



import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english






@require_tokenizers
class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Dict		       =							RoCBertTokenizer
	a__				: str		       =							None
	a__				: str		       =							False
	a__				: Any		       =							True
	a__				: Dict		       =							filter_non_english




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Any:
							super().setUp()

							UpperCAmelCase_=       ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """你""", """好""", """是""", """谁""", """a""", """b""", """c""", """d"""]
							UpperCAmelCase_=       {}
							UpperCAmelCase_=       {}
							for i, value in enumerate(__UpperCAmelCase					):
													UpperCAmelCase_=       i
													UpperCAmelCase_=       i
							UpperCAmelCase_=       os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES["""vocab_file"""]					)
							UpperCAmelCase_=       os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES["""word_shape_file"""]					)
							UpperCAmelCase_=       os.path.join(self.tmpdirname						,  VOCAB_FILES_NAMES["""word_pronunciation_file"""]					)
							with open(self.vocab_file						,  """w"""						,  encoding="""utf-8"""					) as vocab_writer:
													vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)
							with open(self.word_shape_file						,  """w"""						,  encoding="""utf-8"""					) as word_shape_writer:
													json.dump(__UpperCAmelCase						,  __UpperCAmelCase						,  ensure_ascii=__UpperCAmelCase					)
							with open(self.word_pronunciation_file						,  """w"""						,  encoding="""utf-8"""					) as word_pronunciation_writer:
													json.dump(__UpperCAmelCase						,  __UpperCAmelCase						,  ensure_ascii=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.tokenizer_class(self.vocab_file						,  self.word_shape_file						,  self.word_pronunciation_file					)

							UpperCAmelCase_=       tokenizer.tokenize("""你好[SEP]你是谁"""					)
							self.assertListEqual(__UpperCAmelCase						,  ["""你""", """好""", """[SEP]""", """你""", """是""", """谁"""]					)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,  [5, 6, 2, 5, 7, 8]					)
							self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(__UpperCAmelCase					)						,  [5, 6, 2, 5, 7, 8]					)
							self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(__UpperCAmelCase					)						,  [5, 6, 2, 5, 7, 8]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							UpperCAmelCase_=       RoCBertBasicTokenizer()

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Tuple:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Optional[Any]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase						,  strip_accents=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Union[str, Any]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase						,  strip_accents=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Union[str, Any]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Union[str, Any]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase						,  strip_accents=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Optional[int]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase						,  strip_accents=__UpperCAmelCase					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[Any]:
							UpperCAmelCase_=       RoCBertBasicTokenizer(do_lower_case=__UpperCAmelCase						,  never_split=["""[UNK]"""]					)

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Dict:
							UpperCAmelCase_=       ["""[UNK]""", """[CLS]""", """[SEP]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing"""]

							UpperCAmelCase_=       {}
							for i, token in enumerate(__UpperCAmelCase					):
													UpperCAmelCase_=       i
							UpperCAmelCase_=       RoCBertWordpieceTokenizer(vocab=__UpperCAmelCase						,  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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			str:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[Any]:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			str:
							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   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.get_tokenizer()

							# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
							self.assertListEqual([tokenizer.tokenize(__UpperCAmelCase					) for t in ["""Test""", """\xad""", """test"""]]						,  [["""[UNK]"""], [], ["""[UNK]"""]]					)

							if self.test_rust_tokenizer:
													UpperCAmelCase_=       self.get_rust_tokenizer()
													self.assertListEqual(
													    [rust_tokenizer.tokenize(__UpperCAmelCase					) for t in ["""Test""", """\xad""", """test"""]]						,  [["""[UNK]"""], [], ["""[UNK]"""]]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[Any]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
																			UpperCAmelCase_=       self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)

																			UpperCAmelCase_=       F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
																			UpperCAmelCase_=       tokenizer_r.encode_plus(
																			    __UpperCAmelCase						,  return_attention_mask=__UpperCAmelCase						,  return_token_type_ids=__UpperCAmelCase						,  return_offsets_mapping=__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase						,  )

																			UpperCAmelCase_=       tokenizer_r.do_lower_case if hasattr(__UpperCAmelCase						,  """do_lower_case"""					) else False
																			UpperCAmelCase_=       (
																			    [
																			        ((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"""]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			int:
							UpperCAmelCase_=       ["""的""", """人""", """有"""]
							UpperCAmelCase_=       """""".join(__UpperCAmelCase					)
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
																			UpperCAmelCase_=       True
																			UpperCAmelCase_=       self.tokenizer_class.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)
																			UpperCAmelCase_=       self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)

