





"""simple docstring"""




import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


a		:				List[str]  			=    version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
		from nltk import word_tokenize


a		:				Tuple  			=    '''\
@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},
}
'''

a		:				Optional[Any]  			=    '''\
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.
'''

a		:				str  			=    '''
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  __UpperCamelCase       (		datasets.Metric		):






							def 					__a     (     self   )			->				Optional[Any]:
								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 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								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 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0.9   ,      lowerCAmelCase__=3   ,      lowerCAmelCase__=0.5   )			->				int:
								if NLTK_VERSION >= version.Version("3.6.5"   ):
									a   :	List[str]			=       [
									    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:
									a   :	List[Any]			=       [
									    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"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int = 10					, _lowercase		:					int = 1000					, _lowercase		:					bool = True      )				->int:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	    and isinstance(_lowercase					, _lowercase      )
	), "Invalid type of value(s) specified to function!"

	if min_val > max_val:
		raise ValueError("Invalid value for min_val or max_val (min_value < max_value)"      )
	return min_val if option else max_val






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int      )				->int:






	'''simple docstring'''






	return int((number_a + number_a) / 2      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					int					, _lowercase		:					int					, _lowercase		:					int      )				->None:






	'''simple docstring'''






	assert (
	    isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      ) and isinstance(_lowercase					, _lowercase      )
	), 'argument values must be type of "int"'

	if lower > higher:
		raise ValueError("argument value for lower and higher must be(lower > higher)"      )

	if not lower < to_guess < higher:
		raise ValueError(
		    "guess value must be within the range of lower and higher value"      )

	def answer(_lowercase		:					int      ) -> str:
		if number > to_guess:
			return "high"
		elif number < to_guess:
			return "low"
		else:
			return "same"

	print("started..."      )

	a   :	Optional[Any]			=       lower
	a   :	List[Any]			=       higher

	a   :	Tuple			=       []

	while True:
		a   :	List[Any]			=       get_avg(_lowercase					, _lowercase      )
		last_numbers.append(_lowercase      )

		if answer(_lowercase      ) == "low":
			a   :	Optional[int]			=       number
		elif answer(_lowercase      ) == "high":
			a   :	Tuple			=       number
		else:
			break

	print(F"""guess the number : {last_numbers[-1]}"""      )
	print(F"""details : {last_numbers!s}"""      )






def    _SCREAMING_SNAKE_CASE	(			)				->None:






	'''simple docstring'''






	a   :	Tuple			=       int(input("Enter lower value : "      ).strip()      )
	a   :	Dict			=       int(input("Enter high value : "      ).strip()      )
	a   :	Optional[int]			=       int(input("Enter value to guess : "      ).strip()      )
	guess_the_number(_lowercase					, _lowercase					, _lowercase      )


if __name__ == "__main__":
		main()


