



"""simple docstring"""





import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase       :					List[str]             =					'''
@inproceedings{xu-etal-2016-optimizing,
    title = {Optimizing Statistical Machine Translation for Text Simplification},
    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {4},
    year={2016},
    url = {https://www.aclweb.org/anthology/Q16-1029},
    pages = {401--415
},
@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]             =					'''\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
'''


__lowerCAmelCase       :					Dict             =					'''
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.
Args:
    sources: list of source sentences where each sentence should be a string.
    predictions: list of predicted sentences where each sentence should be a string.
    references: list of lists of reference sentences where each sentence should be a string.
Returns:
    sari: sari score
    sacrebleu: sacrebleu score
    exact: exact score

Examples:
    >>> sources=["About 95 species are currently accepted ."]
    >>> predictions=["About 95 you now get in ."]
    >>> references=[["About 95 species are currently known ."]]
    >>> wiki_split = datasets.load_metric("wiki_split")
    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}
'''
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int    ):


						'''simple docstring'''






						def remove_articles(__UpperCamelCase      :			str    ):
												snake_case_   : Tuple						=			re.compile(r"""\b(a|an|the)\b"""					, re.UNICODE    )
												return re.sub(__UpperCamelCase					, """ """					, __UpperCamelCase    )

						def white_space_fix(__UpperCamelCase      :			Optional[Any]    ):
												return " ".join(text.split()    )

						def remove_punc(__UpperCamelCase      :			Any    ):
												snake_case_   : Optional[int]						=			set(string.punctuation    )
												return "".join(ch for ch in text if ch not in exclude    )

						def lower(__UpperCamelCase      :			Dict    ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(__UpperCamelCase    )    )    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			Optional[int]    ):


						'''simple docstring'''






						return int(normalize_answer(__UpperCamelCase    ) == normalize_answer(__UpperCamelCase    )    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			[any(compute_exact(__UpperCamelCase					, __UpperCamelCase    ) for ref in refs    ) for pred, refs in zip(__UpperCamelCase					, __UpperCamelCase    )]
						return (sum(__UpperCamelCase    ) / len(__UpperCamelCase    )) * 1_0_0
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : Dict						=			[rgram for rgrams in rgramslist for rgram in rgrams]
						snake_case_   : List[str]						=			Counter(__UpperCamelCase    )

						snake_case_   : str						=			Counter(__UpperCamelCase    )
						snake_case_   : str						=			Counter()
						for sgram, scount in sgramcounter.items():
												snake_case_   : Optional[int]						=			scount * numref

						snake_case_   : int						=			Counter(__UpperCamelCase    )
						snake_case_   : Tuple						=			Counter()
						for cgram, ccount in cgramcounter.items():
												snake_case_   : str						=			ccount * numref

						# KEEP
						snake_case_   : Optional[Any]						=			sgramcounter_rep & cgramcounter_rep
						snake_case_   : Dict						=			keepgramcounter_rep & rgramcounter
						snake_case_   : Optional[Any]						=			sgramcounter_rep & rgramcounter

						snake_case_   : Optional[int]						=			0
						snake_case_   : Tuple						=			0
						for keepgram in keepgramcountergood_rep:
												keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
												# Fix an alleged bug [2] in the keep score computation.
												# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
												keeptmpscorea += keepgramcountergood_rep[keepgram]
						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						#      a target exactly.
						snake_case_   : Optional[int]						=			1
						snake_case_   : Tuple						=			1
						if len(__UpperCamelCase    ) > 0:
												snake_case_   : int						=			keeptmpscorea / len(__UpperCamelCase    )
						if len(__UpperCamelCase    ) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												snake_case_   : List[str]						=			keeptmpscorea / sum(keepgramcounterall_rep.values()    )
						snake_case_   : Tuple						=			0
						if keepscore_precision > 0 or keepscore_recall > 0:
												snake_case_   : Union[str, Any]						=			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						snake_case_   : Union[str, Any]						=			sgramcounter_rep - cgramcounter_rep
						snake_case_   : Optional[Any]						=			delgramcounter_rep - rgramcounter
						snake_case_   : Tuple						=			sgramcounter_rep - rgramcounter
						snake_case_   : Optional[Any]						=			0
						snake_case_   : Dict						=			0
						for delgram in delgramcountergood_rep:
												deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
												deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						snake_case_   : str						=			1
						if len(__UpperCamelCase    ) > 0:
												snake_case_   : Tuple						=			deltmpscorea / len(__UpperCamelCase    )

						# ADDITION
						snake_case_   : Any						=			set(__UpperCamelCase    ) - set(__UpperCamelCase    )
						snake_case_   : Tuple						=			set(__UpperCamelCase    ) & set(__UpperCamelCase    )
						snake_case_   : Union[str, Any]						=			set(__UpperCamelCase    ) - set(__UpperCamelCase    )

						snake_case_   : int						=			0
						for addgram in addgramcountergood:
												addtmpscore += 1

