"""
Helper methods for the Presidio Streamlit app
"""
from typing import List, Optional, Tuple
import logging
import streamlit as st
from presidio_analyzer import (
    AnalyzerEngine,
    RecognizerResult,
    RecognizerRegistry,
    PatternRecognizer,
    Pattern,
)
from presidio_analyzer.nlp_engine import NlpEngine
from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig

from openai_fake_data_generator import (
    call_completion_model,
    OpenAIParams,
    create_prompt,
)
from presidio_nlp_engine_config import (
    create_nlp_engine_with_spacy,
    create_nlp_engine_with_transformers,
)

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

@st.cache_resource
def nlp_engine_and_registry(
    model_family: str,
    model_path: str,
    ta_key: Optional[str] = None,
    ta_endpoint: Optional[str] = None,
) -> Tuple[NlpEngine, RecognizerRegistry]:
    """Create the NLP Engine instance based on the requested model."""
    if "spacy" in model_family.lower():
        return create_nlp_engine_with_spacy(model_path)
    elif "transformers" in model_family.lower() or "iiiorg" in model_family.lower():
        return create_nlp_engine_with_transformers(model_path)
    else:
        raise ValueError(f"Model family {model_family} not supported")

@st.cache_resource
def analyzer_engine(
    model_family: str,
    model_path: str,
    ta_key: Optional[str] = None,
    ta_endpoint: Optional[str] = None,
) -> AnalyzerEngine:
    """Create the Analyzer Engine instance."""
    nlp_engine, registry = nlp_engine_and_registry(
        model_family, model_path, ta_key, ta_endpoint
    )
    analyzer = AnalyzerEngine(nlp_engine=nlp_engine, registry=registry)
    return analyzer

@st.cache_resource
def anonymizer_engine():
    """Return AnonymizerEngine."""
    return AnonymizerEngine()

@st.cache_data
def get_supported_entities(
    model_family: str, model_path: str, ta_key: str, ta_endpoint: str
):
    """Return supported entities from the Analyzer Engine."""
    return analyzer_engine(
        model_family, model_path, ta_key, ta_endpoint
    ).get_supported_entities() + ["GENERIC_PII"]

@st.cache_data
def analyze(
    model_family: str, model_path: str, ta_key: str, ta_endpoint: str, **kwargs
):
    """Analyze input using Analyzer engine and input arguments."""
    if "entities" not in kwargs or "All" in kwargs["entities"]:
        kwargs["entities"] = None

    if "deny_list" in kwargs and kwargs["deny_list"] is not None:
        ad_hoc_recognizer = create_ad_hoc_deny_list_recognizer(kwargs["deny_list"])
        kwargs["ad_hoc_recognizers"] = [ad_hoc_recognizer] if ad_hoc_recognizer else []
        del kwargs["deny_list"]

    if "regex_params" in kwargs and len(kwargs["regex_params"]) > 0:
        ad_hoc_recognizer = create_ad_hoc_regex_recognizer(*kwargs["regex_params"])
        kwargs["ad_hoc_recognizers"] = [ad_hoc_recognizer] if ad_hoc_recognizer else []
        del kwargs["regex_params"]

    return analyzer_engine(model_family, model_path, ta_key, ta_endpoint).analyze(
        **kwargs
    )

def anonymize(
    text: str,
    operator: str,
    analyze_results: List[RecognizerResult],
    mask_char: Optional[str] = None,
    number_of_chars: Optional[int] = None,
    encrypt_key: Optional[str] = None,
):
    """Anonymize identified input using Presidio Anonymizer."""
    if operator == "mask":
        operator_config = {
            "type": "mask",
            "masking_char": mask_char,
            "chars_to_mask": number_of_chars,
            "from_end": False,
        }
    elif operator == "encrypt":
        operator_config = {"key": encrypt_key}
    elif operator == "highlight":
        operator_config = {"lambda": lambda x: x}
    else:
        operator_config = None

    if operator == "highlight":
        operator = "custom"
    elif operator == "synthesize":
        operator = "replace"

    res = anonymizer_engine().anonymize(
        text,
        analyze_results,
        operators={"DEFAULT": OperatorConfig(operator, operator_config)},
    )
    return res

def annotate(text: str, analyze_results: List[RecognizerResult]):
    """Highlight the identified PII entities on the original text."""
    tokens = []
    results = anonymize(
        text=text,
        operator="highlight",
        analyze_results=analyze_results,
    )
    results = sorted(results.items, key=lambda x: x.start)
    for i, res in enumerate(results):
        if i == 0:
            tokens.append(text[: res.start])
        tokens.append((text[res.start : res.end], res.entity_type))
        if i != len(results) - 1:
            tokens.append(text[res.end : results[i + 1].start])
        else:
            tokens.append(text[res.end :])
    return tokens

def create_fake_data(
    text: str,
    analyze_results: List[RecognizerResult],
    openai_params: OpenAIParams,
):
    """Creates a synthetic version of the text using OpenAI APIs"""
    if not openai_params.openai_key:
        return "Please provide your OpenAI key"
    results = anonymize(text=text, operator="replace", analyze_results=analyze_results)
    prompt = create_prompt(results.text)
    fake = call_completion_model(prompt=prompt, openai_params=openai_params)
    return fake

def create_ad_hoc_deny_list_recognizer(
    deny_list: Optional[List[str]],
) -> Optional[PatternRecognizer]:
    if not deny_list:
        return None
    return PatternRecognizer(supported_entity="GENERIC_PII", deny_list=deny_list)

def create_ad_hoc_regex_recognizer(
    regex: str, entity_type: str, score: float, context: Optional[List[str]] = None
) -> Optional[PatternRecognizer]:
    if not regex:
        return None
    pattern = Pattern(name="Regex pattern", regex=regex, score=score)
    return PatternRecognizer(
        supported_entity=entity_type, patterns=[pattern], context=context
    )