"""simple docstring"""





import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


_UpperCamelCase     :		List[Any]											=					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()





def        a_  (   _lowerCAmelCase						:				int  ):





       '''simple docstring'''

       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None





def        a_  (   _lowerCAmelCase						:				List[str]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				Dict  ):





       '''simple docstring'''

       return max(metric_fn(_lowerCAmelCase     ,				_lowerCAmelCase  ) for gt in ground_truths  )





def        a_  (   _lowerCAmelCase						:				List[Any]     ,				_lowerCAmelCase						:				Optional[int]     ,				_lowerCAmelCase						:				int  ):





       '''simple docstring'''

       lowercase__    :	Any     					=			[line.strip() for line in open(_lowerCAmelCase     ,				'r'  ).readlines()]
       lowercase__    :	Optional[int]     					=			[]

       if args.gold_data_mode == "qa":
              lowercase__    :	Any     					=			pd.read_csv(_lowerCAmelCase     ,				sep='\t'     ,				header=_lowerCAmelCase  )
              for answer_list in data[1]:
                     lowercase__    :	str     					=			ast.literal_eval(_lowerCAmelCase  )
                     answers.append(_lowerCAmelCase  )
       else:
              lowercase__    :	List[Any]     					=			[line.strip() for line in open(_lowerCAmelCase     ,				'r'  ).readlines()]
              lowercase__    :	int     					=			[[reference] for reference in references]

       lowercase__    :	Tuple     					=			0
       for prediction, ground_truths in zip(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              total += 1
              em += metric_max_over_ground_truths(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
              fa += metric_max_over_ground_truths(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )

       lowercase__    :	str     					=			1_0_0.0 * em / total
       lowercase__    :	str     					=			1_0_0.0 * fa / total

       logger.info(f"""F1: {fa:.2f}"""  )
       logger.info(f"""EM: {em:.2f}"""  )





def        a_  (   _lowerCAmelCase						:				Union[str, Any]     ,				_lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Tuple  ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			args.k
       lowercase__    :	Optional[int]     					=			[line.strip() for line in open(_lowerCAmelCase     ,				'r'  ).readlines()]
       lowercase__    :	List[str]     					=			[line.strip() for line in open(_lowerCAmelCase     ,				'r'  ).readlines()]

       lowercase__    :	List[Any]     					=			0
       for hypo, reference in zip(_lowerCAmelCase     ,				_lowerCAmelCase  ):
              lowercase__    :	Union[str, Any]     					=			set(hypo.split('\t'  )[:k]  )
              lowercase__    :	Tuple     					=			set(reference.split('\t'  )  )
              total += 1
              em += len(hypo_provenance & ref_provenance  ) / k

       lowercase__    :	int     					=			1_0_0.0 * em / total
       logger.info(f"""Precision@{k}: {em: .2f}"""  )





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





       '''simple docstring'''

       def strip_title(_lowerCAmelCase						:				List[Any]  ):
              if title.startswith('"'  ):
                     lowercase__    :	int     					=			title[1:]
              if title.endswith('"'  ):
                     lowercase__    :	Optional[int]     					=			title[:-1]
              return title

       lowercase__    :	List[Any]     					=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           _lowerCAmelCase     ,				return_tensors='pt'     ,				padding=_lowerCAmelCase     ,				truncation=_lowerCAmelCase     ,				)['input_ids'].to(args.device  )

       lowercase__    :	Optional[Any]     					=			rag_model.rag.question_encoder(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			question_enc_outputs[0]

       lowercase__    :	str     					=			rag_model.retriever(
           _lowerCAmelCase     ,				question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()     ,				prefix=rag_model.rag.generator.config.prefix     ,				n_docs=rag_model.config.n_docs     ,				return_tensors='pt'     ,				)
       lowercase__    :	List[str]     					=			rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
       lowercase__    :	str     					=			[]
       for docs in all_docs:
              lowercase__    :	Tuple     					=			[strip_title(_lowerCAmelCase  ) for title in docs['title']]
              provenance_strings.append('\t'.join(_lowerCAmelCase  )  )
       return provenance_strings





def        a_  (   _lowerCAmelCase						:				Optional[Any]     ,				_lowerCAmelCase						:				Dict     ,				_lowerCAmelCase						:				Union[str, Any]  ):





