


'''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


__A	=get_logger(__name__)







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

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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=0					):
			accelerator.wait_for_everyone()
			with FSDP.state_dict_type(
			    _lowerCamelCase ,      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(_lowerCamelCase					) != 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
									UpperCAmelCase__  :					Optional[Any]        =     f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
									UpperCAmelCase__  :					Dict        =     os.path.join(_lowerCamelCase ,      _lowerCamelCase					)
									logger.info(f'''Loading model from {input_model_file}'''					)
									UpperCAmelCase__  :					List[Any]        =     torch.load(_lowerCamelCase					)
									logger.info(f'''Model loaded from {input_model_file}'''					)
						elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
									UpperCAmelCase__  :					Any        =     (
									    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
									    if model_index == 0
									    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
									)
									UpperCAmelCase__  :					int        =     os.path.join(_lowerCamelCase ,      _lowerCamelCase					)
									logger.info(f'''Loading model from {input_model_file}'''					)
									UpperCAmelCase__  :					Tuple        =     torch.load(_lowerCamelCase					)
									logger.info(f'''Model loaded from {input_model_file}'''					)
						elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
									UpperCAmelCase__  :					Optional[Any]        =     (
									    os.path.join(_lowerCamelCase ,      f'''{MODEL_NAME}_{model_index}'''					)
									    if f'''{MODEL_NAME}''' not in input_dir
									    else input_dir
									)
									logger.info(f'''Loading model from {ckpt_dir}'''					)
									UpperCAmelCase__  :					Any        =     {'model': model.state_dict()}
									dist_cp.load_state_dict(
									    state_dict=_lowerCamelCase ,      storage_reader=dist_cp.FileSystemReader(_lowerCamelCase					) ,      planner=DefaultLoadPlanner() ,      )
									UpperCAmelCase__  :					List[str]        =     state_dict['model']
									logger.info(f'''Model loaded from {ckpt_dir}'''					)
						model.load_state_dict(_lowerCamelCase					)







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







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=0					):
			accelerator.wait_for_everyone()
			with FSDP.state_dict_type(
			    _lowerCamelCase ,      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:
									UpperCAmelCase__  :					Optional[int]        =     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:
									UpperCAmelCase__  :					int        =     (
									    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
									)
									UpperCAmelCase__  :					int        =     os.path.join(_lowerCamelCase ,      _lowerCamelCase					)
									logger.info(f'''Loading Optimizer state from {input_optimizer_file}'''					)
									UpperCAmelCase__  :					Tuple        =     torch.load(_lowerCamelCase					)
									logger.info(f'''Optimizer state loaded from {input_optimizer_file}'''					)
						else:
									UpperCAmelCase__  :					str        =     (
									    os.path.join(_lowerCamelCase ,      f'''{OPTIMIZER_NAME}_{optimizer_index}'''					)
									    if f'''{OPTIMIZER_NAME}''' not in input_dir
									    else input_dir
									)
									logger.info(f'''Loading Optimizer from {ckpt_dir}'''					)
									UpperCAmelCase__  :					List[Any]        =     load_sharded_optimizer_state_dict(
									    model_state_dict=model.state_dict() ,      optimizer_key="""optimizer""" ,      storage_reader=dist_cp.FileSystemReader(_lowerCamelCase					) ,      )
									UpperCAmelCase__  :					int        =     optim_state['optimizer']
									logger.info(f'''Optimizer loaded from {ckpt_dir}'''					)
						UpperCAmelCase__  :					str        =     FSDP.optim_state_dict_to_load(_lowerCamelCase ,      _lowerCamelCase ,      _lowerCamelCase					)
						optimizer.load_state_dict(_lowerCamelCase					)



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i + 1][j] + matrix[i][j]					)

			return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums					)


if __name__ == "__main__":
		print(f"""{solution() = }""")