


from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
		import tensorflow as tf

		from transformers.models.mpnet.modeling_tf_mpnet import (
		    TFMPNetForMaskedLM,
		    TFMPNetForMultipleChoice,
		    TFMPNetForQuestionAnswering,
		    TFMPNetForSequenceClassification,
		    TFMPNetForTokenClassification,
		    TFMPNetModel,
		)







class 					__lowercase					:



		"""simple docstring"""

		def __init__(   self		,			A		,			A=13		,			A=7		,			A=True		,			A=True		,			A=False		,			A=True		,			A=99		,			A=64		,			A=2		,			A=4		,			A=64		,			A="gelu"		,			A=0.1		,			A=0.1		,			A=5_12		,			A=16		,			A=2		,			A=0.02		,			A=3		,			A=4		,			A=None		,			)     ->   Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase								=     parent
					lowerCamelCase								=     batch_size
					lowerCamelCase								=     seq_length
					lowerCamelCase								=     is_training
					lowerCamelCase								=     use_input_mask
					lowerCamelCase								=     use_token_type_ids
					lowerCamelCase								=     use_labels
					lowerCamelCase								=     vocab_size
					lowerCamelCase								=     hidden_size
					lowerCamelCase								=     num_hidden_layers
					lowerCamelCase								=     num_attention_heads
					lowerCamelCase								=     intermediate_size
					lowerCamelCase								=     hidden_act
					lowerCamelCase								=     hidden_dropout_prob
					lowerCamelCase								=     attention_probs_dropout_prob
					lowerCamelCase								=     max_position_embeddings
					lowerCamelCase								=     type_vocab_size
					lowerCamelCase								=     type_sequence_label_size
					lowerCamelCase								=     initializer_range
					lowerCamelCase								=     num_labels
					lowerCamelCase								=     num_choices
					lowerCamelCase								=     scope







		def 				__A      (   self  )     ->   Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase								=     ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size  )

					lowerCamelCase								=     None
					if self.use_input_mask:
								lowerCamelCase								=     random_attention_mask([self.batch_size, self.seq_length]  )

					lowerCamelCase								=     None
					lowerCamelCase								=     None
					lowerCamelCase								=     None
					if self.use_labels:
								lowerCamelCase								=     ids_tensor([self.batch_size]		,			self.type_sequence_label_size  )
								lowerCamelCase								=     ids_tensor([self.batch_size, self.seq_length]		,			self.num_labels  )
								lowerCamelCase								=     ids_tensor([self.batch_size]		,			self.num_choices  )

					lowerCamelCase								=     MPNetConfig(
					    vocab_size=self.vocab_size		,			hidden_size=self.hidden_size		,			num_hidden_layers=self.num_hidden_layers		,			num_attention_heads=self.num_attention_heads		,			intermediate_size=self.intermediate_size		,			hidden_act=self.hidden_act		,			hidden_dropout_prob=self.hidden_dropout_prob		,			attention_probs_dropout_prob=self.attention_probs_dropout_prob		,			max_position_embeddings=self.max_position_embeddings		,			initializer_range=self.initializer_range		,			)
					return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     TFMPNetModel(config=A  )
					lowerCamelCase								=     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					lowerCamelCase								=     model(A  )
					lowerCamelCase								=     [input_ids, input_mask]
					lowerCamelCase								=     model(A  )
					self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)  )







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     TFMPNetForMaskedLM(config=A  )
					lowerCamelCase								=     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					lowerCamelCase								=     model(A  )
					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.vocab_size)  )







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     TFMPNetForQuestionAnswering(config=A  )
					lowerCamelCase								=     {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					}
					lowerCamelCase								=     model(A  )
					self.parent.assertEqual(result.start_logits.shape		,			(self.batch_size, self.seq_length)  )
					self.parent.assertEqual(result.end_logits.shape		,			(self.batch_size, self.seq_length)  )







