

import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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							=16
_lowerCamelCase							=32







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     return int(x / 2**20     )



class 						a_			:



      """simple docstring"""







      def __enter__( self	:				List[str]							):
           gc.collect()
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
           SCREAMING_SNAKE_CASE 		=torch.cuda.memory_allocated()
           SCREAMING_SNAKE_CASE 		=psutil.Process()

           SCREAMING_SNAKE_CASE 		=self.cpu_mem_used()
           SCREAMING_SNAKE_CASE 		=True
           SCREAMING_SNAKE_CASE 		=threading.Thread(target=self.peak_monitor_func							)
           SCREAMING_SNAKE_CASE 		=True
           peak_monitor_thread.start()
           return self
      def 			_lowerCAmelCase	( self	:				Optional[int]							):
           return self.process.memory_info().rss
      def 			_lowerCAmelCase	( self	:				Union[str, Any]							):
           SCREAMING_SNAKE_CASE 		=-1

           while True:
                SCREAMING_SNAKE_CASE 		=max(self.cpu_mem_used()     ,self.cpu_peak							)

                # can't sleep or will not catch the peak right (this comment is here on purpose)
                # time.sleep(0.001) # 1msec

                if not self.peak_monitoring:
                     break


      def __exit__( self	:				Tuple     ,*snake_case	:				int							):
           SCREAMING_SNAKE_CASE 		=False

           gc.collect()
           torch.cuda.empty_cache()
           SCREAMING_SNAKE_CASE 		=torch.cuda.memory_allocated()
           SCREAMING_SNAKE_CASE 		=torch.cuda.max_memory_allocated()
           SCREAMING_SNAKE_CASE 		=bamb(self.end - self.begin							)
           SCREAMING_SNAKE_CASE 		=bamb(self.peak - self.begin							)

           SCREAMING_SNAKE_CASE 		=self.cpu_mem_used()
           SCREAMING_SNAKE_CASE 		=bamb(self.cpu_end - self.cpu_begin							)
           SCREAMING_SNAKE_CASE 		=bamb(self.cpu_peak - self.cpu_begin							)
           # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


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

 _lowerCamelCase							=mocked_dataloaders  # noqa: F811







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     if os.environ.get('TESTING_MOCKED_DATALOADERS',       lowerCAmelCase_     ) == "1":
          SCREAMING_SNAKE_CASE 		=2

     # New Code #
     # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
     SCREAMING_SNAKE_CASE 		=FullyShardedDataParallelPlugin(
         state_dict_config=FullStateDictConfig(offload_to_cpu=lowerCAmelCase_,       ranka_only=lowerCAmelCase_     ),       optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=lowerCAmelCase_,       ranka_only=lowerCAmelCase_     ),       )

     # Initialize accelerator
     if args.with_tracking:
          SCREAMING_SNAKE_CASE 		=Accelerator(
              cpu=args.cpu,       mixed_precision=args.mixed_precision,       log_with='wandb',       project_dir=args.logging_dir,       fsdp_plugin=lowerCAmelCase_,       )
     else:
          SCREAMING_SNAKE_CASE 		=Accelerator(fsdp_plugin=lowerCAmelCase_     )
     accelerator.print(accelerator.distributed_type     )

     if hasattr(args.checkpointing_steps,       'isdigit'     ):
          if args.checkpointing_steps == "epoch":
               SCREAMING_SNAKE_CASE 		=args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               SCREAMING_SNAKE_CASE 		=int(args.checkpointing_steps     )
          else:
               raise ValueError(
                   F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'     )
     else:
          SCREAMING_SNAKE_CASE 		=None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     SCREAMING_SNAKE_CASE 		=config['lr']
     SCREAMING_SNAKE_CASE 		=int(config['num_epochs']     )
     SCREAMING_SNAKE_CASE 		=int(config['seed']     )
     SCREAMING_SNAKE_CASE 		=int(config['batch_size']     )

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          SCREAMING_SNAKE_CASE 		=vars(lowerCAmelCase_     )
          accelerator.init_trackers('fsdp_glue_no_trainer',       lowerCAmelCase_     )

     SCREAMING_SNAKE_CASE 		=AutoTokenizer.from_pretrained(args.model_name_or_path     )
     SCREAMING_SNAKE_CASE 		=load_dataset('glue',       'mrpc'     )
     SCREAMING_SNAKE_CASE 		=evaluate.load('glue',       'mrpc'     )

     def tokenize_function(lowerCAmelCase_     ):
          # max_length=None => use the model max length (it's actually the default)
          SCREAMING_SNAKE_CASE 		=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():
          SCREAMING_SNAKE_CASE 		=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
     SCREAMING_SNAKE_CASE 		=tokenized_datasets.rename_column('label',       'labels'     )

     # If the batch size is too big we use gradient accumulation
     SCREAMING_SNAKE_CASE 		=1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          SCREAMING_SNAKE_CASE 		=batch_size // MAX_GPU_BATCH_SIZE
          SCREAMING_SNAKE_CASE 		=MAX_GPU_BATCH_SIZE

     def collate_fn(lowerCAmelCase_     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          SCREAMING_SNAKE_CASE 		=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":
               SCREAMING_SNAKE_CASE 		=16
          elif accelerator.mixed_precision != "no":
               SCREAMING_SNAKE_CASE 		=8
          else:
               SCREAMING_SNAKE_CASE 		=None

          return tokenizer.pad(
              lowerCAmelCase_,       padding='longest',       max_length=lowerCAmelCase_,       pad_to_multiple_of=lowerCAmelCase_,       return_tensors='pt',       )

     # Instantiate dataloaders.
     SCREAMING_SNAKE_CASE 		=DataLoader(
         tokenized_datasets['train'],       shuffle=lowerCAmelCase_,       collate_fn=lowerCAmelCase_,       batch_size=lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=DataLoader(
         tokenized_datasets['validation'],       shuffle=lowerCAmelCase_,       collate_fn=lowerCAmelCase_,       batch_size=lowerCAmelCase_     )

     set_seed(lowerCAmelCase_     )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     SCREAMING_SNAKE_CASE 		=AutoModelForSequenceClassification.from_pretrained(
         args.model_name_or_path,       return_dict=lowerCAmelCase_,       low_cpu_mem_usage=lowerCAmelCase_     )

     # New Code #
     # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
     SCREAMING_SNAKE_CASE 		=accelerator.prepare(lowerCAmelCase_     )
     accelerator.print(lowerCAmelCase_     )

     # Instantiate optimizer
     # New Code #
     # For FSDP feature, at present it doesn't support multiple parameter groups,
     # so we need to create a single parameter group for the whole model
     SCREAMING_SNAKE_CASE 		=torch.optim.AdamW(params=model.parameters(),       lr=lowerCAmelCase_,       weight_decay=2e-4     )

     # Instantiate scheduler
     SCREAMING_SNAKE_CASE 		=get_linear_schedule_with_warmup(
         optimizer=lowerCAmelCase_,       num_warmup_steps=10,       num_training_steps=(len(lowerCAmelCase_     ) * num_epochs) // gradient_accumulation_steps,       )

     # New Code #
     # For FSDP feature, prepare everything except the model as we have already prepared the model
     # before creating the optimizer
     # 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.
     SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=accelerator.prepare(
         lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     )

     SCREAMING_SNAKE_CASE 		=0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'     )
               accelerator.load_state(args.resume_from_checkpoint     )
               SCREAMING_SNAKE_CASE 		=os.path.basename(args.resume_from_checkpoint     )
          else:
               # Get the most recent checkpoint
               SCREAMING_SNAKE_CASE 		=[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
               dirs.sort(key=os.path.getctime     )
               SCREAMING_SNAKE_CASE 		=dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          SCREAMING_SNAKE_CASE 		=os.path.splitext(lowerCAmelCase_     )[0]

          if "epoch" in training_difference:
               num_epochs -= int(training_difference.replace('epoch_',       ''     )     )
               SCREAMING_SNAKE_CASE 		=None
          else:
               SCREAMING_SNAKE_CASE 		=int(training_difference.replace('step_',       ''     )     )
               num_epochs -= resume_step // len(lowerCAmelCase_     )
               # If resuming by step, we also need to know exactly how far into the DataLoader we went
               SCREAMING_SNAKE_CASE 		=(num_epochs * len(lowerCAmelCase_     )) - resume_step

