
"""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 			_lowercase  :
	lowercase_								=      42
	lowercase_								=      field(default_factory=__UpperCAmelCase						)
	lowercase_								=      field(default_factory=__UpperCAmelCase						)
	lowercase_								=      field(default_factory=__UpperCAmelCase						)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				int:
			lowerCamelCase	:     Union[str, Any]            =       len(list(m.modules()   )   ) == 1 or isinstance(UpperCAmelCase_						,					nn.Convad   ) or isinstance(UpperCAmelCase_						,					nn.BatchNormad   )
			if has_not_submodules:
					self.traced.append(UpperCAmelCase_   )
					lowerCamelCase	:     List[str]            =       m




	def __call__(					self						,					UpperCAmelCase_   )						->				Union[str, Any]:
			for name, m in self.module.named_modules():
					self.handles.append(m.register_forward_hook(partial(self._forward_hook						,					name=UpperCAmelCase_   )   )   )
			self.module(UpperCAmelCase_   )
			[x.remove() for x in self.handles]
			return self




	@property
	def      _UpperCamelCase       (					self   )						->				Dict:
			# 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 			_lowercase  (							nn.Module						):




	def __init__(					self						,					UpperCAmelCase_   )						->				Optional[Any]:
			super().__init__()

			lowerCamelCase	:     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}"""
					lowerCamelCase	:     str            =       len(UpperCAmelCase_   ) + 1
					feature_blocks.append((F"""res{block_index}""", v)   )

			lowerCamelCase	:     Union[str, Any]            =       nn.ModuleDict(UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				Any:
			return get_trunk_forward_outputs(
			    UpperCAmelCase_						,					out_feat_keys=UpperCAmelCase_						,					feature_blocks=self._feature_blocks						,					)






class 			_lowercase  (							__UpperCAmelCase						):




	def      _UpperCamelCase       (					self   )						->				List[str]:
			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   (						a_     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[Any]            =       RegNetConfig(depths=[2, 7, 17, 1],		hidden_sizes=[8, 8, 8, 8],		groups_width=8     )
		if "in1k" in model_name:
				lowerCamelCase	:     Union[str, Any]            =       RegNetForImageClassification(a_     )
		else:
				lowerCamelCase	:     Optional[int]            =       RegNetModel(a_     )
		# create from model (with small weights)
		lowerCamelCase	:     Any            =       FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=27,		group_width=1010,		w_a=1744,		w_a=6_2_0.8_3,		w_m=2.5_2     )     )     )

		with torch.no_grad():
				lowerCamelCase	:     Any            =       from_model.eval()
				lowerCamelCase	:     List[str]            =       our_model.eval()

				lowerCamelCase	:     str            =       torch.randn((1, 3, 32, 32)     )
				# trace both
				lowerCamelCase	:     Tuple            =       Tracker(a_     )
				lowerCamelCase	:     Optional[int]            =       dest_tracker(a_     ).parametrized

				pprint(dest_tracker.nameamodule     )
				lowerCamelCase	:     List[str]            =       Tracker(a_     )
				lowerCamelCase	:     Union[str, Any]            =       src_tracker(a_     ).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(a_     ):
				lowerCamelCase	:     Any            =       OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								lowerCamelCase	:     Optional[Any]            =       param
				return params_dict

		lowerCamelCase	:     Optional[Any]            =       {}

		lowerCamelCase	:     Optional[Any]            =       to_params_dict(a_     )
		lowerCamelCase	:     List[Any]            =       to_params_dict(a_     )

		for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),		dst_state_dict.items()     ):
				lowerCamelCase	:     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:
				lowerCamelCase	:     List[Any]            =       'classifier.1.weight'
				lowerCamelCase	:     Optional[int]            =       'classifier.1.bias'

		return from_to_ours_keys
def 						UpperCAmelCase   (						a_,		a_ = None,		a_ = True     ):





