




"""simple docstring"""

import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
		from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
		_lowerCamelCase	:      Optional[Any]   =							None

if is_ray_available():
		import ray  # noqa: E402 # isort:skip
		from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
		_lowerCamelCase	:      List[str]   =							None
		_lowerCamelCase	:      List[Any]   =							None
		_lowerCamelCase	:      Union[str, Any]   =							None

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
		A_	:					Union[str, Any]						 =		unittest.skip('''test requires Datasets, Faiss, psutil'''						)(_UpperCAmelCase						)
	return test_case

@require_distributed_retrieval
class 							lowercase   (     __UpperCAmelCase):







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					str						 =		tempfile.mkdtemp()
					A_	:					int						 =		8

					# DPR tok
					A_	:					Optional[Any]						 =		[
					    '''[UNK]''',
					    '''[CLS]''',
					    '''[SEP]''',
					    '''[PAD]''',
					    '''[MASK]''',
					    '''want''',
					    '''##want''',
					    '''##ed''',
					    '''wa''',
					    '''un''',
					    '''runn''',
					    '''##ing''',
					    ''',''',
					    '''low''',
					    '''lowest''',
					]
					A_	:					Optional[Any]						 =		os.path.join(self.tmpdirname   ,  '''dpr_tokenizer'''							)
					os.makedirs(_lowerCamelCase   ,  exist_ok=_lowerCamelCase							)
					A_	:					Dict						 =		os.path.join(_lowerCamelCase   ,  DPR_VOCAB_FILES_NAMES['''vocab_file''']							)
					with open(self.vocab_file   ,  '''w'''   ,  encoding='''utf-8'''							) as vocab_writer:
						vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]							)							)

					# BART tok
					A_	:					List[Any]						 =		[
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''\u0120''',
					    '''\u0120l''',
					    '''\u0120n''',
					    '''\u0120lo''',
					    '''\u0120low''',
					    '''er''',
					    '''\u0120lowest''',
					    '''\u0120newer''',
					    '''\u0120wider''',
					    '''<unk>''',
					]
					A_	:					int						 =		dict(zip(_lowerCamelCase   ,  range(len(_lowerCamelCase							)							)							)							)
					A_	:					Dict						 =		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
					A_	:					str						 =		{'''unk_token''': '''<unk>'''}

					A_	:					Optional[int]						 =		os.path.join(self.tmpdirname   ,  '''bart_tokenizer'''							)
					os.makedirs(_lowerCamelCase   ,  exist_ok=_lowerCamelCase							)
					A_	:					int						 =		os.path.join(_lowerCamelCase   ,  BART_VOCAB_FILES_NAMES['''vocab_file''']							)
					A_	:					str						 =		os.path.join(_lowerCamelCase   ,  BART_VOCAB_FILES_NAMES['''merges_file''']							)
					with open(self.vocab_file   ,  '''w'''   ,  encoding='''utf-8'''							) as fp:
						fp.write(json.dumps(_lowerCamelCase							) + '''\n'''							)
					with open(self.merges_file   ,  '''w'''   ,  encoding='''utf-8'''							) as fp:
						fp.write('''\n'''.join(_lowerCamelCase							)							)







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,  '''dpr_tokenizer'''							)							)







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,  '''bart_tokenizer'''							)							)







				def        a_	(				self :       Union[str, Any]							):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname							)







				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

					A_	:					int						 =		Dataset.from_dict(
					    {
					        '''id''': ['''0''', '''1'''],
					        '''text''': ['''foo''', '''bar'''],
					        '''title''': ['''Foo''', '''Bar'''],
					        '''embeddings''': [np.ones(self.retrieval_vector_size							), 2 * np.ones(self.retrieval_vector_size							)],
					    }							)
					dataset.add_faiss_index('''embeddings'''   ,  string_factory='''Flat'''   ,  metric_type=faiss.METRIC_INNER_PRODUCT							)
					return dataset