       '''simple docstring'''

       with torch.no_grad():
              lowercase__    :	Optional[int]     					=			rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  _lowerCAmelCase     ,				return_tensors='pt'     ,				padding=_lowerCAmelCase     ,				truncation=_lowerCAmelCase  )

              lowercase__    :	int     					=			inputs_dict.input_ids.to(args.device  )
              lowercase__    :	int     					=			inputs_dict.attention_mask.to(args.device  )
              lowercase__    :	Any     					=			rag_model.generate(  # rag_model overwrites generate
                  _lowerCAmelCase     ,				attention_mask=_lowerCAmelCase     ,				num_beams=args.num_beams     ,				min_length=args.min_length     ,				max_length=args.max_length     ,				early_stopping=_lowerCAmelCase     ,				num_return_sequences=1     ,				bad_words_ids=[[0, 0]]     ,				)
              lowercase__    :	List[str]     					=			rag_model.retriever.generator_tokenizer.batch_decode(_lowerCAmelCase     ,				skip_special_tokens=_lowerCAmelCase  )

              if args.print_predictions:
                     for q, a in zip(_lowerCAmelCase     ,				_lowerCAmelCase  ):
                            logger.info('Q: {} - A: {}'.format(_lowerCAmelCase     ,				_lowerCAmelCase  )  )

              return answers





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Union[str, Any]     					=			argparse.ArgumentParser()
       parser.add_argument(
           '--model_type'     ,				choices=['rag_sequence', 'rag_token', 'bart']     ,				type=_lowerCAmelCase     ,				help=(
               'RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'
               ' model_name_or_path'
           )     ,				)
       parser.add_argument(
           '--index_name'     ,				default=_lowerCAmelCase     ,				choices=['exact', 'compressed', 'legacy']     ,				type=_lowerCAmelCase     ,				help='RAG model retriever type'     ,				)
       parser.add_argument(
           '--index_path'     ,				default=_lowerCAmelCase     ,				type=_lowerCAmelCase     ,				help='Path to the retrieval index'     ,				)
       parser.add_argument('--n_docs'     ,				default=5     ,				type=_lowerCAmelCase     ,				help='Number of retrieved docs'  )
       parser.add_argument(
           '--model_name_or_path'     ,				default=_lowerCAmelCase     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				help='Path to pretrained checkpoints or model identifier from huggingface.co/models'     ,				)
       parser.add_argument(
           '--eval_mode'     ,				choices=['e2e', 'retrieval']     ,				default='e2e'     ,				type=_lowerCAmelCase     ,				help=(
               'Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'
               ' precision@k.'
           )     ,				)
       parser.add_argument('--k'     ,				default=1     ,				type=_lowerCAmelCase     ,				help='k for the precision@k calculation'  )
       parser.add_argument(
           '--evaluation_set'     ,				default=_lowerCAmelCase     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				help='Path to a file containing evaluation samples'     ,				)
       parser.add_argument(
           '--gold_data_path'     ,				default=_lowerCAmelCase     ,				type=_lowerCAmelCase     ,				required=_lowerCAmelCase     ,				help='Path to a tab-separated file with gold samples'     ,				)
       parser.add_argument(
           '--gold_data_mode'     ,				default='qa'     ,				type=_lowerCAmelCase     ,				choices=['qa', 'ans']     ,				help=(
               'Format of the gold data file'
               'qa - a single line in the following format: question [tab] answer_list'
               'ans - a single line of the gold file contains the expected answer string'
           )     ,				)
       parser.add_argument(
           '--predictions_path'     ,				type=_lowerCAmelCase     ,				default='predictions.txt'     ,				help='Name of the predictions file, to be stored in the checkpoints directory'     ,				)
       parser.add_argument(
           '--eval_all_checkpoints'     ,				action='store_true'     ,				help='Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'     ,				)
       parser.add_argument(
           '--eval_batch_size'     ,				default=8     ,				type=_lowerCAmelCase     ,				help='Batch size per GPU/CPU for evaluation.'     ,				)
       parser.add_argument(
           '--recalculate'     ,				help='Recalculate predictions even if the prediction file exists'     ,				action='store_true'     ,				)
       parser.add_argument(
           '--num_beams'     ,				default=4     ,				type=_lowerCAmelCase     ,				help='Number of beams to be used when generating answers'     ,				)
       parser.add_argument('--min_length'     ,				default=1     ,				type=_lowerCAmelCase     ,				help='Min length of the generated answers'  )
       parser.add_argument('--max_length'     ,				default=50     ,				type=_lowerCAmelCase     ,				help='Max length of the generated answers'  )

