





"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_	 =logging.get_logger(__name__)





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   List[Any]      =       ConvNextConfig()

						if "tiny" in checkpoint_url:
												_UpperCamelCase     :   Tuple      =       [3, 3, 9, 3]
												_UpperCamelCase     :   str      =       [96, 192, 384, 768]
						if "small" in checkpoint_url:
												_UpperCamelCase     :   int      =       [3, 3, 27, 3]
												_UpperCamelCase     :   List[str]      =       [96, 192, 384, 768]
						if "base" in checkpoint_url:
												_UpperCamelCase     :   List[str]      =       [3, 3, 27, 3]
												_UpperCamelCase     :   Dict      =       [128, 256, 512, 1024]
						if "large" in checkpoint_url:
												_UpperCamelCase     :   str      =       [3, 3, 27, 3]
												_UpperCamelCase     :   Optional[int]      =       [192, 384, 768, 1536]
						if "xlarge" in checkpoint_url:
												_UpperCamelCase     :   Optional[int]      =       [3, 3, 27, 3]
												_UpperCamelCase     :   Dict      =       [256, 512, 1024, 2048]

						if "1k" in checkpoint_url:
												_UpperCamelCase     :   List[Any]      =       1000
												_UpperCamelCase     :   List[Any]      =       '''imagenet-1k-id2label.json'''
												_UpperCamelCase     :   str      =       (1, 1000)
						else:
												_UpperCamelCase     :   Union[str, Any]      =       2_1841
												_UpperCamelCase     :   Dict      =       '''imagenet-22k-id2label.json'''
												_UpperCamelCase     :   Optional[int]      =       (1, 2_1841)

						_UpperCamelCase     :   Optional[int]      =       '''huggingface/label-files'''
						_UpperCamelCase     :   Optional[Any]      =       num_labels
						_UpperCamelCase     :   Optional[Any]      =       json.load(open(hf_hub_download(_lowercase			,		_lowercase			,		repo_type='''dataset'''  )			,		'''r'''  )  )
						_UpperCamelCase     :   str      =       {int(_lowercase  ): v for k, v in idalabel.items()}
						if "1k" not in checkpoint_url:
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9205]
												del idalabel[1_5027]
						_UpperCamelCase     :   Dict      =       idalabel
						_UpperCamelCase     :   Any      =       {v: k for k, v in idalabel.items()}
						_UpperCamelCase     :   List[str]      =       hidden_sizes
						_UpperCamelCase     :   Tuple      =       depths

						return config, expected_shape





def 			a_							(				_lowercase  ):
						if "downsample_layers.0.0" in name:
												_UpperCamelCase     :   Tuple      =       name.replace('''downsample_layers.0.0'''			,		'''embeddings.patch_embeddings'''  )
						if "downsample_layers.0.1" in name:
												_UpperCamelCase     :   Dict      =       name.replace('''downsample_layers.0.1'''			,		'''embeddings.norm'''  )  # we rename to layernorm later on
						if "downsample_layers.1.0" in name:
												_UpperCamelCase     :   Tuple      =       name.replace('''downsample_layers.1.0'''			,		'''stages.1.downsampling_layer.0'''  )
						if "downsample_layers.1.1" in name:
												_UpperCamelCase     :   int      =       name.replace('''downsample_layers.1.1'''			,		'''stages.1.downsampling_layer.1'''  )
						if "downsample_layers.2.0" in name:
												_UpperCamelCase     :   Union[str, Any]      =       name.replace('''downsample_layers.2.0'''			,		'''stages.2.downsampling_layer.0'''  )
						if "downsample_layers.2.1" in name:
												_UpperCamelCase     :   int      =       name.replace('''downsample_layers.2.1'''			,		'''stages.2.downsampling_layer.1'''  )
						if "downsample_layers.3.0" in name:
												_UpperCamelCase     :   int      =       name.replace('''downsample_layers.3.0'''			,		'''stages.3.downsampling_layer.0'''  )
						if "downsample_layers.3.1" in name:
												_UpperCamelCase     :   List[Any]      =       name.replace('''downsample_layers.3.1'''			,		'''stages.3.downsampling_layer.1'''  )
						if "stages" in name and "downsampling_layer" not in name:
												# stages.0.0. for instance should be renamed to stages.0.layers.0.
												_UpperCamelCase     :   Optional[Any]      =       name[: len('''stages.0'''  )] + '''.layers''' + name[len('''stages.0'''  ) :]
						if "stages" in name:
												_UpperCamelCase     :   Dict      =       name.replace('''stages'''			,		'''encoder.stages'''  )
						if "norm" in name:
												_UpperCamelCase     :   str      =       name.replace('''norm'''			,		'''layernorm'''  )
						if "gamma" in name:
												_UpperCamelCase     :   List[str]      =       name.replace('''gamma'''			,		'''layer_scale_parameter'''  )
						if "head" in name:
												_UpperCamelCase     :   List[Any]      =       name.replace('''head'''			,		'''classifier'''  )

						return name





def 			a_							(				):
						_UpperCamelCase     :   List[str]      =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCamelCase     :   List[Any]      =       Image.open(requests.get(_lowercase			,		stream=_lowercase  ).raw  )
						return im





