


'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase  :		List[Any]		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Dict		     =	{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}



class  lowerCAmelCase				(				__SCREAMING_SNAKE_CASE							):
	_lowerCamelCase   :					str			      = 'lxmert'
	_lowerCamelCase   :					str			      = {}



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=9500 ,					snake_case__=1600 ,					snake_case__=400 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=9 ,					snake_case__=5 ,					snake_case__=5 ,					snake_case__=2048 ,					snake_case__=4 ,					snake_case__=6.6_7 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					**snake_case__ ,					):
		lowerCAmelCase					:    List[str]      							= vocab_size
		lowerCAmelCase					:    List[Any]      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= num_attention_heads
		lowerCAmelCase					:    Dict      							= hidden_act
		lowerCAmelCase					:    Tuple      							= intermediate_size
		lowerCAmelCase					:    List[Any]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[int]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    List[str]      							= max_position_embeddings
		lowerCAmelCase					:    Any      							= type_vocab_size
		lowerCAmelCase					:    int      							= initializer_range
		lowerCAmelCase					:    List[str]      							= layer_norm_eps
		lowerCAmelCase					:    Tuple      							= num_qa_labels
		lowerCAmelCase					:    List[str]      							= num_object_labels
		lowerCAmelCase					:    int      							= num_attr_labels
		lowerCAmelCase					:    int      							= l_layers
		lowerCAmelCase					:    List[Any]      							= x_layers
		lowerCAmelCase					:    List[str]      							= r_layers
		lowerCAmelCase					:    Union[str, Any]      							= visual_feat_dim
		lowerCAmelCase					:    Tuple      							= visual_pos_dim
		lowerCAmelCase					:    str      							= visual_loss_normalizer
		lowerCAmelCase					:    List[str]      							= task_matched
		lowerCAmelCase					:    Optional[Any]      							= task_mask_lm
		lowerCAmelCase					:    Union[str, Any]      							= task_obj_predict
		lowerCAmelCase					:    Tuple      							= task_qa
		lowerCAmelCase					:    List[Any]      							= visual_obj_loss
		lowerCAmelCase					:    List[str]      							= visual_attr_loss
		lowerCAmelCase					:    Dict      							= visual_feat_loss
		lowerCAmelCase					:    Any      							= {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
		super().__init__(**_a    )




'''simple docstring'''




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

import datasets


_lowerCAmelCase  :		Dict		     =	'\\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'

_lowerCAmelCase  :		Optional[Any]		     =	'\\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'

_lowerCAmelCase  :		List[Any]		     =	'\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  lowerCAmelCase				(				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 ,					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 ,					):
		lowerCAmelCase					:    List[str]      							= 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'    )
		lowerCAmelCase					:    List[str]      							= [[refs[i] for refs in references] for i in range(snake_case__    )]

		lowerCAmelCase					:    Union[str, Any]      							= CHRF(snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__    )
		lowerCAmelCase					:    Dict      							= 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,
		}
