"""
Gemini LLM binding for LightRAG.

This module provides asynchronous helpers that adapt Google's Gemini models
to the same interface used by the rest of the LightRAG LLM bindings. The
implementation mirrors the OpenAI helpers while relying on the official
``google-genai`` client under the hood.
"""

from __future__ import annotations

import os
from collections.abc import AsyncIterator
from functools import lru_cache
from typing import Any

import numpy as np
from tenacity import (
    retry,
    stop_after_attempt,
    wait_exponential,
    retry_if_exception_type,
)

from lightrag.utils import (
    logger,
    remove_think_tags,
    safe_unicode_decode,
    wrap_embedding_func_with_attrs,
)

import pipmaster as pm

# Install the Google Gemini client and its dependencies on demand
if not pm.is_installed("google-genai"):
    pm.install("google-genai")
if not pm.is_installed("google-api-core"):
    pm.install("google-api-core")

from google import genai  # type: ignore
from google.genai import types  # type: ignore
from google.api_core import exceptions as google_api_exceptions  # type: ignore


class InvalidResponseError(Exception):
    """Custom exception class for triggering retry mechanism when Gemini returns empty responses"""

    pass


@lru_cache(maxsize=8)
def _get_gemini_client(
    api_key: str, base_url: str | None, timeout: int | None = None
) -> genai.Client:
    """
    Create (or fetch cached) Gemini client.

    Args:
        api_key: Google Gemini API key (not used in Vertex AI mode).
        base_url: Optional custom API endpoint.
        timeout: Optional request timeout in milliseconds.

    Returns:
        genai.Client: Configured Gemini client instance.
    """
    client_kwargs: dict[str, Any] = {}

    # Add Vertex AI support
    use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
    if use_vertexai:
        # Vertex AI mode: use project/location, NOT api_key
        client_kwargs["vertexai"] = True
        project = os.getenv("GOOGLE_CLOUD_PROJECT")
        if project:
            location = os.getenv("GOOGLE_CLOUD_LOCATION", "us-central1")
            client_kwargs["project"] = project
            if location:
                client_kwargs["location"] = location
        else:
            raise ValueError(
                "GOOGLE_CLOUD_PROJECT must be set when using Vertex AI mode"
            )
    else:
        # Standard Gemini API mode: use api_key
        client_kwargs["api_key"] = api_key

    if base_url and base_url != "DEFAULT_GEMINI_ENDPOINT" or timeout is not None:
        try:
            http_options_kwargs = {}
            if base_url and base_url != "DEFAULT_GEMINI_ENDPOINT":
                http_options_kwargs["base_url"] = base_url
            if timeout is not None:
                http_options_kwargs["timeout"] = timeout

            client_kwargs["http_options"] = types.HttpOptions(**http_options_kwargs)
        except Exception as e:
            logger.error("Failed to apply custom Gemini http_options: %s", e)
            raise e

    return genai.Client(**client_kwargs)


def _ensure_api_key(api_key: str | None) -> str:
    # In Vertex AI mode, API key is not required
    use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
    if use_vertexai:
        # Return empty string for Vertex AI mode (not used)
        return ""

    key = api_key or os.getenv("LLM_BINDING_API_KEY") or os.getenv("GEMINI_API_KEY")
    if not key:
        raise ValueError(
            "Gemini API key not provided. "
            "Set LLM_BINDING_API_KEY or GEMINI_API_KEY in the environment."
        )
    return key


def _build_generation_config(
    base_config: dict[str, Any] | None,
    system_prompt: str | None,
    keyword_extraction: bool,
) -> types.GenerateContentConfig | None:
    config_data = dict(base_config or {})

    if system_prompt:
        if config_data.get("system_instruction"):
            config_data["system_instruction"] = (
                f"{config_data['system_instruction']}\n{system_prompt}"
            )
        else:
            config_data["system_instruction"] = system_prompt

    if keyword_extraction and not config_data.get("response_mime_type"):
        config_data["response_mime_type"] = "application/json"

