




'''simple docstring'''







import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


lowercase      :   Tuple      =       """
@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\",
}
"""

lowercase      :   Any      =       """\
WIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU
It can be used to evaluate the quality of machine-generated texts.
"""


lowercase      :   Union[str, Any]      =       """
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__				(      __lowercase				):

     def remove_articles(__lowercase				):
          snake_case			:      Any        =			re.compile(r"""\b(a|an|the)\b"""     ,   re.UNICODE				)
          return re.sub(__lowercase     ,   """ """     ,   __lowercase				)

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

     def remove_punc(__lowercase				):
          snake_case			:      Union[str, Any]        =			set(string.punctuation				)
          return "".join(ch for ch in text if ch not in exclude				)

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

     return white_space_fix(remove_articles(remove_punc(lower(__lowercase				)				)				)				)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     return int(normalize_answer(__lowercase				) == normalize_answer(__lowercase				)				)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase				):
     snake_case			:      Any        =			[any(compute_exact(__lowercase     ,   __lowercase				) for ref in refs				) for pred, refs in zip(__lowercase     ,   __lowercase				)]
     return (sum(__lowercase				) / len(__lowercase				)) * 100



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[Any]        =			[rgram for rgrams in rgramslist for rgram in rgrams]
     snake_case			:      Any        =			Counter(__lowercase				)

     snake_case			:      str        =			Counter(__lowercase				)
     snake_case			:      Tuple        =			Counter()
     for sgram, scount in sgramcounter.items():
          snake_case			:      List[str]        =			scount * numref

     snake_case			:      Any        =			Counter(__lowercase				)
     snake_case			:      Dict        =			Counter()
     for cgram, ccount in cgramcounter.items():
          snake_case			:      Optional[Any]        =			ccount * numref

     # KEEP
     snake_case			:      Tuple        =			sgramcounter_rep & cgramcounter_rep
     snake_case			:      List[Any]        =			keepgramcounter_rep & rgramcounter
     snake_case			:      Optional[Any]        =			sgramcounter_rep & rgramcounter

     snake_case			:      Any        =			0
     snake_case			:      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.
     snake_case			:      Dict        =			1
     snake_case			:      List[Any]        =			1
     if len(__lowercase				) > 0:
          snake_case			:      int        =			keeptmpscorea / len(__lowercase				)
     if len(__lowercase				) > 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			:      Union[str, Any]        =			0
     if keepscore_precision > 0 or keepscore_recall > 0:
          snake_case			:      str        =			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

     # DELETION
     snake_case			:      List[str]        =			sgramcounter_rep - cgramcounter_rep
     snake_case			:      Dict        =			delgramcounter_rep - rgramcounter
     snake_case			:      str        =			sgramcounter_rep - rgramcounter
     snake_case			:      int        =			0
     snake_case			:      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.
     snake_case			:      int        =			1
     if len(__lowercase				) > 0:
          snake_case			:      int        =			deltmpscorea / len(__lowercase				)

     # ADDITION
     snake_case			:      Union[str, Any]        =			set(__lowercase				) - set(__lowercase				)
     snake_case			:      Dict        =			set(__lowercase				) & set(__lowercase				)
     snake_case			:      List[Any]        =			set(__lowercase				) - set(__lowercase				)

     snake_case			:      Optional[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.
     snake_case			:      List[str]        =			1
     snake_case			:      int        =			1
     if len(__lowercase				) > 0:
          snake_case			:      str        =			addtmpscore / len(__lowercase				)
     if len(__lowercase				) > 0:
          snake_case			:      Tuple        =			addtmpscore / len(__lowercase				)
     snake_case			:      str        =			0
     if addscore_precision > 0 or addscore_recall > 0:
          snake_case			:      str        =			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

     return (keepscore, delscore_precision, addscore)



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[Any]        =			len(__lowercase				)

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

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

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

     for i in range(0     ,   len(__lowercase				) - 1				):
          if i < len(__lowercase				) - 1:
               snake_case			:      List[Any]        =			cagrams[i] + """ """ + cagrams[i + 1]
               cagrams.append(__lowercase				)
          if i < len(__lowercase				) - 2:
               snake_case			:      List[Any]        =			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
               cagrams.append(__lowercase				)
          if i < len(__lowercase				) - 3:
               snake_case			:      Optional[int]        =			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
               cagrams.append(__lowercase				)

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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase = True     ,   __lowercase = "13a"     ,   __lowercase = True				):
     # 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:
          snake_case			:      Any        =			sentence.lower()

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

     if not return_str:
          snake_case			:      Union[str, Any]        =			normalized_sent.split()

     return normalized_sent



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     if not (len(__lowercase				) == len(__lowercase				) == len(__lowercase				)):
          raise ValueError("""Sources length must match predictions and references lengths."""				)
     snake_case			:      List[Any]        =			0
     for src, pred, refs in zip(__lowercase     ,   __lowercase     ,   __lowercase				):
          sari_score += SARIsent(normalize(__lowercase				)     ,   normalize(__lowercase				)     ,   [normalize(__lowercase				) for sent in refs]				)
     snake_case			:      Any        =			sari_score / len(__lowercase				)
     return 100 * sari_score



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase="exp"     ,   __lowercase=None     ,   __lowercase=False     ,   __lowercase=False     ,   __lowercase=False     ,   ):
     snake_case			:      Tuple        =			len(references[0]				)
     if any(len(__lowercase				) != 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(__lowercase				)]
     snake_case			:      Optional[int]        =			sacrebleu.corpus_bleu(
         __lowercase     ,   __lowercase     ,   smooth_method=__lowercase     ,   smooth_value=__lowercase     ,   force=__lowercase     ,   lowercase=__lowercase     ,   use_effective_order=__lowercase     ,   )
     return output.score







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





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				List[Any]:
          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 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Tuple:
          snake_case			:      Dict        =			{}
          result.update({"""sari""": compute_sari(sources=__a							,predictions=__a							,references=__a							)}							)
          result.update({"""sacrebleu""": compute_sacrebleu(predictions=__a							,references=__a							)}							)
          result.update({"""exact""": compute_em(predictions=__a							,references=__a							)}							)
          return result









'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase      :   Tuple      =       logging.get_logger(__name__)

lowercase      :   Dict      =       {
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}







class 					_a (a__	):





     '''simple docstring'''



     lowerCAmelCase_  :     str          =					"""deta"""
     lowerCAmelCase_  :     Any          =					{
         """hidden_size""": """d_model""",
         """num_attention_heads""": """encoder_attention_heads""",
     }







     def __init__(				self							,__a=None							,__a=900							,__a=2_048							,__a=6							,__a=2_048							,__a=8							,__a=6							,__a=1_024							,__a=8							,__a=0.0							,__a=True							,__a="relu"							,__a=256							,__a=0.1							,__a=0.0							,__a=0.0							,__a=0.02							,__a=1.0							,__a=True							,__a=False							,__a="sine"							,__a=5							,__a=4							,__a=4							,__a=True							,__a=300							,__a=True							,__a=True							,__a=1							,__a=5							,__a=2							,__a=1							,__a=1							,__a=5							,__a=2							,__a=0.1							,__a=0.25							,**__a							,)      ->				int:
          if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""							)
               snake_case			:      int        =			CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]							)
          else:
               if isinstance(__a							,__a							):
                    snake_case			:      str        =			backbone_config.pop("""model_type"""							)
                    snake_case			:      Optional[Any]        =			CONFIG_MAPPING[backbone_model_type]
                    snake_case			:      List[Any]        =			config_class.from_dict(__a							)

          snake_case			:      Any        =			backbone_config
          snake_case			:      Optional[Any]        =			num_queries
          snake_case			:      Dict        =			max_position_embeddings
          snake_case			:      int        =			d_model
          snake_case			:      str        =			encoder_ffn_dim
          snake_case			:      Dict        =			encoder_layers
          snake_case			:      List[Any]        =			encoder_attention_heads
          snake_case			:      int        =			decoder_ffn_dim
          snake_case			:      List[Any]        =			decoder_layers
          snake_case			:      Dict        =			decoder_attention_heads
          snake_case			:      Dict        =			dropout
          snake_case			:      Dict        =			attention_dropout
          snake_case			:      List[Any]        =			activation_dropout
          snake_case			:      Dict        =			activation_function
          snake_case			:      List[Any]        =			init_std
          snake_case			:      Any        =			init_xavier_std
          snake_case			:      List[Any]        =			encoder_layerdrop
          snake_case			:      Optional[Any]        =			auxiliary_loss
          snake_case			:      Optional[int]        =			position_embedding_type
          # deformable attributes
          snake_case			:      Tuple        =			num_feature_levels
          snake_case			:      List[Any]        =			encoder_n_points
          snake_case			:      Dict        =			decoder_n_points
          snake_case			:      int        =			two_stage
          snake_case			:      Any        =			two_stage_num_proposals
          snake_case			:      List[str]        =			with_box_refine
          snake_case			:      Tuple        =			assign_first_stage
          if two_stage is True and with_box_refine is False:
               raise ValueError("""If two_stage is True, with_box_refine must be True."""							)
          # Hungarian matcher
          snake_case			:      Optional[int]        =			class_cost
          snake_case			:      int        =			bbox_cost
          snake_case			:      Optional[int]        =			giou_cost
          # Loss coefficients
          snake_case			:      str        =			mask_loss_coefficient
          snake_case			:      Any        =			dice_loss_coefficient
          snake_case			:      List[Any]        =			bbox_loss_coefficient
          snake_case			:      List[Any]        =			giou_loss_coefficient
          snake_case			:      List[str]        =			eos_coefficient
          snake_case			:      Optional[Any]        =			focal_alpha
          super().__init__(is_encoder_decoder=__a							,**__a							)







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.encoder_attention_heads







     @property
     def 			snake_case_      (				self							)      ->				int:
          return self.d_model







     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      List[str]        =			copy.deepcopy(self.__dict__							)
          snake_case			:      List[str]        =			self.backbone_config.to_dict()
          snake_case			:      List[Any]        =			self.__class__.model_type
          return output



