


import datasets

from .evaluate import evaluate


SCREAMING_SNAKE_CASE :   Dict							      =				"\\n@article{hendrycks2021cuad,\n      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n      journal={arXiv preprint arXiv:2103.06268},\n      year={2021}\n}\n"

SCREAMING_SNAKE_CASE :   Dict							      =				"\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n"

SCREAMING_SNAKE_CASE :   List[str]							      =				"\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': list of possible texts for the answer, as a list of strings\n        depending on a threshold on the confidence probability of each prediction.\n    references: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair (see above),\n        - 'answers': a Dict in the CUAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'aupr': Area Under the Precision-Recall curve\n    'prec_at_80_recall': Precision at 80% recall\n    'prec_at_90_recall': Precision at 90% recall\nExamples:\n    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]\n    >>> cuad_metric = datasets.load_metric(\"cuad\")\n    >>> results = cuad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
							def     UpperCamelCase  (      self) ->     Optional[Any]:




											"""simple docstring"""

											return datasets.MetricInfo(
											    description=_DESCRIPTION,   citation=_CITATION,   inputs_description=_KWARGS_DESCRIPTION,   features=datasets.Features(
											        {
											            'predictions': {
											                'id': datasets.Value('string'),
											                'prediction_text': datasets.features.Sequence(datasets.Value('string')),
											            },
											            'references': {
											                'id': datasets.Value('string'),
											                'answers': datasets.features.Sequence(
											                    {
											                        'text': datasets.Value('string'),
											                        'answer_start': datasets.Value('int32'),
											                    }),
											            },
											        }),   codebase_urls=['https://www.atticusprojectai.org/cuad'],   reference_urls=['https://www.atticusprojectai.org/cuad'],   )




							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase) ->     int:




											"""simple docstring"""

											_lowercase :							List[str]   	=	{prediction['id']: prediction['prediction_text'] for prediction in predictions}
											_lowercase :							Optional[int]   	=	[
											    {
											        'paragraphs': [
											            {
											                'qas': [
											                    {
											                        'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
											                        'id': ref['id'],
											                    }
											                    for ref in references
											                ]
											            }
											        ]
											    }
											]
											_lowercase :							Dict   	=	evaluate(dataset=lowerCamelCase,   predictions=lowerCamelCase)
											return score






from __future__ import annotations

SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				tuple[int, int, int]
SCREAMING_SNAKE_CASE :   Any							      =				tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"ABCDEFGHIJKLMNOPQRSTUVWXYZ"

# -------------------------- default selection --------------------------
# rotors --------------------------
SCREAMING_SNAKE_CASE :   Tuple							      =				"EGZWVONAHDCLFQMSIPJBYUKXTR"
SCREAMING_SNAKE_CASE :   List[str]							      =				"FOBHMDKEXQNRAULPGSJVTYICZW"
SCREAMING_SNAKE_CASE :   Dict							      =				"ZJXESIUQLHAVRMDOYGTNFWPBKC"
# reflector --------------------------
SCREAMING_SNAKE_CASE :   Any							      =				{
    "A": "N",
    "N": "A",
    "B": "O",
    "O": "B",
    "C": "P",
    "P": "C",
    "D": "Q",
    "Q": "D",
    "E": "R",
    "R": "E",
    "F": "S",
    "S": "F",
    "G": "T",
    "T": "G",
    "H": "U",
    "U": "H",
    "I": "V",
    "V": "I",
    "J": "W",
    "W": "J",
    "K": "X",
    "X": "K",
    "L": "Y",
    "Y": "L",
    "M": "Z",
    "Z": "M",
}

# -------------------------- extra rotors --------------------------
SCREAMING_SNAKE_CASE :   List[Any]							      =				"RMDJXFUWGISLHVTCQNKYPBEZOA"
SCREAMING_SNAKE_CASE :   Dict							      =				"SGLCPQWZHKXAREONTFBVIYJUDM"
SCREAMING_SNAKE_CASE :   Optional[int]							      =				"HVSICLTYKQUBXDWAJZOMFGPREN"
SCREAMING_SNAKE_CASE :   List[Any]							      =				"RZWQHFMVDBKICJLNTUXAGYPSOE"
SCREAMING_SNAKE_CASE :   Optional[int]							      =				"LFKIJODBEGAMQPXVUHYSTCZRWN"
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"KOAEGVDHXPQZMLFTYWJNBRCIUS"



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_				)					->		tuple[RotorPositionT, RotorSelectionT, dict[str, str]]:
    # Checks if there are 3 unique rotors

    if (unique_rotsel := len(set(lowerCamelCase_				)				)) < 3:
        _lowercase :							int   	=	F'''Please use 3 unique rotors (not {unique_rotsel})'''
        raise Exception(lowerCamelCase_				)

