






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       a__						(  ):
   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(					__UpperCamelCase      ):



 def __init__(	self   :   Dict ,		UpperCAmelCase__   :   List[str]	):

    """simple docstring"""






    set_hf_deepspeed_config(self	)
    dep_version_check("""accelerate"""	)
    dep_version_check("""deepspeed"""	)
    super().__init__(UpperCAmelCase__	)
class        __snake_case(					__UpperCamelCase      ):



 def __init__(	self   :   str ,		UpperCAmelCase__   :   Tuple	):

    """simple docstring"""






    super().__init__(UpperCAmelCase__	)
    lowercase__						:	Optional[int]         = None
    lowercase__						:	str         = []



 def 						UpperCAmelCase    (	self   :   List[str]	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   str	):

    """simple docstring"""






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



 def 						UpperCAmelCase    (	self   :   int ,		UpperCAmelCase__   :   Optional[int] ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   List[str]=None ,		UpperCAmelCase__   :   Dict=True	):

    """simple docstring"""






    lowercase__     ,					lowercase__						:	str         = self.find_config_node(UpperCAmelCase__	)
    if config is None:
       return

    if config.get(UpperCAmelCase__	) == "auto":
       lowercase__						:	Optional[Any]         = hf_val
       return

    if not must_match:
       return

    lowercase__						:	Optional[int]         = config.get(UpperCAmelCase__	)
    if ds_val is not None and ds_val != hf_val:
       self.mismatches.append(f'''- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}'''	)

 lowercase :       List[Any]		      =		partialmethod(__UpperCamelCase     ,       must_match=__UpperCamelCase      )



 def 						UpperCAmelCase    (	self   :   List[str] ,		UpperCAmelCase__   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Any         = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
    self.fill_match(
        """train_micro_batch_size_per_gpu""" ,		args.per_device_train_batch_size ,		"""per_device_train_batch_size"""	)
    self.fill_match("""gradient_accumulation_steps""" ,		args.gradient_accumulation_steps ,		"""gradient_accumulation_steps"""	)
    self.fill_match("""train_batch_size""" ,		UpperCAmelCase__ ,		"""train_batch_size (calculated)"""	)
    self.fill_match("""gradient_clipping""" ,		args.max_grad_norm ,		"""max_grad_norm"""	)

    self.fill_match("""optimizer.params.lr""" ,		args.learning_rate ,		"""learning_rate"""	)
    self.fill_match("""optimizer.params.betas""" ,		[args.adam_betaa, args.adam_betaa] ,		"""adam_beta1+adam_beta2"""	)
    self.fill_match("""optimizer.params.eps""" ,		args.adam_epsilon ,		"""adam_epsilon"""	)
    self.fill_match("""optimizer.params.weight_decay""" ,		args.weight_decay ,		"""weight_decay"""	)

    self.fill_only("""scheduler.params.warmup_min_lr""" ,		0	)  # not a trainer arg
    self.fill_match("""scheduler.params.warmup_max_lr""" ,		args.learning_rate ,		"""learning_rate"""	)
    # total_num_steps - will get set in trainer_config_finalize

    # fp16
    if args.fpaa or args.fpaa_full_eval:
       lowercase__						:	List[Any]         = """apex""" if args.fpaa_backend == """apex""" else """amp"""
    else:
       lowercase__						:	List[Any]         = None

    if args.save_on_each_node:
       # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
       lowercase__						:	Tuple         = self.config.get("""checkpoint""" ,		{}	)
       lowercase__						:	List[Any]         = args.save_on_each_node

    # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
    # any here unless the user did the work
    self.fill_match(
        """fp16.enabled""" ,		((args.fpaa or args.fpaa_full_eval) and fpaa_backend == """amp""") ,		"""fp16|fp16_full_eval+fp16_backend(amp)""" ,		)

    # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
    # ZeRO features
    self.fill_match("""amp.enabled""" ,		fpaa_backend == """apex""" ,		"""fp16+fp16_backend(apex)"""	)
    self.fill_match("""amp.opt_level""" ,		args.fpaa_opt_level ,		"""fp16_opt_level"""	)

    self.fill_match("""bf16.enabled""" ,		(args.bfaa or args.bfaa_full_eval) ,		"""bf16|bf16_full_eval"""	)

    # deepspeed's default mode is fp16 unless there is a config that says differently
    if self.is_true("""bf16.enabled"""	):
       lowercase__						:	Any         = torch.bfloataa
    elif self.is_false("""fp16.enabled"""	):
       lowercase__						:	Optional[Any]         = torch.floataa
    else:
       lowercase__						:	List[str]         = torch.floataa







 def 						UpperCAmelCase    (	self   :   Optional[int] ,		UpperCAmelCase__   :   List[Any] ,		UpperCAmelCase__   :   Dict ,		UpperCAmelCase__   :   Tuple	):