    # Remove entries that are explicitly set to None to avoid type errors
    sanitized = {
        key: value
        for key, value in config_data.items()
        if value is not None and value != ""
    }

    if not sanitized:
        return None

    return types.GenerateContentConfig(**sanitized)


def _format_history_messages(history_messages: list[dict[str, Any]] | None) -> str:
    if not history_messages:
        return ""

    history_lines: list[str] = []
    for message in history_messages:
        role = message.get("role", "user")
        content = message.get("content", "")
        history_lines.append(f"[{role}] {content}")

    return "\n".join(history_lines)


def _extract_response_text(
    response: Any, extract_thoughts: bool = False
) -> tuple[str, str]:
    """
    Extract text content from Gemini response, separating regular content from thoughts.

    Args:
        response: Gemini API response object
        extract_thoughts: Whether to extract thought content separately

    Returns:
        Tuple of (regular_text, thought_text)
    """
    candidates = getattr(response, "candidates", None)
    if not candidates:
        return ("", "")

    regular_parts: list[str] = []
    thought_parts: list[str] = []

    for candidate in candidates:
        if not getattr(candidate, "content", None):
            continue
        # Use 'or []' to handle None values from parts attribute
        for part in getattr(candidate.content, "parts", None) or []:
            text = getattr(part, "text", None)
            if not text:
                continue

            # Check if this part is thought content using the 'thought' attribute
            is_thought = getattr(part, "thought", False)

            if is_thought and extract_thoughts:
                thought_parts.append(text)
            elif not is_thought:
                regular_parts.append(text)

    return ("\n".join(regular_parts), "\n".join(thought_parts))


@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=60),
    retry=(
        retry_if_exception_type(google_api_exceptions.InternalServerError)
        | retry_if_exception_type(google_api_exceptions.ServiceUnavailable)
        | retry_if_exception_type(google_api_exceptions.ResourceExhausted)
        | retry_if_exception_type(google_api_exceptions.GatewayTimeout)
        | retry_if_exception_type(google_api_exceptions.BadGateway)
        | retry_if_exception_type(google_api_exceptions.DeadlineExceeded)
        | retry_if_exception_type(google_api_exceptions.Aborted)
        | retry_if_exception_type(google_api_exceptions.Unknown)
        | retry_if_exception_type(InvalidResponseError)
    ),
)
async def gemini_complete_if_cache(
    model: str,
    prompt: str,
    system_prompt: str | None = None,
    history_messages: list[dict[str, Any]] | None = None,
    enable_cot: bool = False,
    base_url: str | None = None,
    api_key: str | None = None,
    token_tracker: Any | None = None,
    stream: bool | None = None,
    keyword_extraction: bool = False,
    generation_config: dict[str, Any] | None = None,
    timeout: int | None = None,
    **_: Any,
) -> str | AsyncIterator[str]:
    """
    Complete a prompt using Gemini's API with Chain of Thought (COT) support.

    This function supports automatic integration of reasoning content from Gemini models
    that provide Chain of Thought capabilities via the thinking_config API feature.

    COT Integration:
    - When enable_cot=True: Thought content is wrapped in <think>...</think> tags
    - When enable_cot=False: Thought content is filtered out, only regular content returned
    - Thought content is identified by the 'thought' attribute on response parts
    - Requires thinking_config to be enabled in generation_config for API to return thoughts

    Args:
        model: The Gemini model to use.
        prompt: The prompt to complete.
        system_prompt: Optional system prompt to include.
        history_messages: Optional list of previous messages in the conversation.
        api_key: Optional Gemini API key. If None, uses environment variable.
        base_url: Optional custom API endpoint.
        generation_config: Optional generation configuration dict.
        keyword_extraction: Whether to use JSON response format.
        token_tracker: Optional token usage tracker for monitoring API usage.
        stream: Whether to stream the response.
        hashing_kv: Storage interface (for interface parity with other bindings).
        enable_cot: Whether to include Chain of Thought content in the response.
        timeout: Request timeout in seconds (will be converted to milliseconds for Gemini API).
        **_: Additional keyword arguments (ignored).

