"""simple docstring"""





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
_UpperCamelCase     :		int											=					{"LayoutLMv2Config", "LayoutLMv3Config"}


@is_pipeline_test
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					str										=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      lowerCamelCase__	:					List[str]										=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            lowerCamelCase__	:					List[Any]										=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            lowerCamelCase__	:					Any										=						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      List[Any]:
             lowercase__    :	Any     					=			ZeroShotClassificationPipeline(
                 model=a	,    tokenizer=a	,    candidate_labels=['polics', 'health']							)
             return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Union[str, Any]:
             lowercase__    :	List[str]     					=			classifier('Who are you voting for in 2020?'	,    candidate_labels='politics'							)
             self.assertEqual(a	,    {'sequence': ANY(a							), 'labels': [ANY(a							)], 'scores': [ANY(a							)]}							)

             # No kwarg
             lowercase__    :	int     					=			classifier('Who are you voting for in 2020?'	,    ['politics']							)
             self.assertEqual(a	,    {'sequence': ANY(a							), 'labels': [ANY(a							)], 'scores': [ANY(a							)]}							)

             lowercase__    :	str     					=			classifier('Who are you voting for in 2020?'	,    candidate_labels=['politics']							)
             self.assertEqual(a	,    {'sequence': ANY(a							), 'labels': [ANY(a							)], 'scores': [ANY(a							)]}							)

             lowercase__    :	Tuple     					=			classifier('Who are you voting for in 2020?'	,    candidate_labels='politics, public health'							)
             self.assertEqual(
                 a	,    {'sequence': ANY(a							), 'labels': [ANY(a							), ANY(a							)], 'scores': [ANY(a							), ANY(a							)]}							)
             self.assertAlmostEqual(sum(nested_simplify(outputs['scores']							)							)	,    1.0							)

             lowercase__    :	Tuple     					=			classifier('Who are you voting for in 2020?'	,    candidate_labels=['politics', 'public health']							)
             self.assertEqual(
                 a	,    {'sequence': ANY(a							), 'labels': [ANY(a							), ANY(a							)], 'scores': [ANY(a							), ANY(a							)]}							)
             self.assertAlmostEqual(sum(nested_simplify(outputs['scores']							)							)	,    1.0							)

             lowercase__    :	List[Any]     					=			classifier(
                 'Who are you voting for in 2020?'	,    candidate_labels='politics'	,    hypothesis_template='This text is about {}'							)
             self.assertEqual(a	,    {'sequence': ANY(a							), 'labels': [ANY(a							)], 'scores': [ANY(a							)]}							)

             # https://github.com/huggingface/transformers/issues/13846
             lowercase__    :	str     					=			classifier(['I am happy']	,    ['positive', 'negative']							)
             self.assertEqual(
                 a	,    [
                     {'sequence': ANY(a							), 'labels': [ANY(a							), ANY(a							)], 'scores': [ANY(a							), ANY(a							)]}
                     for i in range(1							)
                 ]	,    )
             lowercase__    :	Any     					=			classifier(['I am happy', 'I am sad']	,    ['positive', 'negative']							)
             self.assertEqual(
                 a	,    [
                     {'sequence': ANY(a							), 'labels': [ANY(a							), ANY(a							)], 'scores': [ANY(a							), ANY(a							)]}
                     for i in range(2							)
                 ]	,    )

             with self.assertRaises(a							):
                    classifier(''	,    candidate_labels='politics'							)

             with self.assertRaises(a							):
                    classifier(a	,    candidate_labels='politics'							)

             with self.assertRaises(a							):
                    classifier('Who are you voting for in 2020?'	,    candidate_labels=''							)

             with self.assertRaises(a							):
                    classifier('Who are you voting for in 2020?'	,    candidate_labels=a							)

             with self.assertRaises(a							):
                    classifier(
                        'Who are you voting for in 2020?'	,    candidate_labels='politics'	,    hypothesis_template='Not formatting template'	,    )

             with self.assertRaises(a							):
                    classifier(
                        'Who are you voting for in 2020?'	,    candidate_labels='politics'	,    hypothesis_template=a	,    )

             self.run_entailment_id(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      Tuple:
             lowercase__    :	int     					=			zero_shot_classifier.model.config
             lowercase__    :	Tuple     					=			config.labelaid
             lowercase__    :	Tuple     					=			zero_shot_classifier.entailment_id

             lowercase__    :	List[Any]     					=			{'LABEL_0': 0, 'LABEL_1': 1, 'LABEL_2': 2}
             self.assertEqual(zero_shot_classifier.entailment_id	,    -1							)

             lowercase__    :	Any     					=			{'entailment': 0, 'neutral': 1, 'contradiction': 2}
             self.assertEqual(zero_shot_classifier.entailment_id	,    0							)

             lowercase__    :	Dict     					=			{'ENTAIL': 0, 'NON-ENTAIL': 1}
             self.assertEqual(zero_shot_classifier.entailment_id	,    0							)

             lowercase__    :	List[Any]     					=			{'ENTAIL': 2, 'NEUTRAL': 1, 'CONTR': 0}
             self.assertEqual(zero_shot_classifier.entailment_id	,    2							)

             lowercase__    :	str     					=			original_labelaid
             self.assertEqual(a	,    zero_shot_classifier.entailment_id							)


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Dict     					=			pipeline(
                 'zero-shot-classification'	,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'	,    framework='pt'	,    )
             # There was a regression in 4.10 for this
             # Adding a test so we don't make the mistake again.
             # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
             zero_shot_classifier(
                 'Who are you voting for in 2020?' * 1_0_0	,    candidate_labels=['politics', 'public health', 'science']							)


      @require_torch
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Tuple     					=			pipeline(
                 'zero-shot-classification'	,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'	,    framework='pt'	,    )
             lowercase__    :	Tuple     					=			zero_shot_classifier(
                 'Who are you voting for in 2020?'	,    candidate_labels=['politics', 'public health', 'science']							)

             self.assertEqual(
                 nested_simplify(a							)	,    {
                     'sequence': 'Who are you voting for in 2020?',
                     'labels': ['science', 'public health', 'politics'],
                     'scores': [0.333, 0.333, 0.333],
                 }	,    )


      @require_tf
      def 	_UpperCAmelCase (     self							) ->      Dict:
             lowercase__    :	Optional[Any]     					=			pipeline(
                 'zero-shot-classification'	,    model='sshleifer/tiny-distilbert-base-cased-distilled-squad'	,    framework='tf'	,    )
             lowercase__    :	Optional[int]     					=			zero_shot_classifier(
                 'Who are you voting for in 2020?'	,    candidate_labels=['politics', 'public health', 'science']							)

             self.assertEqual(
                 nested_simplify(a							)	,    {
                     'sequence': 'Who are you voting for in 2020?',
                     'labels': ['science', 'public health', 'politics'],
                     'scores': [0.333, 0.333, 0.333],
                 }	,    )


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Dict     					=			pipeline('zero-shot-classification'	,    model='roberta-large-mnli'	,    framework='pt'							)
             lowercase__    :	Union[str, Any]     					=			zero_shot_classifier(
                 'Who are you voting for in 2020?'	,    candidate_labels=['politics', 'public health', 'science']							)

             self.assertEqual(
                 nested_simplify(a							)	,    {
                     'sequence': 'Who are you voting for in 2020?',
                     'labels': ['politics', 'public health', 'science'],
                     'scores': [0.976, 0.015, 0.009],
                 }	,    )
             lowercase__    :	str     					=			zero_shot_classifier(
                 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
                 ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
                 ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
                 ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
                 ' machine translation tasks show these models to be superior in quality while being more parallelizable'
                 ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
                 ' English-to-German translation task, improving over the existing best results, including ensembles by'
                 ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
                 ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
                 ' fraction of the training costs of the best models from the literature. We show that the Transformer'
                 ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
                 ' large and limited training data.'	,    candidate_labels=['machine learning', 'statistics', 'translation', 'vision']	,    multi_label=a	,    )
             self.assertEqual(
                 nested_simplify(a							)	,    {
                     'sequence': (
                         'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                         ' networks in an encoder-decoder configuration. The best performing models also connect the'
                         ' encoder and decoder through an attention mechanism. We propose a new simple network'
                         ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                         ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                         ' superior in quality while being more parallelizable and requiring significantly less time to'
                         ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                         ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                         ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                         ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                         ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                         ' other tasks by applying it successfully to English constituency parsing both with large and'
                         ' limited training data.'
                     ),
                     'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                     'scores': [0.817, 0.713, 0.018, 0.018],
                 }	,    )


