






'''simple docstring'''

import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE__      =   1_6
SCREAMING_SNAKE_CASE__      =   3_2







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






class 				a_						:

  def __enter__(      self      )  ->      List[str]:



        """simple docstring"""






        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        UpperCamelCase				  =					torch.cuda.memory_allocated()
        return self


  def __exit__(      self			,		*_SCREAMING_SNAKE_CASE      )  ->      Optional[int]:



        """simple docstring"""






        gc.collect()
        torch.cuda.empty_cache()
        UpperCamelCase				  =					torch.cuda.memory_allocated()
        UpperCamelCase				  =					torch.cuda.max_memory_allocated()
        UpperCamelCase				  =					bamb(self.end - self.begin      )
        UpperCamelCase				  =					bamb(self.peak - self.begin      )
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase = 16		,						__UpperCamelCase = "bert-base-cased"		,						__UpperCamelCase = 320		,						__UpperCamelCase = 160		,						)-> Dict:
      UpperCamelCase				  =					AutoTokenizer.from_pretrained(lowercase_    )
      UpperCamelCase				  =					load_dataset(
          """glue"""		,						"""mrpc"""		,						split={"""train""": F"train[:{n_train}]", """validation""": F"validation[:{n_val}]"}    )

      def tokenize_function(__UpperCamelCase    ):
            # max_length=None => use the model max length (it's actually the default)
            UpperCamelCase				  =					tokenizer(examples["""sentence1"""]		,						examples["""sentence2"""]		,						truncation=lowercase_		,						max_length=lowercase_    )
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      UpperCamelCase				  =					datasets.map(
          lowercase_		,						batched=lowercase_		,						remove_columns=["""idx""", """sentence1""", """sentence2"""]		,						load_from_cache_file=lowercase_    )

      # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
      # transformers library
      UpperCamelCase				  =					tokenized_datasets.rename_column("""label"""		,						"""labels"""    )

      def collate_fn(__UpperCamelCase    ):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(lowercase_		,						padding="""max_length"""		,						max_length=128		,						return_tensors="""pt"""    )
            return tokenizer.pad(lowercase_		,						padding="""longest"""		,						return_tensors="""pt"""    )

      # Instantiate dataloaders.
      UpperCamelCase				  =					DataLoader(
          tokenized_datasets["""train"""]		,						shuffle=lowercase_		,						collate_fn=lowercase_		,						batch_size=lowercase_    )
      UpperCamelCase				  =					DataLoader(
          tokenized_datasets["""validation"""]		,						shuffle=lowercase_		,						collate_fn=lowercase_		,						batch_size=lowercase_    )

      return train_dataloader, eval_dataloader







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase    )-> Tuple:
      # Initialize accelerator
      UpperCamelCase				  =					Accelerator()

      # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
      UpperCamelCase				  =					config["""lr"""]
      UpperCamelCase				  =					int(config["""num_epochs"""]    )
      UpperCamelCase				  =					int(config["""seed"""]    )
      UpperCamelCase				  =					int(config["""batch_size"""]    )
      UpperCamelCase				  =					args.model_name_or_path

      set_seed(lowercase_    )
      UpperCamelCase				  =					get_dataloaders(lowercase_		,						lowercase_		,						lowercase_		,						args.n_train		,						args.n_val    )

      # Instantiate the model (we build the model here so that the seed also control new weights initialization)
      UpperCamelCase				  =					AutoModelForSequenceClassification.from_pretrained(lowercase_		,						return_dict=lowercase_    )

      # Instantiate optimizer
      UpperCamelCase				  =					(
          AdamW
          if accelerator.state.deepspeed_plugin is None
          or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
          else DummyOptim
      )
      UpperCamelCase				  =					optimizer_cls(params=model.parameters()		,						lr=lowercase_    )

      if accelerator.state.deepspeed_plugin is not None:
            UpperCamelCase				  =					accelerator.state.deepspeed_plugin.deepspeed_config[
                """gradient_accumulation_steps"""
            ]
      else:
            UpperCamelCase				  =					1
      UpperCamelCase				  =					(len(lowercase_    ) * num_epochs) // gradient_accumulation_steps

