






"""simple docstring"""

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowercase    			=       version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
			from nltk import word_tokenize


lowercase    			=       '''\
@inproceedings{banarjee2005,
  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},
  author    = {Banerjee, Satanjeev  and Lavie, Alon},
  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},
  month     = jun,
  year      = {2005},
  address   = {Ann Arbor, Michigan},
  publisher = {Association for Computational Linguistics},
  url       = {https://www.aclweb.org/anthology/W05-0909},
  pages     = {65--72},
}
'''

lowercase    			=       '''\
METEOR, an automatic metric for machine translation evaluation
that is based on a generalized concept of unigram matching between the
machine-produced translation and human-produced reference translations.
Unigrams can be matched based on their surface forms, stemmed forms,
and meanings; furthermore, METEOR can be easily extended to include more
advanced matching strategies. Once all generalized unigram matches
between the two strings have been found, METEOR computes a score for
this matching using a combination of unigram-precision, unigram-recall, and
a measure of fragmentation that is designed to directly capture how
well-ordered the matched words in the machine translation are in relation
to the reference.

METEOR gets an R correlation value of 0.347 with human evaluation on the Arabic
data and 0.331 on the Chinese data. This is shown to be an improvement on
using simply unigram-precision, unigram-recall and their harmonic F1
combination.
'''

lowercase    			=       '''
Computes METEOR score of translated segments against one or more references.
Args:
    predictions: list of predictions to score. Each prediction
        should be a string with tokens separated by spaces.
    references: list of reference for each prediction. Each
        reference should be a string with tokens separated by spaces.
    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9
    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3
    gamma: Relative weight assigned to fragmentation penalty. default: 0.5
Returns:
    \'meteor\': meteor score.
Examples:

    >>> meteor = datasets.load_metric(\'meteor\')
    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]
    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]
    >>> results = meteor.compute(predictions=predictions, references=references)
    >>> print(round(results["meteor"], 4))
    0.6944
'''

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







				def     _snake_case					(							self							:						List[str]	)				->					Dict:
										return datasets.MetricInfo(
										    description=_DESCRIPTION						,    citation=_CITATION						,    inputs_description=_KWARGS_DESCRIPTION						,    features=datasets.Features(
										        {
										            "predictions": datasets.Value("string"						,    id="sequence"	),
										            "references": datasets.Value("string"						,    id="sequence"	),
										        }	)						,    codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"]						,    reference_urls=[
										        "https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
										        "https://en.wikipedia.org/wiki/METEOR",
										    ]						,    )







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Optional[int]	)				->					Dict:
										import nltk

										nltk.download("wordnet"	)
										if NLTK_VERSION >= version.Version("3.6.5"	):
																nltk.download("punkt"	)
										if NLTK_VERSION >= version.Version("3.6.6"	):
																nltk.download("omw-1.4"	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict=0.9						,    __lowerCamelCase							:						List[str]=3						,    __lowerCamelCase							:						Tuple=0.5	)				->					Any:
										if NLTK_VERSION >= version.Version("3.6.5"	):
																__magic_name__            =     [
																    meteor_score.single_meteor_score(
																        word_tokenize(__lowerCamelCase	)						,    word_tokenize(__lowerCamelCase	)						,    alpha=__lowerCamelCase						,    beta=__lowerCamelCase						,    gamma=__lowerCamelCase	)
																    for ref, pred in zip(__lowerCamelCase						,    __lowerCamelCase	)
																]
										else:
																__magic_name__            =     [
																    meteor_score.single_meteor_score(__lowerCamelCase						,    __lowerCamelCase						,    alpha=__lowerCamelCase						,    beta=__lowerCamelCase						,    gamma=__lowerCamelCase	)
																    for ref, pred in zip(__lowerCamelCase						,    __lowerCamelCase	)
																]

										return {"meteor": np.mean(__lowerCamelCase	)}











"""simple docstring"""

from __future__ import annotations

lowercase    			=       []






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						for i in range(len(__lowerCamelCase   )   ):
												if board[row][i] == 1:
																		return False
						for i in range(len(__lowerCamelCase   )   ):
												if board[i][column] == 1:
																		return False
						for i, j in zip(range(__lowerCamelCase ,						-1 ,						-1   ) ,						range(__lowerCamelCase ,						-1 ,						-1   )   ):
												if board[i][j] == 1:
																		return False
						for i, j in zip(range(__lowerCamelCase ,						-1 ,						-1   ) ,						range(__lowerCamelCase ,						len(__lowerCamelCase   )   )   ):
												if board[i][j] == 1:
																		return False
						return True






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]] ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						if row >= len(__lowerCamelCase   ):
												solution.append(__lowerCamelCase   )
												printboard(__lowerCamelCase   )
												print()
												return True
						for i in range(len(__lowerCamelCase   )   ):
												if is_safe(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   ):
																		__magic_name__            =     1
																		solve(__lowerCamelCase ,						row + 1   )
																		__magic_name__            =     0
						return False






def _lowerCAmelCase	(  __lowerCamelCase:list[list[int]]   ):

						'''simple docstring'''






						for i in range(len(__lowerCamelCase   )   ):
												for j in range(len(__lowerCamelCase   )   ):
																		if board[i][j] == 1:
																								print("Q" ,						end=" "   )
																		else:
																								print("." ,						end=" "   )
												print()


# n=int(input("The no. of queens"))
lowercase    			=       8
lowercase    			=       [[0 for i in range(n)] for j in range(n)]
solve(board, 0)


print('''The total no. of solutions are :''', len(solution))



