"""
Copyright 2024, Zep Software, Inc.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""

from collections.abc import Iterable
from typing import TYPE_CHECKING

if TYPE_CHECKING:
    from google import genai
    from google.genai import types
else:
    try:
        from google import genai
        from google.genai import types
    except ImportError:
        raise ImportError(
            'google-genai is required for GeminiEmbedder. '
            'Install it with: pip install graphiti-core[google-genai]'
        ) from None

from pydantic import Field

from .client import EmbedderClient, EmbedderConfig

DEFAULT_EMBEDDING_MODEL = 'embedding-001'


class GeminiEmbedderConfig(EmbedderConfig):
    embedding_model: str = Field(default=DEFAULT_EMBEDDING_MODEL)
    api_key: str | None = None


class GeminiEmbedder(EmbedderClient):
    """
    Google Gemini Embedder Client
    """

    def __init__(
        self,
        config: GeminiEmbedderConfig | None = None,
        client: 'genai.Client | None' = None,
    ):
        """
        Initialize the GeminiEmbedder with the provided configuration and client.

        Args:
            config (GeminiEmbedderConfig | None): The configuration for the GeminiEmbedder, including API key, model, base URL, temperature, and max tokens.
            client (genai.Client | None): An optional async client instance to use. If not provided, a new genai.Client is created.
        """
        if config is None:
            config = GeminiEmbedderConfig()

        self.config = config

        if client is None:
            self.client = genai.Client(api_key=config.api_key)
        else:
            self.client = client

    async def create(
        self, input_data: str | list[str] | Iterable[int] | Iterable[Iterable[int]]
    ) -> list[float]:
        """
        Create embeddings for the given input data using Google's Gemini embedding model.

        Args:
            input_data: The input data to create embeddings for. Can be a string, list of strings,
                       or an iterable of integers or iterables of integers.

        Returns:
            A list of floats representing the embedding vector.
        """
        # Generate embeddings
        result = await self.client.aio.models.embed_content(
            model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
            contents=[input_data],  # type: ignore[arg-type]  # mypy fails on broad union type
            config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
        )

        if not result.embeddings or len(result.embeddings) == 0 or not result.embeddings[0].values:
            raise ValueError('No embeddings returned from Gemini API in create()')

        return result.embeddings[0].values

    async def create_batch(self, input_data_list: list[str]) -> list[list[float]]:
        # Generate embeddings
        result = await self.client.aio.models.embed_content(
            model=self.config.embedding_model or DEFAULT_EMBEDDING_MODEL,
            contents=input_data_list,  # type: ignore[arg-type]  # mypy fails on broad union type
            config=types.EmbedContentConfig(output_dimensionality=self.config.embedding_dim),
        )

        if not result.embeddings or len(result.embeddings) == 0:
            raise Exception('No embeddings returned')

        embeddings = []
        for embedding in result.embeddings:
            if not embedding.values:
                raise ValueError('Empty embedding values returned')
            embeddings.append(embedding.values)
        return embeddings