





'''simple docstring'''



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 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, logging


_SCREAMING_SNAKE_CASE    =			logging.get_logger(__name__)






class 							__lowercase				(					__lowercase  ):







	'''simple docstring'''

	a			: List[Any]      =				['''pixel_values''']


	def __init__(self   ,_lowerCamelCase = True   ,_lowerCamelCase = None   ,_lowerCamelCase = PILImageResampling.BILINEAR   ,_lowerCamelCase = True   ,_lowerCamelCase = 1 / 255   ,_lowerCamelCase = True   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,**_lowerCamelCase   ,)							->							Tuple:







				'''simple docstring'''





				super().__init__(**_UpperCamelCase       )
				__lowercase   =							size if size is not None else {'''height''': 224, '''width''': 224}
				__lowercase   =							get_size_dict(_UpperCamelCase       )
				__lowercase   =							do_resize
				__lowercase   =							do_rescale
				__lowercase   =							do_normalize
				__lowercase   =							size
				__lowercase   =							resample
				__lowercase   =							rescale_factor
				__lowercase   =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__lowercase   =							image_std if image_std is not None else IMAGENET_STANDARD_STD


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = PILImageResampling.BILINEAR   ,_lowerCamelCase = None   ,**_lowerCamelCase   ,)							->							Any:







				'''simple docstring'''





				__lowercase   =							get_size_dict(_UpperCamelCase       )
				if "height" not in size or "width" not in size:
							raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"       )
				return resize(
				    _UpperCamelCase   ,size=(size['''height'''], size['''width'''])   ,resample=_UpperCamelCase   ,data_format=_UpperCamelCase   ,**_UpperCamelCase       )


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = None   ,**_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase = None   ,**_lowerCamelCase   ,)							->							List[Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = None   ,_lowerCamelCase = ChannelDimension.FIRST   ,**_lowerCamelCase   ,)							->							Optional[Any]:







				'''simple docstring'''





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

				__lowercase   =							size if size is not None else self.size
				__lowercase   =							get_size_dict(_UpperCamelCase       )

				__lowercase   =							make_list_of_images(_UpperCamelCase       )

				if not valid_images(_UpperCamelCase       ):
							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:
							raise ValueError('''Size must be specified if do_resize is True.'''       )

				if do_rescale and rescale_factor is None:
							raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

				# All transformations expect numpy arrays.
				__lowercase   =							[to_numpy_array(_UpperCamelCase       ) for image in images]

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

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

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

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

				__lowercase   =							{'''pixel_values''': images}
				return BatchFeature(data=_UpperCamelCase   ,tensor_type=_UpperCamelCase       )













from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__snake_case			:			Union[str, Any]			   =   """\
@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},
}
"""

__snake_case			:			Dict			   =   """\
SARI is a metric used for evaluating automatic text simplification systems.
The metric compares the predicted simplified sentences against the reference
and the source sentences. It explicitly measures the goodness of words that are
added, deleted and kept by the system.
Sari = (F1_add + F1_keep + P_del) / 3
where
F1_add: n-gram F1 score for add operation
F1_keep: n-gram F1 score for keep operation
P_del: n-gram precision score for delete operation
n = 4, as in the original paper.

This implementation is adapted from Tensorflow's tensor2tensor implementation [3].
It has two differences with the original GitHub [1] implementation:
  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match
      a target exactly.
  (2) Fixes an alleged bug [2] in the keep score computation.
[1] https://github.com/cocoxu/simplification/blob/master/SARI.py
    (commit 0210f15)
[2] https://github.com/cocoxu/simplification/issues/6
[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py
"""


