'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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





def 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]			)    ->      List[str]:


     '''simple docstring'''




     warnings.warn(
         "The preprocess method is deprecated and will be removed in a future version. Please"
         " use VaeImageProcessor.preprocess instead"					,    SCREAMING_SNAKE_CASE__					,    )
     if isinstance(SCREAMING_SNAKE_CASE__					,    torch.Tensor			):
          return image
     elif isinstance(SCREAMING_SNAKE_CASE__					,    PIL.Image.Image			):
          UpperCAmelCase_           =   [image]

     if isinstance(image[0]					,    PIL.Image.Image			):
          UpperCAmelCase_    , UpperCAmelCase_           =   image[0].size
          UpperCAmelCase_    , UpperCAmelCase_           =   (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          UpperCAmelCase_           =   [np.array(i.resize((w, h)					,    resample=PIL_INTERPOLATION["lanczos"]			)			)[None, :] for i in image]
          UpperCAmelCase_           =   np.concatenate(SCREAMING_SNAKE_CASE__					,    axis=0			)
          UpperCAmelCase_           =   np.array(SCREAMING_SNAKE_CASE__			).astype(np.floataa			) / 255.0
          UpperCAmelCase_           =   image.transpose(0					,    3					,    1					,    2			)
          UpperCAmelCase_           =   2.0 * image - 1.0
          UpperCAmelCase_           =   torch.from_numpy(SCREAMING_SNAKE_CASE__			)
     elif isinstance(image[0]					,    torch.Tensor			):
          UpperCAmelCase_           =   torch.cat(SCREAMING_SNAKE_CASE__					,    dim=0			)
     return image





def 	lowerCAmelCase_				(					snake_case_       : List[str]			)    ->      Optional[Any]:


     '''simple docstring'''




     if isinstance(SCREAMING_SNAKE_CASE__					,    torch.Tensor			):
          return mask
     elif isinstance(SCREAMING_SNAKE_CASE__					,    PIL.Image.Image			):
          UpperCAmelCase_           =   [mask]

     if isinstance(mask[0]					,    PIL.Image.Image			):
          UpperCAmelCase_    , UpperCAmelCase_           =   mask[0].size
          UpperCAmelCase_    , UpperCAmelCase_           =   (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          UpperCAmelCase_           =   [np.array(m.convert("L"			).resize((w, h)					,    resample=PIL_INTERPOLATION["nearest"]			)			)[None, :] for m in mask]
          UpperCAmelCase_           =   np.concatenate(SCREAMING_SNAKE_CASE__					,    axis=0			)
          UpperCAmelCase_           =   mask.astype(np.floataa			) / 255.0
          UpperCAmelCase_           =   0
          UpperCAmelCase_           =   1
          UpperCAmelCase_           =   torch.from_numpy(SCREAMING_SNAKE_CASE__			)
     elif isinstance(mask[0]					,    torch.Tensor			):
          UpperCAmelCase_           =   torch.cat(SCREAMING_SNAKE_CASE__					,    dim=0			)
     return mask






class        __A      ( __A	):
    a__       :						UNetaDModel
    a__       :						RePaintScheduler



    def __init__(self    :    List[str]   ,			__a    :    int   ,			__a    :    Optional[int] ):
         super().__init__()
         self.register_modules(unet=_UpperCamelCase   ,			scheduler=_UpperCamelCase )






    @torch.no_grad()
    def __call__(self    :    List[Any]   ,			__a    :    Union[torch.Tensor, PIL.Image.Image]   ,			__a    :    Union[torch.Tensor, PIL.Image.Image]   ,			__a    :    int = 250   ,			__a    :    float = 0.0   ,			__a    :    int = 10   ,			__a    :    int = 10   ,			__a    :    Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,			__a    :    Optional[str] = "pil"   ,			__a    :    bool = True   ,			):
         UpperCAmelCase_           =   image

