



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


UpperCamelCase__:     List[str]										=    logging.get_logger(__name__)

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

UpperCamelCase__:     Dict										=    {
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__:     List[Any]										=    {
    "bert-base-uncased": 512,
    "bert-large-uncased": 512,
    "bert-base-cased": 512,
    "bert-large-cased": 512,
    "bert-base-multilingual-uncased": 512,
    "bert-base-multilingual-cased": 512,
    "bert-base-chinese": 512,
    "bert-base-german-cased": 512,
    "bert-large-uncased-whole-word-masking": 512,
    "bert-large-cased-whole-word-masking": 512,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 512,
    "bert-large-cased-whole-word-masking-finetuned-squad": 512,
    "bert-base-cased-finetuned-mrpc": 512,
    "bert-base-german-dbmdz-cased": 512,
    "bert-base-german-dbmdz-uncased": 512,
    "TurkuNLP/bert-base-finnish-cased-v1": 512,
    "TurkuNLP/bert-base-finnish-uncased-v1": 512,
    "wietsedv/bert-base-dutch-cased": 512,
}

UpperCamelCase__:     int										=    {
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_INIT_CONFIGURATION
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= BertTokenizer







	def __init__(		self    :  Any	,    __snake_case    :  Tuple=None	,    __snake_case    :  int=None	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  Optional[Any]="[UNK]"	,    __snake_case    :  Tuple="[SEP]"	,    __snake_case    :  Union[str, Any]="[PAD]"	,    __snake_case    :  str="[CLS]"	,    __snake_case    :  Dict="[MASK]"	,    __snake_case    :  str=True	,    __snake_case    :  Tuple=None	,    **__snake_case    :  Optional[Any]	,    )    ->	str:
								super().__init__(
								    __snake_case	,    tokenizer_file=__snake_case	,    do_lower_case=__snake_case	,    unk_token=__snake_case	,    sep_token=__snake_case	,    pad_token=__snake_case	,    cls_token=__snake_case	,    mask_token=__snake_case	,    tokenize_chinese_chars=__snake_case	,    strip_accents=__snake_case	,    **__snake_case	,    )

								UpperCAmelCase				: int											=	json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    normalizer_state.get('''lowercase'''	,    __snake_case	) != do_lower_case
								    or normalizer_state.get('''strip_accents'''	,    __snake_case	) != strip_accents
								    or normalizer_state.get('''handle_chinese_chars'''	,    __snake_case	) != tokenize_chinese_chars
								):
															UpperCAmelCase				: Optional[Any]											=	getattr(__snake_case	,    normalizer_state.pop('''type'''	)	)
															UpperCAmelCase				: Union[str, Any]											=	do_lower_case
															UpperCAmelCase				: str											=	strip_accents
															UpperCAmelCase				: List[Any]											=	tokenize_chinese_chars
															UpperCAmelCase				: Dict											=	normalizer_class(**__snake_case	)

								UpperCAmelCase				: str											=	do_lower_case







	def   A			(		self    :  List[str]	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[Any]=None	)    ->	int:
								UpperCAmelCase				: int											=	[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   A			(		self    :  str	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	)    ->	List[int]:
								UpperCAmelCase				: Dict											=	[self.sep_token_id]
								UpperCAmelCase				: Optional[Any]											=	[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   A			(		self    :  int	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	Tuple[str]:
								UpperCAmelCase				: Union[str, Any]											=	self._tokenizer.model.save(__snake_case	,    name=__snake_case	)
								return tuple(__snake_case	)










'''simple docstring'''






from __future__ import annotations

import time

import numpy as np

UpperCamelCase__:     Tuple										=    [8, 5, 9, 7]
UpperCamelCase__:     int										=    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
UpperCamelCase__:     List[str]										=    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  str	,    __snake_case    :  list[int]	,    __snake_case    :  list[list[int]]	,    __snake_case    :  list[list[int]]	,    )    ->	None:
								UpperCAmelCase				: Union[str, Any]											=	claim_vector
								UpperCAmelCase				: Tuple											=	allocated_resources_table
								UpperCAmelCase				: List[str]											=	maximum_claim_table







	def   A			(		self    :  Optional[Any]	)    ->	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   A			(		self    :  Union[str, Any]	)    ->	list[int]:
								return np.array(self.__claim_vector	) - np.array(
								    self.__processes_resource_summation()	)







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







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







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


if __name__ == "__main__":
						import doctest

						doctest.testmod()