		'''simple docstring'''


		lowerCamelCase	:     Optional[int]            =       'imagenet-1k-id2label.json'
		lowerCamelCase	:     str            =       1000

		lowerCamelCase	:     int            =       'huggingface/label-files'
		lowerCamelCase	:     Any            =       num_labels
		lowerCamelCase	:     Tuple            =       json.load(open(cached_download(hf_hub_url(a_,		a_,		repo_type='dataset'     )     ),		'r'     )     )
		lowerCamelCase	:     List[Any]            =       {int(a_     ): v for k, v in idalabel.items()}

		lowerCamelCase	:     Optional[int]            =       idalabel
		lowerCamelCase	:     Tuple            =       {v: k for k, v in idalabel.items()}

		lowerCamelCase	:     Optional[Any]            =       partial(a_,		num_labels=a_,		idalabel=a_,		labelaid=a_     )

		lowerCamelCase	:     Optional[int]            =       {
		    '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(a_     ) -> Tuple[Dict, Dict]:
				lowerCamelCase	:     Union[str, Any]            =       torch.hub.load_state_dict_from_url(a_,		model_dir=str(a_     ),		map_location='cpu'     )
				# check if we have a head, if yes add it
				lowerCamelCase	:     Optional[Any]            =       files['classy_state_dict']['base_model']['model']
				return model_state_dict["trunk"], model_state_dict["heads"]

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

		lowerCamelCase	:     str            =       get_from_to_our_keys(a_     )

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

				lowerCamelCase	:     Any            =       {}

				lowerCamelCase	:     Union[str, Any]            =       list(from_state_dict.keys()     )
				lowerCamelCase	:     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
						lowerCamelCase	:     Union[str, Any]            =       re.sub(a_,		'',		a_     )
						# 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
						lowerCamelCase	:     int            =       from_to_ours_keys[src_key]
						# store the parameter with our key
						lowerCamelCase	:     Union[str, Any]            =       from_state_dict[key]
						not_used_keys.remove(a_     )
				# check that all keys have been updated
				assert len(a_     ) == 0, F"""Some keys where not used {','.join(a_     )}"""

				logger.info(F"""The following keys were not used: {','.join(a_     )}"""     )

				# save our state dict to disk
				torch.save(a_,		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
				lowerCamelCase	:     Optional[Any]            =       names_to_config[model_name]
				lowerCamelCase	:     Dict            =       RegNetModel
				if "in1k" in model_name:
						lowerCamelCase	:     Any            =       RegNetForImageClassification
				lowerCamelCase	:     int            =       our_model_func(a_     )
				# 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
				lowerCamelCase	:     Optional[Any]            =       our_model.state_dict().keys()
				PreTrainedModel._load_pretrained_model_low_mem(
				    a_,		a_,		[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,		)
				lowerCamelCase	:     List[Any]            =       384
				# we can use the convnext one
				lowerCamelCase	:     Any            =       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k',		size=a_     )
				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   (						a_,		a_,		a_=8     ):





		'''simple docstring'''


		lowerCamelCase	:     List[str]            =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCamelCase	:     List[str]            =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor
def 						UpperCAmelCase   (						a_,		a_=512,		a_=512     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       pil_image.resize((w, h),		resample=Image.BICUBIC,		reducing_gap=1     )
		lowerCamelCase	:     Tuple            =       np.array(pil_image.convert('RGB'     )     )
		lowerCamelCase	:     List[str]            =       arr.astype(np.floataa     ) / 1_2_7.5 - 1
		lowerCamelCase	:     Optional[int]            =       np.transpose(a_,		[2, 0, 1]     )
		lowerCamelCase	:     int            =       torch.from_numpy(a_     ).unsqueeze(0     )
		return image






class 			_lowercase  (							__UpperCAmelCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				Optional[Any]:
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_						,					scheduler=UpperCAmelCase_						,					movq=UpperCAmelCase_						,					)
			lowerCamelCase	:     Any            =       2 ** (len(self.movq.config.block_out_channels   ) - 1)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				str:
			# get the original timestep using init_timestep
			lowerCamelCase	:     List[str]            =       min(int(num_inference_steps * strength   )						,					UpperCAmelCase_   )

			lowerCamelCase	:     Dict            =       max(num_inference_steps - init_timestep						,					0   )
			lowerCamelCase	:     Optional[Any]            =       self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None   )						->				Union[str, Any]:
			if not isinstance(UpperCAmelCase_						,					(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(UpperCAmelCase_   )}"""   )

			lowerCamelCase	:     Optional[Any]            =       image.to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					lowerCamelCase	:     Any            =       image