@torch.no_grad()
def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       get_convnext_config(_lowercase  )
						# load original state_dict from URL
						_UpperCamelCase     :   Union[str, Any]      =       torch.hub.load_state_dict_from_url(_lowercase  )['''model''']
						# rename keys
						for key in state_dict.copy().keys():
												_UpperCamelCase     :   Any      =       state_dict.pop(_lowercase  )
												_UpperCamelCase     :   str      =       val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												_UpperCamelCase     :   List[Any]      =       state_dict.pop(_lowercase  )
												if not key.startswith('''classifier'''  ):
																		_UpperCamelCase     :   int      =       '''convnext.''' + key
												_UpperCamelCase     :   Tuple      =       val

						# load HuggingFace model
						_UpperCamelCase     :   Tuple      =       ConvNextForImageClassification(_lowercase  )
						model.load_state_dict(_lowercase  )
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						_UpperCamelCase     :   int      =       224 if '''224''' in checkpoint_url else 384
						_UpperCamelCase     :   List[Any]      =       ConvNextImageProcessor(size=_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       image_processor(images=prepare_img()			,		return_tensors='''pt'''  ).pixel_values

						_UpperCamelCase     :   str      =       model(_lowercase  ).logits

						# note: the logits below were obtained without center cropping
						if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
												_UpperCamelCase     :   Any      =       torch.tensor([-0.1210, -0.6605, 0.1918]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
												_UpperCamelCase     :   int      =       torch.tensor([-0.4473, -0.1847, -0.6365]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
												_UpperCamelCase     :   Dict      =       torch.tensor([0.4525, 0.7539, 0.0308]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
												_UpperCamelCase     :   List[Any]      =       torch.tensor([0.3561, 0.6350, -0.0384]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
												_UpperCamelCase     :   Dict      =       torch.tensor([0.4174, -0.0989, 0.1489]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
												_UpperCamelCase     :   Any      =       torch.tensor([0.2513, -0.1349, -0.1613]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
												_UpperCamelCase     :   Dict      =       torch.tensor([1.2980, 0.3631, -0.1198]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
												_UpperCamelCase     :   Union[str, Any]      =       torch.tensor([1.2963, 0.1227, 0.1723]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
												_UpperCamelCase     :   Union[str, Any]      =       torch.tensor([1.7956, 0.8390, 0.2820]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
												_UpperCamelCase     :   Dict      =       torch.tensor([-0.2822, -0.0502, -0.0878]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
												_UpperCamelCase     :   Tuple      =       torch.tensor([-0.5672, -0.0730, -0.4348]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
												_UpperCamelCase     :   List[str]      =       torch.tensor([0.2681, 0.2365, 0.6246]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
												_UpperCamelCase     :   int      =       torch.tensor([-0.2642, 0.3931, 0.5116]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
												_UpperCamelCase     :   str      =       torch.tensor([-0.6677, -0.1873, -0.8379]  )
						elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
												_UpperCamelCase     :   List[str]      =       torch.tensor([-0.7749, -0.2967, -0.6444]  )
						else:
												raise ValueError(F"""Unknown URL: {checkpoint_url}"""  )

						assert torch.allclose(logits[0, :3]			,		_lowercase			,		atol=1E-3  )
						assert logits.shape == expected_shape

						Path(_lowercase  ).mkdir(exist_ok=_lowercase  )
						print(F"""Saving model to {pytorch_dump_folder_path}"""  )
						model.save_pretrained(_lowercase  )
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
						image_processor.save_pretrained(_lowercase  )

						print('''Pushing model to the hub...'''  )
						_UpperCamelCase     :   List[str]      =       '''convnext'''
						if "tiny" in checkpoint_url:
												model_name += "-tiny"
						elif "small" in checkpoint_url:
												model_name += "-small"
						elif "base" in checkpoint_url:
												model_name += "-base"
						elif "xlarge" in checkpoint_url:
												model_name += "-xlarge"
						elif "large" in checkpoint_url:
												model_name += "-large"
						if "224" in checkpoint_url:
												model_name += "-224"
						elif "384" in checkpoint_url:
												model_name += "-384"
						if "22k" in checkpoint_url and "1k" not in checkpoint_url:
												model_name += "-22k"
						if "22k" in checkpoint_url and "1k" in checkpoint_url:
												model_name += "-22k-1k"

						model.push_to_hub(
						    repo_path_or_name=Path(_lowercase			,		_lowercase  )			,		organization='''nielsr'''			,		commit_message='''Add model'''			,		)


