


"""simple docstring"""






import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase							:    int					   =	['''small''', '''medium''', '''large''']

_lowerCAmelCase							:    str					   =	'''lm_head.decoder.weight'''
_lowerCAmelCase							:    List[Any]					   =	'''lm_head.weight'''







def        lowerCamelCase_(		_lowerCamelCase			,		_lowerCamelCase       )    ->     List[str]:
   '''simple docstring'''





   _lowerCamelCase   :				str							=     torch.load(_lowerCamelCase       )
   _lowerCamelCase   :				List[Any]							=     d.pop(_lowerCamelCase       )
   os.makedirs(_lowerCamelCase			,		exist_ok=_lowerCamelCase       )
   torch.save(_lowerCamelCase			,		os.path.join(_lowerCamelCase			,		_lowerCamelCase       )       )


if __name__ == "__main__":
  _lowerCAmelCase							:    Dict					   =	argparse.ArgumentParser()
  parser.add_argument('''--dialogpt_path''', default='''.''', type=str)
  _lowerCAmelCase							:    Union[str, Any]					   =	parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase							:    Optional[int]					   =	os.path.join(args.dialogpt_path, f'''{MODEL}_ft.pkl''')
    _lowerCAmelCase							:    List[str]					   =	f'''./DialoGPT-{MODEL}'''
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


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






class 						lowercase_   :






      '''simple docstring'''




      def __init__( self :		Optional[int]					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		str					,   __UpperCAmelCase :		bool = True					,   __UpperCAmelCase :		bool = False    )       ->Tuple:




       """simple docstring"""


       a						    = scheduler
       a						    = optimizers if isinstance(__UpperCAmelCase					,   (list, tuple)    ) else [optimizers]
       a						    = split_batches
       a						    = step_with_optimizer
       a						    = GradientState()







      def  __lowerCAmelCase   ( self :		Tuple					,   *__UpperCAmelCase :		List[Any]					,   **__UpperCAmelCase :		Optional[Any]    )       ->int:




       """simple docstring"""


       if not self.step_with_optimizer:
        # No link between scheduler and optimizer -> just step
        self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )
        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(*__UpperCAmelCase					,   **__UpperCAmelCase    )
       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(__UpperCAmelCase    ):
         # 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(*__UpperCAmelCase					,   **__UpperCAmelCase    )
         else:
          self.scheduler.step(*__UpperCAmelCase					,   **__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Optional[int]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.get_last_lr()







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       return self.scheduler.state_dict()







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		Any    )       ->Tuple:




       """simple docstring"""


       self.scheduler.load_state_dict(__UpperCAmelCase    )







      def  __lowerCAmelCase   ( self :		Dict    )       ->Optional[int]:




       """simple docstring"""


       return self.scheduler.get_lr()







      def  __lowerCAmelCase   ( self :		int					,   *__UpperCAmelCase :		List[str]					,   **__UpperCAmelCase :		Optional[Any]    )       ->List[str]:




       """simple docstring"""


       return self.scheduler.print_lr(*__UpperCAmelCase					,   **__UpperCAmelCase    )


