'''simple docstring'''

import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
A_        =   logging.get_logger()




@dataclass
class 		SCREAMING_SNAKE_CASE_				:



			"""simple docstring"""






			__magic_name__   :							nn.Module
			__magic_name__   :							List[nn.Module]             =					field(default_factory=_UpperCAmelCase			)
			__magic_name__   :							list             =					field(default_factory=_UpperCAmelCase			)
			__magic_name__   :							Dict[str, nn.Module]             =					field(default_factory=_UpperCAmelCase			)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_				):
									snake_case   :					List[Any]							=						len(list(m.modules()				)				) == 1 or isinstance(snake_case_							, nn.Convad				) or isinstance(snake_case_							, nn.BatchNormad				)
									if has_not_submodules:
															self.traced.append(snake_case_				)
															snake_case   :					Optional[Any]							=						m



			def __call__(       self							, snake_case_				):
									for name, m in self.module.named_modules():
															self.handles.append(m.register_forward_hook(partial(self._forward_hook							, name=snake_case_				)				)				)
									self.module(snake_case_				)
									[x.remove() for x in self.handles]
									return self






			@property
			def      lowerCamelCase   (       self				):
									# check the len of the state_dict keys to see if we have learnable params
									return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()				)				) > 0}







class 		SCREAMING_SNAKE_CASE_				(nn.Module			):



			"""simple docstring"""






			def __init__(       self							, snake_case_				):
									super().__init__()

									snake_case   :					List[Tuple[str, nn.Module]]							=						[]
									# - get the stem
									feature_blocks.append(("conv1", model.stem)				)
									# - get all the feature blocks
									for k, v in model.trunk_output.named_children():
															assert k.startswith("block"				), F"""Unexpected layer name {k}"""
															snake_case   :					Optional[Any]							=						len(snake_case_				) + 1
															feature_blocks.append((F"""res{block_index}""", v)				)

									snake_case   :					Tuple							=						nn.ModuleDict(snake_case_				)






			def      lowerCamelCase   (       self							, snake_case_				):
									return get_trunk_forward_outputs(
									    snake_case_							, out_feat_keys=snake_case_							, feature_blocks=self._feature_blocks							, )







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			def      lowerCamelCase   (       self				):
									return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def 					UpperCAmelCase__      (							_lowerCAmelCase						: str)						->		Dict[str, str]:



						"""simple docstring"""

						snake_case   :					str							=						RegNetConfig(depths=[2, 7, 17, 1],			hidden_sizes=[8, 8, 8, 8],			groups_width=8)
						if "in1k" in model_name:
												snake_case   :					Dict							=						RegNetForImageClassification(_lowerCAmelCase)
						else:
												snake_case   :					List[str]							=						RegNetModel(_lowerCAmelCase)
						# create from model (with small weights)
						snake_case   :					Union[str, Any]							=						FakeRegNetVisslWrapper(
						    RegNet(FakeRegNetParams(depth=27,			group_width=1010,			w_a=1744,			w_a=620.83,			w_m=2.52)))

						with torch.no_grad():
												snake_case   :					Optional[Any]							=						from_model.eval()
												snake_case   :					List[Any]							=						our_model.eval()

												snake_case   :					Tuple							=						torch.randn((1, 3, 32, 32))
												# trace both
												snake_case   :					Optional[Any]							=						Tracker(_lowerCAmelCase)
												snake_case   :					Union[str, Any]							=						dest_tracker(_lowerCAmelCase).parametrized

												pprint(dest_tracker.nameamodule)
												snake_case   :					Dict							=						Tracker(_lowerCAmelCase)
												snake_case   :					Optional[int]							=						src_tracker(_lowerCAmelCase).parametrized

