



import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch

import pyarrow as pa
import pytest
import requests
from packaging import version

from datasets import config


if config.PY_VERSION < version.parse('''3.8'''):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata



def 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : Optional[int]=False    )				->			int:






						'''simple docstring'''



						try:
												UpperCAmelCase_=       os.environ[key]
						except KeyError:
												# KEY isn't set, default to `default`.
												UpperCAmelCase_=       default
						else:
												# KEY is set, convert it to True or False.
												try:
																		UpperCAmelCase_=       strtobool(lowerCAmelCase_    )
												except ValueError:
																		# More values are supported, but let's keep the message simple.
																		raise ValueError(F"""If set, {key} must be yes or no."""    )
						return _value


__A       =						parse_flag_from_env('''RUN_SLOW''', default=False)
__A       =						parse_flag_from_env('''RUN_REMOTE''', default=False)
__A       =						parse_flag_from_env('''RUN_LOCAL''', default=True)
__A       =						parse_flag_from_env('''RUN_PACKAGED''', default=True)

# Compression
__A       =						pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
__A       =						pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
__A       =						pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')

# Audio
__A       =						pytest.mark.skipif(
    # On Windows and OS X, soundfile installs sndfile
    find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
    reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)

# Beam
__A       =						pytest.mark.skipif(
    not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
    reason='''test requires apache-beam and a compatible dill version''',
)

# Dill-cloudpickle compatibility
__A       =						pytest.mark.skipif(
    config.DILL_VERSION <= version.parse('''0.3.2'''),
    reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)

# Windows
__A       =						pytest.mark.skipif(
    sys.platform == '''win32''',
    reason='''test should not be run on Windows''',
)



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Optional[int]:






						'''simple docstring'''



						try:
												import faiss  # noqa
						except ImportError:
												UpperCAmelCase_=       unittest.skip("""test requires faiss"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						try:
												import regex  # noqa
						except ImportError:
												UpperCAmelCase_=       unittest.skip("""test requires regex"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			List[str]:






						'''simple docstring'''



						try:
												import elasticsearch  # noqa
						except ImportError:
												UpperCAmelCase_=       unittest.skip("""test requires elasticsearch"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			List[Any]:






						'''simple docstring'''



						try:
												import sqlalchemy  # noqa
						except ImportError:
												UpperCAmelCase_=       unittest.skip("""test requires sqlalchemy"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Tuple    )				->			Union[str, Any]:






						'''simple docstring'''



						if not config.TORCH_AVAILABLE:
												UpperCAmelCase_=       unittest.skip("""test requires PyTorch"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : str    )				->			Any:






						'''simple docstring'''



						if not config.TF_AVAILABLE:
												UpperCAmelCase_=       unittest.skip("""test requires TensorFlow"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Dict    )				->			Union[str, Any]:






						'''simple docstring'''



						if not config.JAX_AVAILABLE:
												UpperCAmelCase_=       unittest.skip("""test requires JAX"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Dict    )				->			List[Any]:






						'''simple docstring'''



						if not config.PIL_AVAILABLE:
												UpperCAmelCase_=       unittest.skip("""test requires Pillow"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : int    )				->			Any:






						'''simple docstring'''



						try:
												import transformers  # noqa F401
						except ImportError:
												return unittest.skip("""test requires transformers"""    )(lowerCAmelCase_    )
						else:
												return test_case



def 	__a					(   lowerCAmelCase_       : str    )				->			List[str]:






						'''simple docstring'''



						try:
												import tiktoken  # noqa F401
						except ImportError:
												return unittest.skip("""test requires tiktoken"""    )(lowerCAmelCase_    )
						else:
												return test_case



def 	__a					(   lowerCAmelCase_       : str    )				->			List[str]:






						'''simple docstring'''



						try:
												import spacy  # noqa F401
						except ImportError:
												return unittest.skip("""test requires spacy"""    )(lowerCAmelCase_    )
						else:
												return test_case



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						def _require_spacy_model(lowerCAmelCase_       : str    ):
												try:
																		import spacy  # noqa F401

																		spacy.load(lowerCAmelCase_    )
												except ImportError:
																		return unittest.skip("""test requires spacy"""    )(lowerCAmelCase_    )
												except OSError:
																		return unittest.skip("""test requires spacy model '{}'""".format(lowerCAmelCase_    )    )(lowerCAmelCase_    )
												else:
																		return test_case

						return _require_spacy_model



def 	__a					(   lowerCAmelCase_       : int    )				->			List[str]:






						'''simple docstring'''



						try:
												import pyspark  # noqa F401
						except ImportError:
												return unittest.skip("""test requires pyspark"""    )(lowerCAmelCase_    )
						else:
												return test_case



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Any:






						'''simple docstring'''



						try:
												import joblibspark  # noqa F401
						except ImportError:
												return unittest.skip("""test requires joblibspark"""    )(lowerCAmelCase_    )
						else:
												return test_case



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Tuple:






						'''simple docstring'''



						if not _run_slow_tests or _run_slow_tests == 0:
												UpperCAmelCase_=       unittest.skip("""test is slow"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Any    )				->			int:






						'''simple docstring'''



						if not _run_local_tests or _run_local_tests == 0:
												UpperCAmelCase_=       unittest.skip("""test is local"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Union[str, Any]    )				->			List[str]:






						'''simple docstring'''



						if not _run_packaged_tests or _run_packaged_tests == 0:
												UpperCAmelCase_=       unittest.skip("""test is packaged"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			str:






						'''simple docstring'''



						if not _run_remote_tests or _run_remote_tests == 0:
												UpperCAmelCase_=       unittest.skip("""test requires remote"""    )(lowerCAmelCase_    )
						return test_case



def 	__a					(   *lowerCAmelCase_       : int    )				->			Any:






						'''simple docstring'''



						def decorate(cls       : Optional[int]    ):
												for name, fn in cls.__dict__.items():
																		if callable(lowerCAmelCase_    ) and name.startswith("""test"""    ):
																								for decorator in decorators:
																														UpperCAmelCase_=       decorator(lowerCAmelCase_    )
																								setattr(cls      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												return cls

						return decorate






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	pass






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: List[Any]		       =							0
	a__				: Tuple		       =							1
	a__				: List[Any]		       =							2



@contextmanager
def 	__a					(   lowerCAmelCase_       : Optional[Any]=OfflineSimulationMode.CONNECTION_FAILS      ,lowerCAmelCase_       : Any=1E-16    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       requests.Session().request

						def timeout_request(lowerCAmelCase_       : Union[str, Any]      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : Optional[Any]      ,**lowerCAmelCase_       : Tuple    ):
												# Change the url to an invalid url so that the connection hangs
												UpperCAmelCase_=       """https://10.255.255.1"""
												if kwargs.get("""timeout"""    ) is None:
																		raise RequestWouldHangIndefinitelyError(
																		    F"""Tried a call to {url} in offline mode with no timeout set. Please set a timeout."""    )
												UpperCAmelCase_=       timeout
												try:
																		return online_request(lowerCAmelCase_      ,lowerCAmelCase_      ,**lowerCAmelCase_    )
												except Exception as e:
																		# The following changes in the error are just here to make the offline timeout error prettier
																		UpperCAmelCase_=       url
																		UpperCAmelCase_=       e.args[0]
																		UpperCAmelCase_=       (max_retry_error.args[0].replace("""10.255.255.1"""      ,F"""OfflineMock[{url}]"""    ),)
																		UpperCAmelCase_=       (max_retry_error,)
																		raise

						def raise_connection_error(lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : str      ,**lowerCAmelCase_       : Optional[int]    ):
												raise requests.ConnectionError("""Offline mode is enabled."""      ,request=lowerCAmelCase_    )

						if mode is OfflineSimulationMode.CONNECTION_FAILS:
												with patch("""requests.Session.send"""      ,lowerCAmelCase_    ):
																		yield
						elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
												# inspired from https://stackoverflow.com/a/904609
												with patch("""requests.Session.request"""      ,lowerCAmelCase_    ):
																		yield
						elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
												with patch("""datasets.config.HF_DATASETS_OFFLINE"""      ,lowerCAmelCase_    ):
																		yield
						else:
												raise ValueError("""Please use a value from the OfflineSimulationMode enum."""    )



@contextmanager
def 	__a					(   *lowerCAmelCase_       : List[Any]      ,**lowerCAmelCase_       : Optional[int]    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_=       str(Path().resolve()    )
						with tempfile.TemporaryDirectory(*lowerCAmelCase_      ,**lowerCAmelCase_    ) as tmp_dir:
												try:
																		os.chdir(lowerCAmelCase_    )
																		yield
												finally:
																		os.chdir(lowerCAmelCase_    )



@contextmanager
def 	__a					(   )				->			List[str]:






						'''simple docstring'''



						import gc

						gc.collect()
						UpperCAmelCase_=       pa.total_allocated_bytes()
						yield
						assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."



