






"""simple docstring"""


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    (A_ :int      ,     A_ :List[Any] ):







  '''simple docstring'''

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






class 		snake_case    (    torch.optim.Optimizer			):





    def __init__(				self      :   Tuple							,						A      :   List[Any]							,						A      :   int=True							,						A      :   Optional[Any]=None     ):


      '''simple docstring'''




      a	:     Dict							=  optimizer
      a	:     Union[str, Any]							=  scaler
      a	:     Tuple							=  AcceleratorState()
      a	:     List[Any]							=  GradientState()
      a	:     Optional[Any]							=  device_placement
      a	:     Optional[Any]							=  False
      a	:     Union[str, Any]							=  None

      # Handle device placement
      if device_placement:
        a	:     Optional[int]							=  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	:     int							=  move_to_device(A							,						self.accelerator_state.device     )
        self.optimizer.load_state_dict(A     )





    @property
    def 							lowerCamelCase__ (				self      :   List[Any]     ):


      '''simple docstring'''




      return self.optimizer.state





    @state.setter
    def 							lowerCamelCase__ (				self      :   Optional[int]							,						A      :   str     ):


      '''simple docstring'''




      a	:     Optional[int]							=  state





    @property
    def 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      return self.optimizer.param_groups





    @param_groups.setter
    def 							lowerCamelCase__ (				self      :   int							,						A      :   List[Any]     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  param_groups





    @property
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      return self.optimizer.defaults





    @defaults.setter
    def 							lowerCamelCase__ (				self      :   Union[str, Any]							,						A      :   Optional[Any]     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  defaults





    def 							lowerCamelCase__ (				self      :   List[Any]							,						A      :   Any     ):


      '''simple docstring'''




      self.optimizer.add_param_group(A     )





    def 							lowerCamelCase__ (				self      :   Any							,						A      :   Dict     ):


      '''simple docstring'''




      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 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return self.optimizer.state_dict()





    def 							lowerCamelCase__ (				self      :   Dict							,						A      :   str=None     ):


      '''simple docstring'''




      if self.gradient_state.sync_gradients:
        a	:     Union[str, Any]							=  'set_to_none' in inspect.signature(self.optimizer.zero_grad     ).parameters
        if accept_arg:
          if set_to_none is None:
            a	:     List[str]							=  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 							lowerCamelCase__ (				self      :   List[str]							,						A      :   Optional[int]=None     ):


      '''simple docstring'''




      if self.gradient_state.sync_gradients:
        if self.accelerator_state.distributed_type == DistributedType.TPU:
          a	:     str							=  {'closure': closure} if closure is not None else {}
          xm.optimizer_step(self.optimizer							,						optimizer_args=A     )
        elif self.scaler is not None:
          a	:     Dict							=  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	:     Optional[int]							=  self.scaler.get_scale()
            a	:     List[str]							=  True
          self.scaler.step(self.optimizer							,						A     )
          self.scaler.update()
          a	:     Union[str, 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	:     Optional[Any]							=  scale_after

        else:
          self.optimizer.step(A     )





    def 							lowerCamelCase__ (				self      :   int							,						A      :   Union[str, Any]     ):


      '''simple docstring'''




      for param_group in self.optimizer.param_groups:
        a	:     Tuple							=  [parameters_map.get(A							,						A     ) for p in param_group['params']]





    @property
    def 							lowerCamelCase__ (				self      :   Optional[Any]     ):


      '''simple docstring'''




      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 							lowerCamelCase__ (				self      :   Dict     ):


      '''simple docstring'''




      return self._is_overflow





    def __getstate__(				self      :   Optional[Any]     ):


      '''simple docstring'''




      return self.__dict__.copy()





    def __setstate__(				self      :   List[Any]							,						A      :   Optional[int]     ):


      '''simple docstring'''




      self.__dict__.update(A     )












"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCamelCase							:       Optional[Any]       					=		logging.get_logger(__name__)

_UpperCamelCase							:       int       					=		{
    'apple/mobilevitv2-1.0': 'https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json',
}






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	'''mobilevitv2'''





    def __init__(				self      :   Any							,						A      :   Any=3							,						A      :   Any=2_5_6							,						A      :   Optional[int]=2							,						A      :   str=2.0							,						A      :   Union[str, Any]="swish"							,						A      :   str=3							,						A      :   Tuple=3_2							,						A      :   Optional[int]=0.1							,						A      :   str=0.02							,						A      :   int=1E-5							,						A      :   List[Any]=5_1_2							,						A      :   str=[6, 1_2, 1_8]							,						A      :   Any=0.1							,						A      :   Union[str, Any]=2_5_5							,						A      :   Optional[Any]=[2, 4, 3]							,						A      :   List[Any]=[1_2_8, 1_9_2, 2_5_6]							,						A      :   Tuple=1.0							,						A      :   Union[str, Any]=2							,						A      :   List[Any]=0.0							,						A      :   int=0.0							,						**A      :   Optional[Any]							,						):


      '''simple docstring'''




      super().__init__(**A     )

      a	:     Optional[Any]							=  num_channels
      a	:     List[Any]							=  image_size
      a	:     Tuple							=  patch_size
      a	:     Optional[Any]							=  expand_ratio
      a	:     Any							=  hidden_act
      a	:     Dict							=  conv_kernel_size
      a	:     Optional[int]							=  output_stride
      a	:     Optional[Any]							=  initializer_range
      a	:     Tuple							=  layer_norm_eps
      a	:     Optional[int]							=  n_attn_blocks
      a	:     Tuple							=  base_attn_unit_dims
      a	:     List[str]							=  width_multiplier
      a	:     Dict							=  ffn_multiplier
      a	:     Dict							=  ffn_dropout
      a	:     Optional[int]							=  attn_dropout
      a	:     Tuple							=  classifier_dropout_prob

      # decode head attributes for semantic segmentation
      a	:     str							=  aspp_out_channels
      a	:     List[Any]							=  atrous_rates
      a	:     Optional[int]							=  aspp_dropout_prob
      a	:     Optional[Any]							=  semantic_loss_ignore_index






class 		snake_case    (    UpperCAmelCase			):
    __magic_name__       	=	version.parse('''1.11'''			)





    @property
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      if self.task == "image-classification":
        return OrderedDict([('logits', {0: 'batch'})]     )
      else:
        return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]     )





    @property
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      return 1E-4




