
'''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   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






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






class 				UpperCAmelCase_			(		torch.optim.Optimizer   ):





 def __init__(      self	,    lowercase_	,    lowercase_=True	,    lowercase_=None):
     snake_case_	:      Dict            =  optimizer
     snake_case_	:      str            =  scaler
     snake_case_	:      Union[str, Any]            =  AcceleratorState()
     snake_case_	:      Optional[Any]            =  GradientState()
     snake_case_	:      str            =  device_placement
     snake_case_	:      Tuple            =  False
     snake_case_	:      Union[str, Any]            =  None

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





 @property
 def     snake_case__						(      self):
     return self.optimizer.state





 @state.setter
 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      Optional[Any]            =  state





 @property
 def     snake_case__						(      self):
     return self.optimizer.param_groups





 @param_groups.setter
 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      List[Any]            =  param_groups





 @property
 def     snake_case__						(      self):
     return self.optimizer.defaults





 @defaults.setter
 def     snake_case__						(      self	,    lowercase_):
     snake_case_	:      str            =  defaults





 def     snake_case__						(      self	,    lowercase_):
     self.optimizer.add_param_group(lowercase_)





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





 def     snake_case__						(      self):
     return self.optimizer.state_dict()





 def     snake_case__						(      self	,    lowercase_=None):
     if self.gradient_state.sync_gradients:
         snake_case_	:      Optional[Any]            =  "set_to_none" in inspect.signature(self.optimizer.zero_grad).parameters
         if accept_arg:
             if set_to_none is None:
                 snake_case_	:      Any            =  False
             self.optimizer.zero_grad(set_to_none=lowercase_)
         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     snake_case__						(      self	,    lowercase_=None):
     if self.gradient_state.sync_gradients:
         if self.accelerator_state.distributed_type == DistributedType.TPU:
             snake_case_	:      str            =  {"closure": closure} if closure is not None else {}
             xm.optimizer_step(self.optimizer	,    optimizer_args=lowercase_)
         elif self.scaler is not None:
             snake_case_	:      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
                 snake_case_	:      List[Any]            =  self.scaler.get_scale()
                 snake_case_	:      Optional[int]            =  True
             self.scaler.step(self.optimizer	,    lowercase_)
             self.scaler.update()
             snake_case_	:      Dict            =  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.
                 snake_case_	:      int            =  scale_after < self._last_scale
             snake_case_	:      Any            =  scale_after

         else:
             self.optimizer.step(lowercase_)





 def     snake_case__						(      self	,    lowercase_):
     for param_group in self.optimizer.param_groups:
         snake_case_	:      int            =  [parameters_map.get(lowercase_	,    lowercase_) for p in param_group["params"]]





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





 @property
 def     snake_case__						(      self):
     return self._is_overflow





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





 def __setstate__(      self	,    lowercase_):
     self.__dict__.update(lowercase_)






'''simple docstring'''
from typing import TYPE_CHECKING

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


a_     =  {
    "configuration_splinter": ["SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP", "SplinterConfig"],
    "tokenization_splinter": ["SplinterTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  ["SplinterTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_     =  [
      "SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SplinterForQuestionAnswering",
      "SplinterForPreTraining",
      "SplinterLayer",
      "SplinterModel",
      "SplinterPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_splinter import SPLINTER_PRETRAINED_CONFIG_ARCHIVE_MAP, SplinterConfig
  from .tokenization_splinter import SplinterTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_splinter_fast import SplinterTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_splinter import (
        SPLINTER_PRETRAINED_MODEL_ARCHIVE_LIST,
        SplinterForPreTraining,
        SplinterForQuestionAnswering,
        SplinterLayer,
        SplinterModel,
        SplinterPreTrainedModel,
    )


else:
  import sys

  a_     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




