





"""simple docstring"""





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		lowercase					(					__lowerCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      RobertaTokenizer
      _SCREAMING_SNAKE_CASE					=      RobertaTokenizerFast
      _SCREAMING_SNAKE_CASE					=      True
      _SCREAMING_SNAKE_CASE					=      {'''cls_token''': '''<s>'''}





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

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            lowerCAmelCase  						=       [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
            ]
            lowerCAmelCase  						=       dict(zip(lowerCAmelCase_      ,						range(len(lowerCAmelCase_ ) ) ) )
            lowerCAmelCase  						=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            lowerCAmelCase  						=       {"""unk_token""": """<unk>"""}

            lowerCAmelCase  						=       os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""vocab_file"""] )
            lowerCAmelCase  						=       os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES["""merges_file"""] )
            with open(self.vocab_file      ,						"""w"""      ,						encoding="""utf-8""" ) as fp:
                  fp.write(json.dumps(lowerCAmelCase_ ) + """\n""" )
            with open(self.merges_file      ,						"""w"""      ,						encoding="""utf-8""" ) as fp:
                  fp.write("""\n""".join(lowerCAmelCase_ ) )





      def     _snake_case			(       self      ,						**lowercase )		->     Tuple:
            kwargs.update(self.special_tokens_map )
            return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**lowerCAmelCase_ )





      def     _snake_case			(       self      ,						**lowercase )		->     Any:
            kwargs.update(self.special_tokens_map )
            return RobertaTokenizerFast.from_pretrained(self.tmpdirname      ,						**lowerCAmelCase_ )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       """lower newer"""
            lowerCAmelCase  						=       """lower newer"""
            return input_text, output_text





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.tokenizer_class(self.vocab_file      ,						self.merges_file      ,						**self.special_tokens_map )
            lowerCAmelCase  						=       """lower newer"""
            lowerCAmelCase  						=       ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            lowerCAmelCase  						=       tokenizer.tokenize(lowerCAmelCase_ )  # , add_prefix_space=True)
            self.assertListEqual(lowerCAmelCase_      ,						lowerCAmelCase_ )

            lowerCAmelCase  						=       tokens + [tokenizer.unk_token]
            lowerCAmelCase  						=       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )      ,						lowerCAmelCase_ )





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("""Hello world!"""      ,						add_special_tokens=lowerCAmelCase_ )      ,						[0, 31_414, 232, 328, 2] )
            self.assertListEqual(
                tokenizer.encode("""Hello world! cécé herlolip 418"""      ,						add_special_tokens=lowerCAmelCase_ )      ,						[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]      ,						)





      @slow
      def     _snake_case			(       self )		->     str:
            lowerCAmelCase  						=       self.tokenizer_class.from_pretrained("""roberta-base""" )

            lowerCAmelCase  						=       tokenizer.encode("""sequence builders"""      ,						add_special_tokens=lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.encode("""multi-sequence build"""      ,						add_special_tokens=lowerCAmelCase_ )

            lowerCAmelCase  						=       tokenizer.encode(
                """sequence builders"""      ,						add_special_tokens=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.encode(
                """sequence builders"""      ,						"""multi-sequence build"""      ,						add_special_tokens=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_ )

            lowerCAmelCase  						=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_      ,						lowerCAmelCase_ )

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       self.get_tokenizer()

            lowerCAmelCase  						=       """Encode this sequence."""
            lowerCAmelCase  						=       tokenizer.byte_encoder[""" """.encode("""utf-8""" )[0]]

            # Testing encoder arguments
            lowerCAmelCase  						=       tokenizer.encode(lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(encoded[0] )[0]
            self.assertNotEqual(lowerCAmelCase_      ,						lowerCAmelCase_ )

            lowerCAmelCase  						=       tokenizer.encode(lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(encoded[0] )[0]
            self.assertEqual(lowerCAmelCase_      ,						lowerCAmelCase_ )

            tokenizer.add_special_tokens({"""bos_token""": """<s>"""} )
            lowerCAmelCase  						=       tokenizer.encode(lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(encoded[1] )[0]
            self.assertNotEqual(lowerCAmelCase_      ,						lowerCAmelCase_ )

            # Testing spaces after special tokens
            lowerCAmelCase  						=       """<mask>"""
            tokenizer.add_special_tokens(
                {"""mask_token""": AddedToken(lowerCAmelCase_      ,						lstrip=lowerCAmelCase_      ,						rstrip=lowerCAmelCase_ )} )  # mask token has a left space
            lowerCAmelCase  						=       tokenizer.convert_tokens_to_ids(lowerCAmelCase_ )

            lowerCAmelCase  						=       """Encode <mask> sequence"""
            lowerCAmelCase  						=       """Encode <mask>sequence"""

            lowerCAmelCase  						=       tokenizer.encode(lowerCAmelCase_ )
            lowerCAmelCase  						=       encoded.index(lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
            self.assertEqual(lowerCAmelCase_      ,						lowerCAmelCase_ )

            lowerCAmelCase  						=       tokenizer.encode(lowerCAmelCase_ )
            lowerCAmelCase  						=       encoded.index(lowerCAmelCase_ )
            lowerCAmelCase  						=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
            self.assertNotEqual(lowerCAmelCase_      ,						lowerCAmelCase_ )





      def     _snake_case			(       self )		->     Tuple:
            pass





      def     _snake_case			(       self )		->     Optional[int]:
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_      ,						**lowerCAmelCase_ )
                        lowerCAmelCase  						=       self.tokenizer_class.from_pretrained(lowerCAmelCase_      ,						**lowerCAmelCase_ )
                        lowerCAmelCase  						=       """A, <mask> AllenNLP sentence."""
                        lowerCAmelCase  						=       tokenizer_r.encode_plus(lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_      ,						return_token_type_ids=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_p.encode_plus(lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_      ,						return_token_type_ids=lowerCAmelCase_ )