    Returns:
        The completed text (with COT content if enable_cot=True) or an async iterator
        of text chunks if streaming. COT content is wrapped in <think>...</think> tags.

    Raises:
        RuntimeError: If the response from Gemini is empty.
        ValueError: If API key is not provided or configured.
    """
    key = _ensure_api_key(api_key)
    # Convert timeout from seconds to milliseconds for Gemini API
    timeout_ms = timeout * 1000 if timeout else None
    client = _get_gemini_client(key, base_url, timeout_ms)

    history_block = _format_history_messages(history_messages)
    prompt_sections = []
    if history_block:
        prompt_sections.append(history_block)
    prompt_sections.append(f"[user] {prompt}")
    combined_prompt = "\n".join(prompt_sections)

    config_obj = _build_generation_config(
        generation_config,
        system_prompt=system_prompt,
        keyword_extraction=keyword_extraction,
    )

    request_kwargs: dict[str, Any] = {
        "model": model,
        "contents": [combined_prompt],
    }
    if config_obj is not None:
        request_kwargs["config"] = config_obj

    if stream:

        async def _async_stream() -> AsyncIterator[str]:
            # COT state tracking for streaming
            cot_active = False
            cot_started = False
            initial_content_seen = False
            usage_metadata = None

            try:
                # Use native async streaming from genai SDK
                # Note: generate_content_stream returns Awaitable[AsyncIterator], need to await first
                stream = await client.aio.models.generate_content_stream(
                    **request_kwargs
                )
                async for chunk in stream:
                    usage = getattr(chunk, "usage_metadata", None)
                    if usage is not None:
                        usage_metadata = usage

                    # Extract both regular and thought content
                    regular_text, thought_text = _extract_response_text(
                        chunk, extract_thoughts=True
                    )

                    if enable_cot:
                        # Process regular content
                        if regular_text:
                            if not initial_content_seen:
                                initial_content_seen = True

                            # Close COT section if it was active
                            if cot_active:
                                yield "</think>"
                                cot_active = False

                            # Process and yield regular content
                            if "\\u" in regular_text:
                                regular_text = safe_unicode_decode(
                                    regular_text.encode("utf-8")
                                )
                            yield regular_text

                        # Process thought content
                        if thought_text:
                            if not initial_content_seen and not cot_started:
                                # Start COT section
                                yield "<think>"
                                cot_active = True
                                cot_started = True

                            # Yield thought content if COT is active
                            if cot_active:
                                if "\\u" in thought_text:
                                    thought_text = safe_unicode_decode(
                                        thought_text.encode("utf-8")
                                    )
                                yield thought_text
                    else:
                        # COT disabled - only yield regular content
                        if regular_text:
                            if "\\u" in regular_text:
                                regular_text = safe_unicode_decode(
                                    regular_text.encode("utf-8")
                                )
                            yield regular_text

                # Ensure COT is properly closed if still active
                if cot_active:
                    yield "</think>"
                    cot_active = False

            except Exception as exc:
                # Try to close COT tag before re-raising
                if cot_active:
                    try:
                        yield "</think>"
                    except Exception:
                        pass
                raise exc
            finally:
                # Track token usage after streaming completes
                if token_tracker and usage_metadata:
                    token_tracker.add_usage(
                        {
                            "prompt_tokens": getattr(
                                usage_metadata, "prompt_token_count", 0
                            ),
                            "completion_tokens": getattr(
                                usage_metadata, "candidates_token_count", 0
                            ),
                            "total_tokens": getattr(
                                usage_metadata, "total_token_count", 0
                            ),
                        }
                    )

        return _async_stream()

    # Non-streaming: use native async client
    response = await client.aio.models.generate_content(**request_kwargs)

    # Extract both regular text and thought text
    regular_text, thought_text = _extract_response_text(response, extract_thoughts=True)