      @slow
      @require_tf
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	List[str]     					=			pipeline('zero-shot-classification'	,    model='roberta-large-mnli'	,    framework='tf'							)
             lowercase__    :	Optional[int]     					=			zero_shot_classifier(
                 'Who are you voting for in 2020?'	,    candidate_labels=['politics', 'public health', 'science']							)

             self.assertEqual(
                 nested_simplify(a							)	,    {
                     'sequence': 'Who are you voting for in 2020?',
                     'labels': ['politics', 'public health', 'science'],
                     'scores': [0.976, 0.015, 0.009],
                 }	,    )
             lowercase__    :	Dict     					=			zero_shot_classifier(
                 'The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'
                 ' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'
                 ' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'
                 ' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'
                 ' machine translation tasks show these models to be superior in quality while being more parallelizable'
                 ' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'
                 ' English-to-German translation task, improving over the existing best results, including ensembles by'
                 ' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'
                 ' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'
                 ' fraction of the training costs of the best models from the literature. We show that the Transformer'
                 ' generalizes well to other tasks by applying it successfully to English constituency parsing both with'
                 ' large and limited training data.'	,    candidate_labels=['machine learning', 'statistics', 'translation', 'vision']	,    multi_label=a	,    )
             self.assertEqual(
                 nested_simplify(a							)	,    {
                     'sequence': (
                         'The dominant sequence transduction models are based on complex recurrent or convolutional neural'
                         ' networks in an encoder-decoder configuration. The best performing models also connect the'
                         ' encoder and decoder through an attention mechanism. We propose a new simple network'
                         ' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'
                         ' and convolutions entirely. Experiments on two machine translation tasks show these models to be'
                         ' superior in quality while being more parallelizable and requiring significantly less time to'
                         ' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'
                         ' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'
                         ' English-to-French translation task, our model establishes a new single-model state-of-the-art'
                         ' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'
                         ' costs of the best models from the literature. We show that the Transformer generalizes well to'
                         ' other tasks by applying it successfully to English constituency parsing both with large and'
                         ' limited training data.'
                     ),
                     'labels': ['translation', 'machine learning', 'vision', 'statistics'],
                     'scores': [0.817, 0.713, 0.018, 0.018],
                 }	,    )


"""simple docstring"""





import argparse
import gc
import json
import os

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


_UpperCamelCase     :		int											=					16
_UpperCamelCase     :		Union[str, Any]											=					32





def        a_  (   _lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       return int(x / 2**20  )


class   UpperCAmelCase_			:


      def __enter__(     self							) ->      Union[str, Any]:
             gc.collect()
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
             lowercase__    :	List[str]     					=			torch.cuda.memory_allocated()
             return self


      def __exit__(     self	,    *a							) ->      Any:
             gc.collect()
             torch.cuda.empty_cache()
             lowercase__    :	Optional[Any]     					=			torch.cuda.memory_allocated()
             lowercase__    :	Union[str, Any]     					=			torch.cuda.max_memory_allocated()
             lowercase__    :	List[Any]     					=			bamb(self.end - self.begin							)
             lowercase__    :	List[Any]     					=			bamb(self.peak - self.begin							)
             # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")





def        a_  (   _lowerCAmelCase						:				Accelerator     ,				_lowerCAmelCase						:				int = 16     ,				_lowerCAmelCase						:				str = "bert-base-cased"     ,				_lowerCAmelCase						:				int = 320     ,				_lowerCAmelCase						:				int = 160     ,				):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			AutoTokenizer.from_pretrained(_lowerCAmelCase  )
       lowercase__    :	Union[str, Any]     					=			load_dataset(
           'glue'     ,				'mrpc'     ,				split={'train': f"""train[:{n_train}]""", 'validation': f"""validation[:{n_val}]"""}  )

       def tokenize_function(_lowerCAmelCase						:				int  ):
              # max_length=None => use the model max length (it's actually the default)
              lowercase__    :	List[str]     					=			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
       lowercase__    :	Union[str, Any]     					=			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
       lowercase__    :	Union[str, Any]     					=			tokenized_datasets.rename_column('label'     ,				'labels'  )

       def collate_fn(_lowerCAmelCase						:				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=128     ,				return_tensors='pt'  )
              return tokenizer.pad(_lowerCAmelCase     ,				padding='longest'     ,				return_tensors='pt'  )

