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import logging
from typing import Optional, List, Tuple, Set
import os
import pandas as pd
from presidio_anonymizer import (
    AnonymizerEngine,
    BatchAnonymizerEngine)
from presidio_analyzer.nlp_engine import NlpEngineProvider

from presidio_analyzer import (
    RecognizerResult,
    EntityRecognizer,
    AnalysisExplanation,
    BatchAnalyzerEngine
)

from presidio_analyzer.nlp_engine import NlpArtifacts

try:
    from flair.data import Sentence
    from flair.models import SequenceTagger
except ImportError:
    print("Flair is not installed")


logger = logging.getLogger("presidio-analyzer")


class FlairRecognizer(EntityRecognizer):
    """
    Wrapper for a flair model, if needed to be used within Presidio Analyzer.

    :example:
    >from presidio_analyzer import AnalyzerEngine, RecognizerRegistry

    >flair_recognizer = FlairRecognizer()

    >registry = RecognizerRegistry()
    >registry.add_recognizer(flair_recognizer)

    >analyzer = AnalyzerEngine(registry=registry)

    >results = analyzer.analyze(
    >    "My name is Christopher and I live in Irbid.",
    >    language="en",
    >    return_decision_process=True,
    >)
    >for result in results:
    >    print(result)
    >    print(result.analysis_explanation)


    """

    ENTITIES = [
        "LOCATION",
        "PERSON",
        "ORGANIZATION",
        # "MISCELLANEOUS"   # - There are no direct correlation with Presidio entities.
    ]

    DEFAULT_EXPLANATION = "Identified as {} by Flair's Named Entity Recognition"

    CHECK_LABEL_GROUPS = [
        ({"LOCATION"}, {"LOC", "LOCATION"}),
        ({"PERSON"}, {"PER", "PERSON"}),
        ({"ORGANIZATION"}, {"ORG"}),
        # ({"MISCELLANEOUS"}, {"MISC"}), # Probably not PII
    ]

    MODEL_LANGUAGES = {
        "de": "flair/ner-german-large"
    }

    PRESIDIO_EQUIVALENCES = {
        "PER": "PERSON",
        "LOC": "LOCATION",
        "ORG": "ORGANIZATION",
        # 'MISC': 'MISCELLANEOUS'   # - Probably not PII
    }

    def __init__(
        self,
        supported_language: str = "de",
        supported_entities: Optional[List[str]] = None,
        check_label_groups: Optional[Tuple[Set, Set]] = None,
        model: SequenceTagger = None,
    ):
        self.check_label_groups = (
            check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS
        )

        supported_entities = supported_entities if supported_entities else self.ENTITIES
        self.model = (
            model
            if model
            else SequenceTagger.load(self.MODEL_LANGUAGES.get(supported_language))
        )

        super().__init__(
            supported_entities=supported_entities,
            supported_language=supported_language,
            name="Flair Analytics",
        )

    def load(self) -> None:
        """Load the model, not used. Model is loaded during initialization."""
        pass

    def get_supported_entities(self) -> List[str]:
        """
        Return supported entities by this model.

        :return: List of the supported entities.
        """
        return self.supported_entities

    # Class to use Flair with Presidio as an external recognizer.
    def analyze(
        self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None
    ) -> List[RecognizerResult]:
        """
        Analyze text using Text Analytics.

        :param text: The text for analysis.
        :param entities: Not working properly for this recognizer.
        :param nlp_artifacts: Not used by this recognizer.
        :param language: Text language. Supported languages in MODEL_LANGUAGES
        :return: The list of Presidio RecognizerResult constructed from the recognized
            Flair detections.
        """

        results = []

        sentences = Sentence(text)
        self.model.predict(sentences)

        # If there are no specific list of entities, we will look for all of it.
        if not entities:
            entities = self.supported_entities

        for entity in entities:
            if entity not in self.supported_entities:
                continue

            for ent in sentences.get_spans("ner"):
                if not self.__check_label(
                    entity, ent.labels[0].value, self.check_label_groups
                ):
                    continue
                textual_explanation = self.DEFAULT_EXPLANATION.format(
                    ent.labels[0].value
                )
                explanation = self.build_flair_explanation(
                    round(ent.score, 2), textual_explanation
                )
                flair_result = self._convert_to_recognizer_result(ent, explanation)

                results.append(flair_result)

        return results

    def _convert_to_recognizer_result(self, entity, explanation) -> RecognizerResult:

        entity_type = self.PRESIDIO_EQUIVALENCES.get(entity.tag, entity.tag)
        flair_score = round(entity.score, 2)

        flair_results = RecognizerResult(
            entity_type=entity_type,
            start=entity.start_position,
            end=entity.end_position,
            score=flair_score,
            analysis_explanation=explanation,
        )

        return flair_results

    def build_flair_explanation(
        self, original_score: float, explanation: str
    ) -> AnalysisExplanation:
        """
        Create explanation for why this result was detected.

        :param original_score: Score given by this recognizer
        :param explanation: Explanation string
        :return:
        """
        explanation = AnalysisExplanation(
            recognizer=self.__class__.__name__,
            original_score=original_score,
            textual_explanation=explanation,
        )
        return explanation

    @staticmethod
    def __check_label(
        entity: str, label: str, check_label_groups: Tuple[Set, Set]
    ) -> bool:
        return any(
            [entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]
        )


if __name__ == "__main__":

    from presidio_analyzer import AnalyzerEngine, RecognizerRegistry

    flair_recognizer = (
        FlairRecognizer(supported_language="de")
    )  # This would download a very large (+2GB) model on the first run


    registry = RecognizerRegistry()
    registry.add_recognizer(flair_recognizer)

    configuration = {
        "nlp_engine_name": "spacy",
        "models": [{"lang_code": "de", "model_name": "de_core_news_lg"}],
    }

    # Create NLP engine based on configuration
    provider = NlpEngineProvider(nlp_configuration=configuration)
    nlp_engine_german = provider.create_engine()

    # Pass the created NLP engine and supported_languages to the AnalyzerEngine
    analyzer = AnalyzerEngine(
        nlp_engine=nlp_engine_german,
        registry=registry,
        supported_languages=["de"]
    )


    # Initialize the anonymizer engine:
    anonymize_engine = AnonymizerEngine()

    # read all excel files in folder, which is spedified in .env file
    for file in os.listdir(os.getenv("DATA_PATH")):
        if file.endswith(".xlsx"):
            print("Read file: " + file)
            df = pd.read_excel(os.path.join(os.getenv("DATA_PATH"), file)).sample(100)

            # analyze the data
            anonymized_df = df["Information"].apply(
                lambda x: analyzer.analyze(text=x, language="de")
            )

            # save the anonymized data to a new file
            anonymized_df.to_excel(
                os.path.join(os.getenv("DATA_PATH"), file + '_anonymized.xlsx')
            )