




"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import AlbertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TF_MODEL_FOR_PRETRAINING_MAPPING
		from transformers.models.albert.modeling_tf_albert import (
		    TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFAlbertForMaskedLM,
		    TFAlbertForMultipleChoice,
		    TFAlbertForPreTraining,
		    TFAlbertForQuestionAnswering,
		    TFAlbertForSequenceClassification,
		    TFAlbertForTokenClassification,
		    TFAlbertModel,
		)

class 							lowercase   :







				def __init__(				self :       List[str]   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       List[Any]=13   ,  _lowerCamelCase :       Optional[int]=7   ,  _lowerCamelCase :       Tuple=True   ,  _lowerCamelCase :       Tuple=True   ,  _lowerCamelCase :       Any=True   ,  _lowerCamelCase :       List[Any]=True   ,  _lowerCamelCase :       Optional[int]=99   ,  _lowerCamelCase :       Any=16   ,  _lowerCamelCase :       str=32   ,  _lowerCamelCase :       Dict=2   ,  _lowerCamelCase :       Optional[Any]=4   ,  _lowerCamelCase :       str=37   ,  _lowerCamelCase :       int="gelu"   ,  _lowerCamelCase :       Union[str, Any]=0.1   ,  _lowerCamelCase :       Dict=0.1   ,  _lowerCamelCase :       int=5_12   ,  _lowerCamelCase :       Tuple=16   ,  _lowerCamelCase :       Union[str, Any]=2   ,  _lowerCamelCase :       Union[str, Any]=0.02   ,  _lowerCamelCase :       str=3   ,  _lowerCamelCase :       Any=4   ,  _lowerCamelCase :       Union[str, Any]=None   ,  ):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		parent
					A_	:					List[Any]						 =		13
					A_	:					Union[str, Any]						 =		7
					A_	:					Optional[int]						 =		True
					A_	:					Union[str, Any]						 =		True
					A_	:					Any						 =		True
					A_	:					int						 =		True
					A_	:					Union[str, Any]						 =		99
					A_	:					Optional[int]						 =		16
					A_	:					Optional[int]						 =		32
					A_	:					List[Any]						 =		2
					A_	:					List[str]						 =		4
					A_	:					Tuple						 =		37
					A_	:					Optional[int]						 =		'''gelu'''
					A_	:					int						 =		0.1
					A_	:					int						 =		0.1
					A_	:					str						 =		5_12
					A_	:					Tuple						 =		16
					A_	:					Dict						 =		2
					A_	:					str						 =		0.02
					A_	:					str						 =		3
					A_	:					int						 =		4
					A_	:					Any						 =		None







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Any						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size							)

					A_	:					List[Any]						 =		None
					if self.use_input_mask:
						A_	:					Optional[Any]						 =		random_attention_mask([self.batch_size, self.seq_length]							)

					A_	:					Tuple						 =		None
					if self.use_token_type_ids:
						A_	:					Dict						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.type_vocab_size							)

					A_	:					List[str]						 =		None
					A_	:					int						 =		None
					A_	:					List[str]						 =		None
					if self.use_labels:
						A_	:					List[Any]						 =		ids_tensor([self.batch_size]   ,  self.type_sequence_label_size							)
						A_	:					Union[str, Any]						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.num_labels							)
						A_	:					List[Any]						 =		ids_tensor([self.batch_size]   ,  self.num_choices							)

					A_	:					Any						 =		AlbertConfig(
					    vocab_size=self.vocab_size   ,  hidden_size=self.hidden_size   ,  embedding_size=self.embedding_size   ,  num_hidden_layers=self.num_hidden_layers   ,  num_attention_heads=self.num_attention_heads   ,  intermediate_size=self.intermediate_size   ,  hidden_act=self.hidden_act   ,  hidden_dropout_prob=self.hidden_dropout_prob   ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,  max_position_embeddings=self.max_position_embeddings   ,  type_vocab_size=self.type_vocab_size   ,  initializer_range=self.initializer_range   ,  )

