


'''simple docstring'''

import argparse
import os

# New Code #
import evaluate
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 import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to ensure out-of-memory errors never
# interrupt training, and builds off the `nlp_example.py` script.
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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							:   Union[str, Any]  						=	16
lowerCamelCase							:   int  						=	32

def _SCREAMING_SNAKE_CASE      (A  , A = 16       )       ->       str:






       """simple docstring"""







       lowercase__      			= AutoTokenizer.from_pretrained('''bert-base-cased'''       )
       lowercase__      			= load_dataset('''glue'''  , '''mrpc'''       )

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

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

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

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

       return train_dataloader, eval_dataloader


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

 lowerCamelCase							:   List[Any]  						=	mocked_dataloaders  # noqa: F811

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Tuple:






       """simple docstring"""







       if os.environ.get('''TESTING_MOCKED_DATALOADERS'''  , A       ) == "1":
              lowercase__      			= 2
       # Initialize accelerator
       lowercase__      			= Accelerator(cpu=args.cpu  , mixed_precision=args.mixed_precision       )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowercase__      			= config['''lr''']
       lowercase__      			= int(config['''num_epochs''']       )
       lowercase__      			= int(config['''seed''']       )
       lowercase__      			= int(config['''batch_size''']       )

       lowercase__      			= evaluate.load('''glue'''  , '''mrpc'''       )

       # New Code #
       # We now can define an inner training loop function. It should take a batch size as the only parameter,
       # and build the dataloaders in there.
       # It also gets our decorator
       @find_executable_batch_size(starting_batch_size=A       )
       def inner_training_loop(A       ):
              # And now just move everything below under this function
              # We need to bring in the Accelerator object from earlier
              nonlocal accelerator
              # And reset all of its attributes that could hold onto any memory:
              accelerator.free_memory()

              # Then we can declare the model, optimizer, and everything else:
              set_seed(A       )

              # Instantiate the model (we build the model here so that the seed also control new weights initialization)
              lowercase__      			= AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''  , return_dict=A       )

              # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
              # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
              # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
              lowercase__      			= model.to(accelerator.device       )

              # Instantiate optimizer
              lowercase__      			= AdamW(params=model.parameters()  , lr=A       )
              lowercase__			,lowercase__      			= get_dataloaders(A  , A       )

              # Instantiate scheduler
              lowercase__      			= get_linear_schedule_with_warmup(
                  optimizer=A  , num_warmup_steps=100  , num_training_steps=(len(A       ) * num_epochs)  , )

              # 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.
              lowercase__			,lowercase__			,lowercase__			,lowercase__			,lowercase__      			= accelerator.prepare(
                  A  , A  , A  , A  , A       )

              # Now we train the model
              for epoch in range(A       ):
                     model.train()
                     for step, batch in enumerate(A       ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device       )
                            lowercase__      			= model(**A       )
                            lowercase__      			= outputs.loss
                            accelerator.backward(A       )
                            optimizer.step()
                            lr_scheduler.step()
                            optimizer.zero_grad()

                     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():
                                   lowercase__      			= model(**A       )
                            lowercase__      			= outputs.logits.argmax(dim=-1       )
                            lowercase__			,lowercase__      			= accelerator.gather_for_metrics((predictions, batch['''labels'''])       )
                            metric.add_batch(
                                predictions=A  , references=A  , )

                     lowercase__      			= metric.compute()
                     # Use accelerator.print to print only on the main process.
                     accelerator.print(f"epoch {epoch}:"  , A       )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
       inner_training_loop()

def _SCREAMING_SNAKE_CASE      ()       ->       Dict:






       """simple docstring"""







       lowercase__      			= 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.'''       )
       lowercase__      			= parser.parse_args()
       lowercase__      			= {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
       training_function(A  , A       )


if __name__ == "__main__":
 main()










'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert isinstance(__UpperCAmelCase,      __UpperCAmelCase	)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_									=				SqlDatasetReader(
										    '''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase,      keep_in_memory=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


@require_sqlalchemy
@pytest.mark.parametrize(
    '''features''',      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],      )
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					snake_case_									=				features.copy() if features else default_expected_features
					snake_case_									=				(
					    Features({feature: Value(__UpperCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      features=__UpperCAmelCase,      cache_dir=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					with contextlib.closing(sqlitea.connect(__UpperCAmelCase	)	) as con:
										snake_case_									=				con.cursor()
										cur.execute('''SELECT * FROM dataset'''	)
										for row in cur:
															yield row


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=1	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=2	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					with pytest.raises(__UpperCAmelCase	):
										SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=0	).write()

