






# 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     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = True		,		_UpperCAmelCase = False			)      ->			str:
     UpperCamelCase_						=  scheduler
     UpperCamelCase_						=  optimizers if isinstance(_UpperCAmelCase		,		(list, tuple)			) else [optimizers]
     UpperCamelCase_						=  split_batches
     UpperCamelCase_						=  step_with_optimizer
     UpperCamelCase_						=  GradientState()




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			List[Any]:
     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
        UpperCamelCase_						=  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  _UpperCAmelCase  (  self			)      ->			List[Any]:
     return self.scheduler.get_last_lr()




  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     return self.scheduler.state_dict()




  def  _UpperCAmelCase  (  self		,		_UpperCAmelCase			)      ->			List[str]:
     self.scheduler.load_state_dict(_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     return self.scheduler.get_lr()




  def  _UpperCAmelCase  (  self		,		*_UpperCAmelCase		,		**_UpperCAmelCase			)      ->			Optional[Any]:
     return self.scheduler.print_lr(*_UpperCAmelCase		,		**_UpperCAmelCase			)













import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--mobilebert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained MobileBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