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     self.num_labels
					lowerCamelCase								=     TFMPNetForSequenceClassification(A  )
					lowerCamelCase								=     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					lowerCamelCase								=     model(A  )
					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)  )







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   Optional[Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.num_choices
					lowerCamelCase								=     TFMPNetForMultipleChoice(A  )
					lowerCamelCase								=     tf.tile(tf.expand_dims(A		,			1  )		,			(1, self.num_choices, 1)  )
					lowerCamelCase								=     tf.tile(tf.expand_dims(A		,			1  )		,			(1, self.num_choices, 1)  )
					lowerCamelCase								=     {
					    '''input_ids''': multiple_choice_inputs_ids,
					    '''attention_mask''': multiple_choice_input_mask,
					}
					lowerCamelCase								=     model(A  )
					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_choices)  )







		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   List[str]:







					'''simple docstring'''







					lowerCamelCase								=     self.num_labels
					lowerCamelCase								=     TFMPNetForTokenClassification(A  )
					lowerCamelCase								=     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					lowerCamelCase								=     model(A  )
					self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.seq_length, self.num_labels)  )







		def 				__A      (   self  )     ->   Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.prepare_config_and_inputs()
					(lowerCamelCase)								=     config_and_inputs
					lowerCamelCase								=     {'''input_ids''': input_ids, '''attention_mask''': input_mask}
					return config, inputs_dict







@require_tf
class 					__lowercase					(       a_ ,       a_ ,       unittest.TestCase     ):



		"""simple docstring"""

		UpperCamelCase    :				List[str] 					=		(
		    (
		        TFMPNetForMaskedLM,
		        TFMPNetForMultipleChoice,
		        TFMPNetForQuestionAnswering,
		        TFMPNetForSequenceClassification,
		        TFMPNetForTokenClassification,
		        TFMPNetModel,
		    )
		    if is_tf_available()
		    else ()
		)
		UpperCamelCase    :				Optional[Any] 					=		(
		    {
		        "feature-extraction": TFMPNetModel,
		        "fill-mask": TFMPNetForMaskedLM,
		        "question-answering": TFMPNetForQuestionAnswering,
		        "text-classification": TFMPNetForSequenceClassification,
		        "token-classification": TFMPNetForTokenClassification,
		        "zero-shot": TFMPNetForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		UpperCamelCase    :				int 					=		False
		UpperCamelCase    :				Dict 					=		False







		def 				__A      (   self  )     ->   Tuple:







					'''simple docstring'''







					lowerCamelCase								=     TFMPNetModelTester(self  )
					lowerCamelCase								=     ConfigTester(self		,			config_class=A		,			hidden_size=37  )







		def 				__A      (   self  )     ->   Union[str, Any]:







					'''simple docstring'''







					self.config_tester.run_common_tests()







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







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_model(*A  )







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_masked_lm(*A  )







		def 				__A      (   self  )     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_question_answering(*A  )







		def 				__A      (   self  )     ->   str:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_sequence_classification(*A  )







		def 				__A      (   self  )     ->   Any:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_multiple_choice(*A  )







		def 				__A      (   self  )     ->   List[Any]:







					'''simple docstring'''







					lowerCamelCase								=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_mpnet_for_token_classification(*A  )







		@slow
		def 				__A      (   self  )     ->   int:







					'''simple docstring'''







					for model_name in ["microsoft/mpnet-base"]:
								lowerCamelCase								=     TFMPNetModel.from_pretrained(A  )
								self.assertIsNotNone(A  )







@require_tf
class 					__lowercase					(       unittest.TestCase     ):



		"""simple docstring"""

		@slow
		def 				__A      (   self  )     ->   Dict:







					'''simple docstring'''







					lowerCamelCase								=     TFMPNetModel.from_pretrained("""microsoft/mpnet-base"""  )
					lowerCamelCase								=     tf.constant([[0, 1, 2, 3, 4, 5]]  )
					lowerCamelCase								=     model(A  )[0]

					lowerCamelCase								=     [1, 6, 7_68]
					self.assertEqual(output.shape		,			A  )

					lowerCamelCase								=     tf.constant(
					    [
					        [
					            [-0.1067172, 0.08216473, 0.0024543],
					            [-0.03465879, 0.8354118, -0.03252288],
					            [-0.06569476, -0.12424111, -0.0494436],
					        ]
					    ]  )
					tf.debugging.assert_near(output[:, :3, :3]		,			A		,			atol=1e-4  )





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,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# 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)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################

UpperCAmelCase      :						str    =		16
UpperCAmelCase      :						Union[str, Any]    =		32






def   __lowerCamelCase	(       lowerCamelCase__		:	Accelerator     ,       lowerCamelCase__		:	int = 16			):





			'''simple docstring'''




			lowerCamelCase								=     AutoTokenizer.from_pretrained("""bert-base-cased"""			)
			lowerCamelCase								=     load_dataset("""glue"""     ,       """mrpc"""			)

			def tokenize_function(lowerCamelCase__		:	List[str]			):
						# max_length=None => use the model max length (it's actually the default)
						lowerCamelCase								=     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
			# starting with the main process first:
			with accelerator.main_process_first():
						lowerCamelCase								=     datasets.map(
						    lowerCamelCase__     ,       batched=lowerCamelCase__     ,       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
			lowerCamelCase								=     tokenized_datasets.rename_column("""label"""     ,       """labels"""			)

			def collate_fn(lowerCamelCase__		:	Optional[Any]			):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						lowerCamelCase								=     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":
									lowerCamelCase								=     16
						elif accelerator.mixed_precision != "no":
									lowerCamelCase								=     8
						else:
									lowerCamelCase								=     None

						return tokenizer.pad(
						    lowerCamelCase__     ,       padding="""longest"""     ,       max_length=lowerCamelCase__     ,       pad_to_multiple_of=lowerCamelCase__     ,       return_tensors="""pt"""     ,       )

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

			return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		UpperCAmelCase      :						Tuple    =		mocked_dataloaders  # noqa: F811






def   __lowerCamelCase	(       lowerCamelCase__		:	List[str]     ,       lowerCamelCase__		:	Optional[int]			):





			'''simple docstring'''




			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""     ,       lowerCamelCase__			) == "1":
						lowerCamelCase								=     2
			# Initialize accelerator
			lowerCamelCase								=     Accelerator(cpu=args.cpu     ,       mixed_precision=args.mixed_precision			)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			lowerCamelCase								=     config["""lr"""]
			lowerCamelCase								=     int(config["""num_epochs"""]			)
			lowerCamelCase								=     int(config["""seed"""]			)
			lowerCamelCase								=     int(config["""batch_size"""]			)

			# New Code #
			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps     ,       """isdigit"""			):
						if args.checkpointing_steps == "epoch":
									lowerCamelCase								=     args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									lowerCamelCase								=     int(args.checkpointing_steps			)
						else:
									raise ValueError(
									    f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
			else:
						lowerCamelCase								=     None

			set_seed(lowerCamelCase__			)

			lowerCamelCase		,		lowerCamelCase								=     get_dataloaders(lowerCamelCase__     ,       lowerCamelCase__			)
			lowerCamelCase								=     evaluate.load("""glue"""     ,       """mrpc"""			)

			# If the batch size is too big we use gradient accumulation
			lowerCamelCase								=     1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						lowerCamelCase								=     batch_size // MAX_GPU_BATCH_SIZE
						lowerCamelCase								=     MAX_GPU_BATCH_SIZE

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			lowerCamelCase								=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""     ,       return_dict=lowerCamelCase__			)

			# 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).
			lowerCamelCase								=     model.to(accelerator.device			)

			# Instantiate optimizer
			lowerCamelCase								=     AdamW(params=model.parameters()     ,       lr=lowerCamelCase__			)

