






"""simple docstring"""


import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
   import torch

_UpperCamelCase :    Any      =			logging.get_logger(__name__)




def 					a			(  )	->		str:
 return importlib.util.find_spec('deepspeed'		) is not None


if is_accelerate_available() and is_deepspeed_available():
   from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
   # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
   # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
   from builtins import object as DeepSpeedConfig






class __a ( snake_case_					):



  def __init__(self	:		List[Any]					,      lowercase_	:		List[str] )		->				str:
   # set global weakref object
   set_hf_deepspeed_config(self )
   dep_version_check('accelerate' )
   dep_version_check('deepspeed' )
   super().__init__(lowercase_ )






class __a ( snake_case_					):



  def __init__(self	:		str					,      lowercase_	:		List[str] )		->				str:
   super().__init__(lowercase_ )
   __A						:							Optional[int]      =						None
   __A						:							Optional[int]      =						[]



  def     A_					(self	:		List[str] )		->				List[Any]:
   if self._dtype is None:
    raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype' )
   return self._dtype



  def     A_					(self	:		Any					,      lowercase_	:		Dict )		->				List[str]:
   __A						:							Union[str, Any]      =						self.get_value(lowercase_ )
   if val is None:
    return False
   else:
    return val == "auto"



  def     A_					(self	:		Optional[int]					,      lowercase_	:		List[str]					,      lowercase_	:		List[str]					,      lowercase_	:		Optional[int]=None					,      lowercase_	:		List[str]=True )		->				str:
   __A     ,       __A						:							List[Any]      =						self.find_config_node(lowercase_ )
   if config is None:
    return

   if config.get(lowercase_ ) == "auto":
    __A						:							Tuple      =						hf_val
    return

   if not must_match:
    return

   __A						:							List[str]      =						config.get(lowercase_ )
   if ds_val is not None and ds_val != hf_val:
    self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}" )

  __lowercase     :     Union[str, Any]       						=       partialmethod(snake_case_			,must_match=snake_case_					)



  def     A_					(self	:		str					,      lowercase_	:		Optional[int] )		->				int:
   __A						:							List[str]      =						args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
   self.fill_match(
       'train_micro_batch_size_per_gpu'					,      args.per_device_train_batch_size					,      'per_device_train_batch_size' )
   self.fill_match('gradient_accumulation_steps'					,      args.gradient_accumulation_steps					,      'gradient_accumulation_steps' )
   self.fill_match('train_batch_size'					,      lowercase_					,      'train_batch_size (calculated)' )
   self.fill_match('gradient_clipping'					,      args.max_grad_norm					,      'max_grad_norm' )

   self.fill_match('optimizer.params.lr'					,      args.learning_rate					,      'learning_rate' )
   self.fill_match('optimizer.params.betas'					,      [args.adam_betaa, args.adam_betaa]					,      'adam_beta1+adam_beta2' )
   self.fill_match('optimizer.params.eps'					,      args.adam_epsilon					,      'adam_epsilon' )
   self.fill_match('optimizer.params.weight_decay'					,      args.weight_decay					,      'weight_decay' )

   self.fill_only('scheduler.params.warmup_min_lr'					,      0 )  # not a trainer arg
   self.fill_match('scheduler.params.warmup_max_lr'					,      args.learning_rate					,      'learning_rate' )
   # total_num_steps - will get set in trainer_config_finalize

   # fp16
   if args.fpaa or args.fpaa_full_eval:
    __A						:							Optional[Any]      =						'apex' if args.fpaa_backend == 'apex' else 'amp'
   else:
    __A						:							Optional[int]      =						None

   if args.save_on_each_node:
    # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
    __A						:							str      =						self.config.get('checkpoint'					,      {} )
    __A						:							Any      =						args.save_on_each_node

   # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
   # any here unless the user did the work
   self.fill_match(
       'fp16.enabled'					,      ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == 'amp')					,      'fp16|fp16_full_eval+fp16_backend(amp)'					,      )

   # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
   # ZeRO features
   self.fill_match('amp.enabled'					,      fpaa_backend == 'apex'					,      'fp16+fp16_backend(apex)' )
   self.fill_match('amp.opt_level'					,      args.fpaa_opt_level					,      'fp16_opt_level' )

   self.fill_match('bf16.enabled'					,      (args.bfaa or args.bfaa_full_eval)					,      'bf16|bf16_full_eval' )

