'''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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							0
		lowercase   :      bool       =							False
		lowercase   :      float       =							3.0

class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     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=__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 		UpperCamelCase__       (   self				) ->     int:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								_lowerCAmelCase										=GradScalerKwargs(init_scale=1024		,						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_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		,						2000				)
								self.assertEqual(scaler._enabled		,						__A				)






		@require_multi_gpu
		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__				)]
								execute_subprocess_async(__A		,						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(100, 200)
							lowercase_     		= accelerator.prepare(model)

							# Check the values changed in kwargs
							lowercase_     		= ''''''
							lowercase_     		= 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)






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_     		= {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ViTMAEForPreTraining''',
							    '''ViTMAELayer''',
							    '''ViTMAEModel''',
							    '''ViTMAEPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TFViTMAEForPreTraining''',
							    '''TFViTMAEModel''',
							    '''TFViTMAEPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_mae import (
														    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMAEForPreTraining,
														    ViTMAELayer,
														    ViTMAEModel,
														    ViTMAEPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
							import sys

							lowercase_     		= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





