
import argparse
import json
import re
import string
import sys

import numpy as np


__magic_name__  :  Tuple     			=   0.5





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      Optional[Any] )->			List[str]:
    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        lowerCAmelCase       (				snake_case__   :      List[Any] )->			Optional[int]:

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

    def white_space_fix(snake_case__   :      List[str] ):
        return " ".join(text.split() )

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

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

    return white_space_fix(remove_articles(remove_punc(lower(snake_case__ ) ) ) )





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





def        lowerCAmelCase       (				snake_case__   :      List[Any]				,					snake_case__   :      Optional[Any]				,					snake_case__   :      Tuple )->			Optional[Any]:
    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        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      List[Any] )->			str:
    return normalize_answer(snake_case__ ) == normalize_answer(snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      List[str]				,					snake_case__   :      Union[str, Any] )->			List[str]:
    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        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      str )->			Dict:
    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_0_0.0 * fa / total
    A_								=					1_0_0.0 * 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__":
     __magic_name__  :  Optional[int]     			=   argparse.ArgumentParser(description='Evaluation for CUAD')
     parser.add_argument('dataset_file', help='Dataset file')
     parser.add_argument('prediction_file', help='Prediction File')
     __magic_name__  :  Dict     			=   parser.parse_args()
     with open(args.dataset_file) as dataset_file:
          __magic_name__  :  str     			=   json.load(dataset_file)
          __magic_name__  :  str     			=   dataset_json['data']
     with open(args.prediction_file) as prediction_file:
          __magic_name__  :  List[Any]     			=   json.load(prediction_file)
     print(json.dumps(evaluate(dataset, predictions)))








import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute





def        lowerCAmelCase       (				snake_case__   :      int = 1				,					snake_case__   :      int = 1 )->			qiskit.result.counts.Counts:
    if isinstance(snake_case__				,					snake_case__ ) or isinstance(snake_case__				,					snake_case__ ):
        raise TypeError("inputs must be integers." )
    if (bit_a < 0) or (bit_a < 0):
        raise ValueError("inputs must be positive." )
    if (math.floor(snake_case__ ) != bit_a) or (math.floor(snake_case__ ) != bit_a):
        raise ValueError("inputs must be exact integers." )
    if (bit_a > 1) or (bit_a > 1):
        raise ValueError("inputs must be less or equal to 1." )

    # build registers
    A_								=					QuantumRegister(2				,					"qr" )
    A_								=					ClassicalRegister(2				,					"cr" )

    A_								=					QuantumCircuit(snake_case__				,					snake_case__ )

    # entanglement the qubits
    quantum_circuit.h(1 )
    quantum_circuit.cx(1				,					0 )

    # send the information
    A_								=					str(snake_case__ ) + str(snake_case__ )

    if c_information == "11":
        quantum_circuit.x(1 )
        quantum_circuit.z(1 )
    elif c_information == "10":
        quantum_circuit.z(1 )
    elif c_information == "01":
        quantum_circuit.x(1 )
    else:
        quantum_circuit.i(1 )

    # unentangled the circuit
    quantum_circuit.cx(1				,					0 )
    quantum_circuit.h(1 )

    # measure the circuit
    quantum_circuit.measure(snake_case__				,					snake_case__ )

    A_								=					Aer.get_backend("aer_simulator" )
    A_								=					execute(snake_case__				,					snake_case__				,					shots=1000 )

    return job.result().get_counts(snake_case__ )


if __name__ == "__main__":
     print(f"""Counts for classical state send: {superdense_coding(1,1)}""")






