



import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


__A   			=datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
				import pyspark

@dataclass
class 		UpperCAmelCase__			(			datasets.BuilderConfig   ):




			'''simple docstring'''






			UpperCamelCase									=       None

def        a						(      _UpperCAmelCase							:					"pyspark.sql.DataFrame" ,   _UpperCAmelCase							:					List[int] ,   ):







		'''simple docstring'''





		import pyspark

		def generate_fn():
				__UpperCAmelCase    :     Optional[int]													=	df.select('''*''' ,   pyspark.sql.functions.spark_partition_id().alias('''part_id'''						)						)
				for partition_id in partition_order:
						__UpperCAmelCase    :     Any													=	df_with_partition_id.select('''*'''						).where(f'part_id = {partition_id}'						).drop('''part_id'''						)
						__UpperCAmelCase    :     Optional[Any]													=	partition_df.collect()
						__UpperCAmelCase    :     Optional[Any]													=	0
						for row in rows:
								yield f'{partition_id}_{row_id}', row.asDict()
								row_id += 1

		return generate_fn

class 		UpperCAmelCase__			(			_BaseExamplesIterable   ):




			'''simple docstring'''






			def __init__(   self							: Optional[Any]       ,       a_							: "pyspark.sql.DataFrame"       ,       a_							: List[str]=None       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	df
					__UpperCAmelCase    :     Any													=	partition_order or range(self.df.rdd.getNumPartitions() )
					__UpperCAmelCase    :     List[str]													=	_generate_iterable_examples(self.df       ,       self.partition_order )




			def __iter__(   self							: Optional[Any] ):
					'''simple docstring'''





					yield from self.generate_examples_fn()




			def  snake_case__     (   self							: int       ,       a_							: np.random.Generator ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	list(range(self.df.rdd.getNumPartitions() ) )
					generator.shuffle(a_ )
					return SparkExamplesIterable(self.df       ,       partition_order=a_ )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: int       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.split_shard_indices_by_worker(a_       ,       a_ )
					return SparkExamplesIterable(self.df       ,       partition_order=a_ )




			@property
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					return len(self.partition_order )

class 		UpperCAmelCase__			(			datasets.DatasetBuilder   ):




			'''simple docstring'''






			UpperCamelCase									=       SparkConfig




			def __init__(   self							: List[str]       ,       a_							: "pyspark.sql.DataFrame"       ,       a_							: str = None       ,       a_							: str = None       ,       **a_							: Any       ,       ):
					'''simple docstring'''





					import pyspark

					__UpperCAmelCase    :     Union[str, Any]													=	pyspark.sql.SparkSession.builder.getOrCreate()
					__UpperCAmelCase    :     Union[str, Any]													=	df
					__UpperCAmelCase    :     List[Any]													=	working_dir

					super().__init__(
					    cache_dir=a_       ,       config_name=str(self.df.semanticHash() )       ,       **a_       ,       )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					def create_cache_and_write_probe(a_							: int ):
							# makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
							# already exist.
							os.makedirs(self._cache_dir       ,       exist_ok=a_ )
							__UpperCAmelCase    :     Tuple													=	os.path.join(self._cache_dir       ,       '''fs_test''' + uuid.uuida().hex )
							# Opening the file in append mode will create a new file unless it already exists, in which case it will not
							# change the file contents.
							open(a_       ,       '''a''' )
							return [probe_file]

					if self._spark.conf.get('''spark.master'''       ,       '''''' ).startswith('''local''' ):
							return

					# If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
					# accessible to the driver.
					# TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
					if self._cache_dir:
							__UpperCAmelCase    :     str													=	(
							    self._spark.sparkContext.parallelize(range(1 )       ,       1 ).mapPartitions(a_ ).collect()
							)
							if os.path.isfile(probe[0] ):
									return

					raise ValueError(
					    '''When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir''' )




