






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

import datasets


UpperCamelCase 				=	'''\
@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",
}
'''

UpperCamelCase 				=	'''\
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.
'''

UpperCamelCase 				=	'''
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       __UpperCAmelCase		(datasets.Metric    ):







			def 							UpperCamelCase      (	self:  List[Any]     ):







									'''simple docstring'''




									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:  Dict      ,	UpperCAmelCase_:  int      ,	UpperCAmelCase_:  Dict      ,	UpperCAmelCase_:  int = CHRF.CHAR_ORDER      ,	UpperCAmelCase_:  int = CHRF.WORD_ORDER      ,	UpperCAmelCase_:  int = CHRF.BETA      ,	UpperCAmelCase_:  bool = False      ,	UpperCAmelCase_:  bool = False      ,	UpperCAmelCase_:  bool = False      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  len(references[0]     )
									if any(len(__snake_case     ) != references_per_prediction for refs in references     ):
															raise ValueError("""Sacrebleu requires the same number of references for each prediction"""     )
									_SCREAMING_SNAKE_CASE	      =  [[refs[i] for refs in references] for i in range(__snake_case     )]

									_SCREAMING_SNAKE_CASE	      =  CHRF(__snake_case      ,	__snake_case      ,	__snake_case      ,	__snake_case      ,	__snake_case      ,	__snake_case     )
									_SCREAMING_SNAKE_CASE	      =  sb_chrf.corpus_score(__snake_case      ,	__snake_case     )

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





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

snake_case							:       str     			=      {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    str          =			'''mobilenet_v2'''
					def __init__(      self   :Optional[Any]			,__snake_case   :List[Any]=3			,__snake_case   :Any=2_24			,__snake_case   :Dict=1.0			,__snake_case   :str=8			,__snake_case   :Union[str, Any]=8			,__snake_case   :Optional[Any]=6			,__snake_case   :str=32			,__snake_case   :Tuple=True			,__snake_case   :Union[str, Any]=True			,__snake_case   :Any="relu6"			,__snake_case   :List[str]=True			,__snake_case   :Dict=0.8			,__snake_case   :Optional[int]=0.02			,__snake_case   :Tuple=0.0_01			,__snake_case   :Dict=2_55			,**__snake_case   :Dict			,)					->    Any:
												super().__init__(**__snake_case      )

												if depth_multiplier <= 0:
																			raise ValueError('depth_multiplier must be greater than zero.'      )

												a__             =     num_channels
												a__             =     image_size
												a__             =     depth_multiplier
												a__             =     depth_divisible_by
												a__             =     min_depth
												a__             =     expand_ratio
												a__             =     output_stride
												a__             =     first_layer_is_expansion
												a__             =     finegrained_output
												a__             =     hidden_act
												a__             =     tf_padding
												a__             =     classifier_dropout_prob
												a__             =     initializer_range
												a__             =     layer_norm_eps
												a__             =     semantic_loss_ignore_index







class        snake_case_   (lowerCamelCase_      ):
					UpperCAmelCase__					:    Tuple          =			version.parse('''1.11'''      )
					@property
					def  lowerCamelCase__(      self   :Any      )					->    Mapping[str, Mapping[int, str]]:
												return OrderedDict([('pixel_values', {0: 'batch'})]      )
					@property
					def  lowerCamelCase__(      self   :List[str]      )					->    Mapping[str, Mapping[int, str]]:
												if self.task == "image-classification":
																			return OrderedDict([('logits', {0: 'batch'})]      )
												else:
																			return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )



					@property
					def  lowerCamelCase__(      self   :Any      )					->    float:
												return 1E-4




