



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case							:   Optional[int]      =    {
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   Any      =    ['BertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   Any      =    [
	    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BertForMaskedLM',
	    'BertForMultipleChoice',
	    'BertForNextSentencePrediction',
	    'BertForPreTraining',
	    'BertForQuestionAnswering',
	    'BertForSequenceClassification',
	    'BertForTokenClassification',
	    'BertLayer',
	    'BertLMHeadModel',
	    'BertModel',
	    'BertPreTrainedModel',
	    'load_tf_weights_in_bert',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   Optional[int]      =    [
	    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFBertEmbeddings',
	    'TFBertForMaskedLM',
	    'TFBertForMultipleChoice',
	    'TFBertForNextSentencePrediction',
	    'TFBertForPreTraining',
	    'TFBertForQuestionAnswering',
	    'TFBertForSequenceClassification',
	    'TFBertForTokenClassification',
	    'TFBertLMHeadModel',
	    'TFBertMainLayer',
	    'TFBertModel',
	    'TFBertPreTrainedModel',
	]
try:
	if not is_tensorflow_text_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   Optional[int]      =    ['TFBertTokenizer']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   Dict      =    [
	    'FlaxBertForCausalLM',
	    'FlaxBertForMaskedLM',
	    'FlaxBertForMultipleChoice',
	    'FlaxBertForNextSentencePrediction',
	    'FlaxBertForPreTraining',
	    'FlaxBertForQuestionAnswering',
	    'FlaxBertForSequenceClassification',
	    'FlaxBertForTokenClassification',
	    'FlaxBertModel',
	    'FlaxBertPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
	from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bert_fast import BertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bert import (
		    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BertForMaskedLM,
		    BertForMultipleChoice,
		    BertForNextSentencePrediction,
		    BertForPreTraining,
		    BertForQuestionAnswering,
		    BertForSequenceClassification,
		    BertForTokenClassification,
		    BertLayer,
		    BertLMHeadModel,
		    BertModel,
		    BertPreTrainedModel,
		    load_tf_weights_in_bert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_bert import (
		    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBertEmbeddings,
		    TFBertForMaskedLM,
		    TFBertForMultipleChoice,
		    TFBertForNextSentencePrediction,
		    TFBertForPreTraining,
		    TFBertForQuestionAnswering,
		    TFBertForSequenceClassification,
		    TFBertForTokenClassification,
		    TFBertLMHeadModel,
		    TFBertMainLayer,
		    TFBertModel,
		    TFBertPreTrainedModel,
		)

	try:
		if not is_tensorflow_text_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bert_tf import TFBertTokenizer

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_bert import (
		    FlaxBertForCausalLM,
		    FlaxBertForMaskedLM,
		    FlaxBertForMultipleChoice,
		    FlaxBertForNextSentencePrediction,
		    FlaxBertForPreTraining,
		    FlaxBertForQuestionAnswering,
		    FlaxBertForSequenceClassification,
		    FlaxBertForTokenClassification,
		    FlaxBertModel,
		    FlaxBertPreTrainedModel,
		)

else:
	import sys

	__snake_case							:   Optional[int]      =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






from __future__ import annotations

import time

import numpy as np

__snake_case							:   Optional[Any]      =    [8, 5, 9, 7]
__snake_case							:   List[Any]      =    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__snake_case							:   Optional[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:		Any       ,			_SCREAMING_SNAKE_CASE:		list[int]       ,			_SCREAMING_SNAKE_CASE:		list[list[int]]       ,			_SCREAMING_SNAKE_CASE:		list[list[int]]       ,			)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       claim_vector
				__lowerCAmelCase      :  Tuple												=       allocated_resources_table
				__lowerCAmelCase      :  Tuple												=       maximum_claim_table

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       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 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       list[int]:

				"""simple docstring"""

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

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

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       dict[int, list[int]]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.__need()
				__lowerCAmelCase      :  int												=       self.__allocated_resources_table
				__lowerCAmelCase      :  Dict												=       self.__available_resources()
				__lowerCAmelCase      :  str												=       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:
							__lowerCAmelCase      :  int												=       False
							for each_need in need_list:
										__lowerCAmelCase      :  Dict												=       True
										for index, need in enumerate(_SCREAMING_SNAKE_CASE):
													if need > available_resources[index]:
																__lowerCAmelCase      :  Dict												=       False
																break
										if execution:
													__lowerCAmelCase      :  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:
																			__lowerCAmelCase      :  Union[str, Any]												=       original_need_index
													print(F"""Process {process_number + 1} is executing.""")
													# remove the process run from stack
													need_list.remove(_SCREAMING_SNAKE_CASE)
													# update available/freed resources stack
													__lowerCAmelCase      :  Dict												=       np.array(_SCREAMING_SNAKE_CASE) + np.array(
													    alloc_resources_table[process_number])
													print(
													    "Updated available resource stack for processes: "
													    + " ".join([str(_SCREAMING_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 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       List[Any]:

				"""simple docstring"""

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()