						# convert the keys -> module dict to keys -> params
						def to_params_dict(_lowerCAmelCase						: Any):
												snake_case   :					int							=						OrderedDict()
												for name, module in dict_with_modules.items():
																		for param_name, param in module.state_dict().items():
																								snake_case   :					Dict							=						param
												return params_dict

						snake_case   :					List[str]							=						{}

						snake_case   :					Any							=						to_params_dict(_lowerCAmelCase)
						snake_case   :					Optional[int]							=						to_params_dict(_lowerCAmelCase)

						for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),			dst_state_dict.items()):
												snake_case   :					Union[str, Any]							=						dest_key
												logger.info(f"""{src_key} -> {dest_key}""")
						# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
						if "in1k" in model_name:
												snake_case   :					Optional[int]							=						"classifier.1.weight"
												snake_case   :					Tuple							=						"classifier.1.bias"

						return from_to_ours_keys


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Path,			_lowerCAmelCase						: str = None,			_lowerCAmelCase						: bool = True)						->		Tuple:



						"""simple docstring"""

						snake_case   :					Any							=						"imagenet-1k-id2label.json"
						snake_case   :					Optional[Any]							=						1000

						snake_case   :					Union[str, Any]							=						"huggingface/label-files"
						snake_case   :					str							=						num_labels
						snake_case   :					Optional[Any]							=						json.load(open(cached_download(hf_hub_url(_lowerCAmelCase,			_lowerCAmelCase,			repo_type="dataset")),			"r"))
						snake_case   :					List[Any]							=						{int(_lowerCAmelCase): v for k, v in idalabel.items()}

						snake_case   :					Union[str, Any]							=						idalabel
						snake_case   :					Tuple							=						{v: k for k, v in idalabel.items()}

						snake_case   :					List[str]							=						partial(_lowerCAmelCase,			num_labels=_lowerCAmelCase,			idalabel=_lowerCAmelCase,			labelaid=_lowerCAmelCase)

						snake_case   :					Any							=						{
						    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1],			hidden_sizes=[2020, 4040, 1_1110, 2_8280],			groups_width=1010),
						    # finetuned on imagenet
						    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
						        depths=[2, 7, 17, 1],			hidden_sizes=[2020, 4040, 1_1110, 2_8280],			groups_width=1010),
						}

						# add seer weights logic
						def load_using_classy_vision(_lowerCAmelCase						: str) -> Tuple[Dict, Dict]:
												snake_case   :					Any							=						torch.hub.load_state_dict_from_url(_lowerCAmelCase,			model_dir=str(_lowerCAmelCase),			map_location="cpu")
												# check if we have a head, if yes add it
												snake_case   :					List[Any]							=						files["classy_state_dict"]["base_model"]["model"]
												return model_state_dict["trunk"], model_state_dict["heads"]

						snake_case   :					Union[str, Any]							=						{
						    "regnet-y-10b-seer": partial(
						        _lowerCAmelCase,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch",			),
						    "regnet-y-10b-seer-in1k": partial(
						        _lowerCAmelCase,			"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch",			),
						}

						snake_case   :					List[str]							=						get_from_to_our_keys(_lowerCAmelCase)

						if not (save_directory / f"""{model_name}.pth""").exists():
												logger.info("Loading original state_dict.")
												snake_case							,				snake_case   :					List[Any]							=						names_to_from_model[model_name]()
												snake_case   :					List[str]							=						from_state_dict_trunk
												if "in1k" in model_name:
																		# add the head
																		snake_case   :					List[Any]							=						{**from_state_dict_trunk, **from_state_dict_head}
												logger.info("Done!")

												snake_case   :					List[Any]							=						{}

												snake_case   :					Dict							=						list(from_state_dict.keys())
												snake_case   :					Optional[Any]							=						r"\.block.-part."
												# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
												for key in from_state_dict.keys():
																		# remove the weird "block[0,1]-part" from the key
																		snake_case   :					str							=						re.sub(_lowerCAmelCase,			"",			_lowerCAmelCase)
																		# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
																		snake_case   :					Optional[int]							=						from_to_ours_keys[src_key]
																		# store the parameter with our key
																		snake_case   :					List[str]							=						from_state_dict[key]
																		not_used_keys.remove(_lowerCAmelCase)
												# check that all keys have been updated
												assert len(_lowerCAmelCase) == 0, f"""Some keys where not used {",".join(_lowerCAmelCase)}"""

												logger.info(f"""The following keys were not used: {",".join(_lowerCAmelCase)}""")

