
'''simple docstring'''







from sklearn.metrics import precision_score

import datasets


_snake_case					: Optional[int]     			=					"""
Precision is the fraction of correctly labeled positive examples out of all of the examples that were labeled as positive. It is computed via the equation:
Precision = TP / (TP + FP)
where TP is the True positives (i.e. the examples correctly labeled as positive) and FP is the False positive examples (i.e. the examples incorrectly labeled as positive).
"""


_snake_case					: List[str]     			=					"""
Args:
    predictions (`list` of `int`): Predicted class labels.
    references (`list` of `int`): Actual class labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`. If `average` is `None`, it should be the label order. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.
    zero_division (`int` or `string`): Sets the value to return when there is a zero division. Defaults to 'warn'.

        - 0: Returns 0 when there is a zero division.
        - 1: Returns 1 when there is a zero division.
        - 'warn': Raises warnings and then returns 0 when there is a zero division.

Returns:
    precision (`float` or `array` of `float`): Precision score or list of precision scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate that fewer negative examples were incorrectly labeled as positive, which means that, generally, higher scores are better.

Examples:

    Example 1-A simple binary example
        >>> precision_metric = datasets.load_metric(\"precision\")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'precision': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> precision_metric = datasets.load_metric(\"precision\")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['precision'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> precision_metric = datasets.load_metric(\"precision\")
        >>> results = precision_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(results)
        {'precision': 0.23529411764705882}

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = precision_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'precision': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'precision': 0.3333333333333333}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'precision': 0.2222222222222222}
        >>> results = precision_metric.compute(predictions=predictions, references=references, average=None)
        >>> print([round(res, 2) for res in results['precision']])
        [0.67, 0.0, 0.0]
"""


_snake_case					: int     			=					"""
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
    and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
    and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
    Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,  _KWARGS_DESCRIPTION      )
class       lowerCAmelCase			(					datasets.Metric      ):




		def      lowercase							(					self   ):
								return datasets.MetricInfo(
								    description=_DESCRIPTION       ,    citation=_CITATION       ,    inputs_description=_KWARGS_DESCRIPTION       ,    features=datasets.Features(
								        {
								            "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
								            "references": datasets.Sequence(datasets.Value("int32"   )   ),
								        }
								        if self.config_name == "multilabel"
								        else {
								            "predictions": datasets.Value("int32"   ),
								            "references": datasets.Value("int32"   ),
								        }   )       ,    reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html"]       ,    )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=1       ,    UpperCamelCase="binary"       ,    UpperCamelCase=None       ,    UpperCamelCase="warn"       ,    ):
								_SCREAMING_SNAKE_CASE					=			precision_score(
								    UpperCamelCase       ,    UpperCamelCase       ,    labels=UpperCamelCase       ,    pos_label=UpperCamelCase       ,    average=UpperCamelCase       ,    sample_weight=UpperCamelCase       ,    zero_division=UpperCamelCase       ,    )
								return {"precision": float(UpperCamelCase   ) if score.size == 1 else score}

'''simple docstring'''







from __future__ import annotations

import random
import string


class       lowerCAmelCase			:




		def __init__(					self       ,    UpperCamelCase = None   ):
								_SCREAMING_SNAKE_CASE					=			passcode or self.__passcode_creator()
								_SCREAMING_SNAKE_CASE					=			self.__make_key_list()
								_SCREAMING_SNAKE_CASE					=			self.__make_shift_key()




		def __str__(					self   ):
								return "".join(self.__passcode   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								for i in range(1       ,    len(UpperCamelCase   )       ,    2   ):
														iterlist[i] *= -1
								return iterlist




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			string.ascii_letters + string.digits
								_SCREAMING_SNAKE_CASE					=			[random.choice(UpperCamelCase   ) for _ in range(random.randint(10       ,    20   )   )]
								return password




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			(
								    string.ascii_letters + string.digits + string.punctuation + " \t\n"
								)

								_SCREAMING_SNAKE_CASE					=			[]

								# creates points known as breakpoints to break the key_list_options at those
								# points and pivot each substring
								_SCREAMING_SNAKE_CASE					=			sorted(set(self.__passcode   )   )
								_SCREAMING_SNAKE_CASE					=			[]

								# algorithm for creating a new shuffled list, keys_l, out of key_list_options
								for i in key_list_options:
														temp_list.extend(UpperCamelCase   )

														# checking breakpoints at which to pivot temporary sublist and add it into
														# keys_l
														if i in breakpoints or i == key_list_options[-1]:
																				keys_l.extend(temp_list[::-1]   )
																				temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
								return keys_l




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			sum(self.__neg_pos([ord(UpperCamelCase   ) for x in self.__passcode]   )   )
								return num if num > 0 else len(self.__passcode   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			""

								# decoding shift like Caesar cipher algorithm implementing negative shift or
								# reverse shift or left shift
								for i in encoded_message:
														_SCREAMING_SNAKE_CASE					=			self.__key_list.index(UpperCamelCase   )
														decoded_message += self.__key_list[
														    (position - self.__shift_key) % -len(self.__key_list   )
														]

								return decoded_message




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			""

								# encoding shift like Caesar cipher algorithm implementing positive shift or
								# forward shift or right shift
								for i in plaintext:
														_SCREAMING_SNAKE_CASE					=			self.__key_list.index(UpperCamelCase   )
														encoded_message += self.__key_list[
														    (position + self.__shift_key) % len(self.__key_list   )
														]

								return encoded_message



def       _a		(						_SCREAMING_SNAKE_CASE		: str = "Hello, this is a modified Caesar cipher"	):
						_SCREAMING_SNAKE_CASE					=			ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(_SCREAMING_SNAKE_CASE	)	)


if __name__ == "__main__":
						import doctest

						doctest.testmod()