from openai import OpenAI, AsyncOpenAI
from typing import Dict, List, Optional
from pydantic import SecretStr

from deepeval.config.settings import get_settings
from deepeval.models.utils import (
    require_secret_api_key,
)
from deepeval.models import DeepEvalBaseEmbeddingModel
from deepeval.models.retry_policy import (
    create_retry_decorator,
    sdk_retries_for,
)
from deepeval.constants import ProviderSlug as PS
from deepeval.utils import require_param

# consistent retry rules
retry_local = create_retry_decorator(PS.LOCAL)


class LocalEmbeddingModel(DeepEvalBaseEmbeddingModel):
    def __init__(
        self,
        model: Optional[str] = None,
        api_key: Optional[str] = None,
        base_url: Optional[str] = None,
        generation_kwargs: Optional[Dict] = None,
        **kwargs,
    ):

        settings = get_settings()
        if api_key is not None:
            # keep it secret, keep it safe from serializings, logging and alike
            self.api_key: Optional[SecretStr] = SecretStr(api_key)
        else:
            self.api_key = get_settings().LOCAL_EMBEDDING_API_KEY

        if base_url is not None:
            base_url = str(base_url).rstrip("/")
        elif settings.LOCAL_EMBEDDING_BASE_URL is not None:
            base_url = str(settings.LOCAL_EMBEDDING_BASE_URL).rstrip("/")

        model = model or settings.LOCAL_EMBEDDING_MODEL_NAME
        # validation
        model = require_param(
            model,
            provider_label="LocalEmbeddingModel",
            env_var_name="LOCAL_EMBEDDING_MODEL_NAME",
            param_hint="model",
        )

        self.base_url = require_param(
            base_url,
            provider_label="LocalEmbeddingModel",
            env_var_name="LOCAL_EMBEDDING_BASE_URL",
            param_hint="base_url",
        )

        # Keep sanitized kwargs for client call to strip legacy keys
        self.kwargs = kwargs
        self.generation_kwargs = generation_kwargs or {}
        super().__init__(model)

    @retry_local
    def embed_text(self, text: str) -> List[float]:
        embedding_model = self.load_model()
        response = embedding_model.embeddings.create(
            model=self.name, input=[text], **self.generation_kwargs
        )
        return response.data[0].embedding

    @retry_local
    def embed_texts(self, texts: List[str]) -> List[List[float]]:
        embedding_model = self.load_model()
        response = embedding_model.embeddings.create(
            model=self.name, input=texts, **self.generation_kwargs
        )
        return [data.embedding for data in response.data]

    @retry_local
    async def a_embed_text(self, text: str) -> List[float]:
        embedding_model = self.load_model(async_mode=True)
        response = await embedding_model.embeddings.create(
            model=self.name, input=[text], **self.generation_kwargs
        )
        return response.data[0].embedding

    @retry_local
    async def a_embed_texts(self, texts: List[str]) -> List[List[float]]:
        embedding_model = self.load_model(async_mode=True)
        response = await embedding_model.embeddings.create(
            model=self.name, input=texts, **self.generation_kwargs
        )
        return [data.embedding for data in response.data]

    ###############################################
    # Model
    ###############################################

    def load_model(self, async_mode: bool = False):
        if not async_mode:
            return self._build_client(OpenAI)
        return self._build_client(AsyncOpenAI)

    def _build_client(self, cls):
        api_key = require_secret_api_key(
            self.api_key,
            provider_label="OpenAI",
            env_var_name="LOCAL_EMBEDDING_API_KEY",
            param_hint="`api_key` to LocalEmbeddingModel(...)",
        )

        client_kwargs = self.kwargs.copy()
        if not sdk_retries_for(PS.LOCAL):
            client_kwargs["max_retries"] = 0

        client_init_kwargs = dict(
            api_key=api_key,
            base_url=self.base_url,
            **client_kwargs,
        )
        try:
            return cls(**client_init_kwargs)
        except TypeError as e:
            # older OpenAI SDKs may not accept max_retries, in that case remove and retry once
            if "max_retries" in str(e):
                client_init_kwargs.pop("max_retries", None)
                return cls(**client_init_kwargs)
            raise

    def get_model_name(self):
        return f"{self.name} (Local Model)"