






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

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
       import torch

if is_vision_available():
       import PIL


__lowerCamelCase	:		Union[str, Any]										=   logging.get_logger(__name__)







def 			A__							(      _a  :					np.ndarray		,  _a  :					Union[int, Iterable[int]]		,  _a  :					bool		,  _a  :					int	):





 '''simple docstring'''







 def constraint_to_multiple_of(_a  :					Union[str, Any]		,  _a  :					List[str]		,  _a  :					str=0		,  _a  :					Any=None	):
  snake_case__  :    Any							       =round(val / multiple	) * multiple

  if max_val is not None and x > max_val:
   snake_case__  :    int							       =math.floor(val / multiple	) * multiple

  if x < min_val:
   snake_case__  :    Dict							       =math.ceil(val / multiple	) * multiple

  return x

 snake_case__  :    str							       =(output_size, output_size) if isinstance(_a		,  _a	) else output_size

 snake_case__       ,   snake_case__  :    Dict							       =get_image_size(_a	)
 snake_case__       ,   snake_case__  :    int							       =output_size

 # determine new height and width
 snake_case__  :    Tuple							       =output_height / input_height
 snake_case__  :    Optional[Any]							       =output_width / input_width

 if keep_aspect_ratio:
  # scale as little as possible
  if abs(1 - scale_width	) < abs(1 - scale_height	):
   # fit width
   snake_case__  :    Optional[int]							       =scale_width
  else:
   # fit height
   snake_case__  :    Any							       =scale_height

 snake_case__  :    Optional[int]							       =constraint_to_multiple_of(scale_height * input_height		,  multiple=_a	)
 snake_case__  :    str							       =constraint_to_multiple_of(scale_width * input_width		,  multiple=_a	)

 return (new_height, new_width)







class  _lowercase   (   _A						):
      _a							:						List[Any]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = None				,			a = PILImageResampling.BILINEAR				,			a = False				,			a = 1				,			a = True				,			a = 1 / 2_5_5				,			a = True				,			a = None				,			a = None				,			**a				,			):
       super().__init__(**a   )
       snake_case__  :    Any							       =size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
       snake_case__  :    List[Any]							       =get_size_dict(a   )
       snake_case__  :    Tuple							       =do_resize
       snake_case__  :    Tuple							       =size
       snake_case__  :    Any							       =keep_aspect_ratio
       snake_case__  :    List[Any]							       =ensure_multiple_of
       snake_case__  :    Tuple							       =resample
       snake_case__  :    str							       =do_rescale
       snake_case__  :    int							       =rescale_factor
       snake_case__  :    Tuple							       =do_normalize
       snake_case__  :    int							       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       snake_case__  :    Any							       =image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						lowercase__					(     self				,			a				,			a				,			a = False				,			a = 1				,			a = PILImageResampling.BICUBIC				,			a = None				,			**a				,			):
       snake_case__  :    Tuple							       =get_size_dict(a   )
       if "height" not in size or "width" not in size:
        raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"   )
       snake_case__  :    Optional[int]							       =get_resize_output_image_size(
           a				,			output_size=(size["""height"""], size["""width"""])				,			keep_aspect_ratio=a				,			multiple=a				,			)
       return resize(a				,			size=a				,			resample=a				,			data_format=a				,			**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a				,			):
       return rescale(a				,			scale=a				,			data_format=a				,			**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a				,			):
       return normalize(a				,			mean=a				,			std=a				,			data_format=a				,			**a   )
      def 						lowercase__					(     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				,			):
       snake_case__  :    Optional[int]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    List[Any]							       =size if size is not None else self.size
       snake_case__  :    int							       =get_size_dict(a   )
       snake_case__  :    Optional[int]							       =keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
       snake_case__  :    int							       =ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
       snake_case__  :    Optional[Any]							       =resample if resample is not None else self.resample
       snake_case__  :    Optional[int]							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    str							       =rescale_factor if rescale_factor is not None else self.rescale_factor
       snake_case__  :    Optional[Any]							       =do_normalize if do_normalize is not None else self.do_normalize
       snake_case__  :    Tuple							       =image_mean if image_mean is not None else self.image_mean
       snake_case__  :    int							       =image_std if image_std is not None else self.image_std

       snake_case__  :    int							       =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 or resample is None:
        raise ValueError("""Size and resample must be specified if do_resize 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.
       snake_case__  :    int							       =[to_numpy_array(a   ) for image in images]

