


"""simple docstring"""






import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


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


UpperCamelCase__				:Optional[int]			   =	"""\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=[\"About 95 species are currently accepted .\"]\n    >>> predictions=[\"About 95 you now get in .\"]\n    >>> references=[[\"About 95 species are currently known .\"]]\n    >>> wiki_split = datasets.load_metric(\"wiki_split\")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {'sari': 21.805555555555557, 'sacrebleu': 14.535768424205482, 'exact': 0.0}\n"""





def 				A_       ( snake_case__		)					->  Optional[Any]:






			def remove_articles(snake_case__		):
						_UpperCamelCase					:List[str]					     =	re.compile(R'''\b(a|an|the)\b'''       ,					re.UNICODE		)
						return re.sub(snake_case__       ,					''' '''       ,					snake_case__		)

			def white_space_fix(snake_case__		):
						return " ".join(text.split()		)

			def remove_punc(snake_case__		):
						_UpperCamelCase					:str					     =	set(string.punctuation		)
						return "".join(ch for ch in text if ch not in exclude		)

			def lower(snake_case__		):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(snake_case__		)		)		)		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  int:
			return int(normalize_answer(snake_case__		) == normalize_answer(snake_case__		)		)





def 				A_       ( snake_case__       ,					snake_case__		)					->  str:
			_UpperCamelCase					:List[Any]					     =	[any(compute_exact(snake_case__       ,					snake_case__		) for ref in refs		) for pred, refs in zip(snake_case__       ,					snake_case__		)]
			return (sum(snake_case__		) / len(snake_case__		)) * 1_00





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)					->  str:
			_UpperCamelCase					:str					     =	[rgram for rgrams in rgramslist for rgram in rgrams]
			_UpperCamelCase					:Union[str, Any]					     =	Counter(snake_case__		)

			_UpperCamelCase					:List[Any]					     =	Counter(snake_case__		)
			_UpperCamelCase					:List[str]					     =	Counter()
			for sgram, scount in sgramcounter.items():
						_UpperCamelCase					:Optional[int]					     =	scount * numref

			_UpperCamelCase					:Optional[Any]					     =	Counter(snake_case__		)
			_UpperCamelCase					:Tuple					     =	Counter()
			for cgram, ccount in cgramcounter.items():
						_UpperCamelCase					:Any					     =	ccount * numref

			# KEEP
			_UpperCamelCase					:str					     =	sgramcounter_rep & cgramcounter_rep
			_UpperCamelCase					:Optional[Any]					     =	keepgramcounter_rep & rgramcounter
			_UpperCamelCase					:Tuple					     =	sgramcounter_rep & rgramcounter

			_UpperCamelCase					:Any					     =	0
			_UpperCamelCase					: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.
			_UpperCamelCase					:str					     =	1
			_UpperCamelCase					:Optional[int]					     =	1
			if len(snake_case__		) > 0:
						_UpperCamelCase					:Tuple					     =	keeptmpscorea / len(snake_case__		)
			if len(snake_case__		) > 0:
						# Fix an alleged bug [2] in the keep score computation.
						# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
						_UpperCamelCase					:int					     =	keeptmpscorea / sum(keepgramcounterall_rep.values()		)
			_UpperCamelCase					:List[str]					     =	0
			if keepscore_precision > 0 or keepscore_recall > 0:
						_UpperCamelCase					:Union[str, Any]					     =	2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

			# DELETION
			_UpperCamelCase					:int					     =	sgramcounter_rep - cgramcounter_rep
			_UpperCamelCase					:List[Any]					     =	delgramcounter_rep - rgramcounter
			_UpperCamelCase					:Dict					     =	sgramcounter_rep - rgramcounter
			_UpperCamelCase					:Tuple					     =	0
			_UpperCamelCase					: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.
			_UpperCamelCase					:Union[str, Any]					     =	1
			if len(snake_case__		) > 0:
						_UpperCamelCase					:Tuple					     =	deltmpscorea / len(snake_case__		)

			# ADDITION
			_UpperCamelCase					:str					     =	set(snake_case__		) - set(snake_case__		)
			_UpperCamelCase					:Union[str, Any]					     =	set(snake_case__		) & set(snake_case__		)
			_UpperCamelCase					:Union[str, Any]					     =	set(snake_case__		) - set(snake_case__		)

