



'''simple docstring'''

import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
							import bitsandbytes as bnb

from copy import deepcopy


__lowerCAmelCase									=   logging.getLogger(__name__)



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			)			->      Dict:
		_a     :      str        =							bnb_quantization_config.load_in_abit
		_a     :      Dict        =							bnb_quantization_config.load_in_abit

		if load_in_abit and not is_abit_bnb_available():
				raise ImportError(
				    'You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'
				    ' make sure you have the latest version of `bitsandbytes` installed.'  )
		if load_in_abit and not is_abit_bnb_available():
				raise ValueError(
				    'You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'
				    'make sure you have the latest version of `bitsandbytes` installed.'  )

		_a     :      str        =							[]
		# custom device map
		if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ) and len(device_map.keys()  ) > 1:
				_a     :      List[Any]        =							[key for key, value in device_map.items() if value in ['disk', 'cpu']]

		# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
		if bnb_quantization_config.skip_modules is None:
				_a     :      Optional[int]        =							get_keys_to_not_convert(lowerCAmelCase_  )

		# add cpu modules to skip modules only for 4-bit modules
		if load_in_abit:
				bnb_quantization_config.skip_modules.extend(lowerCAmelCase_  )
		_a     :      Any        =							bnb_quantization_config.skip_modules

		# We add the modules we want to keep in full precision
		if bnb_quantization_config.keep_in_fpaa_modules is None:
				_a     :      List[str]        =							[]
		_a     :      Optional[Any]        =							bnb_quantization_config.keep_in_fpaa_modules
		modules_to_not_convert.extend(lowerCAmelCase_  )

		# compatibility with peft
		_a     :      Optional[Any]        =							load_in_abit
		_a     :      Dict        =							load_in_abit

		_a     :      Tuple        =							get_parameter_device(lowerCAmelCase_  )
		if model_device.type != "meta":
				# quantization of an already loaded model
				logger.warning(
				    'It is not recommended to quantize a loaded model. '
				    'The model should be instantiated under the `init_empty_weights` context manager.'  )
				_a     :      int        =							replace_with_bnb_layers(lowerCAmelCase_						,			lowerCAmelCase_						,			modules_to_not_convert=lowerCAmelCase_  )
				# convert param to the right dtype
				_a     :      Dict        =							bnb_quantization_config.torch_dtype
				for name, param in model.state_dict().items():
						if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules  ):
								param.to(torch.floataa  )
								if param.dtype != torch.floataa:
										_a     :      Any        =							name.replace('.weight'						,			''  ).replace('.bias'						,			''  )
										_a     :      str        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
										if param is not None:
												param.to(torch.floataa  )
						elif torch.is_floating_point(lowerCAmelCase_  ):
								param.to(lowerCAmelCase_  )
				if model_device.type == "cuda":
						# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
						model.cuda(torch.cuda.current_device()  )
						torch.cuda.empty_cache()
				elif torch.cuda.is_available():
						model.to(torch.cuda.current_device()  )
				else:
						raise RuntimeError('No GPU found. A GPU is needed for quantization.'  )
				logger.info(
				    f"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
				    'We move the model to cuda.'  )
				return model

		elif weights_location is None:
				raise RuntimeError(
				    f"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """  )

		else:
				with init_empty_weights():
						_a     :      str        =							replace_with_bnb_layers(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			modules_to_not_convert=lowerCAmelCase_  )

				_a     :      List[Any]        =							get_quantized_model_device_map(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			max_memory=lowerCAmelCase_						,			no_split_module_classes=lowerCAmelCase_						,			)
				if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
						_a     :      Any        =							True