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

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

					lowerCamelCase	:     List[Any]            =       self.movq.config.scaling_factor * init_latents

			lowerCamelCase	:     List[str]            =       torch.cat([init_latents]						,					dim=0   )

			lowerCamelCase	:     List[Any]            =       init_latents.shape
			lowerCamelCase	:     str            =       randn_tensor(UpperCAmelCase_						,					generator=UpperCAmelCase_						,					device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			# get latents
			lowerCamelCase	:     Union[str, Any]            =       self.scheduler.add_noise(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       init_latents

			return latents




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Tuple:
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'   )

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

			lowerCamelCase	:     Optional[int]            =       [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Optional[Any]:
			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.'   )

			lowerCamelCase	:     str            =       torch.device(F"""cuda:{gpu_id}"""   )

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

			lowerCamelCase	:     Optional[int]            =       None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowerCamelCase      ,       lowerCamelCase	:     Any            =       cpu_offload_with_hook(UpperCAmelCase_						,					UpperCAmelCase_						,					prev_module_hook=UpperCAmelCase_   )

			# We'll offload the last model manually.
			lowerCamelCase	:     Optional[Any]            =       hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			if not hasattr(self.unet						,					'_hf_hook'   ):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(UpperCAmelCase_						,					'_hf_hook'   )
					    and hasattr(module._hf_hook						,					'execution_device'   )
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device   )
			return self.device




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

			lowerCamelCase	:     int            =       guidance_scale > 1.0

			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[str]            =       torch.cat(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       image_embeds.shape[0]

			if do_classifier_free_guidance:
					lowerCamelCase	:     str            =       image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Tuple            =       negative_image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Optional[Any]            =       hint.repeat_interleave(UpperCAmelCase_						,					dim=0   )

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

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

			lowerCamelCase	:     int            =       torch.cat([prepare_image(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) for i in image]						,					dim=0   )
			lowerCamelCase	:     Tuple            =       image.to(dtype=image_embeds.dtype						,					device=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.movq.encode(UpperCAmelCase_   )['latents']
			lowerCamelCase	:     Tuple            =       latents.repeat_interleave(UpperCAmelCase_						,					dim=0   )
			self.scheduler.set_timesteps(UpperCAmelCase_						,					device=UpperCAmelCase_   )
			lowerCamelCase      ,       lowerCamelCase	:     str            =       self.get_timesteps(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       timesteps[:1].repeat(batch_size * num_images_per_prompt   )
			lowerCamelCase      ,       lowerCamelCase	:     int            =       downscale_height_and_width(UpperCAmelCase_						,					UpperCAmelCase_						,					self.movq_scale_factor   )
			lowerCamelCase	:     List[Any]            =       self.prepare_latents(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					image_embeds.dtype						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			for i, t in enumerate(self.progress_bar(UpperCAmelCase_   )   ):
					# expand the latents if we are doing classifier free guidance
					lowerCamelCase	:     List[Any]            =       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

					lowerCamelCase	:     Tuple            =       {'image_embeds': image_embeds, 'hint': hint}
					lowerCamelCase	:     List[Any]            =       self.unet(
					    sample=UpperCAmelCase_						,					timestep=UpperCAmelCase_						,					encoder_hidden_states=UpperCAmelCase_						,					added_cond_kwargs=UpperCAmelCase_						,					return_dict=UpperCAmelCase_						,					)[0]

					if do_classifier_free_guidance:
							lowerCamelCase      ,       lowerCamelCase	:     int            =       noise_pred.split(latents.shape[1]						,					dim=1   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.chunk(2   )
							lowerCamelCase      ,       lowerCamelCase	:     Any            =       variance_pred.chunk(2   )
							lowerCamelCase	:     Any            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowerCamelCase	:     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"]
					):
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.split(latents.shape[1]						,					dim=1   )

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

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

			# post-processing
			lowerCamelCase	:     Optional[Any]            =       self.movq.decode(UpperCAmelCase_						,					force_not_quantize=UpperCAmelCase_   )['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"]:
					lowerCamelCase	:     Optional[Any]            =       image * 0.5 + 0.5
					lowerCamelCase	:     Any            =       image.clamp(0						,					1   )
					lowerCamelCase	:     Any            =       image.cpu().permute(0						,					2						,					3						,					1   ).float().numpy()

			if output_type == "pil":
					lowerCamelCase	:     Tuple            =       self.numpy_to_pil(UpperCAmelCase_   )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_   )






