
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()
UpperCAmelCase__				:  Optional[int]       					=   logging.get_logger()



@dataclass
class 			UpperCamelCase_			:




							'''simple docstring'''

							UpperCamelCase_					   =							42
							UpperCamelCase_					   =							field(default_factory=__lowerCamelCase   )
							UpperCamelCase_					   =							field(default_factory=__lowerCamelCase   )
							UpperCamelCase_					   =							field(default_factory=__lowerCamelCase   )
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Any:
										UpperCamelCase__    :		List[str]     =				len(list(m.modules())) == 1 or isinstance(_lowerCAmelCase     ,						nn.Convad) or isinstance(_lowerCAmelCase     ,						nn.BatchNormad)
										if has_not_submodules:
													self.traced.append(_lowerCAmelCase)
													UpperCamelCase__    :		Optional[int]     =				m
							def __call__(				self     ,						UpperCamelCase) ->	Optional[int]:
										for name, m in self.module.named_modules():
													self.handles.append(m.register_forward_hook(partial(self._forward_hook     ,						name=_lowerCAmelCase)))
										self.module(_lowerCAmelCase)
										[x.remove() for x in self.handles]
										return self






							@property
							def        lowerCAmelCase__ (				self) ->	List[Any]:
										# 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 			UpperCamelCase_			(						nn.Module   ):




							'''simple docstring'''

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

										UpperCamelCase__    :		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}"""
													UpperCamelCase__    :		Dict     =				len(_lowerCAmelCase) + 1
													feature_blocks.append((F"""res{block_index}""", v))

										UpperCamelCase__    :		Optional[int]     =				nn.ModuleDict(_lowerCAmelCase)






							def        lowerCAmelCase__ (				self     ,						UpperCamelCase) ->	Dict:
										return get_trunk_forward_outputs(
										    _lowerCAmelCase     ,						out_feat_keys=_lowerCAmelCase     ,						feature_blocks=self._feature_blocks     ,						)



class 			UpperCamelCase_			(						__lowerCamelCase   ):




							'''simple docstring'''

							def        lowerCAmelCase__ (				self) ->	Dict:
										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       _lowercase					(      __SCREAMING_SNAKE_CASE )	->     Dict[str, str]:
			UpperCamelCase__    :		Dict     =				RegNetConfig(depths=[2, 7, 17, 1]				,    hidden_sizes=[8, 8, 8, 8]				,    groups_width=8 )
			if "in1k" in model_name:
						UpperCamelCase__    :		Optional[Any]     =				RegNetForImageClassification(__lowerCamelCase )
			else:
						UpperCamelCase__    :		Optional[Any]     =				RegNetModel(__lowerCamelCase )
			# create from model (with small weights)
			UpperCamelCase__    :		str     =				FakeRegNetVisslWrapper(
			    RegNet(FakeRegNetParams(depth=27				,    group_width=1010				,    w_a=1744				,    w_a=620.83				,    w_m=2.52 ) ) )

			with torch.no_grad():
						UpperCamelCase__    :		List[Any]     =				from_model.eval()
						UpperCamelCase__    :		Dict     =				our_model.eval()

						UpperCamelCase__    :		Any     =				torch.randn((1, 3, 32, 32) )
						# trace both
						UpperCamelCase__    :		str     =				Tracker(__lowerCamelCase )
						UpperCamelCase__    :		Tuple     =				dest_tracker(__lowerCamelCase ).parametrized

						pprint(dest_tracker.nameamodule )
						UpperCamelCase__    :		List[str]     =				Tracker(__lowerCamelCase )
						UpperCamelCase__    :		List[str]     =				src_tracker(__lowerCamelCase ).parametrized

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

			UpperCamelCase__    :		Optional[int]     =				{}

			UpperCamelCase__    :		int     =				to_params_dict(__lowerCamelCase )
			UpperCamelCase__    :		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() ):
						UpperCamelCase__    :		Optional[int]     =				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:
						UpperCamelCase__    :		str     =				"""classifier.1.weight"""
						UpperCamelCase__    :		Any     =				"""classifier.1.bias"""

			return from_to_ours_keys



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE = None				,    __SCREAMING_SNAKE_CASE = True )	->     Union[str, Any]:
			UpperCamelCase__    :		Dict     =				"""imagenet-1k-id2label.json"""
			UpperCamelCase__    :		Optional[int]     =				1000

