'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
 A_     :						Optional[int]    							={
     '''linear''': PIL.Image.Resampling.BILINEAR,
     '''bilinear''': PIL.Image.Resampling.BILINEAR,
     '''bicubic''': PIL.Image.Resampling.BICUBIC,
     '''lanczos''': PIL.Image.Resampling.LANCZOS,
     '''nearest''': PIL.Image.Resampling.NEAREST,
 }
else:
 A_     :						Optional[Any]    							={
     '''linear''': PIL.Image.LINEAR,
     '''bilinear''': PIL.Image.BILINEAR,
     '''bicubic''': PIL.Image.BICUBIC,
     '''lanczos''': PIL.Image.LANCZOS,
     '''nearest''': PIL.Image.NEAREST,
 }



def snake_case_      (					__snake_case  :							Optional[int])			->					str:
   lowerCAmelCase_			=      (images / 2 + 0.5).clamp(0	,   1)
   lowerCAmelCase_			=      images.cpu().permute(0	,   2	,   3	,   1).float().numpy()
   lowerCAmelCase_			=      numpy_to_pil(__snake_case)
   return images






def snake_case_      (					__snake_case  :							List[str])			->					int:
   if images.ndim == 3:
      lowerCAmelCase_			=      images[None, ...]
   lowerCAmelCase_			=      (images * 255).round().astype('''uint8''')
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      lowerCAmelCase_			=      [Image.fromarray(image.squeeze()	,   mode='''L''') for image in images]
   else:
      lowerCAmelCase_			=      [Image.fromarray(__snake_case) for image in images]

   return pil_images






'''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