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       bool   ,  _lowerCamelCase :       Optional[int]=1_23_45							):



					"""simple docstring"""

					A_	:					str						 =		self.get_dummy_dataset()
					A_	:					Dict						 =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  )
					with patch('''transformers.models.rag.retrieval_rag.load_dataset'''							) as mock_load_dataset:
						A_	:					Tuple						 =		dataset
						A_	:					List[Any]						 =		RagPyTorchDistributedRetriever(
						    _lowerCamelCase   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
						if init_retrieval:
							retriever.init_retrieval(_lowerCamelCase							)
					return retriever







				def        a_	(				self :       Any   ,  _lowerCamelCase :       bool							):



					"""simple docstring"""

					ray.init(local_mode=_lowerCamelCase							)
					A_	:					int						 =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  )
					A_	:					Any						 =		ray.remote(_lowerCamelCase							)
					A_	:					List[str]						 =		[remote_cls.remote() for _ in range(1							)]
					with patch('''transformers.models.rag.retrieval_rag.load_dataset'''							) as mock_load_dataset:
						A_	:					Union[str, Any]						 =		self.get_dummy_dataset()
						A_	:					str						 =		RagRayDistributedRetriever(
						    _lowerCamelCase   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  retrieval_workers=_lowerCamelCase   ,  )
						if init_retrieval:
							retriever.init_retrieval()
					return retriever







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       bool   ,  _lowerCamelCase :       bool   ,  _lowerCamelCase :       int=1_23_45							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		self.get_dummy_dataset()
					A_	:					List[Any]						 =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  index_name='''custom'''   ,  )
					if from_disk:
						A_	:					Any						 =		os.path.join(self.tmpdirname   ,  '''dataset'''							)
						A_	:					int						 =		os.path.join(self.tmpdirname   ,  '''index.faiss'''							)
						dataset.get_index('''embeddings'''							).save(os.path.join(self.tmpdirname   ,  '''index.faiss'''							)							)
						dataset.drop_index('''embeddings'''							)
						dataset.save_to_disk(os.path.join(self.tmpdirname   ,  '''dataset'''							)							)
						del dataset
						A_	:					Optional[int]						 =		RagPyTorchDistributedRetriever(
						    _lowerCamelCase   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  )
					else:
						A_	:					Tuple						 =		RagPyTorchDistributedRetriever(
						    _lowerCamelCase   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  index=CustomHFIndex(config.retrieval_vector_size   ,  _lowerCamelCase							)   ,  )
					if init_retrieval:
						retriever.init_retrieval(_lowerCamelCase							)
					return retriever







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       bool   ,  _lowerCamelCase :       bool							):



					"""simple docstring"""

					ray.init(local_mode=_lowerCamelCase							)
					A_	:					Union[str, Any]						 =		self.get_dummy_dataset()
					A_	:					List[str]						 =		RagConfig(
					    retrieval_vector_size=self.retrieval_vector_size   ,  question_encoder=DPRConfig().to_dict()   ,  generator=BartConfig().to_dict()   ,  index_name='''custom'''   ,  )
					A_	:					List[str]						 =		ray.remote(_lowerCamelCase							)
					A_	:					int						 =		[remote_cls.remote() for _ in range(1							)]
					if from_disk:
						A_	:					List[str]						 =		os.path.join(self.tmpdirname   ,  '''dataset'''							)
						A_	:					str						 =		os.path.join(self.tmpdirname   ,  '''index.faiss'''							)
						dataset.get_index('''embeddings'''							).save(os.path.join(self.tmpdirname   ,  '''index.faiss'''							)							)
						dataset.drop_index('''embeddings'''							)
						dataset.save_to_disk(os.path.join(self.tmpdirname   ,  '''dataset'''							)							)
						del dataset
						A_	:					Optional[int]						 =		RagRayDistributedRetriever(
						    _lowerCamelCase   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  retrieval_workers=_lowerCamelCase   ,  index=CustomHFIndex.load_from_disk(
						        vector_size=config.retrieval_vector_size   ,  dataset_path=config.passages_path   ,  index_path=config.index_path   ,  )   ,  )
					else:
						A_	:					Tuple						 =		RagRayDistributedRetriever(
						    _lowerCamelCase   ,  question_encoder_tokenizer=self.get_dpr_tokenizer()   ,  generator_tokenizer=self.get_bart_tokenizer()   ,  retrieval_workers=_lowerCamelCase   ,  index=CustomHFIndex(config.retrieval_vector_size   ,  _lowerCamelCase							)   ,  )
					if init_retrieval:
						retriever.init_retrieval()
					return retriever







