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							(						__SCREAMING_SNAKE_CASE    : Union[str, Any]					):







    '''simple docstring'''
    return int(x / 2**2_0					)







class 			SCREAMING_SNAKE_CASE__ :

       def __enter__(				self :			Dict       ):
           gc.collect()
           torch.cuda.empty_cache()
           torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
           __snake_case    :							Dict             =  torch.cuda.memory_allocated()
           __snake_case    :							Optional[Any]             =  psutil.Process()

           __snake_case    :							Optional[int]             =  self.cpu_mem_used()
           __snake_case    :							Dict             =  True
           __snake_case    :							Dict             =  threading.Thread(target=self.peak_monitor_func       )
           __snake_case    :							Optional[Any]             =  True
           peak_monitor_thread.start()
           return self

       def        snake_case__  (				self :			Dict       ):
           return self.process.memory_info().rss

       def        snake_case__  (				self :			Union[str, Any]       ):
           __snake_case    :							Union[str, Any]             =  -1

           while True:
               __snake_case    :							Optional[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 :			Any       ,       *_lowerCAmelCase :			List[str]       ):
           __snake_case    :							int             =  False

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

           __snake_case    :							int             =  self.cpu_mem_used()
           __snake_case    :							int             =  bamb(self.cpu_end - self.cpu_begin       )
           __snake_case    :							List[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

     lowercase_   				=       mocked_dataloaders  # noqa: F811







def  __lowerCAmelCase							(						__SCREAMING_SNAKE_CASE    : Union[str, Any]						,				__SCREAMING_SNAKE_CASE    : int					):







    '''simple docstring'''




    if os.environ.get("""TESTING_MOCKED_DATALOADERS"""						,				__SCREAMING_SNAKE_CASE					) == "1":
        __snake_case    :							str             =  2

    # New Code #
    # Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
    __snake_case    :							List[Any]             =  FullyShardedDataParallelPlugin(
        state_dict_config=FullStateDictConfig(offload_to_cpu=__SCREAMING_SNAKE_CASE						,				ranka_only=__SCREAMING_SNAKE_CASE					)						,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=__SCREAMING_SNAKE_CASE						,				ranka_only=__SCREAMING_SNAKE_CASE					)						,				)

    # Initialize accelerator
    if args.with_tracking:
        __snake_case    :							int             =  Accelerator(
            cpu=args.cpu						,				mixed_precision=args.mixed_precision						,				log_with="""wandb"""						,				project_dir=args.logging_dir						,				fsdp_plugin=__SCREAMING_SNAKE_CASE						,				)
    else:
        __snake_case    :							Dict             =  Accelerator(fsdp_plugin=__SCREAMING_SNAKE_CASE					)
    accelerator.print(accelerator.distributed_type					)

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

    # We need to initialize the trackers we use, and also store our configuration
    if args.with_tracking:
        __snake_case    :							Union[str, Any]             =  vars(__SCREAMING_SNAKE_CASE					)
        accelerator.init_trackers("""fsdp_glue_no_trainer"""						,				__SCREAMING_SNAKE_CASE					)

    __snake_case    :							str             =  AutoTokenizer.from_pretrained(args.model_name_or_path					)
    __snake_case    :							Optional[int]             =  load_dataset("""glue"""						,				"""mrpc"""					)
    __snake_case    :							Union[str, Any]             =  evaluate.load("""glue"""						,				"""mrpc"""					)

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

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

    def collate_fn(__SCREAMING_SNAKE_CASE    : Optional[int]					):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        __snake_case    :							Dict             =  1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
        # When using mixed precision we want round multiples of 8/16
        if accelerator.mixed_precision == "fp8":
            __snake_case    :							Any             =  1_6
        elif accelerator.mixed_precision != "no":
            __snake_case    :							List[str]             =  8
        else:
            __snake_case    :							Union[str, Any]             =  None

