'''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
#
########################################################################


__UpperCAmelCase  							=    1_6
__UpperCAmelCase  							=    3_2






def __lowerCAmelCase				(							UpperCamelCase__       )			->					List[Any]:
    return int(x / 2**20       )




class 					a__   :






     def __enter__(				self	:	Union[str, Any]					):





         """simple docstring"""



         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         __lowerCamelCase						=						torch.cuda.memory_allocated()
         __lowerCamelCase						=						psutil.Process()

         __lowerCamelCase						=						self.cpu_mem_used()
         __lowerCamelCase						=						True
         __lowerCamelCase						=						threading.Thread(target=self.peak_monitor_func					)
         __lowerCamelCase						=						True
         peak_monitor_thread.start()
         return self






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]					):





         """simple docstring"""



         return self.process.memory_info().rss






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict					):





         """simple docstring"""



         __lowerCamelCase						=						-1

         while True:
             __lowerCamelCase						=						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[Any]						,       *a	:	List[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						False

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

         __lowerCamelCase						=						self.cpu_mem_used()
         __lowerCamelCase						=						bamb(self.cpu_end - self.cpu_begin					)
         __lowerCamelCase						=						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

  __UpperCAmelCase  							=    mocked_dataloaders  # noqa: F811






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					Any:






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''       ,						UpperCAmelCase_       ) == "1":
        __lowerCamelCase						=						2

    # New Code #
    # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
    __lowerCamelCase						=						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:
        __lowerCamelCase						=						Accelerator(
            cpu=args.cpu       ,						mixed_precision=args.mixed_precision       ,						log_with='''wandb'''       ,						project_dir=args.logging_dir       ,						fsdp_plugin=UpperCAmelCase_       ,						)
    else:
        __lowerCamelCase						=						Accelerator(fsdp_plugin=UpperCAmelCase_       )
    accelerator.print(accelerator.distributed_type       )

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

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

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

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

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

    def collate_fn(UpperCamelCase__       ):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __lowerCamelCase						=						1_28 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __lowerCamelCase						=						16
        elif accelerator.mixed_precision != "no":
            __lowerCamelCase						=						8
        else:
            __lowerCamelCase						=						None

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

    # Instantiate dataloaders.
    __lowerCamelCase						=						DataLoader(
        tokenized_datasets['''train''']       ,						shuffle=UpperCAmelCase_       ,						collate_fn=UpperCAmelCase_       ,						batch_size=UpperCAmelCase_       )
    __lowerCamelCase						=						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)
    __lowerCamelCase						=						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
    __lowerCamelCase						=						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
    __lowerCamelCase						=						torch.optim.AdamW(params=model.parameters()       ,						lr=UpperCAmelCase_       ,						weight_decay=2E-4       )

    # Instantiate scheduler
    __lowerCamelCase						=						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.
    __lowerCamelCase						=						accelerator.prepare(
        UpperCAmelCase_       ,						UpperCAmelCase_       ,						UpperCAmelCase_       ,						UpperCAmelCase_       )

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

        if "epoch" in training_difference:
            num_epochs -= int(training_difference.replace('''epoch_'''       ,						''''''       )       )
            __lowerCamelCase						=						None
        else:
            __lowerCamelCase						=						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
            __lowerCamelCase						=						(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:
                __lowerCamelCase						=						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       )
                __lowerCamelCase						=						model(**UpperCAmelCase_       )
                __lowerCamelCase						=						outputs.loss
                __lowerCamelCase						=						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_       ):
                    __lowerCamelCase						=						f"""step_{overall_step}"""
                    if overall_step % checkpointing_steps == 0:
                        if args.output_dir is not None:
                            __lowerCamelCase						=						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():
                    __lowerCamelCase						=						model(**UpperCAmelCase_       )
                __lowerCamelCase						=						outputs.logits.argmax(dim=-1       )
                __lowerCamelCase						=						accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
                metric.add_batch(
                    predictions=UpperCAmelCase_       ,						references=UpperCAmelCase_       ,						)

            __lowerCamelCase						=						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":
                __lowerCamelCase						=						f"""epoch_{epoch}"""
                if args.output_dir is not None:
                    __lowerCamelCase						=						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 __lowerCAmelCase				(							)			->					str:
    __lowerCamelCase						=						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_       ,						)
    __lowerCamelCase						=						parser.parse_args()
    __lowerCamelCase						=						{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
    training_function(UpperCAmelCase_       ,						UpperCAmelCase_       )


if __name__ == "__main__":
  main()

'''simple docstring'''




from __future__ import annotations






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					list:
    __lowerCamelCase						=						[None] * (len(UpperCamelCase__       ) + len(UpperCamelCase__       ))

    __lowerCamelCase						=						0  # pointer to current index for left Half
    __lowerCamelCase						=						0  # pointer to current index for the right Half
    __lowerCamelCase						=						0  # pointer to current index for the sorted array Half

    while pointera < len(UpperCamelCase__       ) and pointera < len(UpperCamelCase__       ):
        if left_half[pointera] < right_half[pointera]:
            __lowerCamelCase						=						left_half[pointera]
            pointera += 1
            index += 1
        else:
            __lowerCamelCase						=						right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(UpperCamelCase__       ):
        __lowerCamelCase						=						left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(UpperCamelCase__       ):
        __lowerCamelCase						=						right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array






def __lowerCAmelCase				(							UpperCamelCase__       )			->					list:
    if len(UpperCamelCase__       ) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    __lowerCamelCase						=						0 + (len(UpperCamelCase__       ) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    __lowerCamelCase						=						array[:middle]
    __lowerCamelCase						=						array[middle:]

    return merge(merge_sort(UpperCamelCase__       )       ,						merge_sort(UpperCamelCase__       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