    # Apply COT filtering logic based on enable_cot parameter
    if enable_cot:
        # Include thought content wrapped in <think> tags
        if thought_text and thought_text.strip():
            if not regular_text or regular_text.strip() == "":
                # Only thought content available
                final_text = f"<think>{thought_text}</think>"
            else:
                # Both content types present: prepend thought to regular content
                final_text = f"<think>{thought_text}</think>{regular_text}"
        else:
            # No thought content, use regular content only
            final_text = regular_text or ""
    else:
        # Filter out thought content, return only regular content
        final_text = regular_text or ""

    if not final_text:
        raise InvalidResponseError("Gemini response did not contain any text content.")

    if "\\u" in final_text:
        final_text = safe_unicode_decode(final_text.encode("utf-8"))

    final_text = remove_think_tags(final_text)

    usage = getattr(response, "usage_metadata", None)
    if token_tracker and usage:
        token_tracker.add_usage(
            {
                "prompt_tokens": getattr(usage, "prompt_token_count", 0),
                "completion_tokens": getattr(usage, "candidates_token_count", 0),
                "total_tokens": getattr(usage, "total_token_count", 0),
            }
        )

    logger.debug("Gemini response length: %s", len(final_text))
    return final_text


async def gemini_model_complete(
    prompt: str,
    system_prompt: str | None = None,
    history_messages: list[dict[str, Any]] | None = None,
    keyword_extraction: bool = False,
    **kwargs: Any,
) -> str | AsyncIterator[str]:
    hashing_kv = kwargs.get("hashing_kv")
    model_name = None
    if hashing_kv is not None:
        model_name = hashing_kv.global_config.get("llm_model_name")
    if model_name is None:
        model_name = kwargs.pop("model_name", None)
    if model_name is None:
        raise ValueError("Gemini model name not provided in configuration.")

    return await gemini_complete_if_cache(
        model_name,
        prompt,
        system_prompt=system_prompt,
        history_messages=history_messages,
        keyword_extraction=keyword_extraction,
        **kwargs,
    )


@wrap_embedding_func_with_attrs(
    embedding_dim=1536, max_token_size=2048, model_name="gemini-embedding-001"
)
@retry(
    stop=stop_after_attempt(3),
    wait=wait_exponential(multiplier=1, min=4, max=60),
    retry=(
        retry_if_exception_type(google_api_exceptions.InternalServerError)
        | retry_if_exception_type(google_api_exceptions.ServiceUnavailable)
        | retry_if_exception_type(google_api_exceptions.ResourceExhausted)
        | retry_if_exception_type(google_api_exceptions.GatewayTimeout)
        | retry_if_exception_type(google_api_exceptions.BadGateway)
        | retry_if_exception_type(google_api_exceptions.DeadlineExceeded)
        | retry_if_exception_type(google_api_exceptions.Aborted)
        | retry_if_exception_type(google_api_exceptions.Unknown)
    ),
)
async def gemini_embed(
    texts: list[str],
    model: str = "gemini-embedding-001",
    base_url: str | None = None,
    api_key: str | None = None,
    embedding_dim: int | None = None,
    max_token_size: int | None = None,
    task_type: str = "RETRIEVAL_DOCUMENT",
    timeout: int | None = None,
    token_tracker: Any | None = None,
) -> np.ndarray:
    """Generate embeddings for a list of texts using Gemini's API.

    This function uses Google's Gemini embedding model to generate text embeddings.
    It supports dynamic dimension control and automatic normalization for dimensions
    less than 3072.