    # Checks if rotor positions are valid
    _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	rotpos
    if not 0 < rotorposa <= len(lowerCamelCase_				):
        _lowercase :							Union[str, Any]   	=	F'''First rotor position is not within range of 1..26 ({rotorposa}'''
        raise ValueError(lowerCamelCase_				)
    if not 0 < rotorposa <= len(lowerCamelCase_				):
        _lowercase :							Tuple   	=	F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(lowerCamelCase_				)
    if not 0 < rotorposa <= len(lowerCamelCase_				):
        _lowercase :							str   	=	F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
        raise ValueError(lowerCamelCase_				)

    # Validates string and returns dict
    _lowercase :							Optional[int]   	=	_plugboard(lowerCamelCase_				)

    return rotpos, rotsel, pbdict



def        UpperCamelCase_(   lowerCamelCase_				)					->		dict[str, str]:

    # tests the input string if it
    # a) is type string
    # b) has even length (so pairs can be made)
    if not isinstance(lowerCamelCase_							,				lowerCamelCase_				):
        _lowercase :							List[Any]   	=	F'''Plugboard setting isn\'t type string ({type(lowerCamelCase_				)})'''
        raise TypeError(lowerCamelCase_				)
    elif len(lowerCamelCase_				) % 2 != 0:
        _lowercase :							Optional[Any]   	=	F'''Odd number of symbols ({len(lowerCamelCase_				)})'''
        raise Exception(lowerCamelCase_				)
    elif pbstring == "":
        return {}

    pbstring.replace(' '							,				''				)

    # Checks if all characters are unique
    _lowercase :							List[Any]   	=	set()
    for i in pbstring:
        if i not in abc:
            _lowercase :							str   	=	F'''\'{i}\' not in list of symbols'''
            raise Exception(lowerCamelCase_				)
        elif i in tmppbl:
            _lowercase :							Tuple   	=	F'''Duplicate symbol ({i})'''
            raise Exception(lowerCamelCase_				)
        else:
            tmppbl.add(lowerCamelCase_				)
    del tmppbl

    # Created the dictionary
    _lowercase :							List[Any]   	=	{}
    for j in range(0							,				len(lowerCamelCase_				) - 1							,				2				):
        _lowercase :							str   	=	pbstring[j + 1]
        _lowercase :							Union[str, Any]   	=	pbstring[j]

    return pb



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_ = (rotora, rotora, rotora)							,				lowerCamelCase_ = ""							,				)					->		str:
    _lowercase :							int   	=	text.upper()
    _lowercase	,       _lowercase	,       _lowercase :							Optional[int]   	=	_validator(
        lowerCamelCase_							,				lowerCamelCase_							,				plugb.upper()				)

    _lowercase	,       _lowercase	,       _lowercase :							Union[str, Any]   	=	rotor_position
    _lowercase	,       _lowercase	,       _lowercase :							Optional[Any]   	=	rotor_selection
    rotorposa -= 1
    rotorposa -= 1
    rotorposa -= 1

    _lowercase :							Any   	=	[]

    # encryption/decryption process --------------------------
    for symbol in text:
        if symbol in abc:
            # 1st plugboard --------------------------
            if symbol in plugboard:
                _lowercase :							List[str]   	=	plugboard[symbol]

            # rotor ra --------------------------
            _lowercase :							Any   	=	abc.index(lowerCamelCase_				) + rotorposa
            _lowercase :							Any   	=	rotora[index % len(lowerCamelCase_				)]

            # rotor rb --------------------------
            _lowercase :							List[Any]   	=	abc.index(lowerCamelCase_				) + rotorposa
            _lowercase :							Any   	=	rotora[index % len(lowerCamelCase_				)]

            # rotor rc --------------------------
            _lowercase :							Tuple   	=	abc.index(lowerCamelCase_				) + rotorposa
            _lowercase :							Tuple   	=	rotora[index % len(lowerCamelCase_				)]

            # reflector --------------------------
            # this is the reason you don't need another machine to decipher

            _lowercase :							List[Any]   	=	reflector[symbol]

            # 2nd rotors
            _lowercase :							int   	=	abc[rotora.index(lowerCamelCase_				) - rotorposa]
            _lowercase :							str   	=	abc[rotora.index(lowerCamelCase_				) - rotorposa]
            _lowercase :							int   	=	abc[rotora.index(lowerCamelCase_				) - rotorposa]

            # 2nd plugboard
            if symbol in plugboard:
                _lowercase :							Optional[int]   	=	plugboard[symbol]

            # moves/resets rotor positions
            rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Optional[Any]   	=	0
                rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Any   	=	0
                rotorposa += 1
            if rotorposa >= len(lowerCamelCase_				):
                _lowercase :							Any   	=	0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
        result.append(lowerCamelCase_				)

    return "".join(lowerCamelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				"This is my Python script that emulates the Enigma machine from WWII."
 SCREAMING_SNAKE_CASE :   Optional[int]							      =				(1, 1, 1)
 SCREAMING_SNAKE_CASE :   List[Any]							      =				"pictures"
 SCREAMING_SNAKE_CASE :   int							      =				(rotora, rotora, rotora)
 SCREAMING_SNAKE_CASE :   List[Any]							      =				enigma(message, rotor_pos, rotor_sel, pb)

 print("Encrypted message:", en)
 print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))