			_UpperCamelCase					:Union[str, Any]					     =	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.
			_UpperCamelCase					:Tuple					     =	1
			_UpperCamelCase					:str					     =	1
			if len(snake_case__		) > 0:
						_UpperCamelCase					:Any					     =	addtmpscore / len(snake_case__		)
			if len(snake_case__		) > 0:
						_UpperCamelCase					:int					     =	addtmpscore / len(snake_case__		)
			_UpperCamelCase					:List[Any]					     =	0
			if addscore_precision > 0 or addscore_recall > 0:
						_UpperCamelCase					:Any					     =	2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

			return (keepscore, delscore_precision, addscore)





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  List[str]:
			_UpperCamelCase					:List[Any]					     =	len(snake_case__		)

			_UpperCamelCase					:int					     =	ssent.split(''' '''		)
			_UpperCamelCase					:Optional[int]					     =	csent.split(''' '''		)
			_UpperCamelCase					:int					     =	[]
			_UpperCamelCase					:int					     =	[]
			_UpperCamelCase					:Union[str, Any]					     =	[]
			_UpperCamelCase					:Dict					     =	[]
			_UpperCamelCase					:Any					     =	[]
			_UpperCamelCase					:str					     =	[]

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

			for i in range(0       ,					len(snake_case__		) - 1		):
						if i < len(snake_case__		) - 1:
									_UpperCamelCase					:List[Any]					     =	sagrams[i] + ''' ''' + sagrams[i + 1]
									sagrams.append(snake_case__		)
						if i < len(snake_case__		) - 2:
									_UpperCamelCase					:List[Any]					     =	sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2]
									sagrams.append(snake_case__		)
						if i < len(snake_case__		) - 3:
									_UpperCamelCase					:Optional[int]					     =	sagrams[i] + ''' ''' + sagrams[i + 1] + ''' ''' + sagrams[i + 2] + ''' ''' + sagrams[i + 3]
									sagrams.append(snake_case__		)

			for i in range(0       ,					len(snake_case__		) - 1		):
						if i < len(snake_case__		) - 1:
									_UpperCamelCase					:str					     =	cagrams[i] + ''' ''' + cagrams[i + 1]
									cagrams.append(snake_case__		)
						if i < len(snake_case__		) - 2:
									_UpperCamelCase					:Optional[int]					     =	cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2]
									cagrams.append(snake_case__		)
						if i < len(snake_case__		) - 3:
									_UpperCamelCase					:str					     =	cagrams[i] + ''' ''' + cagrams[i + 1] + ''' ''' + cagrams[i + 2] + ''' ''' + cagrams[i + 3]
									cagrams.append(snake_case__		)

			((_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase))					:Dict					     =	SARIngram(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
			((_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase))					:Optional[Any]					     =	SARIngram(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
			((_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase))					:Dict					     =	SARIngram(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
			((_UpperCamelCase)     ,      (_UpperCamelCase)     ,      (_UpperCamelCase))					:Union[str, Any]					     =	SARIngram(snake_case__       ,					snake_case__       ,					snake_case__       ,					snake_case__		)
			_UpperCamelCase					:Optional[Any]					     =	sum([keepascore, keepascore, keepascore, keepascore]		) / 4
			_UpperCamelCase					:Tuple					     =	sum([delascore, delascore, delascore, delascore]		) / 4
			_UpperCamelCase					:List[str]					     =	sum([addascore, addascore, addascore, addascore]		) / 4
			_UpperCamelCase					:Dict					     =	(avgkeepscore + avgdelscore + avgaddscore) / 3
			return finalscore





def 				A_       ( snake_case__       ,					snake_case__ = True       ,					snake_case__ = "13a"       ,					snake_case__ = True		)					->  int:






			if lowercase:
						_UpperCamelCase					:str					     =	sentence.lower()

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

			if not return_str:
						_UpperCamelCase					:Optional[Any]					     =	normalized_sent.split()

			return normalized_sent





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__		)					->  Dict:






			if not (len(snake_case__		) == len(snake_case__		) == len(snake_case__		)):
						raise ValueError('''Sources length must match predictions and references lengths.'''		)
			_UpperCamelCase					:str					     =	0
			for src, pred, refs in zip(snake_case__       ,					snake_case__       ,					snake_case__		):
						sari_score += SARIsent(normalize(snake_case__		)       ,					normalize(snake_case__		)       ,					[normalize(snake_case__		) for sent in refs]		)
			_UpperCamelCase					:List[Any]					     =	sari_score / len(snake_case__		)
			return 1_00 * sari_score





def 				A_       ( snake_case__       ,					snake_case__       ,					snake_case__="exp"       ,					snake_case__=None       ,					snake_case__=False       ,					snake_case__=False       ,					snake_case__=False       ,					)					->  str:
			_UpperCamelCase					:List[Any]					     =	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'''		)
			_UpperCamelCase					:Optional[Any]					     =	[[refs[i] for refs in references] for i in range(snake_case__		)]
			_UpperCamelCase					:Tuple					     =	sacrebleu.corpus_bleu(
			    snake_case__       ,					snake_case__       ,					smooth_method=snake_case__       ,					smooth_value=snake_case__       ,					force=snake_case__       ,					lowercase=snake_case__       ,					use_effective_order=snake_case__       ,					)
			return output.score






