


"""simple docstring"""


import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
	import torch.distributed.checkpoint as dist_cp
	from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
	from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
	from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
	from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase						:				Any             =      get_logger(__name__)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=0	)      ->		int:







		"""simple docstring"""





		os.makedirs(UpperCamelCase__ ,			exist_ok=UpperCamelCase__	)
		with FSDP.state_dict_type(
		    UpperCamelCase__ ,			fsdp_plugin.state_dict_type ,			fsdp_plugin.state_dict_config ,			fsdp_plugin.optim_state_dict_config	):
				A					       = model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						A					       = f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
						A					       = os.path.join(UpperCamelCase__ ,			UpperCamelCase__	)
						if accelerator.process_index == 0:
								logger.info(f'Saving model to {output_model_file}'	)
								torch.save(UpperCamelCase__ ,			UpperCamelCase__	)
								logger.info(f'Model saved to {output_model_file}'	)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						A					       = (
						    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						A					       = os.path.join(UpperCamelCase__ ,			UpperCamelCase__	)
						logger.info(f'Saving model to {output_model_file}'	)
						torch.save(UpperCamelCase__ ,			UpperCamelCase__	)
						logger.info(f'Model saved to {output_model_file}'	)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						A					       = os.path.join(UpperCamelCase__ ,			f'{MODEL_NAME}_{model_index}'	)
						os.makedirs(UpperCamelCase__ ,			exist_ok=UpperCamelCase__	)
						logger.info(f'Saving model to {ckpt_dir}'	)
						A					       = {'model': state_dict}

						dist_cp.save_state_dict(
						    state_dict=UpperCamelCase__ ,			storage_writer=dist_cp.FileSystemWriter(UpperCamelCase__	) ,			planner=DefaultSavePlanner() ,			)
						logger.info(f'Model saved to {ckpt_dir}'	)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=0	)      ->		Optional[int]:







		"""simple docstring"""





		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    UpperCamelCase__ ,			fsdp_plugin.state_dict_type ,			fsdp_plugin.state_dict_config ,			fsdp_plugin.optim_state_dict_config	):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(UpperCamelCase__	) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    'Set the `sync_module_states` flag to `True` so that model states are synced across processes when '
										    'initializing FSDP object'	)
								return
						A					       = f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
						A					       = os.path.join(UpperCamelCase__ ,			UpperCamelCase__	)
						logger.info(f'Loading model from {input_model_file}'	)
						A					       = torch.load(UpperCamelCase__	)
						logger.info(f'Model loaded from {input_model_file}'	)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						A					       = (
						    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						A					       = os.path.join(UpperCamelCase__ ,			UpperCamelCase__	)
						logger.info(f'Loading model from {input_model_file}'	)
						A					       = torch.load(UpperCamelCase__	)
						logger.info(f'Model loaded from {input_model_file}'	)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						A					       = (
						    os.path.join(UpperCamelCase__ ,			f'{MODEL_NAME}_{model_index}'	)
						    if f'{MODEL_NAME}' not in input_dir
						    else input_dir
						)
						logger.info(f'Loading model from {ckpt_dir}'	)
						A					       = {'model': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=UpperCamelCase__ ,			storage_reader=dist_cp.FileSystemReader(UpperCamelCase__	) ,			planner=DefaultLoadPlanner() ,			)
						A					       = state_dict['model']
						logger.info(f'Model loaded from {ckpt_dir}'	)
				model.load_state_dict(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=0	)      ->		List[str]:







		"""simple docstring"""





		os.makedirs(UpperCamelCase__ ,			exist_ok=UpperCamelCase__	)
		with FSDP.state_dict_type(
		    UpperCamelCase__ ,			fsdp_plugin.state_dict_type ,			fsdp_plugin.state_dict_config ,			fsdp_plugin.optim_state_dict_config	):
				A					       = FSDP.optim_state_dict(UpperCamelCase__ ,			UpperCamelCase__	)
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								A					       = (
								    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
								)
								A					       = os.path.join(UpperCamelCase__ ,			UpperCamelCase__	)
								logger.info(f'Saving Optimizer state to {output_optimizer_file}'	)
								torch.save(UpperCamelCase__ ,			UpperCamelCase__	)
								logger.info(f'Optimizer state saved in {output_optimizer_file}'	)
				else:
						A					       = os.path.join(UpperCamelCase__ ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
						os.makedirs(UpperCamelCase__ ,			exist_ok=UpperCamelCase__	)
						logger.info(f'Saving Optimizer state to {ckpt_dir}'	)
						dist_cp.save_state_dict(
						    state_dict={'optimizer': optim_state} ,			storage_writer=dist_cp.FileSystemWriter(UpperCamelCase__	) ,			planner=DefaultSavePlanner() ,			)
						logger.info(f'Optimizer state saved in {ckpt_dir}'	)







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__=0	)      ->		Optional[Any]:







		"""simple docstring"""





		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    UpperCamelCase__ ,			fsdp_plugin.state_dict_type ,			fsdp_plugin.state_dict_config ,			fsdp_plugin.optim_state_dict_config	):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						A					       = None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						A					       = (
						    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
						)
						A					       = os.path.join(UpperCamelCase__ ,			UpperCamelCase__	)
						logger.info(f'Loading Optimizer state from {input_optimizer_file}'	)
						A					       = torch.load(UpperCamelCase__	)
						logger.info(f'Optimizer state loaded from {input_optimizer_file}'	)
				else:
						A					       = (
						    os.path.join(UpperCamelCase__ ,			f'{OPTIMIZER_NAME}_{optimizer_index}'	)
						    if f'{OPTIMIZER_NAME}' not in input_dir
						    else input_dir
						)
						logger.info(f'Loading Optimizer from {ckpt_dir}'	)
						A					       = load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict() ,			optimizer_key='optimizer' ,			storage_reader=dist_cp.FileSystemReader(UpperCamelCase__	) ,			)
						A					       = optim_state['optimizer']
						logger.info(f'Optimizer loaded from {ckpt_dir}'	)
				A					       = FSDP.optim_state_dict_to_load(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)
				optimizer.load_state_dict(UpperCamelCase__	)






"""simple docstring"""


def  __snake_case       (   )      ->		int:







		"""simple docstring"""





		return 1







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else two_pence(x - 2	) + one_pence()







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else five_pence(x - 5	) + two_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else ten_pence(x - 10	) + five_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else twenty_pence(x - 20	) + ten_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else fifty_pence(x - 50	) + twenty_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else one_pound(x - 100	) + fifty_pence(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		return 0 if x < 0 else two_pound(x - 200	) + one_pound(UpperCamelCase__	)







def  __snake_case       (   UpperCamelCase__ = 200	)      ->		int:







		"""simple docstring"""





		return two_pound(UpperCamelCase__	)


if __name__ == "__main__":
	print(solution(int(input().strip())))





