"""simple docstring"""






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       :			str   	=1_6
__lowerCAmelCase       :			Optional[Any]   	=3_2


def      UpperCAmelCase__			(							lowerCAmelCase__	:Accelerator   ,						lowerCAmelCase__	:int = 1_6   ,						lowerCAmelCase__	:str = "bert-base-cased"						)					->       Any:
      '''simple docstring'''


      lowercase								=      AutoTokenizer.from_pretrained(lowerCAmelCase__						)
      lowercase								=      load_dataset("""glue"""   ,						"""mrpc"""						)

      def tokenize_function(lowerCAmelCase__	:List[str]						):
            # max_length=None => use the model max length (it's actually the default)
            lowercase								=      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
      lowercase								=      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
      lowercase								=      tokenized_datasets.rename_column("""label"""   ,						"""labels"""						)

      def collate_fn(lowerCAmelCase__	:Optional[int]						):
            # 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=1_2_8   ,						return_tensors="""pt"""						)
            return tokenizer.pad(lowerCAmelCase__   ,						padding="""longest"""   ,						return_tensors="""pt"""						)

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

      return train_dataloader, eval_dataloader


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[str]   ,						lowerCAmelCase__	:Optional[Any]						)					->       Optional[Any]:
      '''simple docstring'''


      lowercase								=      Accelerator()

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

      set_seed(lowerCAmelCase__						)
      lowercase				,				lowercase								=      get_dataloaders(lowerCAmelCase__   ,						lowerCAmelCase__   ,						lowerCAmelCase__						)

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

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

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

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

      # Now we train the model
      lowercase								=      evaluate.load("""glue"""   ,						"""mrpc"""						)
      lowercase								=      0
      lowercase								=      {}
      for epoch in range(lowerCAmelCase__   ,						lowerCAmelCase__						):
            model.train()
            for step, batch in enumerate(lowerCAmelCase__						):
                  lowercase								=      model(**lowerCAmelCase__						)
                  lowercase								=      outputs.loss
                  lowercase								=      loss / gradient_accumulation_steps
                  accelerator.backward(lowerCAmelCase__						)
                  if step % gradient_accumulation_steps == 0:
                        optimizer.step()
                        lr_scheduler.step()
                        optimizer.zero_grad()

                  overall_step += 1

            model.eval()
            lowercase								=      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():
                        lowercase								=      model(**lowerCAmelCase__						)
                  lowercase								=      outputs.logits.argmax(dim=-1						)
                  # It is slightly faster to call this once, than multiple times
                  lowercase				,				lowercase								=      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:
                              lowercase								=      predictions[: len(eval_dataloader.dataset						) - samples_seen]
                              lowercase								=      references[: len(eval_dataloader.dataset						) - samples_seen]
                        else:
                              samples_seen += references.shape[0]
                  metric.add_batch(
                      predictions=lowerCAmelCase__   ,						references=lowerCAmelCase__   ,						)

            lowercase								=      metric.compute()
            # Use accelerator.print to print only on the main process.
            accelerator.print(f'epoch {epoch}:'   ,						lowerCAmelCase__						)
            lowercase								=      eval_metric["""accuracy"""]

            if best_performance < eval_metric["accuracy"]:
                  lowercase								=      eval_metric["""accuracy"""]

      if args.performance_lower_bound is not None:
            assert (
                args.performance_lower_bound <= best_performance
            ), f'Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'

      accelerator.wait_for_everyone()
      if accelerator.is_main_process:
            with open(os.path.join(args.output_dir   ,						"""all_results.json"""						)   ,						"""w"""						) as f:
                  json.dump(lowerCAmelCase__   ,						lowerCAmelCase__						)


def      UpperCAmelCase__			(							)					->       Dict:
      '''simple docstring'''


      lowercase								=      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(
          """--performance_lower_bound"""   ,						type=lowerCAmelCase__   ,						default=lowerCAmelCase__   ,						help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""   ,						)
      parser.add_argument(
          """--num_epochs"""   ,						type=lowerCAmelCase__   ,						default=3   ,						help="""Number of train epochs."""   ,						)
      lowercase								=      parser.parse_args()
      lowercase								=      {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 4_2, """batch_size""": 1_6}
      training_function(lowerCAmelCase__   ,						lowerCAmelCase__						)


if __name__ == "__main__":
       main()




"""simple docstring"""






from copy import deepcopy





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if arr is None and size is not None:
                  lowercase								=      size
                  lowercase								=      [0] * size
            elif arr is not None:
                  self.init(__lowerCAmelCase		)
            else:
                  raise ValueError("""Either arr or size must be specified"""		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      deepcopy(__lowerCAmelCase		)
            for i in range(1						,			self.size		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        self.tree[j] += self.tree[i]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.tree[:]
            for i in range(self.size - 1						,			0						,			-1		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        arr[j] -= arr[i]
            return arr

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index + (index & (-index))

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index - (index & (-index))

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if index == 0:
                  self.tree[0] += value
                  return
            while index < self.size:
                  self.tree[index] += value
                  lowercase								=      self.next_(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            self.add(__lowerCAmelCase						,			value - self.get(__lowerCAmelCase		)		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if right == 0:
                  return 0
            lowercase								=      self.tree[0]
            right -= 1  # make right inclusive
            while right > 0:
                  result += self.tree[right]
                  lowercase								=      self.prev(__lowerCAmelCase		)
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.prefix(__lowerCAmelCase		) - self.prefix(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.query(__lowerCAmelCase						,			index + 1		)







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            value -= self.tree[0]
            if value < 0:
                  return -1

            lowercase								=      1  # Largest power of 2 <= size
            while j * 2 < self.size:
                  j *= 2

            lowercase								=      0

            while j > 0:
                  if i + j < self.size and self.tree[i + j] <= value:
                        value -= self.tree[i + j]
                        i += j
                  j //= 2
            return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()