                        # token_type_ids should put 0 everywhere
                        self.assertEqual(sum(tokens_r["""token_type_ids"""] )      ,						sum(tokens_p["""token_type_ids"""] ) )

                        # attention_mask should put 1 everywhere, so sum over length should be 1
                        self.assertEqual(
                            sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] )      ,						sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] )      ,						)

                        lowerCAmelCase  						=       tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
                        lowerCAmelCase  						=       tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )

                        # Rust correctly handles the space before the mask while python doesnt
                        self.assertSequenceEqual(tokens_p["""input_ids"""]      ,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
                        self.assertSequenceEqual(tokens_r["""input_ids"""]      ,						[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

                        self.assertSequenceEqual(
                            lowerCAmelCase_      ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
                        self.assertSequenceEqual(
                            lowerCAmelCase_      ,						["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )





      def     _snake_case			(       self )		->     Any:
            for trim_offsets, add_prefix_space in itertools.product([True, False]      ,						repeat=2 ):
                  lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                      self.tmpdirname      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )

                  lowerCAmelCase  						=       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
                  lowerCAmelCase  						=       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

                  self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]      ,						lowerCAmelCase_ )

                  self.assertEqual(post_processor_state["""add_prefix_space"""]      ,						lowerCAmelCase_ )
                  self.assertEqual(post_processor_state["""trim_offsets"""]      ,						lowerCAmelCase_ )





      def     _snake_case			(       self )		->     Any:
            # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
            # `trim_offsets`
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
                        lowerCAmelCase  						=       """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                        lowerCAmelCase  						=       f'{text_of_1_token} {text_of_1_token}'

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(len(lowerCAmelCase_ ) + 1, len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(len(lowerCAmelCase_ ) + 1, len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(len(lowerCAmelCase_ ), len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(0, len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(len(lowerCAmelCase_ ), len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)

                        lowerCAmelCase  						=       f' {text}'

                        # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                        #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                        # )
                        # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                        # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                        # self.assertEqual(
                        #     encoding.offset_mapping[1],
                        #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                        # )

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(1, 1 + len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(1 + len(lowerCAmelCase_ ) + 1, 1 + len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(0, 1 + len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(1 + len(lowerCAmelCase_ ), 1 + len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)

                        lowerCAmelCase  						=       self.rust_tokenizer_class.from_pretrained(
                            lowerCAmelCase_      ,						use_fast=lowerCAmelCase_      ,						add_prefix_space=lowerCAmelCase_      ,						trim_offsets=lowerCAmelCase_ )
                        lowerCAmelCase  						=       tokenizer_r(lowerCAmelCase_      ,						return_offsets_mapping=lowerCAmelCase_      ,						add_special_tokens=lowerCAmelCase_ )
                        self.assertEqual(encoding.offset_mapping[0]      ,						(0, 1 + len(lowerCAmelCase_ )) )
                        self.assertEqual(
                            encoding.offset_mapping[1]      ,						(1 + len(lowerCAmelCase_ ), 1 + len(lowerCAmelCase_ ) + 1 + len(lowerCAmelCase_ ))      ,						)







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										=						16
_SCREAMING_SNAKE_CASE										=						32






def  snake_case  (       snake_case__			:Optional[int])						->   str:
       return int(x / 2**20)



class 				a							:







 """simple docstring"""






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



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






def  snake_case  (       snake_case__			:Accelerator   ,	snake_case__			:int = 16   ,	snake_case__			:str = "bert-base-cased"   ,	snake_case__			:int = 320   ,	snake_case__			:int = 160   ,	)						->   Dict:
       _A        =  AutoTokenizer.from_pretrained(snake_case__)
       _A        =  load_dataset(
           """glue"""   ,	"""mrpc"""   ,	split={"""train""": F'''train[:{n_train}]''', """validation""": F'''validation[:{n_val}]'''})

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

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

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       _A        =  tokenized_datasets.rename_column("""label"""   ,	"""labels""")

       def collate_fn(snake_case__			:List[str]):
              # 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(snake_case__   ,	padding="""max_length"""   ,	max_length=128   ,	return_tensors="""pt""")
              return tokenizer.pad(snake_case__   ,	padding="""longest"""   ,	return_tensors="""pt""")

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

       return train_dataloader, eval_dataloader






def  snake_case  (       snake_case__			:Optional[int]   ,	snake_case__			:Optional[int])						->   Optional[int]:
       # Initialize accelerator
       _A        =  Accelerator()

       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       _A        =  config["""lr"""]
       _A        =  int(config["""num_epochs"""])
       _A        =  int(config["""seed"""])
       _A        =  int(config["""batch_size"""])
       _A        =  args.model_name_or_path

       set_seed(snake_case__)
       _A   ,  _A        =  get_dataloaders(snake_case__   ,	snake_case__   ,	snake_case__   ,	args.n_train   ,	args.n_val)

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

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

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

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

       # Prepare everything
       # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
       # prepare method.
       _A   ,  _A   ,  _A   ,  _A   ,  _A        =  accelerator.prepare(
           snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__   ,	snake_case__)

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

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






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


if __name__ == "__main__":
    main()




