from typing import Tuple
import logging
import spacy
from presidio_analyzer import RecognizerRegistry
from presidio_analyzer.nlp_engine import NlpEngine, NlpEngineProvider
from transformers_class import TransformerRecognizer

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


def create_nlp_engine_with_spacy(
    model_path: str,
) -> Tuple[NlpEngine, RecognizerRegistry]:
    """
    Instantiate an NlpEngine with a spaCy model
    :param model_path: spaCy model path.
    """
    if not spacy.util.is_package(model_path):
        spacy.cli.download(model_path)

    nlp_configuration = {
        "nlp_engine_name": "spacy",
        "models": [{"lang_code": model_path.split('_')[0], "model_name": model_path}],
    }

    nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine()

    registry = RecognizerRegistry()
    # registry.load_predefined_recognizers()
    registry.load_predefined_recognizers(nlp_engine=nlp_engine, languages=["fr", "en"])
    registry.add_recognizers_from_yaml("recognizers.yaml")



    return nlp_engine, registry


def create_nlp_engine_with_transformers(
    model_path: str,
) -> Tuple[NlpEngine, RecognizerRegistry]:
    """
    Instantiate an NlpEngine with a TransformersRecognizer and a small spaCy model.
    The TransformersRecognizer would return results from Transformers models, the spaCy model
    would return NlpArtifacts such as POS and lemmas.
    :param model_path: HuggingFace model path.
    """




    # if not spacy.util.is_package("en_core_web_sm"):
    #     spacy.cli.download("en_core_web_sm")
    # # Using a small spaCy model + a HF NER model
    # transformers_recognizer = TransformersRecognizer(model_path=model_path)
    #
    # if model_path == "StanfordAIMI/stanford-deidentifier-base":
    #     transformers_recognizer.load_transformer(**STANFORD_COFIGURATION)
    # elif model_path == "obi/deid_roberta_i2b2":
    #     transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION)
    # else:
    #     print(f"Warning: Model has no configuration, loading default.")
    #     transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION)

    # Use small spaCy model, no need for both spacy and HF models
    # The transformers model is used here as a recognizer, not as an NlpEngine
    if not spacy.util.is_package(model_path):
        spacy.cli.download(model_path)

    nlp_configuration = {
        "nlp_engine_name": "spacy",
        "models": [{"lang_code": model_path.split('_')[0], "model_name": model_path}],
    }

    nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine()
    registry = RecognizerRegistry()
    registry = load_predefined_recognizers(registry)

    mapping_labels = {"PER": "PERSON", 'LOC': 'LOCATION'}
    model_name = "AliaeAI/camembert_anonymizer_production_v2"  # "Jean-Baptiste/camembert-ner" , "AliaeAI/camembert_anonymizer_production"
    transformers_recognizer = TransformerRecognizer(model_name, mapping_labels)

    registry.add_recognizer(transformers_recognizer)
    registry.remove_recognizer("SpacyRecognizer")



    return nlp_engine, registry



from presidio_analyzer.predefined_recognizers import PhoneRecognizer, EmailRecognizer, CreditCardRecognizer, CryptoRecognizer, DateRecognizer, IpRecognizer, IbanRecognizer, UrlRecognizer
import phonenumbers

def load_predefined_recognizers(registry, lang='fr'):
    # phone number
    phone_recognizer_fr = PhoneRecognizer(supported_language=lang, supported_regions=phonenumbers.SUPPORTED_REGIONS,context=['téléphone'])
    registry.add_recognizer(phone_recognizer_fr)

    # email
    email_recognizer_fr = EmailRecognizer(supported_language=lang, context=["email", "mail", "e-mail"])
    registry.add_recognizer(email_recognizer_fr)

    # credit card
    creditcard_recognizer_fr = CreditCardRecognizer(supported_language=lang,context=["crédit", "carte", "carte de crédit"])
    registry.add_recognizer(creditcard_recognizer_fr)

    # crypto
    crypto_recognizer_fr = CryptoRecognizer(supported_language=lang, context=["crypto"])
    registry.add_recognizer(crypto_recognizer_fr)

    # date time
    date_recognizer_fr = DateRecognizer(supported_language=lang, context=["mois", "date", "jour", "année"])
    registry.add_recognizer(date_recognizer_fr)

    # ip address
    ip_recognizer_fr = IpRecognizer(supported_language=lang, context=["IP", "ip"])
    registry.add_recognizer(ip_recognizer_fr)

    # iban
    iban_recognizer_fr = IbanRecognizer(supported_language=lang, context = ["IBAN", "iban", "bancaire", "compte"])
    registry.add_recognizer(iban_recognizer_fr)

    # URL
    url_recognizer_fr = UrlRecognizer(supported_language=lang, context = ["site", "web"])
    registry.add_recognizer(url_recognizer_fr)

    # load from yaml
    registry.add_recognizers_from_yaml("recognizers.yaml")

    return registry