





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
				from .image_encoder import PaintByExampleImageEncoder
				from .pipeline_paint_by_example import PaintByExamplePipeline








import argparse
import json
import re
import string
import sys

import numpy as np


__A    :   Optional[Any]          =       0.5





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

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

			for token in remove_tokens:
						UpperCAmelCase       		=    ground_truth.replace(UpperCamelCase__      ,      ''''''			)
						UpperCAmelCase       		=    prediction.replace(UpperCamelCase__      ,      ''''''			)

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

			UpperCAmelCase       		=    ground_truth.intersection(UpperCamelCase__			)
			UpperCAmelCase       		=    ground_truth.union(UpperCamelCase__			)
			UpperCAmelCase       		=    len(UpperCamelCase__			) / len(UpperCamelCase__			)
			return jaccard





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			def remove_articles(UpperCamelCase__			):
						return re.sub(R'''\b(a|an|the)\b'''      ,      ''' '''      ,      UpperCamelCase__			)

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

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

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

			return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__			)			)			)			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

			UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase       		=    0, 0, 0

			UpperCAmelCase       		=    '''Parties''' in qa_id

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

									if not match_found:
												fp += 1

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

			return precision, recall





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    process_precisions(UpperCamelCase__			)
			UpperCAmelCase       		=    np.trapz(UpperCamelCase__      ,      UpperCamelCase__			)
			if np.isnan(UpperCamelCase__			):
						return 0
			return aupr





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Optional[int]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			return normalize_answer(UpperCamelCase__			) == normalize_answer(UpperCamelCase__			)





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    str:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    int:







			'''simple docstring'''

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

												precisions.append(UpperCamelCase__			)
												recalls.append(UpperCamelCase__			)

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

												exact_match += metric_max_over_ground_truths(UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)

			UpperCAmelCase       		=    [x for _, x in sorted(zip(UpperCamelCase__      ,      UpperCamelCase__			)			)]
			recalls.sort()

			UpperCAmelCase       		=    100.0 * fa / total
			UpperCAmelCase       		=    100.0 * exact_match / total
			UpperCAmelCase       		=    get_aupr(UpperCamelCase__      ,      UpperCamelCase__			)

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