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

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

			UpperCamelCase__    :		Optional[int]     =				partial(__lowerCamelCase				,    num_labels=__lowerCamelCase				,    idalabel=__lowerCamelCase				,    labelaid=__lowerCamelCase )

			UpperCamelCase__    :		List[str]     =				{
			    """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(__SCREAMING_SNAKE_CASE ) -> Tuple[Dict, Dict]:
						UpperCamelCase__    :		Optional[int]     =				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
						UpperCamelCase__    :		Optional[int]     =				files["""classy_state_dict"""]["""base_model"""]["""model"""]
						return model_state_dict["trunk"], model_state_dict["heads"]

			UpperCamelCase__    :		int     =				{
			    """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'				,    ),
			}

			UpperCamelCase__    :		Union[str, Any]     =				get_from_to_our_keys(__lowerCamelCase )

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

						UpperCamelCase__    :		int     =				{}

						UpperCamelCase__    :		Dict     =				list(from_state_dict.keys() )
						UpperCamelCase__    :		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
									UpperCamelCase__    :		int     =				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
									UpperCamelCase__    :		Optional[Any]     =				from_to_ours_keys[src_key]
									# store the parameter with our key
									UpperCamelCase__    :		Union[str, Any]     =				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
						UpperCamelCase__    :		Union[str, Any]     =				names_to_config[model_name]
						UpperCamelCase__    :		Tuple     =				RegNetModel
						if "in1k" in model_name:
									UpperCamelCase__    :		Optional[Any]     =				RegNetForImageClassification
						UpperCamelCase__    :		Optional[int]     =				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
						UpperCamelCase__    :		int     =				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				,    )
						UpperCamelCase__    :		Optional[int]     =				384
						# we can use the convnext one
						UpperCamelCase__    :		Tuple     =				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__":
							UpperCAmelCase__				:  Tuple       					=   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.''',
							)

							UpperCAmelCase__				:  str       					=   parser.parse_args()

							UpperCAmelCase__				:  Path       					=   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)





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,
)


UpperCAmelCase__				:  Optional[int]       					=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__				:  Optional[int]       					=				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=8 )	->     List[Any]:
			UpperCamelCase__    :		Union[str, Any]     =				height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCamelCase__    :		Tuple     =				width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=512				,    __SCREAMING_SNAKE_CASE=512 )	->     Union[str, Any]:
			UpperCamelCase__    :		List[str]     =				pil_image.resize((w, h)				,    resample=Image.BICUBIC				,    reducing_gap=1 )
			UpperCamelCase__    :		List[str]     =				np.array(pil_image.convert('RGB' ) )
			UpperCamelCase__    :		Tuple     =				arr.astype(np.floataa ) / 127.5 - 1
			UpperCamelCase__    :		Tuple     =				np.transpose(__SCREAMING_SNAKE_CASE				,    [2, 0, 1] )
			UpperCamelCase__    :		int     =				torch.from_numpy(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			return image



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						) ->	Any:
										super().__init__()

										self.register_modules(
										    unet=UpperCamelCase     ,						scheduler=UpperCamelCase     ,						movq=UpperCamelCase     ,						)
										UpperCamelCase__    :		str     =				2 ** (len(self.movq.config.block_out_channels) - 1)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										# get the original timestep using init_timestep
										UpperCamelCase__    :		str     =				min(int(num_inference_steps * strength)     ,						UpperCamelCase)

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

										return timesteps, num_inference_steps - t_start
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase=None) ->	Optional[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)}""")

										UpperCamelCase__    :		int     =				image.to(device=UpperCamelCase     ,						dtype=UpperCamelCase)

										UpperCamelCase__    :		int     =				batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													UpperCamelCase__    :		Tuple     =				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):
																UpperCamelCase__    :		Union[str, Any]     =				[
																    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(UpperCamelCase)
																]
																UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)
													else:
																UpperCamelCase__    :		Any     =				self.movq.encode(UpperCamelCase).latent_dist.sample(UpperCamelCase)

