
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


a_											=						logging.get_logger(__name__)

a_											=						{"tokenizer_file": "tokenizer.json"}

a_											=						{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]
 UpperCAmelCase_		    =						None





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<unk>" ,       lowercase_="<s>" ,       lowercase_="</s>" ,       lowercase_="<pad>" ,       lowercase_=False ,       lowercase_=False ,       **lowercase_ ,       ):
     super().__init__(
         lowercase_ ,       lowercase_ ,       tokenizer_file=lowercase_ ,       unk_token=lowercase_ ,       bos_token=lowercase_ ,       eos_token=lowercase_ ,       pad_token=lowercase_ ,       add_prefix_space=lowercase_ ,       clean_up_tokenization_spaces=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    Optional[Any]     			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
     if pre_tok_state.get("add_prefix_space" ,       lowercase_) != add_prefix_space:
         snake_case_ :    Dict     			=						getattr(lowercase_ ,       pre_tok_state.pop("type"))
         snake_case_ :    Union[str, Any]     			=						add_prefix_space
         snake_case_ :    Optional[Any]     			=						pre_tok_class(**lowercase_)

     snake_case_ :    Any     			=						add_prefix_space





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     snake_case_ :    Dict     			=						kwargs.get("is_split_into_words" ,       lowercase_)
     if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'
             " pretokenized inputs.")

     return super()._batch_encode_plus(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     snake_case_ :    Optional[int]     			=						kwargs.get("is_split_into_words" ,       lowercase_)

     if not (self.add_prefix_space or not is_split_into_words):
         raise Exception(
             F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'
             " pretokenized inputs.")

     return super()._encode_plus(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     snake_case_ :    List[str]     			=						self._tokenizer.model.save(lowercase_ ,       name=lowercase_)
     return tuple(lowercase_)





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						[]
     for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(lowercase_ ,       add_special_tokens=lowercase_) + [self.eos_token_id])

     if len(lowercase_) > self.model_max_length:
         snake_case_ :    Union[str, Any]     			=						input_ids[-self.model_max_length :]
     return input_ids






'''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}""")




