





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						int     		=				0
   _UpperCamelCase :						bool     		=				False
   _UpperCamelCase :						float     		=				3.0







class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      self.assertDictEqual(MockClass().to_kwargs()					,			{}      )
      self.assertDictEqual(MockClass(a=2      ).to_kwargs()					,			{"a": 2}      )
      self.assertDictEqual(MockClass(a=2					,			b=snake_case      ).to_kwargs()					,			{"a": 2, "b": True}      )
      self.assertDictEqual(MockClass(a=2					,			c=2.25      ).to_kwargs()					,			{"a": 2, "c": 2.25}      )





   @require_cuda
   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						List[str]      		=  GradScalerKwargs(init_scale=1_024					,			growth_factor=2      )
      AcceleratorState._reset_state()
      a__	:						List[Any]      		=  Accelerator(mixed_precision="fp16"					,			kwargs_handlers=[scaler_handler]      )
      print(accelerator.use_fpaa      )
      a__	:						int      		=  accelerator.scaler

      # Check the kwargs have been applied
      self.assertEqual(scaler._init_scale					,			1_024.0      )
      self.assertEqual(scaler._growth_factor					,			2.0      )

      # Check the other values are at the default
      self.assertEqual(scaler._backoff_factor					,			0.5      )
      self.assertEqual(scaler._growth_interval					,			2_000      )
      self.assertEqual(scaler._enabled					,			snake_case      )





   @require_multi_gpu
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
      execute_subprocess_async(snake_case					,			env=os.environ.copy()      )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
  SCREAMING_SNAKE_CASE__       :			int			      =						Accelerator(kwargs_handlers=[ddp_scaler])
  SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						torch.nn.Linear(1_0_0, 2_0_0)
  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						accelerator.prepare(model)

  # Check the values changed in kwargs
  SCREAMING_SNAKE_CASE__       :			Dict			      =						""""""
  SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
  if observed_bucket_cap_map != 1_5:
    error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
  if model.find_unused_parameters is not True:
    error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

  # Check the values of the defaults
  if model.dim != 0:
    error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
  if model.broadcast_buffers is not True:
    error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
  if model.gradient_as_bucket_view is not False:
    error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)








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"""):
  SCREAMING_SNAKE_CASE__       :			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:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						{
      """linear""": PIL.Image.LINEAR,
      """bilinear""": PIL.Image.BILINEAR,
      """bicubic""": PIL.Image.BICUBIC,
      """lanczos""": PIL.Image.LANCZOS,
      """nearest""": PIL.Image.NEAREST,
  }






def       _A  (      lowerCamelCase   ):
   a__	:						Optional[int]      		=  (images / 2 + 0.5).clamp(0							, 1   )
   a__	:						List[Any]      		=  images.cpu().permute(0							, 2							, 3							, 1   ).float().numpy()
   a__	:						int      		=  numpy_to_pil(lowerCamelCase   )
   return images






def       _A  (      lowerCamelCase   ):
   if images.ndim == 3:
      a__	:						Tuple      		=  images[None, ...]
   a__	:						Dict      		=  (images * 255).round().astype("uint8"   )
   if images.shape[-1] == 1:
      # special case for grayscale (single channel) images
      a__	:						str      		=  [Image.fromarray(image.squeeze()							, mode="L"   ) for image in images]
   else:
      a__	:						List[Any]      		=  [Image.fromarray(lowerCamelCase   ) for image in images]

   return pil_images

