





"""simple docstring"""





import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
    import torch_xla.core.xla_model as xm  # noqa: F401

    _lowerCamelCase  					=     True
except ImportError:
    _lowerCamelCase  					=     False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_lowerCamelCase  					=     torch.distributed.is_available()




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      str 							=	importlib.util.find_spec(snake_case__		) is not None
 if package_exists:
  try:
   __SCREAMING_SNAKE_CASE			:      int 							=	importlib.metadata.metadata(snake_case__		)
   return True
  except importlib.metadata.PackageNotFoundError:
   return False




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _torch_distributed_available




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 try:
  pass
 except ImportError:
  print(
      '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
      ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
      ''' Bindings for PyTorch*.'''		)
 return (
     importlib.util.find_spec('''torch_ccl'''		) is not None
     or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''		) is not None
 )




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return importlib.metadata.version('''oneccl_bind_pt'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''transformer_engine'''		)




@lru_cache
def 		lowerCAmelCase_     (  lowercase_       :							Dict=True		):

 '''simple docstring'''






 if torch.cuda.is_available():
  return False
 if _tpu_available and check_device:
  try:
   # Will raise a RuntimeError if no XLA configuration is found
   __SCREAMING_SNAKE_CASE			:      List[Any] 							=	xm.xla_device()
   return True
  except RuntimeError:
   return False
 return _tpu_available




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''deepspeed'''		)




def 		lowerCAmelCase_     (  lowercase_       :							int=False		):

 '''simple docstring'''






 if is_tpu_available():
  return not ignore_tpu
 if torch.cuda.is_available():
  return torch.cuda.is_bfaa_supported()
 if is_npu_available():
  return False
 return True




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      List[Any] 							=	_is_package_available('''bitsandbytes'''		)
 if package_exists:
  __SCREAMING_SNAKE_CASE			:      Any 							=	version.parse(importlib.metadata.version('''bitsandbytes'''		)		)
  return compare_versions(snake_case__			,				'''>='''			,				'''0.39.0'''		)
 return False




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Tuple 							=	_is_package_available('''bitsandbytes'''		)
 if package_exists:
  __SCREAMING_SNAKE_CASE			:      Any 							=	version.parse(importlib.metadata.version('''bitsandbytes'''		)		)
  return compare_versions(snake_case__			,				'''>='''			,				'''0.37.2'''		)
 return False




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''bitsandbytes'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''			,				'''False'''		)		) == 1:
  __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	importlib.util.find_spec('''megatron'''		) is not None
  if package_exists:
   try:
    __SCREAMING_SNAKE_CASE			:      Dict 							=	parse(importlib.metadata.version('''megatron-lm'''		)		)
    return compare_versions(snake_case__			,				'''>='''			,				'''2.2.0'''		)
   except Exception as e:
    warnings.warn(F'''Parse Megatron version failed. Exception:{e}'''		)
    return False




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''safetensors'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''transformers'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''datasets'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''aim'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''tensorboard'''		) or _is_package_available('''tensorboardX'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''wandb'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''comet_ml'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''boto3'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 if _is_package_available('''rich'''		):
  if "ACCELERATE_DISABLE_RICH" in os.environ:
   warnings.warn(
       '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''		)
   return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''			,				snake_case__		)
  return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''			,				snake_case__		)
 return False




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''sagemaker'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''tqdm'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return _is_package_available('''mlflow'''		)




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 return is_torch_version('''>='''			,				'''1.12'''		) and torch.backends.mps.is_available() and torch.backends.mps.is_built()




def 		lowerCAmelCase_     (  ):

 '''simple docstring'''






 def get_major_and_minor_from_version(lowercase_       :							Any		):
  return str(version.parse(snake_case__		).major		) + "." + str(version.parse(snake_case__		).minor		)

 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	importlib.metadata.version('''torch'''		)
 if importlib.util.find_spec('''intel_extension_for_pytorch'''		) is None:
  return False
 __SCREAMING_SNAKE_CASE			:      int 							=	"N/A"
 try:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	importlib.metadata.version('''intel_extension_for_pytorch'''		)
 except importlib.metadata.PackageNotFoundError:
  return False
 __SCREAMING_SNAKE_CASE			:      int 							=	get_major_and_minor_from_version(_torch_version		)
 __SCREAMING_SNAKE_CASE			:      str 							=	get_major_and_minor_from_version(_ipex_version		)
 if torch_major_and_minor != ipex_major_and_minor:
  warnings.warn(
      F'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
      F''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''		)
  return False
 return True




@lru_cache
def 		lowerCAmelCase_     (  lowercase_       :							Tuple=False		):

 '''simple docstring'''






 if importlib.util.find_spec('''torch'''		) is None or importlib.util.find_spec('''torch_npu'''		) is None:
  return False

 import torch
 import torch_npu  # noqa: F401

 if check_device:
  try:
   # Will raise a RuntimeError if no NPU is found
   __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.npu.device_count()
   return torch.npu.is_available()
  except RuntimeError:
   return False
 return hasattr(snake_case__			,				'''npu'''		) and torch.npu.is_available()






@lru_cache
def 		lowerCAmelCase_     (  lowercase_       :							Optional[Any]=False		):

 '''simple docstring'''






 if not parse_flag_from_env('''ACCELERATE_USE_XPU'''			,				default=snake_case__		):
  return False
 if is_ipex_available():
  import torch

  if is_torch_version('''<='''			,				'''1.12'''		):
   return False
 else:
  return False

 import intel_extension_for_pytorch  # noqa: F401

 if check_device:
  try:
   # Will raise a RuntimeError if no XPU  is found
   __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	torch.xpu.device_count()
   return torch.xpu.is_available()
  except RuntimeError:
   return False
 return hasattr(snake_case__			,				'''xpu'''		) and torch.xpu.is_available()






