





import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class SCREAMING_SNAKE_CASE_  (   snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     str	 =    MvpTokenizer
						__magic_name__:     str	 =    MvpTokenizerFast
						__magic_name__:     str	 =    True
						__magic_name__:     Any	 =    filter_roberta_detectors






						def UpperCAmelCase_			( self : Optional[Any]		)							->      List[str]:







							"""simple docstring"""


							super().setUp()
							snake_case_						:     str     =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							snake_case_						:     Optional[int]     =		dict(zip(_A						,				range(len(_A		)		)		)		)
							snake_case_						:     Optional[int]     =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							snake_case_						:     Optional[Any]     =		{'unk_token': '<unk>'}

							snake_case_						:     Union[str, Any]     =		os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['vocab_file']		)
							snake_case_						:     Tuple     =		os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['merges_file']		)
							with open(self.vocab_file						,				'w'						,				encoding='utf-8'		) as fp:
								fp.write(json.dumps(_A		) + '\n'		)
							with open(self.merges_file						,				'w'						,				encoding='utf-8'		) as fp:
								fp.write('\n'.join(_A		)		)






						def UpperCAmelCase_			( self : List[Any]						,				**_A : Any		)							->      Union[str, Any]:







							"""simple docstring"""


							kwargs.update(self.special_tokens_map		)
							return self.tokenizer_class.from_pretrained(self.tmpdirname						,				**_A		)






						def UpperCAmelCase_			( self : List[Any]						,				**_A : List[str]		)							->      Any:







							"""simple docstring"""


							kwargs.update(self.special_tokens_map		)
							return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,				**_A		)






						def UpperCAmelCase_			( self : List[str]						,				_A : Optional[int]		)							->      Any:







							"""simple docstring"""


							return "lower newer", "lower newer"






						@cached_property
						def UpperCAmelCase_			( self : List[str]		)							->      List[str]:







							"""simple docstring"""


							return MvpTokenizer.from_pretrained('RUCAIBox/mvp'		)






						@cached_property
						def UpperCAmelCase_			( self : List[Any]		)							->      str:







							"""simple docstring"""


							return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'		)






						@require_torch
						def UpperCAmelCase_			( self : List[str]		)							->      Dict:







							"""simple docstring"""


							snake_case_						:     Any     =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							snake_case_						:     List[Any]     =		[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Optional[int]     =		tokenizer(_A						,				max_length=len(_A		)						,				padding=_A						,				return_tensors='pt'		)
								self.assertIsInstance(_A						,				_A		)

								self.assertEqual((2, 9)						,				batch.input_ids.shape		)
								self.assertEqual((2, 9)						,				batch.attention_mask.shape		)
								snake_case_						:     List[str]     =		batch.input_ids.tolist()[0]
								self.assertListEqual(_A						,				_A		)
								# Test that special tokens are reset






						@require_torch
						def UpperCAmelCase_			( self : str		)							->      int:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		['A long paragraph for summarization.', 'Another paragraph for summarization.']
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Optional[Any]     =		tokenizer(_A						,				padding=_A						,				return_tensors='pt'		)
								# check if input_ids are returned and no labels
								self.assertIn('input_ids'						,				_A		)
								self.assertIn('attention_mask'						,				_A		)
								self.assertNotIn('labels'						,				_A		)
								self.assertNotIn('decoder_attention_mask'						,				_A		)






						@require_torch
						def UpperCAmelCase_			( self : Optional[int]		)							->      Optional[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		[
							    'Summary of the text.',
							    'Another summary.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     str     =		tokenizer(text_target=_A						,				max_length=32						,				padding='max_length'						,				return_tensors='pt'		)
								self.assertEqual(32						,				targets['input_ids'].shape[1]		)






						@require_torch
						def UpperCAmelCase_			( self : List[str]		)							->      Union[str, Any]:







							"""simple docstring"""


							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Optional[int]     =		tokenizer(
								    ['I am a small frog' * 1024, 'I am a small frog']						,				padding=_A						,				truncation=_A						,				return_tensors='pt'		)
								self.assertIsInstance(_A						,				_A		)
								self.assertEqual(batch.input_ids.shape						,				(2, 1024)		)






