

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


lowerCAmelCase__	   = """\
@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},
}
"""

lowerCAmelCase__	   = """\
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.
"""

lowerCAmelCase__	   = """
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			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							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      UpperCamelCase		(    self      ,      lowercase       )  ->						int:
							'''simple docstring'''






							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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase=0.9      ,      lowercase=3      ,      lowercase=0.5       )  ->						Dict:
							'''simple docstring'''






							if NLTK_VERSION >= version.Version("3.6.5"       ):
											A__        =			[
											    meteor_score.single_meteor_score(
											        word_tokenize(lowercase       )      ,      word_tokenize(lowercase       )      ,      alpha=lowercase      ,      beta=lowercase      ,      gamma=lowercase       )
											    for ref, pred in zip(lowercase      ,      lowercase       )
											]
							else:
											A__        =			[
											    meteor_score.single_meteor_score(lowercase      ,      lowercase      ,      alpha=lowercase      ,      beta=lowercase      ,      gamma=lowercase       )
											    for ref, pred in zip(lowercase      ,      lowercase       )
											]

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









from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# If row is equal to the size of the board it means there are a queen in each row in
				# the current board (possible_board)
				if row == n:
								# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
								# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
								boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board]      )
								return

				# We iterate each column in the row to find all possible results in each row
				for col in range(SCREAMING_SNAKE_CASE_      ):
								# We apply that we learned previously. First we check that in the current board
								# (possible_board) there are not other same value because if there is it means
								# that there are a collision in vertical. Then we apply the two formulas we
								# learned before:
								#
								# 45º: y - x = b or 45: row - col = b
								# 135º: y + x = b or row + col = b.
								#
								# And we verify if the results of this two formulas not exist in their variables
								# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
								#
								# If any or these are True it means there is a collision so we continue to the
								# next value in the for loop.
								if (
								    col in possible_board
								    or row - col in diagonal_right_collisions
								    or row + col in diagonal_left_collisions
								):
												continue

								# If it is False we call dfs function again and we update the inputs
								depth_first_search(
								    [*possible_board, col]	,       [*diagonal_right_collisions, row - col]	,       [*diagonal_left_collisions, row + col]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				A__        =			[]
				depth_first_search([]	,       []	,       []	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# Print all the boards
				for board in boards:
								for column in board:
												print(SCREAMING_SNAKE_CASE_      )
								print(""      )

				print(len(SCREAMING_SNAKE_CASE_      )	,       "solutions were found."      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

