"""simple docstring"""




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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
				from .tokenization_albert import AlbertTokenizer
else:
				__lowerCamelCase											=  None

__lowerCamelCase											=  logging.get_logger(__name__)
__lowerCamelCase											=  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase											=  {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json",
    },
}

__lowerCamelCase											=  {
    "albert-base-v1": 5_12,
    "albert-large-v1": 5_12,
    "albert-xlarge-v1": 5_12,
    "albert-xxlarge-v1": 5_12,
    "albert-base-v2": 5_12,
    "albert-large-v2": 5_12,
    "albert-xlarge-v2": 5_12,
    "albert-xxlarge-v2": 5_12,
}

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





						def __init__(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=True		,__UpperCAmelCase=True		,__UpperCAmelCase=False		,__UpperCAmelCase="[CLS]"		,__UpperCAmelCase="[SEP]"		,__UpperCAmelCase="<unk>"		,__UpperCAmelCase="[SEP]"		,__UpperCAmelCase="<pad>"		,__UpperCAmelCase="[CLS]"		,__UpperCAmelCase="[MASK]"		,**__UpperCAmelCase		,)					->				Optional[Any]:
													# Mask token behave like a normal word, i.e. include the space before it and
													# is included in the raw text, there should be a match in a non-normalized sentence.
													A__	     =							(
													    AddedToken(__UpperCAmelCase		,lstrip=__UpperCAmelCase		,rstrip=__UpperCAmelCase		,normalized=__UpperCAmelCase     )
													    if isinstance(__UpperCAmelCase		,__UpperCAmelCase     )
													    else mask_token
													)

													super().__init__(
													    __UpperCAmelCase		,tokenizer_file=__UpperCAmelCase		,do_lower_case=__UpperCAmelCase		,remove_space=__UpperCAmelCase		,keep_accents=__UpperCAmelCase		,bos_token=__UpperCAmelCase		,eos_token=__UpperCAmelCase		,unk_token=__UpperCAmelCase		,sep_token=__UpperCAmelCase		,pad_token=__UpperCAmelCase		,cls_token=__UpperCAmelCase		,mask_token=__UpperCAmelCase		,**__UpperCAmelCase		,)

													A__	     =							do_lower_case
													A__	     =							remove_space
													A__	     =							keep_accents
													A__	     =							vocab_file
													A__	     =							False if not self.vocab_file else True





						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 cls + token_ids_a + sep
													return cls + token_ids_a + sep + token_ids_a + sep





						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     ) * [0] + len(token_ids_a + sep     ) * [1]





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase = None     )					->				Tuple[str]:
													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																				    'tokenizer.'     )

													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     ):
																				copyfile(self.vocab_file		,__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)