   # deepspeed's default mode is fp16 unless there is a config that says differently
   if self.is_true('bf16.enabled' ):
    __A						:							str      =						torch.bfloataa
   elif self.is_false('fp16.enabled' ):
    __A						:							Dict      =						torch.floataa
   else:
    __A						:							int      =						torch.floataa



  def     A_					(self	:		str					,      lowercase_	:		Tuple					,      lowercase_	:		Union[str, Any]					,      lowercase_	:		Any )		->				Optional[int]:
   __A						:							int      =						[
       'zero_optimization.reduce_bucket_size',
       'zero_optimization.stage3_prefetch_bucket_size',
       'zero_optimization.stage3_param_persistence_threshold',
   ]
   __A						:							List[Any]      =						[x for x in hidden_size_based_keys if self.is_auto(lowercase_ )]

   if len(lowercase_ ) > 0:
    if hasattr(model.config					,      'hidden_size' ):
     __A						:							Any      =						model.config.hidden_size
    elif hasattr(model.config					,      'hidden_sizes' ):
     # if there are many hidden sizes pick the largest one
     __A						:							List[Any]      =						max(model.config.hidden_sizes )
    else:
     raise ValueError(
         'The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '
         'therefore it\'s not possible to automatically fill out the following `auto` entries '
         f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
         '`auto` values for these keys with an integer value of your choice.' )

    self.fill_only('zero_optimization.reduce_bucket_size'					,      hidden_size * hidden_size )
    if self.is_zeroa():
     # automatically assign the optimal config values based on model config
     self.fill_only('zero_optimization.stage3_prefetch_bucket_size'					,      0.9 * hidden_size * hidden_size )
     self.fill_only('zero_optimization.stage3_param_persistence_threshold'					,      10 * hidden_size )

        # scheduler
   self.fill_match('scheduler.params.total_num_steps'					,      lowercase_					,      'num_training_steps (calculated)' )
   self.fill_match('scheduler.params.warmup_num_steps'					,      args.get_warmup_steps(lowercase_ )					,      'warmup_steps' )

   if len(self.mismatches ) > 0:
    __A						:							int      =						'\n'.join(self.mismatches )
    raise ValueError(
        'Please correct the following DeepSpeed config values that mismatch TrainingArguments'
        f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'." )






_UpperCamelCase :    Optional[int]      =			None




def 					a			(  UpperCamelCase_				:					int		)	->		Optional[int]:
 # this is a special weakref global object to allow us to get to Deepspeed config from APIs
 # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
 global _hf_deepspeed_config_weak_ref
 # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
 __A						:							Optional[Any]      =						weakref.ref(UpperCamelCase_		)




def 					a			(  )	->		str:
 # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
 global _hf_deepspeed_config_weak_ref
 __A						:							Union[str, Any]      =						None




def 					a			(  )	->		Tuple:
 if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
  return _hf_deepspeed_config_weak_ref().is_zeroa()
 else:
  return False




def 					a			(  )	->		List[Any]:
 if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
  return _hf_deepspeed_config_weak_ref().config
 else:
  return None




def 					a			(  UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					Tuple  ,						UpperCamelCase_				:					Optional[int]  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					List[Any]		)	->		Optional[int]:
 from accelerate.utils import DummyOptim, DummyScheduler

 __A						:							Optional[int]      =						hf_deepspeed_config.config

 # Optimizer + Scheduler
 # Currently supported combos:
 # 1. DS scheduler + DS optimizer: Yes
 # 2. HF scheduler + HF optimizer: Yes
 # 3. DS scheduler + HF optimizer: Yes
 # 4. HF scheduler + DS optimizer: No
 #
 # Unless Offload is enabled in which case it's:
 # 1. DS scheduler + DS optimizer: Yes
 # 2. HF scheduler + HF optimizer: Mostly*
 # 3. DS scheduler + HF optimizer: Mostly*
 # 4. HF scheduler + DS optimizer: No
 #
 # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

 __A						:							Dict      =						None
 if "optimizer" in config:
  if args.adafactor:
   raise ValueError(
       '--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '
       'Only one optimizer can be configured.'		)
  __A						:							str      =						DummyOptim(params=UpperCamelCase_		)
 else:
  if hf_deepspeed_config.is_offload():
   logger.info(
       'Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'
       ' custom optimizer has both CPU and GPU implementation (except LAMB)'		)

  # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
  # But trainer uses AdamW by default.
  __A						:							Union[str, Any]      =						trainer.create_optimizer()
  # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  __A						:							str      =						True

 __A						:							Optional[int]      =						None
 if "scheduler" in config:
  __A						:							str      =						DummyScheduler(UpperCamelCase_		)
 else:
  if isinstance(UpperCamelCase_  ,						UpperCamelCase_		):
   raise ValueError(
       'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
       'Please configure a scheduler in the DeepSpeed config.'		)
  __A						:							Dict      =						trainer.create_scheduler(num_training_steps=UpperCamelCase_  ,						optimizer=UpperCamelCase_		)

 return optimizer, lr_scheduler




def 					a			(  UpperCamelCase_				:					Optional[Any]  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					Tuple=False		)	->		Optional[int]:
 from deepspeed.utils import logger as ds_logger

