

'''simple docstring'''




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     UpperCAmelCase_ (	A__    ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE					=       0
	__SCREAMING_SNAKE_CASE					=       False
	__SCREAMING_SNAKE_CASE					=       3.0

class     UpperCAmelCase_ (	unittest.TestCase    ):



	"""simple docstring"""





	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     int:




				'''simple docstring'''






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

	@require_cuda
	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     str:




				'''simple docstring'''






				UpperCamelCase				:	str    						=					GradScalerKwargs(init_scale=10_24				,			growth_factor=2   )
				AcceleratorState._reset_state()
				UpperCamelCase				:	int    						=					Accelerator(mixed_precision="fp16"				,			kwargs_handlers=[scaler_handler]   )
				print(accelerator.use_fpaa   )
				UpperCamelCase				:	Any    						=					accelerator.scaler

				# Check the kwargs have been applied
				self.assertEqual(scaler._init_scale				,			10_24.0   )
				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				,			a__   )



	@require_multi_gpu
	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     str:




				'''simple docstring'''






				UpperCamelCase				:	Optional[int]    						=					["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
				execute_subprocess_async(a__				,			env=os.environ.copy()   )


if __name__ == "__main__":
				lowerCAmelCase_							=	DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
				lowerCAmelCase_							=	Accelerator(kwargs_handlers=[ddp_scaler])
				lowerCAmelCase_							=	torch.nn.Linear(100, 200)
				lowerCAmelCase_							=	accelerator.prepare(model)

				# Check the values changed in kwargs
				lowerCAmelCase_							=	''
				lowerCAmelCase_							=	model.bucket_bytes_cap // (1024 * 1024)
				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)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    	=		{
    "configuration_upernet": ["UperNetConfig"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "UperNetForSemanticSegmentation",
       "UperNetPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_upernet import UperNetConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
