"""simple docstring"""
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class   __snake_case			(	__lowerCAmelCase			, unittest.TestCase							):
      a__								=						BartTokenizer
      a__								=						BartTokenizerFast
      a__								=						True
      a__								=						filter_roberta_detectors



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        super().setUp()
        a__:	Optional[Any]     				=      [
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            '\u0120',
            '\u0120l',
            '\u0120n',
            '\u0120lo',
            '\u0120low',
            'er',
            '\u0120lowest',
            '\u0120newer',
            '\u0120wider',
            '<unk>',
        ]
        a__:	Dict     				=      dict(zip(lowercase       ,     range(len(lowercase))))
        a__:	Optional[Any]     				=      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
        a__:	int     				=      {'unk_token': '<unk>'}

        a__:	str     				=      os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['vocab_file'])
        a__:	Dict     				=      os.path.join(self.tmpdirname       ,     VOCAB_FILES_NAMES['merges_file'])
        with open(self.vocab_file       ,     'w'       ,     encoding='utf-8') as fp:
          fp.write(json.dumps(lowercase) + '\n')
        with open(self.merges_file       ,     'w'       ,     encoding='utf-8') as fp:
          fp.write('\n'.join(lowercase))



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname       ,     **lowercase)



      def     lowerCamelCase_  (		self       ,     **lowercase)      ->     int:





        '''simple docstring'''





        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,     **lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        return "lower newer", "lower newer"



      @cached_property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return BartTokenizer.from_pretrained('facebook/bart-large')



      @cached_property
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        return BartTokenizerFast.from_pretrained('facebook/bart-large')



      @require_torch
      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	Any     				=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        a__:	Union[str, Any]     				=      [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	Optional[int]     				=      tokenizer(lowercase       ,     max_length=len(lowercase)       ,     padding=lowercase       ,     return_tensors='pt')
          self.assertIsInstance(lowercase       ,     lowercase)

          self.assertEqual((2, 9)       ,     batch.input_ids.shape)
          self.assertEqual((2, 9)       ,     batch.attention_mask.shape)
          a__:	Tuple     				=      batch.input_ids.tolist()[0]
          self.assertListEqual(lowercase       ,     lowercase)
          # Test that special tokens are reset



      @require_torch
      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        a__:	Dict     				=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	str     				=      tokenizer(lowercase       ,     padding=lowercase       ,     return_tensors='pt')
          # check if input_ids are returned and no labels
          self.assertIn('input_ids'       ,     lowercase)
          self.assertIn('attention_mask'       ,     lowercase)
          self.assertNotIn('labels'       ,     lowercase)
          self.assertNotIn('decoder_attention_mask'       ,     lowercase)



      @require_torch
      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	int     				=      [
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	int     				=      tokenizer(text_target=lowercase       ,     max_length=32       ,     padding='max_length'       ,     return_tensors='pt')
          self.assertEqual(32       ,     targets['input_ids'].shape[1])



      @require_torch
      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	Tuple     				=      tokenizer(
              ['I am a small frog' * 10_24, 'I am a small frog']       ,     padding=lowercase       ,     truncation=lowercase       ,     return_tensors='pt')
          self.assertIsInstance(lowercase       ,     lowercase)
          self.assertEqual(batch.input_ids.shape       ,     (2, 10_24))



      @require_torch
      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        a__:	str     				=      ['A long paragraph for summarization.']
        a__:	Union[str, Any]     				=      [
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
          a__:	List[str]     				=      tokenizer(lowercase       ,     return_tensors='pt')
          a__:	Optional[int]     				=      tokenizer(text_target=lowercase       ,     return_tensors='pt')
          a__:	List[str]     				=      inputs['input_ids']
          a__:	Optional[int]     				=      targets['input_ids']
          self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
          self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
          self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
          self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        pass



      def     lowerCamelCase_  (		self)      ->     Dict:





        '''simple docstring'''





        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'):
            a__:	int     				=      self.rust_tokenizer_class.from_pretrained(lowercase       ,     **lowercase)
            a__:	str     				=      self.tokenizer_class.from_pretrained(lowercase       ,     **lowercase)
            a__:	Union[str, Any]     				=      'A, <mask> AllenNLP sentence.'
            a__:	int     				=      tokenizer_r.encode_plus(lowercase       ,     add_special_tokens=lowercase       ,     return_token_type_ids=lowercase)
            a__:	Any     				=      tokenizer_p.encode_plus(lowercase       ,     add_special_tokens=lowercase       ,     return_token_type_ids=lowercase)

            # token_type_ids should put 0 everywhere
            self.assertEqual(sum(tokens_r['token_type_ids'])       ,     sum(tokens_p['token_type_ids']))

            # attention_mask should put 1 everywhere, so sum over length should be 1
            self.assertEqual(
                sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])       ,     sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])       ,     )

            a__:	Any     				=      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
            a__:	int     				=      tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])

            # Rust correctly handles the space before the mask while python doesnt
            self.assertSequenceEqual(tokens_p['input_ids']       ,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])
            self.assertSequenceEqual(tokens_r['input_ids']       ,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2])

            self.assertSequenceEqual(
                lowercase       ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
            self.assertSequenceEqual(
                lowercase       ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])



"""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}'  )


