



"""simple docstring"""






import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


__snake_case							:   Dict      =    '\\n@INPROCEEDINGS{Papineni02bleu:a,\n    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},\n    booktitle = {},\n    year = {2002},\n    pages = {311--318}\n}\n@inproceedings{lin-och-2004-orange,\n    title = "{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation",\n    author = "Lin, Chin-Yew  and\n      Och, Franz Josef",\n    booktitle = "{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics",\n    month = "aug 23{--}aug 27",\n    year = "2004",\n    address = "Geneva, Switzerland",\n    publisher = "COLING",\n    url = "https://www.aclweb.org/anthology/C04-1072",\n    pages = "501--507",\n}\n'

__snake_case							:   Any      =    '\\nBLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.\nQuality is considered to be the correspondence between a machine\'s output and that of a human: "the closer a machine translation is to a professional human translation,\nthe better it is" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and\nremains one of the most popular automated and inexpensive metrics.\n\nScores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.\nThose scores are then averaged over the whole corpus to reach an estimate of the translation\'s overall quality. Intelligibility or grammatical correctness\nare not taken into account[citation needed].\n\nBLEU\'s output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1\nrepresenting more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the\nreference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional\nreference translations will increase the BLEU score.\n'

__snake_case							:   Dict      =    '\nComputes BLEU score of translated segments against one or more references.\nArgs:\n    predictions: list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    smooth: Whether or not to apply Lin et al. 2004 smoothing.\nReturns:\n    \'bleu\': bleu score,\n    \'precisions\': geometric mean of n-gram precisions,\n    \'brevity_penalty\': brevity penalty,\n    \'length_ratio\': ratio of lengths,\n    \'translation_length\': translation_length,\n    \'reference_length\': reference_length\nExamples:\n\n    >>> predictions = [\n    ...     ["hello", "there", "general", "kenobi"],                             # tokenized prediction of the first sample\n    ...     ["foo", "bar", "foobar"]                                             # tokenized prediction of the second sample\n    ... ]\n    >>> references = [\n    ...     [["hello", "there", "general", "kenobi"], ["hello", "there", "!"]],  # tokenized references for the first sample (2 references)\n    ...     [["foo", "bar", "foobar"]]                                           # tokenized references for the second sample (1 reference)\n    ... ]\n    >>> bleu = datasets.load_metric("bleu")\n    >>> results = bleu.compute(predictions=predictions, references=references)\n    >>> print(results["bleu"])\n    1.0\n'



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




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[str]:

				"""simple docstring"""

				return datasets.MetricInfo(
				    description=_DESCRIPTION       ,			citation=_CITATION       ,			inputs_description=_KWARGS_DESCRIPTION       ,			features=datasets.Features(
				        {
				            "predictions": datasets.Sequence(datasets.Value("string"       ,			id="token")       ,			id="sequence"),
				            "references": datasets.Sequence(
				                datasets.Sequence(datasets.Value("string"       ,			id="token")       ,			id="sequence")       ,			id="references"),
				        })       ,			codebase_urls=["https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"]       ,			reference_urls=[
				        "https://en.wikipedia.org/wiki/BLEU",
				        "https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213",
				    ]       ,			)







	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Dict=4       ,			_SCREAMING_SNAKE_CASE:		str=False)				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       compute_bleu(
				    reference_corpus=_SCREAMING_SNAKE_CASE       ,			translation_corpus=_SCREAMING_SNAKE_CASE       ,			max_order=_SCREAMING_SNAKE_CASE       ,			smooth=_SCREAMING_SNAKE_CASE)
				((__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase)						,	(__lowerCAmelCase))      :  Optional[Any]												=       score
				return {
				    "bleu": bleu,
				    "precisions": precisions,
				    "brevity_penalty": bp,
				    "length_ratio": ratio,
				    "translation_length": translation_length,
				    "reference_length": reference_length,
				}




"""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()