




"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
					from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
					a_				:    Dict										=     get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
					from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a_				:    Union[str, Any]										=     12_80_22
a_				:    List[Any]										=     12_80_28

@require_sentencepiece
class __lowercase(      __a	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						Dict     		= MaMaaaTokenizer
					__a						:						Any     		= False
					__a						:						Optional[Any]     		= False
					__a						:						Dict     		= True
					def     snake_case_				(    self     ):
								super().setUp()

								__lowerCamelCase					:	Optional[Any]	       =				["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
								__lowerCamelCase					:	Dict	       =				dict(zip(a_						,	range(len(a_     )     )     )     )
								__lowerCamelCase					:	Tuple	       =				Path(self.tmpdirname     )
								save_json(a_						,	save_dir / VOCAB_FILES_NAMES['vocab_file']     )
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
											copyfile(a_						,	save_dir / VOCAB_FILES_NAMES['spm_file']     )

								__lowerCamelCase					:	Optional[Any]	       =				MaMaaaTokenizer.from_pretrained(self.tmpdirname     )
								tokenizer.save_pretrained(self.tmpdirname     )
					def     snake_case_				(    self						,	**__a     ):
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname						,	**a_     )
					def     snake_case_				(    self						,	__a     ):
								return (
								    "This is a test",
								    "This is a test",
								)
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				"""</s>"""
								__lowerCamelCase					:	str	       =				0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(a_     )						,	a_     )
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(a_     )						,	a_     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				self.get_tokenizer()
								__lowerCamelCase					:	Dict	       =				list(tokenizer.get_vocab().keys()     )

								self.assertEqual(vocab_keys[0]						,	'</s>'     )
								self.assertEqual(vocab_keys[1]						,	'<unk>'     )
								self.assertEqual(vocab_keys[-1]						,	'<s>'     )
								self.assertEqual(len(a_     )						,	tokenizer.vocab_size + len(tokenizer.get_added_vocab()     )     )
					@unittest.skip('Skip this test while all models are still to be uploaded.'     )
					def     snake_case_				(    self     ):
								pass
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				self.get_tokenizer()

								__lowerCamelCase					:	str	       =				tokenizer.tokenize('This is a test'     )
								self.assertListEqual(a_						,	['▁This', '▁is', '▁a', '▁t', 'est']     )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(a_     )						,	[2, 3, 4, 5, 6]						,	)

								__lowerCamelCase					:	List[str]	       =				tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]     )
								self.assertListEqual(a_						,	['▁This', '▁is', '▁a', '▁t', 'est']     )

								__lowerCamelCase					:	Optional[int]	       =				tokenizer.convert_tokens_to_string(a_     )
								self.assertEqual(a_						,	'This is a test'     )





					@slow
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Tuple	       =				{"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=a_						,	model_name='facebook/m2m100_418M'						,	revision='c168bae485c864188cf9aa0e4108b0b6934dc91e'						,	)



@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase(      unittest.TestCase				):




					'''simple docstring'''


					__a						:						Optional[Any]     		= 'facebook/m2m100_418M'
					__a						:						Tuple     		= [
					    'In my opinion, there are two levels of response from the French government.',
					    'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
					]
					__a						:						Tuple     		= [
					    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
					    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
					]

					# fmt: off
					__a						:						List[str]     		= [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]
					@classmethod
					def     snake_case_				(    cls     ):
								__lowerCamelCase					:	MaMaaaTokenizer	       =				MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name						,	src_lang='en'						,	tgt_lang='fr'     )
								__lowerCamelCase					:	Dict	       =				1
								return cls
					def     snake_case_				(    self     ):
								self.assertEqual(self.tokenizer.get_lang_id('ar'     )						,	128006     )
								self.assertEqual(self.tokenizer.get_lang_id('en'     )						,	128022     )
								self.assertEqual(self.tokenizer.get_lang_id('ro'     )						,	128076     )
								self.assertEqual(self.tokenizer.get_lang_id('mr'     )						,	128063     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.tokenizer.get_vocab()
								self.assertEqual(len(a_     )						,	self.tokenizer.vocab_size     )
								self.assertEqual(vocab['<unk>']						,	3     )
								self.assertIn(self.tokenizer.get_lang_token('en'     )						,	a_     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				"""en"""
								__lowerCamelCase					:	str	       =				self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
								self.assertListEqual(self.expected_src_tokens						,	a_     )
					def     snake_case_				(    self     ):
								self.assertIn(a_						,	self.tokenizer.all_special_ids     )
								# fmt: off
								__lowerCamelCase					:	Union[str, Any]	       =				[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
								# fmt: on
								__lowerCamelCase					:	Any	       =				self.tokenizer.decode(a_						,	skip_special_tokens=a_     )
								__lowerCamelCase					:	Optional[Any]	       =				self.tokenizer.decode(generated_ids[1:]						,	skip_special_tokens=a_     )
								self.assertEqual(a_						,	a_     )
								self.assertNotIn(self.tokenizer.eos_token						,	a_     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				tempfile.mkdtemp()
								__lowerCamelCase					:	int	       =				self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(a_     )
								__lowerCamelCase					:	Optional[Any]	       =				MaMaaaTokenizer.from_pretrained(a_     )
								self.assertDictEqual(new_tok.lang_token_to_id						,	a_     )
					@require_torch
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Dict	       =				"""en"""
								__lowerCamelCase					:	Union[str, Any]	       =				"""fr"""

