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__           =							16
A__           =							32





def  _lowerCamelCase					(   a_				: Tuple						,			a_				: Any = 16						,			a_				: List[Any] = "bert-base-cased"):
   lowerCamelCase						:Any						  =     AutoTokenizer.from_pretrained(lowerCAmelCase__)
   lowerCamelCase						:List[str]						  =     load_dataset('''glue'''						,			'''mrpc''')

   def tokenize_function(a_				: Union[str, Any]):
      # max_length=None => use the model max length (it's actually the default)
      lowerCamelCase						:Any						  =     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
   lowerCamelCase						:Any						  =     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
   lowerCamelCase						:str						  =     tokenized_datasets.rename_column('''label'''						,			'''labels''')

   def collate_fn(a_				: 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=1_28						,			return_tensors='''pt''')
      return tokenizer.pad(lowerCAmelCase__						,			padding='''longest'''						,			return_tensors='''pt''')

   # Instantiate dataloaders.
   lowerCamelCase						:Any						  =     DataLoader(
       tokenized_datasets['''train''']						,			shuffle=lowerCAmelCase__						,			collate_fn=lowerCAmelCase__						,			batch_size=lowerCAmelCase__)
   lowerCamelCase						:List[str]						  =     DataLoader(
       tokenized_datasets['''validation''']						,			shuffle=lowerCAmelCase__						,			collate_fn=lowerCAmelCase__						,			batch_size=lowerCAmelCase__)

   return train_dataloader, eval_dataloader





def  _lowerCamelCase					(   a_				: int						,			a_				: Tuple):
   # 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						:Optional[int]						  =     int(config['''num_epochs'''])
   lowerCamelCase						:Optional[int]						  =     int(config['''seed'''])
   lowerCamelCase						:Tuple						  =     int(config['''batch_size'''])
   lowerCamelCase						:Any						  =     args.model_name_or_path

   set_seed(lowerCAmelCase__)
   lowerCamelCase  ,    lowerCamelCase						:Optional[int]						  =     get_dataloaders(lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__)

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase						:List[str]						  =     AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__						,			return_dict=lowerCAmelCase__)

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

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

   # 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						:int						  =     0

   # Now we train the model
   lowerCamelCase						:Dict						  =     evaluate.load('''glue'''						,			'''mrpc''')
   lowerCamelCase						:Optional[Any]						  =     0
   lowerCamelCase						:Optional[Any]						  =     {}
   for epoch in range(lowerCAmelCase__						,			lowerCAmelCase__):
      model.train()
      for step, batch in enumerate(lowerCAmelCase__):
         lowerCamelCase						:int						  =     model(**lowerCAmelCase__)
         lowerCamelCase						:Optional[int]						  =     outputs.loss
         lowerCamelCase						:int						  =     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()
      lowerCamelCase						:Optional[int]						  =     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():
            lowerCamelCase						:str						  =     model(**lowerCAmelCase__)
         lowerCamelCase						:List[str]						  =     outputs.logits.argmax(dim=-1)
         # It is slightly faster to call this once, than multiple times
         lowerCamelCase  ,    lowerCamelCase						:List[Any]						  =     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:
               lowerCamelCase						:List[Any]						  =     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=lowerCAmelCase__						,			references=lowerCAmelCase__						,			)

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

      if best_performance < eval_metric["accuracy"]:
         lowerCamelCase						:int						  =     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  _lowerCamelCase					(   ):
   lowerCamelCase						:str						  =     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.'''						,			)
   lowerCamelCase						:str						  =     parser.parse_args()
   lowerCamelCase						:List[Any]						  =     {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
   training_function(lowerCAmelCase__						,			lowerCAmelCase__)


if __name__ == "__main__":
   main()




import math





def  _lowerCamelCase					(   a_				: int):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,			int(math.sqrt(a_) + 1)						,			6):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def  _lowerCamelCase					(   a_				: float = 0.1):
			lowerCamelCase						:Dict						  =     3
			lowerCamelCase						:List[Any]						  =     3

			while primes / (2 * j - 1) >= ratio:
						for i in range(j * j + j + 1						,			(j + 2) * (j + 2)						,			j + 1):
									primes += is_prime(a_)
						j += 2
			return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()