				_a     :      Union[str, Any]        =							any(x in list(device_map.values()  ) for x in ['cpu', 'disk']  )

				load_checkpoint_in_model(
				    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			dtype=bnb_quantization_config.torch_dtype						,			offload_folder=lowerCAmelCase_						,			offload_state_dict=lowerCAmelCase_						,			keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules						,			offload_abit_bnb=load_in_abit and offload						,			)
				return dispatch_model(lowerCAmelCase_						,			device_map=lowerCAmelCase_						,			offload_dir=lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Optional[int]:
		if device_map is None:
				if torch.cuda.is_available():
						_a     :      List[Any]        =							{'': torch.cuda.current_device()}
				else:
						raise RuntimeError('No GPU found. A GPU is needed for quantization.'  )
				logger.info('The device_map was not initialized.' 'Setting device_map to `{\'\':torch.cuda.current_device()}`.'  )

		if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
				if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
						raise ValueError(
						    'If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '
						    '\'sequential\'.'  )

				_a     :      Union[str, Any]        =							{}
				special_dtypes.update(
				    {
				        name: bnb_quantization_config.torch_dtype
				        for name, _ in model.named_parameters()
				        if any(m in name for m in bnb_quantization_config.skip_modules  )
				    }  )
				special_dtypes.update(
				    {
				        name: torch.floataa
				        for name, _ in model.named_parameters()
				        if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules  )
				    }  )

				_a     :      int        =							{}
				_a     :      str        =							special_dtypes
				_a     :      Tuple        =							no_split_module_classes
				_a     :      Optional[Any]        =							bnb_quantization_config.target_dtype

				# get max_memory for each device.
				if device_map != "sequential":
						_a     :      Optional[Any]        =							get_balanced_memory(
						    lowerCAmelCase_						,			low_zero=(device_map == 'balanced_low_0')						,			max_memory=lowerCAmelCase_						,			**lowerCAmelCase_						,			)

				_a     :      Optional[Any]        =							max_memory
				_a     :      Any        =							infer_auto_device_map(lowerCAmelCase_						,			**lowerCAmelCase_  )

		if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
				# check if don't have any quantized module on the cpu
				_a     :      Tuple        =							bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

				_a     :      Tuple        =							{
				    key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
				}
				for device in ["cpu", "disk"]:
						if device in device_map_without_some_modules.values():
								if bnb_quantization_config.load_in_abit:
										raise ValueError(
										    '\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        '  )
								else:
										logger.info(
										    'Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'  )
				del device_map_without_some_modules
		return device_map



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None  )			->      Tuple:

		if modules_to_not_convert is None:
				_a     :      Optional[int]        =							[]

		_a       ,					_a     :      Tuple        =							_replace_with_bnb_layers(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
		if not has_been_replaced:
				logger.warning(
				    'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
				    ' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'
				    ' Please double check your model architecture, or submit an issue on github if you think this is'
				    ' a bug.'  )
		return model



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=None						,			lowerCAmelCase_=None						,			)			->      List[Any]:
		_a     :      Any        =							False
		for name, module in model.named_children():
				if current_key_name is None:
						_a     :      List[Any]        =							[]
				current_key_name.append(lowerCAmelCase_  )
				if isinstance(lowerCAmelCase_						,			nn.Linear  ) and name not in modules_to_not_convert:
						# Check if the current key is not in the `modules_to_not_convert`
						_a     :      str        =							'.'.join(lowerCAmelCase_  )
						_a     :      Any        =							True
						for key in modules_to_not_convert:
								if (
								    (key in current_key_name_str) and (key + "." in current_key_name_str)
								) or key == current_key_name_str:
										_a     :      Any        =							False
										break
						if proceed:
								# Load bnb module with empty weight and replace ``nn.Linear` module
								if bnb_quantization_config.load_in_abit:
										_a     :      Tuple        =							bnb.nn.LinearabitLt(
										    module.in_features						,			module.out_features						,			module.bias is not None						,			has_fpaa_weights=lowerCAmelCase_						,			threshold=bnb_quantization_config.llm_inta_threshold						,			)
								elif bnb_quantization_config.load_in_abit:
										_a     :      Optional[Any]        =							bnb.nn.Linearabit(
										    module.in_features						,			module.out_features						,			module.bias is not None						,			bnb_quantization_config.bnb_abit_compute_dtype						,			compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant						,			quant_type=bnb_quantization_config.bnb_abit_quant_type						,			)
								else:
										raise ValueError('load_in_8bit and load_in_4bit can\'t be both False'  )
								_a     :      Optional[Any]        =							module.weight.data
								if module.bias is not None:
										_a     :      Union[str, Any]        =							module.bias.data
								bnb_module.requires_grad_(lowerCAmelCase_  )
								setattr(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
								_a     :      Optional[int]        =							True
				if len(list(module.children()  )  ) > 0:
						_a       ,					_a     :      Optional[int]        =							_replace_with_bnb_layers(
						    lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )
						_a     :      Any        =							has_been_replaced | _has_been_replaced
				# Remove the last key for recursion
				current_key_name.pop(-1  )
		return model, has_been_replaced