												# save our state dict to disk
												torch.save(_lowerCAmelCase,			save_directory / f"""{model_name}.pth""")

												del converted_state_dict
						else:
												logger.info("The state_dict was already stored on disk.")
						if push_to_hub:
												logger.info(f"""Token is {os.environ["HF_TOKEN"]}""")
												logger.info("Loading our model.")
												# create our model
												snake_case   :					Any							=						names_to_config[model_name]
												snake_case   :					str							=						RegNetModel
												if "in1k" in model_name:
																		snake_case   :					Dict							=						RegNetForImageClassification
												snake_case   :					Any							=						our_model_func(_lowerCAmelCase)
												# place our model to the meta device (so remove all the weights)
												our_model.to(torch.device("meta"))
												logger.info("Loading state_dict in our model.")
												# load state dict
												snake_case   :					Tuple							=						our_model.state_dict().keys()
												PreTrainedModel._load_pretrained_model_low_mem(
												    _lowerCAmelCase,			_lowerCAmelCase,			[save_directory / f"""{model_name}.pth"""])
												logger.info("Finally, pushing!")
												# push it to hub
												our_model.push_to_hub(
												    repo_path_or_name=save_directory / model_name,			commit_message="Add model",			output_dir=save_directory / model_name,			)
												snake_case   :					Tuple							=						384
												# we can use the convnext one
												snake_case   :					List[str]							=						AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k",			size=_lowerCAmelCase)
												image_processor.push_to_hub(
												    repo_path_or_name=save_directory / model_name,			commit_message="Add image processor",			output_dir=save_directory / model_name,			)


if __name__ == "__main__":
						A_        =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default=None,
						    type=str,
						    help=(
						        "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
						        " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=Path,
						    required=True,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument(
						    "--push_to_hub",
						    default=True,
						    type=bool,
						    required=False,
						    help="If True, push model and image processor to the hub.",
						)

						A_        =   parser.parse_args()

						A_        =   args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

A_        =   "\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int,			_lowerCAmelCase						: Any,			_lowerCAmelCase						: Optional[Any]=8)						->		Optional[int]:



						"""simple docstring"""

						snake_case   :					Any							=						height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						snake_case   :					Tuple							=						width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor


def 					UpperCAmelCase__      (							_lowerCAmelCase						: Tuple,			_lowerCAmelCase						: List[Any]=512,			_lowerCAmelCase						: int=512)						->		Any:



						"""simple docstring"""

						snake_case   :					str							=						pil_image.resize((w, h),			resample=Image.BICUBIC,			reducing_gap=1)
						snake_case   :					List[Any]							=						np.array(pil_image.convert("RGB"))
						snake_case   :					Optional[Any]							=						arr.astype(np.floataa) / 127.5 - 1
						snake_case   :					int							=						np.transpose(_lowerCAmelCase,			[2, 0, 1])
						snake_case   :					str							=						torch.from_numpy(_lowerCAmelCase).unsqueeze(0)
						return image




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			def __init__(       self							, snake_case_							, snake_case_							, snake_case_							, ):
									super().__init__()

									self.register_modules(
									    unet=snake_case_							, scheduler=snake_case_							, movq=snake_case_							, )
									snake_case   :					Union[str, Any]							=						2 ** (len(self.movq.config.block_out_channels				) - 1)



