






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


A_			:Optional[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},
}
'''

A_			:List[str]	    =		'''\
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
'''


A_			:List[Any]	    =		'''
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 				A	(			a_				,a_				,a_				,a_   )	->		List[Any]:
					__UpperCamelCase :   Tuple									=[rgram for rgrams in rgramslist for rgram in rgrams]
					__UpperCamelCase :   int									=Counter(a_   )

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

					__UpperCamelCase :   Tuple									=Counter(a_   )
					__UpperCamelCase :   str									=Counter()
					for cgram, ccount in cgramcounter.items():
										__UpperCamelCase :   List[str]									=ccount * numref

					# KEEP
					__UpperCamelCase :   Optional[Any]									=sgramcounter_rep & cgramcounter_rep
					__UpperCamelCase :   str									=keepgramcounter_rep & rgramcounter
					__UpperCamelCase :   List[str]									=sgramcounter_rep & rgramcounter

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

					# DELETION
					__UpperCamelCase :   Union[str, Any]									=sgramcounter_rep - cgramcounter_rep
					__UpperCamelCase :   List[str]									=delgramcounter_rep - rgramcounter
					__UpperCamelCase :   List[Any]									=sgramcounter_rep - rgramcounter
					__UpperCamelCase :   Optional[Any]									=0
					__UpperCamelCase :   Tuple									=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 :   int									=1
					if len(a_   ) > 0:
										__UpperCamelCase :   List[Any]									=deltmpscorea / len(a_   )

					# ADDITION
					__UpperCamelCase :   Dict									=set(a_   ) - set(a_   )
					__UpperCamelCase :   Optional[int]									=set(a_   ) & set(a_   )
					__UpperCamelCase :   Tuple									=set(a_   ) - set(a_   )

					__UpperCamelCase :   str									=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 :   Dict									=1
					__UpperCamelCase :   Any									=1
					if len(a_   ) > 0:
										__UpperCamelCase :   Optional[Any]									=addtmpscore / len(a_   )
					if len(a_   ) > 0:
										__UpperCamelCase :   Dict									=addtmpscore / len(a_   )
					__UpperCamelCase :   List[str]									=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	(			a_				,a_				,a_   )	->		int:
					__UpperCamelCase :   Optional[Any]									=len(a_   )

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

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

					for i in range(0				,len(a_   ) - 1   ):
										if i < len(a_   ) - 1:
															__UpperCamelCase :   Any									=sagrams[i] + ' ' + sagrams[i + 1]
															sagrams.append(a_   )
										if i < len(a_   ) - 2:
															__UpperCamelCase :   Union[str, Any]									=sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2]
															sagrams.append(a_   )
										if i < len(a_   ) - 3:
															__UpperCamelCase :   int									=sagrams[i] + ' ' + sagrams[i + 1] + ' ' + sagrams[i + 2] + ' ' + sagrams[i + 3]
															sagrams.append(a_   )

					for i in range(0				,len(a_   ) - 1   ):
										if i < len(a_   ) - 1:
															__UpperCamelCase :   Optional[Any]									=cagrams[i] + ' ' + cagrams[i + 1]
															cagrams.append(a_   )
										if i < len(a_   ) - 2:
															__UpperCamelCase :   Dict									=cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2]
															cagrams.append(a_   )
										if i < len(a_   ) - 3:
															__UpperCamelCase :   Dict									=cagrams[i] + ' ' + cagrams[i + 1] + ' ' + cagrams[i + 2] + ' ' + cagrams[i + 3]
															cagrams.append(a_   )

					((__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)) :   Any									=SARIngram(a_				,a_				,a_				,a_   )
					((__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)) :   List[Any]									=SARIngram(a_				,a_				,a_				,a_   )
					((__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)) :   Union[str, Any]									=SARIngram(a_				,a_				,a_				,a_   )
					((__UpperCamelCase)						,							(__UpperCamelCase)						,							(__UpperCamelCase)) :   Optional[int]									=SARIngram(a_				,a_				,a_				,a_   )
					__UpperCamelCase :   Optional[Any]									=sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
					__UpperCamelCase :   Optional[int]									=sum([delascore, delascore, delascore, delascore]   ) / 4
					__UpperCamelCase :   Any									=sum([addascore, addascore, addascore, addascore]   ) / 4
					__UpperCamelCase :   Optional[Any]									=(avgkeepscore + avgdelscore + avgaddscore) / 3
					return finalscore






def 				A	(			a_				,a_ = True				,a_ = "13a"				,a_ = True   )	->		Optional[Any]:
					# Normalization is requried for the ASSET dataset (one of the primary
					# datasets in sentence simplification) to allow using space
					# to split the sentence. Even though Wiki-Auto and TURK datasets,
					# do not require normalization, we do it for consistency.
					# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
					# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

					if lowercase:
										__UpperCamelCase :   Union[str, Any]									=sentence.lower()

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

					if not return_str:
										__UpperCamelCase :   Dict									=normalized_sent.split()

					return normalized_sent




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





				"""simple docstring"""







				def  __lowercase				(					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  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									if not (len(lowerCamelCase__       ) == len(lowerCamelCase__       ) == len(lowerCamelCase__       )):
														raise ValueError('Sources length must match predictions and references lengths.'       )
									__UpperCamelCase :   Optional[Any]									=0
									for src, pred, refs in zip(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):
														sari_score += SARIsent(normalize(lowerCamelCase__       )				,    normalize(lowerCamelCase__       )				,    [normalize(lowerCamelCase__       ) for sent in refs]       )
									__UpperCamelCase :   Tuple									=sari_score / len(lowerCamelCase__       )
									return {"sari": 100 * sari_score}









import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