				def        a_	(				self :       Optional[int]   ,  _lowerCamelCase :       RagRetriever   ,  _lowerCamelCase :       np.array   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

					A_ ,			A_ ,			A_	:					Tuple						 =		retriever.retrieve(_lowerCamelCase   ,  n_docs=_lowerCamelCase							)
					self.assertEqual(retrieved_doc_embeds.shape   ,  (2, n_docs, self.retrieval_vector_size)							)
					self.assertEqual(len(_lowerCamelCase							)   ,  2							)
					self.assertEqual(sorted(doc_dicts[0]							)   ,  ['''embeddings''', '''id''', '''text''', '''title''']							)
					self.assertEqual(len(doc_dicts[0]['''id''']							)   ,  _lowerCamelCase							)
					self.assertEqual(doc_dicts[0]['''id'''][0]   ,  '''1'''							)  # max inner product is reached with second doc
					self.assertEqual(doc_dicts[1]['''id'''][0]   ,  '''0'''							)  # max inner product is reached with first doc
					self.assertListEqual(doc_ids.tolist()   ,  [[1], [0]]							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					int						 =		1
					A_	:					List[Any]						 =		np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]   ,  dtype=np.floataa							)

					self.distributed_retriever_check(
					    self.get_dummy_pytorch_distributed_retriever(init_retrieval=_lowerCamelCase							)   ,  _lowerCamelCase   ,  _lowerCamelCase							)







				def        a_	(				self :       str							):



					"""simple docstring"""

					A_	:					str						 =		1
					A_	:					List[str]						 =		np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]   ,  dtype=np.floataa							)

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowerCamelCase   ,  from_disk=_lowerCamelCase							)   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  )







				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Tuple						 =		1
					A_	:					Optional[Any]						 =		np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]   ,  dtype=np.floataa							)

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=_lowerCamelCase   ,  from_disk=_lowerCamelCase							)   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  )







				@require_ray
				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		1
					A_	:					Dict						 =		np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]   ,  dtype=np.floataa							)

					self.distributed_retriever_check(
					    self.get_dummy_ray_distributed_retriever(init_retrieval=_lowerCamelCase							)   ,  _lowerCamelCase   ,  _lowerCamelCase							)
					ray.shutdown()







				@require_ray
				def        a_	(				self :       str							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		1
					A_	:					List[str]						 =		np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]   ,  dtype=np.floataa							)
					with self.assertRaises(_lowerCamelCase							):
						self.distributed_retriever_check(
						    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowerCamelCase   ,  from_disk=_lowerCamelCase							)   ,  _lowerCamelCase   ,  _lowerCamelCase   ,  )
					ray.shutdown()







				@require_ray
				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					Any						 =		1
					A_	:					Optional[Any]						 =		np.array(
					    [np.ones(self.retrieval_vector_size							), -np.ones(self.retrieval_vector_size							)]   ,  dtype=np.floataa							)

					self.distributed_retriever_check(
					    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=_lowerCamelCase   ,  from_disk=_lowerCamelCase							)   ,  _lowerCamelCase   ,  _lowerCamelCase							)
					ray.shutdown()











"""simple docstring"""

import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# 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)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


_lowerCamelCase	:      Dict   =							16
_lowerCamelCase	:      Union[str, Any]   =							32

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	if args.with_tracking:
		A_	:					Optional[int]						 =		Accelerator(
		    cpu=args.cpu	,   mixed_precision=args.mixed_precision	,   log_with='''all'''	,   project_dir=args.project_dir						)
	else:
		A_	:					str						 =		Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision						)

	if hasattr(args.checkpointing_steps	,   '''isdigit'''						):
		if args.checkpointing_steps == "epoch":
			A_	:					Optional[int]						 =		args.checkpointing_steps
		elif args.checkpointing_steps.isdigit():
			A_	:					Dict						 =		int(args.checkpointing_steps						)
		else:
			raise ValueError(
			    f"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""						)
	else:
		A_	:					Dict						 =		None
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	A_	:					List[Any]						 =		config['''lr''']
	A_	:					Optional[Any]						 =		int(config['''num_epochs''']						)
	A_	:					Optional[Any]						 =		int(config['''seed''']						)
	A_	:					List[str]						 =		int(config['''batch_size''']						)

	# We need to initialize the trackers we use, and also store our configuration
	if args.with_tracking:
		A_	:					Dict						 =		os.path.split(_UpperCAmelCase						)[-1].split('''.'''						)[0]
		accelerator.init_trackers(_UpperCAmelCase	,   _UpperCAmelCase						)

	A_	:					Optional[Any]						 =		AutoTokenizer.from_pretrained('''bert-base-cased'''						)
	A_	:					Any						 =		load_dataset('''glue'''	,   '''mrpc'''						)
	A_	:					Optional[int]						 =		evaluate.load('''glue'''	,   '''mrpc'''						)

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

	# If the batch size is too big we use gradient accumulation
	A_	:					Optional[int]						 =		1
	if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
		A_	:					Union[str, Any]						 =		batch_size // MAX_GPU_BATCH_SIZE
		A_	:					List[str]						 =		MAX_GPU_BATCH_SIZE

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

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

	# Instantiate dataloaders.
	A_	:					List[Any]						 =		DataLoader(
	    tokenized_datasets['''train''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)
	A_	:					Optional[int]						 =		DataLoader(
	    tokenized_datasets['''validation''']	,   shuffle=_UpperCAmelCase	,   collate_fn=_UpperCAmelCase	,   batch_size=_UpperCAmelCase						)

	set_seed(_UpperCAmelCase						)

	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	A_	:					Optional[int]						 =		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_	:					str						 =		model.to(accelerator.device						)

	# Instantiate optimizer
	A_	:					str						 =		AdamW(params=model.parameters()	,   lr=_UpperCAmelCase						)

	# Instantiate scheduler
	A_	:					List[str]						 =		get_linear_schedule_with_warmup(
	    optimizer=_UpperCAmelCase	,   num_warmup_steps=100	,   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_	:					Optional[int]						 =		accelerator.prepare(
	    _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase						)

	# We need to keep track of how many total steps we have iterated over
	A_	:					Tuple						 =		0
	# We also need to keep track of the stating epoch so files are named properly
	A_	:					Union[str, Any]						 =		0

	# Potentially load in the weights and states from a previous save
	if args.resume_from_checkpoint:
		if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
			accelerator.print(f"""Resumed from checkpoint: {args.resume_from_checkpoint}"""						)
			accelerator.load_state(args.resume_from_checkpoint						)
			A_	:					Union[str, Any]						 =		os.path.basename(args.resume_from_checkpoint						)
		else:
			# Get the most recent checkpoint
			A_	:					Union[str, Any]						 =		[f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
			dirs.sort(key=os.path.getctime						)
			A_	:					Optional[int]						 =		dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract `epoch_{i}` or `step_{i}`
		A_	:					Any						 =		os.path.splitext(_UpperCAmelCase						)[0]

		if "epoch" in training_difference:
			A_	:					Any						 =		int(training_difference.replace('''epoch_'''	,   ''''''						)						) + 1
			A_	:					str						 =		None
		else:
			A_	:					str						 =		int(training_difference.replace('''step_'''	,   ''''''						)						)
			A_	:					List[str]						 =		resume_step // len(_UpperCAmelCase						)
			resume_step -= starting_epoch * len(_UpperCAmelCase						)

