'''simple docstring'''


import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__	:     List[Any]      =    get_tests_dir('''fixtures/spiece.model''')






@require_sentencepiece
@require_tokenizers
class 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_		,      unittest.TestCase			):







  '''simple docstring'''






  lowerCamelCase_						:Dict		       =						ErnieMTokenizer
  lowerCamelCase_						:int		       =						False
  lowerCamelCase_						:Tuple		       =						True
  lowerCamelCase_						:Optional[int]		       =						False
  lowerCamelCase_						:List[Any]		       =						False


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCAmelCase_				:  List[Any]  = ErnieMTokenizer(snake_case_ ,						unk_token='<unk>' ,						pad_token='<pad>'		)
       tokenizer.save_pretrained(self.tmpdirname		)


  def _UpperCamelCase	(					self ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = 'this is a test'
       UpperCAmelCase_				:  str  = 'this is a test'
       return input_text, output_text


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  List[str]  = '<pad>'
       UpperCAmelCase_				:  List[str]  = 0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_		) ,						snake_case_		)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_		) ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  int  = list(self.get_tokenizer().get_vocab().keys()		)

       self.assertEqual(vocab_keys[0] ,						'<pad>'		)
       self.assertEqual(vocab_keys[1] ,						'<unk>'		)
       self.assertEqual(vocab_keys[-1] ,						'▁eloquent'		)
       self.assertEqual(len(snake_case_		) ,						3_0_0_0_0		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       self.assertEqual(self.get_tokenizer().vocab_size ,						3_0_0_0_0		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       if not self.test_rust_tokenizer:
            return

       UpperCAmelCase_				:  Optional[Any]  = self.get_tokenizer()
       UpperCAmelCase_				:  Optional[Any]  = self.get_rust_tokenizer()

       UpperCAmelCase_				:  List[Any]  = 'I was born in 92000, and this is falsé.'

       UpperCAmelCase_				:  List[Any]  = tokenizer.tokenize(snake_case_		)
       UpperCAmelCase_				:  Optional[Any]  = rust_tokenizer.tokenize(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Any  = tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       UpperCAmelCase_				:  Dict  = rust_tokenizer.encode(snake_case_ ,						add_special_tokens=snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)

       UpperCAmelCase_				:  Optional[int]  = self.get_rust_tokenizer()
       UpperCAmelCase_				:  Optional[int]  = tokenizer.encode(snake_case_		)
       UpperCAmelCase_				:  int  = rust_tokenizer.encode(snake_case_		)
       self.assertListEqual(snake_case_ ,						snake_case_		)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = ErnieMTokenizer(snake_case_ ,						do_lower_case=snake_case_ ,						unk_token='<unk>' ,						pad_token='<pad>'		)

       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.tokenize('This is a test'		)
       self.assertListEqual(snake_case_ ,						['▁this', '▁is', '▁a', '▁test']		)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_		) ,						[4_8, 2_5, 2_1, 1_2_8_9]		)

       UpperCAmelCase_				:  List[Any]  = tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           snake_case_ ,						['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']		)
       UpperCAmelCase_				:  int  = tokenizer.convert_tokens_to_ids(snake_case_		)
       self.assertListEqual(snake_case_ ,						[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9]		)

       UpperCAmelCase_				:  Optional[Any]  = tokenizer.convert_ids_to_tokens(snake_case_		)
       self.assertListEqual(
           snake_case_ ,						['▁i', '▁was', '▁born', '▁in', '9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.'] ,						)


  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Tuple  = ErnieMTokenizer(snake_case_ ,						unk_token='<unk>' ,						pad_token='<pad>'		)

       UpperCAmelCase_				:  List[Any]  = tokenizer.encode('sequence builders'		)
       UpperCAmelCase_				:  List[str]  = tokenizer.encode('multi-sequence build'		)

       UpperCAmelCase_				:  Union[str, Any]  = tokenizer.build_inputs_with_special_tokens(snake_case_		)
       UpperCAmelCase_				:  Tuple  = tokenizer.build_inputs_with_special_tokens(snake_case_ ,						snake_case_		)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]






  @slow
  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Optional[int]  = {'input_ids': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=snake_case_ ,						model_name='susnato/ernie-m-base_pytorch' ,						sequences=[
               'Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '
               'general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '
               'Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '
               'models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.',
               'BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '
               'conditioning on both left and right context in all layers.',
               'The quick brown fox jumps over the lazy dog.',
           ] ,						)














'''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

__A     						= logging.get_logger(__name__)




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Optional[Any]:







