'''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 lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "config.json"
   @register_to_config
   def __init__(			self			: Tuple,   UpperCamelCase__			: Tuple=2,   UpperCamelCase__			: Optional[Any]=5,   UpperCamelCase__			: Optional[Any]=(2, 5),   UpperCamelCase__			: int="for diffusion",   UpperCamelCase__			: Union[str, Any]=[1, 3],   )     -> Any:
        pass







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "config.json"
   @register_to_config
   def __init__(			self			: Optional[int],   UpperCamelCase__			: Any=2,   UpperCamelCase__			: Dict=5,   UpperCamelCase__			: Optional[Any]=(2, 5),   UpperCamelCase__			: List[Any]="for diffusion",   UpperCamelCase__			: Union[str, Any]=[1, 3],   )     -> List[str]:
        pass







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "config.json"
   @register_to_config
   def __init__(			self			: Tuple,   UpperCamelCase__			: str=2,   UpperCamelCase__			: str=5,   UpperCamelCase__			: Tuple=(2, 5),   UpperCamelCase__			: str="for diffusion",   UpperCamelCase__			: Dict=[1, 3],   UpperCamelCase__			: List[str]=[1, 3],   )     -> List[str]:
        pass







class lowercase_					(		_UpperCamelCase ):







   """simple docstring"""

   __lowerCAmelCase    						=      "config.json"
   @register_to_config
   def __init__(			self			: Dict,   UpperCamelCase__			: List[Any]=2,   UpperCamelCase__			: List[str]=5,   UpperCamelCase__			: List[Any]=(2, 5),   UpperCamelCase__			: int="for diffusion",   UpperCamelCase__			: Optional[Any]=[1, 5],   UpperCamelCase__			: Union[str, Any]=[5, 4],   )     -> Any:
        pass







class lowercase_					(		unittest.TestCase ):







   """simple docstring"""

   def   __UpperCAmelCase      (			self			: int    )     -> List[Any]:
        with self.assertRaises(UpperCamelCase__    ):
             ConfigMixin.load_config('dummy_path'    )
   def   __UpperCAmelCase      (			self			: str    )     -> List[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   __UpperCAmelCase      (			self			: Optional[Any]    )     -> List[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]

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

        # unfreeze configs
        _A								=     dict(UpperCamelCase__    )
        _A								=     dict(UpperCamelCase__    )

        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   __UpperCAmelCase      (			self			: Dict    )     -> Tuple:
        _A								=     logging.get_logger('diffusers.configuration_utils'    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(UpperCamelCase__    ) 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   __UpperCAmelCase      (			self			: Optional[int]    )     -> List[str]:
        _A								=     logging.get_logger('diffusers.configuration_utils'    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(UpperCamelCase__    ) 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   __UpperCAmelCase      (			self			: Optional[Any]    )     -> Dict:
        _A								=     logging.get_logger('diffusers.configuration_utils'    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(UpperCamelCase__    ) 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   __UpperCAmelCase      (			self			: Union[str, Any]    )     -> str:
        _A								=     logging.get_logger('diffusers.configuration_utils'    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(UpperCamelCase__    ) 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   __UpperCAmelCase      (			self			: str    )     -> str:
        _A								=     logging.get_logger('diffusers.configuration_utils'    )
        # 30 for warning
        logger.setLevel(30    )

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

        with CaptureLogger(UpperCamelCase__    ) 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   __UpperCAmelCase      (			self			: List[str]    )     -> Any:
        _A								=     logging.get_logger('diffusers.configuration_utils'    )
        # 30 for warning
        logger.setLevel(30    )

        with CaptureLogger(UpperCamelCase__    ) 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   __UpperCAmelCase      (			self			: Optional[Any]    )     -> Dict:
        # 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(UpperCamelCase__    )

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

             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]




'''simple docstring'''






import argparse
import json
import re
import string
import sys

import numpy as np


_UpperCAmelCase						:    Dict     					=					0.5





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     ['.', ',', ';', ':']

     for token in remove_tokens:
          _A								=     ground_truth.replace(__snake_case      ,		''							)
          _A								=     prediction.replace(__snake_case      ,		''							)

     _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(__snake_case							)
     _A								=     ground_truth.union(__snake_case							)
     _A								=     len(__snake_case							) / len(__snake_case							)
     return jaccard





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any							):

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

     def white_space_fix(__snake_case       : int							):
          return " ".join(text.split()							)

     def remove_punc(__snake_case       : List[str]							):
          _A								=     set(string.punctuation							)
          return "".join(ch for ch in text if ch not in exclude							)

     def lower(__snake_case       : Optional[int]							):
          return text.lower()

     return white_space_fix(remove_articles(remove_punc(lower(__snake_case							)							)							)							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[int]      ,		__snake_case       : Tuple      ,		__snake_case       : List[str]							):
     _A		,					_A		,					_A								=     0, 0, 0

     _A								=     'Parties' in qa_id

     # first check if ground truth is empty
     if len(__snake_case							) == 0:
          if len(__snake_case							) > 0:
               fp += len(__snake_case							)  # false positive for each one
     else:
          for ground_truth in ground_truths:
               assert len(__snake_case							) > 0
               # check if there is a match
               _A								=     False
               for pred in predictions:
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= 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(__snake_case							) > 0
                    if substr_ok:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= IOU_THRESH or ground_truth in pred
                    else:
                         _A								=     get_jaccard(__snake_case      ,		__snake_case							) >= 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    _SCREAMING_SNAKE_CASE		(			__snake_case       : List[Any]							):
     _A								=     precisions[::-1]
     for i in range(1      ,		len(__snake_case							)							):
          _A								=     max(precision_best[i - 1]      ,		precision_best[i]							)
     _A								=     precision_best[::-1]
     return precisions





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Any      ,		__snake_case       : Any							):
     _A								=     process_precisions(__snake_case							)
     _A								=     np.trapz(__snake_case      ,		__snake_case							)
     if np.isnan(__snake_case							):
          return 0
     return aupr





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Union[str, Any]      ,		__snake_case       : List[str]							):
     _A								=     process_precisions(__snake_case							)
     _A								=     0
     for prec, recall in zip(__snake_case      ,		__snake_case							):
          if recall >= recall_thresh:
               _A								=     prec
               break
     return prec_at_recall





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : Dict							):
     return normalize_answer(__snake_case							) == normalize_answer(__snake_case							)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Tuple      ,		__snake_case       : List[Any]							):
     _A								=     0
     for pred in predictions:
          for ground_truth in ground_truths:
               _A								=     metric_fn(__snake_case      ,		__snake_case							)
               if score == 1:  # break the loop when one prediction matches the ground truth
                    break
          if score == 1:
               break
     return score





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Tuple      ,		__snake_case       : Tuple							):
     _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(__snake_case      ,		file=sys.stderr							)
                         continue
                    _A								=     [x['text'] for x in qa['answers']]
                    _A								=     predictions[qa['id']]
                    _A		,					_A								=     compute_precision_recall(__snake_case      ,		__snake_case      ,		qa['id']							)

                    precisions.append(__snake_case							)
                    recalls.append(__snake_case							)

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

                    exact_match += metric_max_over_ground_truths(__snake_case      ,		__snake_case      ,		__snake_case							)

     _A								=     [x for _, x in sorted(zip(__snake_case      ,		__snake_case							)							)]
     recalls.sort()

     _A								=     1_00.0 * fa / total
     _A								=     1_00.0 * exact_match / total
     _A								=     get_aupr(__snake_case      ,		__snake_case							)

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