								__lowerCamelCase					:	Optional[int]	       =				self.tokenizer(self.src_text						,	text_target=self.tgt_text						,	padding=a_						,	return_tensors='pt'     )

								__lowerCamelCase					:	List[str]	       =				shift_tokens_right(
								    batch['labels']						,	self.tokenizer.pad_token_id						,	self.tokenizer.eos_token_id     )

								for k in batch:
											__lowerCamelCase					:	Optional[int]	       =				batch[k].tolist()
								# batch = {k: v.tolist() for k,v in batch.items()}
								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								# batch.decoder_inputs_ids[0][0] ==
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == FR_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
					@require_torch
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[Any]	       =				"""mr"""
								self.assertListEqual(self.tokenizer.prefix_tokens						,	[self.tokenizer.get_lang_id('mr'     )]     )
								self.assertListEqual(self.tokenizer.suffix_tokens						,	[self.tokenizer.eos_token_id]     )

								__lowerCamelCase					:	str	       =				"""zh"""
								self.assertListEqual(self.tokenizer.prefix_tokens						,	[self.tokenizer.get_lang_id('zh'     )]     )
								self.assertListEqual(self.tokenizer.suffix_tokens						,	[self.tokenizer.eos_token_id]     )
					@require_torch
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				"""mr"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens						,	[self.tokenizer.get_lang_id('mr'     )]     )
								self.assertListEqual(self.tokenizer.suffix_tokens						,	[self.tokenizer.eos_token_id]     )
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens						,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )

								__lowerCamelCase					:	Dict	       =				"""zh"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens						,	[self.tokenizer.get_lang_id('zh'     )]     )
								self.assertListEqual(self.tokenizer.suffix_tokens						,	[self.tokenizer.eos_token_id]     )
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens						,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang     )]     )





					@require_torch
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				self.tokenizer._build_translation_inputs('A test'						,	return_tensors='pt'						,	src_lang='en'						,	tgt_lang='ar'     )

								self.assertEqual(
								    nested_simplify(a_     )						,	{
								        # en_XX, A, test, EOS
								        'input_ids': [[128022, 58, 4183, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 128006,
								    }						,	)









"""simple docstring"""
from __future__ import annotations

import pandas as pd





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			__lowerCamelCase					:	Any	       =				[0] * no_of_processes
			# Copy the burst time into remaining_time[]
			for i in range(A__     ):
						__lowerCamelCase					:	Dict	       =				burst_time[i]

			__lowerCamelCase					:	Any	       =				0
			__lowerCamelCase					:	Tuple	       =				0
			__lowerCamelCase					:	Union[str, Any]	       =				999999999
			__lowerCamelCase					:	str	       =				0
			__lowerCamelCase					:	Optional[int]	       =				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:
															__lowerCamelCase					:	List[Any]	       =				remaining_time[j]
															__lowerCamelCase					:	List[str]	       =				j
															__lowerCamelCase					:	Optional[int]	       =				True

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

						__lowerCamelCase					:	Optional[Any]	       =				remaining_time[short]
						if minm == 0:
									__lowerCamelCase					:	Optional[int]	       =				999999999

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

									# Find finish time of current process
									__lowerCamelCase					:	Dict	       =				increment_time + 1

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

									if waiting_time[short] < 0:
												__lowerCamelCase					:	Optional[Any]	       =				0

        # Increment time
						increment_time += 1
			return waiting_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    int							,				A__:    list[int]     )  ->     list[int]:
			__lowerCamelCase					:	List[Any]	       =				[0] * no_of_processes
			for i in range(A__     ):
						__lowerCamelCase					:	Tuple	       =				burst_time[i] + waiting_time[i]
			return turn_around_time





def 						UpperCAmelCase				(			A__:    list[int]							,				A__:    list[int]							,				A__:    int     )  ->     None:
			__lowerCamelCase					:	int	       =				0
			__lowerCamelCase					:	Dict	       =				0
			for i in range(A__     ):
						__lowerCamelCase					:	str	       =				total_waiting_time + waiting_time[i]
						__lowerCamelCase					:	Union[str, 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''')
					a_				:    int										=     int(input())
					a_				:    List[str]										=     [0] * no_of_processes
					a_				:    int										=     [0] * no_of_processes
					a_				:    int										=     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))
										a_						,							a_				:    Union[str, Any]										=     map(int, input().split())

					a_				:    Any										=     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					a_				:    List[str]										=     burst_time
					a_				:    List[Any]										=     no_of_processes
					a_				:    Tuple										=     waiting_time
					a_				:    Optional[Any]										=     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					a_				:    List[Any]										=     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)



