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
#
########################################################################


a  :      List[Any]            =    1_6
a  :      Any            =    3_2
def 		snake_case__	(		lowercase							,				lowercase = 16					):
   lowerCAmelCase_:							List[Any]       =	AutoTokenizer.from_pretrained("bert-base-cased"					)
   lowerCAmelCase_:							Optional[Any]       =	load_dataset("glue"							,				"mrpc"					)

   def tokenize_function(lowercase					):
      # max_length=None => use the model max length (it's actually the default)
      lowerCAmelCase_:							Tuple       =	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
   # starting with the main process first:
   with accelerator.main_process_first():
      lowerCAmelCase_:							List[str]       =	datasets.map(
          lowercase							,				batched=lowercase							,				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
   lowerCAmelCase_:							Dict       =	tokenized_datasets.rename_column("label"							,				"labels"					)

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

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

   # Instantiate dataloaders.
   lowerCAmelCase_:							List[str]       =	DataLoader(
       tokenized_datasets["train"]							,				shuffle=lowercase							,				collate_fn=lowercase							,				batch_size=lowercase					)
   lowerCAmelCase_:							str       =	DataLoader(
       tokenized_datasets["validation"]							,				shuffle=lowercase							,				collate_fn=lowercase							,				batch_size=lowercase					)

   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

 a  :      List[str]            =    mocked_dataloaders  # noqa: F811
def 		snake_case__	(		lowercase							,				lowercase					):
   # For testing only
   if os.environ.get("TESTING_MOCKED_DATALOADERS"							,				lowercase					) == "1":
      lowerCAmelCase_:							str       =	2
   # New Code #
   lowerCAmelCase_:							List[Any]       =	int(args.gradient_accumulation_steps					)
   lowerCAmelCase_:							Union[str, Any]       =	int(args.local_sgd_steps					)
   # Initialize accelerator
   lowerCAmelCase_:							Any       =	Accelerator(
       cpu=args.cpu							,				mixed_precision=args.mixed_precision							,				gradient_accumulation_steps=lowercase					)
   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
   lowerCAmelCase_:							Optional[Any]       =	config["lr"]
   lowerCAmelCase_:							str       =	int(config["num_epochs"]					)
   lowerCAmelCase_:							List[str]       =	int(config["seed"]					)
   lowerCAmelCase_:							Optional[Any]       =	int(config["batch_size"]					)

   lowerCAmelCase_:							str       =	evaluate.load("glue"							,				"mrpc"					)

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

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

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

   # Instantiate scheduler
   lowerCAmelCase_:							List[str]       =	get_linear_schedule_with_warmup(
       optimizer=lowercase							,				num_warmup_steps=100							,				num_training_steps=(len(lowercase					) * 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.
   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	accelerator.prepare(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)

   # Now we train the model
   for epoch in range(lowercase					):
      model.train()
      with LocalSGD(
          accelerator=lowercase							,				model=lowercase							,				local_sgd_steps=lowercase							,				enabled=local_sgd_steps is not None					) as local_sgd:
         for step, batch in enumerate(lowercase					):
            # 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(lowercase					):
               lowerCAmelCase_:							int       =	model(**lowercase					)
               lowerCAmelCase_:							List[Any]       =	output.loss
               accelerator.backward(lowercase					)
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()
               # LocalSGD-specific line
               local_sgd.step()

      model.eval()
      for step, batch in enumerate(lowercase					):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device					)
         with torch.no_grad():
            lowerCAmelCase_:							Dict       =	model(**lowercase					)
         lowerCAmelCase_:							Optional[int]       =	outputs.logits.argmax(dim=-1					)
         lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	accelerator.gather_for_metrics((predictions, batch["labels"])					)
         metric.add_batch(
             predictions=lowercase							,				references=lowercase							,				)

      lowerCAmelCase_:							str       =	metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''							,				lowercase					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Tuple       =	argparse.ArgumentParser(description="Simple example of training script."					)
   parser.add_argument(
       "--mixed_precision"							,				type=lowercase							,				default=lowercase							,				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=lowercase							,				default=1							,				help="The number of minibatches to be ran before gradients are accumulated."							,				)
   parser.add_argument(
       "--local_sgd_steps"							,				type=lowercase							,				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."					)
   lowerCAmelCase_:							Union[str, Any]       =	parser.parse_args()
   lowerCAmelCase_:							Optional[int]       =	{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
   training_function(lowercase							,				lowercase					)


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()