 __A						:							Dict      =						trainer.model
 __A						:							Dict      =						trainer.args

 __A						:							Tuple      =						trainer.accelerator.state.deepspeed_plugin.hf_ds_config

 # resume config update - some bits like `model` and `num_training_steps` only become available during train
 hf_deepspeed_config.trainer_config_finalize(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)

 # set the Deepspeed log level consistent with the Trainer
 ds_logger.setLevel(args.get_process_log_level()		)

 if inference:
  # only Z3 makes sense for the inference
  if not hf_deepspeed_config.is_zeroa():
   raise ValueError('ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'		)

  # in case the training config is re-used for inference
  hf_deepspeed_config.del_config_sub_tree('optimizer'		)
  hf_deepspeed_config.del_config_sub_tree('lr_scheduler'		)
  __A     ,       __A						:							int      =						None, None
  __A						:							Dict      =						None
 else:
  __A						:							Any      =						None  # important for when deepspeed_init is used as re-init
  __A						:							Tuple      =						list(filter(lambda UpperCamelCase_		: p.requires_grad  ,						model.parameters()		)		)
  __A     ,       __A						:							List[str]      =						deepspeed_optim_sched(
      UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)

 # keep for quick debug:
 # from pprint import pprint; pprint(config)

 return optimizer, lr_scheduler




def 					a			(  UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Optional[Any]		)	->		Optional[Any]:
 # it's possible that the user is trying to resume from model_path, which doesn't necessarily
 # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
 # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
 # path contains what looks like a deepspeed checkpoint
 import glob

 __A						:							Dict      =						sorted(glob.glob(F"{checkpoint_path}/global_step*"		)		)

 if len(UpperCamelCase_		) > 0:
  logger.info(F"Attempting to resume from {checkpoint_path}"		)
  # this magically updates self.optimizer and self.lr_scheduler
  __A     ,       __A						:							Union[str, Any]      =						deepspeed_engine.load_checkpoint(
      UpperCamelCase_  ,						load_optimizer_states=UpperCamelCase_  ,						load_lr_scheduler_states=UpperCamelCase_		)
  if load_path is None:
   raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"		)
 else:
  raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"		)













"""simple docstring"""


def 					a			(  UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int		)	->		int:
 return len_board * len_board_column * row + column




def 					a			(  UpperCamelCase_				:					list[list[str]]  ,						UpperCamelCase_				:					str  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					int  ,						UpperCamelCase_				:					set[int]  ,						)	->		bool:

 if board[row][column] != word[word_index]:
  return False

 if word_index == len(UpperCamelCase_		) - 1:
  return True

 __A						:							List[str]      =						[(0, 1), (0, -1), (-1, 0), (1, 0)]
 __A						:							Any      =						len(UpperCamelCase_		)
 __A						:							Union[str, Any]      =						len(board[0]		)
 for direction in traverts_directions:
  __A						:							Any      =						row + direction[0]
  __A						:							List[Any]      =						column + direction[1]
  if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
   continue

  __A						:							Any      =						get_point_key(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)
  if key in visited_points_set:
   continue

  visited_points_set.add(UpperCamelCase_		)
  if exits_word(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						word_index + 1  ,						UpperCamelCase_		):
   return True

  visited_points_set.remove(UpperCamelCase_		)

 return False




def 					a			(  UpperCamelCase_				:					list[list[str]]  ,						UpperCamelCase_				:					str		)	->		bool:
 __A						:							Optional[Any]      =						(
     'The board should be a non empty matrix of single chars strings.'
 )

 __A						:							Dict      =						len(UpperCamelCase_		)
 if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
  raise ValueError(UpperCamelCase_		)

 for row in board:
  if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
   raise ValueError(UpperCamelCase_		)

  for item in row:
   if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) != 1:
    raise ValueError(UpperCamelCase_		)

    # Validate word
 if not isinstance(UpperCamelCase_  ,						UpperCamelCase_		) or len(UpperCamelCase_		) == 0:
  raise ValueError(
      'The word parameter should be a string of length greater than 0.'		)

 __A						:							List[Any]      =						len(board[0]		)
 for i in range(UpperCamelCase_		):
  for j in range(UpperCamelCase_		):
   if exits_word(
       UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						0  ,						{get_point_key(UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_  ,						UpperCamelCase_		)}		):
    return True

 return False


if __name__ == "__main__":
   import doctest

   doctest.testmod()





