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			:						List[str]				  =		16
SCREAMING_SNAKE_CASE			:						List[str]				  =		32



def 	__A		(      _A       ):



 """simple docstring"""


 return int(x / 2**20       )
class  A_			:

  def __enter__(		self			:   Optional[Any]							):
   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			:   Optional[int]  ,				*__SCREAMING_SNAKE_CASE			:   str							):
   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 	__A		(      _A				,					_A = 16				,					_A = "bert-base-cased"				,					_A = 320				,					_A = 160				,					):



 """simple docstring"""


 __a							=	AutoTokenizer.from_pretrained(_A       )
 __a							=	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							=	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							=	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							=	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							=	DataLoader(
     tokenized_datasets["train"]				,					shuffle=_A				,					collate_fn=_A				,					batch_size=_A       )
 __a							=	DataLoader(
     tokenized_datasets["validation"]				,					shuffle=_A				,					collate_fn=_A				,					batch_size=_A       )

 return train_dataloader, eval_dataloader



def 	__A		(      _A				,					_A       ):



 """simple docstring"""


 __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(_A       )
 __a							,  __a							=	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							=	AutoModelForSequenceClassification.from_pretrained(_A				,					return_dict=_A       )

 # 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=_A       )

 if accelerator.state.deepspeed_plugin is not None:
  __a							=	accelerator.state.deepspeed_plugin.deepspeed_config[
      "gradient_accumulation_steps"
  ]
 else:
  __a							=	1
 __a							=	(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							=	get_linear_schedule_with_warmup(
      optimizer=_A				,					num_warmup_steps=0				,					num_training_steps=_A				,					)
 else:
  __a							=	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							=	accelerator.prepare(
     _A				,					_A				,					_A				,					_A				,					_A       )

 # 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(_A				,					_A       ):
  with TorchTracemalloc() as tracemalloc:
   model.train()
   for step, batch in enumerate(_A       ):
    __a							=	model(**_A       )
    __a							=	outputs.loss
    __a							=	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							=	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		(      ):



 """simple docstring"""


 __a							=	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							=	parser.parse_args()
 __a							=	{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
 training_function(_A				,					_A       )


if __name__ == "__main__":
  main()



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		["""BertTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						List[Any]				  =		[
      """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BertForMaskedLM""",
      """BertForMultipleChoice""",
      """BertForNextSentencePrediction""",
      """BertForPreTraining""",
      """BertForQuestionAnswering""",
      """BertForSequenceClassification""",
      """BertForTokenClassification""",
      """BertLayer""",
      """BertLMHeadModel""",
      """BertModel""",
      """BertPreTrainedModel""",
      """load_tf_weights_in_bert""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		[
      """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFBertEmbeddings""",
      """TFBertForMaskedLM""",
      """TFBertForMultipleChoice""",
      """TFBertForNextSentencePrediction""",
      """TFBertForPreTraining""",
      """TFBertForQuestionAnswering""",
      """TFBertForSequenceClassification""",
      """TFBertForTokenClassification""",
      """TFBertLMHeadModel""",
      """TFBertMainLayer""",
      """TFBertModel""",
      """TFBertPreTrainedModel""",
  ]
try:
  if not is_tensorflow_text_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		["""TFBertTokenizer"""]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE			:						Any				  =		[
      """FlaxBertForCausalLM""",
      """FlaxBertForMaskedLM""",
      """FlaxBertForMultipleChoice""",
      """FlaxBertForNextSentencePrediction""",
      """FlaxBertForPreTraining""",
      """FlaxBertForQuestionAnswering""",
      """FlaxBertForSequenceClassification""",
      """FlaxBertForTokenClassification""",
      """FlaxBertModel""",
      """FlaxBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
  from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_fast import BertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bert import (
        BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BertForMaskedLM,
        BertForMultipleChoice,
        BertForNextSentencePrediction,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertForTokenClassification,
        BertLayer,
        BertLMHeadModel,
        BertModel,
        BertPreTrainedModel,
        load_tf_weights_in_bert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_bert import (
        TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFBertEmbeddings,
        TFBertForMaskedLM,
        TFBertForMultipleChoice,
        TFBertForNextSentencePrediction,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertForTokenClassification,
        TFBertLMHeadModel,
        TFBertMainLayer,
        TFBertModel,
        TFBertPreTrainedModel,
    )

  try:
    if not is_tensorflow_text_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bert_tf import TFBertTokenizer

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_bert import (
        FlaxBertForCausalLM,
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
        FlaxBertPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE			:						Optional[int]				  =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