       # Instantiate dataloaders.
       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['train']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )
       lowercase__    :	Dict     					=			DataLoader(
           tokenized_datasets['validation']     ,				shuffle=_lowerCAmelCase     ,				collate_fn=_lowerCAmelCase     ,				batch_size=_lowerCAmelCase  )

       return train_dataloader, eval_dataloader





def        a_  (   _lowerCAmelCase						:				Any     ,				_lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	List[Any]     					=			Accelerator()

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

       set_seed(_lowerCAmelCase  )
       lowercase__					,   lowercase__    :	Tuple     					=			get_dataloaders(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				args.n_train     ,				args.n_val  )

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

       # Instantiate optimizer
       lowercase__    :	List[Any]     					=			(
           AdamW
           if accelerator.state.deepspeed_plugin is None
           or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
           else DummyOptim
       )
       lowercase__    :	Optional[Any]     					=			optimizer_cls(params=model.parameters()     ,				lr=_lowerCAmelCase  )

       if accelerator.state.deepspeed_plugin is not None:
              lowercase__    :	Optional[Any]     					=			accelerator.state.deepspeed_plugin.deepspeed_config[
                  'gradient_accumulation_steps'
              ]
       else:
              lowercase__    :	List[Any]     					=			1
       lowercase__    :	List[Any]     					=			(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
       ):
              lowercase__    :	Optional[int]     					=			get_linear_schedule_with_warmup(
                  optimizer=_lowerCAmelCase     ,				num_warmup_steps=0     ,				num_training_steps=_lowerCAmelCase     ,				)
       else:
              lowercase__    :	Tuple     					=			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.
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__					,   lowercase__    :	Dict     					=			accelerator.prepare(
           _lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

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

       # Now we train the model
       lowercase__    :	Optional[Any]     					=			{}
       for epoch in range(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(_lowerCAmelCase  ):
                            lowercase__    :	List[Any]     					=			model(**_lowerCAmelCase  )
                            lowercase__    :	Dict     					=			outputs.loss
                            lowercase__    :	int     					=			loss / gradient_accumulation_steps
                            accelerator.backward(_lowerCAmelCase  )
                            if step % gradient_accumulation_steps == 0:
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()

                            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
              accelerator.print('Memory before entering the train : {}'.format(bamb(tracemalloc.begin  )  )  )
              accelerator.print('Memory consumed at the end of the train (end-begin): {}'.format(tracemalloc.used  )  )
              accelerator.print('Peak Memory consumed during the train (max-begin): {}'.format(tracemalloc.peaked  )  )
              accelerator.print(
                  'Total Peak Memory consumed during the train (max): {}'.format(
                      tracemalloc.peaked + bamb(tracemalloc.begin  )  )  )
              lowercase__    :	int     					=			tracemalloc.peaked + bamb(tracemalloc.begin  )
              if args.peak_memory_upper_bound is not None:
                     assert (
                         train_total_peak_memory[f"""epoch-{epoch}"""] <= args.peak_memory_upper_bound
                     ), "Peak memory usage exceeded the upper bound"

       accelerator.wait_for_everyone()
       if accelerator.is_main_process:
              with open(os.path.join(args.output_dir     ,				'peak_memory_utilization.json'  )     ,				'w'  ) as f:
                     json.dump(_lowerCAmelCase     ,				_lowerCAmelCase  )





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	int     					=			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(
           '--peak_memory_upper_bound'     ,				type=_lowerCAmelCase     ,				default=_lowerCAmelCase     ,				help='The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'     ,				)
       parser.add_argument(
           '--n_train'     ,				type=_lowerCAmelCase     ,				default=320     ,				help='Number of training examples to use.'     ,				)
       parser.add_argument(
           '--n_val'     ,				type=_lowerCAmelCase     ,				default=160     ,				help='Number of validation examples to use.'     ,				)
       parser.add_argument(
           '--num_epochs'     ,				type=_lowerCAmelCase     ,				default=1     ,				help='Number of train epochs.'     ,				)
       lowercase__    :	Any     					=			parser.parse_args()
       lowercase__    :	Optional[Any]     					=			{'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(_lowerCAmelCase     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       main()

