



'''simple docstring'''

import argparse
import os

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.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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)
#
# 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
#
########################################################################


snake_case_      =  16
snake_case_      =  32






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Union[str, Any]     ,						_SCREAMING_SNAKE_CASE   :Any = 16							):
				SCREAMING_SNAKE_CASE	:    List[Any]          =			AutoTokenizer.from_pretrained('''bert-base-cased'''							)
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			load_dataset('''glue'''     ,						'''mrpc'''							)

				def tokenize_function(_SCREAMING_SNAKE_CASE   :Optional[Any]							):
								# max_length=None => use the model max length (it's actually the default)
								SCREAMING_SNAKE_CASE	:    Tuple          =			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():
								SCREAMING_SNAKE_CASE	:    List[str]          =			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
				SCREAMING_SNAKE_CASE	:    Dict          =			tokenized_datasets.rename_column('''label'''     ,						'''labels'''							)

				def collate_fn(_SCREAMING_SNAKE_CASE   :Union[str, Any]							):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								SCREAMING_SNAKE_CASE	:    List[str]          =			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":
												SCREAMING_SNAKE_CASE	:    int          =			16
								elif accelerator.mixed_precision != "no":
												SCREAMING_SNAKE_CASE	:    str          =			8
								else:
												SCREAMING_SNAKE_CASE	:    str          =			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.
				SCREAMING_SNAKE_CASE	:    List[str]          =			DataLoader(
				    tokenized_datasets['''train''']     ,						shuffle=_SCREAMING_SNAKE_CASE     ,						collate_fn=_SCREAMING_SNAKE_CASE     ,						batch_size=_SCREAMING_SNAKE_CASE							)
				SCREAMING_SNAKE_CASE	:    str          =			DataLoader(
				    tokenized_datasets['''validation''']     ,						shuffle=_SCREAMING_SNAKE_CASE     ,						collate_fn=_SCREAMING_SNAKE_CASE     ,						batch_size=_SCREAMING_SNAKE_CASE							)

				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

	snake_case_      =  mocked_dataloaders  # noqa: F811






def    __lowercase	(_SCREAMING_SNAKE_CASE   :Optional[Any]     ,						_SCREAMING_SNAKE_CASE   :List[str]							):
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''     ,						_SCREAMING_SNAKE_CASE							) == "1":
								SCREAMING_SNAKE_CASE	:    str          =			2
				# New Code #
				SCREAMING_SNAKE_CASE	:    List[Any]          =			int(args.gradient_accumulation_steps							)
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			int(args.local_sgd_steps							)
				# Initialize accelerator
				SCREAMING_SNAKE_CASE	:    Any          =			Accelerator(
				    cpu=args.cpu     ,						mixed_precision=args.mixed_precision     ,						gradient_accumulation_steps=_SCREAMING_SNAKE_CASE							)
				if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
								raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''							)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			config["lr"]
				SCREAMING_SNAKE_CASE	:    str          =			int(config['''num_epochs''']							)
				SCREAMING_SNAKE_CASE	:    List[str]          =			int(config['''seed''']							)
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			int(config['''batch_size''']							)

				SCREAMING_SNAKE_CASE	:    str          =			evaluate.load('''glue'''     ,						'''mrpc'''							)

				set_seed(_SCREAMING_SNAKE_CASE							)
				SCREAMING_SNAKE_CASE	:    Optional[Any]          =			get_dataloaders(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				SCREAMING_SNAKE_CASE	:    Optional[int]          =			AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''     ,						return_dict=_SCREAMING_SNAKE_CASE							)

				# 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).
				SCREAMING_SNAKE_CASE	:    Tuple          =			model.to(accelerator.device							)

				# Instantiate optimizer
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			AdamW(params=model.parameters()     ,						lr=_SCREAMING_SNAKE_CASE							)

