

import argparse
import gc
import os
import threading

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

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase									= 16
lowerCamelCase									= 32




def       UpperCAmelCase__    (      _A				:			Optional[Any]							):





  '''simple docstring'''







  return int(x / 2**20							)







class 						__magic_name__      :

   '''simple docstring'''




   def __enter__(    self	)							->   Tuple:






     """simple docstring"""






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

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



   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     return self.process.memory_info().rss



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     a__  =-1

     while True:
       a__  =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,   *lowercase_	)							->   List[str]:






     """simple docstring"""






     a__  =False

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

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


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

       lowerCamelCase									= mocked_dataloaders  # noqa: F811




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Union[str, Any]							):





  '''simple docstring'''







  if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,				_A							) == "1":
    a__  =2

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

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

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

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

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

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

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

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

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

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

  set_seed(_A							)

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

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

  # 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
  a__  =torch.optim.AdamW(params=model.parameters()     ,				lr=_A     ,				weight_decay=2E-4							)

  # Instantiate scheduler
  a__  =get_linear_schedule_with_warmup(
      optimizer=_A     ,				num_warmup_steps=10     ,				num_training_steps=(len(_A							) * 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.
  a__,				a__,				a__,				a__  =accelerator.prepare(
      _A     ,				_A     ,				_A     ,				_A							)

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

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

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

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

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

      if checkpointing_steps == "epoch":
        a__  =F"""epoch_{epoch}"""
        if args.output_dir is not None:
          a__  =os.path.join(args.output_dir     ,				_A							)
        accelerator.save_state(_A							)
        # 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=_A     ,				)

  if args.with_tracking:
    accelerator.end_training()




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







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


if __name__ == "__main__":
       main()



import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata




def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  a__  =min(re.search('''^ +'''     ,				_A							).end() if t.startswith(''' '''							) else 0 for t in string.splitlines()							)
  return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_A							)]							)


lowerCamelCase									= '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

lowerCamelCase									= '''\
---
---
# Begin of markdown

Some cool dataset card
'''


lowerCamelCase									= '''\
# Begin of markdown

Some cool dataset card
'''







class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     with tempfile.TemporaryDirectory() as tmp_dir:
       a__  =Path(lowercase_	) / '''README.md'''
       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {'''language''': ['''zh''', '''en'''], '''task_ids''': ['''sentiment-classification''']}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertDictEqual(lowercase_,   {}	)

       with open(lowercase_,   '''w+'''	) as readme_file:
         readme_file.write(lowercase_	)
       a__  =DatasetMetadata.from_readme(lowercase_	)
       self.assertEqual(lowercase_,   {}	)



   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - question-answering
            task_ids:
            - open-domain-qa
            task_ids:
            - open-domain-qa
            '''	)
     with self.assertRaises(lowercase_	):
       DatasetMetadata.from_yaml_string(lowercase_	)

     a__  =_dedent(
         '''\
            annotations_creators:
            - found
            language_creators:
            - found
            language:
            - en
            license:
            - unknown
            multilinguality:
            - monolingual
            pretty_name: Test Dataset
            size_categories:
            - 10K<n<100K
            source_datasets:
            - extended|other-yahoo-webscope-l6
            task_categories:
            - text-classification
            task_ids:
            - multi-class-classification
            paperswithcode_id:
            - squad
            configs:
            - en
            train-eval-index:
            - config: en
              task: text-classification
              task_id: multi_class_classification
              splits:
                train_split: train
                eval_split: test
              col_mapping:
                text: text
                label: target
              metrics:
                - type: accuracy
                  name: Accuracy
            extra_gated_prompt: |
              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:
              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:
              1. Researcher shall use the Database only for non-commercial research and educational purposes.
            extra_gated_fields:
              Company: text
              Country: text
              I agree to use this model for non-commerical use ONLY: checkbox
            '''	)
     assert DatasetMetadata.from_yaml_string(lowercase_	)