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







				def        a_	(				self :       Optional[Any]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       Dict   ,  _lowerCamelCase :       List[Any]							):



					"""simple docstring"""

					A_	:					List[str]						 =		TFAlbertModel(config=_lowerCamelCase							)
					# inputs = {'input_ids': input_ids,
					#           'attention_mask': input_mask,
					#           'token_type_ids': token_type_ids}
					# sequence_output, pooled_output = model(**inputs)
					A_	:					Tuple						 =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					A_	:					int						 =		model(_lowerCamelCase							)

					A_	:					List[Any]						 =		[input_ids, input_mask]
					A_	:					Tuple						 =		model(_lowerCamelCase							)

					A_	:					Tuple						 =		model(_lowerCamelCase							)

					self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)							)
					self.parent.assertEqual(result.pooler_output.shape   ,  (self.batch_size, self.hidden_size)							)







				def        a_	(				self :       List[Any]   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Union[str, Any]							):



					"""simple docstring"""

					A_	:					Dict						 =		self.num_labels
					A_	:					Union[str, Any]						 =		TFAlbertForPreTraining(config=_lowerCamelCase							)
					A_	:					List[Any]						 =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					A_	:					Union[str, Any]						 =		model(_lowerCamelCase							)
					self.parent.assertEqual(result.prediction_logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)							)
					self.parent.assertEqual(result.sop_logits.shape   ,  (self.batch_size, self.num_labels)							)







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

					A_	:					str						 =		TFAlbertForMaskedLM(config=_lowerCamelCase							)
					A_	:					List[Any]						 =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					A_	:					Any						 =		model(_lowerCamelCase							)
					self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)							)







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Union[str, Any]   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					int						 =		self.num_labels
					A_	:					Optional[int]						 =		TFAlbertForSequenceClassification(config=_lowerCamelCase							)
					A_	:					List[Any]						 =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					A_	:					Dict						 =		model(_lowerCamelCase							)
					self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.num_labels)							)







				def        a_	(				self :       Dict   ,  _lowerCamelCase :       Dict   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       Union[str, Any]   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       List[Any]							):



					"""simple docstring"""

					A_	:					int						 =		TFAlbertForQuestionAnswering(config=_lowerCamelCase							)
					A_	:					List[Any]						 =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
					A_	:					Any						 =		model(_lowerCamelCase							)
					self.parent.assertEqual(result.start_logits.shape   ,  (self.batch_size, self.seq_length)							)
					self.parent.assertEqual(result.end_logits.shape   ,  (self.batch_size, self.seq_length)							)







				def        a_	(				self :       str   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Dict   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       Any							):



					"""simple docstring"""

					A_	:					Tuple						 =		self.num_choices
					A_	:					Any						 =		TFAlbertForMultipleChoice(config=_lowerCamelCase							)
					A_	:					Optional[Any]						 =		tf.tile(tf.expand_dims(_lowerCamelCase   ,  1							)   ,  (1, self.num_choices, 1)							)
					A_	:					int						 =		tf.tile(tf.expand_dims(_lowerCamelCase   ,  1							)   ,  (1, self.num_choices, 1)							)
					A_	:					Any						 =		tf.tile(tf.expand_dims(_lowerCamelCase   ,  1							)   ,  (1, self.num_choices, 1)							)
					A_	:					Dict						 =		{
					    '''input_ids''': multiple_choice_inputs_ids,
					    '''attention_mask''': multiple_choice_input_mask,
					    '''token_type_ids''': multiple_choice_token_type_ids,
					}
					A_	:					Dict						 =		model(_lowerCamelCase							)
					self.parent.assertListEqual(list(result['''logits'''].shape							)   ,  [self.batch_size, self.num_choices]							)







				def        a_	(				self :       Optional[int]   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       Union[str, Any]   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       Dict							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		self.num_labels
					A_	:					Any						 =		TFAlbertForTokenClassification(config=_lowerCamelCase							)
					A_	:					Optional[Any]						 =		{
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					A_	:					Any						 =		model(_lowerCamelCase							)
					self.parent.assertListEqual(list(result['''logits'''].shape							)   ,  [self.batch_size, self.seq_length, self.num_labels]							)







				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Dict						 =		self.prepare_config_and_inputs()
					(
					    (
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			
					)	:					Optional[int]						 =		config_and_inputs
					A_	:					List[Any]						 =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

@require_tf
class 							lowercase   (     __UpperCAmelCase  ,						__UpperCAmelCase  ,						unittest.TestCase):
				__lowerCAmelCase     :     Union[str, Any]								= (
				    (
				        TFAlbertModel,
				        TFAlbertForPreTraining,
				        TFAlbertForMaskedLM,
				        TFAlbertForSequenceClassification,
				        TFAlbertForQuestionAnswering,
				        TFAlbertForTokenClassification,
				        TFAlbertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				__lowerCAmelCase     :     Optional[Any]								= (
				    {
				        """feature-extraction""": TFAlbertModel,
				        """fill-mask""": TFAlbertForMaskedLM,
				        """question-answering""": TFAlbertForQuestionAnswering,
				        """text-classification""": TFAlbertForSequenceClassification,
				        """token-classification""": TFAlbertForTokenClassification,
				        """zero-shot""": TFAlbertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__lowerCAmelCase     :     Tuple								= False
				__lowerCAmelCase     :     Union[str, Any]								= False







				def        a_	(				self :       Optional[Any]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       str=False							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		super()._prepare_for_class(_lowerCamelCase   ,  _lowerCamelCase   ,  return_labels=_lowerCamelCase							)

					if return_labels:
						if model_class in get_values(_lowerCamelCase							):
							A_	:					Dict						 =		tf.zeros(self.model_tester.batch_size   ,  dtype=tf.intaa							)

					return inputs_dict







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					List[Any]						 =		TFAlbertModelTester(self							)
					A_	:					Dict						 =		ConfigTester(self   ,  config_class=_lowerCamelCase   ,  hidden_size=37							)







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					self.config_tester.run_common_tests()







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					A_	:					Tuple						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_model(*_lowerCamelCase							)







				def        a_	(				self :       Optional[int]							):



					"""simple docstring"""

					A_	:					int						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_pretraining(*_lowerCamelCase							)







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_masked_lm(*_lowerCamelCase							)







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					Any						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_multiple_choice(*_lowerCamelCase							)







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



					"""simple docstring"""

					A_	:					Optional[int]						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_sequence_classification(*_lowerCamelCase							)







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_	:					List[str]						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_albert_for_question_answering(*_lowerCamelCase							)







				@slow
				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					for model_name in TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						A_	:					Tuple						 =		TFAlbertModel.from_pretrained(_lowerCamelCase							)
						self.assertIsNotNone(_lowerCamelCase							)

@require_tf
class 							lowercase   (     unittest.TestCase):







				@slow
				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		TFAlbertForPreTraining.from_pretrained('''albert-base-v2'''							)
					A_	:					Optional[int]						 =		tf.constant([[0, 1, 2, 3, 4, 5]]							)
					A_	:					str						 =		model(_lowerCamelCase							)[0]

					A_	:					Optional[int]						 =		[1, 6, 3_00_00]
					self.assertEqual(output.shape   ,  _lowerCamelCase							)

					A_	:					List[str]						 =		tf.constant(
					    [
					        [
					            [4.595668, 0.74462754, -1.818147],
					            [4.5954347, 0.7454184, -1.8188258],
					            [4.5954905, 0.7448235, -1.8182316],
					        ]
					    ]							)
					tf.debugging.assert_near(output[:, :3, :3]   ,  _lowerCamelCase   ,  atol=1E-4							)











"""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()





