
'''simple docstring'''
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_											=						logging.get_logger(__name__)

a_											=						"▁"

a_											=						{"vocab_file": "prophetnet.tokenizer"}

a_											=						{
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

a_											=						{
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

a_											=						{
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}

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






				snake_case_ :    Any     			=						collections.OrderedDict()
				with open(__SCREAMING_SNAKE_CASE,		"r",		encoding="utf-8"       ) as reader:
								snake_case_ :    Optional[Any]     			=						reader.readlines()
				for index, token in enumerate(__SCREAMING_SNAKE_CASE       ):
								snake_case_ :    str     			=						token.rstrip("\n"       )
								snake_case_ :    Any     			=						index
				return vocab






class    UpperCAmelCase_				(	snake_case__					):
	UpperCAmelCase_		    =						VOCAB_FILES_NAMES
	UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





	def __init__(    self ,       lowercase_ ,       lowercase_="[SEP]" ,       lowercase_="[SEP]" ,       lowercase_="[SEP]" ,       lowercase_="[UNK]" ,       lowercase_="[PAD]" ,       lowercase_="[CLS]" ,       lowercase_="[MASK]" ,       lowercase_ = None ,       **lowercase_ ,       ):
					snake_case_ :    List[str]     			=						{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=lowercase_ ,       eos_token=lowercase_ ,       sep_token=lowercase_ ,       unk_token=lowercase_ ,       pad_token=lowercase_ ,       cls_token=lowercase_ ,       mask_token=lowercase_ ,       sp_model_kwargs=self.sp_model_kwargs ,       **lowercase_ ,       )

					try:
									import sentencepiece as spm
					except ImportError:
									logger.warning(
									    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
									    " pip install sentencepiece")
									raise

					snake_case_ :    List[str]     			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(str(lowercase_))
					snake_case_ :    List[str]     			=						vocab_file

					# Original fairseq vocab and spm vocab must be "aligned":
					# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
					# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
					# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
					# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

					# put special tokens and [unused] tokens into the vocab
					snake_case_ :    Optional[Any]     			=						{"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

					for i in range(10):
									snake_case_ :    List[Any]     			=						F'[unused{i}]'
									snake_case_ :    str     			=						5 + i

					# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
					snake_case_ :    str     			=						12
					snake_case_ :    Optional[Any]     			=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
					for k in self.fairseq_tokens_to_ids.keys():
									self.unique_no_split_tokens.append(lowercase_)





	def __getstate__(    self):
					snake_case_ :    List[str]     			=						self.__dict__.copy()
					snake_case_ :    Dict     			=						None
					return state





	def __setstate__(    self ,       lowercase_):
					snake_case_ :    List[str]     			=						d
					try:
									import sentencepiece as spm
					except ImportError:
									logger.warning(
									    "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
									    " pip install sentencepiece")
									raise

					# for backward compatibility
					if not hasattr(self ,       "sp_model_kwargs"):
									snake_case_ :    Optional[Any]     			=						{}

					snake_case_ :    Dict     			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(self.vocab_file)





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):

					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=lowercase_ ,       token_ids_a=lowercase_ ,       already_has_special_tokens=lowercase_)

					if token_ids_a is None:
									return ([0] * len(lowercase_)) + [1]
					return ([0] * len(lowercase_)) + [1] + ([0] * len(lowercase_)) + [1]





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
					snake_case_ :    int     			=						[self.sep_token_id]

					if token_ids_a is None:
									return len(token_ids_a + sep) * [0]
					return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]





	@property
	def        snake_case__		(    self):
					return len(self.sp_model) + self.fairseq_offset





	def        snake_case__		(    self):
					snake_case_ :    int     			=						{self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab





	def        snake_case__		(    self ,       lowercase_):
					return self.sp_model.encode(lowercase_ ,       out_type=lowercase_)





	def        snake_case__		(    self ,       lowercase_):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					snake_case_ :    List[Any]     			=						self.sp_model.PieceToId(lowercase_)

					# Need to return unknown token if the SP model returned 0
					return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





	def        snake_case__		(    self ,       lowercase_):
					if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
					return self.sp_model.IdToPiece(index - self.fairseq_offset)





	def        snake_case__		(    self ,       lowercase_):
					snake_case_ :    Union[str, Any]     			=						"".join(lowercase_).replace(lowercase_ ,       " ").strip()
					return out_string





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
					if not os.path.isdir(lowercase_):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory')
									return
					snake_case_ :    Optional[Any]     			=						os.path.join(
					    lowercase_ ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

					if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
									copyfile(self.vocab_file ,       lowercase_)
					elif not os.path.isfile(self.vocab_file):
									with open(lowercase_ ,       "wb") as fi:
													snake_case_ :    Optional[Any]     			=						self.sp_model.serialized_model_proto()
													fi.write(lowercase_)

					return (out_vocab_file,)





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):

					if token_ids_a is None:
									return token_ids_a + [self.sep_token_id]
					snake_case_ :    int     			=						[self.sep_token_id]
					return token_ids_a + sep + token_ids_a + sep






'''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}""")




