
def 				a						(							snake_case__:					int						,   snake_case__:					list					):


				'''simple docstring'''
				_enforce_args(snake_case__						,   snake_case__					)
				if n == 0:
								return 0
				lowercase_       =					float('''-inf'''					)
				for i in range(1						,   n + 1					):
								lowercase_       =					max(
								    snake_case__						,   prices[i - 1] + naive_cut_rod_recursive(n - i						,   snake_case__					)					)

				return max_revue




def 				a						(							snake_case__:					int						,   snake_case__:					list					):


				'''simple docstring'''
				_enforce_args(snake_case__						,   snake_case__					)
				lowercase_       =					[float('''-inf'''					) for _ in range(n + 1					)]
				return _top_down_cut_rod_recursive(snake_case__						,   snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					int						,   snake_case__:					list						,   snake_case__:					list					):


				'''simple docstring'''
				if max_rev[n] >= 0:
								return max_rev[n]
				elif n == 0:
								return 0
				else:
								lowercase_       =					float('''-inf'''					)
								for i in range(1						,   n + 1					):
												lowercase_       =					max(
												    snake_case__						,   prices[i - 1] + _top_down_cut_rod_recursive(n - i						,   snake_case__						,   snake_case__					)						,   )

								lowercase_       =					max_revenue

				return max_rev[n]




def 				a						(							snake_case__:					int						,   snake_case__:					list					):


				'''simple docstring'''
				_enforce_args(snake_case__						,   snake_case__					)

				# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
				# length 0.
				lowercase_       =					[float('''-inf'''					) for _ in range(n + 1					)]
				lowercase_       =					0

				for i in range(1						,   n + 1					):
								lowercase_       =					max_rev[i]
								for j in range(1						,   i + 1					):
												lowercase_       =					max(snake_case__						,   prices[j - 1] + max_rev[i - j]					)

								lowercase_       =					max_revenue_i

				return max_rev[n]




def 				a						(							snake_case__:					int						,   snake_case__:					list					):


				'''simple docstring'''
				if n < 0:
								lowercase_       =					F'''n must be greater than or equal to 0. Got n = {n}'''
								raise ValueError(snake_case__					)

				if n > len(snake_case__					):
								lowercase_       =					(
								    '''Each integral piece of rod must have a corresponding price. '''
								    F'''Got n = {n} but length of prices = {len(snake_case__					)}'''
								)
								raise ValueError(snake_case__					)




def 				a						(							):


				'''simple docstring'''
				lowercase_       =					[6, 10, 12, 15, 20, 23]
				lowercase_       =					len(snake_case__					)

				# the best revenue comes from cutting the rod into 6 pieces, each
				# of length 1 resulting in a revenue of 6 * 6 = 36.
				lowercase_       =					36

				lowercase_       =					top_down_cut_rod(snake_case__						,   snake_case__					)
				lowercase_       =					bottom_up_cut_rod(snake_case__						,   snake_case__					)
				lowercase_       =					naive_cut_rod_recursive(snake_case__						,   snake_case__					)

				assert expected_max_revenue == max_rev_top_down
				assert max_rev_top_down == max_rev_bottom_up
				assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
						main()












"""simple docstring"""





import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


_SCREAMING_SNAKE_CASE					:				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},
}
'''

_SCREAMING_SNAKE_CASE					:				int					    =						'''\
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.
'''

_SCREAMING_SNAKE_CASE					:				List[Any]					    =						'''
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    UpperCamelCase       (       self     :							List[str]					)     ->		List[str]:
      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     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]					)     ->		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    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Any       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=0.9       ,						__SCREAMING_SNAKE_CASE     :							Dict=3       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=0.5					)     ->		int:
      if NLTK_VERSION >= version.Version('3.6.5'					):
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(
                word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						word_tokenize(__SCREAMING_SNAKE_CASE					)       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]
      else:
        lowerCamelCase_         =						[
            meteor_score.single_meteor_score(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						alpha=__SCREAMING_SNAKE_CASE       ,						beta=__SCREAMING_SNAKE_CASE       ,						gamma=__SCREAMING_SNAKE_CASE					)
            for ref, pred in zip(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        ]

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