__snake_case			:			int			   =   """
Calculates sari score (between 0 and 100) given a list of source and predicted
sentences, and a list of lists of reference sentences.
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
Examples:
    >>> sources=[\"About 95 species are currently accepted .\"]
    >>> predictions=[\"About 95 you now get in .\"]
    >>> references=[[\"About 95 species are currently known .\",\"About 95 species are now accepted .\",\"95 species are now accepted .\"]]
    >>> sari = datasets.load_metric(\"sari\")
    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)
    >>> print(results)
    {'sari': 26.953601953601954}
"""







def 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Optional[int]			,				UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    int						)     ->      Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  [rgram for rgrams in rgramslist for rgram in rgrams]
						lowerCAmelCase__			  =  Counter(UpperCamelCase_						)

						lowerCAmelCase__			  =  Counter(UpperCamelCase_						)
						lowerCAmelCase__			  =  Counter()
						for sgram, scount in sgramcounter.items():
												lowerCAmelCase__			  =  scount * numref

						lowerCAmelCase__			  =  Counter(UpperCamelCase_						)
						lowerCAmelCase__			  =  Counter()
						for cgram, ccount in cgramcounter.items():
												lowerCAmelCase__			  =  ccount * numref

						# KEEP
						lowerCAmelCase__			  =  sgramcounter_rep & cgramcounter_rep
						lowerCAmelCase__			  =  keepgramcounter_rep & rgramcounter
						lowerCAmelCase__			  =  sgramcounter_rep & rgramcounter

						lowerCAmelCase__			  =  0
						lowerCAmelCase__			  =  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.
						lowerCAmelCase__			  =  1
						lowerCAmelCase__			  =  1
						if len(UpperCamelCase_						) > 0:
												lowerCAmelCase__			  =  keeptmpscorea / len(UpperCamelCase_						)
						if len(UpperCamelCase_						) > 0:
												# Fix an alleged bug [2] in the keep score computation.
												# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
												lowerCAmelCase__			  =  keeptmpscorea / sum(keepgramcounterall_rep.values()						)
						lowerCAmelCase__			  =  0
						if keepscore_precision > 0 or keepscore_recall > 0:
												lowerCAmelCase__			  =  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

						# DELETION
						lowerCAmelCase__			  =  sgramcounter_rep - cgramcounter_rep
						lowerCAmelCase__			  =  delgramcounter_rep - rgramcounter
						lowerCAmelCase__			  =  sgramcounter_rep - rgramcounter
						lowerCAmelCase__			  =  0
						lowerCAmelCase__			  =  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.
						lowerCAmelCase__			  =  1
						if len(UpperCamelCase_						) > 0:
												lowerCAmelCase__			  =  deltmpscorea / len(UpperCamelCase_						)

						# ADDITION
						lowerCAmelCase__			  =  set(UpperCamelCase_						) - set(UpperCamelCase_						)
						lowerCAmelCase__			  =  set(UpperCamelCase_						) & set(UpperCamelCase_						)
						lowerCAmelCase__			  =  set(UpperCamelCase_						) - set(UpperCamelCase_						)

						lowerCAmelCase__			  =  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.
						lowerCAmelCase__			  =  1
						lowerCAmelCase__			  =  1
						if len(UpperCamelCase_						) > 0:
												lowerCAmelCase__			  =  addtmpscore / len(UpperCamelCase_						)
						if len(UpperCamelCase_						) > 0:
												lowerCAmelCase__			  =  addtmpscore / len(UpperCamelCase_						)
						lowerCAmelCase__			  =  0
						if addscore_precision > 0 or addscore_recall > 0:
												lowerCAmelCase__			  =  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

						return (keepscore, delscore_precision, addscore)







def 		_UpperCamelCase      (			UpperCamelCase_				:    Any			,				UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Optional[int]						)     ->      List[Any]:

						"""simple docstring"""



						lowerCAmelCase__			  =  len(UpperCamelCase_						)

						lowerCAmelCase__			  =  ssent.split(' '						)
						lowerCAmelCase__			  =  csent.split(' '						)
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []

						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						lowerCAmelCase__			  =  []
						for rsent in rsents:
												lowerCAmelCase__			  =  rsent.split(' '						)
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  []
												lowerCAmelCase__			  =  []
												ragramslist.append(UpperCamelCase_						)
												for i in range(0			,				len(UpperCamelCase_						) - 1						):
																		if i < len(UpperCamelCase_						) - 1:
																								lowerCAmelCase__			  =  ragrams[i] + ' ' + ragrams[i + 1]
																								ragrams.append(UpperCamelCase_						)
																		if i < len(UpperCamelCase_						) - 2:
																								lowerCAmelCase__			  =  ragrams[i] + ' ' + ragrams[i + 1] + ' ' + ragrams[i + 2]
																								ragrams.append(UpperCamelCase_						)
																		if i < len(UpperCamelCase_						) - 3:
																								lowerCAmelCase__			  =  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:
																		lowerCAmelCase__			  =  sagrams[i] + ' ' + sagrams[i + 1]
																		sagrams.append(UpperCamelCase_						)
												if i < len(UpperCamelCase_						) - 2:
																		lowerCAmelCase__			  =  sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
																		sagrams.append(UpperCamelCase_						)
												if i < len(UpperCamelCase_						) - 3:
																		lowerCAmelCase__			  =  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:
																		lowerCAmelCase__			  =  cagrams[i] + ' ' + cagrams[i + 1]
																		cagrams.append(UpperCamelCase_						)
												if i < len(UpperCamelCase_						) - 2:
																		lowerCAmelCase__			  =  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
																		cagrams.append(UpperCamelCase_						)
												if i < len(UpperCamelCase_						) - 3:
																		lowerCAmelCase__			  =  cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
																		cagrams.append(UpperCamelCase_						)

						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						((lowerCAmelCase__)  ,				(lowerCAmelCase__)  ,				(lowerCAmelCase__))			  =  SARIngram(UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_			,				UpperCamelCase_						)
						lowerCAmelCase__			  =  sum([keepascore, keepascore, keepascore, keepascore]						) / 4
						lowerCAmelCase__			  =  sum([delascore, delascore, delascore, delascore]						) / 4
						lowerCAmelCase__			  =  sum([addascore, addascore, addascore, addascore]						) / 4
						lowerCAmelCase__			  =  (avgkeepscore + avgdelscore + avgaddscore) / 3
						return finalscore







def 		_UpperCamelCase      (			UpperCamelCase_				:    Dict			,				UpperCamelCase_				:    bool = True			,				UpperCamelCase_				:    str = "13a"			,				UpperCamelCase_				:    bool = True						)     ->      List[str]:

						"""simple docstring"""



						if lowercase:
												lowerCAmelCase__			  =  sentence.lower()

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

						if not return_str:
												lowerCAmelCase__			  =  normalized_sent.split()

						return normalized_sent


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






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION   ,  citation=_CITATION   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
												        {
												            'sources': datasets.Value('string'   ,  id='sequence'      ),
												            'predictions': datasets.Value('string'   ,  id='sequence'      ),
												            'references': datasets.Sequence(datasets.Value('string'   ,  id='sequence'      )   ,  id='references'      ),
												        }      )   ,  codebase_urls=[
												        'https://github.com/cocoxu/simplification/blob/master/SARI.py',
												        'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py',
												    ]   ,  reference_urls=['https://www.aclweb.org/anthology/Q16-1029.pdf']   ,  )







						def 					UpperCamelCase__					(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												if not (len(_UpperCamelCase      ) == len(_UpperCamelCase      ) == len(_UpperCamelCase      )):
																		raise ValueError('Sources length must match predictions and references lengths.'      )
												lowerCAmelCase__			  =  0
												for src, pred, refs in zip(_UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):
																		sari_score += SARIsent(normalize(_UpperCamelCase      )   ,  normalize(_UpperCamelCase      )   ,  [normalize(_UpperCamelCase      ) for sent in refs]      )
												lowerCAmelCase__			  =  sari_score / len(_UpperCamelCase      )
												return {"sari": 1_00 * sari_score}





