"""simple docstring"""
import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


lowercase__   			=      logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


lowercase__   			=      namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  a__:	Any     				=      BertTokenizer.from_pretrained('bert-base-uncased'						,	do_lower_case=_SCREAMING_SNAKE_CASE  )
  a__:	Dict     				=      BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'  )
  model.to(args.device  )
  model.eval()

  a__:	Tuple     				=      {
      'BOS': tokenizer.vocab['[unused0]'],
      'EOS': tokenizer.vocab['[unused1]'],
      'PAD': tokenizer.vocab['[PAD]'],
  }

  if args.compute_rouge:
    a__:	Optional[Any]     				=      []
    a__:	Dict     				=      []

    import nltk
    import rouge

    nltk.download('punkt'  )
    a__:	Optional[Any]     				=      rouge.Rouge(
        metrics=['rouge-n', 'rouge-l']						,	max_n=2						,	limit_length=_SCREAMING_SNAKE_CASE						,	length_limit=args.beam_size						,	length_limit_type='words'						,	apply_avg=_SCREAMING_SNAKE_CASE						,	apply_best=_SCREAMING_SNAKE_CASE						,	alpha=0.5						,	weight_factor=1.2						,	stemming=_SCREAMING_SNAKE_CASE						,	)

  # these (unused) arguments are defined to keep the compatibility
  # with the legacy code and will be deleted in a next iteration.
  a__:	Union[str, Any]     				=      ''
  a__:	Any     				=      ''

  a__:	Union[str, Any]     				=      build_data_iterator(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  a__:	int     				=      build_predictor(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  logger.info('***** Running evaluation *****'  )
  logger.info('  Number examples = %d'						,	len(data_iterator.dataset  )  )
  logger.info('  Batch size = %d'						,	args.batch_size  )
  logger.info(''  )
  logger.info('***** Beam Search parameters *****'  )
  logger.info('  Beam size = %d'						,	args.beam_size  )
  logger.info('  Minimum length = %d'						,	args.min_length  )
  logger.info('  Maximum length = %d'						,	args.max_length  )
  logger.info('  Alpha (length penalty) = %.2f'						,	args.alpha  )
  logger.info('  Trigrams %s be blocked'						,	('will' if args.block_trigram else 'will NOT')  )

  for batch in tqdm(_SCREAMING_SNAKE_CASE  ):
    a__:	List[str]     				=      predictor.translate_batch(_SCREAMING_SNAKE_CASE  )
    a__:	Dict     				=      predictor.from_batch(_SCREAMING_SNAKE_CASE  )
    a__:	int     				=      [format_summary(_SCREAMING_SNAKE_CASE  ) for t in translations]
    save_summaries(_SCREAMING_SNAKE_CASE						,	args.summaries_output_dir						,	batch.document_names  )

    if args.compute_rouge:
      reference_summaries += batch.tgt_str
      generated_summaries += summaries

  if args.compute_rouge:
    a__:	Dict     				=      rouge_evaluator.get_scores(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
    a__:	Optional[Any]     				=      format_rouge_scores(_SCREAMING_SNAKE_CASE  )
    save_rouge_scores(_SCREAMING_SNAKE_CASE  )
    print(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  for summary, document_name in zip(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    # Prepare the summary file's name
    if "." in document_name:
      a__:	Any     				=      '.'.join(document_name.split('.'  )[:-1]  )
      a__:	str     				=      document_name.split('.'  )[-1]
      a__:	Optional[Any]     				=      bare_document_name + '_summary.' + extension
    else:
      a__:	Union[str, Any]     				=      document_name + '_summary'

    a__:	Union[str, Any]     				=      os.path.join(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
    with open(_SCREAMING_SNAKE_CASE						,	'w'  ) as output:
      output.write(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Optional[int]:
  a__				,       a__				,       a__:	Optional[Any]     				=      translation
  a__:	Tuple     				=      (
      raw_summary.replace('[unused0]'						,	''  )
      .replace('[unused3]'						,	''  )
      .replace('[PAD]'						,	''  )
      .replace('[unused1]'						,	''  )
      .replace(r' +'						,	' '  )
      .replace(' [unused2] '						,	'. '  )
      .replace('[unused2]'						,	''  )
      .strip()
  )

  return summary







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
      scores['rouge-1']['f']						,	scores['rouge-1']['p']						,	scores['rouge-1']['r']						,	scores['rouge-2']['f']						,	scores['rouge-2']['p']						,	scores['rouge-2']['r']						,	scores['rouge-l']['f']						,	scores['rouge-l']['p']						,	scores['rouge-l']['r']						,	)







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  with open('rouge_scores.txt'						,	'w'  ) as output:
    output.write(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->str:
  a__:	List[Any]     				=      load_and_cache_examples(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
  a__:	Optional[Any]     				=      SequentialSampler(_SCREAMING_SNAKE_CASE  )

  def collate_fn(_SCREAMING_SNAKE_CASE  ):
    return collate(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	block_size=512						,	device=args.device  )

  a__:	Dict     				=      DataLoader(
      _SCREAMING_SNAKE_CASE						,	sampler=_SCREAMING_SNAKE_CASE						,	batch_size=args.batch_size						,	collate_fn=_SCREAMING_SNAKE_CASE						,	)

  return iterator







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  a__:	Dict     				=      CNNDMDataset(args.documents_dir  )
  return dataset







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Union[str, Any]:
  a__:	List[Any]     				=      [x for x in data if not len(x[1]  ) == 0]  # remove empty_files
  a__:	Any     				=      [name for name, _, _ in data]
  a__:	Dict     				=      [' '.join(_SCREAMING_SNAKE_CASE  ) for _, _, summary_list in data]

  a__:	Optional[int]     				=      [encode_for_summarization(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ) for _, story, summary in data]
  a__:	str     				=      torch.tensor(
      [truncate_or_pad(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	tokenizer.pad_token_id  ) for story, _ in encoded_text]  )
  a__:	int     				=      compute_token_type_ids(_SCREAMING_SNAKE_CASE						,	tokenizer.cls_token_id  )
  a__:	str     				=      build_mask(_SCREAMING_SNAKE_CASE						,	tokenizer.pad_token_id  )

  a__:	List[str]     				=      Batch(
      document_names=_SCREAMING_SNAKE_CASE						,	batch_size=len(_SCREAMING_SNAKE_CASE  )						,	src=encoded_stories.to(_SCREAMING_SNAKE_CASE  )						,	segs=encoder_token_type_ids.to(_SCREAMING_SNAKE_CASE  )						,	mask_src=encoder_mask.to(_SCREAMING_SNAKE_CASE  )						,	tgt_str=_SCREAMING_SNAKE_CASE						,	)

  return batch







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Optional[int]     				=      summary_tokens.to('cpu'  ).numpy()
  a__:	List[str]     				=      tokenizer.decode(_SCREAMING_SNAKE_CASE  )
  a__:	Any     				=      summary.split('.'  )
  a__:	int     				=      [s + '.' for s in sentences]
  return sentences







def 		__a  (      )      ->Union[str, Any]:
  a__:	Optional[int]     				=      argparse.ArgumentParser()
  parser.add_argument(
      '--documents_dir'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	required=_SCREAMING_SNAKE_CASE						,	help='The folder where the documents to summarize are located.'						,	)
  parser.add_argument(
      '--summaries_output_dir'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	required=_SCREAMING_SNAKE_CASE						,	help='The folder in wich the summaries should be written. Defaults to the folder where the documents are'						,	)
  parser.add_argument(
      '--compute_rouge'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	required=_SCREAMING_SNAKE_CASE						,	help='Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset.'						,	)
  # EVALUATION options
  parser.add_argument(
      '--no_cuda'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	help='Whether to force the execution on CPU.'						,	)
  parser.add_argument(
      '--batch_size'						,	default=4						,	type=_SCREAMING_SNAKE_CASE						,	help='Batch size per GPU/CPU for training.'						,	)
  # BEAM SEARCH arguments
  parser.add_argument(
      '--min_length'						,	default=50						,	type=_SCREAMING_SNAKE_CASE						,	help='Minimum number of tokens for the summaries.'						,	)
  parser.add_argument(
      '--max_length'						,	default=200						,	type=_SCREAMING_SNAKE_CASE						,	help='Maixmum number of tokens for the summaries.'						,	)
  parser.add_argument(
      '--beam_size'						,	default=5						,	type=_SCREAMING_SNAKE_CASE						,	help='The number of beams to start with for each example.'						,	)
  parser.add_argument(
      '--alpha'						,	default=0.95						,	type=_SCREAMING_SNAKE_CASE						,	help='The value of alpha for the length penalty in the beam search.'						,	)
  parser.add_argument(
      '--block_trigram'						,	default=_SCREAMING_SNAKE_CASE						,	type=_SCREAMING_SNAKE_CASE						,	help='Whether to block the existence of repeating trigrams in the text generated by beam search.'						,	)
  a__:	int     				=      parser.parse_args()