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

    # Instantiate dataloaders.
    __snake_case    :							List[Any]             =  DataLoader(
        tokenized_datasets["""train"""]						,				shuffle=__SCREAMING_SNAKE_CASE						,				collate_fn=__SCREAMING_SNAKE_CASE						,				batch_size=__SCREAMING_SNAKE_CASE					)
    __snake_case    :							Any             =  DataLoader(
        tokenized_datasets["""validation"""]						,				shuffle=__SCREAMING_SNAKE_CASE						,				collate_fn=__SCREAMING_SNAKE_CASE						,				batch_size=__SCREAMING_SNAKE_CASE					)

    set_seed(__SCREAMING_SNAKE_CASE					)

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

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

    # 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
    __snake_case    :							Dict             =  torch.optim.AdamW(params=model.parameters()						,				lr=__SCREAMING_SNAKE_CASE						,				weight_decay=2E-4					)

    # Instantiate scheduler
    __snake_case    :							int             =  get_linear_schedule_with_warmup(
        optimizer=__SCREAMING_SNAKE_CASE						,				num_warmup_steps=1_0						,				num_training_steps=(len(__SCREAMING_SNAKE_CASE					) * 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.
    __snake_case       ,      __snake_case       ,      __snake_case       ,      __snake_case    :							Optional[Any]             =  accelerator.prepare(
        __SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE					)

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

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

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

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

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

            if checkpointing_steps == "epoch":
                __snake_case    :							Any             =  F'''epoch_{epoch}'''
                if args.output_dir is not None:
                    __snake_case    :							List[str]             =  os.path.join(args.output_dir						,				__SCREAMING_SNAKE_CASE					)
                accelerator.save_state(__SCREAMING_SNAKE_CASE					)
        # 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=__SCREAMING_SNAKE_CASE						,				)

    if args.with_tracking:
        accelerator.end_training()







def  __lowerCAmelCase							(						):







    '''simple docstring'''
    __snake_case    :							Optional[int]             =  argparse.ArgumentParser(description="""Simple example of training script."""					)
    parser.add_argument(
        """--mixed_precision"""						,				type=__SCREAMING_SNAKE_CASE						,				default=__SCREAMING_SNAKE_CASE						,				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=__SCREAMING_SNAKE_CASE						,				default=__SCREAMING_SNAKE_CASE						,				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=__SCREAMING_SNAKE_CASE						,				default=__SCREAMING_SNAKE_CASE						,				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=__SCREAMING_SNAKE_CASE						,				default="""."""						,				help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,				)
    parser.add_argument(
        """--logging_dir"""						,				type=__SCREAMING_SNAKE_CASE						,				default="""logs"""						,				help="""Location on where to store experiment tracking logs`"""						,				)
    parser.add_argument(
        """--model_name_or_path"""						,				type=__SCREAMING_SNAKE_CASE						,				help="""Path to pretrained model or model identifier from huggingface.co/models."""						,				required=__SCREAMING_SNAKE_CASE						,				)
    __snake_case    :							Dict             =  parser.parse_args()
    __snake_case    :							Union[str, Any]             =  {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 4_2, """batch_size""": 1_6}
    training_function(__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
     main()





"""simple docstring"""

class    _UpperCAmelCase							:




      def __init__(							self      ,				lowercase_=None							)  ->    Union[str, Any]:
          UpperCAmelCase  	=    data
          UpperCAmelCase  	=    None




      def __repr__(							self							)  ->    Tuple:
          UpperCAmelCase  	=    ''
          UpperCAmelCase  	=    self
          while temp:
              string_rep += F"<{temp.data}> ---> "
              UpperCAmelCase  	=    temp.next
          string_rep += "<END>"
          return string_rep




def 			lowercase__  (    lowerCAmelCase :    int       )       ->					List[Any]:






    """simple docstring"""




    if not elements_list:
        raise Exception('The Elements List is empty'       )

    # Set first element as Head
    UpperCAmelCase  	=    Node(elements_list[0]       )
    UpperCAmelCase  	=    head
    # Loop through elements from position 1
    for data in elements_list[1:]:
        UpperCAmelCase  	=    Node(lowerCAmelCase       )
        UpperCAmelCase  	=    current.next
    return head


SCREAMING_SNAKE_CASE_			    =  [1, 3, 5, 32, 44, 12, 43]
print(F'List: {list_data}')
print('''Creating Linked List from List.''')
SCREAMING_SNAKE_CASE_			    =  make_linked_list(list_data)
print('''Linked List:''')







print(linked_list)



