





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


_lowercase		:  Optional[Any]			       =get_logger(__name__)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=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						):
				lowerCamelCase_	:     List[Any] 						=      model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						lowerCamelCase_	:     Tuple 						=      F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						lowerCamelCase_	:     Dict 						=      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:
						lowerCamelCase_	:     int 						=      (
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						lowerCamelCase_	:     List[str] 						=      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:
						lowerCamelCase_	:     str 						=      os.path.join(lowerCAmelCase__							,F"{MODEL_NAME}_{model_index}"						)
						os.makedirs(lowerCAmelCase__							,exist_ok=lowerCAmelCase__						)
						logger.info(F"Saving model to {ckpt_dir}"						)
						lowerCamelCase_	:     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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=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
						lowerCamelCase_	:     Union[str, Any] 						=      F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
						lowerCamelCase_	:     Tuple 						=      os.path.join(lowerCAmelCase__							,lowerCAmelCase__						)
						logger.info(F"Loading model from {input_model_file}"						)
						lowerCamelCase_	:     Dict 						=      torch.load(lowerCAmelCase__						)
						logger.info(F"Model loaded from {input_model_file}"						)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						lowerCamelCase_	:     Union[str, Any] 						=      (
						    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
						    if model_index == 0
						    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
						)
						lowerCamelCase_	:     Tuple 						=      os.path.join(lowerCAmelCase__							,lowerCAmelCase__						)
						logger.info(F"Loading model from {input_model_file}"						)
						lowerCamelCase_	:     Optional[int] 						=      torch.load(lowerCAmelCase__						)
						logger.info(F"Model loaded from {input_model_file}"						)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						lowerCamelCase_	:     List[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}"						)
						lowerCamelCase_	:     str 						=      {'model': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=lowerCAmelCase__							,storage_reader=dist_cp.FileSystemReader(lowerCAmelCase__						)							,planner=DefaultLoadPlanner()							,)
						lowerCamelCase_	:     Dict 						=      state_dict['model']
						logger.info(F"Model loaded from {ckpt_dir}"						)
				model.load_state_dict(lowerCAmelCase__						)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=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						):
				lowerCamelCase_	:     str 						=      FSDP.optim_state_dict(lowerCAmelCase__							,lowerCAmelCase__						)
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								lowerCamelCase_	:     str 						=      (
								    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
								)
								lowerCamelCase_	:     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:
						lowerCamelCase_	:     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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__=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:
						lowerCamelCase_	:     Any 						=      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:
						lowerCamelCase_	:     List[str] 						=      (
						    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
						)
						lowerCamelCase_	:     List[Any] 						=      os.path.join(lowerCAmelCase__							,lowerCAmelCase__						)
						logger.info(F"Loading Optimizer state from {input_optimizer_file}"						)
						lowerCamelCase_	:     Dict 						=      torch.load(lowerCAmelCase__						)
						logger.info(F"Optimizer state loaded from {input_optimizer_file}"						)
				else:
						lowerCamelCase_	:     Union[str, Any] 						=      (
						    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}"						)
						lowerCamelCase_	:     str 						=      load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()							,optimizer_key='optimizer'							,storage_reader=dist_cp.FileSystemReader(lowerCAmelCase__						)							,)
						lowerCamelCase_	:     List[str] 						=      optim_state['optimizer']
						logger.info(F"Optimizer loaded from {ckpt_dir}"						)
				lowerCamelCase_	:     str 						=      FSDP.optim_state_dict_to_load(lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						)
				optimizer.load_state_dict(lowerCAmelCase__						)







_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


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