


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


snake_case							:	Any					      =				'''\
@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",
}
'''

snake_case							:	str					      =				'''\
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
'''

snake_case							:	List[str]					      =				'''
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				):
					def 			lowerCamelCase__(				self       :Any				) ->   List[Any]:
												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 			lowerCamelCase__(				self       :int      ,__snake_case       :Dict      ,__snake_case       :Tuple      ,__snake_case       :Optional[int]=None      ,__snake_case       :List[str]=True      ,__snake_case       :Any=False				) ->   Optional[int]:
												if rouge_types is None:
																			a__					=					['rouge1', 'rouge2', 'rougeL', 'rougeLsum']

												a__					=					rouge_scorer.RougeScorer(rouge_types=__snake_case      ,use_stemmer=__snake_case				)
												if use_aggregator:
																			a__					=					scoring.BootstrapAggregator()
												else:
																			a__					=					[]

												for ref, pred in zip(__snake_case      ,__snake_case				):
																			a__					=					scorer.score(__snake_case      ,__snake_case				)
																			if use_aggregator:
																										aggregator.add_scores(__snake_case				)
																			else:
																										scores.append(__snake_case				)

												if use_aggregator:
																			a__					=					aggregator.aggregate()
												else:
																			a__					=					{}
																			for key in scores[0]:
																										a__					=					[score[key] for score in scores]

												return result








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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


snake_case							:	str					      =				logging.get_logger(__name__)






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Optional[Any]       ):
							a__					=					b.T
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=1       )
							a__					=					np.sum(np.square(__lowerCAmelCase       ) ,   axis=0       )
							a__					=					np.matmul(__lowerCAmelCase ,   __lowerCAmelCase       )
							a__					=					aa[:, None] - 2 * ab + ba[None, :]
							return d






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    str       ):
							a__					=					x.reshape(-1 ,   3       )
							a__					=					squared_euclidean_distance(__lowerCAmelCase ,   __lowerCAmelCase       )
							return np.argmin(__lowerCAmelCase ,   axis=1       )







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   Optional[Any]   				=      ['''pixel_values''']
					def __init__(				self       :Dict      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :bool = True      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :bool = True      ,__snake_case       :bool = True      ,**__snake_case       :Optional[int]      ,) ->   None:
												super().__init__(**__snake_case				)
												a__					=					size if size is not None else {'height': 2_56, 'width': 2_56}
												a__					=					get_size_dict(__snake_case				)
												a__					=					np.array(__snake_case				) if clusters is not None else None
												a__					=					do_resize
												a__					=					size
												a__					=					resample
												a__					=					do_normalize
												a__					=					do_color_quantize
					def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Dict[str, int]      ,__snake_case       :PILImageResampling = PILImageResampling.BILINEAR      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,**__snake_case       :Any      ,) ->   np.ndarray:
												a__					=					get_size_dict(__snake_case				)
												if "height" not in size or "width" not in size:
																			raise ValueError(F'Size dictionary must contain both height and width keys. Got {size.keys()}'				)
												return resize(
												    __snake_case      ,size=(size['height'], size['width'])      ,resample=__snake_case      ,data_format=__snake_case      ,**__snake_case				)
					def 			lowerCamelCase__(				self       :List[Any]      ,__snake_case       :np.ndarray      ,__snake_case       :Optional[Union[str, ChannelDimension]] = None      ,) ->   np.ndarray:
												a__					=					rescale(image=__snake_case      ,scale=1 / 1_27.5      ,data_format=__snake_case				)
												a__					=					image - 1
												return image



					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :ImageInput      ,__snake_case       :bool = None      ,__snake_case       :Dict[str, int] = None      ,__snake_case       :PILImageResampling = None      ,__snake_case       :bool = None      ,__snake_case       :Optional[bool] = None      ,__snake_case       :Optional[Union[List[List[int]], np.ndarray]] = None      ,__snake_case       :Optional[Union[str, TensorType]] = None      ,__snake_case       :Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST      ,**__snake_case       :List[str]      ,) ->   PIL.Image.Image:
												a__					=					do_resize if do_resize is not None else self.do_resize
												a__					=					size if size is not None else self.size
												a__					=					get_size_dict(__snake_case				)
												a__					=					resample if resample is not None else self.resample
												a__					=					do_normalize if do_normalize is not None else self.do_normalize
												a__					=					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
												a__					=					clusters if clusters is not None else self.clusters
												a__					=					np.array(__snake_case				)

												a__					=					make_list_of_images(__snake_case				)

												if not valid_images(__snake_case				):
																			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 or resample is None:
																			raise ValueError('Size and resample must be specified if do_resize is True.'				)

												if do_color_quantize and clusters is None:
																			raise ValueError('Clusters must be specified if do_color_quantize is True.'				)

												# All transformations expect numpy arrays.
												a__					=					[to_numpy_array(__snake_case				) for image in images]

												if do_resize:
																			a__					=					[self.resize(image=__snake_case      ,size=__snake_case      ,resample=__snake_case				) for image in images]

												if do_normalize:
																			a__					=					[self.normalize(image=__snake_case				) for image in images]

												if do_color_quantize:
																			a__					=					[to_channel_dimension_format(__snake_case      ,ChannelDimension.LAST				) for image in images]
																			# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																			a__					=					np.array(__snake_case				)
																			a__					=					color_quantize(__snake_case      ,__snake_case				).reshape(images.shape[:-1]				)

																			# flatten to (batch_size, height*width)
																			a__					=					images.shape[0]
																			a__					=					images.reshape(__snake_case      ,-1				)

																			# We need to convert back to a list of images to keep consistent behaviour across processors.
																			a__					=					list(__snake_case				)
												else:
																			a__					=					[to_channel_dimension_format(__snake_case      ,__snake_case				) for image in images]

												a__					=					{'input_ids': images}
												return BatchFeature(data=__snake_case      ,tensor_type=__snake_case				)




