


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   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     str=False						) ->   str:





	"""simple docstring"""







	try:
		A								=				os.environ[key]
	except KeyError:
		# KEY isn't set, default to `default`.
		A								=				default
	else:
		# KEY is set, convert it to True or False.
		try:
			A								=				strtobool(UpperCamelCase__						)
		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


_lowercase       :      Dict	=	parse_flag_from_env("RUN_SLOW", default=False)
_lowercase       :      int	=	parse_flag_from_env("RUN_REMOTE", default=False)
_lowercase       :      Optional[int]	=	parse_flag_from_env("RUN_LOCAL", default=True)
_lowercase       :      Union[str, Any]	=	parse_flag_from_env("RUN_PACKAGED", default=True)

# Compression
_lowercase       :      Dict	=	pytest.mark.skipif(not config.LZ4_AVAILABLE, reason="test requires lz4")
_lowercase       :      Dict	=	pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason="test requires py7zr")
_lowercase       :      str	=	pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason="test requires zstandard")

# Audio
_lowercase       :      Any	=	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
_lowercase       :      List[Any]	=	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
_lowercase       :      int	=	pytest.mark.skipif(
    config.DILL_VERSION <= version.parse("0.3.2"),
    reason="test requires dill>0.3.2 for cloudpickle compatibility",
)

# Windows
_lowercase       :      str	=	pytest.mark.skipif(
    sys.platform == "win32",
    reason="test should not be run on Windows",
)





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[Any]						) ->   str:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   Optional[int]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   str:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   Tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   Tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   Dict:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict						) ->   Dict:





	"""simple docstring"""







	def _require_spacy_model(UpperCamelCase__:     Optional[int]						):
		try:
			import spacy  # noqa F401

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

	return _require_spacy_model





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]						) ->   int:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   Tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple						) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int						) ->   Optional[Any]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   *UpperCamelCase__:     str						) ->   Tuple:





	"""simple docstring"""







	def decorate(cls:     Any						):
		for name, fn in cls.__dict__.items():
			if callable(UpperCamelCase__						) and name.startswith("""test"""						):
				for decorator in decorators:
					A								=				decorator(UpperCamelCase__						)
				setattr(cls							,					UpperCamelCase__							,					UpperCamelCase__						)
		return cls

	return decorate






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				pass






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				lowerCAmelCase 		= 0
				lowerCAmelCase 		= 1
				lowerCAmelCase 		= 2





@contextmanager
def   _lowerCAmelCase				(   UpperCamelCase__:     List[Any]=OfflineSimulationMode.CONNECTION_FAILS							,					UpperCamelCase__:     int=1e-16						) ->   Any:





	"""simple docstring"""







	A								=				requests.Session().request

	def timeout_request(UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     List[Any]							,					**UpperCamelCase__:     Union[str, Any]						):
		# Change the url to an invalid url so that the connection hangs
		A								=				"""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.'						)
		A								=				timeout
		try:
			return online_request(UpperCamelCase__							,					UpperCamelCase__							,					**UpperCamelCase__						)
		except Exception as e:
			# The following changes in the error are just here to make the offline timeout error prettier
			A								=				url
			A								=				e.args[0]
			A								=				(max_retry_error.args[0].replace("""10.255.255.1"""							,					f'OfflineMock[{url}]'						),)
			A								=				(max_retry_error,)
			raise

	def raise_connection_error(UpperCamelCase__:     Any							,					UpperCamelCase__:     str							,					**UpperCamelCase__:     List[Any]						):
		raise requests.ConnectionError("""Offline mode is enabled."""							,					request=UpperCamelCase__						)

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





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





	"""simple docstring"""







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





@contextmanager
def   _lowerCAmelCase				(   ) ->   int:





	"""simple docstring"""







	import gc

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





@contextmanager
def   _lowerCAmelCase				(   ) ->   Optional[int]:





	"""simple docstring"""







	import gc

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     int						) ->   Tuple:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   List[str]:





	"""simple docstring"""







	import decorator
	from requests.exceptions import HTTPError

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

	return decorator.decorator(_wrapper							,					UpperCamelCase__						)






class       _UpperCamelCase    :

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__				,   a__   )	->				List[Any]:
					A								=				returncode
					A								=				stdout
					A								=				stderr





async def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple						) ->   List[str]:





	"""simple docstring"""







	while True:
		A								=				await stream.readline()
		if line:
			callback(UpperCamelCase__						)
		else:
			break





async def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Dict=None							,					UpperCamelCase__:     List[Any]=None							,					UpperCamelCase__:     List[Any]=None							,					UpperCamelCase__:     Optional[int]=False							,					UpperCamelCase__:     List[Any]=False						) ->   _RunOutput:





	"""simple docstring"""







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

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

	# 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)

	A								=				[]
	A								=				[]

	def tee(UpperCamelCase__:     Dict							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     str							,					UpperCamelCase__:     str=""						):
		A								=				line.decode("""utf-8"""						).rstrip()
		sink.append(UpperCamelCase__						)
		if not quiet:
			print(UpperCamelCase__							,					UpperCamelCase__							,					file=UpperCamelCase__						)

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





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     Tuple=None							,					UpperCamelCase__:     str=1_80							,					UpperCamelCase__:     Optional[Any]=False							,					UpperCamelCase__:     Optional[int]=True						) ->   _RunOutput:





	"""simple docstring"""







	A								=				asyncio.get_event_loop()
	A								=				loop.run_until_complete(
	    _stream_subprocess(UpperCamelCase__							,					env=UpperCamelCase__							,					stdin=UpperCamelCase__							,					timeout=UpperCamelCase__							,					quiet=UpperCamelCase__							,					echo=UpperCamelCase__						)						)

	A								=				""" """.join(UpperCamelCase__						)
	if result.returncode > 0:
		A								=				"""\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   _lowerCAmelCase				(   ) ->   Any:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   ) ->   List[Any]:





	"""simple docstring"""







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






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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# 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
#
########################################################################


_lowercase       :      Optional[Any]	=	16
_lowercase       :      List[Any]	=	32





def   _lowerCAmelCase				(   UpperCamelCase__:     Accelerator							,					UpperCamelCase__:     int = 16						) ->   Tuple:





	"""simple docstring"""







	A								=				AutoTokenizer.from_pretrained("""bert-base-cased"""						)
	A								=				load_dataset("""glue"""							,					"""mrpc"""						)

	def tokenize_function(UpperCamelCase__:     List[Any]						):
		# max_length=None => use the model max length (it's actually the default)
		A								=				tokenizer(examples["""sentence1"""]							,					examples["""sentence2"""]							,					truncation=UpperCamelCase__							,					max_length=UpperCamelCase__						)
		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():
		A								=				datasets.map(
		    UpperCamelCase__							,					batched=UpperCamelCase__							,					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
	A								=				tokenized_datasets.rename_column("""label"""							,					"""labels"""						)

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

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

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

	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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any						) ->   int:





	"""simple docstring"""







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

	A								=				evaluate.load("""glue"""							,					"""mrpc"""						)

	# If the batch size is too big we use gradient accumulation
	A								=				1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A								=				batch_size // MAX_GPU_BATCH_SIZE
		A								=				MAX_GPU_BATCH_SIZE

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

	# 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).
	A								=				model.to(accelerator.device						)

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

	# Instantiate scheduler
	A								=				get_linear_schedule_with_warmup(
	    optimizer=UpperCamelCase__							,					num_warmup_steps=1_00							,					num_training_steps=(len(UpperCamelCase__						) * num_epochs) // gradient_accumulation_steps							,					)

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

	# Now we train the model
	for epoch in range(UpperCamelCase__						):
		model.train()
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			A								=				model(**UpperCamelCase__						)
			A								=				outputs.loss
			A								=				loss / gradient_accumulation_steps
			accelerator.backward(UpperCamelCase__						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		A								=				0
		for step, batch in enumerate(UpperCamelCase__						):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device						)
			with torch.no_grad():
				A								=				model(**UpperCamelCase__						)
			A								=				outputs.logits.argmax(dim=-1						)
			A					,			A								=				accelerator.gather((predictions, batch["""labels"""])						)
			# New Code #
			# First we check if it's a distributed system
			if accelerator.use_distributed:
				# Then see if we're on the last batch of our eval dataloader
				if step == len(UpperCamelCase__						) - 1:
					# Last batch needs to be truncated on distributed systems as it contains additional samples
					A								=				predictions[: len(eval_dataloader.dataset						) - samples_seen]
					A								=				references[: len(eval_dataloader.dataset						) - samples_seen]
				else:
					# Otherwise we add the number of samples seen
					samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
			metric.add_batch(
			    predictions=UpperCamelCase__							,					references=UpperCamelCase__							,					)

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





def   _lowerCAmelCase				(   ) ->   Dict:





	"""simple docstring"""







	A								=				argparse.ArgumentParser(description="""Simple example of training script."""						)
	parser.add_argument(
	    """--mixed_precision"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					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."""						)
	A								=				parser.parse_args()
	A								=				{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(UpperCamelCase__							,					UpperCamelCase__						)


if __name__ == "__main__":
				main()