						@require_torch
						def UpperCAmelCase_			( self : List[Any]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		['A long paragraph for summarization.']
							snake_case_						:     Tuple     =		[
							    'Summary of the text.',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
								snake_case_						:     Any     =		tokenizer(_A						,				text_target=_A						,				return_tensors='pt'		)
								snake_case_						:     Union[str, Any]     =		inputs['input_ids']
								snake_case_						:     Dict     =		inputs['labels']
								self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
								self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
								self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
								self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)






						def UpperCAmelCase_			( self : int		)							->      Optional[Any]:







							"""simple docstring"""


							pass






						def UpperCAmelCase_			( self : Any		)							->      List[str]:







							"""simple docstring"""


							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
									snake_case_						:     Tuple     =		self.rust_tokenizer_class.from_pretrained(_A						,				**_A		)
									snake_case_						:     Tuple     =		self.tokenizer_class.from_pretrained(_A						,				**_A		)
									snake_case_						:     Optional[int]     =		'A, <mask> AllenNLP sentence.'
									snake_case_						:     Optional[Any]     =		tokenizer_r.encode_plus(_A						,				add_special_tokens=_A						,				return_token_type_ids=_A		)
									snake_case_						:     Dict     =		tokenizer_p.encode_plus(_A						,				add_special_tokens=_A						,				return_token_type_ids=_A		)

									# token_type_ids should put 0 everywhere
									self.assertEqual(sum(tokens_r['token_type_ids']		)						,				sum(tokens_p['token_type_ids']		)		)

									# attention_mask should put 1 everywhere, so sum over length should be 1
									self.assertEqual(
									    sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)						,				sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)						,				)

									snake_case_						:     Any     =		tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
									snake_case_						:     List[str]     =		tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)

									# Rust correctly handles the space before the mask while python doesnt
									self.assertSequenceEqual(tokens_p['input_ids']						,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
									self.assertSequenceEqual(tokens_r['input_ids']						,				[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

									self.assertSequenceEqual(
									    _A						,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
									self.assertSequenceEqual(
									    _A						,				['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)








from __future__ import annotations

import pandas as pd


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Optional[Any]     =		[0] * no_of_processes
	snake_case_						:     Tuple     =		[0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(__a				):
		snake_case_						:     Union[str, Any]     =		burst_time[i]

	snake_case_						:     Optional[Any]     =		0
	snake_case_						:     Dict     =		0
	snake_case_						:     Any     =		9_99_99_99_99
	snake_case_						:     Tuple     =		0
	snake_case_						:     List[Any]     =		False

	# Process until all processes are completed
	while complete != no_of_processes:
		for j in range(__a				):
			if arrival_time[j] <= increment_time and remaining_time[j] > 0:
				if remaining_time[j] < minm:
					snake_case_						:     str     =		remaining_time[j]
					snake_case_						:     Any     =		j
					snake_case_						:     List[str]     =		True

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

		snake_case_						:     Any     =		remaining_time[short]
		if minm == 0:
			snake_case_						:     Dict     =		9_99_99_99_99

		if remaining_time[short] == 0:
			complete += 1
			snake_case_						:     List[str]     =		False

			# Find finish time of current process
			snake_case_						:     List[str]     =		increment_time + 1

			# Calculate waiting time
			snake_case_						:     Any     =		finish_time - arrival_time[short]
			snake_case_						:     Any     =		finar - burst_time[short]

			if waiting_time[short] < 0:
				snake_case_						:     Optional[int]     =		0

        # Increment time
		increment_time += 1
	return waiting_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     Tuple     =		[0] * no_of_processes
	for i in range(__a				):
		snake_case_						:     str     =		burst_time[i] + waiting_time[i]
	return turn_around_time


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	snake_case_						:     int     =		0
	snake_case_						:     Optional[Any]     =		0
	for i in range(__a				):
		snake_case_						:     int     =		total_waiting_time + waiting_time[i]
		snake_case_						:     Optional[Any]     =		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""")
						_SCREAMING_SNAKE_CASE 		=						int(input())
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						[0] * no_of_processes
						_SCREAMING_SNAKE_CASE 		=						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))
												_SCREAMING_SNAKE_CASE		,							_SCREAMING_SNAKE_CASE 		=						map(int, input().split())

						_SCREAMING_SNAKE_CASE 		=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

						calculate_average_times(waiting_time, turn_around_time, no_of_processes)

						_SCREAMING_SNAKE_CASE 		=						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)

