



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase		:				Optional[Any]      		=get_tests_dir("fixtures/test_sentencepiece.model")

_lowercase		:				Optional[Any]      		={'target_lang': 'fi', 'source_lang': 'en'}
_lowercase		:				List[Any]      		='>>zh<<'
_lowercase		:				int      		='Helsinki-NLP/'

if is_torch_available():
		_lowercase		:				Tuple      		='pt'
elif is_tf_available():
		_lowercase		:				Any      		='tf'
else:
		_lowercase		:				Any      		='jax'

@require_sentencepiece
class        snake_case__    (A__ ,     unittest.TestCase		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[int]      =					MarianTokenizer
						__lowerCAmelCase					:Union[str, Any]      =					False
						__lowerCAmelCase					:List[str]      =					True

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									super().setUp()
									a__       :						Dict							=  ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
									a__       :						Tuple							=  dict(zip(_snake_case ,       range(len(_snake_case  )  )  )  )
									a__       :						Dict							=  Path(self.tmpdirname  )
									save_json(_snake_case ,       save_dir / VOCAB_FILES_NAMES["""vocab"""]  )
									save_json(_snake_case ,       save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]  )
									if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
												copyfile(_snake_case ,       save_dir / VOCAB_FILES_NAMES["""source_spm"""]  )
												copyfile(_snake_case ,       save_dir / VOCAB_FILES_NAMES["""target_spm"""]  )

									a__       :						List[str]							=  MarianTokenizer.from_pretrained(self.tmpdirname  )
									tokenizer.save_pretrained(self.tmpdirname  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				MarianTokenizer:
									"""simple docstring"""




									return MarianTokenizer.from_pretrained(self.tmpdirname ,       **_snake_case  )

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




									return (
									    "This is a test",
									    "This is a test",
									)

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  """</s>"""
									a__       :						Optional[Any]							=  0

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_snake_case  ) ,       _snake_case  )
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_snake_case  ) ,       _snake_case  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						str							=  list(self.get_tokenizer().get_vocab().keys()  )

									self.assertEqual(vocab_keys[0] ,       """</s>"""  )
									self.assertEqual(vocab_keys[1] ,       """<unk>"""  )
									self.assertEqual(vocab_keys[-1] ,       """<pad>"""  )
									self.assertEqual(len(_snake_case  ) ,       9  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.assertEqual(self.get_tokenizer().vocab_size ,       9  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						List[str]							=  MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''  )
									a__       :						List[Any]							=  en_de_tokenizer(["""I am a small frog"""] ,       return_tensors=_snake_case  )
									self.assertIsInstance(_snake_case ,       _snake_case  )
									a__       :						str							=  [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
									self.assertListEqual(_snake_case ,       batch.input_ids[0]  )

									a__       :						int							=  tempfile.mkdtemp()
									en_de_tokenizer.save_pretrained(_snake_case  )
									a__       :						Tuple							=  [x.name for x in Path(_snake_case  ).glob("""*"""  )]
									self.assertIn("""source.spm""" ,       _snake_case  )
									MarianTokenizer.from_pretrained(_snake_case  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						List[Any]							=  self.get_tokenizer()

									a__       :						Tuple							=  tok(
									    ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""] ,       padding=_snake_case ,       truncation=_snake_case ,       return_tensors=_snake_case  )
									self.assertIsInstance(_snake_case ,       _snake_case  )
									self.assertEqual(batch.input_ids.shape ,       (2, 5_1_2)  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						List[Any]							=  self.get_tokenizer()
									a__       :						List[str]							=  tok(["""I am a tiny frog""", """I am a small frog"""] ,       padding=_snake_case ,       return_tensors=_snake_case  )
									self.assertIsInstance(_snake_case ,       _snake_case  )
									self.assertEqual(batch_smaller.input_ids.shape ,       (2, 1_0)  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Any							=  {"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_snake_case ,       model_name="""Helsinki-NLP/opus-mt-en-de""" ,       revision="""1a8c2263da11e68e50938f97e10cd57820bd504c""" ,       decode_kwargs={"""use_source_tokenizer""": True} ,       )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Tuple							=  MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""  )

									a__       :						int							=  """Tämä on testi"""
									a__       :						str							=  """This is a test"""

									a__       :						Optional[Any]							=  [7_6, 7, 2_0_4_7, 2]
									a__       :						Optional[int]							=  [6_9, 1_2, 1_1, 9_4_0, 2]

									a__       :						Any							=  tokenizer(_snake_case  ).input_ids
									self.assertListEqual(_snake_case ,       _snake_case  )

									a__       :						Dict							=  tokenizer(text_target=_snake_case  ).input_ids
									self.assertListEqual(_snake_case ,       _snake_case  )

									a__       :						Any							=  tokenizer.decode(_snake_case ,       skip_special_tokens=_snake_case  )
									self.assertEqual(_snake_case ,       _snake_case  )