						# Define 0/0=1 instead of 0 to give higher scores for predictions that match
						# a target exactly.
						snake_case_   : Tuple						=			1
						snake_case_   : Optional[Any]						=			1
						if len(__UpperCamelCase    ) > 0:
												snake_case_   : Dict						=			addtmpscore / len(__UpperCamelCase    )
						if len(__UpperCamelCase    ) > 0:
												snake_case_   : Tuple						=			addtmpscore / len(__UpperCamelCase    )
						snake_case_   : int						=			0
						if addscore_precision > 0 or addscore_recall > 0:
												snake_case_   : Optional[int]						=			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			List[str]    ):


						'''simple docstring'''






						snake_case_   : List[str]						=			len(__UpperCamelCase    )

						snake_case_   : Union[str, Any]						=			ssent.split(""" """    )
						snake_case_   : Optional[Any]						=			csent.split(""" """    )
						snake_case_   : int						=			[]
						snake_case_   : int						=			[]
						snake_case_   : Optional[int]						=			[]
						snake_case_   : List[str]						=			[]
						snake_case_   : str						=			[]
						snake_case_   : Union[str, Any]						=			[]

						snake_case_   : Dict						=			[]
						snake_case_   : List[str]						=			[]
						snake_case_   : int						=			[]
						snake_case_   : Dict						=			[]
						for rsent in rsents:
												snake_case_   : Any						=			rsent.split(""" """    )
												snake_case_   : List[str]						=			[]
												snake_case_   : Tuple						=			[]
												snake_case_   : Optional[Any]						=			[]
												ragramslist.append(__UpperCamelCase    )
												for i in range(0					, len(__UpperCamelCase    ) - 1    ):
																		if i < len(__UpperCamelCase    ) - 1:
																								snake_case_   : Optional[int]						=			ragrams[i] + """ """ + ragrams[i + 1]
																								ragrams.append(__UpperCamelCase    )
																		if i < len(__UpperCamelCase    ) - 2:
																								snake_case_   : Union[str, Any]						=			ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																								ragrams.append(__UpperCamelCase    )
																		if i < len(__UpperCamelCase    ) - 3:
																								snake_case_   : Union[str, Any]						=			ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																								ragrams.append(__UpperCamelCase    )
												ragramslist.append(__UpperCamelCase    )
												ragramslist.append(__UpperCamelCase    )
												ragramslist.append(__UpperCamelCase    )

						for i in range(0					, len(__UpperCamelCase    ) - 1    ):
												if i < len(__UpperCamelCase    ) - 1:
																		snake_case_   : Optional[Any]						=			sagrams[i] + """ """ + sagrams[i + 1]
																		sagrams.append(__UpperCamelCase    )
												if i < len(__UpperCamelCase    ) - 2:
																		snake_case_   : List[str]						=			sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
																		sagrams.append(__UpperCamelCase    )
												if i < len(__UpperCamelCase    ) - 3:
																		snake_case_   : Optional[Any]						=			sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
																		sagrams.append(__UpperCamelCase    )

						for i in range(0					, len(__UpperCamelCase    ) - 1    ):
												if i < len(__UpperCamelCase    ) - 1:
																		snake_case_   : Union[str, Any]						=			cagrams[i] + """ """ + cagrams[i + 1]
																		cagrams.append(__UpperCamelCase    )
												if i < len(__UpperCamelCase    ) - 2:
																		snake_case_   : Any						=			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
																		cagrams.append(__UpperCamelCase    )
												if i < len(__UpperCamelCase    ) - 3:
																		snake_case_   : Optional[int]						=			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
																		cagrams.append(__UpperCamelCase    )

						(snake_case_)   : Tuple						=			SARIngram(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						(snake_case_)   : Any						=			SARIngram(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						(snake_case_)   : Any						=			SARIngram(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						(snake_case_)   : str						=			SARIngram(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						snake_case_   : Dict						=			sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
						snake_case_   : Tuple						=			sum([delascore, delascore, delascore, delascore]    ) / 4
						snake_case_   : Any						=			sum([addascore, addascore, addascore, addascore]    ) / 4
						snake_case_   : Tuple						=			(avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			bool = True					, __UpperCamelCase      :			str = "13a"					, __UpperCamelCase      :			bool = True    ):


						'''simple docstring'''






						if lowercase:
												snake_case_   : Optional[int]						=			sentence.lower()

						if tokenizer in ["13a", "intl"]:
												if version.parse(sacrebleu.__version__    ).major >= 2:
																		snake_case_   : str						=			sacrebleu.metrics.bleu._get_tokenizer(__UpperCamelCase    )()(__UpperCamelCase    )
												else:
																		snake_case_   : Optional[int]						=			sacrebleu.TOKENIZERS[tokenizer]()(__UpperCamelCase    )
						elif tokenizer == "moses":
												snake_case_   : int						=			sacremoses.MosesTokenizer().tokenize(__UpperCamelCase					, return_str=__UpperCamelCase					, escape=__UpperCamelCase    )
						elif tokenizer == "penn":
												snake_case_   : Tuple						=			sacremoses.MosesTokenizer().penn_tokenize(__UpperCamelCase					, return_str=__UpperCamelCase    )
						else:
												snake_case_   : str						=			sentence

						if not return_str:
												snake_case_   : List[Any]						=			normalized_sent.split()

						return normalized_sent
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Dict					, __UpperCamelCase      :			Tuple    ):


