



import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
		import os

		# The slow tests are often failing with OOM error on GPU
		# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
		# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
		_lowercase		:				Tuple      		="platform"
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class        snake_case__    :







						"""simple docstring"""




						__lowerCAmelCase					:List[str]      =					PegasusConfig
						__lowerCAmelCase					:Optional[int]      =					{}
						__lowerCAmelCase					:Dict      =					"gelu"

						def __init__(       self ,       __lowercase ,       __lowercase=1_3 ,       __lowercase=7 ,       __lowercase=True ,       __lowercase=False ,       __lowercase=9_9 ,       __lowercase=3_2 ,       __lowercase=5 ,       __lowercase=4 ,       __lowercase=3_7 ,       __lowercase=0.1 ,       __lowercase=0.1 ,       __lowercase=2_0 ,       __lowercase=2 ,       __lowercase=1 ,       __lowercase=0 ,       ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						Optional[int]							=  parent
									a__       :						Optional[int]							=  batch_size
									a__       :						List[Any]							=  seq_length
									a__       :						int							=  is_training
									a__       :						Any							=  use_labels
									a__       :						Tuple							=  vocab_size
									a__       :						int							=  hidden_size
									a__       :						Dict							=  num_hidden_layers
									a__       :						Union[str, Any]							=  num_attention_heads
									a__       :						Tuple							=  intermediate_size

									a__       :						Any							=  hidden_dropout_prob
									a__       :						int							=  attention_probs_dropout_prob
									a__       :						Dict							=  max_position_embeddings
									a__       :						Optional[int]							=  eos_token_id
									a__       :						Any							=  pad_token_id
									a__       :						Dict							=  bos_token_id

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




									a__       :						Dict							=  ids_tensor([self.batch_size, self.seq_length - 1] ,       self.vocab_size  ).clip(3 ,       self.vocab_size  )
									a__       :						int							=  np.expand_dims(np.array([self.eos_token_id] * self.batch_size  ) ,       1  )
									a__       :						Optional[int]							=  np.concatenate([input_ids, eos_tensor] ,       axis=1  )

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

									a__       :						List[str]							=  self.config_cls(
									    vocab_size=self.vocab_size ,       d_model=self.hidden_size ,       encoder_layers=self.num_hidden_layers ,       decoder_layers=self.num_hidden_layers ,       encoder_attention_heads=self.num_attention_heads ,       decoder_attention_heads=self.num_attention_heads ,       encoder_ffn_dim=self.intermediate_size ,       decoder_ffn_dim=self.intermediate_size ,       dropout=self.hidden_dropout_prob ,       attention_dropout=self.attention_probs_dropout_prob ,       max_position_embeddings=self.max_position_embeddings ,       eos_token_ids=[2] ,       bos_token_id=self.bos_token_id ,       pad_token_id=self.pad_token_id ,       decoder_start_token_id=self.pad_token_id ,       **self.config_updates ,       )
									a__       :						Union[str, Any]							=  prepare_pegasus_inputs_dict(__lowercase ,       __lowercase ,       __lowercase  )
									return config, inputs_dict

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




									a__       :						Dict							=  2_0
									a__       :						str							=  model_class_name(__lowercase  )

									a__       :						Union[str, Any]							=  model.encode(inputs_dict["""input_ids"""]  )

									a__       :						Dict							=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									a__       :						Dict							=  model.init_cache(decoder_input_ids.shape[0] ,       __lowercase ,       __lowercase  )
									a__       :						List[Any]							=  jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,       dtype="""i4"""  )

									a__       :						Tuple							=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )
									a__       :						List[Any]							=  model.decode(
									    decoder_input_ids[:, :-1] ,       __lowercase ,       decoder_attention_mask=__lowercase ,       past_key_values=__lowercase ,       decoder_position_ids=__lowercase ,       )

									a__       :						Any							=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4"""  )
									a__       :						Tuple							=  model.decode(
									    decoder_input_ids[:, -1:] ,       __lowercase ,       decoder_attention_mask=__lowercase ,       past_key_values=outputs_cache.past_key_values ,       decoder_position_ids=__lowercase ,       )

									a__       :						List[str]							=  model.decode(__lowercase ,       __lowercase  )

									a__       :						Tuple							=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1E-3 ,       msg=F'''Max diff is {diff}'''  )

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




									a__       :						Tuple							=  2_0
									a__       :						List[Any]							=  model_class_name(__lowercase  )

									a__       :						Dict							=  model.encode(inputs_dict["""input_ids"""]  )

									a__       :						Optional[Any]							=  (
									    inputs_dict["""decoder_input_ids"""],
									    inputs_dict["""decoder_attention_mask"""],
									)

									a__       :						Any							=  jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])  ),
									    ] ,       axis=-1 ,       )

									a__       :						Any							=  model.init_cache(decoder_input_ids.shape[0] ,       __lowercase ,       __lowercase  )
									a__       :						List[str]							=  jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :] ,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,       )

									a__       :						Tuple							=  model.decode(
									    decoder_input_ids[:, :-1] ,       __lowercase ,       decoder_attention_mask=__lowercase ,       past_key_values=__lowercase ,       decoder_position_ids=__lowercase ,       )
									a__       :						List[str]							=  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,       dtype="""i4"""  )
									a__       :						Optional[Any]							=  model.decode(
									    decoder_input_ids[:, -1:] ,       __lowercase ,       past_key_values=outputs_cache.past_key_values ,       decoder_attention_mask=__lowercase ,       decoder_position_ids=__lowercase ,       )

									a__       :						Any							=  model.decode(__lowercase ,       __lowercase ,       decoder_attention_mask=__lowercase  )

									a__       :						int							=  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
									self.parent.assertTrue(diff < 1E-3 ,       msg=F'''Max diff is {diff}'''  )




def      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Optional[int]						,       _lowercase    :       Union[str, Any]						,       _lowercase    :       List[str]=None						,       _lowercase    :       Optional[int]=None						,       )							->	Any:







			"""simple docstring"""
			if attention_mask is None:
						a__       :						Union[str, Any]							=  np.not_equal(_lowercase						,       config.pad_token_id).astype(np.inta)
			if decoder_attention_mask is None:
						a__       :						int							=  np.concatenate(
						    [
						        np.ones(decoder_input_ids[:, :1].shape						,       dtype=np.inta),
						        np.not_equal(decoder_input_ids[:, 1:]						,       config.pad_token_id).astype(np.inta),
						    ]						,       axis=-1						,       )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			}





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







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					(
						    (
						        FlaxPegasusForConditionalGeneration,
						        FlaxPegasusModel,
						    )
						    if is_flax_available()
						    else ()
						)
						__lowerCAmelCase					:Optional[Any]      =					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
						__lowerCAmelCase					:Union[str, Any]      =					True
						__lowerCAmelCase					:Dict      =					False
						__lowerCAmelCase					:int      =					False
						__lowerCAmelCase					:Dict      =					False

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




									a__       :						List[str]							=  FlaxPegasusModelTester(self  )
									a__       :						List[str]							=  ConfigTester(self ,       config_class=__lowercase  )

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




									self.config_tester.run_common_tests()

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




									a__       :						int							=  self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward(__lowercase ,       __lowercase ,       __lowercase  )

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




									a__       :						Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
												self.model_tester.check_use_cache_forward_with_attn_mask(__lowercase ,       __lowercase ,       __lowercase  )

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




									a__       :						Any							=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															a__       :						str							=  self._prepare_for_class(__lowercase ,       __lowercase  )
															a__       :						Union[str, Any]							=  model_class(__lowercase  )

															@jax.jit
															def encode_jitted(__lowercase ,       __lowercase=None ,       **__lowercase  ):
																		return model.encode(input_ids=__lowercase ,       attention_mask=__lowercase  )

															with self.subTest("""JIT Enabled"""  ):
																		a__       :						List[str]							=  encode_jitted(**__lowercase  ).to_tuple()

															with self.subTest("""JIT Disabled"""  ):
																		with jax.disable_jit():
																					a__       :						str							=  encode_jitted(**__lowercase  ).to_tuple()

															self.assertEqual(len(__lowercase  ) ,       len(__lowercase  )  )
															for jitted_output, output in zip(__lowercase ,       __lowercase  ):
																		self.assertEqual(jitted_output.shape ,       output.shape  )

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




									a__       :						Tuple							=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												with self.subTest(model_class.__name__  ):
															a__       :						Tuple							=  model_class(__lowercase  )
															a__       :						int							=  model.encode(inputs_dict["""input_ids"""] ,       inputs_dict["""attention_mask"""]  )

															a__       :						Union[str, Any]							=  {
															    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
															    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
															    """encoder_outputs""": encoder_outputs,
															}

															@jax.jit
															def decode_jitted(__lowercase ,       __lowercase ,       __lowercase  ):
																		return model.decode(
																		    decoder_input_ids=__lowercase ,       decoder_attention_mask=__lowercase ,       encoder_outputs=__lowercase ,       )

															with self.subTest("""JIT Enabled"""  ):
																		a__       :						str							=  decode_jitted(**__lowercase  ).to_tuple()

															with self.subTest("""JIT Disabled"""  ):
																		with jax.disable_jit():
																					a__       :						List[str]							=  decode_jitted(**__lowercase  ).to_tuple()

															self.assertEqual(len(__lowercase  ) ,       len(__lowercase  )  )
															for jitted_output, output in zip(__lowercase ,       __lowercase  ):
																		self.assertEqual(jitted_output.shape ,       output.shape  )

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




									for model_class_name in self.all_model_classes:
												a__       :						Union[str, Any]							=  model_class_name.from_pretrained("""google/pegasus-large""" ,       from_pt=__lowercase  )
												a__       :						Optional[int]							=  np.ones((1, 1)  )
												a__       :						Dict							=  model(__lowercase  )
												self.assertIsNotNone(__lowercase  )

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




									a__       :						Dict							=  FlaxPegasusForConditionalGeneration.from_pretrained("""google/pegasus-xsum"""  )
									a__       :						Union[str, Any]							=  PegasusTokenizer.from_pretrained("""google/pegasus-xsum"""  )

									a__       :						List[str]							=  [
									    """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
									    """ The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
									]

									a__       :						Optional[int]							=  [
									    """California's largest electricity provider has turned off power to hundreds of thousands of customers.""",
									    """Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.""",
									]

									a__       :						List[str]							=  tokenizer(__lowercase ,       return_tensors="""np""" ,       truncation=__lowercase ,       max_length=5_1_2 ,       padding=__lowercase  )
									a__       :						Tuple							=  model.generate(**__lowercase ,       num_beams=2  ).sequences
									a__       :						int							=  tokenizer.batch_decode(__lowercase ,       skip_special_tokens=__lowercase  )
									assert tgt_text == decoded









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()