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					return datasets.DatasetInfo(features=self.config.features )




			def  snake_case__     (   self							: Dict       ,       a_							: datasets.download.download_manager.DownloadManager ):
					'''simple docstring'''





					return [datasets.SplitGenerator(name=datasets.Split.TRAIN )]




			def  snake_case__     (   self							: Any       ,       a_							: Optional[Any] ):
					'''simple docstring'''





					import pyspark

					def get_arrow_batch_size(a_							: Dict ):
							for batch in it:
									yield pa.RecordBatch.from_pydict({'''batch_bytes''': [batch.nbytes]} )

					__UpperCAmelCase    :     Dict													=	self.df.count()
					__UpperCAmelCase    :     Optional[int]													=	df_num_rows if df_num_rows <= 1_00 else 1_00
					# Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
					__UpperCAmelCase    :     Optional[Any]													=	(
					    self.df.limit(a_ )
					    .repartition(1 )
					    .mapInArrow(a_       ,       '''batch_bytes: long''' )
					    .agg(pyspark.sql.functions.sum('''batch_bytes''' ).alias('''sample_bytes''' ) )
					    .collect()[0]
					    .sample_bytes
					    / sample_num_rows
					)
					__UpperCAmelCase    :     Optional[Any]													=	approx_bytes_per_row * df_num_rows
					if approx_total_size > max_shard_size:
							# Make sure there is at least one row per partition.
							__UpperCAmelCase    :     Dict													=	min(a_       ,       int(approx_total_size / max_shard_size ) )
							__UpperCAmelCase    :     List[Any]													=	self.df.repartition(a_ )




			def  snake_case__     (   self							: Any       ,       a_							: str       ,       a_							: str       ,       a_							: int       ,       ):
					'''simple docstring'''





					import pyspark

					__UpperCAmelCase    :     Dict													=	ParquetWriter if file_format == '''parquet''' else ArrowWriter
					__UpperCAmelCase    :     List[str]													=	os.path.join(self._working_dir       ,       os.path.basename(a_ ) ) if self._working_dir else fpath
					__UpperCAmelCase    :     str													=	file_format == '''parquet'''

					# Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
					# pickling the SparkContext.
					__UpperCAmelCase    :     Tuple													=	self.config.features
					__UpperCAmelCase    :     Dict													=	self._writer_batch_size
					__UpperCAmelCase    :     Union[str, Any]													=	self._fs.storage_options

					def write_arrow(a_							: str ):
							# Within the same SparkContext, no two task attempts will share the same attempt ID.
							__UpperCAmelCase    :     int													=	pyspark.TaskContext().taskAttemptId()
							__UpperCAmelCase    :     Any													=	next(a_       ,       a_ )
							if first_batch is None:
									# Some partitions might not receive any data.
									return pa.RecordBatch.from_arrays(
									    [[task_id], [0], [0]]       ,       names=['''task_id''', '''num_examples''', '''num_bytes''']       ,       )
							__UpperCAmelCase    :     Dict													=	0
							__UpperCAmelCase    :     Tuple													=	writer_class(
							    features=a_       ,       path=working_fpath.replace('''SSSSS'''       ,       F'{shard_id:05d}' ).replace('''TTTTT'''       ,       F'{task_id:05d}' )       ,       writer_batch_size=a_       ,       storage_options=a_       ,       embed_local_files=a_       ,       )
							__UpperCAmelCase    :     Optional[Any]													=	pa.Table.from_batches([first_batch] )
							writer.write_table(a_ )
							for batch in it:
									if max_shard_size is not None and writer._num_bytes >= max_shard_size:
											__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	writer.finalize()
											writer.close()
											yield pa.RecordBatch.from_arrays(
											    [[task_id], [num_examples], [num_bytes]]       ,       names=['''task_id''', '''num_examples''', '''num_bytes''']       ,       )
											shard_id += 1
											__UpperCAmelCase    :     Dict													=	writer_class(
											    features=writer._features       ,       path=working_fpath.replace('''SSSSS'''       ,       F'{shard_id:05d}' ).replace('''TTTTT'''       ,       F'{task_id:05d}' )       ,       writer_batch_size=a_       ,       storage_options=a_       ,       embed_local_files=a_       ,       )
									__UpperCAmelCase    :     Union[str, Any]													=	pa.Table.from_batches([batch] )
									writer.write_table(a_ )

