

"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_xlnet import XLNetTokenizer
else:
     a					:List[Any]    = None


a					:Optional[int]    = logging.get_logger(__name__)

a					:Union[str, Any]    = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a					:Optional[int]    = {
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
    },
}

a					:Dict    = {
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

a					:int    = "▁"

# Segments (not really needed)
a					:Dict    = 0
a					:Optional[int]    = 1
a					:Tuple    = 2
a					:List[str]    = 3
a					:Optional[Any]    = 4

class      __a							(UpperCamelCase_):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Tuple       = VOCAB_FILES_NAMES
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = PRETRAINED_VOCAB_FILES_MAP
     _SCREAMING_SNAKE_CASE						:Tuple       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _SCREAMING_SNAKE_CASE						:str       = """left"""
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = XLNetTokenizer

     def __init__(						self	,    _a=None	,    _a=None	,    _a=False	,    _a=True	,    _a=False	,    _a="<s>"	,    _a="</s>"	,    _a="<unk>"	,    _a="<sep>"	,    _a="<pad>"	,    _a="<cls>"	,    _a="<mask>"	,    _a=["<eop>", "<eod>"]	,    **_a	,    )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AddedToken(_a	,    lstrip=_a	,    rstrip=_a  ) if isinstance(_a	,    _a  ) else mask_token

           super().__init__(
               vocab_file=_a	,    tokenizer_file=_a	,    do_lower_case=_a	,    remove_space=_a	,    keep_accents=_a	,    bos_token=_a	,    eos_token=_a	,    unk_token=_a	,    sep_token=_a	,    pad_token=_a	,    cls_token=_a	,    mask_token=_a	,    additional_special_tokens=_a	,    **_a	,    )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	3
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	do_lower_case
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	remove_space
           SCREAMING_SNAKE_CASE__   :      int      		=	keep_accents
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	vocab_file
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	False if not self.vocab_file else True

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	[self.cls_token_id]
           if token_ids_a is None:
                 return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls

     def 					_a			(						self	,    _a	,    _a = None  )  ->      List[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	[self.sep_token_id]
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	[2]

           if token_ids_a is None:
                 return len(token_ids_a + sep  ) * [0] + cls_segment_id
           return len(token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1] + cls_segment_id





     def 					_a			(						self	,    _a	,    _a = None  )  ->      Tuple[str]:


           """simple docstring"""


           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                     """tokenizer."""  )

           if not os.path.isdir(_a  ):
                 logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                 return
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	os.path.join(
               _a	,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )

           if os.path.abspath(self.vocab_file  ) != os.path.abspath(_a  ):
                 copyfile(self.vocab_file	,    _a  )

           return (out_vocab_file,)







"""simple docstring"""


from __future__ import annotations

import time

import numpy as np

a					:Optional[Any]    = [8, 5, 9, 7]
a					:List[Any]    = [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
a					:int    = [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a	,    )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	claim_vector
           SCREAMING_SNAKE_CASE__   :      Any      		=	allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Any      		=	maximum_claim_table

     def 					_a			(						self  )  ->      list[int]:


           """simple docstring"""


           return [
               sum(p_item[i] for p_item in self.__allocated_resources_table  )
               for i in range(len(self.__allocated_resources_table[0]  )  )
           ]

     def 					_a			(						self  )  ->      list[int]:


           """simple docstring"""


           return np.array(self.__claim_vector  ) - np.array(
               self.__processes_resource_summation()  )

     def 					_a			(						self  )  ->      list[list[int]]:


           """simple docstring"""


           return [
               list(np.array(self.__maximum_claim_table[i]  ) - np.array(_a  )  )
               for i, allocated_resource in enumerate(self.__allocated_resources_table  )
           ]

     def 					_a			(						self  )  ->      dict[int, list[int]]:


           """simple docstring"""


           return {self.__need().index(_a  ): i for i in self.__need()}

     def 					_a			(						self	,    **_a  )  ->      None:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.__need()
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.__allocated_resources_table
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.__available_resources()
           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.__need_index_manager()
           for kw, val in kwargs.items():
                 if kw and val is True:
                       self.__pretty_data()
           print("""_""" * 50 + """\n"""  )
           while need_list:
                 SCREAMING_SNAKE_CASE__   :      List[str]      		=	False
                 for each_need in need_list:
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	True
                       for index, need in enumerate(_a  ):
                             if need > available_resources[index]:
                                   SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	False
                                   break
                       if execution:
                             SCREAMING_SNAKE_CASE__   :      Any      		=	True
                             # get the original index of the process from ind_ctrl db
                             for original_need_index, need_clone in need_index_manager.items():
                                   if each_need == need_clone:
                                         SCREAMING_SNAKE_CASE__   :      Tuple      		=	original_need_index
                             print(f'''Process {process_number + 1} is executing.'''  )
                             # remove the process run from stack
                             need_list.remove(_a  )
                             # update available/freed resources stack
                             SCREAMING_SNAKE_CASE__   :      Dict      		=	np.array(_a  ) + np.array(
                                 alloc_resources_table[process_number]  )
                             print(
                                 """Updated available resource stack for processes: """
                                 + """ """.join([str(_a  ) for x in available_resources]  )  )
                             break
                 if safe:
                       print("""The process is in a safe state.\n"""  )
                 else:
                       print("""System in unsafe state. Aborting...\n"""  )
                       break





     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           print(""" """ * 9 + """Allocated Resource Table"""  )
           for item in self.__allocated_resources_table:
                 print(
                     f'''P{self.__allocated_resources_table.index(_a  ) + 1}'''
                     + """ """.join(f'''{it:>8}''' for it in item  )
                     + """\n"""  )
           print(""" """ * 9 + """System Resource Table"""  )
           for item in self.__maximum_claim_table:
                 print(
                     f'''P{self.__maximum_claim_table.index(_a  ) + 1}'''
                     + """ """.join(f'''{it:>8}''' for it in item  )
                     + """\n"""  )
           print(
               """Current Usage by Active Processes: """
               + """ """.join(str(_a  ) for x in self.__claim_vector  )  )
           print(
               """Initial Available Resources:       """
               + """ """.join(str(_a  ) for x in self.__available_resources()  )  )
           time.sleep(1  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()




