





'''simple docstring'''

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
#
########################################################################


lowercase_         =			16
lowercase_         =			32





def lowerCamelCase						(					__lowerCamelCase			:   Dict   )						->Tuple:
    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       snake_case_(		self	)		->       Tuple:
        return self.process.memory_info().rss


    def       snake_case_(		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			,		*A	)		->       Union[str, Any]:
        _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

      lowercase_         =			mocked_dataloaders  # noqa: F811





def lowerCamelCase						(					__lowerCamelCase			:   Any ,	__lowerCamelCase			:   int   )						->Optional[int]:
    # For testing only
    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			:   Tuple   ):
        # 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			:   Optional[Any]   ):
        # 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 lowerCamelCase						(					)						->List[str]:
    _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()










'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



