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


a  :      str            =    1_6
a  :      Any            =    3_2
def 		snake_case__	(		lowercase							,				lowercase = 16							,				lowercase = "bert-base-cased"					):
   lowerCAmelCase_:							Optional[int]       =	AutoTokenizer.from_pretrained(lowercase					)
   lowerCAmelCase_:							Tuple       =	load_dataset("glue"							,				"mrpc"					)

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

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

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   lowerCAmelCase_:							Union[str, Any]       =	tokenized_datasets.rename_column("label"							,				"labels"					)

   def collate_fn(lowercase					):
      # 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(lowercase							,				padding="max_length"							,				max_length=128							,				return_tensors="pt"					)
      return tokenizer.pad(lowercase							,				padding="longest"							,				return_tensors="pt"					)

   # Instantiate dataloaders.
   lowerCAmelCase_:							str       =	DataLoader(
       tokenized_datasets["train"]							,				shuffle=lowercase							,				collate_fn=lowercase							,				batch_size=lowercase					)
   lowerCAmelCase_:							List[Any]       =	DataLoader(
       tokenized_datasets["validation"]							,				shuffle=lowercase							,				collate_fn=lowercase							,				batch_size=lowercase					)

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

   lowerCAmelCase_:							Optional[int]       =	metric.compute()
   return eval_metric["accuracy"]
def 		snake_case__	(		lowercase							,				lowercase					):
   # Initialize accelerator
   lowerCAmelCase_:							Union[str, Any]       =	Accelerator()

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

   set_seed(lowercase					)
   lowerCAmelCase_  ,	lowerCAmelCase_:							Tuple       =	get_dataloaders(lowercase							,				lowercase							,				lowercase					)

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

   # Instantiate optimizer
   lowerCAmelCase_:							List[str]       =	(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowerCAmelCase_:							int       =	optimizer_cls(params=model.parameters()							,				lr=lowercase					)

   if accelerator.state.deepspeed_plugin is not None:
      lowerCAmelCase_:							Dict       =	accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      lowerCAmelCase_:							List[Any]       =	1
   lowerCAmelCase_:							int       =	(len(lowercase					) * num_epochs) // gradient_accumulation_steps

   # Instantiate scheduler
   if (
       accelerator.state.deepspeed_plugin is None
       or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
   ):
      lowerCAmelCase_:							Tuple       =	get_linear_schedule_with_warmup(
          optimizer=lowercase							,				num_warmup_steps=0							,				num_training_steps=lowercase							,				)
   else:
      lowerCAmelCase_:							Dict       =	DummyScheduler(lowercase							,				total_num_steps=lowercase							,				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.
   lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							List[Any]       =	accelerator.prepare(
       lowercase							,				lowercase							,				lowercase							,				lowercase							,				lowercase					)

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

   if args.partial_train_epoch is not None:
      lowerCAmelCase_:							Optional[int]       =	args.partial_train_epoch

   if args.resume_from_checkpoint:
      accelerator.load_state(args.resume_from_checkpoint					)
      lowerCAmelCase_:							Optional[Any]       =	args.resume_from_checkpoint.split("epoch_"					)[1]
      lowerCAmelCase_:							Optional[Any]       =	""
      for char in epoch_string:
         if char.isdigit():
            state_epoch_num += char
         else:
            break
      lowerCAmelCase_:							Optional[int]       =	int(lowercase					) + 1
      lowerCAmelCase_:							Optional[int]       =	evaluation_loop(lowercase							,				lowercase							,				lowercase							,				lowercase					)
      accelerator.print("resumed checkpoint performance:"							,				lowercase					)
      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:
         lowerCAmelCase_:							List[Any]       =	json.load(lowercase					)
         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
   lowerCAmelCase_:							Any       =	{}
   for epoch in range(lowercase							,				lowercase					):
      model.train()
      for step, batch in enumerate(lowercase					):
         lowerCAmelCase_:							List[Any]       =	model(**lowercase					)
         lowerCAmelCase_:							Optional[int]       =	outputs.loss
         lowerCAmelCase_:							str       =	loss / gradient_accumulation_steps
         accelerator.backward(lowercase					)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

         overall_step += 1
      lowerCAmelCase_:							Dict       =	F'''epoch_{epoch}'''
      lowerCAmelCase_:							Optional[Any]       =	os.path.join(args.output_dir							,				lowercase					)
      accelerator.save_state(lowercase					)
      lowerCAmelCase_:							Optional[int]       =	evaluation_loop(lowercase							,				lowercase							,				lowercase							,				lowercase					)
      lowerCAmelCase_:							List[str]       =	accuracy
      lowerCAmelCase_:							int       =	lr_scheduler.get_lr()[0]
      lowerCAmelCase_:							List[Any]       =	optimizer.param_groups[0]["lr"]
      lowerCAmelCase_:							Dict       =	epoch
      lowerCAmelCase_:							int       =	overall_step
      accelerator.print(F'''epoch {epoch}:'''							,				lowercase					)

      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(lowercase							,				lowercase					)
def 		snake_case__	(		):
   lowerCAmelCase_:							Any       =	argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."					)
   parser.add_argument(
       "--model_name_or_path"							,				type=lowercase							,				default="bert-base-cased"							,				help="Path to pretrained model or model identifier from huggingface.co/models."							,				required=lowercase							,				)
   parser.add_argument(
       "--output_dir"							,				type=lowercase							,				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=lowercase							,				default=lowercase							,				help="If the training should continue from a checkpoint folder."							,				)
   parser.add_argument(
       "--partial_train_epoch"							,				type=lowercase							,				default=lowercase							,				help="If passed, the training will stop after this number of epochs."							,				)
   parser.add_argument(
       "--num_epochs"							,				type=lowercase							,				default=2							,				help="Number of train epochs."							,				)
   lowerCAmelCase_:							List[Any]       =	parser.parse_args()
   lowerCAmelCase_:							int       =	{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

   training_function(lowercase							,				lowercase					)


if __name__ == "__main__":
 main()
def 		snake_case__	(		lowercase							,				lowercase					):

   if density <= 0:
      raise ValueError("Impossible fluid density"					)
   if bulk_modulus <= 0:
      raise ValueError("Impossible bulk modulus"					)

   return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()