


'''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 	lowerCAmelCase__			(			_UpperCAmelCase	):
			lowerCAmelCase :	Optional[int]          =     0
			lowerCAmelCase :	Any          =     False
			lowerCAmelCase :	str          =     3.0






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     str )   ->List[Any]:



					'''simple docstring'''







					self.assertDictEqual(MockClass().to_kwargs()	,  {} )
					self.assertDictEqual(MockClass(a=2 ).to_kwargs()	,  {"a": 2} )
					self.assertDictEqual(MockClass(a=2	,  b=lowercase__ ).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  :     int )   ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      GradScalerKwargs(init_scale=10_24	,  growth_factor=2 )
					AcceleratorState._reset_state()
					_UpperCAmelCase		:       List[Any]								=      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	,  20_00 )
					self.assertEqual(scaler._enabled	,  lowercase__ )






			@require_multi_gpu
			def 							lowerCAmelCase__    (		self  :     Tuple )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
					execute_subprocess_async(lowercase__	,  env=os.environ.copy() )


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

	# Check the values changed in kwargs
	lowerCamelCase__    	=     ''
	lowerCamelCase__    	=     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 Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	int          =     "pixel_values"
			lowerCAmelCase :	Dict          =     False
			lowerCAmelCase :	Union[str, Any]          =     TimmBackboneConfig




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					requires_backends(self	,  "timm" )
					super().__init__(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      config

					if config.backbone is None:
							raise ValueError("backbone is not set in the config. Please set it to a timm model name." )

					if config.backbone not in timm.list_models():
							raise ValueError(F"""backbone {config.backbone} is not supported by timm.""" )

					if hasattr(lowerCamelCase__	,  "out_features" ) and config.out_features is not None:
							raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )

					_UpperCAmelCase		:       Optional[Any]								=      getattr(lowerCamelCase__	,  "use_pretrained_backbone"	,  lowerCamelCase__ )
					if pretrained is None:
							raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )

					# We just take the final layer by default. This matches the default for the transformers models.
					_UpperCAmelCase		:       int								=      config.out_indices if getattr(lowerCamelCase__	,  "out_indices"	,  lowerCamelCase__ ) is not None else (-1,)

					_UpperCAmelCase		:       List[Any]								=      timm.create_model(
					    config.backbone	,  pretrained=lowerCamelCase__	,  features_only=config.features_only	,  in_chans=config.num_channels	,  out_indices=lowerCamelCase__	,  **lowerCamelCase__	,  )
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					_UpperCAmelCase		:       List[str]								=      self._backbone.return_layers
					_UpperCAmelCase		:       Optional[int]								=      {layer["module"]: str(lowerCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
					super()._init_backbone(lowerCamelCase__ )




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     List[str]	,  lowerCamelCase__  :     str	,  *lowerCamelCase__  :     Tuple	,  **lowerCamelCase__  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					requires_backends(cls	,  ["vision", "timm"] )
					from ...models.timm_backbone import TimmBackboneConfig

					_UpperCAmelCase		:       Any								=      kwargs.pop("config"	,  TimmBackboneConfig() )

					_UpperCAmelCase		:       Dict								=      kwargs.pop("use_timm_backbone"	,  lowerCamelCase__ )
					if not use_timm:
							raise ValueError("use_timm_backbone must be True for timm backbones" )

					_UpperCAmelCase		:       str								=      kwargs.pop("num_channels"	,  config.num_channels )
					_UpperCAmelCase		:       Dict								=      kwargs.pop("features_only"	,  config.features_only )
					_UpperCAmelCase		:       str								=      kwargs.pop("use_pretrained_backbone"	,  config.use_pretrained_backbone )
					_UpperCAmelCase		:       Optional[Any]								=      kwargs.pop("out_indices"	,  config.out_indices )
					_UpperCAmelCase		:       Dict								=      TimmBackboneConfig(
					    backbone=lowerCamelCase__	,  num_channels=lowerCamelCase__	,  features_only=lowerCamelCase__	,  use_pretrained_backbone=lowerCamelCase__	,  out_indices=lowerCamelCase__	,  )
					return super()._from_config(lowerCamelCase__	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					pass






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     List[Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  **lowerCamelCase__  :     Dict )   ->Union[BackboneOutput, Tuple[Tensor, ...]]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      return_dict if return_dict is not None else self.config.use_return_dict
					_UpperCAmelCase		:       Optional[int]								=      (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					_UpperCAmelCase		:       Dict								=      output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
							raise ValueError("Cannot output attentions for timm backbones at the moment" )

					if output_hidden_states:
							# We modify the return layers to include all the stages of the backbone
							_UpperCAmelCase		:       Optional[int]								=      self._all_layers
							_UpperCAmelCase		:       List[str]								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      self._return_layers
							_UpperCAmelCase		:       Tuple								=      tuple(hidden_states[i] for i in self.out_indices )
					else:
							_UpperCAmelCase		:       Any								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      None

					_UpperCAmelCase		:       Dict								=      tuple(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      tuple(lowerCamelCase__ ) if hidden_states is not None else None

					if not return_dict:
							_UpperCAmelCase		:       Dict								=      (feature_maps,)
							if output_hidden_states:
									_UpperCAmelCase		:       List[str]								=      output + (hidden_states,)
							return output

					return BackboneOutput(feature_maps=lowerCamelCase__	,  hidden_states=lowerCamelCase__	,  attentions=lowerCamelCase__ )