         UpperCAmelCase_           =   _preprocess_image(_UpperCamelCase )
         UpperCAmelCase_           =   original_image.to(device=self.device   ,			dtype=self.unet.dtype )
         UpperCAmelCase_           =   _preprocess_mask(_UpperCamelCase )
         UpperCAmelCase_           =   mask_image.to(device=self.device   ,			dtype=self.unet.dtype )

         UpperCAmelCase_           =   original_image.shape[0]

         # sample gaussian noise to begin the loop
         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.""" )

         UpperCAmelCase_           =   original_image.shape
         UpperCAmelCase_           =   randn_tensor(_UpperCamelCase   ,			generator=_UpperCamelCase   ,			device=self.device   ,			dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(_UpperCamelCase   ,			_UpperCamelCase   ,			_UpperCamelCase   ,			self.device )
         UpperCAmelCase_           =   eta

         UpperCAmelCase_           =   self.scheduler.timesteps[0] + 1
         UpperCAmelCase_           =   generator[0] if isinstance(_UpperCamelCase   ,			_UpperCamelCase ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
              if t < t_last:
                   # predict the noise residual
                   UpperCAmelCase_           =   self.unet(_UpperCamelCase   ,			_UpperCamelCase ).sample
                   # compute previous image: x_t -> x_t-1
                   UpperCAmelCase_           =   self.scheduler.step(_UpperCamelCase   ,			_UpperCamelCase   ,			_UpperCamelCase   ,			_UpperCamelCase   ,			_UpperCamelCase   ,			_UpperCamelCase ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   UpperCAmelCase_           =   self.scheduler.undo_step(_UpperCamelCase   ,			_UpperCamelCase   ,			_UpperCamelCase )
              UpperCAmelCase_           =   t

         UpperCAmelCase_           =   (image / 2 + 0.5).clamp(0   ,			1 )
         UpperCAmelCase_           =   image.cpu().permute(0   ,			2   ,			3   ,			1 ).numpy()
         if output_type == "pil":
              UpperCAmelCase_           =   self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=_UpperCamelCase )







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def   __SCREAMING_SNAKE_CASE					(*SCREAMING_SNAKE_CASE__       ):
						if not isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
												snake_case_   =	list(SCREAMING_SNAKE_CASE__       )
						for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
												snake_case_   =	None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''CUDA out of memory.''',  # CUDA OOM
						    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
						    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
						]
						if isinstance(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ) and len(exception.args       ) == 1:
												return any(err in exception.args[0] for err in _statements       )
						return False


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__ = None  ,		SCREAMING_SNAKE_CASE__ = 128       ):
						if function is None:
												return functools.partial(SCREAMING_SNAKE_CASE__  ,		starting_batch_size=SCREAMING_SNAKE_CASE__       )

						snake_case_   =	starting_batch_size

						def decorator(*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       ):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												snake_case_   =	list(inspect.signature(SCREAMING_SNAKE_CASE__       ).parameters.keys()       )
												# Guard against user error
												if len(SCREAMING_SNAKE_CASE__       ) < (len(SCREAMING_SNAKE_CASE__       ) + 1):
																		snake_case_   =	''', '''.join([F'''{arg}={value}''' for arg, value in zip(params[1:]  ,		args[1:]       )]       )
																		raise TypeError(
																		    F'''Batch size was passed into `{function.__name__}` as the first argument when called.'''
																		    F'''Remove this as the decorator already does so: `{function.__name__}({arg_str})`'''       )
												while True:
																		if batch_size == 0:
																								raise RuntimeError('''No executable batch size found, reached zero.'''       )
																		try:
																								return function(SCREAMING_SNAKE_CASE__  ,		*SCREAMING_SNAKE_CASE__  ,		**SCREAMING_SNAKE_CASE__       )
																		except Exception as e:
																								if should_reduce_batch_size(SCREAMING_SNAKE_CASE__       ):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator