




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 (	)							->						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    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			Optional[int]			,							snake_case       :			Optional[Any]					):
          # set global weakref object
          set_hf_deepspeed_config(self					)
          dep_version_check('''accelerate'''					)
          dep_version_check('''deepspeed'''					)
          super().__init__(snake_case					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      def __init__(  self       :			List[Any]			,							snake_case       :			Dict					):
          super().__init__(snake_case					)
          __UpperCamelCase			=    None
          __UpperCamelCase			=    []







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







      def    snake_case						(  self       :			str			,							snake_case       :			int					):
          __UpperCamelCase			=    self.get_value(snake_case					)
          if val is None:
              return False
          else:
              return val == "auto"







      def    snake_case						(  self       :			str			,							snake_case       :			str			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[int]=None			,							snake_case       :			Any=True					):
          __UpperCamelCase		,   __UpperCamelCase			=    self.find_config_node(snake_case					)
          if config is None:
              return

          if config.get(snake_case					) == "auto":
              __UpperCamelCase			=    hf_val
              return

          if not must_match:
              return

          __UpperCamelCase			=    config.get(snake_case					)
          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}"					)

      lowerCAmelCase__      :			List[str]     =       partialmethod(_SCREAMING_SNAKE_CASE				,      must_match=_SCREAMING_SNAKE_CASE )







      def    snake_case						(  self       :			List[Any]			,							snake_case       :			Optional[Any]					):
          __UpperCamelCase			=    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'''			,							snake_case			,							'''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:
              __UpperCamelCase			=    '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
          else:
              __UpperCamelCase			=    None

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







      def    snake_case						(  self       :			Dict			,							snake_case       :			Optional[Any]			,							snake_case       :			Optional[int]			,							snake_case       :			Optional[int]					):
          __UpperCamelCase			=    [
              '''zero_optimization.reduce_bucket_size''',
              '''zero_optimization.stage3_prefetch_bucket_size''',
              '''zero_optimization.stage3_param_persistence_threshold''',
          ]
          __UpperCamelCase			=    [x for x in hidden_size_based_keys if self.is_auto(snake_case					)]

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

          if len(self.mismatches					) > 0:
              __UpperCamelCase			=    '''\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 (	lowercase_   )							->						str:

    """simple docstring"""


    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    __UpperCamelCase			=    weakref.ref(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[int]:

    """simple docstring"""


    global _hf_deepspeed_config_weak_ref
    __UpperCamelCase			=    None




def        __SCREAMING_SNAKE_CASE (	)							->						Optional[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        __SCREAMING_SNAKE_CASE (	)							->						Optional[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 (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    from accelerate.utils import DummyOptim, DummyScheduler

    __UpperCamelCase			=    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)

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

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

    return optimizer, lr_scheduler




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=False   )							->						Tuple:

    """simple docstring"""


    from deepspeed.utils import logger as ds_logger

    __UpperCamelCase			=    trainer.model
    __UpperCamelCase			=    trainer.args

    __UpperCamelCase			=    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(lowercase_       ,  lowercase_       ,  lowercase_   )

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

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

    return optimizer, lr_scheduler




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    import glob

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

    if len(lowercase_   ) > 0:
        logger.info(F"Attempting to resume from {checkpoint_path}"   )
        # this magically updates self.optimizer and self.lr_scheduler
        __UpperCamelCase		,   __UpperCamelCase			=    deepspeed_engine.load_checkpoint(
            lowercase_       ,  load_optimizer_states=lowercase_       ,  load_lr_scheduler_states=lowercase_   )
        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 json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
 from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

 from .test_feature_extraction_speechta import floats_list


a_ 		=  get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")

@require_torch
class 			_lowerCamelCase    (      unittest.TestCase ):

      """simple docstring"""

      def    snake_case						(  self       :			int					):
          __UpperCamelCase			=    tempfile.mkdtemp()

          __UpperCamelCase			=    SpeechTaTokenizer(snake_case					)
          tokenizer.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    {
              '''feature_size''': 1,
              '''padding_value''': 0.0,
              '''sampling_rate''': 16000,
              '''do_normalize''': False,
              '''num_mel_bins''': 80,
              '''hop_length''': 16,
              '''win_length''': 64,
              '''win_function''': '''hann_window''',
              '''fmin''': 80,
              '''fmax''': 7600,
              '''mel_floor''': 1E-10,
              '''reduction_factor''': 2,
              '''return_attention_mask''': True,
          }

          __UpperCamelCase			=    os.path.join(self.tmpdirname			,							snake_case					)
          with open(self.feature_extraction_file			,							'''w'''			,							encoding='''utf-8'''					) as fp:
              fp.write(json.dumps(snake_case					) + '''\n'''					)







      def    snake_case						(  self       :			Union[str, Any]			,							**snake_case       :			List[Any]					):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			List[Any]			,							**snake_case       :			List[str]					):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,							**snake_case					)







      def    snake_case						(  self       :			Union[str, Any]					):
          shutil.rmtree(self.tmpdirname					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_tokenizer()
          __UpperCamelCase			=    self.get_feature_extractor()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          processor.save_pretrained(self.tmpdirname					)
          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(self.tmpdirname					)

          self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer.get_vocab()					)
          self.assertIsInstance(processor.tokenizer			,							snake_case					)

          self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor.to_json_string()					)
          self.assertIsInstance(processor.feature_extractor			,							snake_case					)







      def    snake_case						(  self       :			Tuple					):
          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=self.get_tokenizer()			,							feature_extractor=self.get_feature_extractor()					)
          processor.save_pretrained(self.tmpdirname					)

          __UpperCamelCase			=    self.get_tokenizer(bos_token='''(BOS)'''			,							eos_token='''(EOS)'''					)
          __UpperCamelCase			=    self.get_feature_extractor(do_normalize=snake_case			,							padding_value=1.0					)

          __UpperCamelCase			=    SpeechTaProcessor.from_pretrained(
              self.tmpdirname			,							bos_token='''(BOS)'''			,							eos_token='''(EOS)'''			,							do_normalize=snake_case			,							padding_value=1.0					)

          self.assertEqual(processor.tokenizer.get_vocab()			,							tokenizer_add_kwargs.get_vocab()					)
          self.assertIsInstance(processor.tokenizer			,							snake_case					)

          self.assertEqual(processor.feature_extractor.to_json_string()			,							feature_extractor_add_kwargs.to_json_string()					)
          self.assertIsInstance(processor.feature_extractor			,							snake_case					)







      def    snake_case						(  self       :			List[str]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio=snake_case			,							return_tensors='''np'''					)

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1E-2					)







      def    snake_case						(  self       :			List[Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    floats_list((3, 1000)					)

          __UpperCamelCase			=    feature_extractor(audio_target=snake_case			,							return_tensors='''np'''					)
          __UpperCamelCase			=    processor(audio_target=snake_case			,							return_tensors='''np'''					)

          for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()			,							input_processor[key].sum()			,							delta=1E-2					)







      def    snake_case						(  self       :			Union[str, Any]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    '''This is a test string'''

          __UpperCamelCase			=    processor(text_target=snake_case					)
          __UpperCamelCase			=    tokenizer(snake_case					)

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]			,							encoded_processor[key]					)







      def    snake_case						(  self       :			str					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          __UpperCamelCase			=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __UpperCamelCase			=    processor.batch_decode(snake_case					)
          __UpperCamelCase			=    tokenizer.batch_decode(snake_case					)

          self.assertListEqual(snake_case			,							snake_case					)







      def    snake_case						(  self       :			Optional[int]					):
          __UpperCamelCase			=    self.get_feature_extractor()
          __UpperCamelCase			=    self.get_tokenizer()

          __UpperCamelCase			=    SpeechTaProcessor(tokenizer=snake_case			,							feature_extractor=snake_case					)

          self.assertListEqual(
              processor.model_input_names			,							feature_extractor.model_input_names			,							msg='''`processor` and `feature_extractor` model input names do not match'''			,							)






