


from __future__ import annotations

import pandas as pd






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			List[str]         =					[0] * no_of_processes
		lowerCAmelCase__   :			Optional[int]         =					[0] * no_of_processes
		# Copy the burst time into remaining_time[]
		for i in range(UpperCamelCase__       ):
				lowerCAmelCase__   :			Tuple         =					burst_time[i]

		lowerCAmelCase__   :			Optional[Any]         =					0
		lowerCAmelCase__   :			Optional[Any]         =					0
		lowerCAmelCase__   :			Tuple         =					999_999_999
		lowerCAmelCase__   :			Optional[Any]         =					0
		lowerCAmelCase__   :			Optional[Any]         =					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:
										lowerCAmelCase__   :			List[Any]         =					remaining_time[j]
										lowerCAmelCase__   :			List[str]         =					j
										lowerCAmelCase__   :			List[str]         =					True

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

				lowerCAmelCase__   :			Optional[Any]         =					remaining_time[short]
				if minm == 0:
						lowerCAmelCase__   :			Optional[int]         =					999_999_999

				if remaining_time[short] == 0:
						complete += 1
						lowerCAmelCase__   :			Union[str, Any]         =					False

						# Find finish time of current process
						lowerCAmelCase__   :			Optional[int]         =					increment_time + 1

						# Calculate waiting time
						lowerCAmelCase__   :			Union[str, Any]         =					finish_time - arrival_time[short]
						lowerCAmelCase__   :			Optional[int]         =					finar - burst_time[short]

						if waiting_time[short] < 0:
								lowerCAmelCase__   :			Union[str, Any]         =					0

        # Increment time
				increment_time += 1
		return waiting_time






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Optional[Any]         =					[0] * no_of_processes
		for i in range(UpperCamelCase__       ):
				lowerCAmelCase__   :			Optional[int]         =					burst_time[i] + waiting_time[i]
		return turn_around_time






def      lowerCamelCase_    (       _a	,   _a	,   _a       ):




		"""simple docstring"""






		lowerCAmelCase__   :			Tuple         =					0
		lowerCAmelCase__   :			List[Any]         =					0
		for i in range(UpperCamelCase__       ):
				lowerCAmelCase__   :			Optional[Any]         =					total_waiting_time + waiting_time[i]
				lowerCAmelCase__   :			Optional[int]         =					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						=		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)











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


__A				:    Dict       =   logging.get_logger(__name__)

__A				:    Any       =   {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__A				:    Tuple       =   {
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


__A				:    List[Any]       =   {
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}

class 	A_       (a_			):
	UpperCAmelCase__					=			VOCAB_FILES_NAMES
	UpperCAmelCase__					=			PRETRAINED_VOCAB_FILES_MAP
	UpperCAmelCase__					=			PRETRAINED_INIT_CONFIGURATION
	UpperCAmelCase__					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCAmelCase__					=			SqueezeBertTokenizer
	def __init__(    self							,					_A=None							,					_A=None							,					_A=True							,					_A="[UNK]"							,					_A="[SEP]"							,					_A="[PAD]"							,					_A="[CLS]"							,					_A="[MASK]"							,					_A=True							,					_A=None							,					**_A							,					):






				'''simple docstring'''







				super().__init__(
				    _A							,					tokenizer_file=_A							,					do_lower_case=_A							,					unk_token=_A							,					sep_token=_A							,					pad_token=_A							,					cls_token=_A							,					mask_token=_A							,					tokenize_chinese_chars=_A							,					strip_accents=_A							,					**_A							,					)

				UpperCAmelCase   		=				json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
				if (
				    normalizer_state.get('''lowercase'''							,					_A		) != do_lower_case
				    or normalizer_state.get('''strip_accents'''							,					_A		) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''							,					_A		) != tokenize_chinese_chars
				):
							UpperCAmelCase   		=				getattr(_A							,					normalizer_state.pop('''type'''		)		)
							UpperCAmelCase   		=				do_lower_case
							UpperCAmelCase   		=				strip_accents
							UpperCAmelCase   		=				tokenize_chinese_chars
							UpperCAmelCase   		=				normalizer_class(**_A		)

				UpperCAmelCase   		=				do_lower_case
	def      _lowercase				(    self							,					_A							,					_A=None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

				if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

				return output
	def      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				[self.sep_token_id]
				UpperCAmelCase   		=				[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      _lowercase				(    self							,					_A							,					_A = None		):






				'''simple docstring'''







				UpperCAmelCase   		=				self._tokenizer.model.save(_A							,					name=_A		)
				return tuple(_A		)



