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


SCREAMING_SNAKE_CASE :	List[Any]							     =		get_logger(__name__)



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 os.makedirs(_A							,				exist_ok=_A					)
 with FSDP.state_dict_type(
     _A							,				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(_A							,				_A					)
   if accelerator.process_index == 0:
    logger.info(f"""Saving model to {output_model_file}"""					)
    torch.save(_A							,				_A					)
    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(_A							,				_A					)
   logger.info(f"""Saving model to {output_model_file}"""					)
   torch.save(_A							,				_A					)
   logger.info(f"""Model saved to {output_model_file}"""					)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   __a       =  os.path.join(_A							,				f"""{MODEL_NAME}_{model_index}"""					)
   os.makedirs(_A							,				exist_ok=_A					)
   logger.info(f"""Saving model to {ckpt_dir}"""					)
   __a       =  {"model": state_dict}

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



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 accelerator.wait_for_everyone()
 with FSDP.state_dict_type(
     _A							,				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(_A					) != 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(_A							,				_A					)
   logger.info(f"""Loading model from {input_model_file}"""					)
   __a       =  torch.load(_A					)
   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(_A							,				_A					)
   logger.info(f"""Loading model from {input_model_file}"""					)
   __a       =  torch.load(_A					)
   logger.info(f"""Model loaded from {input_model_file}"""					)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   __a       =  (
       os.path.join(_A							,				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=_A							,				storage_reader=dist_cp.FileSystemReader(_A					)							,				planner=DefaultLoadPlanner()							,				)
   __a       =  state_dict["model"]
   logger.info(f"""Model loaded from {ckpt_dir}"""					)
  model.load_state_dict(_A					)



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 os.makedirs(_A							,				exist_ok=_A					)
 with FSDP.state_dict_type(
     _A							,				fsdp_plugin.state_dict_type							,				fsdp_plugin.state_dict_config							,				fsdp_plugin.optim_state_dict_config					):
  __a       =  FSDP.optim_state_dict(_A							,				_A					)
  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(_A							,				_A					)
    logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""					)
    torch.save(_A							,				_A					)
    logger.info(f"""Optimizer state saved in {output_optimizer_file}"""					)
  else:
   __a       =  os.path.join(_A							,				f"""{OPTIMIZER_NAME}_{optimizer_index}"""					)
   os.makedirs(_A							,				exist_ok=_A					)
   logger.info(f"""Saving Optimizer state to {ckpt_dir}"""					)
   dist_cp.save_state_dict(
       state_dict={"optimizer": optim_state}							,				storage_writer=dist_cp.FileSystemWriter(_A					)							,				planner=DefaultSavePlanner()							,				)
   logger.info(f"""Optimizer state saved in {ckpt_dir}"""					)






def   __A		(			_A							,				_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 accelerator.wait_for_everyone()
 with FSDP.state_dict_type(
     _A							,				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(_A							,				_A					)
   logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""					)
   __a       =  torch.load(_A					)
   logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""					)
  else:
   __a       =  (
       os.path.join(_A							,				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(_A					)							,				)
   __a       =  optim_state["optimizer"]
   logger.info(f"""Optimizer loaded from {ckpt_dir}"""					)
  __a       =  FSDP.optim_state_dict_to_load(_A							,				_A							,				_A					)
  optimizer.load_state_dict(_A					)



class    A_ :

  def __init__(		self :      List[Any]       ):
   __a       =  {}  # Mapping from char to TrieNode
   __a       =  False

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      list[str]       ):
   for word in words:
    self.insert(__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     __a       =  TrieNode()
    __a       =  curr.nodes[char]
   __a       =  True

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self
   for char in word:
    if char not in curr.nodes:
     return False
    __a       =  curr.nodes[char]
   return curr.is_leaf

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):

   def _delete(__SCREAMING_SNAKE_CASE :      TrieNode     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int       ) -> bool:
    if index == len(__SCREAMING_SNAKE_CASE       ):
     # If word does not exist
     if not curr.is_leaf:
      return False
     __a       =  False
     return len(curr.nodes       ) == 0
    __a       =  word[index]
    __a       =  curr.nodes.get(__SCREAMING_SNAKE_CASE       )
    # If char not in current trie node
    if not char_node:
     return False
    # Flag to check if node can be deleted
    __a       =  _delete(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							index + 1       )
    if delete_curr:
     del curr.nodes[char]
     return len(curr.nodes       ) == 0
    return delete_curr

   _delete(self     ,							__SCREAMING_SNAKE_CASE     ,							0       )



def   __A		(			_A							,				_A					):



 """simple docstring"""


 if node.is_leaf:
  print(_A							,				end=" "					)

 for key, value in node.nodes.items():
  print_words(_A							,				word + key					)



def   __A		(			):



 """simple docstring"""


 __a       =  "banana bananas bandana band apple all beast".split()
 __a       =  TrieNode()
 root.insert_many(_A					)
 # print_words(root, "")
 assert all(root.find(_A					) for word in words					)
 assert root.find("banana"					)
 assert not root.find("bandanas"					)
 assert not root.find("apps"					)
 assert root.find("apple"					)
 assert root.find("all"					)
 root.delete("all"					)
 assert not root.find("all"					)
 root.delete("banana"					)
 assert not root.find("banana"					)
 assert root.find("bananas"					)
 return True



def   __A		(			_A							,				_A					):



 """simple docstring"""


 print(str(_A					)							,				"works!" if passes else "doesn't work :("					)



def   __A		(			):



 """simple docstring"""


 assert test_trie()



def   __A		(			):



 """simple docstring"""


 print_results("Testing trie functionality"							,				test_trie()					)


if __name__ == "__main__":
  main()


