

import argparse
import json
import re
import string
import sys

import numpy as np


_lowerCamelCase							:  Optional[Any]     =				0.5

def      a__	(						UpperCAmelCase				:     int							,					UpperCAmelCase				:     Tuple     )    ->		Optional[int]:
  UpperCAmelCase			:				List[Any]										=					[".", ",", ";", ":"]

  for token in remove_tokens:
    UpperCAmelCase			:				List[Any]										=					ground_truth.replace(_lowerCamelCase							,					''''''     )
    UpperCAmelCase			:				List[Any]										=					prediction.replace(_lowerCamelCase							,					''''''     )

  UpperCAmelCase			:				List[Any]										=					ground_truth.lower(), prediction.lower()
  UpperCAmelCase			:				Dict										=					ground_truth.replace('''/'''							,					''' '''     ), prediction.replace('''/'''							,					''' '''     )
  UpperCAmelCase			:				str										=					set(ground_truth.split(''' '''     )     ), set(prediction.split(''' '''     )     )

  UpperCAmelCase			:				Optional[int]										=					ground_truth.intersection(_lowerCamelCase     )
  UpperCAmelCase			:				Union[str, Any]										=					ground_truth.union(_lowerCamelCase     )
  UpperCAmelCase			:				Dict										=					len(_lowerCamelCase     ) / len(_lowerCamelCase     )
  return jaccard

def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		Optional[Any]:


  def remove_articles(UpperCAmelCase				:     Any     ):
    return re.sub(r'''\b(a|an|the)\b'''							,					''' '''							,					_lowerCamelCase     )

  def white_space_fix(UpperCAmelCase				:     Any     ):
    return " ".join(text.split()     )

  def remove_punc(UpperCAmelCase				:     List[Any]     ):
    UpperCAmelCase			:				Tuple										=					set(string.punctuation     )
    return "".join(ch for ch in text if ch not in exclude     )

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

  return white_space_fix(remove_articles(remove_punc(lower(_lowerCamelCase     )     )     )     )

def      a__	(						UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     Optional[Any]							,					UpperCAmelCase				:     Tuple     )    ->		Any:
  UpperCAmelCase			:				Optional[int]										=					0, 0, 0

  UpperCAmelCase			:				Optional[int]										=					"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
      UpperCAmelCase			:				Tuple										=					False
      for pred in predictions:
        if substr_ok:
          UpperCAmelCase			:				Optional[Any]										=					get_jaccard(_lowerCamelCase							,					_lowerCamelCase     ) >= IOU_THRESH or ground_truth in pred
        else:
          UpperCAmelCase			:				Tuple										=					get_jaccard(_lowerCamelCase							,					_lowerCamelCase     ) >= IOU_THRESH
        if is_match:
          UpperCAmelCase			:				str										=					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.)
      UpperCAmelCase			:				Tuple										=					False
      for ground_truth in ground_truths:
        assert len(_lowerCamelCase     ) > 0
        if substr_ok:
          UpperCAmelCase			:				Tuple										=					get_jaccard(_lowerCamelCase							,					_lowerCamelCase     ) >= IOU_THRESH or ground_truth in pred
        else:
          UpperCAmelCase			:				Dict										=					get_jaccard(_lowerCamelCase							,					_lowerCamelCase     ) >= IOU_THRESH
        if is_match:
          UpperCAmelCase			:				List[Any]										=					True

      if not match_found:
        fp += 1

  UpperCAmelCase			:				Union[str, Any]										=					tp / (tp + fp) if tp + fp > 0 else np.nan
  UpperCAmelCase			:				Dict										=					tp / (tp + fn) if tp + fn > 0 else np.nan

  return precision, recall

def      a__	(						UpperCAmelCase				:     Optional[int]     )    ->		Dict:
  UpperCAmelCase			:				int										=					precisions[::-1]
  for i in range(1							,					len(_lowerCamelCase     )     ):
    UpperCAmelCase			:				Union[str, Any]										=					max(precision_best[i - 1]							,					precision_best[i]     )
  UpperCAmelCase			:				Optional[Any]										=					precision_best[::-1]
  return precisions

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     str     )    ->		List[str]:
  UpperCAmelCase			:				Tuple										=					process_precisions(_lowerCamelCase     )
  UpperCAmelCase			:				List[Any]										=					np.trapz(_lowerCamelCase							,					_lowerCamelCase     )
  if np.isnan(_lowerCamelCase     ):
    return 0
  return aupr

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Any							,					UpperCAmelCase				:     List[str]     )    ->		Any:
  UpperCAmelCase			:				Any										=					process_precisions(_lowerCamelCase     )
  UpperCAmelCase			:				List[str]										=					0
  for prec, recall in zip(_lowerCamelCase							,					_lowerCamelCase     ):
    if recall >= recall_thresh:
      UpperCAmelCase			:				Optional[int]										=					prec
      break
  return prec_at_recall

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     Optional[Any]     )    ->		int:
  return normalize_answer(_lowerCamelCase     ) == normalize_answer(_lowerCamelCase     )

def      a__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     str							,					UpperCAmelCase				:     List[Any]     )    ->		Union[str, Any]:
  UpperCAmelCase			:				Optional[Any]										=					0
  for pred in predictions:
    for ground_truth in ground_truths:
      UpperCAmelCase			:				Optional[Any]										=					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__	(						UpperCAmelCase				:     Optional[int]							,					UpperCAmelCase				:     int     )    ->		int:
  UpperCAmelCase			:				Any										=					0
  UpperCAmelCase			:				List[str]										=					[]
  UpperCAmelCase			:				Union[str, Any]										=					[]
  for article in dataset:
    for paragraph in article["paragraphs"]:
      for qa in paragraph["qas"]:
        total += 1
        if qa["id"] not in predictions:
          UpperCAmelCase			:				Union[str, Any]										=					"Unanswered question " + qa["id"] + " will receive score 0."
          print(_lowerCamelCase							,					file=sys.stderr     )
          continue
        UpperCAmelCase			:				Any										=					[x["text"] for x in qa["answers"]]
        UpperCAmelCase			:				int										=					predictions[qa["id"]]
        UpperCAmelCase			:				Tuple										=					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     )

  UpperCAmelCase			:				Optional[int]										=					[x for _, x in sorted(zip(_lowerCamelCase							,					_lowerCamelCase     )     )]
  recalls.sort()

  UpperCAmelCase			:				Union[str, Any]										=					100.0 * fa / total
  UpperCAmelCase			:				Dict										=					100.0 * exact_match / total
  UpperCAmelCase			:				str										=					get_aupr(_lowerCamelCase							,					_lowerCamelCase     )

  UpperCAmelCase			:				Optional[int]										=					get_prec_at_recall(_lowerCamelCase							,					_lowerCamelCase							,					recall_thresh=0.9     )
  UpperCAmelCase			:				Optional[Any]										=					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__":
     _lowerCamelCase							:  Tuple     =				argparse.ArgumentParser(description="Evaluation for CUAD")
     parser.add_argument("dataset_file", help="Dataset file")
     parser.add_argument("prediction_file", help="Prediction File")
     _lowerCamelCase							:  Tuple     =				parser.parse_args()
     with open(args.dataset_file) as dataset_file:
          _lowerCamelCase							:  Union[str, Any]     =				json.load(dataset_file)
          _lowerCamelCase							:  Union[str, Any]     =				dataset_json["data"]
     with open(args.prediction_file) as prediction_file:
          _lowerCamelCase							:  Optional[Any]     =				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  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->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 = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       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
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

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

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )