

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							:  str     =				1_6
_lowerCamelCase							:  Union[str, Any]     =				3_2

def      a__	(						UpperCAmelCase				:     Any     )    ->		List[str]:
  return int(x / 2**20     )





class    __UpperCAmelCase	:



   def __enter__(				self				:	Tuple		):
     gc.collect()
     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
     UpperCAmelCase			:				Dict										=					torch.cuda.memory_allocated()
     UpperCAmelCase			:				Dict										=					psutil.Process()

     UpperCAmelCase			:				List[str]										=					self.cpu_mem_used()
     UpperCAmelCase			:				Union[str, Any]										=					True
     UpperCAmelCase			:				Tuple										=					threading.Thread(target=self.peak_monitor_func		)
     UpperCAmelCase			:				Optional[Any]										=					True
     peak_monitor_thread.start()
     return self



   def 			__magic_name__       (				self				:	List[Any]		):
     return self.process.memory_info().rss



   def 			__magic_name__       (				self				:	Any		):
     UpperCAmelCase			:				Optional[int]										=					-1

     while True:
       UpperCAmelCase			:				int										=					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				:	Optional[int],						*__A				:	int		):
     UpperCAmelCase			:				str										=					False

     gc.collect()
     torch.cuda.empty_cache()
     UpperCAmelCase			:				List[str]										=					torch.cuda.memory_allocated()
     UpperCAmelCase			:				str										=					torch.cuda.max_memory_allocated()
     UpperCAmelCase			:				Any										=					bamb(self.end - self.begin		)
     UpperCAmelCase			:				List[Any]										=					bamb(self.peak - self.begin		)

     UpperCAmelCase			:				Any										=					self.cpu_mem_used()
     UpperCAmelCase			:				Optional[int]										=					bamb(self.cpu_end - self.cpu_begin		)
     UpperCAmelCase			:				Optional[Any]										=					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							:  Tuple     =				mocked_dataloaders  # noqa: F811

def      a__	(						UpperCAmelCase				:     str							,					UpperCAmelCase				:     Optional[int]     )    ->		Any:
  # For testing only
  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''							,					UpperCAmelCase     ) == "1":
    UpperCAmelCase			:				List[str]										=					2

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

  # Initialize accelerator
  if args.with_tracking:
    UpperCAmelCase			:				List[str]										=					Accelerator(
        cpu=args.cpu							,					mixed_precision=args.mixed_precision							,					log_with='''wandb'''							,					project_dir=args.logging_dir							,					fsdp_plugin=UpperCAmelCase							,					)
  else:
    UpperCAmelCase			:				int										=					Accelerator(fsdp_plugin=UpperCAmelCase     )
  accelerator.print(accelerator.distributed_type     )

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

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

  UpperCAmelCase			:				Tuple										=					AutoTokenizer.from_pretrained(args.model_name_or_path     )
  UpperCAmelCase			:				Optional[int]										=					load_dataset('''glue'''							,					'''mrpc'''     )
  UpperCAmelCase			:				Any										=					evaluate.load('''glue'''							,					'''mrpc'''     )

  def tokenize_function(UpperCAmelCase				:     Any     ):
    # max_length=None => use the model max length (it's actually the default)
    UpperCAmelCase			:				List[str]										=					tokenizer(examples['''sentence1''']							,					examples['''sentence2''']							,					truncation=UpperCAmelCase							,					max_length=UpperCAmelCase     )
    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():
    UpperCAmelCase			:				Optional[Any]										=					datasets.map(
        UpperCAmelCase							,					batched=UpperCAmelCase							,					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
  UpperCAmelCase			:				List[Any]										=					tokenized_datasets.rename_column('''label'''							,					'''labels'''     )

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

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

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

  # Instantiate dataloaders.
  UpperCAmelCase			:				Optional[Any]										=					DataLoader(
      tokenized_datasets['''train''']							,					shuffle=UpperCAmelCase							,					collate_fn=UpperCAmelCase							,					batch_size=UpperCAmelCase     )
  UpperCAmelCase			:				Tuple										=					DataLoader(
      tokenized_datasets['''validation''']							,					shuffle=UpperCAmelCase							,					collate_fn=UpperCAmelCase							,					batch_size=UpperCAmelCase     )

  set_seed(UpperCAmelCase     )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  UpperCAmelCase			:				List[str]										=					AutoModelForSequenceClassification.from_pretrained(
      args.model_name_or_path							,					return_dict=UpperCAmelCase							,					low_cpu_mem_usage=UpperCAmelCase     )

  # New Code #
  # For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
  UpperCAmelCase			:				List[Any]										=					accelerator.prepare(UpperCAmelCase     )
  accelerator.print(UpperCAmelCase     )

  # 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
  UpperCAmelCase			:				int										=					torch.optim.AdamW(params=model.parameters()							,					lr=UpperCAmelCase							,					weight_decay=2E-4     )

  # Instantiate scheduler
  UpperCAmelCase			:				int										=					get_linear_schedule_with_warmup(
      optimizer=UpperCAmelCase							,					num_warmup_steps=10							,					num_training_steps=(len(UpperCAmelCase     ) * 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.
  UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					accelerator.prepare(
      UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase							,					UpperCAmelCase     )

  UpperCAmelCase			:				Any										=					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     )
      UpperCAmelCase			:				List[Any]										=					os.path.basename(args.resume_from_checkpoint     )
    else:
      # Get the most recent checkpoint
      UpperCAmelCase			:				int										=					[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
      dirs.sort(key=os.path.getctime     )
      UpperCAmelCase			:				str										=					dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
    # Extract `epoch_{i}` or `step_{i}`
    UpperCAmelCase			:				Tuple										=					os.path.splitext(UpperCAmelCase     )[0]

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

    # Now we train the model
  for epoch in range(UpperCAmelCase     ):
    # New Code #
    # context manager to track the peak memory usage during the training epoch
    with TorchTracemalloc() as tracemalloc:
      model.train()
      if args.with_tracking:
        UpperCAmelCase			:				Union[str, Any]										=					0
      for step, batch in enumerate(UpperCAmelCase     ):
        # 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     )
        UpperCAmelCase			:				Dict										=					model(**UpperCAmelCase     )
        UpperCAmelCase			:				str										=					outputs.loss
        UpperCAmelCase			:				str										=					loss / gradient_accumulation_steps
        # We keep track of the loss at each epoch
        if args.with_tracking:
          total_loss += loss.detach().float()
        accelerator.backward(UpperCAmelCase     )
        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(UpperCAmelCase							,					UpperCAmelCase     ):
          UpperCAmelCase			:				Any										=					f'''step_{overall_step}'''
          if overall_step % checkpointing_steps == 0:
            if args.output_dir is not None:
              UpperCAmelCase			:				Dict										=					os.path.join(args.output_dir							,					UpperCAmelCase     )
            accelerator.save_state(UpperCAmelCase     )
        # 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=UpperCAmelCase							,					)

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

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

      if checkpointing_steps == "epoch":
        UpperCAmelCase			:				Optional[Any]										=					f'''epoch_{epoch}'''
        if args.output_dir is not None:
          UpperCAmelCase			:				Dict										=					os.path.join(args.output_dir							,					UpperCAmelCase     )
        accelerator.save_state(UpperCAmelCase     )
        # 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=UpperCAmelCase							,					)

  if args.with_tracking:
    accelerator.end_training()

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				Any										=					argparse.ArgumentParser(description='''Simple example of training script.'''     )
  parser.add_argument(
      '''--mixed_precision'''							,					type=UpperCAmelCase							,					default=UpperCAmelCase							,					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=UpperCAmelCase							,					default=UpperCAmelCase							,					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=UpperCAmelCase							,					default=UpperCAmelCase							,					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=UpperCAmelCase							,					default='''.'''							,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''							,					)
  parser.add_argument(
      '''--logging_dir'''							,					type=UpperCAmelCase							,					default='''logs'''							,					help='''Location on where to store experiment tracking logs`'''							,					)
  parser.add_argument(
      '''--model_name_or_path'''							,					type=UpperCAmelCase							,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''							,					required=UpperCAmelCase							,					)
  UpperCAmelCase			:				str										=					parser.parse_args()
  UpperCAmelCase			:				List[str]										=					{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
  training_function(UpperCAmelCase							,					UpperCAmelCase     )


if __name__ == "__main__":
     main()











"""simple docstring"""

import argparse

import torch

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


logging.set_verbosity_info()
UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)




def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Optional[Any]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						WavLMForSequenceClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""projector.weight"""]
					__snake_case        =						downstream_dict["""projector.bias"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.weight"""]
					__snake_case        =						downstream_dict["""model.post_net.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForAudioFrameClassification.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""model.linear.weight"""]
					__snake_case        =						downstream_dict["""model.linear.bias"""]
					return model




def    snake_case__						(							lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		Dict					,       lowerCAmelCase :		Dict		):
					__snake_case        =						WavLMForXVector.from_pretrained(lowerCAmelCase					,       config=lowerCAmelCase		)
					__snake_case        =						downstream_dict["""connector.weight"""]
					__snake_case        =						downstream_dict["""connector.bias"""]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
										__snake_case        =						downstream_dict[
										    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
										]
										__snake_case        =						downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

					__snake_case        =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
					__snake_case        =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
					__snake_case        =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
					__snake_case        =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
					__snake_case        =						downstream_dict["""objective.W"""]
					return model




@torch.no_grad()
def    snake_case__						(							lowerCAmelCase :		str					,       lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]		):
					__snake_case        =						torch.load(lowerCAmelCase					,       map_location="""cpu"""		)

					__snake_case        =						checkpoint["""Downstream"""]

					__snake_case        =						WavLMConfig.from_pretrained(lowerCAmelCase		)
					__snake_case        =						WavaVecaFeatureExtractor.from_pretrained(
					    lowerCAmelCase					,       return_attention_mask=lowerCAmelCase					,       do_normalize=lowerCAmelCase		)

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

					if hf_config.use_weighted_layer_sum:
										__snake_case        =						checkpoint["""Featurizer"""]["""weights"""]

					hf_feature_extractor.save_pretrained(lowerCAmelCase		)
					hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
		UpperCamelCase__			:     str			       =				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.")
		UpperCamelCase__			:     Tuple			       =				parser.parse_args()
		convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


