



"""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

SCREAMING_SNAKE_CASE				:     Tuple          =	logging.get_logger(__name__)






def 	__UpperCAmelCase					(       )							->	Tuple:






   """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	(   __lowercase    ):







  def __init__(self       ,	lowerCamelCase				):




     '''simple docstring'''




     set_hf_deepspeed_config(self				)
     dep_version_check("""accelerate"""				)
     dep_version_check("""deepspeed"""				)
     super().__init__(lowerCamelCase				)





class       __lowerCamelCase	(   __lowercase    ):







  def __init__(self       ,	lowerCamelCase				):




     '''simple docstring'''




     super().__init__(lowerCamelCase				)
     _lowerCAmelCase					     =		None
     _lowerCAmelCase					     =		[]







  def 							A__						(self				):




     '''simple docstring'''




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







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     _lowerCAmelCase					     =		self.get_value(lowerCamelCase				)
     if val is None:
        return False
     else:
        return val == "auto"







  def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase=None       ,	lowerCamelCase=True				):




     '''simple docstring'''




     _lowerCAmelCase     ,						_lowerCAmelCase					     =		self.find_config_node(lowerCamelCase				)
     if config is None:
        return

     if config.get(lowerCamelCase				) == "auto":
        _lowerCAmelCase					     =		hf_val
        return

     if not must_match:
        return

     _lowerCAmelCase					     =		config.get(lowerCamelCase				)
     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}"""				)

  __UpperCamelCase									=   partialmethod(__lowercase   ,							must_match=__lowercase    )







  def 							A__						(self       ,	lowerCamelCase				):




     '''simple docstring'''




     _lowerCAmelCase					     =		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"""       ,	lowerCamelCase       ,	"""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:
        _lowerCAmelCase					     =		"""apex""" if args.fpaa_backend == """apex""" else """amp"""
     else:
        _lowerCAmelCase					     =		None

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







  def 							A__						(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase				):




     '''simple docstring'''




     _lowerCAmelCase					     =		[
         """zero_optimization.reduce_bucket_size""",
         """zero_optimization.stage3_prefetch_bucket_size""",
         """zero_optimization.stage3_param_persistence_threshold""",
     ]
     _lowerCAmelCase					     =		[x for x in hidden_size_based_keys if self.is_auto(lowerCamelCase				)]

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

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





SCREAMING_SNAKE_CASE				:     List[str]          =	None






def 	__UpperCAmelCase					(       snake_case_  :  Any   )							->	Any:






   """simple docstring"""


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






def 	__UpperCAmelCase					(       )							->	List[Any]:






   """simple docstring"""


   global _hf_deepspeed_config_weak_ref
   _lowerCAmelCase					     =		None






def 	__UpperCAmelCase					(       )							->	List[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 	__UpperCAmelCase					(       )							->	Optional[int]:






   """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 	__UpperCAmelCase					(       snake_case_  :  Tuple   ,	snake_case_  :  Any   ,	snake_case_  :  int   ,	snake_case_  :  Any   ,	snake_case_  :  Tuple   )							->	Optional[int]:






   """simple docstring"""


   from accelerate.utils import DummyOptim, DummyScheduler

   _lowerCAmelCase					     =		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)

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

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

   return optimizer, lr_scheduler






def 	__UpperCAmelCase					(       snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str=False   )							->	Optional[Any]:






   """simple docstring"""


   from deepspeed.utils import logger as ds_logger

   _lowerCAmelCase					     =		trainer.model
   _lowerCAmelCase					     =		trainer.args

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

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

   return optimizer, lr_scheduler






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  str   )							->	List[Any]:






   """simple docstring"""


   import glob

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

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




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}






def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		hf_pointer.shape

   assert hf_shape == value.shape, (
       F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
       F""" {value.shape} for {full_name}"""
   )

   if weight_type == "weight":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_g":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_v":
      _lowerCAmelCase					     =		value
   elif weight_type == "bias":
      _lowerCAmelCase					     =		value
   else:
      _lowerCAmelCase					     =		value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		int(items[1]   )

   if type_id == 0:
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""   )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
             F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
             " found."
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
      elif "weight" in name:
         assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
             F"""{full_name} has size {value.shape}, but"""
             F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)