



"""simple docstring"""
import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :int						=			XCLIPTextConfig()

						# derive patch size from model name
						snake_case_ :Union[str, Any]						=			model_name.find("""patch"""	)
						snake_case_ :List[str]						=			int(model_name[start_idx + len("""patch"""	) : start_idx + len("""patch"""	) + 2]	)
						snake_case_ :Any						=			XCLIPVisionConfig(patch_size=_lowercase,					num_frames=_lowercase	)

						if "large" in model_name:
												snake_case_ :Optional[Any]						=			768
												snake_case_ :Union[str, Any]						=			3072
												snake_case_ :Any						=			12

												snake_case_ :Any						=			1024
												snake_case_ :str						=			4096
												snake_case_ :Union[str, Any]						=			16
												snake_case_ :Union[str, Any]						=			24
												snake_case_ :Tuple						=			768
												snake_case_ :Any						=			3072

						if model_name == "xclip-large-patch14-16-frames":
												snake_case_ :Any						=			336

						snake_case_ :Any						=			XCLIPConfig.from_text_vision_configs(_lowercase,					_lowercase	)

						if "large" in model_name:
												snake_case_ :List[Any]						=			768

						return config
def    A_  (			_lowercase	):







						'''simple docstring'''


						if name == "token_embedding.weight":
												snake_case_ :Optional[Any]						=			name.replace("""token_embedding.weight""",					"""text_model.embeddings.token_embedding.weight"""	)
						if name == "positional_embedding":
												snake_case_ :Tuple						=			name.replace("""positional_embedding""",					"""text_model.embeddings.position_embedding.weight"""	)
						if "ln_1" in name:
												snake_case_ :Dict						=			name.replace("""ln_1""",					"""layer_norm1"""	)
						if "ln_2" in name:
												snake_case_ :str						=			name.replace("""ln_2""",					"""layer_norm2"""	)
						if "c_fc" in name:
												snake_case_ :str						=			name.replace("""c_fc""",					"""fc1"""	)
						if "c_proj" in name:
												snake_case_ :int						=			name.replace("""c_proj""",					"""fc2"""	)
						if name.startswith("""transformer.resblocks"""	):
												snake_case_ :Union[str, Any]						=			name.replace("""transformer.resblocks""",					"""text_model.encoder.layers"""	)
						if "attn.out_proj" in name and "message" not in name:
												snake_case_ :Union[str, Any]						=			name.replace("""attn.out_proj""",					"""self_attn.out_proj"""	)
						if "ln_final" in name:
												snake_case_ :Union[str, Any]						=			name.replace("""ln_final""",					"""text_model.final_layer_norm"""	)
						# visual encoder
						if name == "visual.class_embedding":
												snake_case_ :Any						=			name.replace("""visual.class_embedding""",					"""vision_model.embeddings.class_embedding"""	)
						if name == "visual.positional_embedding":
												snake_case_ :Optional[int]						=			name.replace("""visual.positional_embedding""",					"""vision_model.embeddings.position_embedding.weight"""	)
						if name.startswith("""visual.transformer.resblocks"""	):
												snake_case_ :Union[str, Any]						=			name.replace("""visual.transformer.resblocks""",					"""vision_model.encoder.layers"""	)
						if "visual.conv1" in name:
												snake_case_ :int						=			name.replace("""visual.conv1""",					"""vision_model.embeddings.patch_embedding"""	)
						if "visual.ln_pre" in name:
												snake_case_ :Any						=			name.replace("""visual.ln_pre""",					"""vision_model.pre_layernorm"""	)
						if "visual.ln_post" in name:
												snake_case_ :str						=			name.replace("""visual.ln_post""",					"""vision_model.post_layernorm"""	)
						if "visual.proj" in name:
												snake_case_ :Union[str, Any]						=			name.replace("""visual.proj""",					"""visual_projection.weight"""	)
						if "text_projection" in name:
												snake_case_ :Dict						=			name.replace("""text_projection""",					"""text_projection.weight"""	)
						# things on top
						if "prompts_visual_proj" in name:
												snake_case_ :List[str]						=			name.replace("""prompts_visual_proj""",					"""prompts_visual_projection"""	)
						if "prompts_visual_ln" in name:
												snake_case_ :Dict						=			name.replace("""prompts_visual_ln""",					"""prompts_visual_layernorm"""	)
						# mit
						if name == "mit.positional_embedding":
												snake_case_ :str						=			name.replace("""positional""",					"""position"""	)
						if name.startswith("""mit.resblocks"""	):
												snake_case_ :Dict						=			name.replace("""mit.resblocks""",					"""mit.encoder.layers"""	)
						# prompts generator
						if name.startswith("""prompts_generator.norm"""	):
												snake_case_ :Union[str, Any]						=			name.replace("""prompts_generator.norm""",					"""prompts_generator.layernorm"""	)