@contextmanager
def 	__a					(   )				->			Dict:






						'''simple docstring'''



						import gc

						gc.collect()
						UpperCAmelCase_=       pa.total_allocated_bytes()
						yield
						assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."



def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : List[Any]    )				->			List[str]:






						'''simple docstring'''



						return deepcopy(lowerCAmelCase_    ).integers(0      ,1_00      ,10    ).tolist() == deepcopy(lowerCAmelCase_    ).integers(0      ,1_00      ,10    ).tolist()



def 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						import decorator
						from requests.exceptions import HTTPError

						def _wrapper(lowerCAmelCase_       : Optional[int]      ,*lowerCAmelCase_       : List[Any]      ,**lowerCAmelCase_       : Optional[Any]    ):
												try:
																		return func(*lowerCAmelCase_      ,**lowerCAmelCase_    )
												except HTTPError as err:
																		if str(lowerCAmelCase_    ).startswith("""500"""    ) or str(lowerCAmelCase_    ).startswith("""502"""    ):
																								pytest.xfail(str(lowerCAmelCase_    )    )
																		raise err

						return decorator.decorator(_wrapper      ,lowerCAmelCase_    )






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Tuple						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						List[str]						,  __UpperCAmelCase     :						Any					)      ->			List[Any]:
							UpperCAmelCase_=       returncode
							UpperCAmelCase_=       stdout
							UpperCAmelCase_=       stderr



async def 	__a					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : List[str]    )				->			str:






						'''simple docstring'''



						while True:
												UpperCAmelCase_=       await stream.readline()
												if line:
																		callback(lowerCAmelCase_    )
												else:
																		break



async def 	__a					(   lowerCAmelCase_       : Optional[Any]      ,lowerCAmelCase_       : int=None      ,lowerCAmelCase_       : Union[str, Any]=None      ,lowerCAmelCase_       : Tuple=None      ,lowerCAmelCase_       : int=False      ,lowerCAmelCase_       : Dict=False    )				->			_RunOutput:






						'''simple docstring'''



						if echo:
												print("""\nRunning: """      ,""" """.join(lowerCAmelCase_    )    )

						UpperCAmelCase_=       await asyncio.create_subprocess_exec(
						    cmd[0]      ,*cmd[1:]      ,stdin=lowerCAmelCase_      ,stdout=asyncio.subprocess.PIPE      ,stderr=asyncio.subprocess.PIPE      ,env=lowerCAmelCase_      ,)

						# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
						# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
						#
						# If it starts hanging, will need to switch to the following code. The problem is that no data
						# will be seen until it's done and if it hangs for example there will be no debug info.
						# out, err = await p.communicate()
						# return _RunOutput(p.returncode, out, err)

						UpperCAmelCase_=       []
						UpperCAmelCase_=       []

						def tee(lowerCAmelCase_       : Any      ,lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Any=""    ):
												UpperCAmelCase_=       line.decode("""utf-8"""    ).rstrip()
												sink.append(lowerCAmelCase_    )
												if not quiet:
																		print(lowerCAmelCase_      ,lowerCAmelCase_      ,file=lowerCAmelCase_    )

    # XXX: the timeout doesn't seem to make any difference here
						await asyncio.wait(
						    [
						        _read_stream(p.stdout      ,lambda lowerCAmelCase_    : tee(lowerCAmelCase_      ,lowerCAmelCase_      ,sys.stdout      ,label="""stdout:"""    )    ),
						        _read_stream(p.stderr      ,lambda lowerCAmelCase_    : tee(lowerCAmelCase_      ,lowerCAmelCase_      ,sys.stderr      ,label="""stderr:"""    )    ),
						    ]      ,timeout=lowerCAmelCase_      ,)
						return _RunOutput(await p.wait()      ,lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int=None      ,lowerCAmelCase_       : int=None      ,lowerCAmelCase_       : str=1_80      ,lowerCAmelCase_       : Dict=False      ,lowerCAmelCase_       : Any=True    )				->			_RunOutput:






						'''simple docstring'''