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_				):
									# get the original timestep using init_timestep
									snake_case   :					List[Any]							=						min(int(num_inference_steps * strength				)							, snake_case_				)

									snake_case   :					Optional[int]							=						max(num_inference_steps - init_timestep							, 0				)
									snake_case   :					int							=						self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start



			def      lowerCamelCase   (       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_=None				):
									if not isinstance(snake_case_							, (torch.Tensor, PIL.Image.Image, list)				):
															raise ValueError(
															    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case_				)}"""				)

									snake_case   :					Union[str, Any]							=						image.to(device=snake_case_							, dtype=snake_case_				)

									snake_case   :					str							=						batch_size * num_images_per_prompt

									if image.shape[1] == 4:
															snake_case   :					Dict							=						image

									else:
															if isinstance(snake_case_							, snake_case_				) and len(snake_case_				) != batch_size:
																					raise ValueError(
																					    F"""You have passed a list of generators of length {len(snake_case_				)}, but requested an effective batch"""
																					    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""				)

															elif isinstance(snake_case_							, snake_case_				):
																					snake_case   :					Optional[Any]							=						[
																					    self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(snake_case_				)
																					]
																					snake_case   :					Optional[Any]							=						torch.cat(snake_case_							, dim=0				)
															else:
																					snake_case   :					List[Any]							=						self.movq.encode(snake_case_				).latent_dist.sample(snake_case_				)

															snake_case   :					List[str]							=						self.movq.config.scaling_factor * init_latents

									snake_case   :					int							=						torch.cat([init_latents]							, dim=0				)

									snake_case   :					str							=						init_latents.shape
									snake_case   :					Dict							=						randn_tensor(snake_case_							, generator=snake_case_							, device=snake_case_							, dtype=snake_case_				)

									# get latents
									snake_case   :					int							=						self.scheduler.add_noise(snake_case_							, snake_case_							, snake_case_				)

									snake_case   :					Optional[int]							=						init_latents

									return latents



			def      lowerCamelCase   (       self							, snake_case_=0				):
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError("Please install accelerate via `pip install accelerate`"				)

									snake_case   :					Dict							=						torch.device(F"""cuda:{gpu_id}"""				)

									snake_case   :					List[str]							=						[
									    self.unet,
									    self.movq,
									]
									for cpu_offloaded_model in models:
															if cpu_offloaded_model is not None:
																					cpu_offload(snake_case_							, snake_case_				)



			def      lowerCamelCase   (       self							, snake_case_=0				):
									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."				)

									snake_case   :					Dict							=						torch.device(F"""cuda:{gpu_id}"""				)

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

									snake_case   :					Any							=						None
									for cpu_offloaded_model in [self.unet, self.movq]:
															snake_case							,				snake_case   :					Tuple							=						cpu_offload_with_hook(snake_case_							, snake_case_							, prev_module_hook=snake_case_				)

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



			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def      lowerCamelCase   (       self				):
									if not hasattr(self.unet							, "_hf_hook"				):
															return self.device
									for module in self.unet.modules():
															if (
															    hasattr(snake_case_							, "_hf_hook"				)
															    and hasattr(module._hf_hook							, "execution_device"				)
															    and module._hf_hook.execution_device is not None
															):
																					return torch.device(module._hf_hook.execution_device				)
									return self.device






			@torch.no_grad()
			@replace_example_docstring(snake_case_				)
			def __call__(       self							, snake_case_							, snake_case_							, snake_case_							, snake_case_							, snake_case_ = 512							, snake_case_ = 512							, snake_case_ = 100							, snake_case_ = 4.0							, snake_case_ = 0.3							, snake_case_ = 1							, snake_case_ = None							, snake_case_ = "pil"							, snake_case_ = True							, ):
									snake_case   :					List[Any]							=						self._execution_device

									snake_case   :					int							=						guidance_scale > 1.0

									if isinstance(snake_case_							, snake_case_				):
															snake_case   :					Optional[Any]							=						torch.cat(snake_case_							, dim=0				)
									if isinstance(snake_case_							, snake_case_				):
															snake_case   :					int							=						torch.cat(snake_case_							, dim=0				)
									if isinstance(snake_case_							, snake_case_				):
															snake_case   :					List[Any]							=						torch.cat(snake_case_							, dim=0				)

									snake_case   :					Tuple							=						image_embeds.shape[0]

									if do_classifier_free_guidance:
															snake_case   :					Union[str, Any]							=						image_embeds.repeat_interleave(snake_case_							, dim=0				)
															snake_case   :					str							=						negative_image_embeds.repeat_interleave(snake_case_							, dim=0				)
															snake_case   :					Dict							=						hint.repeat_interleave(snake_case_							, dim=0				)

