





import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
    from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder



def A_(		A__		,				A__		,				A__		,				A__=None		,				A__=None		,				A__=None		,				A__=None		,				A__=None		,				)  ->		int:
       if attention_mask is None:
              a__						:     int			=    input_ids.ne(config.pad_token_id    )
       if decoder_attention_mask is None:
              a__						:     Dict			=    decoder_input_ids.ne(config.pad_token_id    )
       if head_mask is None:
              a__						:     Optional[Any]			=    torch.ones(config.encoder_layers		,				config.encoder_attention_heads		,				device=A__    )
       if decoder_head_mask is None:
              a__						:     int			=    torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=A__    )
       if cross_attn_head_mask is None:
              a__						:     Tuple			=    torch.ones(config.decoder_layers		,				config.decoder_attention_heads		,				device=A__    )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=False ,  lowercase=99 ,  lowercase=16 ,  lowercase=2 ,  lowercase=4 ,  lowercase=4 ,  lowercase="relu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=0.0 ,  lowercase=0.0 ,  lowercase=20 ,  lowercase=2 ,  lowercase=1 ,  lowercase=0 ,  )      ->						int:







          '''simple docstring'''

          a__						:     Tuple			=    parent
          a__						:     Optional[Any]			=    batch_size
          a__						:     Tuple			=    seq_length
          a__						:     Dict			=    is_training
          a__						:     Tuple			=    use_labels
          a__						:     Tuple			=    vocab_size
          a__						:     List[str]			=    hidden_size
          a__						:     List[str]			=    num_hidden_layers
          a__						:     Tuple			=    num_attention_heads
          a__						:     Dict			=    intermediate_size
          a__						:     Dict			=    hidden_act
          a__						:     int			=    hidden_dropout_prob
          a__						:     str			=    attention_probs_dropout_prob
          a__						:     List[Any]			=    encoder_layerdrop
          a__						:     Any			=    decoder_layerdrop
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Dict			=    eos_token_id
          a__						:     int			=    pad_token_id
          a__						:     str			=    bos_token_id

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







          '''simple docstring'''

          a__						:     Optional[int]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)
          a__						:     Any			=    self.eos_token_id  # Eos Token
          a__						:     List[str]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for M2M100 the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          a__						:     str			=    input_ids.clamp(self.pad_token_id + 1)
          a__						:     List[Any]			=    decoder_input_ids.clamp(self.pad_token_id + 1)

          a__						:     str			=    self.get_config()
          a__						:     Any			=    prepare_mam_aaa_inputs_dict(lowercase ,  lowercase ,  lowercase)
          return config, inputs_dict

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          return MaMaaaConfig(
              vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  encoder_layerdrop=self.encoder_layerdrop ,  decoder_layerdrop=self.decoder_layerdrop ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_id=self.eos_token_id ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  )

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[str]			=    self.prepare_config_and_inputs()
          return config, inputs_dict

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Dict:







          '''simple docstring'''

          a__						:     str			=    MaMaaaModel(config=lowercase).get_decoder().to(lowercase).eval()
          a__						:     str			=    inputs_dict['input_ids']
          a__						:     Any			=    inputs_dict['attention_mask']
          a__						:     Optional[int]			=    inputs_dict['head_mask']

          # first forward pass
          a__						:     int			=    model(lowercase ,  attention_mask=lowercase ,  head_mask=lowercase ,  use_cache=lowercase)

          a__						:     int			=    outputs.to_tuple()

          # create hypothetical multiple next token and extent to next_input_ids
          a__						:     int			=    ids_tensor((self.batch_size, 3) ,  config.vocab_size)
          a__						:     Optional[int]			=    ids_tensor((self.batch_size, 3) ,  2)

          # append to next input_ids and
          a__						:     Tuple			=    torch.cat([input_ids, next_tokens] ,  dim=-1)
          a__						:     Optional[Any]			=    torch.cat([attention_mask, next_attn_mask] ,  dim=-1)

          a__						:     Union[str, Any]			=    model(lowercase ,  attention_mask=lowercase)['last_hidden_state']
          a__						:     int			=    model(lowercase ,  attention_mask=lowercase ,  past_key_values=lowercase)[
              'last_hidden_state'
          ]

          # select random slice
          a__						:     List[str]			=    ids_tensor((1,) ,  output_from_past.shape[-1]).item()
          a__						:     Tuple			=    output_from_no_past[:, -3:, random_slice_idx].detach()
          a__						:     List[str]			=    output_from_past[:, :, random_slice_idx].detach()

          self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(lowercase ,  lowercase ,  atol=1e-2))

   def 							__lowercase		( self ,  lowercase ,  lowercase)      ->						Dict:







          '''simple docstring'''

          a__						:     Tuple			=    MaMaaaModel(config=lowercase).to(lowercase).eval()
          a__						:     List[str]			=    model(**lowercase)

          a__						:     List[Any]			=    outputs.encoder_last_hidden_state
          a__						:     Optional[int]			=    outputs.last_hidden_state

          with tempfile.TemporaryDirectory() as tmpdirname:
                 a__						:     Optional[int]			=    model.get_encoder()
                 encoder.save_pretrained(lowercase)
                 a__						:     Tuple			=    MaMaaaEncoder.from_pretrained(lowercase).to(lowercase)

          a__						:     Optional[Any]			=    encoder(inputs_dict['input_ids'] ,  attention_mask=inputs_dict['attention_mask'])[
              0
          ]

          self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3)

          with tempfile.TemporaryDirectory() as tmpdirname:
                 a__						:     int			=    model.get_decoder()
                 decoder.save_pretrained(lowercase)
                 a__						:     int			=    MaMaaaDecoder.from_pretrained(lowercase).to(lowercase)

          a__						:     int			=    decoder(
              input_ids=inputs_dict['decoder_input_ids'] ,  attention_mask=inputs_dict['decoder_attention_mask'] ,  encoder_hidden_states=lowercase ,  encoder_attention_mask=inputs_dict['attention_mask'] ,  )[0]

          self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3)





@require_torch
class 			A__   (							snake_case_			,  snake_case_			,  snake_case_			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Any	  =  (
       (
           MaMaaaModel,
           MaMaaaForConditionalGeneration,
       )
       if is_torch_available()
       else ()
   )
   __A       :    Tuple	  =  (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
   __A       :    Dict	  =  (
       {
           '''conversational''': MaMaaaForConditionalGeneration,
           '''feature-extraction''': MaMaaaModel,
           '''summarization''': MaMaaaForConditionalGeneration,
           '''text2text-generation''': MaMaaaForConditionalGeneration,
           '''translation''': MaMaaaForConditionalGeneration,
       }
       if is_torch_available()
       else {}
   )
   __A       :    List[Any]	  =  True
   __A       :    Optional[Any]	  =  True
   __A       :    Union[str, Any]	  =  False
   __A       :    Tuple	  =  False

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						List[Any]:







          '''simple docstring'''

          if pipeline_test_casse_name == "TranslationPipelineTests":
                 # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                 # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                 return True

          return False

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







          '''simple docstring'''

          a__						:     Tuple			=    MaMaaaModelTester(self)
          a__						:     Optional[Any]			=    ConfigTester(self ,  config_class=lowercase)

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







          '''simple docstring'''

          self.config_tester.run_common_tests()

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







          '''simple docstring'''

          a__						:     str			=    self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
                 a__						:     int			=    model_class(lowercase)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                        model.save_pretrained(lowercase)
                        a__						:     str			=    model_class.from_pretrained(lowercase ,  output_loading_info=lowercase)
                 self.assertEqual(info['missing_keys'] ,  [])

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Any			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase)

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                 a__						:     Optional[Any]			=    model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 a__						:     List[Any]			=    copy.deepcopy(self._prepare_for_class(lowercase ,  lowercase))

                 if not self.is_encoder_decoder:
                        a__						:     List[Any]			=    inputs['input_ids']
                        del inputs["input_ids"]
                 else:
                        a__						:     str			=    inputs['input_ids']
                        a__						:     List[str]			=    inputs.get('decoder_input_ids' ,  lowercase)
                        del inputs["input_ids"]
                        inputs.pop('decoder_input_ids' ,  lowercase)

                 a__						:     List[str]			=    model.get_input_embeddings()
                 if not self.is_encoder_decoder:
                        a__						:     Tuple			=    wte(lowercase)
                 else:
                        a__						:     Optional[int]			=    wte(lowercase)
                        a__						:     Optional[int]			=    wte(lowercase)

                 with torch.no_grad():
                        model(**lowercase)[0]

   def 							__lowercase		( self)      ->						List[str]:







          '''simple docstring'''

          a__						:     int			=    self.model_tester.prepare_config_and_inputs()
          a__						:     str			=    input_dict['input_ids']
          a__						:     int			=    input_ids.ne(1).to(lowercase)
          a__						:     Dict			=    MaMaaaForConditionalGeneration(lowercase).eval().to(lowercase)
          if torch_device == "cuda":
                 model.half()
          model.generate(lowercase ,  attention_mask=lowercase)
          model.generate(num_beams=4 ,  do_sample=lowercase ,  early_stopping=lowercase ,  num_return_sequences=3)



def A_(		A__    )  ->		int:
       return torch.tensor(A__		,				dtype=torch.long		,				device=A__    )


lowercase				:	Any      						=  1e-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   @cached_property
   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M')