    Args:
        texts: List of texts to embed.
        model: The Gemini embedding model to use. Default is "gemini-embedding-001".
        base_url: Optional custom API endpoint.
        api_key: Optional Gemini API key. If None, uses environment variables.
        embedding_dim: Optional embedding dimension for dynamic dimension reduction.
            **IMPORTANT**: This parameter is automatically injected by the EmbeddingFunc wrapper.
            Do NOT manually pass this parameter when calling the function directly.
            The dimension is controlled by the @wrap_embedding_func_with_attrs decorator
            or the EMBEDDING_DIM environment variable.
            Supported range: 128-3072. Recommended values: 768, 1536, 3072.
        max_token_size: Maximum tokens per text. This parameter is automatically
            injected by the EmbeddingFunc wrapper when the underlying function
            signature supports it (via inspect.signature check). Gemini API will
            automatically truncate texts exceeding this limit (autoTruncate=True
            by default), so no client-side truncation is needed.
        task_type: Task type for embedding optimization. Default is "RETRIEVAL_DOCUMENT".
            Supported types: SEMANTIC_SIMILARITY, CLASSIFICATION, CLUSTERING,
            RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, CODE_RETRIEVAL_QUERY,
            QUESTION_ANSWERING, FACT_VERIFICATION.
        timeout: Request timeout in seconds (will be converted to milliseconds for Gemini API).
        token_tracker: Optional token usage tracker for monitoring API usage.

    Returns:
        A numpy array of embeddings, one per input text. For dimensions < 3072,
        the embeddings are L2-normalized to ensure optimal semantic similarity performance.

    Raises:
        ValueError: If API key is not provided or configured.
        RuntimeError: If the response from Gemini is invalid or empty.

    Note:
        - For dimension 3072: Embeddings are already normalized by the API
        - For dimensions < 3072: Embeddings are L2-normalized after retrieval
        - Normalization ensures accurate semantic similarity via cosine distance
        - Gemini API automatically truncates texts exceeding max_token_size (autoTruncate=True)
    """
    # Note: max_token_size is received but not used for client-side truncation.
    # Gemini API handles truncation automatically with autoTruncate=True (default).
    _ = max_token_size  # Acknowledge parameter to avoid unused variable warning

    key = _ensure_api_key(api_key)
    # Convert timeout from seconds to milliseconds for Gemini API
    timeout_ms = timeout * 1000 if timeout else None
    client = _get_gemini_client(key, base_url, timeout_ms)

    # Prepare embedding configuration
    config_kwargs: dict[str, Any] = {}

    # Add task_type to config
    if task_type:
        config_kwargs["task_type"] = task_type

    # Add output_dimensionality if embedding_dim is provided
    if embedding_dim is not None:
        config_kwargs["output_dimensionality"] = embedding_dim

    # Create config object if we have parameters
    config_obj = types.EmbedContentConfig(**config_kwargs) if config_kwargs else None

    request_kwargs: dict[str, Any] = {
        "model": model,
        "contents": texts,
    }
    if config_obj is not None:
        request_kwargs["config"] = config_obj

    # Use native async client for embedding
    response = await client.aio.models.embed_content(**request_kwargs)

    # Extract embeddings from response
    if not hasattr(response, "embeddings") or not response.embeddings:
        raise RuntimeError("Gemini response did not contain embeddings.")

    # Convert embeddings to numpy array
    embeddings = np.array(
        [np.array(e.values, dtype=np.float32) for e in response.embeddings]
    )

    # Apply L2 normalization for dimensions < 3072
    # The 3072 dimension embedding is already normalized by Gemini API
    if embedding_dim and embedding_dim < 3072:
        # Normalize each embedding vector to unit length
        norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
        # Avoid division by zero
        norms = np.where(norms == 0, 1, norms)
        embeddings = embeddings / norms
        logger.debug(
            f"Applied L2 normalization to {len(embeddings)} embeddings of dimension {embedding_dim}"
        )

    # Track token usage if tracker is provided
    # Note: Gemini embedding API may not provide usage metadata
    if token_tracker and hasattr(response, "usage_metadata"):
        usage = response.usage_metadata
        token_counts = {
            "prompt_tokens": getattr(usage, "prompt_token_count", 0),
            "total_tokens": getattr(usage, "total_token_count", 0),
        }
        token_tracker.add_usage(token_counts)

    logger.debug(
        f"Generated {len(embeddings)} Gemini embeddings with dimension {embeddings.shape[1]}"
    )

    return embeddings


__all__ = [
    "gemini_complete_if_cache",
    "gemini_model_complete",
    "gemini_embed",
]
