





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_lowerCAmelCase							:					Tuple		    =		16
_lowerCAmelCase							:					Optional[int]		    =		32

def    __snake_case  (    _lowerCAmelCase       :       Accelerator , _lowerCAmelCase       :       int = 16		)       ->  List[str]:
   A_    : Tuple								=	AutoTokenizer.from_pretrained("bert-base-cased"		)
   A_    : Tuple								=	load_dataset("glue" , "mrpc"		)

   def tokenize_function(_lowerCAmelCase       :       List[Any]		):
      # max_length=None => use the model max length (it's actually the default)
      A_    : int								=	tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCAmelCase , max_length=_lowerCAmelCase		)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      A_    : Dict								=	datasets.map(
          _lowerCAmelCase , batched=_lowerCAmelCase , remove_columns=["idx", "sentence1", "sentence2"] , )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   A_    : Union[str, Any]								=	tokenized_datasets.rename_column("label" , "labels"		)

   def collate_fn(_lowerCAmelCase       :       Dict		):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      A_    : str								=	128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         A_    : Union[str, Any]								=	16
      elif accelerator.mixed_precision != "no":
         A_    : Optional[Any]								=	8
      else:
         A_    : List[Any]								=	None

      return tokenizer.pad(
          _lowerCAmelCase , padding="longest" , max_length=_lowerCAmelCase , pad_to_multiple_of=_lowerCAmelCase , return_tensors="pt" , )

   # Instantiate dataloaders.
   A_    : List[str]								=	DataLoader(
       tokenized_datasets["train"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase		)
   A_    : Dict								=	DataLoader(
       tokenized_datasets["validation"] , shuffle=_lowerCAmelCase , collate_fn=_lowerCAmelCase , batch_size=_lowerCAmelCase		)

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
 from accelerate.test_utils.training import mocked_dataloaders

 _lowerCAmelCase							:					Tuple		    =		mocked_dataloaders  # noqa: F811

def    __snake_case  (    _lowerCAmelCase       :       Any , _lowerCAmelCase       :       Union[str, Any]		)       ->  str:
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS" , _lowerCAmelCase		) == "1":
      A_    : List[Any]								=	2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      A_    : Tuple								=	Accelerator(
          cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir		)
   else:
      A_    : Optional[int]								=	Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision		)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   A_    : Union[str, Any]								=	config['lr']
   A_    : Optional[Any]								=	int(config["num_epochs"]		)
   A_    : Optional[int]								=	int(config["seed"]		)
   A_    : List[Any]								=	int(config["batch_size"]		)
   set_seed(_lowerCAmelCase		)

   A_    : List[str]								=	get_dataloaders(_lowerCAmelCase , _lowerCAmelCase		)
   A_    : Union[str, Any]								=	evaluate.load("glue" , "mrpc"		)

   # If the batch size is too big we use gradient accumulation
   A_    : Optional[int]								=	1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      A_    : Union[str, Any]								=	batch_size // MAX_GPU_BATCH_SIZE
      A_    : Dict								=	MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   A_    : Any								=	AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=_lowerCAmelCase		)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   A_    : int								=	model.to(accelerator.device		)

   # Instantiate optimizer
   A_    : List[str]								=	AdamW(params=model.parameters() , lr=_lowerCAmelCase		)

   # Instantiate scheduler
   A_    : int								=	get_linear_schedule_with_warmup(
       optimizer=_lowerCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCAmelCase		) * num_epochs) // gradient_accumulation_steps , )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   A_    : Dict								=	accelerator.prepare(
       _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase		)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      A_    : Optional[Any]								=	os.path.split(_lowerCAmelCase		)[-1].split("."		)[0]
      accelerator.init_trackers(_lowerCAmelCase , _lowerCAmelCase		)

   # Now we train the model
   for epoch in range(_lowerCAmelCase		):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         A_    : Tuple								=	0
      for step, batch in enumerate(_lowerCAmelCase		):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device		)
         A_    : str								=	model(**_lowerCAmelCase		)
         A_    : Optional[Any]								=	outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         A_    : Tuple								=	loss / gradient_accumulation_steps
         accelerator.backward(_lowerCAmelCase		)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_lowerCAmelCase		):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device		)
         with torch.no_grad():
            A_    : Any								=	model(**_lowerCAmelCase		)
         A_    : int								=	outputs.logits.argmax(dim=-1		)
         A_    : Any								=	accelerator.gather_for_metrics((predictions, batch["labels"])		)
         metric.add_batch(
             predictions=_lowerCAmelCase , references=_lowerCAmelCase , )

      A_    : List[str]								=	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"epoch {epoch}:" , _lowerCAmelCase		)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 "accuracy": eval_metric["accuracy"],
                 "f1": eval_metric["f1"],
                 "train_loss": total_loss.item() / len(_lowerCAmelCase		),
                 "epoch": epoch,
             } , step=_lowerCAmelCase , )

          # New Code #
          # When a run is finished, you should call `accelerator.end_training()`
          # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()

def    __snake_case  (    )       ->  int:
   A_    : str								=	argparse.ArgumentParser(description="Simple example of training script."		)
   parser.add_argument(
       "--mixed_precision" , type=_lowerCAmelCase , default=_lowerCAmelCase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
       "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
       "and an Nvidia Ampere GPU." , )
   parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU."		)
   parser.add_argument(
       "--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
   parser.add_argument(
       "--project_dir" , type=_lowerCAmelCase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
   A_    : Any								=	parser.parse_args()
   A_    : Tuple								=	{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
   training_function(_lowerCAmelCase , _lowerCAmelCase		)


if __name__ == "__main__":
 main()

import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
a_    					=       logging.get_logger(__name__)





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

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

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


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)



