




from __future__ import annotations

import time

import numpy as np

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




class 					snake_case__							:
				"""simple docstring"""



				def __init__(   self       :  Any      ,   __lowerCamelCase       :  list[int]      ,   __lowerCamelCase       :  list[list[int]]      ,   __lowerCamelCase       :  list[list[int]]      ,   )      ->  None:
							a					=    claim_vector
							a					=    allocated_resources_table
							a					=    maximum_claim_table





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





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





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





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


if __name__ == "__main__":
		import doctest

		doctest.testmod()












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_big_bird import BigBirdTokenizer
else:
		__lowerCAmelCase	:       Optional[int]     =	None

__lowerCAmelCase	:       Optional[Any]     =	logging.get_logger(__name__)
__lowerCAmelCase	:       Optional[int]     =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	:       List[Any]     =	{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

__lowerCAmelCase	:       List[str]     =	{
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}


__lowerCAmelCase	:       Any     =	'▁'




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Any											=      VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_ :							Dict											=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_ :							str											=      BigBirdTokenizer
				SCREAMING_SNAKE_CASE_ :							str											=      ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_ :							List[int]											=      []





				def __init__(   self       :  int      ,   __lowerCamelCase       :  Any=None      ,   __lowerCamelCase       :  List[str]=None      ,   __lowerCamelCase       :  Optional[int]="<unk>"      ,   __lowerCamelCase       :  int="<s>"      ,   __lowerCamelCase       :  Optional[Any]="</s>"      ,   __lowerCamelCase       :  Tuple="<pad>"      ,   __lowerCamelCase       :  Tuple="[SEP]"      ,   __lowerCamelCase       :  Dict="[MASK]"      ,   __lowerCamelCase       :  Tuple="[CLS]"      ,   **__lowerCamelCase       :  Optional[Any]      ,   )      ->  List[Any]:
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else bos_token
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else eos_token
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else unk_token
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else pad_token
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else cls_token
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else sep_token

							# Mask token behave like a normal word, i.e. include the space before it
							a					=    AddedToken(__lowerCamelCase      ,   lstrip=__lowerCamelCase      ,   rstrip=__lowerCamelCase							) if isinstance(__lowerCamelCase      ,   __lowerCamelCase							) else mask_token

							super().__init__(
							    __lowerCamelCase      ,   tokenizer_file=__lowerCamelCase      ,   bos_token=__lowerCamelCase      ,   eos_token=__lowerCamelCase      ,   unk_token=__lowerCamelCase      ,   sep_token=__lowerCamelCase      ,   pad_token=__lowerCamelCase      ,   cls_token=__lowerCamelCase      ,   mask_token=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

							a					=    vocab_file
							a					=    False if not self.vocab_file else True





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  List[int]:
							a					=    [self.sep_token_id]
							a					=    [self.cls_token_id]
							if token_ids_a is None:
										return cls + token_ids_a + sep
							return cls + token_ids_a + sep + token_ids_a + sep





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None      ,   __lowerCamelCase       :  bool = False							)      ->  List[int]:

							if already_has_special_tokens:
										if token_ids_a is not None:
													raise ValueError(
													    "You should not supply a second sequence if the provided sequence of "
													    "ids is already formatted with special tokens for the model."							)
										return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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





				def 							__UpperCAmelCase							(   self       :  Optional[int]      ,   __lowerCamelCase       :  List[int]      ,   __lowerCamelCase       :  Optional[List[int]] = None							)      ->  List[int]:
							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 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[str] = None							)      ->  Tuple[str]:
							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(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a					=    os.path.join(
							    __lowerCamelCase      ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

							return (out_vocab_file,)






