




import argparse
import json
import os

import evaluate
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


_lowerCAmelCase						:   int		=   1_6
_lowerCAmelCase						:   Union[str, Any]		=   3_2



def 						lowerCAmelCase		(		_lowerCAmelCase							:   Accelerator						,							_lowerCAmelCase							:   int = 16						,							_lowerCAmelCase							:   str = "bert-base-cased"	):




 """simple docstring"""







 UpperCAmelCase__							 =						AutoTokenizer.from_pretrained(_lowerCAmelCase	)
 UpperCAmelCase__							 =						load_dataset("glue"						,							"mrpc"	)

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

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

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

 def collate_fn(_lowerCAmelCase							:   List[Any]	):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(_lowerCAmelCase						,							padding="max_length"						,							max_length=128						,							return_tensors="pt"	)
  return tokenizer.pad(_lowerCAmelCase						,							padding="longest"						,							return_tensors="pt"	)

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

 return train_dataloader, eval_dataloader



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   List[str]						,							_lowerCAmelCase							:   int	):




 """simple docstring"""







 model.eval()
 UpperCAmelCase__							 =						0
 for step, batch in enumerate(_lowerCAmelCase	):
  # We could avoid this line since we set the accelerator with `device_placement=True`.
  batch.to(accelerator.device	)
  with torch.no_grad():
   UpperCAmelCase__							 =						model(**_lowerCAmelCase	)
  UpperCAmelCase__							 =						outputs.logits.argmax(dim=-1	)
  # It is slightly faster to call this once, than multiple times
  UpperCAmelCase__ , UpperCAmelCase__							 =						accelerator.gather(
      (predictions, batch["labels"])	)  # If we are in a multiprocess environment, the last batch has duplicates
  if accelerator.use_distributed:
   if step == len(_lowerCAmelCase	) - 1:
    UpperCAmelCase__							 =						predictions[: len(eval_dataloader.dataset	) - samples_seen]
    UpperCAmelCase__							 =						references[: len(eval_dataloader.dataset	) - samples_seen]
   else:
    samples_seen += references.shape[0]
  metric.add_batch(
      predictions=_lowerCAmelCase						,							references=_lowerCAmelCase						,							)

 UpperCAmelCase__							 =						metric.compute()
 return eval_metric["accuracy"]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   List[Any]						,							_lowerCAmelCase							:   Dict	):




 """simple docstring"""







 UpperCAmelCase__							 =						Accelerator()

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

 set_seed(_lowerCAmelCase	)
 UpperCAmelCase__ , UpperCAmelCase__							 =						get_dataloaders(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)

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

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

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

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

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

 # We need to keep track of how many total steps we have iterated over
 UpperCAmelCase__							 =						0
 # We also need to keep track of the stating epoch so files are named properly
 UpperCAmelCase__							 =						0
 UpperCAmelCase__							 =						evaluate.load("glue"						,							"mrpc"	)
 UpperCAmelCase__							 =						num_epochs

 if args.partial_train_epoch is not None:
  UpperCAmelCase__							 =						args.partial_train_epoch

 if args.resume_from_checkpoint:
  accelerator.load_state(args.resume_from_checkpoint	)
  UpperCAmelCase__							 =						args.resume_from_checkpoint.split("epoch_"	)[1]
  UpperCAmelCase__							 =						""
  for char in epoch_string:
   if char.isdigit():
    state_epoch_num += char
   else:
    break
  UpperCAmelCase__							 =						int(_lowerCAmelCase	) + 1
  UpperCAmelCase__							 =						evaluation_loop(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
  accelerator.print("resumed checkpoint performance:"						,							_lowerCAmelCase	)
  accelerator.print("resumed checkpoint's scheduler's lr:"						,							lr_scheduler.get_lr()[0]	)
  accelerator.print("resumed optimizers's lr:"						,							optimizer.param_groups[0]["lr"]	)
  with open(os.path.join(args.output_dir						,							F'''state_{starting_epoch-1}.json'''	)						,							"r"	) as f:
   UpperCAmelCase__							 =						json.load(_lowerCAmelCase	)
   assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
   assert (
       resumed_state["lr"] == lr_scheduler.get_lr()[0]
   ), "Scheduler learning rate mismatch, loading from checkpoint failed"
   assert (
       resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
   ), "Optimizer learning rate mismatch, loading from checkpoint failed"
   assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
   return

    # Now we train the model
 UpperCAmelCase__							 =						{}
 for epoch in range(_lowerCAmelCase						,							_lowerCAmelCase	):
  model.train()
  for step, batch in enumerate(_lowerCAmelCase	):
   UpperCAmelCase__							 =						model(**_lowerCAmelCase	)
   UpperCAmelCase__							 =						outputs.loss
   UpperCAmelCase__							 =						loss / gradient_accumulation_steps
   accelerator.backward(_lowerCAmelCase	)
   if step % gradient_accumulation_steps == 0:
    optimizer.step()
    lr_scheduler.step()
    optimizer.zero_grad()

   overall_step += 1
  UpperCAmelCase__							 =						F'''epoch_{epoch}'''
  UpperCAmelCase__							 =						os.path.join(args.output_dir						,							_lowerCAmelCase	)
  accelerator.save_state(_lowerCAmelCase	)
  UpperCAmelCase__							 =						evaluation_loop(_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase	)
  UpperCAmelCase__							 =						accuracy
  UpperCAmelCase__							 =						lr_scheduler.get_lr()[0]
  UpperCAmelCase__							 =						optimizer.param_groups[0]["lr"]
  UpperCAmelCase__							 =						epoch
  UpperCAmelCase__							 =						overall_step
  accelerator.print(F'''epoch {epoch}:'''						,							_lowerCAmelCase	)

  accelerator.wait_for_everyone()
  if accelerator.is_main_process:
   with open(os.path.join(args.output_dir						,							F'''state_{epoch}.json'''	)						,							"w"	) as f:
    json.dump(_lowerCAmelCase						,							_lowerCAmelCase	)



def 						lowerCAmelCase		(		):




 """simple docstring"""







 UpperCAmelCase__							 =						argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."	)
 parser.add_argument(
     "--model_name_or_path"						,							type=_lowerCAmelCase						,							default="bert-base-cased"						,							help="Path to pretrained model or model identifier from huggingface.co/models."						,							required=_lowerCAmelCase						,							)
 parser.add_argument(
     "--output_dir"						,							type=_lowerCAmelCase						,							default="."						,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."						,							)
 parser.add_argument(
     "--resume_from_checkpoint"						,							type=_lowerCAmelCase						,							default=_lowerCAmelCase						,							help="If the training should continue from a checkpoint folder."						,							)
 parser.add_argument(
     "--partial_train_epoch"						,							type=_lowerCAmelCase						,							default=_lowerCAmelCase						,							help="If passed, the training will stop after this number of epochs."						,							)
 parser.add_argument(
     "--num_epochs"						,							type=_lowerCAmelCase						,							default=2						,							help="Number of train epochs."						,							)
 UpperCAmelCase__							 =						parser.parse_args()
 UpperCAmelCase__							 =						{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

 training_function(_lowerCAmelCase						,							_lowerCAmelCase	)


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,
)


_lowerCAmelCase						:   Union[str, Any]		=   {
    "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:
  _lowerCAmelCase						:   str		=   ["BertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   int		=   [
      "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:
  _lowerCAmelCase						:   Dict		=   [
      "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:
  _lowerCAmelCase						:   str		=   ["TFBertTokenizer"]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase						:   Optional[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

  _lowerCAmelCase						:   Union[str, Any]		=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





