
import logging
import re

import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor


__lowerCamelCase				=			logging.getLogger(__name__)

__lowerCamelCase				=			50  # max width of layer names
__lowerCamelCase				=			70  # max width of quantizer names


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]       ):
       snake_case			:  int     							= parser.add_argument_group("quant_trainer arguments"       )
       group.add_argument("--wprec"							,   type=__lowerCamelCase							,   default=8							,   help="weight precision"       )
       group.add_argument("--aprec"							,   type=__lowerCamelCase							,   default=8							,   help="activation precision"       )
       group.add_argument("--quant-per-tensor"							,   action="store_true"							,   help="per tensor weight scaling"       )
       group.add_argument("--quant-disable"							,   action="store_true"							,   help="disable all quantizers"       )
       group.add_argument("--quant-disable-embeddings"							,   action="store_true"							,   help="disable all embeddings quantizers"       )
       group.add_argument("--quant-disable-keyword"							,   type=__lowerCamelCase							,   nargs="+"							,   help="disable quantizers by keyword"       )
       group.add_argument("--quant-disable-layer-module"							,   type=__lowerCamelCase							,   help="disable quantizers by keyword under layer."       )
       group.add_argument("--quant-enable-layer-module"							,   type=__lowerCamelCase							,   help="enable quantizers by keyword under layer"       )
       group.add_argument("--calibrator"							,   default="max"							,   help="which quantization range calibrator to use"       )
       group.add_argument("--percentile"							,   default=__lowerCamelCase							,   type=__lowerCamelCase							,   help="percentile for PercentileCalibrator"       )
       group.add_argument("--fuse-qkv"							,   action="store_true"							,   help="use the same scale factor for qkv"       )
       group.add_argument("--clip-gelu"							,   metavar="N"							,   type=__lowerCamelCase							,   help="clip gelu output maximum value to N"       )
       group.add_argument(
           "--recalibrate-weights"							,   action="store_true"							,   help=(
               "recalibrate weight amaxes by taking the max of the weights."
               " amaxes will be computed with the current quantization granularity (axis)."
           )							,   )


def      UpperCamelCase   (       __lowerCamelCase	:					Tuple       ):

       if args.calibrator == "max":
              snake_case			:  Tuple     							= "max"
       elif args.calibrator == "percentile":
              if args.percentile is None:
                     raise ValueError("Specify --percentile when using percentile calibrator"       )
              snake_case			:  Tuple     							= "histogram"
       elif args.calibrator == "mse":
              snake_case			:  List[str]     							= "histogram"
       else:
              raise ValueError(f"""Invalid calibrator {args.calibrator}"""       )

       snake_case			:  Optional[int]     							= QuantDescriptor(num_bits=args.aprec							,   calib_method=__lowerCamelCase       )
       snake_case			:  Optional[int]     							= QuantDescriptor(num_bits=args.wprec							,   axis=(None if args.quant_per_tensor else (0,))       )
       quant_nn.QuantLinear.set_default_quant_desc_input(__lowerCamelCase       )
       quant_nn.QuantLinear.set_default_quant_desc_weight(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					str							,   __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					Union[str, Any]=False							,   __lowerCamelCase	:					List[Any]=False       ):
       logger.info("Configuring Model for Quantization"       )
       logger.info(f"""using quantization package {pytorch_quantization.__file__}"""       )

       if not calib:
              if args.quant_disable_embeddings:
                     set_quantizer_by_name(__lowerCamelCase							,   ["embeddings"]							,   which="weight"							,   _disabled=__lowerCamelCase       )

              if args.quant_disable:
                     set_quantizer_by_name(__lowerCamelCase							,   [""]							,   _disabled=__lowerCamelCase       )

              if args.quant_disable_keyword:
                     set_quantizer_by_name(__lowerCamelCase							,   args.quant_disable_keyword							,   _disabled=__lowerCamelCase       )

              if args.quant_disable_layer_module:
                     set_quantizer_by_name(__lowerCamelCase							,   [r"layer.\d+." + args.quant_disable_layer_module]							,   _disabled=__lowerCamelCase       )

              if args.quant_enable_layer_module:
                     set_quantizer_by_name(__lowerCamelCase							,   [r"layer.\d+." + args.quant_enable_layer_module]							,   _disabled=__lowerCamelCase       )

              if args.recalibrate_weights:
                     recalibrate_weights(__lowerCamelCase       )

              if args.fuse_qkv:
                     fuse_qkv(__lowerCamelCase							,   __lowerCamelCase       )

       if args.clip_gelu:
              clip_gelu(__lowerCamelCase							,   args.clip_gelu       )

