"""simple docstring"""





from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_UpperCamelCase       :						List[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},
}
'''

_UpperCamelCase       :						List[Any]     =   '''\
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
'''


_UpperCamelCase       :						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.
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_					(				_lowerCAmelCase  :			Any       ,			_lowerCAmelCase  :			str       ,			_lowerCAmelCase  :			Optional[int]       ,			_lowerCAmelCase  :			int					):





       '''simple docstring'''

       lowercase__      :		List[Any]          =     [rgram for rgrams in rgramslist for rgram in rgrams]
       lowercase__      :		Optional[int]          =     Counter(_A					)

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

       lowercase__      :		Optional[int]          =     Counter(_A					)
       lowercase__      :		str          =     Counter()
       for cgram, ccount in cgramcounter.items():
              lowercase__      :		List[str]          =     ccount * numref

       # KEEP
       lowercase__      :		Union[str, Any]          =     sgramcounter_rep & cgramcounter_rep
       lowercase__      :		Dict          =     keepgramcounter_rep & rgramcounter
       lowercase__      :		Any          =     sgramcounter_rep & rgramcounter

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

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

       # ADDITION
       lowercase__      :		Optional[int]          =     set(_A					) - set(_A					)
       lowercase__      :		Union[str, Any]          =     set(_A					) & set(_A					)
       lowercase__      :		str          =     set(_A					) - set(_A					)

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

       return (keepscore, delscore_precision, addscore)





def     a_					(				_lowerCAmelCase  :			int       ,			_lowerCAmelCase  :			Optional[Any]       ,			_lowerCAmelCase  :			str					):





       '''simple docstring'''

       lowercase__      :		Dict          =     len(_A					)

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

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

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





def     a_					(				_lowerCAmelCase  :			Optional[int]       ,			_lowerCAmelCase  :			bool = True       ,			_lowerCAmelCase  :			str = "13a"       ,			_lowerCAmelCase  :			bool = True					):





       '''simple docstring'''

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

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

       if not return_str:
              lowercase__      :		Optional[int]          =     normalized_sent.split()

       return normalized_sent






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


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             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		,      a		,      a		,      a							)	-> Union[str, Any]:


             if not (len(__lowerCAmelCase							) == len(__lowerCAmelCase							) == len(__lowerCAmelCase							)):
                    raise ValueError('Sources length must match predictions and references lengths.'							)
             lowercase__      :		List[str]          =     0
             for src, pred, refs in zip(__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase							):
                    sari_score += SARIsent(normalize(__lowerCAmelCase							)		,      normalize(__lowerCAmelCase							)		,      [normalize(__lowerCAmelCase							) for sent in refs]							)
             lowercase__      :		Tuple          =     sari_score / len(__lowerCAmelCase							)
             return {"sari": 1_0_0 * sari_score}


"""simple docstring"""





import copy
from typing import Dict, Optional

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


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


      def 		_UpperCAmelCase	(       self							)	-> Dict[str, any]:
             lowercase__      :		str          =     copy.deepcopy(self.__dict__							)
             lowercase__      :		Union[str, Any]          =     self.backbone_config.to_dict()
             lowercase__      :		str          =     self.__class__.model_type
             return output

