



'''simple docstring'''
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   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			unittest.TestCase					):
	"""simple docstring"""


	A__    :    Dict						=				BartTokenizer
	A__    :    List[Any]						=				BartTokenizerFast
	A__    :    str						=				True
	A__    :    Optional[Any]						=				filter_roberta_detectors


	def      a__ (		self			) ->							int:
				super().setUp()
				A:      Tuple														=   [
				    """l""",
				    """o""",
				    """w""",
				    """e""",
				    """r""",
				    """s""",
				    """t""",
				    """i""",
				    """d""",
				    """n""",
				    """\u0120""",
				    """\u0120l""",
				    """\u0120n""",
				    """\u0120lo""",
				    """\u0120low""",
				    """er""",
				    """\u0120lowest""",
				    """\u0120newer""",
				    """\u0120wider""",
				    """<unk>""",
				]
				A:      Tuple														=   dict(zip(A					,   range(len(A			)			)			)			)
				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[str]														=   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      a__ (		self					,   **A			) ->							Any:
				kwargs.update(self.special_tokens_map			)
				return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **A			)


	def      a__ (		self					,   **A			) ->							Tuple:
				kwargs.update(self.special_tokens_map			)
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,   **A			)


	def      a__ (		self					,   A			) ->							List[Any]:
				return "lower newer", "lower newer"


	@cached_property
	def      a__ (		self			) ->							List[Any]:
				return BartTokenizer.from_pretrained("""facebook/bart-large"""			)


	@cached_property
	def      a__ (		self			) ->							Dict:
				return BartTokenizerFast.from_pretrained("""facebook/bart-large"""			)


	@require_torch
	def      a__ (		self			) ->							int:
				A:      str														=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
				A:      Optional[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:      Tuple														=   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			)
							A:      Union[str, Any]														=   batch.input_ids.tolist()[0]
							self.assertListEqual(A					,   A			)
							# Test that special tokens are reset


	@require_torch
	def      a__ (		self			) ->							Any:
				A:      List[str]														=   ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							A:      List[str]														=   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      a__ (		self			) ->							List[Any]:
				A:      str														=   [
				    """Summary of the text.""",
				    """Another summary.""",
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							A:      int														=   tokenizer(text_target=A					,   max_length=32					,   padding="""max_length"""					,   return_tensors="""pt"""			)
							self.assertEqual(32					,   targets["""input_ids"""].shape[1]			)


	@require_torch
	def      a__ (		self			) ->							Tuple:
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							A:      Optional[Any]														=   tokenizer(
							    ["""I am a small frog""" * 10_24, """I am a small frog"""]					,   padding=A					,   truncation=A					,   return_tensors="""pt"""			)
							self.assertIsInstance(A					,   A			)
							self.assertEqual(batch.input_ids.shape					,   (2, 10_24)			)


	@require_torch
	def      a__ (		self			) ->							Tuple:
				A:      List[str]														=   ["""A long paragraph for summarization."""]
				A:      Any														=   [
				    """Summary of the text.""",
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							A:      Dict														=   tokenizer(A					,   return_tensors="""pt"""			)
							A:      List[Any]														=   tokenizer(text_target=A					,   return_tensors="""pt"""			)
							A:      int														=   inputs["""input_ids"""]
							A:      Optional[int]														=   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      a__ (		self			) ->							Optional[Any]:
				pass





	def      a__ (		self			) ->							List[str]:
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'			):
										A:      Any														=   self.rust_tokenizer_class.from_pretrained(A					,   **A			)
										A:      Optional[Any]														=   self.tokenizer_class.from_pretrained(A					,   **A			)
										A:      Optional[int]														=   """A, <mask> AllenNLP sentence."""
										A:      Optional[Any]														=   tokenizer_r.encode_plus(A					,   add_special_tokens=A					,   return_token_type_ids=A			)
										A:      Any														=   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"""]			)					,   )

										A:      Optional[Any]														=   tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]			)
										A:      List[Any]														=   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(
										    A					,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)
										self.assertSequenceEqual(
										    A					,   ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]			)










'''simple docstring'''
from __future__ import annotations



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			A:      int														=   [0] * len(lowerCamelCase__				)
			for i in range(1							,							len(lowerCamelCase__				)				):
						A:      Dict														=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]							,							lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(lowerCamelCase__				)
			]



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)


if __name__ == "__main__":
					# process id's
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =[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
					__SCREAMING_SNAKE_CASE :      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
					__SCREAMING_SNAKE_CASE :      Tuple             =calculate_waiting_times(duration_times)
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_turnaround_times(duration_times, waiting_times)

					# get the average times
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_average_waiting_time(waiting_times)
					__SCREAMING_SNAKE_CASE :      Union[str, Any]             =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}""")





