





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__SCREAMING_SNAKE_CASE     :str       =				{
    '''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LayoutLMv2Config'''],
    '''processing_layoutlmv2''': ['''LayoutLMv2Processor'''],
    '''tokenization_layoutlmv2''': ['''LayoutLMv2Tokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Optional[Any]       =				['''LayoutLMv2TokenizerFast''']

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :Any       =				['''LayoutLMv2FeatureExtractor''']
  __SCREAMING_SNAKE_CASE     :List[Any]       =				['''LayoutLMv2ImageProcessor''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE     :int       =				[
      '''LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''LayoutLMv2ForQuestionAnswering''',
      '''LayoutLMv2ForSequenceClassification''',
      '''LayoutLMv2ForTokenClassification''',
      '''LayoutLMv2Layer''',
      '''LayoutLMv2Model''',
      '''LayoutLMv2PreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
  from .processing_layoutlmva import LayoutLMvaProcessor
  from .tokenization_layoutlmva import LayoutLMvaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_layoutlmva import (
        LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        LayoutLMvaForQuestionAnswering,
        LayoutLMvaForSequenceClassification,
        LayoutLMvaForTokenClassification,
        LayoutLMvaLayer,
        LayoutLMvaModel,
        LayoutLMvaPreTrainedModel,
    )
else:
  import sys

  __SCREAMING_SNAKE_CASE     :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)












"""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					)}




