



import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm
def 						snake_case__							(       __lowercase				,  __lowercase							)		->		Any:







						"""simple docstring"""

						if isinstance(snake_case_				,  (list, tuple)							):
												return honor_type(snake_case_				,  (move_to_device(snake_case_				,  snake_case_							) for t in state)							)
						elif isinstance(snake_case_				,  snake_case_							):
												return type(snake_case_							)({k: move_to_device(snake_case_				,  snake_case_							) for k, v in state.items()}							)
						elif isinstance(snake_case_				,  torch.Tensor							):
												return state.to(snake_case_							)
						return state




class 				lowerCAmelCase__	(						torch.optim.Optimizer    ):







					def __init__(							self   :  Any      ,		_A   :  int      ,		_A   :  Optional[Any]=True      ,		_A   :  str=None):
											A__						:				Union[str, Any]         =			optimizer
											A__						:				Optional[Any]         =			scaler
											A__						:				Any         =			AcceleratorState()
											A__						:				Optional[Any]         =			GradientState()
											A__						:				str         =			device_placement
											A__						:				List[Any]         =			False
											A__						:				List[str]         =			None

											# Handle device placement
											if device_placement:
																	A__						:				List[str]         =			self.optimizer.state_dict()
																	if self.accelerator_state.distributed_type == DistributedType.TPU:
																							xm.send_cpu_data_to_device(_A      ,		self.accelerator_state.device)
																	else:
																							A__						:				Any         =			move_to_device(_A      ,		self.accelerator_state.device)
																	self.optimizer.load_state_dict(_A)







					@property
					def 			_lowercase      (							self   :  List[str]):
											return self.optimizer.state







					@state.setter
					def 			_lowercase      (							self   :  int      ,		_A   :  Optional[int]):
											A__						:				Tuple         =			state







					@property
					def 			_lowercase      (							self   :  List[Any]):
											return self.optimizer.param_groups







					@param_groups.setter
					def 			_lowercase      (							self   :  List[Any]      ,		_A   :  List[str]):
											A__						:				Optional[Any]         =			param_groups







					@property
					def 			_lowercase      (							self   :  Any):
											return self.optimizer.defaults







					@defaults.setter
					def 			_lowercase      (							self   :  int      ,		_A   :  int):
											A__						:				Optional[Any]         =			defaults







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  Any):
											self.optimizer.add_param_group(_A)







					def 			_lowercase      (							self   :  str      ,		_A   :  Optional[int]):







											if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
																	xm.send_cpu_data_to_device(_A      ,		self.accelerator_state.device)
											self.optimizer.load_state_dict(_A)







					def 			_lowercase      (							self   :  Tuple):
											return self.optimizer.state_dict()







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  Union[str, Any]=None):







											if self.gradient_state.sync_gradients:
																	A__						:				List[Any]         =			"set_to_none" in inspect.signature(self.optimizer.zero_grad).parameters
																	if accept_arg:
																							if set_to_none is None:
																													A__						:				Any         =			False
																							self.optimizer.zero_grad(set_to_none=_A)
																	else:
																							if set_to_none is not None:
																													raise ValueError("`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.")
																							self.optimizer.zero_grad()







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  Any=None):







											if self.gradient_state.sync_gradients:
																	if self.accelerator_state.distributed_type == DistributedType.TPU:
																							A__						:				Any         =			{"closure": closure} if closure is not None else {}
																							xm.optimizer_step(self.optimizer      ,		optimizer_args=_A)
																	elif self.scaler is not None:
																							A__						:				Optional[int]         =			False
																							if self._last_scale is None:
																													# `get_scale` is an async operation requiring full synchronization
																													# on CPU and GPUs before finishing. As a result, we store away
																													# the prior one to reduce the call overhead
																													A__						:				int         =			self.scaler.get_scale()
																													A__						:				Optional[int]         =			True
																							self.scaler.step(self.optimizer      ,		_A)
																							self.scaler.update()
																							A__						:				List[Any]         =			self.scaler.get_scale()
																							if not new_scale:
																													# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																													A__						:				Optional[int]         =			scale_after < self._last_scale
																							A__						:				Dict         =			scale_after

																	else:
																							self.optimizer.step(_A)







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  Optional[Any]):







											for param_group in self.optimizer.param_groups:
																	A__						:				List[Any]         =			[parameters_map.get(_A      ,		_A) for p in param_group["params"]]







					@property
					def 			_lowercase      (							self   :  Dict):
											warnings.warn(
											    "The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use "
											    "`optimizer.step_was_skipped` instead."      ,		_A      ,		)
											return self._is_overflow







					@property
					def 			_lowercase      (							self   :  int):
											return self._is_overflow







					def __getstate__(							self   :  Dict):
											return self.__dict__.copy()







					def __setstate__(							self   :  Dict      ,		_A   :  Any):
											self.__dict__.update(_A)






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


a_    :				Union[str, Any]					=			logging.get_logger(__name__)

a_    :				Optional[Any]					=			{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							"""vit-hybrid"""





  def __init__(  self	,  A=None	,  A=7_68	,  A=12	,  A=12	,  A=30_72	,  A="gelu"	,  A=0.0	,  A=0.0	,  A=0.02	,  A=1E-12	,  A=2_24	,  A=1	,  A=3	,  A=[1, 10_24, 24, 24]	,  A=True	,  **A	,  )		->   Union[str, Any]:





       '''simple docstring'''







       super().__init__(**A						)

       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''						)
            __magic_name__				=       {
                '''global_padding''': '''same''',
                '''layer_type''': '''bottleneck''',
                '''depths''': [3, 4, 9],
                '''out_features''': ['''stage3'''],
                '''embedding_dynamic_padding''': True,
            }

       if isinstance(A	,  A						):
            if "model_type" in backbone_config:
                 __magic_name__				=       CONFIG_MAPPING[backbone_config['''model_type''']]
            else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
                 __magic_name__				=       BitConfig
            __magic_name__				=       backbone_config_class(**A						)

       __magic_name__				=       backbone_featmap_shape
       __magic_name__				=       backbone_config
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       num_channels
       __magic_name__				=       qkv_bias







  def 			__A      (  self						)		->   Dict[str, any]:





       '''simple docstring'''







       __magic_name__				=       copy.deepcopy(self.__dict__						)
       __magic_name__				=       self.backbone_config.to_dict()
       __magic_name__				=       self.__class__.model_type
       return output