

"""simple docstring"""




import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_	  =			get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
     from transformers.models.plbart.modeling_plbart import shift_tokens_right

SCREAMING_SNAKE_CASE_	  =			5_0003
SCREAMING_SNAKE_CASE_	  =			5_0002


@require_sentencepiece
@require_tokenizers
class 		_A       (			__UpperCAmelCase				,       unittest.TestCase					):
      __a								=	PLBartTokenizer
      __a								=	None
      __a								=	False





      def 					_lowerCamelCase  (  self		)   ->					str:
         super().setUp()

         # We have a SentencePiece fixture for testing
         lowerCamelCase__						=			PLBartTokenizer(__SCREAMING_SNAKE_CASE    ,						language_codes="base"    ,						keep_accents=__SCREAMING_SNAKE_CASE		)
         tokenizer.save_pretrained(self.tmpdirname		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			PLBartTokenizer(__SCREAMING_SNAKE_CASE    ,						language_codes="base"    ,						keep_accents=__SCREAMING_SNAKE_CASE		)

         lowerCamelCase__						=			tokenizer.tokenize("This is a test"		)
         self.assertListEqual(__SCREAMING_SNAKE_CASE    ,						["▁This", "▁is", "▁a", "▁t", "est"]		)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)    ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,						)

         lowerCamelCase__						=			tokenizer.tokenize("I was born in 92000, and this is falsé."		)
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]    ,						)
         lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]    ,						)

         lowerCamelCase__						=			tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		)
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]    ,						)

         lowerCamelCase__						=			tokenizer.vocab_size
         lowerCamelCase__						=			[tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		) for x in range(end - 4    ,						__SCREAMING_SNAKE_CASE		)]

         self.assertListEqual(__SCREAMING_SNAKE_CASE    ,						["__java__", "__python__", "__en_XX__", "<mask>"]		)

         lowerCamelCase__						=			"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
         lowerCamelCase__						=			tokenizer(__SCREAMING_SNAKE_CASE		).input_ids
         self.assertEqual(
             tokenizer.decode(__SCREAMING_SNAKE_CASE    ,						skip_special_tokens=__SCREAMING_SNAKE_CASE    ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE		)    ,						__SCREAMING_SNAKE_CASE    ,						)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			PLBartTokenizer(__SCREAMING_SNAKE_CASE    ,						language_codes="multi"    ,						keep_accents=__SCREAMING_SNAKE_CASE		)

         lowerCamelCase__						=			tokenizer.tokenize("This is a test"		)
         self.assertListEqual(__SCREAMING_SNAKE_CASE    ,						["▁This", "▁is", "▁a", "▁t", "est"]		)

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)    ,						[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,						)

         lowerCamelCase__						=			tokenizer.tokenize("I was born in 92000, and this is falsé."		)
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "9",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "é",
                 ".",
             ]    ,						)
         lowerCamelCase__						=			tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]    ,						)

         lowerCamelCase__						=			tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		)
         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						[
                 SPIECE_UNDERLINE + "I",
                 SPIECE_UNDERLINE + "was",
                 SPIECE_UNDERLINE + "b",
                 "or",
                 "n",
                 SPIECE_UNDERLINE + "in",
                 SPIECE_UNDERLINE + "",
                 "<unk>",
                 "2",
                 "0",
                 "0",
                 "0",
                 ",",
                 SPIECE_UNDERLINE + "and",
                 SPIECE_UNDERLINE + "this",
                 SPIECE_UNDERLINE + "is",
                 SPIECE_UNDERLINE + "f",
                 "al",
                 "s",
                 "<unk>",
                 ".",
             ]    ,						)
         lowerCamelCase__						=			tokenizer.vocab_size
         lowerCamelCase__						=			[tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		) for x in range(end - 7    ,						__SCREAMING_SNAKE_CASE		)]

         self.assertListEqual(
             __SCREAMING_SNAKE_CASE    ,						["__java__", "__python__", "__en_XX__", "__javascript__", "__php__", "__ruby__", "__go__"]		)
         lowerCamelCase__						=			"java.lang.Exception, python.lang.Exception, javascript, php, ruby, go"
         lowerCamelCase__						=			tokenizer(__SCREAMING_SNAKE_CASE		).input_ids
         self.assertEqual(
             tokenizer.decode(__SCREAMING_SNAKE_CASE    ,						skip_special_tokens=__SCREAMING_SNAKE_CASE    ,						clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE		)    ,						__SCREAMING_SNAKE_CASE    ,						)






