



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 		a__      (		_UpperCamelCase      :			Accelerator			,_UpperCamelCase      :			int = 16			,_UpperCamelCase      :			str = "bert-base-cased"				):
   __lowerCamelCase    				=     AutoTokenizer.from_pretrained(__a				)
   __lowerCamelCase    				=     load_dataset('''glue'''			,'''mrpc'''				)

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

   def collate_fn(_UpperCamelCase      :			List[str]				):
      # 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=1_28			,return_tensors='''pt'''				)
      return tokenizer.pad(__a			,padding='''longest'''			,return_tensors='''pt'''				)

   # Instantiate dataloaders.
   __lowerCamelCase    				=     DataLoader(
       tokenized_datasets['''train''']			,shuffle=__a			,collate_fn=__a			,batch_size=__a				)
   __lowerCamelCase    				=     DataLoader(
       tokenized_datasets['''validation''']			,shuffle=__a			,collate_fn=__a			,batch_size=__a				)

   return train_dataloader, eval_dataloader





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

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





def 		a__      (		_UpperCamelCase      :			int			,_UpperCamelCase      :			Any				):
   __lowerCamelCase    				=     Accelerator()

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

   set_seed(__a				)
   __lowerCamelCase    				=     get_dataloaders(__a			,__a			,__a				)

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

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

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

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

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

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

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

      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(__a			,__a				)





def 		a__      (		):
   __lowerCamelCase    				=     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(
       '''--resume_from_checkpoint'''			,type=__a			,default=__a			,help='''If the training should continue from a checkpoint folder.'''			,)
   parser.add_argument(
       '''--partial_train_epoch'''			,type=__a			,default=__a			,help='''If passed, the training will stop after this number of epochs.'''			,)
   parser.add_argument(
       '''--num_epochs'''			,type=__a			,default=2			,help='''Number of train epochs.'''			,)
   __lowerCamelCase    				=     parser.parse_args()
   __lowerCamelCase    				=     {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

   training_function(__a			,__a				)


if __name__ == "__main__":
  main()









def   __UpperCAmelCase					(      __a :     str      )     ->	list:

					"""simple docstring"""

					if n_term == "":
										return []
					_a      :       list   =						[]
					for temp in range(int(__a      )      ):
										series.append(F"""1/{temp + 1}""" if series else '''1'''      )
					return series


if __name__ == "__main__":
			a__												=						input('''Enter the last number (nth term) of the Harmonic Series''')
			print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
			print(harmonic_series(nth_term))