						UpperCAmelCase_=       asyncio.get_event_loop()
						UpperCAmelCase_=       loop.run_until_complete(
						    _stream_subprocess(lowerCAmelCase_      ,env=lowerCAmelCase_      ,stdin=lowerCAmelCase_      ,timeout=lowerCAmelCase_      ,quiet=lowerCAmelCase_      ,echo=lowerCAmelCase_    )    )

						UpperCAmelCase_=       """ """.join(lowerCAmelCase_    )
						if result.returncode > 0:
												UpperCAmelCase_=       """\n""".join(result.stderr    )
												raise RuntimeError(
												    F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
												    F"""The combined stderr from workers follows:\n{stderr}"""    )

						# check that the subprocess actually did run and produced some output, should the test rely on
						# the remote side to do the testing
						if not result.stdout and not result.stderr:
												raise RuntimeError(F"""'{cmd_str}' produced no output."""    )

						return result



def 	__a					(   )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       os.environ.get("""PYTEST_XDIST_WORKER"""      ,"""gw0"""    )
						UpperCAmelCase_=       re.sub(r"""^gw"""      ,""""""      ,lowerCAmelCase_      ,0      ,re.M    )
						return int(lowerCAmelCase_    )



def 	__a					(   )				->			Optional[int]:






						'''simple docstring'''



						UpperCAmelCase_=       2_95_00
						UpperCAmelCase_=       pytest_xdist_worker_id()
						return port + uniq_delta









import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


__A       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : int = 16      ,lowerCAmelCase_       : str = "bert-base-cased"    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       AutoTokenizer.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       load_dataset("""glue"""      ,"""mrpc"""    )

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

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

						# 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(lowerCAmelCase_       : List[Any]    ):
												# 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(lowerCAmelCase_      ,padding="""max_length"""      ,max_length=1_28      ,return_tensors="""pt"""    )
												return tokenizer.pad(lowerCAmelCase_      ,padding="""longest"""      ,return_tensors="""pt"""    )

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

						return train_dataloader, eval_dataloader



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						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(lowerCAmelCase_    )
						UpperCAmelCase_,       UpperCAmelCase_=       get_dataloaders(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

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

						# 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=lowerCAmelCase_    )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												UpperCAmelCase_=       1
						UpperCAmelCase_=       (len(lowerCAmelCase_    ) * 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=lowerCAmelCase_      ,num_warmup_steps=0      ,num_training_steps=lowerCAmelCase_      ,)
						else:
												UpperCAmelCase_=       DummyScheduler(lowerCAmelCase_      ,total_num_steps=lowerCAmelCase_      ,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_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_=       accelerator.prepare(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# 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_=       evaluate.load("""glue"""      ,"""mrpc"""    )
						UpperCAmelCase_=       0
						UpperCAmelCase_=       {}
						for epoch in range(lowerCAmelCase_      ,lowerCAmelCase_    ):
												model.train()
												for step, batch in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.loss
																		UpperCAmelCase_=       loss / gradient_accumulation_steps
																		accelerator.backward(lowerCAmelCase_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												UpperCAmelCase_=       0
												for step, batch in enumerate(lowerCAmelCase_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.logits.argmax(dim=-1    )
																		# It is slightly faster to call this once, than multiple times
																		UpperCAmelCase_,       UpperCAmelCase_=       accelerator.gather(
																		    (predictions, batch["""labels"""])    )  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(lowerCAmelCase_    ) - 1:
																														UpperCAmelCase_=       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																														UpperCAmelCase_=       references[: len(eval_dataloader.dataset    ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

												UpperCAmelCase_=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""      ,lowerCAmelCase_    )
												UpperCAmelCase_=       eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		UpperCAmelCase_=       eval_metric["""accuracy"""]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

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



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
						parser.add_argument(
						    """--model_name_or_path"""      ,type=lowerCAmelCase_      ,default="""bert-base-cased"""      ,help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,required=lowerCAmelCase_      ,)
						parser.add_argument(
						    """--output_dir"""      ,type=lowerCAmelCase_      ,default="""."""      ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,)
						parser.add_argument(
						    """--performance_lower_bound"""      ,type=lowerCAmelCase_      ,default=lowerCAmelCase_      ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""      ,)
						parser.add_argument(
						    """--num_epochs"""      ,type=lowerCAmelCase_      ,default=3      ,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(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()