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      int:
		# Create a copy of the model
		with init_empty_weights():
				_a     :      Optional[int]        =							deepcopy(lowerCAmelCase_  )  # this has 0 cost since it is done inside `init_empty_weights` context manager`

		_a     :      int        =							find_tied_parameters(lowerCAmelCase_  )
		# For compatibility with Accelerate < 0.18
		if isinstance(lowerCAmelCase_						,			lowerCAmelCase_  ):
				_a     :      str        =							sum(list(tied_params.values()  )						,			[]  ) + list(tied_params.keys()  )
		else:
				_a     :      List[str]        =							sum(lowerCAmelCase_						,			[]  )
		_a     :      Union[str, Any]        =							len(lowerCAmelCase_  ) > 0

		# Check if it is a base model
		_a     :      str        =							False
		if hasattr(lowerCAmelCase_						,			'base_model_prefix'  ):
				_a     :      str        =							not hasattr(lowerCAmelCase_						,			model.base_model_prefix  )

		# Ignore this for base models (BertModel, GPT2Model, etc.)
		if (not has_tied_params) and is_base_model:
				return []

		# otherwise they have an attached head
		_a     :      Optional[int]        =							list(model.named_children()  )
		_a     :      int        =							[list_modules[-1][0]]

		# add last module together with tied weights
		_a     :      str        =							set(lowerCAmelCase_  ) - set(lowerCAmelCase_  )
		_a     :      Optional[Any]        =							list(set(lowerCAmelCase_  )  ) + list(lowerCAmelCase_  )

		# remove ".weight" from the keys
		_a     :      List[str]        =							['.weight', '.bias']
		_a     :      Any        =							[]
		for name in list_untouched:
				for name_to_remove in names_to_remove:
						if name_to_remove in name:
								_a     :      int        =							name.replace(lowerCAmelCase_						,			''  )
				filtered_module_names.append(lowerCAmelCase_  )

		return filtered_module_names



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		for m in model.modules():
				if isinstance(lowerCAmelCase_						,			bnb.nn.Linearabit  ):
						return True
		return False



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		return next(parameter.parameters()  ).device



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_  )			->      List[Any]:
		# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
		if fpaa_statistics is None:
				set_module_tensor_to_device(lowerCAmelCase_						,			lowerCAmelCase_						,			0						,			dtype=lowerCAmelCase_						,			value=lowerCAmelCase_  )
				_a     :      List[Any]        =							param_name
				_a     :      List[Any]        =							model
				if "." in tensor_name:
						_a     :      Tuple        =							tensor_name.split('.'  )
						for split in splits[:-1]:
								_a     :      Union[str, Any]        =							getattr(lowerCAmelCase_						,			lowerCAmelCase_  )
								if new_module is None:
										raise ValueError(f"""{module} has no attribute {split}."""  )
								_a     :      Dict        =							new_module
						_a     :      List[Any]        =							splits[-1]
				# offload weights
				_a     :      List[str]        =							False
				offload_weight(module._parameters[tensor_name]						,			lowerCAmelCase_						,			lowerCAmelCase_						,			index=lowerCAmelCase_  )
				if hasattr(module._parameters[tensor_name]						,			'SCB'  ):
						offload_weight(
						    module._parameters[tensor_name].SCB						,			param_name.replace('weight'						,			'SCB'  )						,			lowerCAmelCase_						,			index=lowerCAmelCase_						,			)
		else:
				offload_weight(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			index=lowerCAmelCase_  )
				offload_weight(lowerCAmelCase_						,			param_name.replace('weight'						,			'SCB'  )						,			lowerCAmelCase_						,			index=lowerCAmelCase_  )

