'''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 			__lowercase		(						_lowerCamelCase						):







					"""simple docstring"""






					SCREAMING_SNAKE_CASE__		   =						0
					SCREAMING_SNAKE_CASE__		   =						False
					SCREAMING_SNAKE_CASE__		   =						3.0






class 			__lowercase		(						unittest.TestCase						):







					"""simple docstring"""






					def lowerCAmelCase     (		self    ):
									# 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=_lowerCamelCase    ).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 lowerCAmelCase     (		self    ):
									# If no defaults are changed, `to_kwargs` returns an empty dict.
									__UpperCamelCase	:	List[str]		    =  GradScalerKwargs(init_scale=1_0_2_4     ,			growth_factor=2    )
									AcceleratorState._reset_state()
									__UpperCamelCase	:	List[str]		    =  Accelerator(mixed_precision='fp16'     ,			kwargs_handlers=[scaler_handler]    )
									print(accelerator.use_fpaa    )
									__UpperCamelCase	:	List[str]		    =  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     ,			2_0_0_0    )
									self.assertEqual(scaler._enabled     ,			_lowerCamelCase    )







					@require_multi_gpu
					def lowerCAmelCase     (		self    ):
									__UpperCamelCase	:	List[Any]		    =  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__    )]
									execute_subprocess_async(_lowerCamelCase     ,			env=os.environ.copy()    )


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

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




'''simple docstring'''
a=  '''0.21.0'''

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
  from .utils import rich