"""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,
)


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

_lowerCamelCase  					=     '''
    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 		lowerCAmelCase_     (  lowercase_       :							Optional[int]			,				lowercase_       :							Optional[Any]			,				lowercase_       :							Optional[int]=8		):

 '''simple docstring'''






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




def 		lowerCAmelCase_     (  lowercase_       :							str			,				lowercase_       :							Tuple=512			,				lowercase_       :							Any=512		):

 '''simple docstring'''






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






class       snake_case		(       __UpperCAmelCase       ):

      def __init__(      self       :Dict						,							_lowerCamelCase       :UNetaDConditionModel						,							_lowerCamelCase       :DDPMScheduler						,							_lowerCamelCase       :VQModel						,							):
       super().__init__()

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

      def 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :List[Any]						,							_lowerCamelCase       :List[Any]						):
       # get the original timestep using init_timestep
       __SCREAMING_SNAKE_CASE			:      str 							=	min(int(num_inference_steps * strength						)						,							_lowerCamelCase						)

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

       return timesteps, num_inference_steps - t_start

      def 						SCREAMING_SNAKE_CASE_      (      self       :Any						,							_lowerCamelCase       :str						,							_lowerCamelCase       :int						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :Dict=None						):
       if not isinstance(_lowerCamelCase						,							(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(_lowerCamelCase						)}'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image.to(device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	batch_size * num_images_per_prompt

       if image.shape[1] == 4:
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	image

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

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

        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.movq.config.scaling_factor * init_latents

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

       __SCREAMING_SNAKE_CASE			:      int 							=	init_latents.shape
       __SCREAMING_SNAKE_CASE			:      int 							=	randn_tensor(_lowerCamelCase						,							generator=_lowerCamelCase						,							device=_lowerCamelCase						,							dtype=_lowerCamelCase						)

       # get latents
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	self.scheduler.add_noise(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	init_latents

       return latents

      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :List[Any]=0						):
       if is_accelerate_available():
        from accelerate import cpu_offload
       else:
        raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.device(f'''cuda:{gpu_id}'''						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	[
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
        if cpu_offloaded_model is not None:
         cpu_offload(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[Any]						,							_lowerCamelCase       :Optional[Any]=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.'''						)

       __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.device(f'''cuda:{gpu_id}'''						)

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

       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	None
       for cpu_offloaded_model in [self.unet, self.movq]:
        __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	cpu_offload_with_hook(_lowerCamelCase						,							_lowerCamelCase						,							prev_module_hook=_lowerCamelCase						)

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

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       if not hasattr(self.unet						,							'''_hf_hook'''						):
        return self.device
       for module in self.unet.modules():
        if (
            hasattr(_lowerCamelCase						,							'''_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(_lowerCamelCase						)
      def __call__(      self       :List[str]						,							_lowerCamelCase       :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase       :Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]						,							_lowerCamelCase       :Union[torch.FloatTensor, List[torch.FloatTensor]]						,							_lowerCamelCase       :torch.FloatTensor						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = 5_1_2						,							_lowerCamelCase       :int = 1_0_0						,							_lowerCamelCase       :float = 4.0						,							_lowerCamelCase       :float = 0.3						,							_lowerCamelCase       :int = 1						,							_lowerCamelCase       :Optional[Union[torch.Generator, List[torch.Generator]]] = None						,							_lowerCamelCase       :Optional[str] = "pil"						,							_lowerCamelCase       :bool = True						,							):
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self._execution_device

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	guidance_scale > 1.0

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.cat(_lowerCamelCase						,							dim=0						)

       __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_embeds.shape[0]

       if do_classifier_free_guidance:
        __SCREAMING_SNAKE_CASE			:      Tuple 							=	image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE			:      Any 							=	negative_image_embeds.repeat_interleave(_lowerCamelCase						,							dim=0						)
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	hint.repeat_interleave(_lowerCamelCase						,							dim=0						)

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

       if not isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	[image]
       if not all(isinstance(_lowerCamelCase						,							(PIL.Image.Image, torch.Tensor)						) for i in image						):
        raise ValueError(
            f'''Input is in incorrect format: {[type(_lowerCamelCase						) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	torch.cat([prepare_image(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						) for i in image]						,							dim=0						)
       __SCREAMING_SNAKE_CASE			:      Dict 							=	image.to(dtype=image_embeds.dtype						,							device=_lowerCamelCase						)

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

        __SCREAMING_SNAKE_CASE			:      Any 							=	{'''image_embeds''': image_embeds, '''hint''': hint}
        __SCREAMING_SNAKE_CASE			:      int 							=	self.unet(
            sample=_lowerCamelCase						,							timestep=_lowerCamelCase						,							encoder_hidden_states=_lowerCamelCase						,							added_cond_kwargs=_lowerCamelCase						,							return_dict=_lowerCamelCase						,							)[0]

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

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

        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.scheduler.step(
            _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							generator=_lowerCamelCase						,							)[0]

       # post-processing
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.movq.decode(_lowerCamelCase						,							force_not_quantize=_lowerCamelCase						)['''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"]:
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	image * 0.5 + 0.5
        __SCREAMING_SNAKE_CASE			:      str 							=	image.clamp(0						,							1						)
        __SCREAMING_SNAKE_CASE			:      int 							=	image.cpu().permute(0						,							2						,							3						,							1						).float().numpy()

       if output_type == "pil":
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	self.numpy_to_pil(_lowerCamelCase						)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase						)