       # if args.local_rank in [-1, 0] and not calib:
       print_quant_summary(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					str       ):
       logger.info("Enabling Calibration"       )
       for name, module in model.named_modules():
              if name.endswith("_quantizer"       ):
                     if module._calibrator is not None:
                            module.disable_quant()
                            module.enable_calib()
                     else:
                            module.disable()
                     logger.info(f"""{name:80}: {module}"""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Tuple							,   __lowerCamelCase	:					List[str]       ):
       logger.info("Loading calibrated amax"       )
       for name, module in model.named_modules():
              if name.endswith("_quantizer"       ):
                     if module._calibrator is not None:
                            if isinstance(module._calibrator							,   calib.MaxCalibrator       ):
                                   module.load_calib_amax()
                            else:
                                   module.load_calib_amax("percentile"							,   percentile=args.percentile       )
                            module.enable_quant()
                            module.disable_calib()
                     else:
                            module.enable()
       model.cuda()
       print_quant_summary(__lowerCamelCase       )


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					List[str]       ):

       def fusea(__lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Dict       ):
              for mod in [qq, qk, qv]:
                     if not hasattr(__lowerCamelCase							,   "_amax"       ):
                            print("          WARNING: NO AMAX BUFFER"       )
                            return
              snake_case			:  List[Any]     							= qq._amax.detach().item()
              snake_case			:  Dict     							= qk._amax.detach().item()
              snake_case			:  Tuple     							= qv._amax.detach().item()

              snake_case			:  Optional[Any]     							= max(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )
              qq._amax.fill_(__lowerCamelCase       )
              qk._amax.fill_(__lowerCamelCase       )
              qv._amax.fill_(__lowerCamelCase       )
              logger.info(f"""          q={q:5.2f} k={k:5.2f} v={v:5.2f} -> {amax:5.2f}"""       )

       for name, mod in model.named_modules():
              if name.endswith(".attention.self"       ):
                     logger.info(f"""FUSE_QKV: {name:{name_width}}"""       )
                     fusea(mod.matmul_q_input_quantizer							,   mod.matmul_k_input_quantizer							,   mod.matmul_v_input_quantizer       )
                     if args.quant_per_tensor:
                            fusea(mod.query._weight_quantizer							,   mod.key._weight_quantizer							,   mod.value._weight_quantizer       )


def      UpperCamelCase   (       __lowerCamelCase	:					List[Any]							,   __lowerCamelCase	:					str       ):

       for name, mod in model.named_modules():
              if name.endswith(".output.dense"       ) and not name.endswith("attention.output.dense"       ):
                     snake_case			:  Tuple     							= mod._input_quantizer._amax.data.detach().item()
                     mod._input_quantizer._amax.data.detach().clamp_(max=__lowerCamelCase       )
                     snake_case			:  str     							= mod._input_quantizer._amax.data.detach().item()
                     logger.info(f"""CLIP_GELU: {name:{name_width}} amax: {amax_init:5.2f} -> {amax:5.2f}"""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):

       for name, mod in model.named_modules():
              if hasattr(__lowerCamelCase							,   "_weight_quantizer"       ) and mod._weight_quantizer.axis is not None:
                     snake_case			:  Tuple     							= mod.weight.shape[0]
                     snake_case			:  Tuple     							= mod._weight_quantizer._amax.detach()
                     snake_case			:  Optional[Any]     							= torch.ones(__lowerCamelCase							,   dtype=amax.dtype							,   device=amax.device       ) * amax
                     print(f"""expanding {name} {amax} -> {mod._weight_quantizer._amax}"""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):

       for name, mod in model.named_modules():
              if hasattr(__lowerCamelCase							,   "_weight_quantizer"       ):
                     if not hasattr(mod.weight_quantizer							,   "_amax"       ):
                            print("RECALIB: {name:{name_width}} WARNING: NO AMAX BUFFER"       )
                            continue

                     # determine which axes to reduce across
                     # e.g. a 4D tensor quantized per axis 0 should reduce over (1,2,3)
                     snake_case			:  List[Any]     							= set() if mod._weight_quantizer.axis is None else set(mod._weight_quantizer.axis       )
                     snake_case			:  Optional[Any]     							= set(range(len(mod.weight.size()       )       )       ) - axis_set
                     snake_case			:  int     							= pytorch_quantization.utils.reduce_amax(mod.weight							,   axis=__lowerCamelCase							,   keepdims=__lowerCamelCase       ).detach()
                     logger.info(f"""RECALIB: {name:{name_width}} {mod._weight_quantizer._amax.flatten()} -> {amax.flatten()}"""       )
                     snake_case			:  List[Any]     							= amax