						return name
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case_ :Dict						=			orig_state_dict.pop(_lowercase	)

												if "attn.in_proj" in key:
																		snake_case_ :Optional[Any]						=			key.split("""."""	)
																		if key.startswith("""visual"""	):
																								snake_case_ :Any						=			key_split[3]
																								snake_case_ :Optional[Any]						=			config.vision_config.hidden_size
																								if "message_attn" in key:
																														if "weight" in key:
																																				snake_case_ :str						=			val[
																																				    :dim, :
																																				]
																																				snake_case_ :Optional[int]						=			val[
																																				    dim : dim * 2, :
																																				]
																																				snake_case_ :Union[str, Any]						=			val[
																																				    -dim:, :
																																				]
																														else:
																																				snake_case_ :Dict						=			val[
																																				    :dim
																																				]
																																				snake_case_ :Optional[int]						=			val[
																																				    dim : dim * 2
																																				]
																																				snake_case_ :Optional[int]						=			val[
																																				    -dim:
																																				]
																								else:
																														if "weight" in key:
																																				snake_case_ :Optional[Any]						=			val[
																																				    :dim, :
																																				]
																																				snake_case_ :List[str]						=			val[
																																				    dim : dim * 2, :
																																				]
																																				snake_case_ :Dict						=			val[
																																				    -dim:, :
																																				]
																														else:
																																				snake_case_ :Union[str, Any]						=			val[:dim]
																																				snake_case_ :Union[str, Any]						=			val[
																																				    dim : dim * 2
																																				]
																																				snake_case_ :Union[str, Any]						=			val[-dim:]
																		elif key.startswith("""mit"""	):
																								snake_case_ :Tuple						=			key_split[2]
																								snake_case_ :Union[str, Any]						=			config.vision_config.mit_hidden_size
																								if "weight" in key:
																														snake_case_ :Optional[int]						=			val[:dim, :]
																														snake_case_ :Optional[int]						=			val[dim : dim * 2, :]
																														snake_case_ :str						=			val[-dim:, :]
																								else:
																														snake_case_ :str						=			val[:dim]
																														snake_case_ :Any						=			val[dim : dim * 2]
																														snake_case_ :int						=			val[-dim:]
																		else:
																								snake_case_ :Tuple						=			key_split[2]
																								snake_case_ :Any						=			config.text_config.hidden_size
																								if "weight" in key:
																														snake_case_ :Dict						=			val[:dim, :]
																														snake_case_ :Dict						=			val[
																														    dim : dim * 2, :
																														]
																														snake_case_ :List[str]						=			val[-dim:, :]
																								else:
																														snake_case_ :Any						=			val[:dim]
																														snake_case_ :Tuple						=			val[
																														    dim : dim * 2
																														]
																														snake_case_ :List[str]						=			val[-dim:]
												else:
																		snake_case_ :Optional[int]						=			rename_key(_lowercase	)
																		if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																								snake_case_ :Optional[Any]						=			val.T
																		snake_case_ :Tuple						=			val

						return orig_state_dict
def    A_  (			_lowercase	):







						'''simple docstring'''


						if num_frames == 8:
												snake_case_ :str						=			"""eating_spaghetti_8_frames.npy"""
						elif num_frames == 16:
												snake_case_ :int						=			"""eating_spaghetti.npy"""
						elif num_frames == 32:
												snake_case_ :List[str]						=			"""eating_spaghetti_32_frames.npy"""
						snake_case_ :int						=			hf_hub_download(
						    repo_id="""hf-internal-testing/spaghetti-video""",					filename=_lowercase,					repo_type="""dataset""",					)
						snake_case_ :Union[str, Any]						=			np.load(_lowercase	)
						return list(_lowercase	)
def    A_  (			_lowercase,					_lowercase=None,					_lowercase=False	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{
						    # fully supervised kinetics-400 checkpoints
						    """xclip-base-patch32""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth""",
						    """xclip-base-patch32-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"""
						    ),
						    """xclip-base-patch16""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth""",
						    """xclip-base-patch16-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"""
						    ),
						    """xclip-large-patch14""": """https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb""",
						    """xclip-large-patch14-16-frames""": """https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f""",
						    # fully supervised kinetics-600 checkpoints
						    """xclip-base-patch16-kinetics-600""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"""
						    ),
						    """xclip-base-patch16-kinetics-600-16-frames""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"""
						    ),
						    """xclip-large-patch14-kinetics-600""": """https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be""",
						    # few shot
						    """xclip-base-patch16-hmdb-2-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"""
						    ),
						    """xclip-base-patch16-hmdb-4-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"""
						    ),
						    """xclip-base-patch16-hmdb-8-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"""
						    ),
						    """xclip-base-patch16-hmdb-16-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"""
						    ),
						    """xclip-base-patch16-ucf-2-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"""
						    ),
						    """xclip-base-patch16-ucf-4-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"""
						    ),
						    """xclip-base-patch16-ucf-8-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"""
						    ),
						    """xclip-base-patch16-ucf-16-shot""": (
						        """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"""
						    ),
						    # zero shot
						    """xclip-base-patch16-zero-shot""": """https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth""",
						}