    """simple docstring"""






    lowercase__						:	Dict         = [
        """zero_optimization.reduce_bucket_size""",
        """zero_optimization.stage3_prefetch_bucket_size""",
        """zero_optimization.stage3_param_persistence_threshold""",
    ]
    lowercase__						:	List[str]         = [x for x in hidden_size_based_keys if self.is_auto(UpperCAmelCase__	)]

    if len(UpperCAmelCase__	) > 0:
       if hasattr(model.config ,		"""hidden_size"""	):
          lowercase__						:	Optional[Any]         = model.config.hidden_size
       elif hasattr(model.config ,		"""hidden_sizes"""	):
          # if there are many hidden sizes pick the largest one
          lowercase__						:	Union[str, Any]         = 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""" ,		UpperCAmelCase__ ,		"""num_training_steps (calculated)"""	)
    self.fill_match("""scheduler.params.warmup_num_steps""" ,		args.get_warmup_steps(UpperCAmelCase__	) ,		"""warmup_steps"""	)

    if len(self.mismatches	) > 0:
       lowercase__						:	Union[str, 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       a__						(  A					):
   # 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)
   lowercase__						:	Dict         = weakref.ref(A					)
def       a__						(  ):
   # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
   global _hf_deepspeed_config_weak_ref
   lowercase__						:	Optional[Any]         = None
def       a__						(  ):
   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__						(  ):
   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__						(  A		,				A		,				A		,				A		,				A					):
   from accelerate.utils import DummyOptim, DummyScheduler

   lowercase__						:	int         = hf_deepspeed_config.config

   # Optimizer + Scheduler
   # Currently supported combos:
   # 1. DS scheduler + DS optimizer: Yes
   # 2. HF scheduler + HF optimizer: Yes
   # 3. DS scheduler + HF optimizer: Yes
   # 4. HF scheduler + DS optimizer: No
   #
   # Unless Offload is enabled in which case it's:
   # 1. DS scheduler + DS optimizer: Yes
   # 2. HF scheduler + HF optimizer: Mostly*
   # 3. DS scheduler + HF optimizer: Mostly*
   # 4. HF scheduler + DS optimizer: No
   #
   # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

   lowercase__						:	Union[str, 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."""					)
      lowercase__						:	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.
      lowercase__						:	Union[str, Any]         = trainer.create_optimizer()
      # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
      lowercase__						:	List[str]         = True

   lowercase__						:	Union[str, Any]         = None
   if "scheduler" in config:
      lowercase__						:	str         = 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."""					)
      lowercase__						:	List[str]         = trainer.create_scheduler(num_training_steps=A		,				optimizer=A					)

   return optimizer, lr_scheduler
def       a__						(  A		,				A		,				A=False					):
   from deepspeed.utils import logger as ds_logger

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

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

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

   return optimizer, lr_scheduler






def       a__						(  A		,				A					):
   # 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

   lowercase__						:	Dict         = 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
      lowercase__     ,					lowercase__						:	Dict         = 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}'''					)










import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a    =       logging.get_logger(__name__)
def       a__						(  A		,				A		,				A					):
   lowercase__						:	int         = WavLMForSequenceClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	Dict         = downstream_dict["""projector.weight"""]
   lowercase__						:	int         = downstream_dict["""projector.bias"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.post_net.linear.weight"""]
   lowercase__						:	List[str]         = downstream_dict["""model.post_net.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Tuple         = WavLMForAudioFrameClassification.from_pretrained(A		,				config=A					)
   lowercase__						:	List[Any]         = downstream_dict["""model.linear.weight"""]
   lowercase__						:	Optional[int]         = downstream_dict["""model.linear.bias"""]
   return model
def       a__						(  A		,				A		,				A					):
   lowercase__						:	Optional[int]         = WavLMForXVector.from_pretrained(A		,				config=A					)
   lowercase__						:	List[str]         = downstream_dict["""connector.weight"""]
   lowercase__						:	int         = downstream_dict["""connector.bias"""]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
      lowercase__						:	Optional[Any]         = downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      lowercase__						:	List[str]         = downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   lowercase__						:	Union[str, Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
   lowercase__						:	Optional[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
   lowercase__						:	Any         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
   lowercase__						:	List[Any]         = downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
   lowercase__						:	List[Any]         = downstream_dict["""objective.W"""]
   return model
@torch.no_grad()
def       a__						(  A		,				A		,				A		,				A					):
   lowercase__						:	List[Any]         = torch.load(A		,				map_location="""cpu"""					)

   lowercase__						:	Tuple         = checkpoint["""Downstream"""]

   lowercase__						:	List[str]         = WavLMConfig.from_pretrained(A					)
   lowercase__						:	Dict         = WavaVecaFeatureExtractor.from_pretrained(
       A		,				return_attention_mask=A		,				do_normalize=A					)

   lowercase__						:	Union[str, Any]         = hf_config.architectures[0]
   if arch.endswith("""ForSequenceClassification"""					):
      lowercase__						:	Union[str, Any]         = convert_classification(A		,				A		,				A					)
   elif arch.endswith("""ForAudioFrameClassification"""					):
      lowercase__						:	Dict         = convert_diarization(A		,				A		,				A					)
   elif arch.endswith("""ForXVector"""					):
      lowercase__						:	Dict         = convert_xvector(A		,				A		,				A					)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''					)

   if hf_config.use_weighted_layer_sum:
      lowercase__						:	Union[str, Any]         = checkpoint["""Featurizer"""]["""weights"""]

   hf_feature_extractor.save_pretrained(A					)
   hf_model.save_pretrained(A					)


if __name__ == "__main__":
 a    =       argparse.ArgumentParser()
 parser.add_argument(
     '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
 )
 parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
 parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
 a    =       parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


