

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

import datasets


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

_UpperCAmelCase		:   Tuple										=       """\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"""

_UpperCAmelCase		:   Optional[int]										=       """\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    'score' (float): The chrF (chrF++) score,\n    'char_order' (int): The character n-gram order,\n    'word_order' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    'beta' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {'score': 84.64214891738334, 'char_order': 6, 'word_order': 0, 'beta': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {'score': 82.87263732906315, 'char_order': 6, 'word_order': 2, 'beta': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {'score': 92.12853119829202, 'char_order': 6, 'word_order': 2, 'beta': 2}\n"""







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




				def     a							(					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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case = CHRF.CHAR_ORDER      ,   snake_case = CHRF.WORD_ORDER      ,   snake_case = CHRF.BETA      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   ):
											snake_case_							=    len(references[0]       )
											if any(len(UpperCamelCase__       ) != references_per_prediction for refs in references       ):
																		raise ValueError('Sacrebleu requires the same number of references for each prediction'       )
											snake_case_							=    [[refs[i] for refs in references] for i in range(UpperCamelCase__       )]

											snake_case_							=    CHRF(UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__       )
											snake_case_							=    sb_chrf.corpus_score(UpperCamelCase__      ,   UpperCamelCase__       )

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







"""simple docstring"""


from typing import Dict, List, Optional, Tuple, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
			import torch


__A						   =			logging.get_logger(__name__)





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  ["""pixel_values"""]

				def __init__(	self							:				List[Any]     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Optional[Dict[str, int]] = None     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BILINEAR     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Union[int, float] = 1 / 2_5_5     ,  UpperCamelCase__							:				bool = True     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  **UpperCamelCase__							:				Optional[int]     ,  )->   None:



						'''simple docstring'''


						super().__init__(**UpperCamelCase__)
						__lowerCAmelCase:       int          =			size if size is not None else {"shortest_edge": 2_5_6}
						__lowerCAmelCase:       str          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						__lowerCAmelCase:       Any          =			crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
						__lowerCAmelCase:       Optional[Any]          =			get_size_dict(UpperCamelCase__     ,  param_name="crop_size")
						__lowerCAmelCase:       str          =			do_resize
						__lowerCAmelCase:       Any          =			size
						__lowerCAmelCase:       Dict          =			resample
						__lowerCAmelCase:       Tuple          =			do_center_crop
						__lowerCAmelCase:       str          =			crop_size
						__lowerCAmelCase:       List[Any]          =			do_rescale
						__lowerCAmelCase:       int          =			rescale_factor
						__lowerCAmelCase:       List[Any]          =			do_normalize
						__lowerCAmelCase:       Any          =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						__lowerCAmelCase:       Optional[Any]          =			image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				PILImageResampling = PILImageResampling.BICUBIC     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Dict     ,  )->   np.ndarray:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Optional[int]     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Dict[str, int]     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Tuple     ,  )->   np.ndarray:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			get_size_dict(UpperCamelCase__)
						if "height" not in size or "width" not in size:
								raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(UpperCamelCase__     ,  size=(size["height"], size["width"])     ,  data_format=UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				float     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Optional[int])->   np.ndarray:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				np.ndarray     ,  UpperCamelCase__							:				Union[float, List[float]]     ,  UpperCamelCase__							:				Union[float, List[float]]     ,  UpperCamelCase__							:				Optional[Union[str, ChannelDimension]] = None     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   np.ndarray:



						'''simple docstring'''


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

				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				ImageInput     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				PILImageResampling = None     ,  UpperCamelCase__							:				bool = None     ,  UpperCamelCase__							:				Dict[str, int] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[float] = None     ,  UpperCamelCase__							:				Optional[bool] = None     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  UpperCamelCase__							:				Optional[Union[float, List[float]]] = None     ,  UpperCamelCase__							:				Optional[Union[str, TensorType]] = None     ,  UpperCamelCase__							:				Union[str, ChannelDimension] = ChannelDimension.FIRST     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Any          =			do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase:       str          =			size if size is not None else self.size
						__lowerCAmelCase:       Tuple          =			get_size_dict(UpperCamelCase__     ,  default_to_square=UpperCamelCase__)
						__lowerCAmelCase:       List[str]          =			resample if resample is not None else self.resample
						__lowerCAmelCase:       str          =			do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCAmelCase:       Tuple          =			crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase:       List[Any]          =			get_size_dict(UpperCamelCase__     ,  param_name="crop_size")
						__lowerCAmelCase:       List[Any]          =			do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase:       Optional[int]          =			rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase:       Optional[int]          =			do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase:       Union[str, Any]          =			image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase:       Tuple          =			image_std if image_std is not None else self.image_std

						__lowerCAmelCase:       Union[str, Any]          =			make_list_of_images(UpperCamelCase__)

						if not valid_images(UpperCamelCase__):
								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.
						__lowerCAmelCase:       Tuple          =			[to_numpy_array(UpperCamelCase__) for image in images]

						if do_resize:
								__lowerCAmelCase:       Union[str, Any]          =			[self.resize(image=UpperCamelCase__     ,  size=UpperCamelCase__     ,  resample=UpperCamelCase__) for image in images]

						if do_center_crop:
								__lowerCAmelCase:       Optional[Any]          =			[self.center_crop(image=UpperCamelCase__     ,  size=UpperCamelCase__) for image in images]

						if do_rescale:
								__lowerCAmelCase:       Optional[Any]          =			[self.rescale(image=UpperCamelCase__     ,  scale=UpperCamelCase__) for image in images]

						if do_normalize:
								__lowerCAmelCase:       List[str]          =			[self.normalize(image=UpperCamelCase__     ,  mean=UpperCamelCase__     ,  std=UpperCamelCase__) for image in images]

						__lowerCAmelCase:       Optional[Any]          =			[to_channel_dimension_format(UpperCamelCase__     ,  UpperCamelCase__) for image in images]

						__lowerCAmelCase:       List[str]          =			{"pixel_values": images}
						return BatchFeature(data=UpperCamelCase__     ,  tensor_type=UpperCamelCase__)




				def 							lowercase_     (	self							:				int     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				List[Tuple] = None)->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       Union[str, Any]          =			outputs.logits

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

								if is_torch_tensor(UpperCamelCase__):
										__lowerCAmelCase:       Optional[int]          =			target_sizes.numpy()

								__lowerCAmelCase:       List[Any]          =			[]

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

						return semantic_segmentation


