



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 						__a (     SCREAMING_SNAKE_CASE				):
	SCREAMING_SNAKE_CASE       =       0
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       3.0






class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Optional[Any])->							Optional[Any]:
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						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=2.2_5).to_kwargs()				,	{"""a""": 2, """c""": 2.2_5})



	@require_cuda
	def 		UpperCamelCase							(						self							:    str)->							Optional[Any]:
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						__lowerCAmelCase	   =GradScalerKwargs(init_scale=10_24				,	growth_factor=2)
						AcceleratorState._reset_state()
						__lowerCAmelCase	   =Accelerator(mixed_precision="""fp16"""				,	kwargs_handlers=[scaler_handler])
						print(accelerator.use_fpaa)
						__lowerCAmelCase	   =accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale				,	1_0_2_4.0)
						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				,	20_00)
						self.assertEqual(scaler._enabled				,	snake_case_)



	@require_multi_gpu
	def 		UpperCamelCase							(						self							:    str)->							Optional[Any]:
						__lowerCAmelCase	   =["""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__":
				lowercase_	       =			DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
				lowercase_	       =			Accelerator(kwargs_handlers=[ddp_scaler])
				lowercase_	       =			torch.nn.Linear(1_00, 2_00)
				lowercase_	       =			accelerator.prepare(model)

				# Check the values changed in kwargs
				lowercase_	       =			''''''
				lowercase_	       =			model.bucket_bytes_cap // (10_24 * 10_24)
				if observed_bucket_cap_map != 15:
								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'''):
				lowercase_	       =			{
				    '''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:
				lowercase_	       =			{
				    '''linear''': PIL.Image.LINEAR,
				    '''bilinear''': PIL.Image.BILINEAR,
				    '''bicubic''': PIL.Image.BICUBIC,
				    '''lanczos''': PIL.Image.LANCZOS,
				    '''nearest''': PIL.Image.NEAREST,
				}






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






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str	)  ->						str:
					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(__lowerCamelCase	) for image in images]

					return pil_images




