"""simple docstring"""




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCamelCase											=  logging.get_logger(__name__)

__lowerCamelCase											=  {"vocab_file": "sentencepiece.bpe.model"}

__lowerCamelCase											=  {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

__lowerCamelCase											=  {
    "camembert-base": 5_12,
}

__lowerCamelCase											=  "▁"
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						List[str]													=		VOCAB_FILES_NAMES
						lowerCAmelCase__  :						Union[str, Any]													=		PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__  :						Any													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__  :						List[str]													=		['input_ids', 'attention_mask']





						def __init__(       self		,__UpperCAmelCase		,__UpperCAmelCase="<s>"		,__UpperCAmelCase="</s>"		,__UpperCAmelCase="</s>"		,__UpperCAmelCase="<s>"		,__UpperCAmelCase="<unk>"		,__UpperCAmelCase="<pad>"		,__UpperCAmelCase="<mask>"		,__UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]		,__UpperCAmelCase = None		,**__UpperCAmelCase		,)					->				None:
													# Mask token behave like a normal word, i.e. include the space before it
													A__	     =							AddedToken(__UpperCAmelCase		,lstrip=__UpperCAmelCase		,rstrip=__UpperCAmelCase     ) if isinstance(__UpperCAmelCase		,__UpperCAmelCase     ) else mask_token

													A__	     =							{} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    bos_token=__UpperCAmelCase		,eos_token=__UpperCAmelCase		,unk_token=__UpperCAmelCase		,sep_token=__UpperCAmelCase		,cls_token=__UpperCAmelCase		,pad_token=__UpperCAmelCase		,mask_token=__UpperCAmelCase		,additional_special_tokens=__UpperCAmelCase		,sp_model_kwargs=self.sp_model_kwargs		,**__UpperCAmelCase		,)
													A__	     =							spm.SentencePieceProcessor(**self.sp_model_kwargs     )
													self.sp_model.Load(str(__UpperCAmelCase     )     )
													A__	     =							vocab_file
													# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
													# sentencepiece vocabulary (this is the case for <s> and </s>
													A__	     =							{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
													A__	     =							len(self.fairseq_tokens_to_ids     )
													A__	     =							len(self.sp_model     ) + len(self.fairseq_tokens_to_ids     )
													A__	     =							{v: k for k, v in self.fairseq_tokens_to_ids.items()}





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				List[int]:

													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													A__	     =							[self.cls_token_id]
													A__	     =							[self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = False     )					->				List[int]:
													if already_has_special_tokens:
																				return super().get_special_tokens_mask(
																				    token_ids_a=__UpperCAmelCase		,token_ids_a=__UpperCAmelCase		,already_has_special_tokens=__UpperCAmelCase     )

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





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				List[int]:
													A__	     =							[self.sep_token_id]
													A__	     =							[self.cls_token_id]

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





						@property
						def 				snake_case__	(       self     )					->				str:
													return len(self.fairseq_tokens_to_ids     ) + len(self.sp_model     )





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							{self.convert_ids_to_tokens(__UpperCAmelCase     ): i for i in range(self.vocab_size     )}
													vocab.update(self.added_tokens_encoder     )
													return vocab





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				List[str]:
													return self.sp_model.encode(__UpperCAmelCase		,out_type=__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Tuple:
													if token in self.fairseq_tokens_to_ids:
																				return self.fairseq_tokens_to_ids[token]
													elif self.sp_model.PieceToId(__UpperCAmelCase     ) == 0:
																				# Convert sentence piece unk token to fairseq unk token index
																				return self.unk_token_id
													return self.fairseq_offset + self.sp_model.PieceToId(__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				List[str]:
													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		,__UpperCAmelCase     )					->				int:
													A__	     =							[]
													A__	     =							''
													A__	     =							False
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											if not prev_is_special:
																																		out_string += " "
																											out_string += self.sp_model.decode(__UpperCAmelCase     ) + token
																											A__	     =							True
																											A__	     =							[]
																				else:
																											current_sub_tokens.append(__UpperCAmelCase     )
																											A__	     =							False
													out_string += self.sp_model.decode(__UpperCAmelCase     )
													return out_string.strip()





						def __getstate__(       self     )					->				str:
													A__	     =							self.__dict__.copy()
													A__	     =							None
													return state





						def __setstate__(       self		,__UpperCAmelCase     )					->				Optional[int]:
													A__	     =							d

													# for backward compatibility
													if not hasattr(self		,'sp_model_kwargs'     ):
																				A__	     =							{}

													A__	     =							spm.SentencePieceProcessor(**self.sp_model_kwargs     )
													self.sp_model.Load(self.vocab_file     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				Tuple[str]:
													if not os.path.isdir(__UpperCAmelCase     ):
																				logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
																				return
													A__	     =							os.path.join(
													    __UpperCAmelCase		,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

													if os.path.abspath(self.vocab_file     ) != os.path.abspath(__UpperCAmelCase     ) and os.path.isfile(self.vocab_file     ):
																				copyfile(self.vocab_file		,__UpperCAmelCase     )
													elif not os.path.isfile(self.vocab_file     ):
																				with open(__UpperCAmelCase		,'wb'     ) as fi:
																											A__	     =							self.sp_model.serialized_model_proto()
																											fi.write(__UpperCAmelCase     )

													return (out_vocab_file,)






"""simple docstring"""




from __future__ import annotations

import pandas as pd






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[0] * no_of_processes
							A__	     =							[0] * no_of_processes
							# Copy the burst time into remaining_time[]
							for i in range(UpperCamelCase__				):
														A__	     =							burst_time[i]

							A__	     =							0
							A__	     =							0
							A__	     =							999_999_999
							A__	     =							0
							A__	     =							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:
																																			A__	     =							remaining_time[j]
																																			A__	     =							j
																																			A__	     =							True

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

														A__	     =							remaining_time[short]
														if minm == 0:
																					A__	     =							999_999_999

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

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

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

																					if waiting_time[short] < 0:
																												A__	     =							0

        # Increment time
														increment_time += 1
							return waiting_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							[0] * no_of_processes
							for i in range(UpperCamelCase__				):
														A__	     =							burst_time[i] + waiting_time[i]
							return turn_around_time






def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							0
							A__	     =							0
							for i in range(UpperCamelCase__				):
														A__	     =							total_waiting_time + waiting_time[i]
														A__	     =							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")
				__lowerCamelCase											=  int(input())
				__lowerCamelCase											=  [0] * no_of_processes
				__lowerCamelCase											=  [0] * no_of_processes
				__lowerCamelCase											=  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))
								__lowerCamelCase				,   __lowerCamelCase											=  map(int, input().split())

				__lowerCamelCase											=  calculate_waitingtime(arrival_time, burst_time, no_of_processes)

				__lowerCamelCase											=  burst_time
				__lowerCamelCase											=  no_of_processes
				__lowerCamelCase											=  waiting_time
				__lowerCamelCase											=  calculate_turnaroundtime(bt, n, wt)

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				__lowerCamelCase											=  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)





