
import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


lowerCamelCase__     :		Any        = version.parse(importlib_metadata.version("""nltk"""))
if NLTK_VERSION >= version.Version("""3.6.4"""):
     from nltk import word_tokenize


lowerCamelCase__     :		Union[str, Any]        = """\
@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},
}
"""

lowerCamelCase__     :		List[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.
"""

lowerCamelCase__     :		Dict        = """
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    _snake_case (						datasets.Metric				):


 def   lowercase__  (       self):


   '''simple docstring'''




   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   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   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=0.9       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=0.5):


   '''simple docstring'''




   if NLTK_VERSION >= version.Version("""3.6.5"""):
     lowercase__	: Dict     		=   [
         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:
     lowercase__	: List[Any]     		=   [
         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_)}








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__     :		Tuple        = {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		Optional[int]        = [
         """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MgpstrModel""",
         """MgpstrPreTrainedModel""",
         """MgpstrForSceneTextRecognition""",
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     lowerCamelCase__     :		List[Any]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






