

'''simple docstring'''



from typing import TYPE_CHECKING

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


UpperCAmelCase				: Tuple         =  {
    'configuration_flava': [
        'FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'FlavaConfig',
        'FlavaImageCodebookConfig',
        'FlavaImageConfig',
        'FlavaMultimodalConfig',
        'FlavaTextConfig',
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				: str         =  ['FlavaFeatureExtractor']
  UpperCAmelCase				: Optional[Any]         =  ['FlavaImageProcessor']
  UpperCAmelCase				: int         =  ['FlavaProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase				: Any         =  [
      'FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'FlavaForPreTraining',
      'FlavaImageCodebook',
      'FlavaImageModel',
      'FlavaModel',
      'FlavaMultimodalModel',
      'FlavaPreTrainedModel',
      'FlavaTextModel',
  ]

if TYPE_CHECKING:
  from .configuration_flava import (
      FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
      FlavaConfig,
      FlavaImageCodebookConfig,
      FlavaImageConfig,
      FlavaMultimodalConfig,
      FlavaTextConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_flava import FlavaFeatureExtractor
    from .image_processing_flava import FlavaImageProcessor
    from .processing_flava import FlavaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flava import (
        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlavaForPreTraining,
        FlavaImageCodebook,
        FlavaImageModel,
        FlavaModel,
        FlavaMultimodalModel,
        FlavaPreTrainedModel,
        FlavaTextModel,
    )

else:
  import sys

  UpperCAmelCase				: List[str]         =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








'''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      a__ (						a__   ,					a__		):




     """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 		lowerCAmelCase__     (   torch.optim.Optimizer      ):



 """simple docstring"""



 def __init__(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Dict=True      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   optimizer
      __SCREAMING_SNAKE_CASE						     =   scaler
      __SCREAMING_SNAKE_CASE						     =   AcceleratorState()
      __SCREAMING_SNAKE_CASE						     =   GradientState()
      __SCREAMING_SNAKE_CASE						     =   device_placement
      __SCREAMING_SNAKE_CASE						     =   False
      __SCREAMING_SNAKE_CASE						     =   None

      # Handle device placement
      if device_placement:
           __SCREAMING_SNAKE_CASE						     =   self.optimizer.state_dict()
           if self.accelerator_state.distributed_type == DistributedType.TPU:
                xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
           else:
                __SCREAMING_SNAKE_CASE						     =   move_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
           self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
 @property
 def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


      """simple docstring"""




      return self.optimizer.state
 @state.setter
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   state
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.optimizer.param_groups
 @param_groups.setter
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   param_groups
 @property
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				Any:


      """simple docstring"""




      return self.optimizer.defaults
 @defaults.setter
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   defaults
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				List[str]:


      """simple docstring"""




      self.optimizer.add_param_group(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Dict:


      """simple docstring"""




      if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
      self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.optimizer.state_dict()
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]=None    )						->				Any:


      """simple docstring"""




      if self.gradient_state.sync_gradients:
           __SCREAMING_SNAKE_CASE						     =   """set_to_none""" in inspect.signature(self.optimizer.zero_grad    ).parameters
           if accept_arg:
                if set_to_none is None:
                     __SCREAMING_SNAKE_CASE						     =   False
                self.optimizer.zero_grad(set_to_none=__SCREAMING_SNAKE_CASE    )
           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    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				Dict:


      """simple docstring"""




      if self.gradient_state.sync_gradients:
           if self.accelerator_state.distributed_type == DistributedType.TPU:
                __SCREAMING_SNAKE_CASE						     =   {"""closure""": closure} if closure is not None else {}
                xm.optimizer_step(self.optimizer      ,							optimizer_args=__SCREAMING_SNAKE_CASE    )
           elif self.scaler is not None:
                __SCREAMING_SNAKE_CASE						     =   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
                     __SCREAMING_SNAKE_CASE						     =   self.scaler.get_scale()
                     __SCREAMING_SNAKE_CASE						     =   True
                self.scaler.step(self.optimizer      ,							__SCREAMING_SNAKE_CASE    )
                self.scaler.update()
                __SCREAMING_SNAKE_CASE						     =   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.
                     __SCREAMING_SNAKE_CASE						     =   scale_after < self._last_scale
                __SCREAMING_SNAKE_CASE						     =   scale_after

           else:
                self.optimizer.step(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				Any:


      """simple docstring"""




      for param_group in self.optimizer.param_groups:
           __SCREAMING_SNAKE_CASE						     =   [parameters_map.get(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) for p in param_group["""params"""]]
 @property
 def    UpperCAmelCase__	(	self      : int    )						->				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."""      ,							__SCREAMING_SNAKE_CASE      ,							)
      return self._is_overflow
 @property
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[int]:


      """simple docstring"""




      return self._is_overflow
 def __getstate__(	self      : Tuple    )						->				str:


      """simple docstring"""




      return self.__dict__.copy()


 def __setstate__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Any:


      """simple docstring"""




      self.__dict__.update(__SCREAMING_SNAKE_CASE    )






