



import argparse
import json
import re
import string
import sys

import numpy as np


UpperCamelCase_									=   0.5







def       lowerCamelCase_  (    _a       :						str   ,		_a       :						str    ):



    '''simple docstring'''




    UpperCAmelCase_     :       List[Any]  		=					[""".""", """,""", """;""", """:"""]

    for token in remove_tokens:
        UpperCAmelCase_     :       Dict  		=					ground_truth.replace(_a   ,		""""""    )
        UpperCAmelCase_     :       List[Any]  		=					prediction.replace(_a   ,		""""""    )

    UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					ground_truth.lower(), prediction.lower()
    UpperCAmelCase_		, UpperCAmelCase_     :       Dict  		=					ground_truth.replace("""/"""   ,		""" """    ), prediction.replace("""/"""   ,		""" """    )
    UpperCAmelCase_		, UpperCAmelCase_     :       Tuple  		=					set(ground_truth.split(""" """    )    ), set(prediction.split(""" """    )    )

    UpperCAmelCase_     :       Tuple  		=					ground_truth.intersection(_a    )
    UpperCAmelCase_     :       Optional[Any]  		=					ground_truth.union(_a    )
    UpperCAmelCase_     :       Tuple  		=					len(_a    ) / len(_a    )
    return jaccard







def       lowerCamelCase_  (    _a       :						Optional[int]    ):



    '''simple docstring'''




    def remove_articles(_a       :						Optional[int]    ):
        return re.sub(r"""\b(a|an|the)\b"""   ,		""" """   ,		_a    )

    def white_space_fix(_a       :						Optional[int]    ):
        return " ".join(text.split()    )

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

    def lower(_a       :						int    ):
        return text.lower()

    return white_space_fix(remove_articles(remove_punc(lower(_a    )    )    )    )







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Optional[int]   ,		_a       :						int    ):



    '''simple docstring'''




    UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					0, 0, 0

    UpperCAmelCase_     :       int  		=					"""Parties""" in qa_id

    # first check if ground truth is empty
    if len(_a    ) == 0:
        if len(_a    ) > 0:
            fp += len(_a    )  # false positive for each one
    else:
        for ground_truth in ground_truths:
            assert len(_a    ) > 0
            # check if there is a match
            UpperCAmelCase_     :       int  		=					False
            for pred in predictions:
                if substr_ok:
                    UpperCAmelCase_     :       int  		=					get_jaccard(_a   ,		_a    ) >= IOU_THRESH or ground_truth in pred
                else:
                    UpperCAmelCase_     :       Tuple  		=					get_jaccard(_a   ,		_a    ) >= IOU_THRESH
                if is_match:
                    UpperCAmelCase_     :       Union[str, Any]  		=					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_     :       List[Any]  		=					False
            for ground_truth in ground_truths:
                assert len(_a    ) > 0
                if substr_ok:
                    UpperCAmelCase_     :       str  		=					get_jaccard(_a   ,		_a    ) >= IOU_THRESH or ground_truth in pred
                else:
                    UpperCAmelCase_     :       Union[str, Any]  		=					get_jaccard(_a   ,		_a    ) >= IOU_THRESH
                if is_match:
                    UpperCAmelCase_     :       str  		=					True

            if not match_found:
                fp += 1

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

    return precision, recall







def       lowerCamelCase_  (    _a       :						Optional[int]    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    _a       :						List[str]   ,		_a       :						str    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[int]  		=					process_precisions(_a    )
    UpperCAmelCase_     :       Tuple  		=					np.trapz(_a   ,		_a    )
    if np.isnan(_a    ):
        return 0
    return aupr







def       lowerCamelCase_  (    _a       :						Union[str, Any]   ,		_a       :						Optional[Any]   ,		_a       :						Union[str, Any]    ):



    '''simple docstring'''




    UpperCAmelCase_     :       Optional[Any]  		=					process_precisions(_a    )
    UpperCAmelCase_     :       List[Any]  		=					0
    for prec, recall in zip(_a   ,		_a    ):
        if recall >= recall_thresh:
            UpperCAmelCase_     :       Union[str, Any]  		=					prec
            break
    return prec_at_recall







def       lowerCamelCase_  (    _a       :						List[Any]   ,		_a       :						Tuple    ):



    '''simple docstring'''




    return normalize_answer(_a    ) == normalize_answer(_a    )







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						Optional[Any]   ,		_a       :						Optional[int]    ):



    '''simple docstring'''




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







def       lowerCamelCase_  (    _a       :						Optional[int]   ,		_a       :						List[Any]    ):



    '''simple docstring'''




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

                precisions.append(_a    )
                recalls.append(_a    )

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

                exact_match += metric_max_over_ground_truths(_a   ,		_a   ,		_a    )

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

    UpperCAmelCase_     :       List[str]  		=					1_0_0.0 * fa / total
    UpperCAmelCase_     :       Tuple  		=					1_0_0.0 * exact_match / total
    UpperCAmelCase_     :       Union[str, Any]  		=					get_aupr(_a   ,		_a    )

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






import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
		import torch


UpperCAmelCase_    =logging.get_logger(__name__)


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=21							,			UpperCAmelCase_=[]							,			UpperCAmelCase_=None    ):
											lowerCAmelCase =		model
											lowerCAmelCase =		trace_batch_nums
											lowerCAmelCase =		abort_after_batch_num

											# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
											lowerCAmelCase =		collections.deque([]							,			UpperCAmelCase_    )
											lowerCAmelCase =		[]
											lowerCAmelCase =		0
											lowerCAmelCase =		0
											lowerCAmelCase =		False
											lowerCAmelCase =		'''                 '''

											self.analyse_model()

											self.register_forward_hook()



				def    __snake_case		(			self							,			UpperCAmelCase_=None    ):
											if frame is not None:
																		self.expand_frame(UpperCAmelCase_    )
											self.frames.append('''\n'''.join(self.frame    )    )
											lowerCAmelCase =		[]  # start a new frame



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											self.frame.append(UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											print('''\n'''.join(self.frames    )    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											print(F"""\nDetected inf/nan during batch_number={self.batch_number}"""    )
											print(F"""Last {len(self.frames    )} forward frames:"""    )
											print(F"""{"abs min":8} {"abs max":8} metadata"""    )
											print('''\n'''.join(self.frames    )    )
											print('''\n\n'''    )
											lowerCAmelCase =		[]



				def    __snake_case		(			self    ):
											# extract the fully qualified module names, to be able to report at run time. e.g.:
											# encoder.block.2.layer.0.SelfAttention.o
											#
											# for shared weights only the first shared module name will be registered
											lowerCAmelCase =		{m: name for name, m in self.model.named_modules()}
											# self.longest_module_name = max(len(v) for v in self.module_names.values())



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											if torch.is_tensor(UpperCAmelCase_    ):
																		self.expand_frame(get_abs_min_max(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		if detect_overflow(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									lowerCAmelCase =		True
											elif var is None:
																		self.expand_frame(F"""{"None":>17} {ctx}"""    )
											else:
																		self.expand_frame(F"""{"not a tensor":>17} {ctx}"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""\n\n{self.prefix} *** Starting batch number={self.batch_number} ***"""    )
											self.expand_frame(F"""{"abs min":8} {"abs max":8} metadata"""    )



				def    __snake_case		(			self    ):
											self.expand_frame(F"""{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n"""    )



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											self.expand_frame(F"""{self.prefix} {self.module_names[module]} {module.__class__.__name__}"""    )

											# params
											for name, p in module.named_parameters(recurse=UpperCAmelCase_    ):
																		self.analyse_variable(UpperCAmelCase_							,			UpperCAmelCase_    )

											# inputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									self.analyse_variable(UpperCAmelCase_							,			F"""input[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''input'''    )

											# outputs
											if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																		for i, x in enumerate(UpperCAmelCase_    ):
																									# possibly a tuple of tuples
																									if isinstance(UpperCAmelCase_							,			UpperCAmelCase_    ):
																																for j, y in enumerate(UpperCAmelCase_    ):
																																							self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}][{j}]"""    )
																									else:
																																self.analyse_variable(UpperCAmelCase_							,			F"""output[{i}]"""    )
											else:
																		self.analyse_variable(UpperCAmelCase_							,			'''output'''    )

											self.save_frame()



				def    __snake_case		(			self    ):
											self.model.apply(self._register_forward_hook    )



				def    __snake_case		(			self							,			UpperCAmelCase_    ):
											module.register_forward_hook(self.forward_hook    )






				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											# - input is a tuple of packed inputs (could be non-Tensors)
											# - output could be a Tensor or a tuple of Tensors and non-Tensors

											lowerCAmelCase =		False

											lowerCAmelCase =		True if self.batch_number in self.trace_batch_nums else False
											if trace_mode:
																		self.reset_saved_frames()

											if self.total_calls == 0:
																		self.batch_start_frame()
											self.total_calls += 1

											# count batch numbers - the very first forward hook of the batch will be called when the
											# batch completes - i.e. it gets called very last - we know this batch has finished
											if module == self.model:
																		self.batch_number += 1
																		lowerCAmelCase =		True

											self.create_frame(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

											# if last_frame_of_batch:
											#     self.batch_end_frame()

											if trace_mode:
																		self.trace_frames()

											if last_frame_of_batch:
																		self.batch_start_frame()

											if self.detected_overflow and not trace_mode:
																		self.dump_saved_frames()

																		# now we can abort, as it's pointless to continue running
																		raise ValueError(
																		    '''DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '''
																		    '''Please scroll up above this traceback to see the activation values prior to this event.'''    )

											# abort after certain batch if requested to do so
											if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
																		raise ValueError(
																		    F"""DebugUnderflowOverflow: aborting after {self.batch_number} batches due to"""
																		    F""" `abort_after_batch_num={self.abort_after_batch_num}` arg"""    )




def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		var.abs()
							return F"""{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}"""



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		False
							if torch.isnan(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has nans"""				)
							if torch.isinf(_snake_case				).any().item():
														lowerCAmelCase =		True
														print(F"""{ctx} has infs"""				)

							# if needed to monitor large elements can enable the following
							if 0:  # and detected:
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			100				)]
														if naaa.numel() > 0:
																					print(F"""{ctx}:  n100={naaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			1000				)]
														if naaaa.numel() > 0:
																					print(F"""{ctx}: n1000={naaaa.numel()}"""				)
														lowerCAmelCase =		var[torch.ge(var.abs()    ,			10000				)]
														if naaaaa.numel() > 0:
																					print(F"""{ctx}: n10000={naaaaa.numel()}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e}"""				)

							if 0:
														print(F"""min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})"""				)

							return detected


class 				__UpperCamelCase      (			__UpperCAmelCase	):





				'''simple docstring'''



				__a				:			Tuple					    ="""underflow_overflow"""
				__a				:			Dict					    ="""tpu_metrics_debug"""



