
import argparse
import json
import re
import string
import sys

import numpy as np


_A       =					0.5



def 		lowercase_     (  A__					,						A__						)				->				Tuple:
    """simple docstring"""

    snake_case						=   [".", ",", ";", ":"]

    for token in remove_tokens:
        snake_case						=   ground_truth.replace(UpperCAmelCase_					,						""						)
        snake_case						=   prediction.replace(UpperCAmelCase_					,						""						)

    snake_case	,				snake_case						=   ground_truth.lower(), prediction.lower()
    snake_case	,				snake_case						=   ground_truth.replace("/"					,						" "						), prediction.replace("/"					,						" "						)
    snake_case	,				snake_case						=   set(ground_truth.split(" "						)						), set(prediction.split(" "						)						)

    snake_case						=   ground_truth.intersection(UpperCAmelCase_						)
    snake_case						=   ground_truth.union(UpperCAmelCase_						)
    snake_case						=   len(UpperCAmelCase_						) / len(UpperCAmelCase_						)
    return jaccard



def 		lowercase_     (  A__						)				->				List[str]:
    """simple docstring"""

    def remove_articles(A__						):
        return re.sub(r"\b(a|an|the)\b"					,						" "					,						UpperCAmelCase_						)

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

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

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

    return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_						)						)						)						)



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

    snake_case	,				snake_case	,				snake_case						=   0, 0, 0

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

            if not match_found:
                fp += 1

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

    return precision, recall



def 		lowercase_     (  A__						)				->				str:
    """simple docstring"""

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



def 		lowercase_     (  A__					,						A__						)				->				List[str]:
    """simple docstring"""

    snake_case						=   process_precisions(UpperCAmelCase_						)
    snake_case						=   np.trapz(UpperCAmelCase_					,						UpperCAmelCase_						)
    if np.isnan(UpperCAmelCase_						):
        return 0
    return aupr



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Union[str, Any]:
    """simple docstring"""

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



def 		lowercase_     (  A__					,						A__						)				->				List[str]:
    """simple docstring"""

    return normalize_answer(UpperCAmelCase_						) == normalize_answer(UpperCAmelCase_						)



def 		lowercase_     (  A__					,						A__					,						A__						)				->				Any:
    """simple docstring"""

    snake_case						=   0
    for pred in predictions:
        for ground_truth in ground_truths:
            snake_case						=   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 		lowercase_     (  A__					,						A__						)				->				Any:
    """simple docstring"""

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

    snake_case						=   [x for _, x in sorted(zip(UpperCAmelCase_					,						UpperCAmelCase_						)						)]
    recalls.sort()

    snake_case						=   100.0 * fa / total
    snake_case						=   100.0 * exact_match / total
    snake_case						=   get_aupr(UpperCAmelCase_					,						UpperCAmelCase_						)

    snake_case						=   get_prec_at_recall(UpperCAmelCase_					,						UpperCAmelCase_					,						recall_thresh=0.9						)
    snake_case						=   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       =					argparse.ArgumentParser(description="Evaluation for CUAD")
   parser.add_argument("dataset_file", help="Dataset file")
   parser.add_argument("prediction_file", help="Prediction File")
   _A       =					parser.parse_args()
   with open(args.dataset_file) as dataset_file:
      _A       =					json.load(dataset_file)
      _A       =					dataset_json['data']
   with open(args.prediction_file) as prediction_file:
      _A       =					json.load(prediction_file)
   print(json.dumps(evaluate(dataset, predictions)))




"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       "swiftformer"

    def __init__(  self:			Tuple     ,    UpperCamelCase:			int=3     ,    UpperCamelCase:			Union[str, Any]=[3, 3, 6, 4]     ,    UpperCamelCase:			List[str]=[48, 56, 1_12, 2_20]     ,    UpperCamelCase:			List[Any]=4     ,    UpperCamelCase:			List[Any]=[True, True, True, True]     ,    UpperCamelCase:			str="gelu"     ,    UpperCamelCase:			Tuple=3     ,    UpperCamelCase:			Dict=2     ,    UpperCamelCase:			int=1     ,    UpperCamelCase:			List[Any]=0.0     ,    UpperCamelCase:			Any=True     ,    UpperCamelCase:			List[Any]=1e-5     ,    UpperCamelCase:			List[Any]=1e-5     ,    **UpperCamelCase:			Any     ,    ):






         """simple docstring"""


         super().__init__(**UpperCamelCase							)
         A__				      =	num_channels
         A__				      =	depths
         A__				      =	embed_dims
         A__				      =	mlp_ratio
         A__				      =	downsamples
         A__				      =	hidden_act
         A__				      =	down_patch_size
         A__				      =	down_stride
         A__				      =	down_pad
         A__				      =	drop_path_rate
         A__				      =	use_layer_scale
         A__				      =	layer_scale_init_value
         A__				      =	batch_norm_eps



class        a    (							_lowerCamelCase					):



    """simple docstring"""



    UpperCAmelCase    			=       version.parse("1.11"					)

    @property
    def 					UpperCamelCase						(  self:			int							):






         """simple docstring"""


         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]							)





    @property
    def 					UpperCamelCase						(  self:			Any							):






         """simple docstring"""


         return 1e-4