    # Now we train the model
     for epoch in range(lowerCAmelCase_     ):
          # New Code #
          # context manager to track the peak memory usage during the training epoch
          with TorchTracemalloc() as tracemalloc:
               model.train()
               if args.with_tracking:
                    SCREAMING_SNAKE_CASE 		=0
               for step, batch in enumerate(lowerCAmelCase_     ):
                    # We need to skip steps until we reach the resumed step
                    if args.resume_from_checkpoint and epoch == 0:
                         if resume_step is not None and step < resume_step:
                              pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device     )
                    SCREAMING_SNAKE_CASE 		=model(**lowerCAmelCase_     )
                    SCREAMING_SNAKE_CASE 		=outputs.loss
                    SCREAMING_SNAKE_CASE 		=loss / gradient_accumulation_steps
                    # We keep track of the loss at each epoch
                    if args.with_tracking:
                         total_loss += loss.detach().float()
                    accelerator.backward(lowerCAmelCase_     )
                    if step % gradient_accumulation_steps == 0:
                         optimizer.step()
                         lr_scheduler.step()
                         optimizer.zero_grad()
                         # accelerator.print(lr_scheduler.get_lr())

                    overall_step += 1

                    if isinstance(lowerCAmelCase_,       lowerCAmelCase_     ):
                         SCREAMING_SNAKE_CASE 		=F'step_{overall_step}'
                         if overall_step % checkpointing_steps == 0:
                              if args.output_dir is not None:
                                   SCREAMING_SNAKE_CASE 		=os.path.join(args.output_dir,       lowerCAmelCase_     )
                              accelerator.save_state(lowerCAmelCase_     )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin     )     )     )
          accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used     )     )
          accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked     )     )
          accelerator.print(
              'Total Peak Memory consumed during the train (max): {}'.format(
                  tracemalloc.peaked + bamb(tracemalloc.begin     )     )     )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       'train_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin     ),
                   },       step=lowerCAmelCase_,       )

          # New Code #
          # context manager to track the peak memory usage during the evaluation
          with TorchTracemalloc() as tracemalloc:
               model.eval()
               for step, batch in enumerate(lowerCAmelCase_     ):
                    # We could avoid this line since we set the accelerator with `device_placement=True`.
                    batch.to(accelerator.device     )
                    with torch.no_grad():
                         SCREAMING_SNAKE_CASE 		=model(**lowerCAmelCase_     )
                    SCREAMING_SNAKE_CASE 		=outputs.logits.argmax(dim=-1     )
                    SCREAMING_SNAKE_CASE		,    SCREAMING_SNAKE_CASE 		=accelerator.gather_for_metrics((predictions, batch['labels'])     )
                    metric.add_batch(
                        predictions=lowerCAmelCase_,       references=lowerCAmelCase_,       )

               SCREAMING_SNAKE_CASE 		=metric.compute()
               # Use accelerator.print to print only on the main process.
               accelerator.print(F'epoch {epoch}:',       lowerCAmelCase_     )
               if args.with_tracking:
                    accelerator.log(
                        {
                            'accuracy': eval_metric['accuracy'],
                            'f1': eval_metric['f1'],
                            'train_loss': total_loss.item() / len(lowerCAmelCase_     ),
                        },       step=lowerCAmelCase_,       )

               if checkpointing_steps == "epoch":
                    SCREAMING_SNAKE_CASE 		=F'epoch_{epoch}'
                    if args.output_dir is not None:
                         SCREAMING_SNAKE_CASE 		=os.path.join(args.output_dir,       lowerCAmelCase_     )
                    accelerator.save_state(lowerCAmelCase_     )
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
          accelerator.print('Memory before entering the eval : {}'.format(bamb(tracemalloc.begin     )     )     )
          accelerator.print('Memory consumed at the end of the eval (end-begin): {}'.format(tracemalloc.used     )     )
          accelerator.print('Peak Memory consumed during the eval (max-begin): {}'.format(tracemalloc.peaked     )     )
          accelerator.print(
              'Total Peak Memory consumed during the eval (max): {}'.format(tracemalloc.peaked + bamb(tracemalloc.begin     )     )     )
          # Logging the peak memory usage of the GPU to the tracker
          if args.with_tracking:
               accelerator.log(
                   {
                       'eval_total_peak_memory': tracemalloc.peaked + bamb(tracemalloc.begin     ),
                   },       step=lowerCAmelCase_,       )

     if args.with_tracking:
          accelerator.end_training()







def      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=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(
         '--checkpointing_steps',       type=lowerCAmelCase_,       default=lowerCAmelCase_,       help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.',       )
     parser.add_argument(
         '--resume_from_checkpoint',       type=lowerCAmelCase_,       default=lowerCAmelCase_,       help='If the training should continue from a checkpoint folder.',       )
     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(
         '--output_dir',       type=lowerCAmelCase_,       default='.',       help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.',       )
     parser.add_argument(
         '--logging_dir',       type=lowerCAmelCase_,       default='logs',       help='Location on where to store experiment tracking logs`',       )
     parser.add_argument(
         '--model_name_or_path',       type=lowerCAmelCase_,       help='Path to pretrained model or model identifier from huggingface.co/models.',       required=lowerCAmelCase_,       )
     SCREAMING_SNAKE_CASE 		=parser.parse_args()
     SCREAMING_SNAKE_CASE 		={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
     training_function(lowerCAmelCase_,       lowerCAmelCase_     )


if __name__ == "__main__":
 main()




import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase							={
    "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      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     for attribute in key.split('.'     ):
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     )

     if weight_type is not None:
          SCREAMING_SNAKE_CASE 		=getattr(lowerCAmelCase_,       lowerCAmelCase_     ).shape
     else:
          SCREAMING_SNAKE_CASE 		=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":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "weight_g":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "weight_v":
          SCREAMING_SNAKE_CASE 		=value
     elif weight_type == "bias":
          SCREAMING_SNAKE_CASE 		=value
     else:
          SCREAMING_SNAKE_CASE 		=value

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=[]
     SCREAMING_SNAKE_CASE 		=fairseq_model.state_dict()

     SCREAMING_SNAKE_CASE 		=hf_model.feature_extractor

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

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

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=full_name.split('conv_layers.'     )[-1]
     SCREAMING_SNAKE_CASE 		=name.split('.'     )
     SCREAMING_SNAKE_CASE 		=int(items[0]     )
     SCREAMING_SNAKE_CASE 		=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.'
               )
               SCREAMING_SNAKE_CASE 		=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.'
               )
               SCREAMING_SNAKE_CASE 		=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."
               )
               SCREAMING_SNAKE_CASE 		=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.'
               )
               SCREAMING_SNAKE_CASE 		=value
               logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
     else:
          unused_weights.append(lowerCAmelCase_     )







def      snake_case__							(  lowerCAmelCase_     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=HubertConfig()
     SCREAMING_SNAKE_CASE 		=model.config

     SCREAMING_SNAKE_CASE 		=fs_config.activation_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=fs_config.attention_dropout
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=eval(fs_config.extractor_conv_feature_layers     )
     SCREAMING_SNAKE_CASE 		=[x[0] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[1] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		=[x[2] for x in conv_layers]
     SCREAMING_SNAKE_CASE 		='gelu'
     SCREAMING_SNAKE_CASE 		='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
     SCREAMING_SNAKE_CASE 		=False
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.activation_fn
     SCREAMING_SNAKE_CASE 		=fs_config.dropout
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_embed_dim
     SCREAMING_SNAKE_CASE 		=0.02
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_ffn_embed_dim
     SCREAMING_SNAKE_CASE 		=1e-5
     SCREAMING_SNAKE_CASE 		=0.0
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_attention_heads
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos_groups
     SCREAMING_SNAKE_CASE 		=fs_config.conv_pos
     SCREAMING_SNAKE_CASE 		=len(lowerCAmelCase_     )
     SCREAMING_SNAKE_CASE 		=fs_config.encoder_layers

     return config







@torch.no_grad()
def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=distilhubert().model.model

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

     SCREAMING_SNAKE_CASE 		=WavaVecaFeatureExtractor(
         feature_size=1,       sampling_rate=16000,       padding_value=0,       do_normalize=lowerCAmelCase_,       return_attention_mask=lowerCAmelCase_,       )
     SCREAMING_SNAKE_CASE 		=HubertModel(lowerCAmelCase_     )

     recursively_load_weights(lowerCAmelCase_,       lowerCAmelCase_     )

     feature_extractor.save_pretrained(lowerCAmelCase_     )
     hf_model.save_pretrained(lowerCAmelCase_     )


if __name__ == "__main__":
 _lowerCamelCase							=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")
 _lowerCamelCase							=parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