       parser.add_argument(
           '--print_predictions'     ,				action='store_true'     ,				help='If True, prints predictions while evaluating.'     ,				)
       parser.add_argument(
           '--print_docs'     ,				action='store_true'     ,				help='If True, prints docs retried while generating.'     ,				)
       lowercase__    :	Optional[int]     					=			parser.parse_args()
       lowercase__    :	Tuple     					=			torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
       return args





def        a_  (   _lowerCAmelCase						:				List[str]  ):





       '''simple docstring'''

       lowercase__    :	int     					=			{}
       if args.model_type is None:
              lowercase__    :	Union[str, Any]     					=			infer_model_type(args.model_name_or_path  )
              assert args.model_type is not None
       if args.model_type.startswith('rag'  ):
              lowercase__    :	Tuple     					=			RagTokenForGeneration if args.model_type == 'rag_token' else RagSequenceForGeneration
              lowercase__    :	Tuple     					=			args.n_docs
              if args.index_name is not None:
                     lowercase__    :	Any     					=			args.index_name
              if args.index_path is not None:
                     lowercase__    :	Tuple     					=			args.index_path
       else:
              lowercase__    :	Tuple     					=			BartForConditionalGeneration

       lowercase__    :	Optional[Any]     					=			(
           [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info('Evaluate the following checkpoints: %s'     ,				_lowerCAmelCase  )

       lowercase__    :	Dict     					=			get_scores if args.eval_mode == 'e2e' else get_precision_at_k
       lowercase__    :	Union[str, Any]     					=			evaluate_batch_eae if args.eval_mode == 'e2e' else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path  ) and (not args.recalculate):
                     logger.info('Calculating metrics based on an existing predictions file: {}'.format(args.predictions_path  )  )
                     score_fn(_lowerCAmelCase     ,				args.predictions_path     ,				args.gold_data_path  )
                     continue

              logger.info('***** Running evaluation for {} *****'.format(_lowerCAmelCase  )  )
              logger.info('  Batch size = %d'     ,				args.eval_batch_size  )
              logger.info('  Predictions will be stored under {}'.format(args.predictions_path  )  )

              if args.model_type.startswith('rag'  ):
                     lowercase__    :	Tuple     					=			RagRetriever.from_pretrained(_lowerCAmelCase     ,				**_lowerCAmelCase  )
                     lowercase__    :	List[str]     					=			model_class.from_pretrained(_lowerCAmelCase     ,				retriever=_lowerCAmelCase     ,				**_lowerCAmelCase  )
                     model.retriever.init_retrieval()
              else:
                     lowercase__    :	Optional[int]     					=			model_class.from_pretrained(_lowerCAmelCase     ,				**_lowerCAmelCase  )
              model.to(args.device  )

              with open(args.evaluation_set     ,				'r'  ) as eval_file, open(args.predictions_path     ,				'w'  ) as preds_file:
                     lowercase__    :	Dict     					=			[]
                     for line in tqdm(_lowerCAmelCase  ):
                            questions.append(line.strip()  )
                            if len(_lowerCAmelCase  ) == args.eval_batch_size:
                                   lowercase__    :	List[str]     					=			evaluate_batch_fn(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
                                   preds_file.write('\n'.join(_lowerCAmelCase  ) + '\n'  )
                                   preds_file.flush()
                                   lowercase__    :	Union[str, Any]     					=			[]
                     if len(_lowerCAmelCase  ) > 0:
                            lowercase__    :	Union[str, Any]     					=			evaluate_batch_fn(_lowerCAmelCase     ,				_lowerCAmelCase     ,				_lowerCAmelCase  )
                            preds_file.write('\n'.join(_lowerCAmelCase  )  )
                            preds_file.flush()

                     score_fn(_lowerCAmelCase     ,				args.predictions_path     ,				args.gold_data_path  )


if __name__ == "__main__":
       _UpperCamelCase     :		Any											=					get_args()
       main(args)


"""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()

