



# 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 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase = True    ,	lowerCAmelCase = False     ):
							UpperCAmelCase_						=				scheduler
							UpperCAmelCase_						=				optimizers if isinstance(lowerCAmelCase    ,	(list, tuple)     ) else [optimizers]
							UpperCAmelCase_						=				split_batches
							UpperCAmelCase_						=				step_with_optimizer
							UpperCAmelCase_						=				GradientState()



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



					def    A__		(			self     ):
							return self.scheduler.get_last_lr()



					def    A__		(			self     ):
							return self.scheduler.state_dict()



					def    A__		(			self    ,	lowerCAmelCase     ):
							self.scheduler.load_state_dict(lowerCAmelCase     )



					def    A__		(			self     ):
							return self.scheduler.get_lr()



					def    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							return self.scheduler.print_lr(*lowerCAmelCase    ,	**lowerCAmelCase     )






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__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[Any]:
		# Initialise PyTorch model
		UpperCAmelCase_						=				MobileBertConfig.from_json_file(__SCREAMING_SNAKE_CASE )
		print(f'''Building PyTorch model from configuration: {config}''' )
		UpperCAmelCase_						=				MobileBertForPreTraining(__SCREAMING_SNAKE_CASE )
		# Load weights from tf checkpoint
		UpperCAmelCase_						=				load_tf_weights_in_mobilebert(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		# Save pytorch-model
		print(f'''Save PyTorch model to {pytorch_dump_path}''' )
		torch.save(model.state_dict()			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE     				=						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."
			)
			SCREAMING_SNAKE_CASE     				=						parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)

