


'''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


a_      			=				16
a_      			=				32





def __snake_case      (   UpperCAmelCase_		:		Accelerator      ,       UpperCAmelCase_		:		int = 16      ,       UpperCAmelCase_		:		str = "bert-base-cased"	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(a_	)
 lowerCamelCase_  				=						load_dataset("glue"      ,       "mrpc"	)

 def tokenize_function(UpperCAmelCase_		:		List[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_		:		Dict	):
  # 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=128      ,       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 __snake_case      (   UpperCAmelCase_		:		Optional[Any]      ,       UpperCAmelCase_		:		Union[str, Any]	):
 # Initialize accelerator
 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

 # Now we train the model
 lowerCamelCase_  				=						evaluate.load("glue"      ,       "mrpc"	)
 lowerCamelCase_  				=						0
 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

  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()
  # Use accelerator.print to print only on the main process.
  accelerator.print(F'''epoch {epoch}:'''      ,       a_	)
  lowerCamelCase_  				=						eval_metric['''accuracy''']

  if best_performance < eval_metric["accuracy"]:
   lowerCamelCase_  				=						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(a_      ,       a_	)





def __snake_case      (   ):
 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(
     "--performance_lower_bound"      ,       type=a_      ,       default=a_      ,       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=a_      ,       default=3      ,       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()







'''simple docstring'''

from __future__ import annotations

import pandas as pd





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						[0] * no_of_processes
 lowerCamelCase_  				=						[0] * no_of_processes
 # Copy the burst time into remaining_time[]
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i]

 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						999999999
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						False

 # Process until all processes are completed
 while complete != no_of_processes:
  for j in range(UpperCAmelCase_	):
   if arrival_time[j] <= increment_time and remaining_time[j] > 0:
    if remaining_time[j] < minm:
     lowerCamelCase_  				=						remaining_time[j]
     lowerCamelCase_  				=						j
     lowerCamelCase_  				=						True

  if not check:
   increment_time += 1
   continue
  remaining_time[short] -= 1

  lowerCamelCase_  				=						remaining_time[short]
  if minm == 0:
   lowerCamelCase_  				=						999999999

  if remaining_time[short] == 0:
   complete += 1
   lowerCamelCase_  				=						False

   # Find finish time of current process
   lowerCamelCase_  				=						increment_time + 1

   # Calculate waiting time
   lowerCamelCase_  				=						finish_time - arrival_time[short]
   lowerCamelCase_  				=						finar - burst_time[short]

   if waiting_time[short] < 0:
    lowerCamelCase_  				=						0

        # Increment time
  increment_time += 1
 return waiting_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int      ,       UpperCAmelCase_		:		list[int]	):
 lowerCamelCase_  				=						[0] * no_of_processes
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						burst_time[i] + waiting_time[i]
 return turn_around_time





def __snake_case      (   UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		list[int]      ,       UpperCAmelCase_		:		int	):
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						0
 for i in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						total_waiting_time + waiting_time[i]
  lowerCamelCase_  				=						total_turn_around_time + turn_around_time[i]
 print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}'''	)
 print("Average turn around time ="      ,       total_turn_around_time / no_of_processes	)


if __name__ == "__main__":
     print("""Enter how many process you want to analyze""")
     a_						:       Dict      			=				int(input())
     a_						:       Any      			=				[0] * no_of_processes
     a_						:       Optional[int]      			=				[0] * no_of_processes
     a_						:       Tuple      			=				list(range(1, no_of_processes + 1))

     for i in range(no_of_processes):
          print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
          a_			,     a_						:       str      			=				map(int, input().split())

     a_						:       List[Any]      			=				calculate_waitingtime(arrival_time, burst_time, no_of_processes)

     a_						:       int      			=				burst_time
     a_						:       Union[str, Any]      			=				no_of_processes
     a_						:       Optional[int]      			=				waiting_time
     a_						:       Any      			=				calculate_turnaroundtime(bt, n, wt)

     calculate_average_times(waiting_time, turn_around_time, no_of_processes)

     a_						:       Optional[int]      			=				pd.DataFrame(
         list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
         columns=[
             """Process""",
             """BurstTime""",
             """ArrivalTime""",
             """WaitingTime""",
             """TurnAroundTime""",
         ],
     )

     # Printing the dataFrame
     pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
     print(fcfs)



