





"""simple docstring"""



import absl  # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import six  # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring

import datasets


a_					=     """\
@inproceedings{lin-2004-rouge,
    title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
    author = \"Lin, Chin-Yew\",
    booktitle = \"Text Summarization Branches Out\",
    month = jul,
    year = \"2004\",
    address = \"Barcelona, Spain\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W04-1013\",
    pages = \"74--81\",
}
"""

a_					=     """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.

Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.

This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""

a_					=     """
Calculates average rouge scores for a list of hypotheses and 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.
    rouge_types: A list of rouge types to calculate.
        Valid names:
        `\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
        `\"rougeL\"`: Longest common subsequence based scoring.
        `\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
        See details in https://github.com/huggingface/datasets/issues/617
    use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
    use_aggregator: Return aggregates if this is set to True
Returns:
    rouge1: rouge_1 (precision, recall, f1),
    rouge2: rouge_2 (precision, recall, f1),
    rougeL: rouge_l (precision, recall, f1),
    rougeLsum: rouge_lsum (precision, recall, f1)
Examples:

    >>> rouge = datasets.load_metric('rouge')
    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> results = rouge.compute(predictions=predictions, references=references)
    >>> print(list(results.keys()))
    ['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
    >>> print(results[\"rouge1\"])
    AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
    >>> print(results[\"rouge1\"].mid.fmeasure)
    1.0
"""






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





 """simple docstring"""


 def    UpperCamelCase__(       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/google-research/google-research/tree/master/rouge''']   ,		reference_urls=[
             '''https://en.wikipedia.org/wiki/ROUGE_(metric)''',
             '''https://github.com/google-research/google-research/tree/master/rouge''',
         ]   ,		)







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase=None   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False    ):



     '''simple docstring'''




     if rouge_types is None:
         __A       :		Dict							=	['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

     __A       :		Dict							=	rouge_scorer.RougeScorer(rouge_types=__lowerCamelCase   ,		use_stemmer=__lowerCamelCase    )
     if use_aggregator:
         __A       :		Dict							=	scoring.BootstrapAggregator()
     else:
         __A       :		Optional[Any]							=	[]

     for ref, pred in zip(__lowerCamelCase   ,		__lowerCamelCase    ):
         __A       :		List[Any]							=	scorer.score(__lowerCamelCase   ,		__lowerCamelCase    )
         if use_aggregator:
             aggregator.add_scores(__lowerCamelCase    )
         else:
             scores.append(__lowerCamelCase    )

     if use_aggregator:
         __A       :		Optional[Any]							=	aggregator.aggregate()
     else:
         __A       :		List[Any]							=	{}
         for key in scores[0]:
             __A       :		Tuple							=	[score[key] for score in scores]

     return result











"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a_					=     logging.get_logger(__name__)


if is_vision_available():
       import PIL






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	["""pixel_values"""]

 def __init__(       self   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		__lowerCamelCase = 1 / 255   ,		__lowerCamelCase = True   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = True   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase    )
     __A       :		Optional[int]							=	size if size is not None else {'''shortest_edge''': 224}
     __A       :		Optional[int]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     __A       :		Dict							=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     __A       :		str							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase   ,		param_name='''crop_size'''    )

     __A       :		Dict							=	do_resize
     __A       :		Optional[Any]							=	size
     __A       :		Optional[Any]							=	resample
     __A       :		Dict							=	do_center_crop
     __A       :		Any							=	crop_size
     __A       :		Optional[Any]							=	do_rescale
     __A       :		List[Any]							=	rescale_factor
     __A       :		Union[str, Any]							=	do_normalize
     __A       :		Any							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
     __A       :		Optional[int]							=	image_std if image_std is not None else OPENAI_CLIP_STD
     __A       :		Dict							=	do_convert_rgb

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = PILImageResampling.BICUBIC   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase   ,		default_to_square=__lowerCamelCase    )
     if "shortest_edge" not in size:
         raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
     __A       :		Optional[int]							=	get_resize_output_image_size(__lowerCamelCase   ,		size=size['''shortest_edge''']   ,		default_to_square=__lowerCamelCase    )
     return resize(__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Optional[Any]							=	get_size_dict(__lowerCamelCase    )
     if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""    )
     return center_crop(__lowerCamelCase   ,		size=(size['''height'''], size['''width'''])   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     return rescale(__lowerCamelCase   ,		scale=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )

 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     return normalize(__lowerCamelCase   ,		mean=__lowerCamelCase   ,		std=__lowerCamelCase   ,		data_format=__lowerCamelCase   ,		**__lowerCamelCase    )







 def    UpperCamelCase__(       self   ,		__lowerCamelCase   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = None   ,		__lowerCamelCase = ChannelDimension.FIRST   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Tuple							=	do_resize if do_resize is not None else self.do_resize
     __A       :		Any							=	size if size is not None else self.size
     __A       :		List[str]							=	get_size_dict(__lowerCamelCase   ,		param_name='''size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[Any]							=	resample if resample is not None else self.resample
     __A       :		Any							=	do_center_crop if do_center_crop is not None else self.do_center_crop
     __A       :		Dict							=	crop_size if crop_size is not None else self.crop_size
     __A       :		List[Any]							=	get_size_dict(__lowerCamelCase   ,		param_name='''crop_size'''   ,		default_to_square=__lowerCamelCase    )
     __A       :		List[str]							=	do_rescale if do_rescale is not None else self.do_rescale
     __A       :		List[Any]							=	rescale_factor if rescale_factor is not None else self.rescale_factor
     __A       :		Tuple							=	do_normalize if do_normalize is not None else self.do_normalize
     __A       :		str							=	image_mean if image_mean is not None else self.image_mean
     __A       :		int							=	image_std if image_std is not None else self.image_std
     __A       :		Union[str, Any]							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

     __A       :		int							=	make_list_of_images(__lowerCamelCase    )

     if not valid_images(__lowerCamelCase    ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

     if do_resize and size is None:
         raise ValueError('''Size must be specified if do_resize is True.'''    )

     if do_center_crop and crop_size is None:
         raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

     if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

     if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

     # PIL RGBA images are converted to RGB
     if do_convert_rgb:
         __A       :		List[str]							=	[convert_to_rgb(__lowerCamelCase    ) for image in images]

     # All transformations expect numpy arrays.
     __A       :		Optional[int]							=	[to_numpy_array(__lowerCamelCase    ) for image in images]

     if do_resize:
         __A       :		List[str]							=	[self.resize(image=__lowerCamelCase   ,		size=__lowerCamelCase   ,		resample=__lowerCamelCase    ) for image in images]

     if do_center_crop:
         __A       :		Dict							=	[self.center_crop(image=__lowerCamelCase   ,		size=__lowerCamelCase    ) for image in images]

     if do_rescale:
         __A       :		str							=	[self.rescale(image=__lowerCamelCase   ,		scale=__lowerCamelCase    ) for image in images]

     if do_normalize:
         __A       :		Optional[Any]							=	[self.normalize(image=__lowerCamelCase   ,		mean=__lowerCamelCase   ,		std=__lowerCamelCase    ) for image in images]

     __A       :		str							=	[to_channel_dimension_format(__lowerCamelCase   ,		__lowerCamelCase    ) for image in images]

     __A       :		Union[str, Any]							=	{'''pixel_values''': images}
     return BatchFeature(data=__lowerCamelCase   ,		tensor_type=__lowerCamelCase    )