@require_torch
@require_sentencepiece
@require_tokenizers
class 		_A       (			unittest.TestCase					):
      __a								=	'''uclanlp/plbart-python-en_XX'''
      __a								=	[
          '''def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])''',
          '''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''',
      ]
      __a								=	[
          '''Returns the maximum value of a b c.''',
          '''Sums the values of a b c.''',
      ]
      __a								=	[
          134,
          5452,
          3_3460,
          3_3441,
          3_3463,
          3_3465,
          3_3463,
          3_3449,
          988,
          20,
          3_3456,
          19,
          3_3456,
          771,
          39,
          4258,
          889,
          3318,
          3_3441,
          3_3463,
          3_3465,
          3_3463,
          3_3449,
          2471,
          2,
          PYTHON_CODE,
      ]





      @classmethod
      def 					_lowerCamelCase  (  cls		)   ->					Any:
         lowerCamelCase__						=			PLBartTokenizer.from_pretrained(
             cls.checkpoint_name    ,						language_codes="base"    ,						src_lang="python"    ,						tgt_lang="en_XX"		)
         lowerCamelCase__						=			1
         return cls





      def 					_lowerCamelCase  (  self		)   ->					Any:
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__java__"]    ,						50001		)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__python__"]    ,						50002		)
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["__en_XX__"]    ,						50003		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
         self.assertListEqual(self.expected_src_tokens    ,						__SCREAMING_SNAKE_CASE		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         self.assertIn(__SCREAMING_SNAKE_CASE    ,						self.tokenizer.all_special_ids		)
         lowerCamelCase__						=			[EN_CODE, 9037, 33442, 57, 752, 153, 14, 56, 18, 9, 2]
         lowerCamelCase__						=			self.tokenizer.decode(__SCREAMING_SNAKE_CASE    ,						skip_special_tokens=__SCREAMING_SNAKE_CASE		)
         lowerCamelCase__						=			self.tokenizer.decode(generated_ids[1:]    ,						skip_special_tokens=__SCREAMING_SNAKE_CASE		)
         self.assertEqual(__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE		)
         self.assertNotIn(self.tokenizer.eos_token    ,						__SCREAMING_SNAKE_CASE		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			["def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])" * 20]
         self.assertIsInstance(src_text[0]    ,						__SCREAMING_SNAKE_CASE		)
         lowerCamelCase__						=			10
         lowerCamelCase__						=			self.tokenizer(__SCREAMING_SNAKE_CASE    ,						max_length=__SCREAMING_SNAKE_CASE    ,						truncation=__SCREAMING_SNAKE_CASE		).input_ids[0]
         self.assertEqual(ids[-2]    ,						2		)
         self.assertEqual(ids[-1]    ,						__SCREAMING_SNAKE_CASE		)
         self.assertEqual(len(__SCREAMING_SNAKE_CASE		)    ,						__SCREAMING_SNAKE_CASE		)





      def 					_lowerCamelCase  (  self		)   ->					int:
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "__java__"]		)    ,						[50004, 50001]		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			tempfile.mkdtemp()
         lowerCamelCase__						=			self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE		)
         lowerCamelCase__						=			PLBartTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE		)
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids    ,						__SCREAMING_SNAKE_CASE		)





      @require_torch
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			self.tokenizer(self.src_text    ,						text_target=self.tgt_text    ,						padding=__SCREAMING_SNAKE_CASE    ,						return_tensors="pt"		)
         lowerCamelCase__						=			shift_tokens_right(batch["labels"]    ,						self.tokenizer.pad_token_id		)

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         self.assertEqual(batch.input_ids[1][-2:].tolist()    ,						[2, PYTHON_CODE]		)
         self.assertEqual(batch.decoder_input_ids[1][0]    ,						__SCREAMING_SNAKE_CASE		)
         self.assertEqual(batch.decoder_input_ids[1][-1]    ,						2		)
         self.assertEqual(batch.labels[1][-2:].tolist()    ,						[2, EN_CODE]		)





      @require_torch
      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			self.tokenizer(
             self.src_text    ,						text_target=self.tgt_text    ,						padding=__SCREAMING_SNAKE_CASE    ,						truncation=__SCREAMING_SNAKE_CASE    ,						max_length=len(self.expected_src_tokens		)    ,						return_tensors="pt"    ,						)
         lowerCamelCase__						=			shift_tokens_right(batch["labels"]    ,						self.tokenizer.pad_token_id		)

         self.assertIsInstance(__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE		)

         self.assertEqual((2, 26)    ,						batch.input_ids.shape		)
         self.assertEqual((2, 26)    ,						batch.attention_mask.shape		)
         lowerCamelCase__						=			batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens    ,						__SCREAMING_SNAKE_CASE		)
         self.assertEqual(2    ,						batch.decoder_input_ids[0, -1]		)  # EOS
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens    ,						[]		)
         self.assertEqual(self.tokenizer.suffix_tokens    ,						[self.tokenizer.eos_token_id, PYTHON_CODE]		)





