




"""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 lowerCAmelCase__      (	datasets.Metric		):






			'''simple docstring'''



			def    A_       (    self    ):
							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    , lowercase    ):
							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    , lowercase    , lowercase    , lowercase=0.9    , lowercase=3    , lowercase=0.5    ):
							if NLTK_VERSION >= version.Version('3.6.5'    ):
											_lowerCamelCase	:     List[Any]       =       [
											    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:
											_lowerCamelCase	:     Optional[Any]       =       [
											    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    )}





"""simple docstring"""






import operator as op







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Dict       =       []
				_lowerCamelCase	:     List[str]       =       lambda lowercase__				,       lowercase__						: int(x / y						)  # noqa: E731 integer division operation
				_lowerCamelCase	:     Optional[int]       =       {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8						)				,       'Action'.center(12						)				,       'Stack'				,       sep=' | '						)
				print('-' * (30 + len(lowercase__						))						)

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowercase__						)  # append x to stack
												# output in tabular format
												print(x.rjust(8						)				,       ('push(' + x + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)
								else:
												_lowerCamelCase	:     int       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												_lowerCamelCase	:     Tuple       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + a + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												stack.append(
												    str(opr[x](int(lowercase__						)				,       int(lowercase__						)						)						)						)  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8						)				,       ('push(' + a + x + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '				,       )

				return int(stack[0]						)


if __name__ == "__main__":
			lowercase__			=		input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))