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

										UpperCamelCase__    :		List[Any]     =				torch.cat([init_latents]     ,						dim=0)

										UpperCamelCase__    :		Union[str, Any]     =				init_latents.shape
										UpperCamelCase__    :		Union[str, Any]     =				randn_tensor(UpperCamelCase     ,						generator=UpperCamelCase     ,						device=UpperCamelCase     ,						dtype=UpperCamelCase)

										# get latents
										UpperCamelCase__    :		Optional[int]     =				self.scheduler.add_noise(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				init_latents

										return latents
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	str:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('Please install accelerate via `pip install accelerate`')

										UpperCamelCase__    :		Any     =				torch.device(F"""cuda:{gpu_id}""")

										UpperCamelCase__    :		int     =				[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(UpperCamelCase     ,						UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	Tuple:
										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__    :		int     =				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)

										UpperCamelCase__    :		Dict     =				None
										for cpu_offloaded_model in [self.unet, self.movq]:
													UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				cpu_offload_with_hook(UpperCamelCase     ,						UpperCamelCase     ,						prev_module_hook=UpperCamelCase)

										# We'll offload the last model manually.
										UpperCamelCase__    :		List[Any]     =				hook
							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        lowerCAmelCase__ (				self) ->	Optional[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 = 5_12     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 1_00     ,						UpperCamelCase = 4.0     ,						UpperCamelCase = 0.3     ,						UpperCamelCase = 1     ,						UpperCamelCase = None     ,						UpperCamelCase = "pil"     ,						UpperCamelCase = True     ,						) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				self._execution_device

										UpperCamelCase__    :		Union[str, Any]     =				guidance_scale > 1.0

										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Dict     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		List[Any]     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)

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

										if do_classifier_free_guidance:
													UpperCamelCase__    :		List[str]     =				image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		Any     =				negative_image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		str     =				hint.repeat_interleave(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		int     =				torch.cat([negative_image_embeds, image_embeds]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)
										UpperCamelCase__    :		Tuple     =				torch.cat([hint, hint]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)

										if not isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Tuple     =				[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""")

										UpperCamelCase__    :		List[str]     =				torch.cat([prepare_image(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) for i in image]     ,						dim=0)
										UpperCamelCase__    :		int     =				image.to(dtype=image_embeds.dtype     ,						device=UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				self.movq.encode(UpperCamelCase)['latents']
										UpperCamelCase__    :		str     =				latents.repeat_interleave(UpperCamelCase     ,						dim=0)
										self.scheduler.set_timesteps(UpperCamelCase     ,						device=UpperCamelCase)
										UpperCamelCase__   ,			UpperCamelCase__    :		List[str]     =				self.get_timesteps(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				timesteps[:1].repeat(batch_size * num_images_per_prompt)
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[Any]     =				downscale_height_and_width(UpperCamelCase     ,						UpperCamelCase     ,						self.movq_scale_factor)
										UpperCamelCase__    :		int     =				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
													UpperCamelCase__    :		List[Any]     =				torch.cat([latents] * 2) if do_classifier_free_guidance else latents

													UpperCamelCase__    :		List[str]     =				{'image_embeds': image_embeds, 'hint': hint}
													UpperCamelCase__    :		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:
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				noise_pred.chunk(2)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				variance_pred.chunk(2)
																UpperCamelCase__    :		Tuple     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																UpperCamelCase__    :		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"]
													):
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)

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

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

										# post-processing
										UpperCamelCase__    :		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"]:
													UpperCamelCase__    :		Any     =				image * 0.5 + 0.5
													UpperCamelCase__    :		Tuple     =				image.clamp(0     ,						1)
													UpperCamelCase__    :		Tuple     =				image.cpu().permute(0     ,						2     ,						3     ,						1).float().numpy()

										if output_type == "pil":
													UpperCamelCase__    :		Dict     =				self.numpy_to_pil(UpperCamelCase)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=UpperCamelCase)