						snake_case_ :Optional[int]						=			model_to_url[model_name]
						snake_case_ :int						=			8
						if "16-frames" in model_name:
												snake_case_ :List[Any]						=			16
						elif "shot" in model_name:
												snake_case_ :Dict						=			32

						snake_case_ :Optional[int]						=			get_xclip_config(_lowercase,					_lowercase	)
						snake_case_ :Optional[Any]						=			XCLIPModel(_lowercase	)
						model.eval()

						if "drive" in checkpoint_url:
												snake_case_ :List[str]						=			"""pytorch_model.bin"""
												gdown.cached_download(_lowercase,					_lowercase,					quiet=_lowercase	)
												snake_case_ :List[Any]						=			torch.load(_lowercase,					map_location="""cpu"""	)["""model"""]
						else:
												snake_case_ :Tuple						=			torch.hub.load_state_dict_from_url(_lowercase	)["""model"""]

						snake_case_ :Union[str, Any]						=			convert_state_dict(_lowercase,					_lowercase	)

						snake_case_ :str						=			XCLIPModel(_lowercase	)
						snake_case_,       snake_case_ :Optional[int]						=			model.load_state_dict(_lowercase,					strict=_lowercase	)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						snake_case_ :List[str]						=			336 if model_name == """xclip-large-patch14-16-frames""" else 224
						snake_case_ :List[Any]						=			VideoMAEImageProcessor(size=_lowercase	)
						snake_case_ :Any						=			CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
						snake_case_ :str						=			CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""	)
						snake_case_ :Optional[Any]						=			XCLIPProcessor(image_processor=_lowercase,					tokenizer=_lowercase	)

						snake_case_ :Optional[int]						=			prepare_video(_lowercase	)
						snake_case_ :Optional[Any]						=			processor(
						    text=["""playing sports""", """eating spaghetti""", """go shopping"""],					videos=_lowercase,					return_tensors="""pt""",					padding=_lowercase	)

						print("""Shape of pixel values:""",					inputs.pixel_values.shape	)

						with torch.no_grad():
												snake_case_ :List[Any]						=			model(**_lowercase	)

						# Verify outputs
						snake_case_ :List[Any]						=			outputs.logits_per_video
						snake_case_ :Any						=			logits_per_video.softmax(dim=1	)
						print("""Probs:""",					_lowercase	)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												snake_case_ :Union[str, Any]						=			torch.tensor([[0.0019, 0.9951, 0.0030]]	)
						elif model_name == "xclip-base-patch32-16-frames":
												snake_case_ :str						=			torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]	)
						elif model_name == "xclip-base-patch16":
												snake_case_ :Tuple						=			torch.tensor([[0.0083, 0.9681, 0.0236]]	)
						elif model_name == "xclip-base-patch16-16-frames":
												snake_case_ :Any						=			torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]	)
						elif model_name == "xclip-large-patch14":
												snake_case_ :str						=			torch.tensor([[0.0062, 0.9864, 0.0075]]	)
						elif model_name == "xclip-large-patch14-16-frames":
												snake_case_ :Tuple						=			torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]	)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												snake_case_ :List[Any]						=			torch.tensor([[0.0555, 0.8914, 0.0531]]	)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												snake_case_ :Union[str, Any]						=			torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]]	)
						elif model_name == "xclip-large-patch14-kinetics-600":
												snake_case_ :List[Any]						=			torch.tensor([[0.0036, 0.9920, 0.0045]]	)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												snake_case_ :Dict						=			torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]	)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												snake_case_ :Union[str, Any]						=			torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]	)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												snake_case_ :str						=			torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]	)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												snake_case_ :str						=			torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]	)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												snake_case_ :int						=			torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]	)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												snake_case_ :Optional[int]						=			torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]	)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												snake_case_ :Any						=			torch.tensor([[0.0027, 0.9904, 0.0070]]	)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												snake_case_ :Tuple						=			torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]	)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												snake_case_ :Union[str, Any]						=			torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-03]]	)
						else:
												raise ValueError(f"""Model name {model_name} not supported"""	)
						assert torch.allclose(_lowercase,					_lowercase,					atol=1e-3	)
						print("""Looks ok!"""	)

