


'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


a_       :     Dict 							=     0.5






def 						_A					(lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:Any		) ->	Union[str, Any]:




     '''simple docstring'''



     _a         =				['.', ',', ';', ':']

     for token in remove_tokens:
          _a         =				ground_truth.replace(lowerCAmelCase__					,    ''		)
          _a         =				prediction.replace(lowerCAmelCase__					,    ''		)

     _a   , _a         =				ground_truth.lower(), prediction.lower()
     _a   , _a         =				ground_truth.replace('/'					,    ' '		), prediction.replace('/'					,    ' '		)
     _a   , _a         =				set(ground_truth.split(' '		)		), set(prediction.split(' '		)		)

     _a         =				ground_truth.intersection(lowerCAmelCase__		)
     _a         =				ground_truth.union(lowerCAmelCase__		)
     _a         =				len(lowerCAmelCase__		) / len(lowerCAmelCase__		)
     return jaccard






def 						_A					(lowerCAmelCase__	:List[Any]		) ->	str:




     '''simple docstring'''



     def remove_articles(lowerCAmelCase__	:Union[str, Any]		):
          return re.sub(r'\b(a|an|the)\b'					,    ' '					,    lowerCAmelCase__		)

     def white_space_fix(lowerCAmelCase__	:Optional[Any]		):
          return " ".join(text.split()		)

     def remove_punc(lowerCAmelCase__	:int		):
          _a         =				set(string.punctuation		)
          return "".join(ch for ch in text if ch not in exclude		)

     def lower(lowerCAmelCase__	:str		):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase__		)		)		)		)






def 						_A					(lowerCAmelCase__	:Optional[int]					,    lowerCAmelCase__	:str					,    lowerCAmelCase__	:int		) ->	Dict:




     '''simple docstring'''



     _a   , _a   , _a         =				0, 0, 0

     _a         =				'Parties' in qa_id

     # first check if ground truth is empty
     if len(lowerCAmelCase__		) == 0:
          if len(lowerCAmelCase__		) > 0:
               fp += len(lowerCAmelCase__		)  # false positive for each one
     else:
          for ground_truth in ground_truths:
               assert len(lowerCAmelCase__		) > 0
               # check if there is a match
               _a         =				False
               for pred in predictions:
                    if substr_ok:
                         _a         =				get_jaccard(lowerCAmelCase__					,    lowerCAmelCase__		) >= IOU_THRESH or ground_truth in pred
                    else:
                         _a         =				get_jaccard(lowerCAmelCase__					,    lowerCAmelCase__		) >= IOU_THRESH
                    if is_match:
                         _a         =				True

               if match_found:
                    tp += 1
               else:
                    fn += 1

        # now also get any fps by looping through preds
          for pred in predictions:
               # Check if there's a match. if so, don't count (don't want to double count based on the above)
               # but if there's no match, then this is a false positive.
               # (Note: we get the true positives in the above loop instead of this loop so that we don't double count
               # multiple predictions that are matched with the same answer.)
               _a         =				False
               for ground_truth in ground_truths:
                    assert len(lowerCAmelCase__		) > 0
                    if substr_ok:
                         _a         =				get_jaccard(lowerCAmelCase__					,    lowerCAmelCase__		) >= IOU_THRESH or ground_truth in pred
                    else:
                         _a         =				get_jaccard(lowerCAmelCase__					,    lowerCAmelCase__		) >= IOU_THRESH
                    if is_match:
                         _a         =				True

               if not match_found:
                    fp += 1

     _a         =				tp / (tp + fp) if tp + fp > 0 else np.nan
     _a         =				tp / (tp + fn) if tp + fn > 0 else np.nan

     return precision, recall






def 						_A					(lowerCAmelCase__	:Tuple		) ->	Tuple:




     '''simple docstring'''



     _a         =				precisions[::-1]
     for i in range(1					,    len(lowerCAmelCase__		)		):
          _a         =				max(precision_best[i - 1]					,    precision_best[i]		)
     _a         =				precision_best[::-1]
     return precisions






def 						_A					(lowerCAmelCase__	:Union[str, Any]					,    lowerCAmelCase__	:int		) ->	Any:




     '''simple docstring'''



     _a         =				process_precisions(lowerCAmelCase__		)
     _a         =				np.trapz(lowerCAmelCase__					,    lowerCAmelCase__		)
     if np.isnan(lowerCAmelCase__		):
          return 0
     return aupr






def 						_A					(lowerCAmelCase__	:str					,    lowerCAmelCase__	:Optional[int]					,    lowerCAmelCase__	:Optional[int]		) ->	Optional[Any]:




     '''simple docstring'''



     _a         =				process_precisions(lowerCAmelCase__		)
     _a         =				0
     for prec, recall in zip(lowerCAmelCase__					,    lowerCAmelCase__		):
          if recall >= recall_thresh:
               _a         =				prec
               break
     return prec_at_recall






def 						_A					(lowerCAmelCase__	:List[str]					,    lowerCAmelCase__	:Any		) ->	Any:




     '''simple docstring'''



     return normalize_answer(lowerCAmelCase__		) == normalize_answer(lowerCAmelCase__		)






def 						_A					(lowerCAmelCase__	:Optional[int]					,    lowerCAmelCase__	:str					,    lowerCAmelCase__	:Optional[Any]		) ->	Any:




     '''simple docstring'''



     _a         =				0
     for pred in predictions:
          for ground_truth in ground_truths:
               _a         =				metric_fn(lowerCAmelCase__					,    lowerCAmelCase__		)
               if score == 1:  # break the loop when one prediction matches the ground truth
                    break
          if score == 1:
               break
     return score






def 						_A					(lowerCAmelCase__	:Optional[Any]					,    lowerCAmelCase__	:Optional[Any]		) ->	Union[str, Any]:




     '''simple docstring'''



     _a         =				_a         =				_a         =				0
     _a         =				[]
     _a         =				[]
     for article in dataset:
          for paragraph in article["paragraphs"]:
               for qa in paragraph["qas"]:
                    total += 1
                    if qa["id"] not in predictions:
                         _a         =				'Unanswered question ' + qa['id'] + ' will receive score 0.'
                         print(lowerCAmelCase__					,    file=sys.stderr		)
                         continue
                    _a         =				[x['text'] for x in qa['answers']]
                    _a         =				predictions[qa['id']]
                    _a   , _a         =				compute_precision_recall(lowerCAmelCase__					,    lowerCAmelCase__					,    qa['id']		)

                    precisions.append(lowerCAmelCase__		)
                    recalls.append(lowerCAmelCase__		)

                    if precision == 0 and recall == 0:
                         fa += 0
                    else:
                         fa += 2 * (precision * recall) / (precision + recall)

                    exact_match += metric_max_over_ground_truths(lowerCAmelCase__					,    lowerCAmelCase__					,    lowerCAmelCase__		)

     _a         =				[x for _, x in sorted(zip(lowerCAmelCase__					,    lowerCAmelCase__		)		)]
     recalls.sort()

     _a         =				1_0_0.0 * fa / total
     _a         =				1_0_0.0 * exact_match / total
     _a         =				get_aupr(lowerCAmelCase__					,    lowerCAmelCase__		)

     _a         =				get_prec_at_recall(lowerCAmelCase__					,    lowerCAmelCase__					,    recall_thresh=0.9		)
     _a         =				get_prec_at_recall(lowerCAmelCase__					,    lowerCAmelCase__					,    recall_thresh=0.8		)

     return {
         "exact_match": exact_match,
         "f1": fa,
         "aupr": aupr,
         "prec_at_80_recall": prec_at_aa_recall,
         "prec_at_90_recall": prec_at_aa_recall,
     }


if __name__ == "__main__":
 a_       :     str 							=     argparse.ArgumentParser(description="Evaluation for CUAD")
 parser.add_argument("dataset_file", help="Dataset file")
 parser.add_argument("prediction_file", help="Prediction File")
 a_       :     Optional[int] 							=     parser.parse_args()
 with open(args.dataset_file) as dataset_file:
  a_       :     Optional[int] 							=     json.load(dataset_file)
  a_       :     str 							=     dataset_json["data"]
 with open(args.prediction_file) as prediction_file:
  a_       :     Union[str, Any] 							=     json.load(prediction_file)
 print(json.dumps(evaluate(dataset, predictions)))





'''simple docstring'''






import copy
from typing import Dict, Optional

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


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     Union[str, 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 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				CONFIG_MAPPING['swin'](
               image_size=2_24   ,					in_channels=3   ,					patch_size=4   ,					embed_dim=96   ,					depths=[2, 2, 6, 2]   ,					num_heads=[3, 6, 12, 24]   ,					window_size=7   ,					drop_path_rate=0.3   ,					use_absolute_embeddings=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

      _a         =				ignore_value
      _a         =				num_queries
      _a         =				no_object_weight
      _a         =				class_weight
      _a         =				mask_weight
      _a         =				dice_weight
      _a         =				contrastive_weight
      _a         =				contrastive_temperature
      _a         =				train_num_points
      _a         =				oversample_ratio
      _a         =				importance_sample_ratio
      _a         =				init_std
      _a         =				init_xavier_std
      _a         =				layer_norm_eps
      _a         =				is_training
      _a         =				use_auxiliary_loss
      _a         =				output_auxiliary_logits
      _a         =				strides
      _a         =				task_seq_len
      _a         =				text_encoder_width
      _a         =				text_encoder_context_length
      _a         =				text_encoder_num_layers
      _a         =				text_encoder_vocab_size
      _a         =				text_encoder_proj_layers
      _a         =				text_encoder_n_ctx
      _a         =				conv_dim
      _a         =				mask_dim
      _a         =				hidden_dim
      _a         =				encoder_feedforward_dim
      _a         =				norm
      _a         =				encoder_layers
      _a         =				decoder_layers
      _a         =				use_task_norm
      _a         =				num_attention_heads
      _a         =				dropout
      _a         =				dim_feedforward
      _a         =				pre_norm
      _a         =				enforce_input_proj
      _a         =				query_dec_layers
      _a         =				common_stride
      _a         =				decoder_layers

      super().__init__(**__magic_name__    )




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

