'''simple docstring'''




import argparse
import json
import re
import string
import sys

import numpy as np


__snake_case   :      str						    =  0.5





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any]   )	->      List[Any]:
    A_     =   ['''.''', ''',''', ''';''', ''':''']

    for token in remove_tokens:
        A_     =   ground_truth.replace(_UpperCamelCase,   ''''''   )
        A_     =   prediction.replace(_UpperCamelCase,   ''''''   )

    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(_UpperCamelCase   )
    A_     =   ground_truth.union(_UpperCamelCase   )
    A_     =   len(_UpperCamelCase   ) / len(_UpperCamelCase   )
    return jaccard





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      List[str]:

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

    def white_space_fix(_UpperCamelCase    :	List[Any]   ):
        return " ".join(text.split()   )

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

    def lower(_UpperCamelCase    :	Tuple   ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_UpperCamelCase   )   )   )   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Optional[Any],   _UpperCamelCase    :	Any   )	->      Any:
    A_   ,A_   ,A_     =   0, 0, 0

    A_     =   '''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
            A_     =   False
            for pred in predictions:
                if substr_ok:
                    A_     =   get_jaccard(_UpperCamelCase,   _UpperCamelCase   ) >= IOU_THRESH or ground_truth in pred
                else:
                    A_     =   get_jaccard(_UpperCamelCase,   _UpperCamelCase   ) >= 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(_UpperCamelCase   ) > 0
                if substr_ok:
                    A_     =   get_jaccard(_UpperCamelCase,   _UpperCamelCase   ) >= IOU_THRESH or ground_truth in pred
                else:
                    A_     =   get_jaccard(_UpperCamelCase,   _UpperCamelCase   ) >= 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      _UpperCAmelCase    (							_UpperCamelCase    :	List[str]   )	->      List[str]:
    A_     =   precisions[::-1]
    for i in range(1,   len(_UpperCamelCase   )   ):
        A_     =   max(precision_best[i - 1],   precision_best[i]   )
    A_     =   precision_best[::-1]
    return precisions





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Optional[int]   )	->      Union[str, Any]:
    A_     =   process_precisions(_UpperCamelCase   )
    A_     =   np.trapz(_UpperCamelCase,   _UpperCamelCase   )
    if np.isnan(_UpperCamelCase   ):
        return 0
    return aupr





def      _UpperCAmelCase    (							_UpperCamelCase    :	List[str],   _UpperCamelCase    :	Dict,   _UpperCamelCase    :	Tuple   )	->      Optional[Any]:
    A_     =   process_precisions(_UpperCamelCase   )
    A_     =   0
    for prec, recall in zip(_UpperCamelCase,   _UpperCamelCase   ):
        if recall >= recall_thresh:
            A_     =   prec
            break
    return prec_at_recall





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any]   )	->      Union[str, Any]:
    return normalize_answer(_UpperCamelCase   ) == normalize_answer(_UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	int,   _UpperCamelCase    :	Tuple   )	->      List[str]:
    A_     =   0
    for pred in predictions:
        for ground_truth in ground_truths:
            A_     =   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      _UpperCAmelCase    (							_UpperCamelCase    :	List[str],   _UpperCamelCase    :	str   )	->      List[str]:
    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(_UpperCamelCase,   file=sys.stderr   )
                    continue
                A_     =   [x['''text'''] for x in qa['''answers''']]
                A_     =   predictions[qa['''id''']]
                A_   ,A_     =   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   )

    A_     =   [x for _, x in sorted(zip(_UpperCamelCase,   _UpperCamelCase   )   )]
    recalls.sort()

    A_     =   1_0_0.0 * fa / total
    A_     =   1_0_0.0 * exact_match / total
    A_     =   get_aupr(_UpperCamelCase,   _UpperCamelCase   )

    A_     =   get_prec_at_recall(_UpperCamelCase,   _UpperCamelCase,   recall_thresh=0.9   )
    A_     =   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__":
  __snake_case   :      List[str]						    =  argparse.ArgumentParser(description='Evaluation for CUAD')
  parser.add_argument('dataset_file', help='Dataset file')
  parser.add_argument('prediction_file', help='Prediction File')
  __snake_case   :      Dict						    =  parser.parse_args()
  with open(args.dataset_file) as dataset_file:
    __snake_case   :      List[Any]						    =  json.load(dataset_file)
    __snake_case   :      str						    =  dataset_json['data']
  with open(args.prediction_file) as prediction_file:
    __snake_case   :      Union[str, Any]						    =  json.load(prediction_file)
  print(json.dumps(evaluate(dataset, predictions)))







'''simple docstring'''




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					str    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Tuple    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Union[str, Any]:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     _SCREAMING_SNAKE_CASE=[1, 3]    ,     )		->     Dict:
           pass




class 	__UpperCAmelCase				(				_UpperCamelCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				'config.json'

       @register_to_config
       def __init__(      self    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=(2, 5)    ,     _SCREAMING_SNAKE_CASE="for diffusion"    ,     _SCREAMING_SNAKE_CASE=[1, 5]    ,     _SCREAMING_SNAKE_CASE=[5, 4]    ,     )		->     List[str]:
           pass




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               ConfigMixin.load_config('''dummy_path''' )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   SampleObject()
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           A_     =   SampleObject(_name_or_path='''lalala''' )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           A_     =   SampleObject(c=6 )
           A_     =   obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           A_     =   SampleObject(1    ,     c=6 )
           A_     =   obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 					__A       (      self )		->     int:
           A_     =   SampleObject()
           A_     =   obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_SCREAMING_SNAKE_CASE )
               A_     =   SampleObject.from_config(SampleObject.load_config(_SCREAMING_SNAKE_CASE ) )
               A_     =   new_obj.config

           # unfreeze configs
           A_     =   dict(_SCREAMING_SNAKE_CASE )
           A_     =   dict(_SCREAMING_SNAKE_CASE )

           assert config.pop('''c''' ) == (2, 5)  # instantiated as tuple
           assert new_config.pop('''c''' ) == [2, 5]  # saved & loaded as list because of json
           config.pop('''_use_default_values''' )
           assert config == new_config

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDIMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     List[str]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   EulerAncestralDiscreteScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   PNDMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Tuple:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DDPMScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler'''    ,     prediction_type='''sample'''    ,     beta_end=8    ,     )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger_a:
               A_     =   DDPMScheduler.from_pretrained('''google/ddpm-celebahq-256'''    ,     beta_start=88 )

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 					__A       (      self )		->     List[Any]:
           A_     =   logging.get_logger('''diffusers.configuration_utils''' )
           # 30 for warning
           logger.setLevel(30 )

           with CaptureLogger(_SCREAMING_SNAKE_CASE ) as cap_logger:
               A_     =   DPMSolverMultistepScheduler.from_pretrained(
                   '''hf-internal-testing/tiny-stable-diffusion-torch'''    ,     subfolder='''scheduler''' )

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 					__A       (      self )		->     Optional[int]:
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           A_     =   SampleObject()

           A_     =   {k: v for k, v in config.config.items() if not k.startswith('''_''' )}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys() ) == set(config.config._use_default_values )

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_SCREAMING_SNAKE_CASE )

               # now loading it with SampleObject2 should put f into `_use_default_values`
               A_     =   SampleObjecta.from_config(_SCREAMING_SNAKE_CASE )

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           A_     =   SampleObjecta.from_config(config.config )
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]






