






'''simple docstring'''

import numpy as np
from nltk.translate import meteor_score

import datasets
from datasets.config import importlib_metadata, version


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


SCREAMING_SNAKE_CASE__     =       '\\n@inproceedings{banarjee2005,\n  title     = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n  author    = {Banerjee, Satanjeev  and Lavie, Alon},\n  booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n  month     = jun,\n  year      = {2005},\n  address   = {Ann Arbor, Michigan},\n  publisher = {Association for Computational Linguistics},\n  url       = {https://www.aclweb.org/anthology/W05-0909},\n  pages     = {65--72},\n}\n'

SCREAMING_SNAKE_CASE__     =       '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'

SCREAMING_SNAKE_CASE__     =       '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n    predictions: list of predictions to score. Each prediction\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\n    alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n    beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n    gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n    \'meteor\': meteor score.\nExamples:\n\n    >>> meteor = datasets.load_metric(\'meteor\')\n    >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n    >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n    >>> results = meteor.compute(predictions=predictions, references=references)\n    >>> print(round(results["meteor"], 4))\n    0.6944\n'






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,	_KWARGS_DESCRIPTION    )
class     a_						(      datasets.Metric    ):

  def   A__						(				self				)  ->				Union[str, Any]:



        """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   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """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   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=0.9      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=0.5				)  ->				List[Any]:



        """simple docstring"""






        if NLTK_VERSION >= version.Version("""3.6.5"""				):
              UpperCamelCase      = [
                  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:
              UpperCamelCase      = [
                  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				)}










'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. 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 ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""Salesforce/blip-image-captioning-base"""
  lowercase							=				(
      """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
      """image to caption, and returns a text that contains the description in English."""
  )
  lowercase							=				"""image_captioner"""
  lowercase							=				AutoModelForVisionaSeq

  lowercase							=				["""image"""]
  lowercase							=				["""text"""]

  def __init__(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        requires_backends(self      ,		["""vision"""]				)
        super().__init__(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.pre_processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.model.generate(**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        return self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE				)[0].strip()