			# Instantiate scheduler
			lowerCamelCase								=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase__     ,       num_warmup_steps=100     ,       num_training_steps=(len(lowerCamelCase__			) * 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.
			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     accelerator.prepare(
			    lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__			)

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

			# We need to load the checkpoint back in before training here with `load_state`
			# The total number of epochs is adjusted based on where the state is being loaded from,
			# as we assume continuation of the same training script
			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			)
									lowerCamelCase								=     os.path.basename(args.resume_from_checkpoint			)
						else:
									# Get the most recent checkpoint
									lowerCamelCase								=     [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
									dirs.sort(key=os.path.getctime			)
									lowerCamelCase								=     dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						lowerCamelCase								=     os.path.splitext(lowerCamelCase__			)[0]

						if "epoch" in training_difference:
									lowerCamelCase								=     int(training_difference.replace("""epoch_"""     ,       """"""			)			) + 1
									lowerCamelCase								=     None
						else:
									lowerCamelCase								=     int(training_difference.replace("""step_"""     ,       """"""			)			)
									lowerCamelCase								=     resume_step // len(lowerCamelCase__			)
									resume_step -= starting_epoch * len(lowerCamelCase__			)

    # Now we train the model
			for epoch in range(lowerCamelCase__     ,       lowerCamelCase__			):
						model.train()
						# New Code #
						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
									lowerCamelCase								=     accelerator.skip_first_batches(lowerCamelCase__     ,       lowerCamelCase__			)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									lowerCamelCase								=     train_dataloader
						for step, batch in enumerate(lowerCamelCase__			):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device			)
									lowerCamelCase								=     model(**lowerCamelCase__			)
									lowerCamelCase								=     outputs.loss
									lowerCamelCase								=     loss / gradient_accumulation_steps
									accelerator.backward(lowerCamelCase__			)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
									# New Code #
									overall_step += 1

									# New Code #
									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
									# These are saved to folders named `step_{overall_step}`
									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
									# If mixed precision was used, will also save a "scalar.bin" file
									if isinstance(lowerCamelCase__     ,       lowerCamelCase__			):
												lowerCamelCase								=     f'step_{overall_step}'
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
															accelerator.save_state(lowerCamelCase__			)

						model.eval()
						for step, batch in enumerate(lowerCamelCase__			):
									# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
									batch.to(accelerator.device			)
									with torch.no_grad():
												lowerCamelCase								=     model(**lowerCamelCase__			)
									lowerCamelCase								=     outputs.logits.argmax(dim=-1			)
									lowerCamelCase		,		lowerCamelCase								=     accelerator.gather_for_metrics((predictions, batch["""labels"""])			)
									metric.add_batch(
									    predictions=lowerCamelCase__     ,       references=lowerCamelCase__     ,       )

						lowerCamelCase								=     metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(f'epoch {epoch}:'     ,       lowerCamelCase__			)

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `epoch_{epoch}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if checkpointing_steps == "epoch":
									lowerCamelCase								=     f'epoch_{epoch}'
									if args.output_dir is not None:
												lowerCamelCase								=     os.path.join(args.output_dir     ,       lowerCamelCase__			)
									accelerator.save_state(lowerCamelCase__			)






def   __lowerCamelCase	(       ):





			'''simple docstring'''




			lowerCamelCase								=     argparse.ArgumentParser(description="""Simple example of training script."""			)
			parser.add_argument(
			    """--mixed_precision"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       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=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""     ,       )
			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(
			    """--resume_from_checkpoint"""     ,       type=lowerCamelCase__     ,       default=lowerCamelCase__     ,       help="""If the training should continue from a checkpoint folder."""     ,       )
			lowerCamelCase								=     parser.parse_args()
			lowerCamelCase								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(lowerCamelCase__     ,       lowerCamelCase__			)


if __name__ == "__main__":
		main()

