




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__					(      snake_case__   ):
						_a							:					int      =					0
						_a							:					bool      =					False
						_a							:					float      =					3.0






class     a__					(      unittest.TestCase   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""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    ):




											"""simple docstring"""



											__lowerCAmelCase            = GradScalerKwargs(init_scale=1_0_2_4	,    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	,    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	,    2_0_0_0    )
											self.assertEqual(scaler._enabled	,    _A    )






						@require_multi_gpu
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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

					# Check the values changed in kwargs
					UpperCamelCase__					    =    """"""
					UpperCamelCase__					    =    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 _LazyModule


UpperCamelCase__					    =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_tapex import TapexTokenizer


else:
					import sys

					UpperCamelCase__					    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)




