'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class     __UpperCAmelCase       ( __a		):
  __A :				UNetaDModel
  __A :				ScoreSdeVeScheduler





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     super().__init__()
     self.register_modules(unet=_lowerCamelCase    ,     scheduler=_lowerCamelCase )







  @torch.no_grad()
  def __call__(	self    ,     _lowerCamelCase = 1    ,     _lowerCamelCase = 2000    ,     _lowerCamelCase = None    ,     _lowerCamelCase = "pil"    ,     _lowerCamelCase = True    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      self.unet.config.sample_size
     lowerCAmelCase_			=      (batch_size, 3, img_size, img_size)

     lowerCAmelCase_			=      self.unet

     lowerCAmelCase_			=      randn_tensor(_lowerCamelCase    ,     generator=_lowerCamelCase ) * self.scheduler.init_noise_sigma
     lowerCAmelCase_			=      sample.to(self.device )

     self.scheduler.set_timesteps(_lowerCamelCase )
     self.scheduler.set_sigmas(_lowerCamelCase )

     for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
        lowerCAmelCase_			=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device )

        # correction step
        for _ in range(self.scheduler.config.correct_steps ):
           lowerCAmelCase_			=      self.unet(_lowerCamelCase    ,     _lowerCamelCase ).sample
           lowerCAmelCase_			=      self.scheduler.step_correct(_lowerCamelCase    ,     _lowerCamelCase    ,     generator=_lowerCamelCase ).prev_sample

        # prediction step
        lowerCAmelCase_			=      model(_lowerCamelCase    ,     _lowerCamelCase ).sample
        lowerCAmelCase_			=      self.scheduler.step_pred(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     generator=_lowerCamelCase )

        lowerCAmelCase_	,lowerCAmelCase_			=      output.prev_sample, output.prev_sample_mean

     lowerCAmelCase_			=      sample_mean.clamp(0    ,     1 )
     lowerCAmelCase_			=      sample.cpu().permute(0    ,     2    ,     3    ,     1 ).numpy()
     if output_type == "pil":
        lowerCAmelCase_			=      self.numpy_to_pil(_lowerCamelCase )

     if not return_dict:
        return (sample,)

     return ImagePipelineOutput(images=_lowerCamelCase )






'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def snake_case_      (					*__snake_case  :							Optional[int])			->					int:
   if not isinstance(__snake_case	,   __snake_case):
      lowerCAmelCase_			=      list(__snake_case)
   for i in range(len(__snake_case)):
      lowerCAmelCase_			=      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 snake_case_      (					__snake_case  :							Exception)			->					bool:
   lowerCAmelCase_			=      [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(__snake_case	,   __snake_case) and len(exception.args) == 1:
      return any(err in exception.args[0] for err in _statements)
   return False






def snake_case_      (					__snake_case  :							callable = None	,   __snake_case  :							int = 128)			->					List[Any]:
   if function is None:
      return functools.partial(__snake_case	,   starting_batch_size=__snake_case)

   lowerCAmelCase_			=      starting_batch_size

   def decorator(*__snake_case  :							List[str]	,   **__snake_case  :							Union[str, Any]):
      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()
      lowerCAmelCase_			=      list(inspect.signature(__snake_case).parameters.keys())
      # Guard against user error
      if len(__snake_case) < (len(__snake_case) + 1):
         lowerCAmelCase_			=      ''', '''.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(__snake_case	,   *__snake_case	,   **__snake_case)
         except Exception as e:
            if should_reduce_batch_size(__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





