
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_ 				=			get_logger(__name__)


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





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

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


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





  """simple docstring"""
  accelerator.wait_for_everyone()
  with FSDP.state_dict_type(
      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(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
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
      __SCREAMING_SNAKE_CASE      :	int					=		os.path.join(snake_case       ,			snake_case							)
      logger.info(F'''Loading model from {input_model_file}'''							)
      __SCREAMING_SNAKE_CASE      :	str					=		torch.load(snake_case							)
      logger.info(F'''Model loaded from {input_model_file}'''							)
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      __SCREAMING_SNAKE_CASE      :	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'''
      )
      __SCREAMING_SNAKE_CASE      :	Tuple					=		os.path.join(snake_case       ,			snake_case							)
      logger.info(F'''Loading model from {input_model_file}'''							)
      __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		torch.load(snake_case							)
      logger.info(F'''Model loaded from {input_model_file}'''							)
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		(
          os.path.join(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}'''							)
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{'''model''': model.state_dict()}
      dist_cp.load_state_dict(
          state_dict=snake_case       ,			storage_reader=dist_cp.FileSystemReader(snake_case							)       ,			planner=DefaultLoadPlanner()       ,			)
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		state_dict['''model''']
      logger.info(F'''Model loaded from {ckpt_dir}'''							)
    model.load_state_dict(snake_case							)


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





  """simple docstring"""
  os.makedirs(snake_case       ,			exist_ok=snake_case							)
  with FSDP.state_dict_type(
      snake_case       ,			fsdp_plugin.state_dict_type       ,			fsdp_plugin.state_dict_config       ,			fsdp_plugin.optim_state_dict_config							):
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		FSDP.optim_state_dict(snake_case       ,			snake_case							)
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      if accelerator.process_index == 0:
        __SCREAMING_SNAKE_CASE      :	Dict					=		(
            F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
        )
        __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		os.path.join(snake_case       ,			snake_case							)
        logger.info(F'''Saving Optimizer state to {output_optimizer_file}'''							)
        torch.save(snake_case       ,			snake_case							)
        logger.info(F'''Optimizer state saved in {output_optimizer_file}'''							)
    else:
      __SCREAMING_SNAKE_CASE      :	List[str]					=		os.path.join(snake_case       ,			F'''{OPTIMIZER_NAME}_{optimizer_index}'''							)
      os.makedirs(snake_case       ,			exist_ok=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(snake_case							)       ,			planner=DefaultSavePlanner()       ,			)
      logger.info(F'''Optimizer state saved in {ckpt_dir}'''							)


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





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




"""simple docstring"""
import random

def 				_snake_case      (   __snake_case							:      List[Any]						, __snake_case							:      List[Any]						, __snake_case							:      int   ):




   """simple docstring"""







   _lowerCamelCase	:					List[str]   =		a[left_index]
   _lowerCamelCase	:					Dict   =		left_index + 1
   for j in range(left_index + 1						, __snake_case   ):
      if a[j] < pivot:
         _lowerCamelCase   ,    _lowerCamelCase	:					List[str]   =		a[i], a[j]
         i += 1
   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		a[i - 1], a[left_index]
   return i - 1

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]						, __snake_case							:      List[str]   ):




   """simple docstring"""







   if left < right:
      _lowerCamelCase	:					Any   =		random.randint(__snake_case						, right - 1   )
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		(
          a[left],
          a[pivot],
      )  # switches the pivot with the left most bound
      _lowerCamelCase	:					List[str]   =		partition(__snake_case						, __snake_case						, __snake_case   )
      quick_sort_random(
          __snake_case						, __snake_case						, __snake_case   )  # recursive quicksort to the left of the pivot point
      quick_sort_random(
          __snake_case						, pivot_index + 1						, __snake_case   )  # recursive quicksort to the right of the pivot point

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Union[str, Any]   =		input("""Enter numbers separated by a comma:\n"""   ).strip()
   _lowerCamelCase	:					int   =		[int(__snake_case   ) for item in user_input.split(""","""   )]

   quick_sort_random(__snake_case						, 0						, len(__snake_case   )   )

   print(__snake_case   )


if __name__ == "__main__":
 main()

