
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


_lowercase											=    logging.get_logger(__name__)


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						["pixel_values"]


       def __init__(    self						,       __A = True						,       __A = None						,       __A = PILImageResampling.BICUBIC						,       __A = True						,       __A = None						,       __A = True						,       __A = 1 / 255						,       __A = True						,       __A = IMAGENET_DEFAULT_MEAN						,       __A = IMAGENET_DEFAULT_STD						,       **__A						,       )						->    None:
              super().__init__(**__A				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=size if size is not None else {'''shortest_edge''': 224}
              SCREAMING_SNAKE_CASE_							:				List[str]		=get_size_dict(__A						,       default_to_square=__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
              SCREAMING_SNAKE_CASE_							:				int		=get_size_dict(__A						,       param_name='''crop_size'''				)

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=do_resize
              SCREAMING_SNAKE_CASE_							:				Tuple		=size
              SCREAMING_SNAKE_CASE_							:				Tuple		=resample
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=do_center_crop
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=crop_size
              SCREAMING_SNAKE_CASE_							:				List[str]		=do_rescale
              SCREAMING_SNAKE_CASE_							:				List[str]		=rescale_factor
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=do_normalize
              SCREAMING_SNAKE_CASE_							:				int		=image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=image_std if image_std is not None else IMAGENET_DEFAULT_STD


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = PILImageResampling.BICUBIC						,       __A = None						,       **__A						,       )						->    np.ndarray:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=get_size_dict(__A						,       default_to_square=__A				)
              # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
              if "shortest_edge" in size:
                     SCREAMING_SNAKE_CASE_							:				str		=int((256 / 224) * size['''shortest_edge''']				)
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=get_resize_output_image_size(__A						,       size=__A						,       default_to_square=__A				)
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		={'''height''': output_size[0], '''width''': output_size[1]}
              if "height" not in size_dict or "width" not in size_dict:
                     raise ValueError(
                         F'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'				)
              return resize(
                  __A						,       size=(size_dict['''height'''], size_dict['''width'''])						,       resample=__A						,       data_format=__A						,       **__A				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = None						,       **__A						,       )						->    np.ndarray:
              SCREAMING_SNAKE_CASE_							:				List[str]		=get_size_dict(__A				)
              if "height" not in size or "width" not in size:
                     raise ValueError(F'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'				)
              return center_crop(__A						,       size=(size['''height'''], size['''width'''])						,       data_format=__A						,       **__A				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A = None						,       **__A						,       )						->    np.ndarray:
              return rescale(__A						,       scale=__A						,       data_format=__A						,       **__A				)


       def 	_snake_case				(    self						,       __A						,       __A						,       __A						,       __A = None						,       **__A						,       )						->    np.ndarray:
              return normalize(__A						,       mean=__A						,       std=__A						,       data_format=__A						,       **__A				)





       def 	_snake_case				(    self						,       __A						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = None						,       __A = ChannelDimension.FIRST						,       **__A						,       )						->    BatchFeature:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=do_resize if do_resize is not None else self.do_resize
              SCREAMING_SNAKE_CASE_							:				int		=resample if resample is not None else self.resample
              SCREAMING_SNAKE_CASE_							:				Any		=do_center_crop if do_center_crop is not None else self.do_center_crop
              SCREAMING_SNAKE_CASE_							:				Dict		=do_rescale if do_rescale is not None else self.do_rescale
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rescale_factor if rescale_factor is not None else self.rescale_factor
              SCREAMING_SNAKE_CASE_							:				str		=do_normalize if do_normalize is not None else self.do_normalize
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image_mean if image_mean is not None else self.image_mean
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=image_std if image_std is not None else self.image_std

              SCREAMING_SNAKE_CASE_							:				Any		=size if size is not None else self.size
              SCREAMING_SNAKE_CASE_							:				List[Any]		=get_size_dict(__A						,       default_to_square=__A				)
              SCREAMING_SNAKE_CASE_							:				List[str]		=crop_size if crop_size is not None else self.crop_size
              SCREAMING_SNAKE_CASE_							:				Dict		=get_size_dict(__A						,       param_name='''crop_size'''				)

              SCREAMING_SNAKE_CASE_							:				Any		=make_list_of_images(__A				)

              if not valid_images(__A				):
                     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.'''				)

              # All transformations expect numpy arrays.
              SCREAMING_SNAKE_CASE_							:				str		=[to_numpy_array(__A				) for image in images]

              if do_resize:
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=[self.resize(__A						,       __A						,       __A				) for image in images]

              if do_center_crop:
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[self.center_crop(__A						,       __A				) for image in images]

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

              if do_normalize:
                     SCREAMING_SNAKE_CASE_							:				int		=[self.normalize(__A						,       __A						,       __A				) for image in images]

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

              SCREAMING_SNAKE_CASE_							:				List[str]		={'''pixel_values''': images}
              return BatchFeature(data=__A						,       tensor_type=__A				)


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


_lowercase											=    """\
@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\",
}
"""

_lowercase											=    """\
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
"""

_lowercase											=    """
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 			lowercase_		(      datasets.Metric       ):


       def 	_snake_case				(    self				)						->    Union[str, 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 	_snake_case				(    self						,       __A						,       __A						,       __A=None						,       __A=True						,       __A=False				)						->    List[str]:
              if rouge_types is None:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=['''rouge1''', '''rouge2''', '''rougeL''', '''rougeLsum''']

              SCREAMING_SNAKE_CASE_							:				Tuple		=rouge_scorer.RougeScorer(rouge_types=__A						,       use_stemmer=__A				)
              if use_aggregator:
                     SCREAMING_SNAKE_CASE_							:				List[str]		=scoring.BootstrapAggregator()
              else:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=[]

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

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

              return result
