
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class    UpperCAmelCase_				:





	def __init__(    self ,       lowercase_=2 ,       lowercase_=3 ,       lowercase_=64 ,       lowercase_=None):
					snake_case_ :    int     			=						np.random.default_rng(lowercase_)
					snake_case_ :    List[Any]     			=						length
					snake_case_ :    Optional[Any]     			=						rng.normal(size=(length,)).astype(np.floataa)
					snake_case_ :    Tuple     			=						a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)).astype(np.floataa)





	def __len__(    self):
					return self.length





	def __getitem__(    self ,       lowercase_):
					return {"x": self.x[i], "y": self.y[i]}






class    UpperCAmelCase_				(	torch.nn.Module					):





	def __init__(    self ,       lowercase_=0 ,       lowercase_=0 ,       lowercase_=False):
					super().__init__()
					snake_case_ :    Optional[int]     			=						torch.nn.Parameter(torch.tensor([2, 3]).float())
					snake_case_ :    List[Any]     			=						torch.nn.Parameter(torch.tensor([2, 3]).float())
					snake_case_ :    List[Any]     			=						True





	def        snake_case__		(    self ,       lowercase_=None):
					if self.first_batch:
									print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}')
									snake_case_ :    List[Any]     			=						False
					return x * self.a[0] + self.b[0]






class    UpperCAmelCase_				(	torch.nn.Module					):





	def __init__(    self ,       lowercase_=0 ,       lowercase_=0 ,       lowercase_=False):
					super().__init__()
					snake_case_ :    str     			=						torch.nn.Parameter(torch.tensor(lowercase_).float())
					snake_case_ :    List[str]     			=						torch.nn.Parameter(torch.tensor(lowercase_).float())
					snake_case_ :    str     			=						True





	def        snake_case__		(    self ,       lowercase_=None):
					if self.first_batch:
									print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}')
									snake_case_ :    str     			=						False
					return x * self.a + self.b


def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE = 1_6       ):
				"""simple docstring"""






				from datasets import load_dataset
				from transformers import AutoTokenizer

				snake_case_ :    Dict     			=						AutoTokenizer.from_pretrained("bert-base-cased"       )
				snake_case_ :    Union[str, Any]     			=						{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
				snake_case_ :    Optional[int]     			=						load_dataset("csv",		data_files=__SCREAMING_SNAKE_CASE       )
				snake_case_ :    Optional[Any]     			=						datasets["train"].unique("label"       )

				snake_case_ :    List[Any]     			=						{v: i for i, v in enumerate(__SCREAMING_SNAKE_CASE       )}

				def tokenize_function(__SCREAMING_SNAKE_CASE       ):
								# max_length=None => use the model max length (it's actually the default)
								snake_case_ :    str     			=						tokenizer(
								    examples["sentence1"],		examples["sentence2"],		truncation=__SCREAMING_SNAKE_CASE,		max_length=__SCREAMING_SNAKE_CASE,		padding="max_length"       )
								if "label" in examples:
												snake_case_ :    Optional[int]     			=						[label_to_id[l] for l in examples["label"]]
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				snake_case_ :    str     			=						datasets.map(
				    __SCREAMING_SNAKE_CASE,		batched=__SCREAMING_SNAKE_CASE,		remove_columns=["sentence1", "sentence2", "label"],		)

				def collate_fn(__SCREAMING_SNAKE_CASE       ):
								# 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(__SCREAMING_SNAKE_CASE,		padding="max_length",		max_length=1_2_8,		return_tensors="pt"       )
								return tokenizer.pad(__SCREAMING_SNAKE_CASE,		padding="longest",		return_tensors="pt"       )

				# Instantiate dataloaders.
				snake_case_ :    Union[str, Any]     			=						DataLoader(tokenized_datasets["train"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=2       )
				snake_case_ :    List[str]     			=						DataLoader(tokenized_datasets["validation"],		shuffle=__SCREAMING_SNAKE_CASE,		collate_fn=__SCREAMING_SNAKE_CASE,		batch_size=1       )

				return train_dataloader, eval_dataloader






'''simple docstring'''
from __future__ import annotations

from statistics import mean

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						[0] * no_of_processes
    snake_case_ :    Optional[int]     			=						[0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    str     			=						burst_time[i]
    snake_case_ :    list[int]     			=						[]

    snake_case_ :    Tuple     			=						0
    snake_case_ :    Optional[int]     			=						0

    # When processes are not completed,
    # A process whose arrival time has passed \
    # and has remaining execution time is put into the ready_process.
    # The shortest process in the ready_process, target_process is executed.

    while completed != no_of_processes:
        snake_case_ :    Any     			=						[]
        snake_case_ :    Dict     			=						-1

        for i in range(__SCREAMING_SNAKE_CASE       ):
            if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
                ready_process.append(__SCREAMING_SNAKE_CASE       )

        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Dict     			=						ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    snake_case_ :    Optional[int]     			=						i
            total_time += burst_time[target_process]
            completed += 1
            snake_case_ :    Tuple     			=						0
            snake_case_ :    int     			=						(
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						[0] * no_of_processes
    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Dict     			=						burst_time[i] + waiting_time[i]
    return turn_around_time


if __name__ == "__main__":
  print("[TEST CASE 01]")

  a_											=						4
  a_											=						[2, 5, 3, 7]
  a_											=						[0, 0, 0, 0]
  a_											=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  a_											=						calculate_turnaroundtime(
      burst_time, no_of_processes, waiting_time
  )

  # Printing the Result
  print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
  for i, process_id in enumerate(list(range(1, 5))):
    print(
        f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
        f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
    )
  print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
  print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")




