




import argparse
import json
import re
import string
import sys

import numpy as np


__SCREAMING_SNAKE_CASE			:      Any      			=0.5

def 	lowerCAmelCase_	(     lowerCamelCase:				List[str]   , lowerCamelCase:				Tuple  )    ->	List[str]:


      '''simple docstring'''




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

      for token in remove_tokens:
            lowerCamelCase__					     = ground_truth.replace(lowerCamelCase   , ''  )
            lowerCamelCase__					     = prediction.replace(lowerCamelCase   , ''  )

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

      lowerCamelCase__					     = ground_truth.intersection(lowerCamelCase  )
      lowerCamelCase__					     = ground_truth.union(lowerCamelCase  )
      lowerCamelCase__					     = len(lowerCamelCase  ) / len(lowerCamelCase  )
      return jaccard

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict  )    ->	List[Any]:


      '''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:				Dict  ):
            lowerCamelCase__					     = set(string.punctuation  )
            return "".join(ch for ch in text if ch not in exclude  )

      def lower(lowerCamelCase:				List[Any]  ):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase  )  )  )  )

def 	lowerCAmelCase_	(     lowerCamelCase:				List[str]   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				Optional[Any]  )    ->	Tuple:


      '''simple docstring'''




      lowerCamelCase__     ,						lowerCamelCase__     ,						lowerCamelCase__					     = 0, 0, 0

      lowerCamelCase__					     = '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
                  lowerCamelCase__					     = False
                  for pred in predictions:
                        if substr_ok:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
                        else:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH
                        if is_match:
                              lowerCamelCase__					     = 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.)
                  lowerCamelCase__					     = False
                  for ground_truth in ground_truths:
                        assert len(lowerCamelCase  ) > 0
                        if substr_ok:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH or ground_truth in pred
                        else:
                              lowerCamelCase__					     = get_jaccard(lowerCamelCase   , lowerCamelCase  ) >= IOU_THRESH
                        if is_match:
                              lowerCamelCase__					     = True

                  if not match_found:
                        fp += 1

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

      return precision, recall

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict  )    ->	Optional[Any]:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[Any]   , lowerCamelCase:				Any  )    ->	List[Any]:


      '''simple docstring'''




      lowerCamelCase__					     = process_precisions(lowerCamelCase  )
      lowerCamelCase__					     = np.trapz(lowerCamelCase   , lowerCamelCase  )
      if np.isnan(lowerCamelCase  ):
            return 0
      return aupr

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[Any]   , lowerCamelCase:				int   , lowerCamelCase:				str  )    ->	int:


      '''simple docstring'''




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

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Tuple  )    ->	Tuple:


      '''simple docstring'''




      return normalize_answer(lowerCamelCase  ) == normalize_answer(lowerCamelCase  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[int]   , lowerCamelCase:				Optional[Any]   , lowerCamelCase:				Dict  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = 0
      for pred in predictions:
            for ground_truth in ground_truths:
                  lowerCamelCase__					     = 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 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Optional[Any]  )    ->	Optional[Any]:


      '''simple docstring'''




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

      lowerCamelCase__					     = [x for _, x in sorted(zip(lowerCamelCase   , lowerCamelCase  )  )]
      recalls.sort()

      lowerCamelCase__					     = 100.0 * fa / total
      lowerCamelCase__					     = 100.0 * exact_match / total
      lowerCamelCase__					     = get_aupr(lowerCamelCase   , lowerCamelCase  )

      lowerCamelCase__					     = get_prec_at_recall(lowerCamelCase   , lowerCamelCase   , recall_thresh=0.9  )
      lowerCamelCase__					     = 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__":
      __SCREAMING_SNAKE_CASE			:      Optional[Any]      			=argparse.ArgumentParser(description="""Evaluation for CUAD""")
      parser.add_argument("""dataset_file""", help="""Dataset file""")
      parser.add_argument("""prediction_file""", help="""Prediction File""")
      __SCREAMING_SNAKE_CASE			:      Optional[int]      			=parser.parse_args()
      with open(args.dataset_file) as dataset_file:
            __SCREAMING_SNAKE_CASE			:      Tuple      			=json.load(dataset_file)
            __SCREAMING_SNAKE_CASE			:      str      			=dataset_json["""data"""]
      with open(args.prediction_file) as prediction_file:
            __SCREAMING_SNAKE_CASE			:      int      			=json.load(prediction_file)
      print(json.dumps(evaluate(dataset, predictions)))





from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

           lowerCamelCase__					     = (image / 2 + 0.5).clamp(0		,     1	)
           lowerCamelCase__					     = image.cpu().permute(0		,     2		,     3		,     1	).numpy()
           if output_type == "pil":
                 lowerCamelCase__					     = self.numpy_to_pil(A__	)

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)