



'''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 						a_		(     snake_case    ):
							UpperCAmelCase			:		Dict        =				0
							UpperCAmelCase			:		str        =				False
							UpperCAmelCase			:		Tuple        =				3.0






class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						int			)			->					int:
										# 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=2.2_5			).to_kwargs()      ,						{'a': 2, 'c': 2.2_5}			)


							@require_cuda
							def    UpperCamelCase     (				self   :						int			)			->					Dict:
										# If no defaults are changed, `to_kwargs` returns an empty dict.
										snake_case:    List[Any]      							=GradScalerKwargs(init_scale=1_0_2_4      ,						growth_factor=2			)
										AcceleratorState._reset_state()
										snake_case:    Any      							=Accelerator(mixed_precision='fp16'      ,						kwargs_handlers=[scaler_handler]			)
										print(accelerator.use_fpaa			)
										snake_case:    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      ,						A_			)




							@require_multi_gpu
							def    UpperCamelCase     (				self   :						List[Any]			)			->					Union[str, Any]:
										snake_case:    Dict      							=['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__			)]
										execute_subprocess_async(A_      ,						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 // (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)








"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__magic_name__						: Any    =				{'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				['GLPNFeatureExtractor']
					__magic_name__						: Union[str, Any]    =				['GLPNImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: int    =				[
					    'GLPN_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'GLPNForDepthEstimation',
					    'GLPNLayer',
					    'GLPNModel',
					    'GLPNPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_glpn import GLPNFeatureExtractor
										from .image_processing_glpn import GLPNImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_glpn import (
										    GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GLPNForDepthEstimation,
										    GLPNLayer,
										    GLPNModel,
										    GLPNPreTrainedModel,
										)


else:
					import sys

					__magic_name__						: Optional[int]    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