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

       if do_rescale:
        snake_case__  :    List[Any]							       =[self.rescale(image=a				,			scale=a   ) for image in images]

       if do_normalize:
        snake_case__  :    str							       =[self.normalize(image=a				,			mean=a				,			std=a   ) for image in images]

       snake_case__  :    List[Any]							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Union[str, Any]							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    Optional[Any]							       =outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
        if len(a   ) != len(a   ):
         raise ValueError(
             """Make sure that you pass in as many target sizes as the batch dimension of the logits"""   )

        if is_torch_tensor(a   ):
         snake_case__  :    Optional[Any]							       =target_sizes.numpy()

        snake_case__  :    Optional[Any]							       =[]

        for idx in range(len(a   )   ):
         snake_case__  :    List[str]							       =torch.nn.functional.interpolate(
             logits[idx].unsqueeze(dim=0   )				,			size=target_sizes[idx]				,			mode="""bilinear"""				,			align_corners=a   )
         snake_case__  :    List[Any]							       =resized_logits[0].argmax(dim=0   )
         semantic_segmentation.append(a   )
       else:
        snake_case__  :    List[str]							       =logits.argmax(dim=1   )
        snake_case__  :    Optional[int]							       =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

       return semantic_segmentation













import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


__lowerCamelCase	:		Dict										=   """\
@inproceedings{popovic-2015-chrf,
    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",
    month = sep,
    year = \"2015\",
    address = \"Lisbon, Portugal\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W15-3049\",
    doi = \"10.18653/v1/W15-3049\",
    pages = \"392--395\",
}
@inproceedings{popovic-2017-chrf,
    title = \"chr{F}++: words helping character n-grams\",
    author = \"Popovi{\'c}, Maja\",
    booktitle = \"Proceedings of the Second Conference on Machine Translation\",
    month = sep,
    year = \"2017\",
    address = \"Copenhagen, Denmark\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://aclanthology.org/W17-4770\",
    doi = \"10.18653/v1/W17-4770\",
    pages = \"612--618\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

__lowerCamelCase	:		Optional[int]										=   """\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
"""

__lowerCamelCase	:		List[str]										=   """
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    'score' (float): The chrF (chrF++) score,
    'char_order' (int): The character n-gram order,
    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    'beta' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]
        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]
        >>> chrf = datasets.load_metric(\"chrf\")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,				_KWARGS_DESCRIPTION						)
class  _lowercase   (   datasets.Metric						):
      def 						lowercase__					(     self   ):
       if version.parse(scb.__version__   ) < version.parse("""1.4.12"""   ):
        raise ImportWarning(
            """To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
            """You can install it with `pip install \"sacrebleu>=1.4.12\"`."""   )
       return datasets.MetricInfo(
           description=_DESCRIPTION				,			citation=_CITATION				,			homepage="""https://github.com/mjpost/sacreBLEU#chrf--chrf"""				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
               {
                   """predictions""": datasets.Value("""string"""				,			id="""sequence"""   ),
                   """references""": datasets.Sequence(datasets.Value("""string"""				,			id="""sequence"""   )				,			id="""references"""   ),
               }   )				,			codebase_urls=["""https://github.com/mjpost/sacreBLEU#chrf--chrf"""]				,			reference_urls=[
               """https://github.com/m-popovic/chrF""",
           ]				,			)



      def 						lowercase__					(     self				,			a				,			a				,			a = CHRF.CHAR_ORDER				,			a = CHRF.WORD_ORDER				,			a = CHRF.BETA				,			a = False				,			a = False				,			a = False				,			):
       snake_case__  :    str							       =len(references[0]   )
       if any(len(a   ) != references_per_prediction for refs in references   ):
        raise ValueError("""Sacrebleu requires the same number of references for each prediction"""   )
       snake_case__  :    List[Any]							       =[[refs[i] for refs in references] for i in range(a   )]

       snake_case__  :    int							       =CHRF(a				,			a				,			a				,			a				,			a				,			a   )
       snake_case__  :    int							       =sb_chrf.corpus_score(a				,			a   )

       return {
           "score": output.score,
           "char_order": output.char_order,
           "word_order": output.word_order,
           "beta": output.beta,
       }





