



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		(					snake_case_):






	"""simple docstring"""


	a__ :   Optional[int]						  =				0
	a__ :   Union[str, Any]						  =				False
	a__ :   Union[str, Any]						  =				3.0






class 						lowercase		(					unittest.TestCase):






	"""simple docstring"""


	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple	)     ->					List[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=__UpperCAmelCase	).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      :		Optional[Any]	)     ->					str:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							UpperCAmelCase_=  GradScalerKwargs(init_scale=1_024		,    growth_factor=2	)
							AcceleratorState._reset_state()
							UpperCAmelCase_=  Accelerator(mixed_precision="""fp16"""		,    kwargs_handlers=[scaler_handler]	)
							print(accelerator.use_fpaa	)
							UpperCAmelCase_=  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		,    2_000	)
							self.assertEqual(scaler._enabled		,    __UpperCAmelCase	)




	@require_multi_gpu
	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]	)     ->					Optional[Any]:
							UpperCAmelCase_=  ["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
							execute_subprocess_async(__UpperCAmelCase		,    env=os.environ.copy()	)


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

	# Check the values changed in kwargs
	__A							       =		''''''
	__A							       =		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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate




@dataclass(frozen=snake_case_							)
class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					field(default='''language-modeling'''			,   metadata={'''include_in_asdict_even_if_is_default''': True}							)

	lowerCAmelCase						=					Features({'''text''': Value('''string'''							)}							)
	lowerCAmelCase						=					Features({}							)
	lowerCAmelCase						=					"text"




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict[str, str]:
								return {self.text_column: "text"}