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[int]							,   __lowerCamelCase	:					str=25							,   __lowerCamelCase	:					Optional[int]=180							,   __lowerCamelCase	:					List[Any]=None       ):

       if ignore is None:
              snake_case			:  str     							= []
       elif not isinstance(__lowerCamelCase							,   __lowerCamelCase       ):
              snake_case			:  int     							= [ignore]

       snake_case			:  Tuple     							= 0
       for name, mod in model.named_modules():
              if not hasattr(__lowerCamelCase							,   "weight"       ):
                     continue
              snake_case			:  List[str]     							= max(__lowerCamelCase							,   len(__lowerCamelCase       )       )

       for name, mod in model.named_modules():
              snake_case			:  Dict     							= getattr(__lowerCamelCase							,   "_input_quantizer"							,   __lowerCamelCase       )
              snake_case			:  Any     							= getattr(__lowerCamelCase							,   "_weight_quantizer"							,   __lowerCamelCase       )
              if not hasattr(__lowerCamelCase							,   "weight"       ):
                     continue
              if type(__lowerCamelCase       ) in ignore:
                     continue
              if [True for s in ignore if type(__lowerCamelCase       ) is str and s in name]:
                     continue
              snake_case			:  str     							= f"""Act:{input_q.extra_repr()}"""
              snake_case			:  str     							= f"""Wgt:{weight_q.extra_repr()}"""
              snake_case			:  Any     							= f"""{name:{name_width}} {act_str} {wgt_str}"""
              if len(__lowerCamelCase       ) <= line_width:
                     logger.info(__lowerCamelCase       )
              else:
                     logger.info(f"""{name:{name_width}} {act_str}"""       )
                     logger.info(f"""{'  ':{name_width}} {wgt_str}"""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Any       ):
       snake_case			:  Union[str, Any]     							= 0
       for name, mod in model.named_modules():
              if isinstance(__lowerCamelCase							,   pytorch_quantization.nn.TensorQuantizer       ):
                     print(f"""{name:80} {mod}"""       )
                     count += 1
       print(f"""{count} TensorQuantizers found in model"""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					str       ):
       snake_case			:  str     							= getattr(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )
       if quantizer_mod is not None:
              assert hasattr(__lowerCamelCase							,   __lowerCamelCase       )
              setattr(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )
       else:
              logger.warning(f"""{name} has no {quantizer}"""       )


def      UpperCamelCase   (       __lowerCamelCase	:					Optional[Any]							,   __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					List[Any]="both"							,   **__lowerCamelCase	:					Optional[Any]       ):
       snake_case			:  Union[str, Any]     							= f"""Warning: changing {which} quantizers of {name:{qname_width}}"""
       for k, v in kwargs.items():
              s += f""" {k}={v}"""
              if which in ["input", "both"]:
                     set_quantizer(__lowerCamelCase							,   __lowerCamelCase							,   "_input_quantizer"							,   __lowerCamelCase							,   __lowerCamelCase       )
              if which in ["weight", "both"]:
                     set_quantizer(__lowerCamelCase							,   __lowerCamelCase							,   "_weight_quantizer"							,   __lowerCamelCase							,   __lowerCamelCase       )
       logger.info(__lowerCamelCase       )




def      UpperCamelCase   (       __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					int							,   **__lowerCamelCase	:					Any       ):

       for name, mod in model.named_modules():
              if hasattr(__lowerCamelCase							,   "_input_quantizer"       ) or hasattr(__lowerCamelCase							,   "_weight_quantizer"       ):
                     for n in names:
                            if re.search(__lowerCamelCase							,   __lowerCamelCase       ):
                                   set_quantizers(__lowerCamelCase							,   __lowerCamelCase							,   **__lowerCamelCase       )
              elif name.endswith("_quantizer"       ):
                     for n in names:
                            if re.search(__lowerCamelCase							,   __lowerCamelCase       ):
                                   snake_case			:  Optional[int]     							= f"""Warning: changing {name:{name_width}}"""
                                   for k, v in kwargs.items():
                                          s += f""" {k}={v}"""
                                          setattr(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )
                                   logger.info(__lowerCamelCase       )







from typing import List, Optional, Union

import torch

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
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def      UpperCamelCase   (       __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Dict							,   __lowerCamelCase	:					int=8       ):
       snake_case			:  Optional[int]     							= height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       snake_case			:  Union[str, Any]     							= width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor






class     UpperCAmelCase						(		A_					):



   def __init__(self						:   int       ,		snake_case__						:   UNetaDConditionModel       ,		snake_case__						:   DDPMScheduler       ,		snake_case__						:   VQModel       ,		)						->      Tuple:


          '''simple docstring'''


          super().__init__()

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   Optional[Any]       ,		snake_case__						:   int       ,		snake_case__						:   List[Any]       ,		snake_case__						:   List[str]					)						->      Optional[Any]:


          '''simple docstring'''


          if latents is None:
                 snake_case			:  int     							= randn_tensor(snake_case__       ,		generator=snake_case__       ,		device=snake_case__       ,		dtype=snake_case__					)
          else:
                 if latents.shape != shape:
                        raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""					)
                 snake_case			:  Tuple     							= latents.to(snake_case__					)

          snake_case			:  int     							= latents * scheduler.init_noise_sigma
          return latents



   def 							_SCREAMING_SNAKE_CASE  (self						:   str       ,		snake_case__						:   Optional[Any]=0					)						->      Tuple:


          '''simple docstring'''


          if is_accelerate_available():
                 from accelerate import cpu_offload
          else:
                 raise ImportError("Please install accelerate via `pip install accelerate`"					)

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

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Tuple       ,		snake_case__						:   Union[str, Any]=0					)						->      Union[str, Any]:


          '''simple docstring'''


          if is_accelerate_available() and is_accelerate_version(">="       ,		"0.17.0.dev0"					):
                 from accelerate import cpu_offload_with_hook
          else:
                 raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."					)

          snake_case			:  Union[str, Any]     							= torch.device(f"""cuda:{gpu_id}"""					)

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

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

          # We'll offload the last model manually.
          snake_case			:  Tuple     							= hook



   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 							_SCREAMING_SNAKE_CASE  (self						:   List[str]					)						->      str:


          '''simple docstring'''


          if not hasattr(self.unet       ,		"_hf_hook"					):
                 return self.device
          for module in self.unet.modules():
                 if (
                     hasattr(snake_case__       ,		"_hf_hook"					)
                     and hasattr(module._hf_hook       ,		"execution_device"					)
                     and module._hf_hook.execution_device is not None
                 ):
                        return torch.device(module._hf_hook.execution_device					)
          return self.device



   @torch.no_grad()
   @replace_example_docstring(snake_case__					)
   def __call__(self						:   int       ,		snake_case__						:   Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		snake_case__						:   Union[torch.FloatTensor, List[torch.FloatTensor]]       ,		snake_case__						:   int = 5_12       ,		snake_case__						:   int = 5_12       ,		snake_case__						:   int = 1_00       ,		snake_case__						:   float = 4.0       ,		snake_case__						:   int = 1       ,		snake_case__						:   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,		snake_case__						:   Optional[torch.FloatTensor] = None       ,		snake_case__						:   Optional[str] = "pil"       ,		snake_case__						:   bool = True       ,		)						->      Dict:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self._execution_device

          snake_case			:  List[str]     							= guidance_scale > 1.0

          if isinstance(snake_case__       ,		snake_case__					):
                 snake_case			:  Optional[int]     							= torch.cat(snake_case__       ,		dim=0					)
          snake_case			:  Optional[int]     							= image_embeds.shape[0] * num_images_per_prompt
          if isinstance(snake_case__       ,		snake_case__					):
                 snake_case			:  List[Any]     							= torch.cat(snake_case__       ,		dim=0					)

          if do_classifier_free_guidance:
                 snake_case			:  str     							= image_embeds.repeat_interleave(snake_case__       ,		dim=0					)
                 snake_case			:  Optional[int]     							= negative_image_embeds.repeat_interleave(snake_case__       ,		dim=0					)

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

          self.scheduler.set_timesteps(snake_case__       ,		device=snake_case__					)
          snake_case			:  Optional[int]     							= self.scheduler.timesteps

          snake_case			:  int     							= self.unet.config.in_channels

          snake_case							,   snake_case			:  Any     							= downscale_height_and_width(snake_case__       ,		snake_case__       ,		self.movq_scale_factor					)

          # create initial latent
          snake_case			:  List[Any]     							= self.prepare_latents(
              (batch_size, num_channels_latents, height, width)       ,		image_embeds.dtype       ,		snake_case__       ,		snake_case__       ,		snake_case__       ,		self.scheduler       ,		)

          for i, t in enumerate(self.progress_bar(snake_case__					)					):
                 # expand the latents if we are doing classifier free guidance
                 snake_case			:  List[str]     							= torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

                 snake_case			:  Tuple     							= {"image_embeds": image_embeds}
                 snake_case			:  str     							= self.unet(
                     sample=snake_case__       ,		timestep=snake_case__       ,		encoder_hidden_states=snake_case__       ,		added_cond_kwargs=snake_case__       ,		return_dict=snake_case__       ,		)[0]

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

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

                 # compute the previous noisy sample x_t -> x_t-1
                 snake_case			:  Tuple     							= self.scheduler.step(
                     snake_case__       ,		snake_case__       ,		snake_case__       ,		generator=snake_case__       ,		)[0]
          # post-processing
          snake_case			:  Optional[Any]     							= self.movq.decode(snake_case__       ,		force_not_quantize=snake_case__					)["sample"]

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

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

          if output_type == "pil":
                 snake_case			:  Any     							= self.numpy_to_pil(snake_case__					)

          if not return_dict:
                 return (image,)

          return ImagePipelineOutput(images=snake_case__					)