						'''simple docstring'''






						if not (len(__UpperCamelCase    ) == len(__UpperCamelCase    ) == len(__UpperCamelCase    )):
												raise ValueError("""Sources length must match predictions and references lengths."""    )
						snake_case_   : Dict						=			0
						for src, pred, refs in zip(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    ):
												sari_score += SARIsent(normalize(__UpperCamelCase    )					, normalize(__UpperCamelCase    )					, [normalize(__UpperCamelCase    ) for sent in refs]    )
						snake_case_   : Dict						=			sari_score / len(__UpperCamelCase    )
						return 1_0_0 * sari_score
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			List[Any]					, __UpperCamelCase      :			List[Any]="exp"					, __UpperCamelCase      :			Dict=None					, __UpperCamelCase      :			Optional[int]=False					, __UpperCamelCase      :			str=False					, __UpperCamelCase      :			int=False					, ):


						'''simple docstring'''






						snake_case_   : str						=			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_   : List[str]						=			[[refs[i] for refs in references] for i in range(__UpperCamelCase    )]
						snake_case_   : List[Any]						=			sacrebleu.corpus_bleu(
						    __UpperCamelCase					, __UpperCamelCase					, smooth_method=__UpperCamelCase					, smooth_value=__UpperCamelCase					, force=__UpperCamelCase					, lowercase=__UpperCamelCase					, use_effective_order=__UpperCamelCase					, )
						return output.score






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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







								'''simple docstring'''

								return datasets.MetricInfo(
								    description=_DESCRIPTION							,    citation=_CITATION							,    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/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
								        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
								        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
								        """https://github.com/mjpost/sacreBLEU""",
								    ]							,    reference_urls=[
								        """https://www.aclweb.org/anthology/Q16-1029.pdf""",
								        """https://github.com/mjpost/sacreBLEU""",
								        """https://en.wikipedia.org/wiki/BLEU""",
								        """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
								    ]							,    )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : int						=			{}
								result.update({"""sari""": compute_sari(sources=_lowercase							,    predictions=_lowercase							,    references=_lowercase   )}   )
								result.update({"""sacrebleu""": compute_sacrebleu(predictions=_lowercase							,    references=_lowercase   )}   )
								result.update({"""exact""": compute_em(predictions=_lowercase							,    references=_lowercase   )}   )
								return result






"""simple docstring"""





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase       :					Tuple      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Dict      					=					{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''swin'''

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





		def __init__(					self							,    _lowercase=2_2_4							,    _lowercase=4							,    _lowercase=3							,    _lowercase=9_6							,    _lowercase=[2, 2, 6, 2]							,    _lowercase=[3, 6, 1_2, 2_4]							,    _lowercase=7							,    _lowercase=4.0							,    _lowercase=True							,    _lowercase=0.0							,    _lowercase=0.0							,    _lowercase=0.1							,    _lowercase="gelu"							,    _lowercase=False							,    _lowercase=0.02							,    _lowercase=1E-5							,    _lowercase=3_2							,    _lowercase=None							,    _lowercase=None							,    **_lowercase							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								super().__init__(**_lowercase   )

								snake_case_   : str						=			image_size
								snake_case_   : int						=			patch_size
								snake_case_   : Optional[int]						=			num_channels
								snake_case_   : Union[str, Any]						=			embed_dim
								snake_case_   : Optional[int]						=			depths
								snake_case_   : Optional[int]						=			len(_lowercase   )
								snake_case_   : Optional[Any]						=			num_heads
								snake_case_   : Optional[Any]						=			window_size
								snake_case_   : Optional[Any]						=			mlp_ratio
								snake_case_   : Optional[Any]						=			qkv_bias
								snake_case_   : Optional[Any]						=			hidden_dropout_prob
								snake_case_   : Tuple						=			attention_probs_dropout_prob
								snake_case_   : Union[str, Any]						=			drop_path_rate
								snake_case_   : List[Any]						=			hidden_act
								snake_case_   : str						=			use_absolute_embeddings
								snake_case_   : str						=			layer_norm_eps
								snake_case_   : Optional[Any]						=			initializer_range
								snake_case_   : Any						=			encoder_stride
								# 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
								snake_case_   : Tuple						=			int(embed_dim * 2 ** (len(_lowercase   ) - 1)   )
								snake_case_   : Tuple						=			["""stem"""] + [f'stage{idx}' for idx in range(1							,    len(_lowercase   ) + 1   )]
								snake_case_			,		snake_case_   : Any						=			get_aligned_output_features_output_indices(
								    out_features=_lowercase							,    out_indices=_lowercase							,    stage_names=self.stage_names   )




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       version.parse('''1.11'''		)





		@property
		def    UpperCAmelCase__							(					self   ) ->						Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								return OrderedDict(
								    [
								        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
								    ]   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						float:







								'''simple docstring'''

								return 1E-4

