



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

import datasets


A__  :  Optional[Any]								=					'''\
@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",
}
'''

A__  :  Optional[Any]								=					'''\
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.
'''

A__  :  Optional[int]								=					'''
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 				__snake_case						(     datasets.Metric						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									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    UpperCAmelCase__			(       self     :       Any				,				A_     :       str				,				A_     :       List[str]				,				A_     :       int = CHRF.CHAR_ORDER				,				A_     :       int = CHRF.WORD_ORDER				,				A_     :       int = CHRF.BETA				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       bool = False				,				):
									lowerCAmelCase_			:     Union[str, Any]									= 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''')
									lowerCAmelCase_			:     Any									= [[refs[i] for refs in references] for i in range(A_)]

									lowerCAmelCase_			:     List[str]									= CHRF(A_				,				A_				,				A_				,				A_				,				A_				,				A_)
									lowerCAmelCase_			:     List[Any]									= sb_chrf.corpus_score(A_				,				A_)

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










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A__  :  List[str]								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''maskformer-swin'''

							_a			      = {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(       self     :       Union[str, Any]				,				A_     :       Dict=2_2_4				,				A_     :       Optional[Any]=4				,				A_     :       List[str]=3				,				A_     :       str=9_6				,				A_     :       Optional[Any]=[2, 2, 6, 2]				,				A_     :       Tuple=[3, 6, 1_2, 2_4]				,				A_     :       List[Any]=7				,				A_     :       List[Any]=4.0				,				A_     :       List[str]=True				,				A_     :       Dict=0.0				,				A_     :       int=0.0				,				A_     :       str=0.1				,				A_     :       Optional[int]="gelu"				,				A_     :       List[Any]=False				,				A_     :       int=0.02				,				A_     :       int=1e-5				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				**A_     :       List[Any]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     Dict									= image_size
									lowerCAmelCase_			:     Optional[Any]									= patch_size
									lowerCAmelCase_			:     Tuple									= num_channels
									lowerCAmelCase_			:     Any									= embed_dim
									lowerCAmelCase_			:     List[str]									= depths
									lowerCAmelCase_			:     Union[str, Any]									= len(A_)
									lowerCAmelCase_			:     List[str]									= num_heads
									lowerCAmelCase_			:     Dict									= window_size
									lowerCAmelCase_			:     Optional[int]									= mlp_ratio
									lowerCAmelCase_			:     Dict									= qkv_bias
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[int]									= drop_path_rate
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     str									= use_absolute_embeddings
									lowerCAmelCase_			:     List[str]									= layer_norm_eps
									lowerCAmelCase_			:     int									= initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase_			:     str									= int(embed_dim * 2 ** (len(A_) - 1))
									lowerCAmelCase_			:     Optional[Any]									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     int									= get_aligned_output_features_output_indices(
									    out_features=A_				,				out_indices=A_				,				stage_names=self.stage_names)





