



"""simple docstring"""






from sklearn.metrics import fa_score

import datasets


__snake_case							:   Tuple      =    '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


__snake_case							:   int      =    '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `\'binary\'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `\'binary\'`.\n\n        - \'binary\': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'weighted\': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `\'macro\'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


__snake_case							:   Dict      =    '\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n'



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,		_KWARGS_DESCRIPTION							)
class 			A__   (  datasets.Metric							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       int:

				"""simple docstring"""

				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,			citation=_CITATION       ,			inputs_description=_KWARGS_DESCRIPTION       ,			features=datasets.Features(
				        {
				            "predictions": datasets.Sequence(datasets.Value("int32")),
				            "references": datasets.Sequence(datasets.Value("int32")),
				        }
				        if self.config_name == "multilabel"
				        else {
				            "predictions": datasets.Value("int32"),
				            "references": datasets.Value("int32"),
				        })       ,			reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,			)







	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		List[Any]=None       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=1       ,			_SCREAMING_SNAKE_CASE:		Optional[int]="binary"       ,			_SCREAMING_SNAKE_CASE:		List[Any]=None)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       fa_score(
				    _SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE       ,			labels=_SCREAMING_SNAKE_CASE       ,			pos_label=_SCREAMING_SNAKE_CASE       ,			average=_SCREAMING_SNAKE_CASE       ,			sample_weight=_SCREAMING_SNAKE_CASE)
				return {"f1": float(_SCREAMING_SNAKE_CASE) if score.size == 1 else score}




"""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()