																			UpperCAmelCase_=       tokenizer_p.encode(__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer_r.encode(__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase					)

																			UpperCAmelCase_=       tokenizer_r.convert_ids_to_tokens(__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer_p.convert_ids_to_tokens(__UpperCAmelCase					)

																			# it is expected that each Chinese character is not preceded by "##"
																			self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)
																			self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)

																			UpperCAmelCase_=       False
																			UpperCAmelCase_=       self.rust_tokenizer_class.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)
																			UpperCAmelCase_=       self.tokenizer_class.from_pretrained(__UpperCAmelCase						,  **__UpperCAmelCase					)

																			UpperCAmelCase_=       tokenizer_r.encode(__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer_p.encode(__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase					)

																			UpperCAmelCase_=       tokenizer_r.convert_ids_to_tokens(__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer_p.convert_ids_to_tokens(__UpperCAmelCase					)

																			# it is expected that only the first Chinese character is not preceded by "##".
																			UpperCAmelCase_=       [
																			    F"""##{token}""" if idx != 0 else token for idx, token in enumerate(__UpperCAmelCase					)
																			]
																			self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)
																			self.assertListEqual(__UpperCAmelCase						,  __UpperCAmelCase					)




	@slow
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.tokenizer_class(self.vocab_file						,  self.word_shape_file						,  self.word_pronunciation_file					)

							UpperCAmelCase_=       tokenizer.encode("""你好"""						,  add_special_tokens=__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.encode("""你是谁"""						,  add_special_tokens=__UpperCAmelCase					)

							UpperCAmelCase_=       tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
							UpperCAmelCase_=       tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,  __UpperCAmelCase					)

							assert encoded_sentence == [1] + text + [2]
							assert encoded_pair == [1] + text + [2] + text_a + [2]




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.get_tokenizers(do_lower_case=__UpperCAmelCase					)
							for tokenizer in tokenizers:
													with self.subTest(F"""{tokenizer.__class__.__name__}"""					):
																			UpperCAmelCase_=       """你好，你是谁"""
																			UpperCAmelCase_=       tokenizer.tokenize(__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer.convert_tokens_to_shape_ids(__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer.convert_tokens_to_pronunciation_ids(__UpperCAmelCase					)
																			UpperCAmelCase_=       tokenizer.prepare_for_model(
																			    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase					)

																			UpperCAmelCase_=       tokenizer.encode_plus(__UpperCAmelCase						,  add_special_tokens=__UpperCAmelCase					)

																			self.assertEqual(__UpperCAmelCase						,  __UpperCAmelCase					)









import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__A       =						16
__A       =						32



def 	__a					(   lowerCAmelCase_       : Accelerator      ,lowerCAmelCase_       : int = 16      ,lowerCAmelCase_       : str = "bert-base-cased"    )				->			Tuple:






						'''simple docstring'''



						UpperCAmelCase_=       AutoTokenizer.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       load_dataset("""glue"""      ,"""mrpc"""    )

						def tokenize_function(lowerCAmelCase_       : List[Any]    ):
												# max_length=None => use the model max length (it's actually the default)
												UpperCAmelCase_=       tokenizer(examples["""sentence1"""]      ,examples["""sentence2"""]      ,truncation=lowerCAmelCase_      ,max_length=lowerCAmelCase_    )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						UpperCAmelCase_=       datasets.map(
						    lowerCAmelCase_      ,batched=lowerCAmelCase_      ,remove_columns=["""idx""", """sentence1""", """sentence2"""]      ,load_from_cache_file=lowerCAmelCase_    )

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						UpperCAmelCase_=       tokenized_datasets.rename_column("""label"""      ,"""labels"""    )

						def collate_fn(lowerCAmelCase_       : List[Any]    ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(lowerCAmelCase_      ,padding="""max_length"""      ,max_length=1_28      ,return_tensors="""pt"""    )
												return tokenizer.pad(lowerCAmelCase_      ,padding="""longest"""      ,return_tensors="""pt"""    )

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

						return train_dataloader, eval_dataloader



def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       Accelerator()