									snake_case   :					int							=						torch.cat([negative_image_embeds, image_embeds]							, dim=0				).to(dtype=self.unet.dtype							, device=snake_case_				)
									snake_case   :					Optional[Any]							=						torch.cat([hint, hint]							, dim=0				).to(dtype=self.unet.dtype							, device=snake_case_				)

									if not isinstance(snake_case_							, snake_case_				):
															snake_case   :					Dict							=						[image]
									if not all(isinstance(snake_case_							, (PIL.Image.Image, torch.Tensor)				) for i in image				):
															raise ValueError(
															    F"""Input is in incorrect format: {[type(snake_case_				) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""				)

									snake_case   :					Union[str, Any]							=						torch.cat([prepare_image(snake_case_							, snake_case_							, snake_case_				) for i in image]							, dim=0				)
									snake_case   :					List[Any]							=						image.to(dtype=image_embeds.dtype							, device=snake_case_				)

									snake_case   :					int							=						self.movq.encode(snake_case_				)["latents"]
									snake_case   :					Tuple							=						latents.repeat_interleave(snake_case_							, dim=0				)
									self.scheduler.set_timesteps(snake_case_							, device=snake_case_				)
									snake_case							,				snake_case   :					str							=						self.get_timesteps(snake_case_							, snake_case_							, snake_case_				)
									snake_case   :					Dict							=						timesteps[:1].repeat(batch_size * num_images_per_prompt				)
									snake_case							,				snake_case   :					Union[str, Any]							=						downscale_height_and_width(snake_case_							, snake_case_							, self.movq_scale_factor				)
									snake_case   :					List[Any]							=						self.prepare_latents(
									    snake_case_							, snake_case_							, snake_case_							, snake_case_							, image_embeds.dtype							, snake_case_							, snake_case_				)
									for i, t in enumerate(self.progress_bar(snake_case_				)				):
															# expand the latents if we are doing classifier free guidance
															snake_case   :					Union[str, Any]							=						torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

															snake_case   :					Any							=						{"image_embeds": image_embeds, "hint": hint}
															snake_case   :					List[str]							=						self.unet(
															    sample=snake_case_							, timestep=snake_case_							, encoder_hidden_states=snake_case_							, added_cond_kwargs=snake_case_							, return_dict=snake_case_							, )[0]

															if do_classifier_free_guidance:
																					snake_case							,				snake_case   :					List[str]							=						noise_pred.split(latents.shape[1]							, dim=1				)
																					snake_case							,				snake_case   :					Optional[Any]							=						noise_pred.chunk(2				)
																					snake_case							,				snake_case   :					Tuple							=						variance_pred.chunk(2				)
																					snake_case   :					Optional[Any]							=						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																					snake_case   :					Any							=						torch.cat([noise_pred, variance_pred_text]							, dim=1				)

															if not (
															    hasattr(self.scheduler.config							, "variance_type"				)
															    and self.scheduler.config.variance_type in ["learned", "learned_range"]
															):
																					snake_case							,				snake_case   :					Optional[Any]							=						noise_pred.split(latents.shape[1]							, dim=1				)

															# compute the previous noisy sample x_t -> x_t-1

															snake_case   :					Union[str, Any]							=						self.scheduler.step(
															    snake_case_							, snake_case_							, snake_case_							, generator=snake_case_							, )[0]

									# post-processing
									snake_case   :					Dict							=						self.movq.decode(snake_case_							, force_not_quantize=snake_case_				)["sample"]

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

									if output_type in ["np", "pil"]:
															snake_case   :					Any							=						image * 0.5 + 0.5
															snake_case   :					List[str]							=						image.clamp(0							, 1				)
															snake_case   :					List[str]							=						image.cpu().permute(0							, 2							, 3							, 1				).float().numpy()

									if output_type == "pil":
															snake_case   :					str							=						self.numpy_to_pil(snake_case_				)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=snake_case_				)





