


'''simple docstring'''






import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")




class 	a       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = False  )   ->							str:
      _a      =							scheduler
      _a      =							optimizers if isinstance(__magic_name__ ,			(list, tuple)  ) else [optimizers]
      _a      =							split_batches
      _a      =							step_with_optimizer
      _a      =							GradientState()
 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							List[Any]:
      if not self.step_with_optimizer:
           # No link between scheduler and optimizer -> just step
           self.scheduler.step(*__magic_name__ ,			**__magic_name__  )
           return

      # Otherwise, first make sure the optimizer was stepped.
      if not self.gradient_state.sync_gradients:
           if self.gradient_state.adjust_scheduler:
                self.scheduler._step_count += 1
           return

      for opt in self.optimizers:
           if opt.step_was_skipped:
                return
      if self.split_batches:
           # Split batches -> the training dataloader batch size is not changed so one step per training step
           self.scheduler.step(*__magic_name__ ,			**__magic_name__  )
      else:
           # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
           # num_processes steps per training step
           _a      =							AcceleratorState().num_processes
           for _ in range(__magic_name__  ):
                # Special case when using OneCycle and `drop_last` was not used
                if hasattr(self.scheduler ,			'total_steps'  ):
                     if self.scheduler._step_count <= self.scheduler.total_steps:
                          self.scheduler.step(*__magic_name__ ,			**__magic_name__  )
                else:
                     self.scheduler.step(*__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return self.scheduler.get_last_lr()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return self.scheduler.state_dict()
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      self.scheduler.load_state_dict(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.scheduler.get_lr()




 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							List[str]:
      return self.scheduler.print_lr(*__magic_name__ ,			**__magic_name__  )





'''simple docstring'''






from typing import TYPE_CHECKING

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


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

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

