






"""simple docstring"""





from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

_SCREAMING_SNAKE_CASE					:				List[str]					    =						TypeVar('''KEY''')
_SCREAMING_SNAKE_CASE					:				int					    =						TypeVar('''VAL''')






@dataclass(frozen=__snake_case  ,      slots=__snake_case		)
class      a						(					Generic[KEY, VAL]		):
    SCREAMING_SNAKE_CASE						:     KEY
    SCREAMING_SNAKE_CASE						:     VAL






class      a						(					_Item		):






    def __init__(       self     :							Tuple					)     ->		None:
      super().__init__(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def __bool__(       self     :							Dict					)     ->		bool:
      return False
_SCREAMING_SNAKE_CASE					:				Optional[int]					    =						_DeletedItem()






class      a						(					MutableMapping[KEY, VAL]		):






    def __init__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							int = 8       ,						__SCREAMING_SNAKE_CASE     :							float = 0.75					)     ->		None:
      lowerCamelCase_         =						initial_block_size
      lowerCamelCase_         =						[None] * initial_block_size
      assert 0.0 < capacity_factor < 1.0
      lowerCamelCase_         =						capacity_factor
      lowerCamelCase_         =						0






    def    UpperCamelCase       (       self     :							str       ,						__SCREAMING_SNAKE_CASE     :							KEY					)     ->		int:
      return hash(__SCREAMING_SNAKE_CASE					) % len(self._buckets					)






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		int:
      return (ind + 1) % len(self._buckets					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							int       ,						__SCREAMING_SNAKE_CASE     :							KEY       ,						__SCREAMING_SNAKE_CASE     :							VAL					)     ->		bool:
      lowerCamelCase_         =						self._buckets[ind]
      if not stored:
        lowerCamelCase_         =						_Item(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        self._len += 1
        return True
      elif stored.key == key:
        lowerCamelCase_         =						_Item(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)
        return True
      else:
        return False






    def    UpperCamelCase       (       self     :							List[str]					)     ->		bool:
      lowerCamelCase_         =						len(self._buckets					) * self._capacity_factor
      return len(self					) >= int(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		bool:
      if len(self._buckets					) <= self._initial_block_size:
        return False
      lowerCamelCase_         =						len(self._buckets					) * self._capacity_factor / 2
      return len(self					) < limit






    def    UpperCamelCase       (       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							int					)     ->		None:
      lowerCamelCase_         =						self._buckets
      lowerCamelCase_         =						[None] * new_size
      lowerCamelCase_         =						0
      for item in old_buckets:
        if item:
          self._add_item(item.key       ,						item.val					)






    def    UpperCamelCase       (       self     :							Optional[int]					)     ->		None:
      self._resize(len(self._buckets					) * 2					)






    def    UpperCamelCase       (       self     :							List[str]					)     ->		None:
      self._resize(len(self._buckets					) // 2					)






    def    UpperCamelCase       (       self     :							Optional[int]       ,						__SCREAMING_SNAKE_CASE     :							KEY					)     ->		Iterator[int]:
      lowerCamelCase_         =						self._get_bucket_index(__SCREAMING_SNAKE_CASE					)
      for _ in range(len(self._buckets					)					):
        yield ind
        lowerCamelCase_         =						self._get_next_ind(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							KEY       ,						__SCREAMING_SNAKE_CASE     :							VAL					)     ->		None:
      for ind in self._iterate_buckets(__SCREAMING_SNAKE_CASE					):
        if self._try_set(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					):
          break






    def __setitem__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							KEY       ,						__SCREAMING_SNAKE_CASE     :							VAL					)     ->		None:
      if self._is_full():
        self._size_up()

      self._add_item(__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE					)






    def __delitem__(       self     :							Dict       ,						__SCREAMING_SNAKE_CASE     :							KEY					)     ->		None:
      for ind in self._iterate_buckets(__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						self._buckets[ind]
        if item is None:
          raise KeyError(__SCREAMING_SNAKE_CASE					)
        if item is _deleted:
          continue
        if item.key == key:
          lowerCamelCase_         =						_deleted
          self._len -= 1
          break
      if self._is_sparse():
        self._size_down()






    def __getitem__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							KEY					)     ->		VAL:
      for ind in self._iterate_buckets(__SCREAMING_SNAKE_CASE					):
        lowerCamelCase_         =						self._buckets[ind]
        if item is None:
          break
        if item is _deleted:
          continue
        if item.key == key:
          return item.val
      raise KeyError(__SCREAMING_SNAKE_CASE					)






    def __len__(       self     :							str					)     ->		int:
      return self._len






    def __iter__(       self     :							Optional[Any]					)     ->		Iterator[KEY]:
      yield from (item.key for item in self._buckets if item)






    def __repr__(       self     :							str					)     ->		str:
      lowerCamelCase_         =						' ,'.join(
          F'''{item.key}: {item.val}''' for item in self._buckets if item					)
      return F'''HashMap({val_string})'''












"""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					)}