							if writer._num_bytes > 0:
									__UpperCAmelCase							,					__UpperCAmelCase    :     Dict													=	writer.finalize()
									writer.close()
									yield pa.RecordBatch.from_arrays(
									    [[task_id], [num_examples], [num_bytes]]       ,       names=['''task_id''', '''num_examples''', '''num_bytes''']       ,       )

							if working_fpath != fpath:
									for file in os.listdir(os.path.dirname(a_ ) ):
											__UpperCAmelCase    :     List[str]													=	os.path.join(os.path.dirname(a_ )       ,       os.path.basename(a_ ) )
											shutil.move(a_       ,       a_ )

					__UpperCAmelCase    :     str													=	(
					    self.df.mapInArrow(a_       ,       '''task_id: long, num_examples: long, num_bytes: long''' )
					    .groupBy('''task_id''' )
					    .agg(
					        pyspark.sql.functions.sum('''num_examples''' ).alias('''total_num_examples''' )       ,       pyspark.sql.functions.sum('''num_bytes''' ).alias('''total_num_bytes''' )       ,       pyspark.sql.functions.count('''num_bytes''' ).alias('''num_shards''' )       ,       pyspark.sql.functions.collect_list('''num_examples''' ).alias('''shard_lengths''' )       ,       )
					    .collect()
					)
					for row in stats:
							yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)




			def  snake_case__     (   self							: Optional[int]       ,       a_							: "datasets.SplitGenerator"       ,       a_							: str = "arrow"       ,       a_							: Optional[Union[str, int]] = None       ,       a_							: Optional[int] = None       ,       **a_							: Union[str, Any]       ,       ):
					'''simple docstring'''





					self._validate_cache_dir()

					__UpperCAmelCase    :     Optional[int]													=	convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE )
					self._repartition_df_if_needed(a_ )
					__UpperCAmelCase    :     List[str]													=	not is_remote_filesystem(self._fs )
					__UpperCAmelCase    :     Union[str, Any]													=	os.path.join if is_local else posixpath.join

					__UpperCAmelCase    :     str													=	'''-TTTTT-SSSSS-of-NNNNN'''
					__UpperCAmelCase    :     List[Any]													=	F'{self.name}-{split_generator.name}{SUFFIX}.{file_format}'
					__UpperCAmelCase    :     Dict													=	path_join(self._output_dir       ,       a_ )

					__UpperCAmelCase    :     Any													=	0
					__UpperCAmelCase    :     Tuple													=	0
					__UpperCAmelCase    :     Any													=	0
					__UpperCAmelCase    :     Optional[Any]													=	[]
					__UpperCAmelCase    :     Dict													=	[]

					for task_id, content in self._prepare_split_single(a_       ,       a_       ,       a_ ):
							(
							    (
							    __UpperCAmelCase
							)							,					(
							    __UpperCAmelCase
							)							,					(
							    __UpperCAmelCase
							)							,					(
							    __UpperCAmelCase
							)							,					
							)    :     List[Any]													=	content
							if num_bytes > 0:
									total_num_examples += num_examples
									total_num_bytes += num_bytes
									total_shards += num_shards
									task_id_and_num_shards.append((task_id, num_shards) )
									all_shard_lengths.extend(a_ )

					__UpperCAmelCase    :     Optional[Any]													=	total_num_examples
					__UpperCAmelCase    :     Optional[Any]													=	total_num_bytes

					# should rename everything at the end
					logger.debug(F'Renaming {total_shards} shards.' )
					if total_shards > 1:
							__UpperCAmelCase    :     int													=	all_shard_lengths

							# Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
							# pickling error due to pickling the SparkContext.
							__UpperCAmelCase    :     List[str]													=	self._fs

							# use the -SSSSS-of-NNNNN pattern
							def _rename_shard(
							    a_							: int       ,       a_							: int       ,       a_							: int       ,       ):
									rename(
									    a_       ,       fpath.replace('''SSSSS'''       ,       F'{shard_id:05d}' ).replace('''TTTTT'''       ,       F'{task_id:05d}' )       ,       fpath.replace('''TTTTT-SSSSS'''       ,       F'{global_shard_id:05d}' ).replace('''NNNNN'''       ,       F'{total_shards:05d}' )       ,       )

