
'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
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__   :		List[str]											=	get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class  _a					(_lowercase	,       unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE   =	XGLMTokenizer
       SCREAMING_SNAKE_CASE   =	XGLMTokenizerFast
       SCREAMING_SNAKE_CASE   =	True
       SCREAMING_SNAKE_CASE   =	True






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

          # We have a SentencePiece fixture for testing
          _SCREAMING_SNAKE_CASE  			=					XGLMTokenizer(A__							,				keep_accents=A__   )
          tokenizer.save_pretrained(self.tmpdirname   )






       def 							UpperCamelCase					(				self   )     ->      List[str]:
          _SCREAMING_SNAKE_CASE  			=					"""<pad>"""
          _SCREAMING_SNAKE_CASE  			=					1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(A__   )							,				A__   )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(A__   )							,				A__   )






       def 							UpperCamelCase					(				self   )     ->      str:
          _SCREAMING_SNAKE_CASE  			=					list(self.get_tokenizer().get_vocab().keys()   )

          self.assertEqual(vocab_keys[0]							,				"""<s>"""   )
          self.assertEqual(vocab_keys[1]							,				"""<pad>"""   )
          self.assertEqual(len(A__   )							,				10_08   )






       def 							UpperCamelCase					(				self   )     ->      int:
          self.assertEqual(self.get_tokenizer().vocab_size							,				10_08   )






       def 							UpperCamelCase					(				self   )     ->      List[Any]:
          _SCREAMING_SNAKE_CASE  			=					XGLMTokenizer(A__							,				keep_accents=A__   )

          _SCREAMING_SNAKE_CASE  			=					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 [2_85, 46, 10, 1_70, 3_82]]							,				)

          _SCREAMING_SNAKE_CASE  			=					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""",
                  """.""",
              ]							,				)
          _SCREAMING_SNAKE_CASE  			=					tokenizer.convert_tokens_to_ids(A__   )
          self.assertListEqual(
              A__							,				[
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              ]							,				)

          _SCREAMING_SNAKE_CASE  			=					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>""",
                  """.""",
              ]							,				)






       @cached_property
       def 							UpperCamelCase					(				self   )     ->      Any:
          return XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""   )






       def 							UpperCamelCase					(				self   )     ->      Tuple:
          with tempfile.NamedTemporaryFile() as f:
             shutil.copyfile(A__							,				f.name   )
             _SCREAMING_SNAKE_CASE  			=					XGLMTokenizer(f.name							,				keep_accents=A__   )
             _SCREAMING_SNAKE_CASE  			=					pickle.dumps(A__   )
          pickle.loads(A__   )






       def 							UpperCamelCase					(				self   )     ->      Union[str, Any]:
          if not self.test_rust_tokenizer:
             return

          _SCREAMING_SNAKE_CASE  			=					self.get_tokenizer()
          _SCREAMING_SNAKE_CASE  			=					self.get_rust_tokenizer()

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

          _SCREAMING_SNAKE_CASE  			=					tokenizer.tokenize(A__   )
          _SCREAMING_SNAKE_CASE  			=					rust_tokenizer.tokenize(A__   )
          self.assertListEqual(A__							,				A__   )

          _SCREAMING_SNAKE_CASE  			=					tokenizer.encode(A__							,				add_special_tokens=A__   )
          _SCREAMING_SNAKE_CASE  			=					rust_tokenizer.encode(A__							,				add_special_tokens=A__   )
          self.assertListEqual(A__							,				A__   )

          _SCREAMING_SNAKE_CASE  			=					self.get_rust_tokenizer()
          _SCREAMING_SNAKE_CASE  			=					tokenizer.encode(A__   )
          _SCREAMING_SNAKE_CASE  			=					rust_tokenizer.encode(A__   )
          self.assertListEqual(A__							,				A__   )






       @slow
       def 							UpperCamelCase					(				self   )     ->      str:
          _SCREAMING_SNAKE_CASE  			=					"""Hello World!"""
          _SCREAMING_SNAKE_CASE  			=					[2, 3_12_27, 44_47, 35]

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






       @slow
       def 							UpperCamelCase					(				self   )     ->      str:
          _SCREAMING_SNAKE_CASE  			=					(
              """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"""
          )
          # fmt: off
          _SCREAMING_SNAKE_CASE  			=					[2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
          # fmt: on

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






       @slow
       def 							UpperCamelCase					(				self   )     ->      Optional[Any]:
          # fmt: off
          _SCREAMING_SNAKE_CASE  			=					{
              """input_ids""": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
              """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]]
          }  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=A__							,				model_name="""facebook/xglm-564M"""							,				padding=A__							,				)











from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