    # Now we train the model
	for epoch in range(_UpperCAmelCase	,   _UpperCAmelCase						):
		model.train()
		if args.with_tracking:
			A_	:					Optional[Any]						 =		0
		if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
			# We need to skip steps until we reach the resumed step
			A_	:					int						 =		accelerator.skip_first_batches(_UpperCAmelCase	,   _UpperCAmelCase						)
			overall_step += resume_step
		else:
			# After the first iteration though, we need to go back to the original dataloader
			A_	:					Dict						 =		train_dataloader
		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_	:					Any						 =		model(**_UpperCAmelCase						)
			A_	:					Any						 =		outputs.loss
			A_	:					str						 =		loss / gradient_accumulation_steps
			# We keep track of the loss at each epoch
			if args.with_tracking:
				total_loss += loss.detach().float()
			accelerator.backward(_UpperCAmelCase						)
			if step % gradient_accumulation_steps == 0:
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

			overall_step += 1

			if isinstance(_UpperCAmelCase	,   _UpperCAmelCase						):
				A_	:					List[Any]						 =		f"""step_{overall_step}"""
				if overall_step % checkpointing_steps == 0:
					if args.output_dir is not None:
						A_	:					Tuple						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
					accelerator.save_state(_UpperCAmelCase						)

		model.eval()
		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_	:					Dict						 =		model(**_UpperCAmelCase						)
			A_	:					List[str]						 =		outputs.logits.argmax(dim=-1						)
			A_ ,			A_	:					Optional[int]						 =		accelerator.gather_for_metrics((predictions, batch['''labels'''])						)
			metric.add_batch(
			    predictions=_UpperCAmelCase	,   references=_UpperCAmelCase	,   )

		A_	:					Union[str, Any]						 =		metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""	,   _UpperCAmelCase						)
		if args.with_tracking:
			accelerator.log(
			    {
			        '''accuracy''': eval_metric['''accuracy'''],
			        '''f1''': eval_metric['''f1'''],
			        '''train_loss''': total_loss.item() / len(_UpperCAmelCase						),
			        '''epoch''': epoch,
			    }	,   step=_UpperCAmelCase	,   )

		if checkpointing_steps == "epoch":
			A_	:					str						 =		f"""epoch_{epoch}"""
			if args.output_dir is not None:
				A_	:					int						 =		os.path.join(args.output_dir	,   _UpperCAmelCase						)
			accelerator.save_state(_UpperCAmelCase						)

	if args.with_tracking:
		accelerator.end_training()

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					List[Any]						 =		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.'''						)
	parser.add_argument(
	    '''--checkpointing_steps'''	,   type=_UpperCAmelCase	,   default=_UpperCAmelCase	,   help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''	,   )
	parser.add_argument(
	    '''--resume_from_checkpoint'''	,   type=_UpperCAmelCase	,   default=_UpperCAmelCase	,   help='''If the training should continue from a checkpoint folder.'''	,   )
	parser.add_argument(
	    '''--with_tracking'''	,   action='''store_true'''	,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,   )
	parser.add_argument(
	    '''--output_dir'''	,   type=_UpperCAmelCase	,   default='''.'''	,   help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''	,   )
	parser.add_argument(
	    '''--project_dir'''	,   type=_UpperCAmelCase	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
	A_	:					Any						 =		parser.parse_args()
	A_	:					List[Any]						 =		{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
	training_function(_UpperCAmelCase	,   _UpperCAmelCase						)


if __name__ == "__main__":
		main()





