





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     :     int							=	get_logger(__name__)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[str]=0						):





  """simple docstring"""



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

      dist_cp.save_state_dict(
          state_dict=SCREAMING_SNAKE_CASE	,	storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE						)	,	planner=DefaultSavePlanner()	,	)
      logger.info(f'''Model saved to {ckpt_dir}'''						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Optional[int]=0						):





  """simple docstring"""



  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      SCREAMING_SNAKE_CASE	,	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(SCREAMING_SNAKE_CASE						) != 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__     : Any  =f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
      a__     : List[Any]  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      logger.info(f'''Loading model from {input_model_file}'''						)
      a__     : List[str]  =torch.load(SCREAMING_SNAKE_CASE						)
      logger.info(f'''Model loaded from {input_model_file}'''						)
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      a__     : 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'''
      )
      a__     : Optional[Any]  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      logger.info(f'''Loading model from {input_model_file}'''						)
      a__     : List[Any]  =torch.load(SCREAMING_SNAKE_CASE						)
      logger.info(f'''Model loaded from {input_model_file}'''						)
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      a__     : int  =(
          os.path.join(SCREAMING_SNAKE_CASE	,	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__     : List[Any]  ={"model": model.state_dict()}
      dist_cp.load_state_dict(
          state_dict=SCREAMING_SNAKE_CASE	,	storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE						)	,	planner=DefaultLoadPlanner()	,	)
      a__     : Optional[Any]  =state_dict["model"]
      logger.info(f'''Model loaded from {ckpt_dir}'''						)
    model.load_state_dict(SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       str=0						):





  """simple docstring"""



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





def 					_A	(		SCREAMING_SNAKE_CASE					:       Dict	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Any	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       List[str]=0						):





  """simple docstring"""



  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      SCREAMING_SNAKE_CASE	,	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__     : str  =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__     : Union[str, Any]  =(
          f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
      )
      a__     : List[str]  =os.path.join(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
      logger.info(f'''Loading Optimizer state from {input_optimizer_file}'''						)
      a__     : Any  =torch.load(SCREAMING_SNAKE_CASE						)
      logger.info(f'''Optimizer state loaded from {input_optimizer_file}'''						)
    else:
      a__     : Union[str, Any]  =(
          os.path.join(SCREAMING_SNAKE_CASE	,	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__     : int  =load_sharded_optimizer_state_dict(
          model_state_dict=model.state_dict()	,	optimizer_key="optimizer"	,	storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE						)	,	)
      a__     : str  =optim_state["optimizer"]
      logger.info(f'''Optimizer loaded from {ckpt_dir}'''						)
    a__     : List[Any]  =FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    optimizer.load_state_dict(SCREAMING_SNAKE_CASE						)













from __future__ import annotations

import math
def 					_A	(		SCREAMING_SNAKE_CASE					:       int						):





  """simple docstring"""



  if num <= 0:
    a__     : List[str]  =f'''{num}: Invalid input, please enter a positive integer.'''
    raise ValueError(SCREAMING_SNAKE_CASE						)

  a__     : Union[str, Any]  =[True] * (num + 1)
  a__     : Union[str, Any]  =[]
  a__     : str  =2
  a__     : Any  =int(math.sqrt(SCREAMING_SNAKE_CASE						)						)

  while start <= end:
    # If start is a prime
    if sieve[start] is True:
      prime.append(SCREAMING_SNAKE_CASE						)

      # Set multiples of start be False
      for i in range(start * start	,	num + 1	,	SCREAMING_SNAKE_CASE						):
        if sieve[i] is True:
          a__     : Optional[int]  =False

    start += 1

  for j in range(end + 1	,	num + 1						):
    if sieve[j] is True:
      prime.append(SCREAMING_SNAKE_CASE						)

  return prime


if __name__ == "__main__":
     print(prime_sieve(int(input("""Enter a positive integer: """).strip())))






