code: 
  
  
  import json
  import os
  import unittest
  
  from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
  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 			a      (      UpperCamelCase_      ,unittest.TestCase   ):
  		__lowercase													=		BartTokenizer
  		__lowercase													=		BartTokenizerFast
  		__lowercase													=		True
  		__lowercase													=		filter_roberta_detectors
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						super().setUp()
  						A__     :							Optional[Any]   				=[
  						    '''l''',
  						    '''o''',
  						    '''w''',
  						    '''e''',
  						    '''r''',
  						    '''s''',
  						    '''t''',
  						    '''i''',
  						    '''d''',
  						    '''n''',
  						    '''\u0120''',
  						    '''\u0120l''',
  						    '''\u0120n''',
  						    '''\u0120lo''',
  						    '''\u0120low''',
  						    '''er''',
  						    '''\u0120lowest''',
  						    '''\u0120newer''',
  						    '''\u0120wider''',
  						    '''<unk>''',
  						]
  						A__     :							int   				=dict(zip(__UpperCamelCase	,     range(len(__UpperCamelCase							)							)							)							)
  						A__     :							Optional[int]   				=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
  						A__     :							List[Any]   				={'''unk_token''': '''<unk>'''}
  
  						A__     :							Union[str, Any]   				=os.path.join(self.tmpdirname	,     VOCAB_FILES_NAMES['''vocab_file''']							)
  						A__     :							List[Any]   				=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(__UpperCamelCase							) + '''\n'''							)
  						with open(self.merges_file	,     '''w'''	,     encoding='''utf-8'''							) as fp:
  										fp.write('''\n'''.join(__UpperCamelCase							)							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     **__UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						kwargs.update(self.special_tokens_map							)
  						return self.tokenizer_class.from_pretrained(self.tmpdirname	,     **__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     **__UpperCamelCase							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						kwargs.update(self.special_tokens_map							)
  						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,     **__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Any:
  						'''simple docstring'''
  
  
  						return "lower newer", "lower newer"
  
  
  
  
  
  
  		@cached_property
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						return BartTokenizer.from_pretrained('''facebook/bart-large'''							)
  
  
  
  
  
  
  		@cached_property
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						return BartTokenizerFast.from_pretrained('''facebook/bart-large'''							)
  
  
  
  
  
  
  		@require_torch
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  						A__     :							Union[str, Any]   				=[0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
  
  						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  										A__     :							Union[str, Any]   				=tokenizer(__UpperCamelCase	,     max_length=len(__UpperCamelCase							)	,     padding=__UpperCamelCase	,     return_tensors='''pt'''							)
  										self.assertIsInstance(__UpperCamelCase	,     __UpperCamelCase							)
  
  										self.assertEqual((2, 9)	,     batch.input_ids.shape							)
  										self.assertEqual((2, 9)	,     batch.attention_mask.shape							)
  										A__     :							Optional[Any]   				=batch.input_ids.tolist()[0]
  										self.assertListEqual(__UpperCamelCase	,     __UpperCamelCase							)
  										# Test that special tokens are reset
  
  
  
  
  
  
  		@require_torch
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						A__     :							Optional[Any]   				=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  										A__     :							Tuple   				=tokenizer(__UpperCamelCase	,     padding=__UpperCamelCase	,     return_tensors='''pt'''							)
  										# check if input_ids are returned and no labels
  										self.assertIn('''input_ids'''	,     __UpperCamelCase							)
  										self.assertIn('''attention_mask'''	,     __UpperCamelCase							)
  										self.assertNotIn('''labels'''	,     __UpperCamelCase							)
  										self.assertNotIn('''decoder_attention_mask'''	,     __UpperCamelCase							)
  
  
  
  
  
  
  		@require_torch
  		def       lowerCAmelCase_  (  self							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, Any]   				=[
  						    '''Summary of the text.''',
  						    '''Another summary.''',
  						]
  						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  										A__     :							int   				=tokenizer(text_target=__UpperCamelCase	,     max_length=32	,     padding='''max_length'''	,     return_tensors='''pt'''							)
  										self.assertEqual(32	,     targets['''input_ids'''].shape[1]							)
  
  
  
  
  