				# Instantiate scheduler
				SCREAMING_SNAKE_CASE	:    List[str]          =			get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE     ,						num_warmup_steps=1_00     ,						num_training_steps=(len(_SCREAMING_SNAKE_CASE							) * 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.
				SCREAMING_SNAKE_CASE	:    str          =			accelerator.prepare(
				    _SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)

				# Now we train the model
				for epoch in range(_SCREAMING_SNAKE_CASE							):
								model.train()
								with LocalSGD(
								    accelerator=_SCREAMING_SNAKE_CASE     ,						model=_SCREAMING_SNAKE_CASE     ,						local_sgd_steps=_SCREAMING_SNAKE_CASE     ,						enabled=local_sgd_steps is not None							) as local_sgd:
												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							)
																# New code #
																# We use the new `accumulate` context manager to perform gradient accumulation
																# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																with accelerator.accumulate(_SCREAMING_SNAKE_CASE							):
																				SCREAMING_SNAKE_CASE	:    int          =			model(**_SCREAMING_SNAKE_CASE							)
																				SCREAMING_SNAKE_CASE	:    List[Any]          =			output.loss
																				accelerator.backward(_SCREAMING_SNAKE_CASE							)
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# LocalSGD-specific line
																				local_sgd.step()

								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():
																SCREAMING_SNAKE_CASE	:    Dict          =			model(**_SCREAMING_SNAKE_CASE							)
												SCREAMING_SNAKE_CASE	:    Optional[int]          =			outputs.logits.argmax(dim=-1							)
												SCREAMING_SNAKE_CASE	:    Tuple          =			accelerator.gather_for_metrics((predictions, batch['''labels'''])							)
												metric.add_batch(
												    predictions=_SCREAMING_SNAKE_CASE     ,						references=_SCREAMING_SNAKE_CASE     ,						)

								SCREAMING_SNAKE_CASE	:    str          =			metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'''epoch {epoch}:'''     ,						_SCREAMING_SNAKE_CASE							)






def    __lowercase	():
				SCREAMING_SNAKE_CASE	:    Tuple          =			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.'''     ,						)
				# New Code #
				parser.add_argument(
				    '''--gradient_accumulation_steps'''     ,						type=_SCREAMING_SNAKE_CASE     ,						default=1     ,						help='''The number of minibatches to be ran before gradients are accumulated.'''     ,						)
				parser.add_argument(
				    '''--local_sgd_steps'''     ,						type=_SCREAMING_SNAKE_CASE     ,						default=8     ,						help='''Number of local SGD steps or None to disable local SGD'''							)
				parser.add_argument('''--cpu'''     ,						action='''store_true'''     ,						help='''If passed, will train on the CPU.'''							)
				SCREAMING_SNAKE_CASE	:    Union[str, Any]          =			parser.parse_args()
				SCREAMING_SNAKE_CASE	:    Optional[int]          =			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
				training_function(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	main()





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 		snake_case__	(		lowercase							,				lowercase					):
   assert isinstance(lowercase							,				lowercase					)
   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 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      lowerCAmelCase_:							Optional[int]       =	SqlDatasetReader(
          "dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase							,				keep_in_memory=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
@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 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   lowerCAmelCase_:							List[str]       =	features.copy() if features else default_expected_features
   lowerCAmelCase_:							Any       =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   lowerCAmelCase_:							int       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				features=lowercase							,				cache_dir=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
def 		snake_case__	(		lowercase					):
   with contextlib.closing(sqlitea.connect(lowercase					)					) as con:
      lowerCAmelCase_:							Any       =	con.cursor()
      cur.execute("SELECT * FROM dataset"					)
      for row in cur:
         yield row
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[Any]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							str       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=1					).write()

   lowerCAmelCase_:							Union[str, Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							str       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Optional[Any]       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=2					).write()

   lowerCAmelCase_:							Optional[Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	iter_sql_file(lowercase					)

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







@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Any       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   with pytest.raises(lowercase					):
      SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=0					).write()