



'''simple docstring'''

import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase     		=   get_tests_dir('fixtures/test_sentencepiece.model')

@require_sentencepiece
@require_tokenizers
class __snake_case(				lowerCamelCase__		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		XLMRobertaTokenizer
						UpperCAmelCase							:   Dict									=		XLMRobertaTokenizerFast
						UpperCAmelCase							:   Dict									=		True
						UpperCAmelCase							:   Optional[Any]									=		True


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							super().setUp()

							# We have a SentencePiece fixture for testing
							lowerCAmelCase   =     XLMRobertaTokenizer(A_     ,	keep_accents=A_				)
							tokenizer.save_pretrained(self.tmpdirname				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     "<pad>"
							lowerCAmelCase   =     1

							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				)     ->     Optional[int]:
							lowerCAmelCase   =     list(self.get_tokenizer().get_vocab().keys()				)

							self.assertEqual(vocab_keys[0]     ,	"""<s>"""				)
							self.assertEqual(vocab_keys[1]     ,	"""<pad>"""				)
							self.assertEqual(vocab_keys[-1]     ,	"""<mask>"""				)
							self.assertEqual(len(A_				)     ,	1002				)


						def     __snake_case   ( self				)     ->     int:
							self.assertEqual(self.get_tokenizer().vocab_size     ,	1002				)


						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     XLMRobertaTokenizer(A_     ,	keep_accents=A_				)

							lowerCAmelCase   =     tokenizer.tokenize("""This is a test"""				)
							self.assertListEqual(A_     ,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]				)

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(A_				)     ,	[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]     ,	)

							lowerCAmelCase   =     tokenizer.tokenize("""I was born in 92000, and this is falsé."""				)
							self.assertListEqual(
							    A_     ,	[
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """9""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """é""",
							        """.""",
							    ]     ,	)
							lowerCAmelCase   =     tokenizer.convert_tokens_to_ids(A_				)
							self.assertListEqual(
							    A_     ,	[
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ]     ,	)

							lowerCAmelCase   =     tokenizer.convert_ids_to_tokens(A_				)
							self.assertListEqual(
							    A_     ,	[
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """<unk>""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """<unk>""",
							        """.""",
							    ]     ,	)


						def     __snake_case   ( self				)     ->     Tuple:




							if not self.test_slow_tokenizer:
								# as we don't have a slow version, we can't compare the outputs between slow and fast versions
								return

							lowerCAmelCase   =     (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'				):
									lowerCAmelCase   =     self.rust_tokenizer_class.from_pretrained(A_     ,	**A_				)
									lowerCAmelCase   =     self.tokenizer_class.from_pretrained(A_     ,	**A_				)

									lowerCAmelCase   =     tempfile.mkdtemp()

									lowerCAmelCase   =     tokenizer_r.save_pretrained(A_				)
									lowerCAmelCase   =     tokenizer_p.save_pretrained(A_				)

									# Checks it save with the same files + the tokenizer.json file for the fast one
									self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)
									lowerCAmelCase   =     tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f				)
									self.assertSequenceEqual(A_     ,	A_				)

									# Checks everything loads correctly in the same way
									lowerCAmelCase   =     tokenizer_r.from_pretrained(A_				)
									lowerCAmelCase   =     tokenizer_p.from_pretrained(A_				)

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
										self.assertTrue(hasattr(A_     ,	A_				)				)
										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

									shutil.rmtree(A_				)

									# Save tokenizer rust, legacy_format=True
									lowerCAmelCase   =     tempfile.mkdtemp()

									lowerCAmelCase   =     tokenizer_r.save_pretrained(A_     ,	legacy_format=A_				)
									lowerCAmelCase   =     tokenizer_p.save_pretrained(A_				)

									# Checks it save with the same files
									self.assertSequenceEqual(A_     ,	A_				)

									# Checks everything loads correctly in the same way
									lowerCAmelCase   =     tokenizer_r.from_pretrained(A_				)
									lowerCAmelCase   =     tokenizer_p.from_pretrained(A_				)

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
										self.assertTrue(hasattr(A_     ,	A_				)				)

									shutil.rmtree(A_				)

									# Save tokenizer rust, legacy_format=False
									lowerCAmelCase   =     tempfile.mkdtemp()

									lowerCAmelCase   =     tokenizer_r.save_pretrained(A_     ,	legacy_format=A_				)
									lowerCAmelCase   =     tokenizer_p.save_pretrained(A_				)

									# Checks it saved the tokenizer.json file
									self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files				)				)

