





"""simple docstring"""



from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


a_				=    """\
@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_				=    """\
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_				=    """
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 	__lowercase  (     snake_case_						:						Tuple       ,snake_case_						:						int       ,snake_case_						:						Tuple       ,snake_case_						:						str		)     ->List[Any]:



    '''simple docstring'''







    __A					:		int						=  [rgram for rgrams in rgramslist for rgram in rgrams]
    __A					:		Union[str, Any]						=  Counter(snake_case_		)

    __A					:		Tuple						=  Counter(snake_case_		)
    __A					:		Any						=  Counter()
    for sgram, scount in sgramcounter.items():
        __A					:		Union[str, Any]						=  scount * numref

    __A					:		List[Any]						=  Counter(snake_case_		)
    __A					:		int						=  Counter()
    for cgram, ccount in cgramcounter.items():
        __A					:		Union[str, Any]						=  ccount * numref

    # KEEP
    __A					:		List[Any]						=  sgramcounter_rep & cgramcounter_rep
    __A					:		List[Any]						=  keepgramcounter_rep & rgramcounter
    __A					:		Dict						=  sgramcounter_rep & rgramcounter

    __A					:		Tuple						=  0
    __A					:		List[str]						=  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.
    __A					:		Any						=  1
    __A					:		int						=  1
    if len(snake_case_		) > 0:
        __A					:		Any						=  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)
        __A					:		Union[str, Any]						=  keeptmpscorea / sum(keepgramcounterall_rep.values()		)
    __A					:		Optional[Any]						=  0
    if keepscore_precision > 0 or keepscore_recall > 0:
        __A					:		Optional[Any]						=  2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

    # DELETION
    __A					:		Tuple						=  sgramcounter_rep - cgramcounter_rep
    __A					:		List[str]						=  delgramcounter_rep - rgramcounter
    __A					:		List[str]						=  sgramcounter_rep - rgramcounter
    __A					:		List[Any]						=  0
    __A					:		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.
    __A					:		List[Any]						=  1
    if len(snake_case_		) > 0:
        __A					:		Dict						=  deltmpscorea / len(snake_case_		)

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

    __A					:		Optional[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.
    __A					:		str						=  1
    __A					:		List[Any]						=  1
    if len(snake_case_		) > 0:
        __A					:		Dict						=  addtmpscore / len(snake_case_		)
    if len(snake_case_		) > 0:
        __A					:		Optional[Any]						=  addtmpscore / len(snake_case_		)
    __A					:		List[Any]						=  0
    if addscore_precision > 0 or addscore_recall > 0:
        __A					:		List[Any]						=  2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

    return (keepscore, delscore_precision, addscore)







def 	__lowercase  (     snake_case_						:						int       ,snake_case_						:						Tuple       ,snake_case_						:						str		)     ->List[Any]:



    '''simple docstring'''







    __A					:		Dict						=  len(snake_case_		)

    __A					:		Optional[int]						=  ssent.split(''' '''		)
    __A					:		List[Any]						=  csent.split(''' '''		)
    __A					:		Dict						=  []
    __A					:		Optional[int]						=  []
    __A					:		str						=  []
    __A					:		int						=  []
    __A					:		Tuple						=  []
    __A					:		List[str]						=  []

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

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







def 	__lowercase  (     snake_case_						:						List[Any]       ,snake_case_						:						bool = True       ,snake_case_						:						str = "13a"       ,snake_case_						:						bool = True		)     ->Optional[int]:



    '''simple docstring'''







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

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

    if not return_str:
        __A					:		List[str]						=  normalized_sent.split()

    return normalized_sent






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





 """simple docstring"""


 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      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						):



     '''simple docstring'''




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











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_				=    {
    """configuration_layoutlm""": ["""LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LayoutLMConfig""", """LayoutLMOnnxConfig"""],
    """tokenization_layoutlm""": ["""LayoutLMTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    ["""LayoutLMTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """LayoutLMForMaskedLM""",
           """LayoutLMForSequenceClassification""",
           """LayoutLMForTokenClassification""",
           """LayoutLMForQuestionAnswering""",
           """LayoutLMModel""",
           """LayoutLMPreTrainedModel""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_				=    [
           """TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFLayoutLMForMaskedLM""",
           """TFLayoutLMForSequenceClassification""",
           """TFLayoutLMForTokenClassification""",
           """TFLayoutLMForQuestionAnswering""",
           """TFLayoutLMMainLayer""",
           """TFLayoutLMModel""",
           """TFLayoutLMPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
       from .tokenization_layoutlm import LayoutLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_layoutlm import (
                  LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LayoutLMForMaskedLM,
                  LayoutLMForQuestionAnswering,
                  LayoutLMForSequenceClassification,
                  LayoutLMForTokenClassification,
                  LayoutLMModel,
                  LayoutLMPreTrainedModel,
              )
       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_layoutlm import (
                  TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFLayoutLMForMaskedLM,
                  TFLayoutLMForQuestionAnswering,
                  TFLayoutLMForSequenceClassification,
                  TFLayoutLMForTokenClassification,
                  TFLayoutLMMainLayer,
                  TFLayoutLMModel,
                  TFLayoutLMPreTrainedModel,
              )

else:
       import sys

       a_				=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




