



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  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					0
							UpperCamelCase__				:      bool         =					False
							UpperCamelCase__				:      float         =					3.0




class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							def _a      (							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=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 _a      (							self		):
											# If no defaults are changed, `to_kwargs` returns an empty dict.
											a__				      =		GradScalerKwargs(init_scale=1_024  ,  growth_factor=2		)
											AcceleratorState._reset_state()
											a__				      =		Accelerator(mixed_precision="""fp16"""  ,  kwargs_handlers=[scaler_handler]		)
											print(accelerator.use_fpaa		)
											a__				      =		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  ,  a_		)







							@require_multi_gpu
							def _a      (							self		):
											a__				      =		["""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 // (1_024 * 1_024)
	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,
)


UpperCAmelCase							 =					{
    """configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FalconForCausalLM""",
	    """FalconModel""",
	    """FalconPreTrainedModel""",
	    """FalconForSequenceClassification""",
	    """FalconForTokenClassification""",
	    """FalconForQuestionAnswering""",
	]


if TYPE_CHECKING:
	from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_falcon import (
		    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FalconForCausalLM,
		    FalconForQuestionAnswering,
		    FalconForSequenceClassification,
		    FalconForTokenClassification,
		    FalconModel,
		    FalconPreTrainedModel,
		)


else:
	import sys

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

