
'''simple docstring'''





import os
import unittest

from transformers import RealmTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.realm.tokenization_realm import RealmTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english


@require_tokenizers
class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     int    					=       RealmTokenizer
 UpperCAmelCase__     :     Union[str, Any]    					=       RealmTokenizerFast
 UpperCAmelCase__     :     List[Any]    					=       True
 UpperCAmelCase__     :     Union[str, Any]    					=       True
 UpperCAmelCase__     :     Union[str, Any]    					=       filter_non_english






 def 						lowercase_    (      self	:str      )					->      Optional[int]:







       '''simple docstring'''



       super().setUp()

       __A													=      [
           '[UNK]',
           '[CLS]',
           '[SEP]',
           '[PAD]',
           '[MASK]',
           'want',
           '##want',
           '##ed',
           'wa',
           'un',
           'runn',
           '##ing',
           ',',
           'low',
           'lowest',
       ]
       __A													=      os.path.join(self.tmpdirname						,					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]      )      )






 def 						lowercase_    (      self	:Any						,					_A	:Optional[Any]      )					->      List[str]:







       '''simple docstring'''



       __A													=      'UNwant\u00E9d,running'
       __A													=      'unwanted, running'
       return input_text, output_text






 def 						lowercase_    (      self	:str      )					->      List[Any]:







       '''simple docstring'''



       __A													=      self.tokenizer_class(self.vocab_file      )

       __A													=      tokenizer.tokenize('UNwant\u00E9d,running'      )
       self.assertListEqual(_A						,					['un', '##want', '##ed', ',', 'runn', '##ing']      )
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_A      )						,					[9, 6, 7, 12, 10, 11]      )






 def 						lowercase_    (      self	:int      )					->      Any:







       '''simple docstring'''



       if not self.test_rust_tokenizer:
             return

       __A													=      self.get_tokenizer()
       __A													=      self.get_rust_tokenizer()

       __A													=      'UNwant\u00E9d,running'

       __A													=      tokenizer.tokenize(_A      )
       __A													=      rust_tokenizer.tokenize(_A      )
       self.assertListEqual(_A						,					_A      )

       __A													=      tokenizer.encode(_A						,					add_special_tokens=_A      )
       __A													=      rust_tokenizer.encode(_A						,					add_special_tokens=_A      )
       self.assertListEqual(_A						,					_A      )

       __A													=      self.get_rust_tokenizer()
       __A													=      tokenizer.encode(_A      )
       __A													=      rust_tokenizer.encode(_A      )
       self.assertListEqual(_A						,					_A      )

       # With lower casing
       __A													=      self.get_tokenizer(do_lower_case=_A      )
       __A													=      self.get_rust_tokenizer(do_lower_case=_A      )

       __A													=      'UNwant\u00E9d,running'

       __A													=      tokenizer.tokenize(_A      )
       __A													=      rust_tokenizer.tokenize(_A      )
       self.assertListEqual(_A						,					_A      )

       __A													=      tokenizer.encode(_A						,					add_special_tokens=_A      )
       __A													=      rust_tokenizer.encode(_A						,					add_special_tokens=_A      )
       self.assertListEqual(_A						,					_A      )

       __A													=      self.get_rust_tokenizer()
       __A													=      tokenizer.encode(_A      )
       __A													=      rust_tokenizer.encode(_A      )
       self.assertListEqual(_A						,					_A      )






 def 						lowercase_    (      self	:Tuple      )					->      List[Any]:







       '''simple docstring'''



       __A													=      BasicTokenizer()

       self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'      )						,					['ah', '\u535A', '\u63A8', 'zz']      )






 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )						,					['hello', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )						,					['hello']      )






 def 						lowercase_    (      self	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A						,					strip_accents=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )						,					['hällo', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )						,					['h\u00E9llo']      )






 def 						lowercase_    (      self	:int      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A						,					strip_accents=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )						,					['hallo', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )						,					['hello']      )






 def 						lowercase_    (      self	:Tuple      )					->      Any:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )						,					['hallo', '!', 'how', 'are', 'you', '?']      )
       self.assertListEqual(tokenizer.tokenize('H\u00E9llo'      )						,					['hello']      )






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '      )						,					['HeLLo', '!', 'how', 'Are', 'yoU', '?']      )






 def 						lowercase_    (      self	:str      )					->      Any:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A						,					strip_accents=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )						,					['HäLLo', '!', 'how', 'Are', 'yoU', '?']      )






 def 						lowercase_    (      self	:List[Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A						,					strip_accents=_A      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '      )						,					['HaLLo', '!', 'how', 'Are', 'yoU', '?']      )






 def 						lowercase_    (      self	:Any      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      BasicTokenizer(do_lower_case=_A						,					never_split=['[UNK]']      )

       self.assertListEqual(
           tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'      )						,					['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']      )






 def 						lowercase_    (      self	:Any      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

       __A													=      {}
       for i, token in enumerate(_A      ):
             __A													=      i
       __A													=      WordpieceTokenizer(vocab=_A						,					unk_token='[UNK]'      )

       self.assertListEqual(tokenizer.tokenize(''      )						,					[]      )

       self.assertListEqual(tokenizer.tokenize('unwanted running'      )						,					['un', '##want', '##ed', 'runn', '##ing']      )

       self.assertListEqual(tokenizer.tokenize('unwantedX running'      )						,					['[UNK]', 'runn', '##ing']      )






 def 						lowercase_    (      self	:List[str]      )					->      Optional[Any]:







       '''simple docstring'''



       self.assertTrue(_is_whitespace(' '      )      )
       self.assertTrue(_is_whitespace('\t'      )      )
       self.assertTrue(_is_whitespace('\r'      )      )
       self.assertTrue(_is_whitespace('\n'      )      )
       self.assertTrue(_is_whitespace('\u00A0'      )      )

       self.assertFalse(_is_whitespace('A'      )      )
       self.assertFalse(_is_whitespace('-'      )      )






 def 						lowercase_    (      self	:int      )					->      Tuple:







       '''simple docstring'''



       self.assertTrue(_is_control('\u0005'      )      )

       self.assertFalse(_is_control('A'      )      )
       self.assertFalse(_is_control(' '      )      )
       self.assertFalse(_is_control('\t'      )      )
       self.assertFalse(_is_control('\r'      )      )






 def 						lowercase_    (      self	:Dict      )					->      Any:







       '''simple docstring'''



       self.assertTrue(_is_punctuation('-'      )      )
       self.assertTrue(_is_punctuation('$'      )      )
       self.assertTrue(_is_punctuation('`'      )      )
       self.assertTrue(_is_punctuation('.'      )      )

       self.assertFalse(_is_punctuation('A'      )      )
       self.assertFalse(_is_punctuation(' '      )      )






 def 						lowercase_    (      self	:int      )					->      Dict:







       '''simple docstring'''



       __A													=      self.get_tokenizer()

       # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
       self.assertListEqual([tokenizer.tokenize(_A      ) for t in ['Test', '\xad', 'test']]						,					[['[UNK]'], [], ['[UNK]']]      )

       if self.test_rust_tokenizer:
             __A													=      self.get_rust_tokenizer()
             self.assertListEqual(
                 [rust_tokenizer.tokenize(_A      ) for t in ['Test', '\xad', 'test']]						,					[['[UNK]'], [], ['[UNK]']]      )






 @slow
 def 						lowercase_    (      self	:int      )					->      List[Any]:







       '''simple docstring'''



       __A													=      self.tokenizer_class.from_pretrained('bert-base-uncased'      )

       __A													=      tokenizer.encode('sequence builders'						,					add_special_tokens=_A      )
       __A													=      tokenizer.encode('multi-sequence build'						,					add_special_tokens=_A      )

       __A													=      tokenizer.build_inputs_with_special_tokens(_A      )
       __A													=      tokenizer.build_inputs_with_special_tokens(_A						,					_A      )

       assert encoded_sentence == [101] + text + [102]
       assert encoded_pair == [101] + text + [102] + text_a + [102]






 def 						lowercase_    (      self	:Optional[int]      )					->      str:







       '''simple docstring'''



       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                   __A													=      self.rust_tokenizer_class.from_pretrained(_A						,					**_A      )

                   __A													=      F'A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'
                   __A													=      tokenizer_r.encode_plus(
                       _A						,					return_attention_mask=_A						,					return_token_type_ids=_A						,					return_offsets_mapping=_A						,					add_special_tokens=_A						,					)

                   __A													=      tokenizer_r.do_lower_case if hasattr(_A						,					'do_lower_case'      ) else False
                   __A													=      (
                       [
                           ((0, 0), tokenizer_r.cls_token),
                           ((0, 1), 'A'),
                           ((1, 2), ','),
                           ((3, 5), 'na'),
                           ((5, 6), '##ï'),
                           ((6, 8), '##ve'),
                           ((9, 15), tokenizer_r.mask_token),
                           ((16, 21), 'Allen'),
                           ((21, 23), '##NL'),
                           ((23, 24), '##P'),
                           ((25, 33), 'sentence'),
                           ((33, 34), '.'),
                           ((0, 0), tokenizer_r.sep_token),
                       ]
                       if not do_lower_case
                       else [
                           ((0, 0), tokenizer_r.cls_token),
                           ((0, 1), 'a'),
                           ((1, 2), ','),
                           ((3, 8), 'naive'),
                           ((9, 15), tokenizer_r.mask_token),
                           ((16, 21), 'allen'),
                           ((21, 23), '##nl'),
                           ((23, 24), '##p'),
                           ((25, 33), 'sentence'),
                           ((33, 34), '.'),
                           ((0, 0), tokenizer_r.sep_token),
                       ]
                   )

                   self.assertEqual(
                       [e[1] for e in expected_results]						,					tokenizer_r.convert_ids_to_tokens(tokens['input_ids']      )      )
                   self.assertEqual([e[0] for e in expected_results]						,					tokens['offset_mapping']      )






 @slow
 def 						lowercase_    (      self	:List[Any]      )					->      Tuple:







       '''simple docstring'''



       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
                   __A													=      self.rust_tokenizer_class.from_pretrained(_A						,					**_A      )
                   __A													=      self.tokenizer_class.from_pretrained(_A						,					**_A      )
                   __A													=      [['Hello world!', 'Nice to meet you!'], ['The cute cat.', 'The adorable dog.']]

                   __A													=      tokenizer_r.batch_encode_candidates(_A						,					max_length=10						,					return_tensors='np'      )
                   __A													=      tokenizer_p.batch_encode_candidates(_A						,					max_length=10						,					return_tensors='np'      )

                   __A													=      (2, 2, 10)

                   self.assertEqual(encoded_sentence_r['input_ids'].shape						,					_A      )
                   self.assertEqual(encoded_sentence_r['attention_mask'].shape						,					_A      )
                   self.assertEqual(encoded_sentence_r['token_type_ids'].shape						,					_A      )

                   self.assertEqual(encoded_sentence_p['input_ids'].shape						,					_A      )
                   self.assertEqual(encoded_sentence_p['attention_mask'].shape						,					_A      )
                   self.assertEqual(encoded_sentence_p['token_type_ids'].shape						,					_A      )












"""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()





