


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__     			=							logging.get_logger(__name__)
def        UpperCAmelCase_      (							)  ->    str:







 """simple docstring"""


 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    lowerCamelCase__     ( lowerCAmelCase):





     def __init__(self	,  UpperCAmelCase )  ->     str:
      # set global weakref object
      set_hf_deepspeed_config(self )
      dep_version_check('''accelerate''' )
      dep_version_check('''deepspeed''' )
      super().__init__(UpperCAmelCase )







class    lowerCamelCase__     ( lowerCAmelCase):





     def __init__(self	,  UpperCAmelCase )  ->     Union[str, Any]:
      super().__init__(UpperCAmelCase )
      _lowercase							   =None
      _lowercase							   =[]





     def   __A      (self )  ->     Optional[int]:
      if self._dtype is None:
       raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype''' )
      return self._dtype





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





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase=None	,  UpperCAmelCase=True )  ->     str:
      _lowercase   ,					_lowercase							   =self.find_config_node(UpperCAmelCase )
      if config is None:
       return

      if config.get(UpperCAmelCase ) == "auto":
       _lowercase							   =hf_val
       return

      if not must_match:
       return

      _lowercase							   =config.get(UpperCAmelCase )
      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}" )

     SCREAMING_SNAKE_CASE__       =    partialmethod(lowerCAmelCase     ,							must_match=lowerCAmelCase)





     def   __A      (self	,  UpperCAmelCase )  ->     Dict:
      _lowercase							   =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'''	,  UpperCAmelCase	,  '''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:
       _lowercase							   ='''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
      else:
       _lowercase							   =None

      if args.save_on_each_node:
       # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
       _lowercase							   =self.config.get('''checkpoint'''	,  {} )
       _lowercase							   =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''' ):
       _lowercase							   =torch.bfloataa
      elif self.is_false('''fp16.enabled''' ):
       _lowercase							   =torch.floataa
      else:
       _lowercase							   =torch.floataa





     def   __A      (self	,  UpperCAmelCase	,  UpperCAmelCase	,  UpperCAmelCase )  ->     Union[str, Any]:
      _lowercase							   =[
          '''zero_optimization.reduce_bucket_size''',
          '''zero_optimization.stage3_prefetch_bucket_size''',
          '''zero_optimization.stage3_param_persistence_threshold''',
      ]
      _lowercase							   =[x for x in hidden_size_based_keys if self.is_auto(UpperCAmelCase )]

      if len(UpperCAmelCase ) > 0:
       if hasattr(model.config	,  '''hidden_size''' ):
        _lowercase							   =model.config.hidden_size
       elif hasattr(model.config	,  '''hidden_sizes''' ):
        # if there are many hidden sizes pick the largest one
        _lowercase							   =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'''	,  1_0 * hidden_size )

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

      if len(self.mismatches ) > 0:
       _lowercase							   ='''\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__     			=							None
def        UpperCAmelCase_      (							__snake_case		)  ->    Dict:







 """simple docstring"""


 global _hf_deepspeed_config_weak_ref
 # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
 _lowercase							   =weakref.ref(__snake_case		)
def        UpperCAmelCase_      (							)  ->    str:







 """simple docstring"""


 global _hf_deepspeed_config_weak_ref
 _lowercase							   =None
def        UpperCAmelCase_      (							)  ->    Union[str, Any]:







 """simple docstring"""


 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        UpperCAmelCase_      (							)  ->    Any:







 """simple docstring"""


 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        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)  ->    str:







 """simple docstring"""


 from accelerate.utils import DummyOptim, DummyScheduler

 _lowercase							   =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)

 _lowercase							   =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.'''		)
  _lowercase							   =DummyOptim(params=__snake_case		)
 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.
  _lowercase							   =trainer.create_optimizer()
  # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
  _lowercase							   =True

 _lowercase							   =None
 if "scheduler" in config:
  _lowercase							   =DummyScheduler(__snake_case		)
 else:
  if isinstance(__snake_case  ,		__snake_case		):
   raise ValueError(
       '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
       '''Please configure a scheduler in the DeepSpeed config.'''		)
  _lowercase							   =trainer.create_scheduler(num_training_steps=__snake_case  ,		optimizer=__snake_case		)

 return optimizer, lr_scheduler
def        UpperCAmelCase_      (							__snake_case  ,		__snake_case  ,		__snake_case=False		)  ->    Optional[int]:







 """simple docstring"""


 from deepspeed.utils import logger as ds_logger

 _lowercase							   =trainer.model
 _lowercase							   =trainer.args

 _lowercase							   =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(__snake_case  ,		__snake_case  ,		__snake_case		)

 # 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'''		)
  _lowercase   ,					_lowercase							   =None, None
  _lowercase							   =None
 else:
  _lowercase							   =None  # important for when deepspeed_init is used as re-init
  _lowercase							   =list(filter(lambda __snake_case		: p.requires_grad  ,		model.parameters()		)		)
  _lowercase   ,					_lowercase							   =deepspeed_optim_sched(
      __snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case  ,		__snake_case		)

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

 return optimizer, lr_scheduler



def        UpperCAmelCase_      (							__snake_case  ,		__snake_case		)  ->    List[str]:







 """simple docstring"""


 import glob

 _lowercase							   =sorted(glob.glob(F"{checkpoint_path}/global_step*"		)		)

 if len(__snake_case		) > 0:
  logger.info(F"Attempting to resume from {checkpoint_path}"		)
  # this magically updates self.optimizer and self.lr_scheduler
  _lowercase   ,					_lowercase							   =deepspeed_engine.load_checkpoint(
      __snake_case  ,		load_optimizer_states=__snake_case  ,		load_lr_scheduler_states=__snake_case		)
  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}"		)





import requests

try:
  from django.contrib.auth import authenticate, login
  from django.shortcuts import redirect, render
except ImportError:
  UpperCAmelCase__     			=							UpperCAmelCase__     			=							UpperCAmelCase__     			=							UpperCAmelCase__     			=							print
def        UpperCAmelCase_      (							__snake_case		)  ->    List[str]:







 """simple docstring"""


 _lowercase							   ='''secretKey'''
 _lowercase							   ='''https://www.google.com/recaptcha/api/siteverify'''

 # when method is not POST, direct user to login page
 if request.method != "POST":
  return render(__snake_case  ,		'''login.html'''		)

 # from the frontend, get username, password, and client_key
 _lowercase							   =request.POST.get('''username'''		)
 _lowercase							   =request.POST.get('''password'''		)
 _lowercase							   =request.POST.get('''g-recaptcha-response'''		)

 # post recaptcha response to Google's recaptcha api
 _lowercase							   =requests.post(__snake_case  ,		data={'''secret''': secret_key, '''response''': client_key}		)
 # if the recaptcha api verified our keys
 if response.json().get('''success'''  ,		__snake_case		):
  # authenticate the user
  _lowercase							   =authenticate(__snake_case  ,		username=__snake_case  ,		password=__snake_case		)
  if user_in_database:
   login(__snake_case  ,		__snake_case		)
   return redirect('''/your-webpage'''		)
 return render(__snake_case  ,		'''login.html'''		)