						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						UpperCAmelCase_=       config["""lr"""]
						UpperCAmelCase_=       int(config["""num_epochs"""]    )
						UpperCAmelCase_=       int(config["""seed"""]    )
						UpperCAmelCase_=       int(config["""batch_size"""]    )
						UpperCAmelCase_=       args.model_name_or_path

						set_seed(lowerCAmelCase_    )
						UpperCAmelCase_,       UpperCAmelCase_=       get_dataloaders(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						UpperCAmelCase_=       AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_      ,return_dict=lowerCAmelCase_    )

						# Instantiate optimizer
						UpperCAmelCase_=       (
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						UpperCAmelCase_=       optimizer_cls(params=model.parameters()      ,lr=lowerCAmelCase_    )

						if accelerator.state.deepspeed_plugin is not None:
												UpperCAmelCase_=       accelerator.state.deepspeed_plugin.deepspeed_config[
												    """gradient_accumulation_steps"""
												]
						else:
												UpperCAmelCase_=       1
						UpperCAmelCase_=       (len(lowerCAmelCase_    ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												UpperCAmelCase_=       get_linear_schedule_with_warmup(
												    optimizer=lowerCAmelCase_      ,num_warmup_steps=0      ,num_training_steps=lowerCAmelCase_      ,)
						else:
												UpperCAmelCase_=       DummyScheduler(lowerCAmelCase_      ,total_num_steps=lowerCAmelCase_      ,warmup_num_steps=0    )

						# 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_=       accelerator.prepare(
						    lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_    )

						# We need to keep track of how many total steps we have iterated over
						UpperCAmelCase_=       0
						# We also need to keep track of the stating epoch so files are named properly
						UpperCAmelCase_=       0

						# Now we train the model
						UpperCAmelCase_=       evaluate.load("""glue"""      ,"""mrpc"""    )
						UpperCAmelCase_=       0
						UpperCAmelCase_=       {}
						for epoch in range(lowerCAmelCase_      ,lowerCAmelCase_    ):
												model.train()
												for step, batch in enumerate(lowerCAmelCase_    ):
																		UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.loss
																		UpperCAmelCase_=       loss / gradient_accumulation_steps
																		accelerator.backward(lowerCAmelCase_    )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												UpperCAmelCase_=       0
												for step, batch in enumerate(lowerCAmelCase_    ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device    )
																		with torch.no_grad():
																								UpperCAmelCase_=       model(**lowerCAmelCase_    )
																		UpperCAmelCase_=       outputs.logits.argmax(dim=-1    )
																		# It is slightly faster to call this once, than multiple times
																		UpperCAmelCase_,       UpperCAmelCase_=       accelerator.gather(
																		    (predictions, batch["""labels"""])    )  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(lowerCAmelCase_    ) - 1:
																														UpperCAmelCase_=       predictions[: len(eval_dataloader.dataset    ) - samples_seen]
																														UpperCAmelCase_=       references[: len(eval_dataloader.dataset    ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=lowerCAmelCase_      ,references=lowerCAmelCase_      ,)

												UpperCAmelCase_=       metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F"""epoch {epoch}:"""      ,lowerCAmelCase_    )
												UpperCAmelCase_=       eval_metric["""accuracy"""]

												if best_performance < eval_metric["accuracy"]:
																		UpperCAmelCase_=       eval_metric["""accuracy"""]

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), F"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir      ,"""all_results.json"""    )      ,"""w"""    ) as f:
																		json.dump(lowerCAmelCase_      ,lowerCAmelCase_    )



def 	__a					(   )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""    )
						parser.add_argument(
						    """--model_name_or_path"""      ,type=lowerCAmelCase_      ,default="""bert-base-cased"""      ,help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,required=lowerCAmelCase_      ,)
						parser.add_argument(
						    """--output_dir"""      ,type=lowerCAmelCase_      ,default="""."""      ,help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""      ,)
						parser.add_argument(
						    """--performance_lower_bound"""      ,type=lowerCAmelCase_      ,default=lowerCAmelCase_      ,help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""      ,)
						parser.add_argument(
						    """--num_epochs"""      ,type=lowerCAmelCase_      ,default=3      ,help="""Number of train epochs."""      ,)
						UpperCAmelCase_=       parser.parse_args()
						UpperCAmelCase_=       {"""lr""": 2E-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
						training_function(lowerCAmelCase_      ,lowerCAmelCase_    )


if __name__ == "__main__":
	main()