									# Checks everything loads correctly in the same way
									lowerCAmelCase   =     tokenizer_r.from_pretrained(A_				)
									lowerCAmelCase   =     tokenizer_p.from_pretrained(A_				)

									# Check special tokens are set accordingly on Rust and Python
									for key in tokenizer_pp.special_tokens_map:
										self.assertTrue(hasattr(A_     ,	A_				)				)

									shutil.rmtree(A_				)


						@cached_property
						def     __snake_case   ( self				)     ->     Optional[int]:
							return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""				)


						def     __snake_case   ( self				)     ->     Tuple:




							with tempfile.NamedTemporaryFile() as f:
								shutil.copyfile(A_     ,	f.name				)
								lowerCAmelCase   =     XLMRobertaTokenizer(f.name     ,	keep_accents=A_				)
								lowerCAmelCase   =     pickle.dumps(A_				)
							pickle.loads(A_				)


						def     __snake_case   ( self				)     ->     Optional[Any]:




							if not self.test_rust_tokenizer:
								return

							lowerCAmelCase   =     self.get_tokenizer()
							lowerCAmelCase   =     self.get_rust_tokenizer()

							lowerCAmelCase   =     "I was born in 92000, and this is falsé."

							lowerCAmelCase   =     tokenizer.tokenize(A_				)
							lowerCAmelCase   =     rust_tokenizer.tokenize(A_				)
							self.assertListEqual(A_     ,	A_				)

							lowerCAmelCase   =     tokenizer.encode(A_     ,	add_special_tokens=A_				)
							lowerCAmelCase   =     rust_tokenizer.encode(A_     ,	add_special_tokens=A_				)
							self.assertListEqual(A_     ,	A_				)

							lowerCAmelCase   =     self.get_rust_tokenizer()
							lowerCAmelCase   =     tokenizer.encode(A_				)
							lowerCAmelCase   =     rust_tokenizer.encode(A_				)
							self.assertListEqual(A_     ,	A_				)


						@slow
						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     "Hello World!"
							lowerCAmelCase   =     [0, 3_5378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(A_     ,	self.big_tokenizer.encode(A_				)				)


						@slow
						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     (
							    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
							    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
							)
							lowerCAmelCase   =     [
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    17_9459,
							    12_4850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    1_0114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    2_2376,
							    642,
							    1221,
							    1_5190,
							    3_4153,
							    450,
							    5608,
							    959,
							    1119,
							    5_7702,
							    136,
							    186,
							    47,
							    1098,
							    2_9367,
							    47,
							    # 4426, # What fairseq tokenizes from "<unk>": "_<"
							    # 3678, # What fairseq tokenizes from "<unk>": "unk"
							    # 2740, # What fairseq tokenizes from "<unk>": ">"
							    3,  # What we tokenize from "<unk>": "<unk>"
							    6,  # Residue from the tokenization: an extra sentencepiece underline
							    4,
							    6044,
							    237,
							    6284,
							    5_0901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(A_     ,	self.big_tokenizer.encode(A_				)				)




						@slow
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     {"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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]], "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, 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], [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]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=A_     ,	model_name="""xlm-roberta-base"""     ,	revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3"""     ,	)




'''simple docstring'''

from __future__ import annotations

import pandas as pd






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	lowerCAmelCase   =     [0] * no_of_processes
	# Copy the burst time into remaining_time[]
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i]

	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	lowerCAmelCase   =     999_999_999
	lowerCAmelCase   =     0
	lowerCAmelCase   =     False

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

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

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

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

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

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

			if waiting_time[short] < 0:
				lowerCAmelCase   =     0

        # Increment time
		increment_time += 1
	return waiting_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						list[int]							)      ->						list[int]:







	"""simple docstring"""
	lowerCAmelCase   =     [0] * no_of_processes
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     burst_time[i] + waiting_time[i]
	return turn_around_time






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						list[int]							,		_SCREAMING_SNAKE_CASE					:						int							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     0
	lowerCAmelCase   =     0
	for i in range(_SCREAMING_SNAKE_CASE							):
		lowerCAmelCase   =     total_waiting_time + waiting_time[i]
		lowerCAmelCase   =     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')
				UpperCAmelCase     		=   int(input())
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   [0] * no_of_processes
				UpperCAmelCase     		=   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))
								UpperCAmelCase		,       UpperCAmelCase     		=   map(int, input().split())

				UpperCAmelCase     		=   calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

				calculate_average_times(waiting_time, turn_around_time, no_of_processes)

				UpperCAmelCase     		=   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)