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


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

def      lowerCAmelCase_					(  _lowercase    :       Accelerator						,       _lowercase    :       int = 16						,       _lowercase    :       str = "bert-base-cased")							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  AutoTokenizer.from_pretrained(_lowercase)
			a__       :						Any							=  load_dataset("""glue"""						,       """mrpc""")

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

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

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

			def collate_fn(_lowercase    :       Optional[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(_lowercase						,       padding="""max_length"""						,       max_length=128						,       return_tensors="""pt""")
						return tokenizer.pad(_lowercase						,       padding="""longest"""						,       return_tensors="""pt""")

			# Instantiate dataloaders.
			a__       :						int							=  DataLoader(
			    tokenized_datasets["""train"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)
			a__       :						Union[str, Any]							=  DataLoader(
			    tokenized_datasets["""validation"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)

			return train_dataloader, eval_dataloader

def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Dict)							->	int:







			"""simple docstring"""
			# Initialize accelerator
			a__       :						Optional[int]							=  Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			a__       :						Union[str, Any]							=  config["""lr"""]
			a__       :						List[str]							=  int(config["""num_epochs"""])
			a__       :						List[str]							=  int(config["""seed"""])
			a__       :						Tuple							=  int(config["""batch_size"""])
			a__       :						int							=  args.model_name_or_path

			set_seed(_lowercase)
			a__	,						a__       :						int							=  get_dataloaders(_lowercase						,       _lowercase						,       _lowercase)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__       :						Optional[Any]							=  AutoModelForSequenceClassification.from_pretrained(_lowercase						,       return_dict=_lowercase)

			# Instantiate optimizer
			a__       :						int							=  (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			a__       :						Optional[Any]							=  optimizer_cls(params=model.parameters()						,       lr=_lowercase)

			if accelerator.state.deepspeed_plugin is not None:
						a__       :						Dict							=  accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						a__       :						List[str]							=  1
			a__       :						List[Any]							=  (len(_lowercase) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						a__       :						Dict							=  get_linear_schedule_with_warmup(
						    optimizer=_lowercase						,       num_warmup_steps=0						,       num_training_steps=_lowercase						,       )
			else:
						a__       :						Dict							=  DummyScheduler(_lowercase						,       total_num_steps=_lowercase						,       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.
			a__	,						a__	,						a__	,						a__	,						a__       :						List[Any]							=  accelerator.prepare(
			    _lowercase						,       _lowercase						,       _lowercase						,       _lowercase						,       _lowercase)

			# We need to keep track of how many total steps we have iterated over
			a__       :						Optional[int]							=  0
			# We also need to keep track of the stating epoch so files are named properly
			a__       :						Optional[int]							=  0

			# Now we train the model
			a__       :						Tuple							=  evaluate.load("""glue"""						,       """mrpc""")
			a__       :						List[Any]							=  0
			a__       :						Tuple							=  {}
			for epoch in range(_lowercase						,       _lowercase):
						model.train()
						for step, batch in enumerate(_lowercase):
									a__       :						Union[str, Any]							=  model(**_lowercase)
									a__       :						Tuple							=  outputs.loss
									a__       :						Any							=  loss / gradient_accumulation_steps
									accelerator.backward(_lowercase)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

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

						a__       :						Optional[Any]							=  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''						,       _lowercase)
						a__       :						Any							=  eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									a__       :						List[str]							=  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(_lowercase						,       _lowercase)

def      lowerCAmelCase_					(  )							->	Tuple:







			"""simple docstring"""
			a__       :						Optional[Any]							=  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
			parser.add_argument(
			    """--model_name_or_path"""						,       type=_lowercase						,       default="""bert-base-cased"""						,       help="""Path to pretrained model or model identifier from huggingface.co/models."""						,       required=_lowercase						,       )
			parser.add_argument(
			    """--output_dir"""						,       type=_lowercase						,       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=_lowercase						,       default=_lowercase						,       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=_lowercase						,       default=3						,       help="""Number of train epochs."""						,       )
			a__       :						Any							=  parser.parse_args()
			a__       :						Dict							=  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(_lowercase						,       _lowercase)


if __name__ == "__main__":
		main()