		set_module_tensor_to_device(lowerCAmelCase_						,			lowerCAmelCase_						,			'meta'						,			dtype=lowerCAmelCase_						,			value=torch.empty(*param.size()  )  )












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

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							int     ,       __snake_case    :							Tuple=8  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_    :      List[Any]            =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




def      lowercase__      (     __snake_case    :							Any     ,       __snake_case    :							int=512     ,       __snake_case    :							Dict=512  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						pil_image.resize((w, h)     ,       resample=Image.BICUBIC     ,       reducing_gap=1  )
					UpperCAmelCase_    :      Dict            =						np.array(pil_image.convert('RGB'  )  )
					UpperCAmelCase_    :      Any            =						arr.astype(np.floataa  ) / 127.5 - 1
					UpperCAmelCase_    :      Dict            =						np.transpose(__snake_case     ,       [2, 0, 1]  )
					UpperCAmelCase_    :      List[str]            =						torch.from_numpy(__snake_case  ).unsqueeze(0  )
					return image






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							Union[str, Any]:
							super().__init__()

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



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							# get the original timestep using init_timestep
							UpperCAmelCase_    :      Any            =						min(int(num_inference_steps * strength   )				,		_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						max(num_inference_steps - init_timestep				,		0   )
							UpperCAmelCase_    :      str            =						self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							Tuple:
							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_    :      List[str]            =						image.to(device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						batch_size * num_images_per_prompt

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

												UpperCAmelCase_    :      int            =						self.movq.config.scaling_factor * init_latents

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

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

							# get latents
							UpperCAmelCase_    :      str            =						self.scheduler.add_noise(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						init_latents

							return latents



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

							UpperCAmelCase_    :      Optional[Any]            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      Optional[Any]            =						[
							    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   )	->							Union[str, 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.'   )

							UpperCAmelCase_    :      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)

							UpperCAmelCase_    :      Dict            =						None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						cpu_offload_with_hook(_UpperCamelCase				,		_UpperCamelCase				,		prev_module_hook=_UpperCamelCase   )

							# We'll offload the last model manually.
							UpperCAmelCase_    :      Any            =						hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							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 = 5_1_2				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 0.3				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							str:
							UpperCAmelCase_    :      Any            =						self._execution_device

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

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						torch.cat(_UpperCamelCase				,		dim=0   )
							UpperCAmelCase_    :      Optional[Any]            =						image_embeds.shape[0]
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      int            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )
												UpperCAmelCase_    :      int            =						negative_image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.cat([negative_image_embeds, image_embeds]				,		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_    :      str            =						torch.cat([prepare_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ) for i in image]				,		dim=0   )
							UpperCAmelCase_    :      Any            =						image.to(dtype=image_embeds.dtype				,		device=_UpperCamelCase   )

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

												UpperCAmelCase_    :      str            =						{'image_embeds': image_embeds}
												UpperCAmelCase_    :      Union[str, 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_    :      Tuple            =						noise_pred.split(latents.shape[1]				,		dim=1   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						variance_pred.chunk(2   )
																	UpperCAmelCase_    :      Dict            =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase_    :      Tuple            =						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_    :      int            =						noise_pred.split(latents.shape[1]				,		dim=1   )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_    :      List[str]            =						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_    :      List[str]            =						image * 0.5 + 0.5
												UpperCAmelCase_    :      List[Any]            =						image.clamp(0				,		1   )
												UpperCAmelCase_    :      Dict            =						image.cpu().permute(0				,		2				,		3				,		1   ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[Any]            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase   )