						if pytorch_dump_folder_path is not None:
												print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
												model.save_pretrained(_lowercase	)

						if push_to_hub:
												print("""Pushing model, processor and slow tokenizer files to the hub..."""	)
												model.push_to_hub(_lowercase,					organization="""nielsr"""	)
												processor.push_to_hub(_lowercase,					organization="""nielsr"""	)
												slow_tokenizer.push_to_hub(_lowercase,					organization="""nielsr"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="xclip-base-patch32",
						    type=str,
						    help="Name of the model.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
						)
						parser.add_argument(
						    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
						)

						__a      =				parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> int:
								super().__init__()
								self.register_modules(transformer=lowerCAmelCase__      ,					vae=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )

								# create a imagenet -> id dictionary for easier use
								a      :		str	=				{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(","      ):
											a      :		int	=				int(lowerCAmelCase__      )
									a      :		Any	=				dict(sorted(self.labels.items()      )      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[int]:

								if not isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				list(lowerCAmelCase__      )

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = 4.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 50      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[ImagePipelineOutput, Tuple]:
								a      :		Dict	=				len(lowerCAmelCase__      )
								a      :		Tuple	=				self.transformer.config.sample_size
								a      :		Tuple	=				self.transformer.config.in_channels

								a      :		Optional[int]	=				randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=self.transformer.dtype      ,					)
								a      :		Tuple	=				torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								a      :		List[str]	=				torch.tensor(lowerCAmelCase__      ,					device=self.device      ).reshape(-1      )
								a      :		str	=				torch.tensor([1000] * batch_size      ,					device=self.device      )
								a      :		Dict	=				torch.cat([class_labels, class_null]      ,					0      ) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCAmelCase__      )

								for t in self.progress_bar(self.scheduler.timesteps      ):
									if guidance_scale > 1:
										a      :		Any	=				latent_model_input[: len(lowerCAmelCase__      ) // 2]
										a      :		Tuple	=				torch.cat([half, half]      ,					dim=0      )
									a      :		List[str]	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )

									a      :		Dict	=				t
									if not torch.is_tensor(lowerCAmelCase__      ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										a      :		Optional[int]	=				latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
											a      :		Union[str, Any]	=				torch.floataa if is_mps else torch.floataa
										else:
											a      :		str	=				torch.intaa if is_mps else torch.intaa
										a      :		List[str]	=				torch.tensor([timesteps]      ,					dtype=lowerCAmelCase__      ,					device=latent_model_input.device      )
									elif len(timesteps.shape      ) == 0:
										a      :		List[Any]	=				timesteps[None].to(latent_model_input.device      )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									a      :		Dict	=				timesteps.expand(latent_model_input.shape[0]      )
									# predict noise model_output
									a      :		Union[str, Any]	=				self.transformer(
									    lowerCAmelCase__      ,					timestep=lowerCAmelCase__      ,					class_labels=lowerCAmelCase__      ).sample

									# perform guidance
									if guidance_scale > 1:
										a,       a      :		Any	=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										a,       a      :		Union[str, Any]	=				torch.split(lowerCAmelCase__      ,					len(lowerCAmelCase__      ) // 2      ,					dim=0      )

										a      :		Dict	=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										a      :		Optional[int]	=				torch.cat([half_eps, half_eps]      ,					dim=0      )

										a      :		Optional[int]	=				torch.cat([eps, rest]      ,					dim=1      )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										a,       a      :		str	=				torch.split(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dim=1      )
									else:
										a      :		Any	=				noise_pred

									# compute previous image: x_t -> x_t-1
									a      :		Optional[int]	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								if guidance_scale > 1:
									a,       a      :		Tuple	=				latent_model_input.chunk(2      ,					dim=0      )
								else:
									a      :		Tuple	=				latent_model_input

								a      :		Optional[Any]	=				1 / self.vae.config.scaling_factor * latents
								a      :		Any	=				self.vae.decode(lowerCAmelCase__      ).sample

								a      :		Tuple	=				(samples / 2 + 0.5).clamp(0      ,					1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a      :		Optional[Any]	=				samples.cpu().permute(0      ,					2      ,					3      ,					1      ).float().numpy()

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

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