   """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 a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   Optional[Any]:
      """simple docstring"""






      set_hf_deepspeed_config(self)
      dep_version_check('accelerate')
      dep_version_check('deepspeed')
      super().__init__(__a)




class a_     (     UpperCamelCase_				):







   def __init__(self       , __a)      ->   List[str]:
      """simple docstring"""






      super().__init__(__a)
      __snake_case					:   Dict  				=			None
      __snake_case					:   Optional[Any]  				=			[]







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      if self._dtype is None:
         raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype')
      return self._dtype







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   Dict  				=			self.get_value(__a)
      if val is None:
         return False
      else:
         return val == "auto"







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a=None       , __a=True)      ->   int:
      """simple docstring"""






      __snake_case				,__snake_case					:   str  				=			self.find_config_node(__a)
      if config is None:
         return

      if config.get(__a) == "auto":
         __snake_case					:   List[Any]  				=			hf_val
         return

      if not must_match:
         return

      __snake_case					:   Optional[Any]  				=			config.get(__a)
      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}""")

   _snake_case	   =				partialmethod(UpperCamelCase_		,   must_match=UpperCamelCase_				)







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   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'       , __a       , '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:
         __snake_case					:   Optional[Any]  				=			'apex' if args.fpaa_backend == 'apex' else 'amp'
      else:
         __snake_case					:   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
         __snake_case					:   Union[str, Any]  				=			self.config.get('checkpoint'       , {})
         __snake_case					:   List[str]  				=			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'):
         __snake_case					:   List[str]  				=			torch.bfloataa
      elif self.is_false('fp16.enabled'):
         __snake_case					:   Union[str, Any]  				=			torch.floataa
      else:
         __snake_case					:   List[str]  				=			torch.floataa







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a)      ->   Any:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			[
          'zero_optimization.reduce_bucket_size',
          'zero_optimization.stage3_prefetch_bucket_size',
          'zero_optimization.stage3_param_persistence_threshold',
      ]
      __snake_case					:   int  				=			[x for x in hidden_size_based_keys if self.is_auto(__a)]

      if len(__a) > 0:
         if hasattr(model.config       , 'hidden_size'):
            __snake_case					:   Optional[int]  				=			model.config.hidden_size
         elif hasattr(model.config       , 'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            __snake_case					:   Tuple  				=			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'       , __a       , 'num_training_steps (calculated)')
      self.fill_match('scheduler.params.warmup_num_steps'       , args.get_warmup_steps(__a)       , 'warmup_steps')

      if len(self.mismatches) > 0:
         __snake_case					:   Any  				=			'\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'.""")


__A     						= None




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	)     ->			Union[str, Any]:







   """simple docstring"""
   # 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)
   __snake_case					:   List[Any]  				=			weakref.ref(A	)




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Union[str, Any]:







   """simple docstring"""
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   __snake_case					:   Optional[Any]  				=			None




def 							_SCREAMING_SNAKE_CASE	(					)     ->			str:







   """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 							_SCREAMING_SNAKE_CASE	(					)     ->			List[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 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							int	,		A	:							Optional[Any]	,		A	:							Optional[Any]	,		A	:							str	)     ->			Union[str, Any]:







   """simple docstring"""
   from accelerate.utils import DummyOptim, DummyScheduler

   __snake_case					:   Tuple  				=			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)

   __snake_case					:   Any  				=			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.'	)
      __snake_case					:   Union[str, Any]  				=			DummyOptim(params=A	)
   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.
      __snake_case					:   Optional[int]  				=			trainer.create_optimizer()
      # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
      __snake_case					:   Dict  				=			True

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

   return optimizer, lr_scheduler




def 							_SCREAMING_SNAKE_CASE	(					A	:							Dict	,		A	:							List[Any]	,		A	:							str=False	)     ->			Union[str, Any]:







   """simple docstring"""
   from deepspeed.utils import logger as ds_logger

   __snake_case					:   int  				=			trainer.model
   __snake_case					:   Any  				=			trainer.args

   __snake_case					:   List[str]  				=			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(A	,		A	,		A	)

   # 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'	)
      __snake_case				,__snake_case					:   Any  				=			None, None
      __snake_case					:   int  				=			None
   else:
      __snake_case					:   str  				=			None  # important for when deepspeed_init is used as re-init
      __snake_case					:   Optional[int]  				=			list(filter(lambda A	: p.requires_grad	,		model.parameters()	)	)
      __snake_case				,__snake_case					:   List[Any]  				=			deepspeed_optim_sched(
          A	,		A	,		A	,		A	,		A	)

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

   return optimizer, lr_scheduler





def 							_SCREAMING_SNAKE_CASE	(					A	:							Any	,		A	:							int	)     ->			List[Any]:







   """simple docstring"""
   # 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

   __snake_case					:   int  				=			sorted(glob.glob(F"""{checkpoint_path}/global_step*"""	)	)

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