






from __future__ import annotations

import time

import numpy as np

__UpperCAmelCase								=						[8, 5, 9, 7]
__UpperCAmelCase								=						[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__UpperCAmelCase								=						[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]



class   lowerCamelCase      :







		'''simple docstring'''



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		)	->							None:
							UpperCAmelCase_    :      List[str]            =						claim_vector
							UpperCAmelCase_    :      Union[str, Any]            =						allocated_resources_table
							UpperCAmelCase_    :      Any            =						maximum_claim_table



		def 					__UpperCAmelCase			(							self   )	->							list[int]:
							return [
							    sum(p_item[i] for p_item in self.__allocated_resources_table   )
							    for i in range(len(self.__allocated_resources_table[0]   )   )
							]



		def 					__UpperCAmelCase			(							self   )	->							list[int]:
							return np.array(self.__claim_vector   ) - np.array(
							    self.__processes_resource_summation()   )



		def 					__UpperCAmelCase			(							self   )	->							list[list[int]]:
							return [
							    list(np.array(self.__maximum_claim_table[i]   ) - np.array(__UpperCAmelCase   )   )
							    for i, allocated_resource in enumerate(self.__allocated_resources_table   )
							]



		def 					__UpperCAmelCase			(							self   )	->							dict[int, list[int]]:
							return {self.__need().index(__UpperCAmelCase   ): i for i in self.__need()}



		def 					__UpperCAmelCase			(							self				,		**_UpperCamelCase   )	->							None:
							UpperCAmelCase_    :      List[str]            =						self.__need()
							UpperCAmelCase_    :      Any            =						self.__allocated_resources_table
							UpperCAmelCase_    :      Any            =						self.__available_resources()
							UpperCAmelCase_    :      Dict            =						self.__need_index_manager()
							for kw, val in kwargs.items():
												if kw and val is True:
																	self.__pretty_data()
							print('_' * 5_0 + '\n'   )
							while need_list:
												UpperCAmelCase_    :      str            =						False
												for each_need in need_list:
																	UpperCAmelCase_    :      str            =						True
																	for index, need in enumerate(__UpperCAmelCase   ):
																						if need > available_resources[index]:
																											UpperCAmelCase_    :      List[str]            =						False
																											break
																	if execution:
																						UpperCAmelCase_    :      Union[str, 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:
																																UpperCAmelCase_    :      str            =						original_need_index
																						print(f"Process {process_number + 1} is executing."   )
																						# remove the process run from stack
																						need_list.remove(__UpperCAmelCase   )
																						# update available/freed resources stack
																						UpperCAmelCase_    :      int            =						np.array(__UpperCAmelCase   ) + np.array(
																						    alloc_resources_table[process_number]   )
																						print(
																						    'Updated available resource stack for processes: '
																						    + ' '.join([str(__UpperCAmelCase   ) 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 					__UpperCAmelCase			(							self   )	->							Dict:
							print(' ' * 9 + 'Allocated Resource Table'   )
							for item in self.__allocated_resources_table:
												print(
												    f"P{self.__allocated_resources_table.index(__UpperCAmelCase   ) + 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(__UpperCAmelCase   ) + 1}"
												    + ' '.join(f"{it:>8}" for it in item   )
												    + '\n'   )
							print(
							    'Current Usage by Active Processes: '
							    + ' '.join(str(__UpperCAmelCase   ) for x in self.__claim_vector   )   )
							print(
							    'Initial Available Resources:       '
							    + ' '.join(str(__UpperCAmelCase   ) for x in self.__available_resources()   )   )
							time.sleep(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()














import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


UpperCAmelCase__				   =		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				   =		{
    "vocab_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"
        ),
        "google/electra-base-generator": "https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt",
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "google/electra-small-generator": (
            "https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-generator": (
            "https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-generator": (
            "https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"
        ),
        "google/electra-small-discriminator": (
            "https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-base-discriminator": (
            "https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"
        ),
        "google/electra-large-discriminator": (
            "https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": 512,
    "google/electra-base-generator": 512,
    "google/electra-large-generator": 512,
    "google/electra-small-discriminator": 512,
    "google/electra-base-discriminator": 512,
    "google/electra-large-discriminator": 512,
}

UpperCAmelCase__				   =		{
    "google/electra-small-generator": {"do_lower_case": True},
    "google/electra-base-generator": {"do_lower_case": True},
    "google/electra-large-generator": {"do_lower_case": True},
    "google/electra-small-discriminator": {"do_lower_case": True},
    "google/electra-base-discriminator": {"do_lower_case": True},
    "google/electra-large-discriminator": {"do_lower_case": True},
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  VOCAB_FILES_NAMES
      __snake_case       =  PRETRAINED_VOCAB_FILES_MAP
      __snake_case       =  PRETRAINED_INIT_CONFIGURATION
      __snake_case       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __snake_case       =  ElectraTokenizer







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						int=None ,					__UpperCAmelCase      :						str=None ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						str="[UNK]" ,					__UpperCAmelCase      :						Any="[SEP]" ,					__UpperCAmelCase      :						str="[PAD]" ,					__UpperCAmelCase      :						Optional[Any]="[CLS]" ,					__UpperCAmelCase      :						Union[str, Any]="[MASK]" ,					__UpperCAmelCase      :						List[str]=True ,					__UpperCAmelCase      :						Tuple=None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->str:




       """simple docstring"""


       super().__init__(
           __UpperCAmelCase ,					tokenizer_file=__UpperCAmelCase ,					do_lower_case=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					sep_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					cls_token=__UpperCAmelCase ,					mask_token=__UpperCAmelCase ,					tokenize_chinese_chars=__UpperCAmelCase ,					strip_accents=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

       a		  =      json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
       if (
           normalizer_state.get('''lowercase''' ,					__UpperCAmelCase  ) != do_lower_case
           or normalizer_state.get('''strip_accents''' ,					__UpperCAmelCase  ) != strip_accents
           or normalizer_state.get('''handle_chinese_chars''' ,					__UpperCAmelCase  ) != tokenize_chinese_chars
       ):
        a		  =      getattr(__UpperCAmelCase ,					normalizer_state.pop('''type'''  )  )
        a		  =      do_lower_case
        a		  =      strip_accents
        a		  =      tokenize_chinese_chars
        a		  =      normalizer_class(**__UpperCAmelCase  )

       a		  =      do_lower_case







      def       __lowerCAmelCase					(      self      :						List[Any] ,					__UpperCAmelCase      :						Optional[int] ,					__UpperCAmelCase      :						Tuple=None  )    ->str:




       """simple docstring"""


       a		  =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

       return output







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						List[int] ,					__UpperCAmelCase      :						Optional[List[int]] = None  )    ->List[int]:




       """simple docstring"""


       a		  =      [self.sep_token_id]
       a		  =      [self.cls_token_id]
       if token_ids_a is None:
        return len(cls + token_ids_a + sep  ) * [0]
       return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]







      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[str] = None  )    ->Tuple[str]:




       """simple docstring"""


       a		  =      self._tokenizer.model.save(__UpperCAmelCase ,					name=__UpperCAmelCase  )
       return tuple(__UpperCAmelCase  )