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







          '''simple docstring'''

          a__						:     Union[str, Any]			=    MaMaaaModel.from_pretrained('facebook/m2m100_418M').to(lowercase)
          a__						:     str			=    _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]])
          a__						:     Dict			=    _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]])
          a__						:     Optional[int]			=    prepare_mam_aaa_inputs_dict(model.config ,  lowercase ,  lowercase)
          with torch.no_grad():
                 a__						:     Any			=    model(**lowercase)[0]
          a__						:     Tuple			=    torch.Size((1, 11, 1024))
          self.assertEqual(output.shape ,  lowercase)
          # change to expected output here
          a__						:     Dict			=    torch.tensor(
              [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]] ,  device=lowercase)
          self.assertTrue(torch.allclose(output[:, :3, :3] ,  lowercase ,  atol=lowercase))

   def 							__lowercase		( self)      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M').to(lowercase)

          # change to intended input
          a__						:     Tuple			=    _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]])
          a__						:     Union[str, Any]			=    _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]])
          a__						:     Dict			=    prepare_mam_aaa_inputs_dict(model.config ,  lowercase ,  lowercase)
          with torch.no_grad():
                 a__						:     Dict			=    model(**lowercase)[0]
          a__						:     Union[str, Any]			=    torch.Size((1, 11, model.config.vocab_size))
          self.assertEqual(output.shape ,  lowercase)
          # change to expected output here
          a__						:     Any			=    torch.tensor(
              [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]] ,  device=lowercase)
          self.assertTrue(torch.allclose(output[:, :3, :3] ,  lowercase ,  atol=lowercase))

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     str			=    MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M').to(lowercase)
          a__						:     Optional[int]			=    MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' ,  src_lang='fr' ,  tgt_lang='en')

          a__						:     Union[str, Any]			=    [
              'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
              'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
              'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
              ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
              ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
          ]

          # The below article tests that we don't add any hypotheses outside of the top n_beams
          a__						:     Optional[Any]			=    tokenizer(lowercase ,  padding=lowercase ,  return_tensors='pt')

          a__						:     Dict			=    model.generate(
              input_ids=dct['input_ids'].to(lowercase) ,  attention_mask=dct['attention_mask'].to(lowercase) ,  num_beams=5 ,  forced_bos_token_id=tokenizer.get_lang_id('en') ,  )

          a__						:     Optional[int]			=    [
              'The NSA case highlights the total absence of intelligence debate',
              'I think there are two levels of response from the French government.',
              'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
              ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
              ' communications in France.',
          ]

          a__						:     List[str]			=    tokenizer.batch_decode(
              hypotheses_batch.tolist() ,  clean_up_tokenization_spaces=lowercase ,  skip_special_tokens=lowercase)
          assert generated == expected_en









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


lowercase				:	Optional[int]      						=  1_6
lowercase				:	Optional[int]      						=  3_2



def A_     (		A__    )  ->		Tuple:
       return int(x / 2**20    )





class 			A__   :




   """simple docstring"""



   def __enter__( self)      ->						Dict:







          '''simple docstring'''

          gc.collect()
          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
          a__						:     Union[str, Any]			=    torch.cuda.memory_allocated()
          return self

   def __exit__( self ,  *lowercase)      ->						List[Any]:







          '''simple docstring'''

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



def A_     (		A__		,				A__ = 16		,				A__ = "bert-base-cased"		,				A__ = 320		,				A__ = 160		,				)  ->		Dict:
       a__						:     int			=    AutoTokenizer.from_pretrained(A__    )
       a__						:     str			=    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)
              a__						:     int			=    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
       a__						:     List[str]			=    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
       a__						:     Optional[int]			=    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.
       a__						:     Dict			=    DataLoader(
           tokenized_datasets['train']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )
       a__						:     Optional[int]			=    DataLoader(
           tokenized_datasets['validation']		,				shuffle=A__		,				collate_fn=A__		,				batch_size=A__    )

       return train_dataloader, eval_dataloader



def A_     (		A__		,				A__    )  ->		Any:
       # Initialize accelerator
       a__						:     Optional[int]			=    Accelerator()

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

       set_seed(A__    )
       a__			,							a__						:     Any			=    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)
       a__						:     Optional[int]			=    AutoModelForSequenceClassification.from_pretrained(A__		,				return_dict=A__    )

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

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

       # We need to keep track of how many total steps we have iterated over
       a__						:     Optional[Any]			=    0
       # We also need to keep track of the stating epoch so files are named properly
       a__						:     Union[str, Any]			=    0

       # Now we train the model
       a__						:     List[str]			=    {}
       for epoch in range(A__		,				A__    ):
              with TorchTracemalloc() as tracemalloc:
                     model.train()
                     for step, batch in enumerate(A__    ):
                            a__						:     Optional[Any]			=    model(**A__    )
                            a__						:     List[Any]			=    outputs.loss
                            a__						:     Union[str, Any]			=    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    )    )    )
              a__						:     List[str]			=    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 A_     (		)  ->		int:
       a__						:     Union[str, Any]			=    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.'		,				)
       a__						:     Dict			=    parser.parse_args()
       a__						:     List[str]			=    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
       training_function(A__		,				A__    )


if __name__ == "__main__":
    main()