  # Select device (distibuted not available)
  a__:	Union[str, Any]     				=      torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'  )

  # Check the existence of directories
  if not args.summaries_output_dir:
    a__:	Dict     				=      args.documents_dir

  if not documents_dir_is_valid(args.documents_dir  ):
    raise FileNotFoundError(
        'We could not find the directory you specified for the documents to summarize, or it was empty. Please'
        ' specify a valid path.'  )
  os.makedirs(args.summaries_output_dir						,	exist_ok=_SCREAMING_SNAKE_CASE  )

  evaluate(_SCREAMING_SNAKE_CASE  )







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->Any:
  if not os.path.exists(_SCREAMING_SNAKE_CASE  ):
    return False

  a__:	Optional[Any]     				=      os.listdir(_SCREAMING_SNAKE_CASE  )
  if len(_SCREAMING_SNAKE_CASE  ) == 0:
    return False

  return True


if __name__ == "__main__":
 main()



"""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

lowercase__   			=      logging.get_logger(__name__)







def 		__a  (      )      ->Dict:
  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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     int:





        '''simple docstring'''





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







class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        super().__init__(lowercase)
        a__:	List[Any]     				=      None
        a__:	int     				=      []



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.get_value(lowercase)
        if val is None:
          return False
        else:
          return val == "auto"



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase=None       ,     lowercase=True)      ->     int:





        '''simple docstring'''





        a__				,       a__:	int     				=      self.find_config_node(lowercase)
        if config is None:
          return

        if config.get(lowercase) == "auto":
          a__:	Optional[int]     				=      hf_val
          return

        if not must_match:
          return

        a__:	List[Any]     				=      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}')

      a__								=						partialmethod(__lowerCAmelCase			, must_match=__lowerCAmelCase							)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	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__:	Tuple     				=      '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__:	Union[str, Any]     				=      self.config.get('checkpoint'       ,     {})
          a__:	Tuple     				=      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__:	Optional[int]     				=      torch.bfloataa
        elif self.is_false('fp16.enabled'):
          a__:	Tuple     				=      torch.floataa
        else:
          a__:	Tuple     				=      torch.floataa



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Any:





        '''simple docstring'''





        a__:	List[Any]     				=      [
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        a__:	Union[str, 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[str]     				=      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\'.')







lowercase__   			=      None







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  # 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__:	Any     				=      weakref.ref(_SCREAMING_SNAKE_CASE  )







def 		__a  (      )      ->List[Any]:
  # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
  global _hf_deepspeed_config_weak_ref
  a__:	Any     				=      None







def 		__a  (      )      ->Union[str, 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().is_zeroa()
  else:
    return False







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().config
  else:
    return None







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  from accelerate.utils import DummyOptim, DummyScheduler

  a__:	Optional[Any]     				=      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=_SCREAMING_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.
    a__:	Any     				=      trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    a__:	str     				=      True

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

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False  )      ->Dict:
  from deepspeed.utils import logger as ds_logger

  a__:	Optional[int]     				=      trainer.model
  a__:	List[Any]     				=      trainer.args

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

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

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  # 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__:	Tuple     				=      sorted(glob.glob(F'{checkpoint_path}/global_step*'  )  )

  if len(_SCREAMING_SNAKE_CASE  ) > 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(
        _SCREAMING_SNAKE_CASE						,	load_optimizer_states=_SCREAMING_SNAKE_CASE						,	load_lr_scheduler_states=_SCREAMING_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}'  )