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

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	str:

					"""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					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	{}
					result.update({'''sari''': compute_sari(sources=SCREAMING_SNAKE_CASE__					,				predictions=SCREAMING_SNAKE_CASE__					,				references=SCREAMING_SNAKE_CASE__   )}   )
					result.update({'''sacrebleu''': compute_sacrebleu(predictions=SCREAMING_SNAKE_CASE__					,				references=SCREAMING_SNAKE_CASE__   )}   )
					result.update({'''exact''': compute_em(predictions=SCREAMING_SNAKE_CASE__					,				references=SCREAMING_SNAKE_CASE__   )}   )
					return result



from typing import Dict, List, Optional, 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_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Dict						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = None							,				a = PILImageResampling.BILINEAR							,				a = True							,				a = 1 / 2_55							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""shortest_edge""": 3_84}
								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)

								_UpperCamelCase         =						do_resize
								_UpperCamelCase         =						size
								# Default value set here for backwards compatibility where the value in config is None
								_UpperCamelCase         =						crop_pct if crop_pct is not None else 2_24 / 2_56
								_UpperCamelCase         =						resample
								_UpperCamelCase         =						do_rescale
								_UpperCamelCase         =						rescale_factor
								_UpperCamelCase         =						do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCamelCase         =						image_std if image_std is not None else IMAGENET_STANDARD_STD

						def 			A_					(      self							,				a							,				a							,				a							,				a = PILImageResampling.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)
								if "shortest_edge" not in size:
										raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'					)
								_UpperCamelCase         =						size["""shortest_edge"""]

								if shortest_edge < 3_84:
										# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
										_UpperCamelCase         =						int(shortest_edge / crop_pct					)
										_UpperCamelCase         =						get_resize_output_image_size(a							,				size=a							,				default_to_square=a					)
										_UpperCamelCase         =						resize(image=a							,				size=a							,				resample=a							,				data_format=a							,				**a					)
										# then crop to (shortest_edge, shortest_edge)
										return center_crop(image=a							,				size=(shortest_edge, shortest_edge)							,				data_format=a							,				**a					)
								else:
										# warping (no cropping) when evaluated at 384 or larger
										return resize(
										    a							,				size=(shortest_edge, shortest_edge)							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   Optional[int]:


								'''simple docstring'''

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

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

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







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

								_UpperCamelCase         =						do_resize if do_resize is not None else self.do_resize
								_UpperCamelCase         =						crop_pct if crop_pct is not None else self.crop_pct
								_UpperCamelCase         =						resample if resample is not None else self.resample
								_UpperCamelCase         =						do_rescale if do_rescale is not None else self.do_rescale
								_UpperCamelCase         =						rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCamelCase         =						do_normalize if do_normalize is not None else self.do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else self.image_mean
								_UpperCamelCase         =						image_std if image_std is not None else self.image_std

								_UpperCamelCase         =						size if size is not None else self.size
								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)

								_UpperCamelCase         =						make_list_of_images(a					)

								if not valid_images(a					):
										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 or resample is None:
										raise ValueError("""Size and resample must be specified if do_resize is True."""					)

								if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
										raise ValueError("""crop_pct must be specified if size < 384."""					)

								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.
								_UpperCamelCase         =						[to_numpy_array(a					) for image in images]

								if do_resize:
										_UpperCamelCase         =						[self.resize(image=a							,				size=a							,				crop_pct=a							,				resample=a					) for image in images]

								if do_rescale:
										_UpperCamelCase         =						[self.rescale(image=a							,				scale=a					) for image in images]

								if do_normalize:
										_UpperCamelCase         =						[self.normalize(image=a							,				mean=a							,				std=a					) for image in images]

								_UpperCamelCase         =						[to_channel_dimension_format(a							,				a					) for image in images]

								_UpperCamelCase         =						{"""pixel_values""": images}
								return BatchFeature(data=a							,				tensor_type=a					)



