding="""longest"""							,					return_tensors="""pt"""						).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Dict   	=		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}"						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		self.text_encoder(text_input_ids.to(lowerCAmelCase__						)						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		text_encoder_output.text_embeds

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		prompt_embeds.repeat_interleave(lowerCAmelCase__							,					dim=0						)
					# in Shap-E it normalize the prompt_embeds and then later rescale it
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		prompt_embeds / torch.linalg.norm(lowerCAmelCase__							,					dim=-1							,					keepdim=lowerCAmelCase__						)

					if do_classifier_free_guidance:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.zeros_like(lowerCAmelCase__						)

							# 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
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.cat([negative_prompt_embeds, prompt_embeds]						)

					# Rescale the features to have unit variance
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		math.sqrt(prompt_embeds.shape[1]						) * prompt_embeds

					return prompt_embeds



			@torch.no_grad()
			@replace_example_docstring(lowerCAmelCase__						)
			def __call__(							self					:			Union[str, Any]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			int = 1							,					lowerCAmelCase__					:			int = 2_5							,					lowerCAmelCase__					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					lowerCAmelCase__					:			Optional[torch.FloatTensor] = None							,					lowerCAmelCase__					:			float = 4.0							,					lowerCAmelCase__					:			int = 6_4							,					lowerCAmelCase__					:			Optional[str] = "pil"							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		str   	=		1
					elif isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		len(lowerCAmelCase__						)
					else:
							raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__						)}"						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		self._execution_device

					__SCREAMING_SNAKE_CASE	:		str   	=		batch_size * num_images_per_prompt

					__SCREAMING_SNAKE_CASE	:		str   	=		guidance_scale > 1.0
					__SCREAMING_SNAKE_CASE	:		int   	=		self._encode_prompt(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

					# prior

					self.scheduler.set_timesteps(lowerCAmelCase__							,					device=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.scheduler.timesteps

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.prior.config.num_embeddings
					__SCREAMING_SNAKE_CASE	:		int   	=		self.prior.config.embedding_dim

					__SCREAMING_SNAKE_CASE	:		str   	=		self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)							,					prompt_embeds.dtype							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					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
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		latents.reshape(latents.shape[0]							,					lowerCAmelCase__							,					lowerCAmelCase__						)

					for i, t in enumerate(self.progress_bar(lowerCAmelCase__						)						):
							# expand the latents if we are doing classifier free guidance
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
							__SCREAMING_SNAKE_CASE	:		str   	=		self.scheduler.scale_model_input(lowerCAmelCase__							,					lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	:		str   	=		self.prior(
							    lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					proj_embedding=lowerCAmelCase__							,					).predicted_image_embedding

							# remove the variance
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		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:
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Any   	=		noise_pred.chunk(2						)
									__SCREAMING_SNAKE_CASE	:		str   	=		noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

							__SCREAMING_SNAKE_CASE	:		str   	=		self.scheduler.step(
							    lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					sample=lowerCAmelCase__							,					).prev_sample

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

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

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.stack(lowerCAmelCase__						)

					if output_type not in ["np", "pil"]:
							raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}"						)

					__SCREAMING_SNAKE_CASE	:		str   	=		images.cpu().numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE	:		int   	=		[self.numpy_to_pil(lowerCAmelCase__						) 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=lowerCAmelCase__						)