      # Instantiate scheduler
      if (
          accelerator.state.deepspeed_plugin is None
          or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
      ):
            UpperCamelCase				  =					get_linear_schedule_with_warmup(
                optimizer=lowercase_		,						num_warmup_steps=0		,						num_training_steps=lowercase_		,						)
      else:
            UpperCamelCase				  =					DummyScheduler(lowercase_		,						total_num_steps=lowercase_		,						warmup_num_steps=0    )

      # Prepare everything
      # 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				  =					accelerator.prepare(
          lowercase_		,						lowercase_		,						lowercase_		,						lowercase_		,						lowercase_    )

      # We need to keep track of how many total steps we have iterated over
      UpperCamelCase				  =					0
      # We also need to keep track of the stating epoch so files are named properly
      UpperCamelCase				  =					0

      # Now we train the model
      UpperCamelCase				  =					{}
      for epoch in range(lowercase_		,						lowercase_    ):
            with TorchTracemalloc() as tracemalloc:
                  model.train()
                  for step, batch in enumerate(lowercase_    ):
                        UpperCamelCase				  =					model(**lowercase_    )
                        UpperCamelCase				  =					outputs.loss
                        UpperCamelCase				  =					loss / gradient_accumulation_steps
                        accelerator.backward(lowercase_    )
                        if step % gradient_accumulation_steps == 0:
                              optimizer.step()
                              lr_scheduler.step()
                              optimizer.zero_grad()

                        overall_step += 1

        # 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    )    )    )
            UpperCamelCase				  =					tracemalloc.peaked + bamb(tracemalloc.begin    )
            if args.peak_memory_upper_bound is not None:
                  assert (
                      train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
                  ), "Peak memory usage exceeded the upper bound"

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir		,						"""peak_memory_utilization.json"""    )		,						"""w"""    ) as f:
                  json.dump(lowercase_		,						lowercase_    )







def     lowercase__ (      )-> Dict:
      UpperCamelCase				  =					argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
      parser.add_argument(
          """--model_name_or_path"""		,						type=lowercase_		,						default="""bert-base-cased"""		,						help="""Path to pretrained model or model identifier from huggingface.co/models."""		,						required=lowercase_		,						)
      parser.add_argument(
          """--output_dir"""		,						type=lowercase_		,						default="""."""		,						help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,						)
      parser.add_argument(
          """--peak_memory_upper_bound"""		,						type=lowercase_		,						default=lowercase_		,						help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""		,						)
      parser.add_argument(
          """--n_train"""		,						type=lowercase_		,						default=320		,						help="""Number of training examples to use."""		,						)
      parser.add_argument(
          """--n_val"""		,						type=lowercase_		,						default=160		,						help="""Number of validation examples to use."""		,						)
      parser.add_argument(
          """--num_epochs"""		,						type=lowercase_		,						default=1		,						help="""Number of train epochs."""		,						)
      UpperCamelCase				  =					parser.parse_args()
      UpperCamelCase				  =					{"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
      training_function(lowercase_		,						lowercase_    )


if __name__ == "__main__":
     main()



import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       : Any         =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		DatasetInfosDict(
	    {
	        """default""": DatasetInfo(
	            features=Features(
	                {
	                    """tokens""": Sequence(Value("""string"""					)					),
	                    """ner_tags""": Sequence(
	                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]					)					),
	                    """langs""": Sequence(Value("""string"""					)					),
	                    """spans""": Sequence(Value("""string"""					)					),
	                }					)    ,      splits=[
	                {
	                    """name""": """train""",
	                    """num_bytes""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes    ,      expected[split].num_bytes					)
		else:
			result == expected