      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         lowerCamelCase__						=			self.tokenizer(self.src_text    ,						padding=__SCREAMING_SNAKE_CASE    ,						truncation=__SCREAMING_SNAKE_CASE    ,						max_length=3    ,						return_tensors="pt"		)
         lowerCamelCase__						=			self.tokenizer(
             text_target=self.tgt_text    ,						padding=__SCREAMING_SNAKE_CASE    ,						truncation=__SCREAMING_SNAKE_CASE    ,						max_length=10    ,						return_tensors="pt"		)
         lowerCamelCase__						=			targets["input_ids"]
         lowerCamelCase__						=			shift_tokens_right(__SCREAMING_SNAKE_CASE    ,						self.tokenizer.pad_token_id		)

         self.assertEqual(batch.input_ids.shape[1]    ,						3		)
         self.assertEqual(batch.decoder_input_ids.shape[1]    ,						10		)





      @require_torch
      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			self.tokenizer._build_translation_inputs(
             "A test"    ,						return_tensors="pt"    ,						src_lang="en_XX"    ,						tgt_lang="java"		)

         self.assertEqual(
             nested_simplify(__SCREAMING_SNAKE_CASE		)    ,						{
                 # A, test, EOS, en_XX
                 "input_ids": [[150, 242, 2, 50003]],
                 "attention_mask": [[1, 1, 1, 1]],
                 # java
                 "forced_bos_token_id": 50001,
             }    ,						)







"""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


SCREAMING_SNAKE_CASE_     :			int	  =			16
SCREAMING_SNAKE_CASE_     :			int	  =			32



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   return int(x / 2**20   )


class 		_A       :





      def __enter__(  self		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         return self





      def __exit__(  self    ,						*SCREAMING_SNAKE_CASE__		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         lowerCamelCase__						=			bamb(self.end - self.begin		)
         lowerCamelCase__						=			bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	UpperCAmelCase__   (				A__	,    A__ = 16	,    A__ = "bert-base-cased"	,    A__ = 320	,    A__ = 160	,    )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			AutoTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			load_dataset(
       "glue"	,    "mrpc"	,    split={"train": f'train[:{n_train}]', "validation": f'validation[:{n_val}]'}   )

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

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

   # 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(A__   ):
      # 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(A__	,    padding="max_length"	,    max_length=128	,    return_tensors="pt"   )
      return tokenizer.pad(A__	,    padding="longest"	,    return_tensors="pt"   )

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

   return train_dataloader, eval_dataloader



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   # Initialize accelerator
   lowerCamelCase__						=			Accelerator()

   # 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"]   )
   lowerCamelCase__						=			args.model_name_or_path

   set_seed(A__   )
   lowerCamelCase__					,       lowerCamelCase__						=			get_dataloaders(A__	,    A__	,    A__	,    args.n_train	,    args.n_val   )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase__						=			AutoModelForSequenceClassification.from_pretrained(A__	,    return_dict=A__   )

   # Instantiate optimizer
   lowerCamelCase__						=			(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowerCamelCase__						=			optimizer_cls(params=model.parameters()	,    lr=A__   )

   if accelerator.state.deepspeed_plugin is not None:
      lowerCamelCase__						=			accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      lowerCamelCase__						=			1
   lowerCamelCase__						=			(len(A__   ) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      lowerCamelCase__						=			get_linear_schedule_with_warmup(
          optimizer=A__	,    num_warmup_steps=0	,    num_training_steps=A__	,    )
   else:
      lowerCamelCase__						=			DummyScheduler(A__	,    total_num_steps=A__	,    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.
   lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
       A__	,    A__	,    A__	,    A__	,    A__   )

   # 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

   # Now we train the model
   lowerCamelCase__						=			{}
   for epoch in range(A__	,    A__   ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(A__   ):
            lowerCamelCase__						=			model(**A__   )
            lowerCamelCase__						=			outputs.loss
            lowerCamelCase__						=			loss / gradient_accumulation_steps
            accelerator.backward(A__   )
            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   )   )   )
      lowerCamelCase__						=			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(A__	,    A__   )



def 	UpperCAmelCase__   (				)      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."   )
   parser.add_argument(
       "--model_name_or_path"	,    type=A__	,    default="bert-base-cased"	,    help="Path to pretrained model or model identifier from huggingface.co/models."	,    required=A__	,    )
   parser.add_argument(
       "--output_dir"	,    type=A__	,    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=A__	,    default=A__	,    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=A__	,    default=320	,    help="Number of training examples to use."	,    )
   parser.add_argument(
       "--n_val"	,    type=A__	,    default=160	,    help="Number of validation examples to use."	,    )
   parser.add_argument(
       "--num_epochs"	,    type=A__	,    default=1	,    help="Number of train epochs."	,    )
   lowerCamelCase__						=			parser.parse_args()
   lowerCamelCase__						=			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(A__	,    A__   )


if __name__ == "__main__":
     main()