  
  		@require_torch
  		def       lowerCAmelCase_  (  self							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  										A__     :							Any   				=tokenizer(
  										    ['''I am a small frog''' * 10_24, '''I am a small frog''']	,     padding=__UpperCamelCase	,     truncation=__UpperCamelCase	,     return_tensors='''pt'''							)
  										self.assertIsInstance(__UpperCamelCase	,     __UpperCamelCase							)
  										self.assertEqual(batch.input_ids.shape	,     (2, 10_24)							)
  
  
  
  
  
  
  		@require_torch
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=['''A long paragraph for summarization.''']
  						A__     :							Dict   				=[
  						    '''Summary of the text.''',
  						]
  						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
  										A__     :							Tuple   				=tokenizer(__UpperCamelCase	,     return_tensors='''pt'''							)
  										A__     :							Any   				=tokenizer(text_target=__UpperCamelCase	,     return_tensors='''pt'''							)
  										A__     :							Tuple   				=inputs['''input_ids''']
  										A__     :							str   				=targets['''input_ids''']
  										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       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						pass
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
  										with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'							):
  														A__     :							str   				=self.rust_tokenizer_class.from_pretrained(__UpperCamelCase	,     **__UpperCamelCase							)
  														A__     :							Optional[Any]   				=self.tokenizer_class.from_pretrained(__UpperCamelCase	,     **__UpperCamelCase							)
  														A__     :							int   				='''A, <mask> AllenNLP sentence.'''
  														A__     :							Union[str, Any]   				=tokenizer_r.encode_plus(__UpperCamelCase	,     add_special_tokens=__UpperCamelCase	,     return_token_type_ids=__UpperCamelCase							)
  														A__     :							List[str]   				=tokenizer_p.encode_plus(__UpperCamelCase	,     add_special_tokens=__UpperCamelCase	,     return_token_type_ids=__UpperCamelCase							)
  
  														# 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''']							)	,     )
  
  														A__     :							Optional[Any]   				=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
  														A__     :							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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
  														self.assertSequenceEqual(tokens_r['''input_ids''']	,     [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]							)
  
  														self.assertSequenceEqual(
  														    __UpperCamelCase	,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
  														self.assertSequenceEqual(
  														    __UpperCamelCase	,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']							)
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							list[int]:
  				A__     :							Optional[int]   				=[0] * len(snake_case_	)
  				for i in range(1,	len(snake_case_	)	):
  								A__     :							List[Any]   				=duration_times[i - 1] + waiting_times[i - 1]
  				return waiting_times
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							list[int]:
  				return [
  				    duration_time + waiting_times[i]
  				    for i, duration_time in enumerate(snake_case_	)
  				]
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							float:
  				return sum(snake_case_	) / len(snake_case_	)
  
  
  if __name__ == "__main__":
  						# process id's
  						__lowerCamelCase       :       List[str]				   =			[1, 2, 3]
  
  						# ensure that we actually have processes
  						if len(processes) == 0:
  												print("Zero amount of processes")
  												raise SystemExit(0)
  
  						# duration time of all processes
  						__lowerCamelCase       :       Dict				   =			[19, 8, 9]
  
  						# ensure we can match each id to a duration time
  						if len(duration_times) != len(processes):
  												print("Unable to match all id's with their duration time")
  												raise SystemExit(0)
  
  						# get the waiting times and the turnaround times
  						__lowerCamelCase       :       int				   =			calculate_waiting_times(duration_times)
  						__lowerCamelCase       :       Optional[int]				   =			calculate_turnaround_times(duration_times, waiting_times)
  
  						# get the average times
  						__lowerCamelCase       :       List[str]				   =			calculate_average_waiting_time(waiting_times)
  						__lowerCamelCase       :       Dict				   =			calculate_average_turnaround_time(turnaround_times)
  
  						# print all the results
  						print("Process ID\tDuration Time\tWaiting Time\tTurnaround Time")
  						for i, process in enumerate(processes):
  												print(
  												    F"{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"
  												    F"{turnaround_times[i]}"
  												)
  						print(F"Average waiting time = {average_waiting_time}")
  						print(F"Average turn around time = {average_turnaround_time}")
  
  
  
  