							__UpperCAmelCase    :     Tuple													=	[]
							__UpperCAmelCase    :     Any													=	0
							for i in range(len(a_ ) ):
									__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[int]													=	task_id_and_num_shards[i]
									for shard_id in range(a_ ):
											args.append([task_id, shard_id, global_shard_id] )
											global_shard_id += 1
							self._spark.sparkContext.parallelize(a_       ,       len(a_ ) ).map(lambda a_ : _rename_shard(*a_ ) ).collect()
					else:
							# don't use any pattern
							__UpperCAmelCase    :     Dict													=	0
							__UpperCAmelCase    :     Any													=	task_id_and_num_shards[0][0]
							self._rename(
							    fpath.replace('''SSSSS'''       ,       F'{shard_id:05d}' ).replace('''TTTTT'''       ,       F'{task_id:05d}' )       ,       fpath.replace(a_       ,       '''''' )       ,       )




			def  snake_case__     (   self							: int       ,       a_							: "datasets.SplitGenerator"       ,       ):
					'''simple docstring'''





					return SparkExamplesIterable(self.df )










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


__A   			=1_6
__A   			=3_2

def        a						(      _UpperCAmelCase							:					Accelerator ,   _UpperCAmelCase							:					int = 16						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	AutoTokenizer.from_pretrained('''bert-base-cased'''						)
		__UpperCAmelCase    :     Any													=	load_dataset('''glue''' ,   '''mrpc'''						)

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

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

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

		# Instantiate dataloaders.
		__UpperCAmelCase    :     Optional[Any]													=	DataLoader(
		    tokenized_datasets['''train'''] ,   shuffle=_UpperCAmelCase ,   collate_fn=_UpperCAmelCase ,   batch_size=_UpperCAmelCase						)
		__UpperCAmelCase    :     str													=	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

				__A   			=mocked_dataloaders  # noqa: F811

def        a						(      _UpperCAmelCase							:					List[str] ,   _UpperCAmelCase							:					Any						):







		'''simple docstring'''





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

		__UpperCAmelCase    :     Optional[Any]													=	evaluate.load('''glue''' ,   '''mrpc'''						)

		# If the batch size is too big we use gradient accumulation
		__UpperCAmelCase    :     List[str]													=	1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__UpperCAmelCase    :     Tuple													=	batch_size // MAX_GPU_BATCH_SIZE
				__UpperCAmelCase    :     List[str]													=	MAX_GPU_BATCH_SIZE

		set_seed(_UpperCAmelCase						)
		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	get_dataloaders(_UpperCAmelCase ,   _UpperCAmelCase						)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__UpperCAmelCase    :     List[str]													=	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).
		__UpperCAmelCase    :     Tuple													=	model.to(accelerator.device						)

		# Instantiate optimizer
		__UpperCAmelCase    :     Any													=	AdamW(params=model.parameters() ,   lr=_UpperCAmelCase						)

		# Instantiate scheduler
		__UpperCAmelCase    :     str													=	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.
		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     List[str]													=	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						)
						__UpperCAmelCase    :     int													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.loss
						__UpperCAmelCase    :     str													=	loss / gradient_accumulation_steps
						accelerator.backward(_UpperCAmelCase						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__UpperCAmelCase    :     Tuple													=	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():
								__UpperCAmelCase    :     Dict													=	model(**_UpperCAmelCase						)
						__UpperCAmelCase    :     str													=	outputs.logits.argmax(dim=-1						)
						__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	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
										__UpperCAmelCase    :     Dict													=	predictions[: len(eval_dataloader.dataset						) - samples_seen]
										__UpperCAmelCase    :     List[str]													=	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 ,   )

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

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	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.'''						)
		__UpperCAmelCase    :     int													=	parser.parse_args()
		__UpperCAmelCase    :     Union[str, Any]													=	{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
		training_function(_UpperCAmelCase ,   _UpperCAmelCase						)


if __name__ == "__main__":
				main()