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint_url""",
						    default="""https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth""",
						    type=str,
						    help="""URL of the original ConvNeXT checkpoint you'd like to convert.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)

						UpperCamelCase_	 =parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










"""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_	 =logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase_	 ="""
    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 					_a		( _lowerCAmelCase							):
				UpperCamelCase								=       42




class 					_a		( _lowerCAmelCase							):






				def __init__(					self						:       int,					lowerCAmelCase__						:       PriorTransformer,					lowerCAmelCase__						:       CLIPTextModelWithProjection,					lowerCAmelCase__						:       CLIPTokenizer,					lowerCAmelCase__						:       HeunDiscreteScheduler,					lowerCAmelCase__						:       ShapERenderer,					)      -> Optional[int]:




										'''simple docstring'''



										super().__init__()

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






				def 							snake_case      (					self						:       List[Any],					lowerCAmelCase__						:       Any,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[str]				)      -> Optional[int]:




										'''simple docstring'''



										if latents is None:
																_UpperCamelCase     :   Any      =       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}"""				)
																_UpperCamelCase     :   List[Any]      =       latents.to(lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       latents * scheduler.init_noise_sigma
										return latents






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       str=0				)      -> Dict:




										'''simple docstring'''



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

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

										_UpperCamelCase     :   List[str]      =       [self.text_encoder, self.prior]
										for cpu_offloaded_model in models:
																if cpu_offloaded_model is not None:
																						cpu_offload(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       int,					lowerCAmelCase__						:       Tuple=0				)      -> List[Any]:




										'''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.'''				)

										_UpperCamelCase     :   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)

										_UpperCamelCase     :   Tuple      =       None
										for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
																_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       cpu_offload_with_hook(lowerCAmelCase__,					lowerCAmelCase__,					prev_module_hook=lowerCAmelCase__				)

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

										# We'll offload the last model manually.
										_UpperCamelCase     :   Union[str, Any]      =       hook






				@property
				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''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 							snake_case      (					self						:       Tuple,					lowerCAmelCase__						:       Optional[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       Any,					)      -> List[str]:




										'''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
										_UpperCamelCase     :   Optional[Any]      =       0
										# get prompt text embeddings
										_UpperCamelCase     :   Union[str, Any]      =       self.tokenizer(
										    lowerCAmelCase__,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowerCAmelCase__,					return_tensors='''pt''',					)
										_UpperCamelCase     :   Union[str, Any]      =       text_inputs.input_ids
										_UpperCamelCase     :   Union[str, Any]      =       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__				):
																_UpperCamelCase     :   Tuple      =       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}"""				)

										_UpperCamelCase     :   Dict      =       self.text_encoder(text_input_ids.to(lowerCAmelCase__				)				)
										_UpperCamelCase     :   Optional[int]      =       text_encoder_output.text_embeds

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

										if do_classifier_free_guidance:
																_UpperCamelCase     :   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
																_UpperCamelCase     :   Optional[Any]      =       torch.cat([negative_prompt_embeds, prompt_embeds]				)

										# Rescale the features to have unit variance
										_UpperCamelCase     :   Union[str, Any]      =       math.sqrt(prompt_embeds.shape[1]				) * prompt_embeds

										return prompt_embeds






				@torch.no_grad()
				@replace_example_docstring(lowerCAmelCase__				)
				def __call__(					self						:       Optional[int],					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,					)      -> Any:




										'''simple docstring'''



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

										_UpperCamelCase     :   Dict      =       self._execution_device

										_UpperCamelCase     :   List[Any]      =       batch_size * num_images_per_prompt

										_UpperCamelCase     :   List[str]      =       guidance_scale > 1.0
										_UpperCamelCase     :   str      =       self._encode_prompt(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

										# prior

										self.scheduler.set_timesteps(lowerCAmelCase__,					device=lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       self.scheduler.timesteps

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

										_UpperCamelCase     :   Tuple      =       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
										_UpperCamelCase     :   Dict      =       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
																_UpperCamelCase     :   Optional[int]      =       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents
																_UpperCamelCase     :   Dict      =       self.scheduler.scale_model_input(lowerCAmelCase__,					lowerCAmelCase__				)

																_UpperCamelCase     :   Tuple      =       self.prior(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					proj_embedding=lowerCAmelCase__,					).predicted_image_embedding

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

																_UpperCamelCase     :   Dict      =       self.scheduler.step(
																    lowerCAmelCase__,					timestep=lowerCAmelCase__,					sample=lowerCAmelCase__,					).prev_sample

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

										_UpperCamelCase     :   List[Any]      =       []
										for i, latent in enumerate(lowerCAmelCase__				):
																_UpperCamelCase     :   Union[str, 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__				)

										_UpperCamelCase     :   str      =       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}"""				)

										_UpperCamelCase     :   Dict      =       images.cpu().numpy()

										if output_type == "pil":
																_UpperCamelCase     :   Any      =       [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__				)



