#
#  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
#  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.
#

import asyncio
import functools
import inspect
import json
import logging
import sys
import time
from copy import deepcopy
from functools import wraps
from typing import Any

import requests
from quart import (
    jsonify,
    request,
    has_app_context,
)
from werkzeug.exceptions import BadRequest as WerkzeugBadRequest

try:
    from quart.exceptions import BadRequest as QuartBadRequest
except ImportError:  # pragma: no cover - optional dependency
    QuartBadRequest = None

from peewee import OperationalError

from common.constants import ActiveEnum, LLMType
from api.utils.json_encode import CustomJSONEncoder
from common.mcp_tool_call_conn import MCPToolCallSession, close_multiple_mcp_toolcall_sessions
from api.db.services.tenant_llm_service import LLMFactoriesService
from common.connection_utils import timeout
from common.constants import RetCode
from common import settings
from common.misc_utils import thread_pool_exec

requests.models.complexjson.dumps = functools.partial(json.dumps, cls=CustomJSONEncoder)


def _safe_jsonify(payload: dict):
    if has_app_context():
        return jsonify(payload)
    return payload


async def _coerce_request_data() -> dict:
    """Fetch JSON body with sane defaults; fallback to form data."""
    if hasattr(request, "_cached_payload"):
        return request._cached_payload
    payload: Any = None

    body_bytes = await request.get_data()
    has_body = bool(body_bytes)
    content_type = (request.content_type or "").lower()
    is_json = content_type.startswith("application/json")

    if not has_body:
        payload = {}
    elif is_json:
        payload = await request.get_json(force=False, silent=False)
        if isinstance(payload, dict):
            payload = payload or {}
        elif isinstance(payload, str):
            raise AttributeError("'str' object has no attribute 'get'")
        else:
            raise TypeError("JSON payload must be an object.")
    else:
        form = await request.form
        payload = form.to_dict() if form else None
        if payload is None:
            raise TypeError("Request body is not a valid form payload.")

    request._cached_payload = payload
    return payload


async def get_request_json():
    return await _coerce_request_data()


def serialize_for_json(obj):
    """
    Recursively serialize objects to make them JSON serializable.
    Handles ModelMetaclass and other non-serializable objects.
    """
    if hasattr(obj, "__dict__"):
        # For objects with __dict__, try to serialize their attributes
        try:
            return {key: serialize_for_json(value) for key, value in obj.__dict__.items() if not key.startswith("_")}
        except (AttributeError, TypeError):
            return str(obj)
    elif hasattr(obj, "__name__"):
        # For classes and metaclasses, return their name
        return f"<{obj.__module__}.{obj.__name__}>" if hasattr(obj, "__module__") else f"<{obj.__name__}>"
    elif isinstance(obj, (list, tuple)):
        return [serialize_for_json(item) for item in obj]
    elif isinstance(obj, dict):
        return {key: serialize_for_json(value) for key, value in obj.items()}
    elif isinstance(obj, (str, int, float, bool)) or obj is None:
        return obj
    else:
        # Fallback: convert to string representation
        return str(obj)


def get_data_error_result(code=RetCode.DATA_ERROR, message="Sorry! Data missing!"):
    if sys.exc_info()[0] is not None:
        logging.exception(message)
    else:
        logging.error(message)
    result_dict = {"code": code, "message": message}
    response = {}
    for key, value in result_dict.items():
        if value is None and key != "code":
            continue
        else:
            response[key] = value
    return _safe_jsonify(response)


def server_error_response(e):
    # Quart invokes this handler outside the original except block, so we must pass exc_info manually.
    logging.error("Unhandled exception during request", exc_info=(type(e), e, e.__traceback__))
    try:
        msg = repr(e).lower()
        if getattr(e, "code", None) == 401 or ("unauthorized" in msg) or ("401" in msg):
            resp = get_json_result(code=RetCode.UNAUTHORIZED, message="Unauthorized")
            resp.status_code = RetCode.UNAUTHORIZED
            return resp
    except Exception as ex:
        logging.warning(f"error checking authorization: {ex}")

    if repr(e).find("index_not_found_exception") >= 0:
        return get_json_result(code=RetCode.EXCEPTION_ERROR, message="No chunk found, please upload file and parse it.")

    if "not_found" in str(e):
        return get_error_data_result(message="No chunk found! Check the chunk status please!")

    return get_json_result(code=RetCode.EXCEPTION_ERROR, message=repr(e))


def validate_request(*args, **kwargs):
    def process_args(input_arguments):
        no_arguments = []
        error_arguments = []
        for arg in args:
            if arg not in input_arguments:
                no_arguments.append(arg)
        for k, v in kwargs.items():
            config_value = input_arguments.get(k, None)
            if config_value is None:
                no_arguments.append(k)
            elif isinstance(v, (tuple, list)):
                if config_value not in v:
                    error_arguments.append((k, set(v)))
            elif config_value != v:
                error_arguments.append((k, v))
        if no_arguments or error_arguments:
            error_string = ""
            if no_arguments:
                error_string += "required argument are missing: {}; ".format(",".join(no_arguments))
            if error_arguments:
                error_string += "required argument values: {}".format(",".join(["{}={}".format(a[0], a[1]) for a in error_arguments]))
            return error_string
        return None

    def wrapper(func):
        @wraps(func)
        async def decorated_function(*_args, **_kwargs):
            exception_types = (AttributeError, TypeError, WerkzeugBadRequest)
            if QuartBadRequest is not None:
                exception_types = exception_types + (QuartBadRequest,)
            if args or kwargs:
                try:
                    input_arguments = await _coerce_request_data()
                except exception_types:
                    input_arguments = {}
            else:
                input_arguments = await _coerce_request_data()
            errs = process_args(input_arguments)
            if errs:
                return get_json_result(code=RetCode.ARGUMENT_ERROR, message=errs)
            if inspect.iscoroutinefunction(func):
                return await func(*_args, **_kwargs)
            return func(*_args, **_kwargs)

        return decorated_function

    return wrapper


def not_allowed_parameters(*params):
    def decorator(func):
        async def wrapper(*args, **kwargs):
            input_arguments = await _coerce_request_data()
            for param in params:
                if param in input_arguments:
                    return get_json_result(code=RetCode.ARGUMENT_ERROR, message=f"Parameter {param} isn't allowed")
            if inspect.iscoroutinefunction(func):
                return await func(*args, **kwargs)
            return func(*args, **kwargs)

        return wrapper

    return decorator


def active_required(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        from api.db.services import UserService
        from api.apps import current_user

        user_id = current_user.id
        usr = UserService.filter_by_id(user_id)
        # check is_active
        if not usr or not usr.is_active == ActiveEnum.ACTIVE.value:
            return get_json_result(code=RetCode.FORBIDDEN, message="User isn't active, please activate first.")
        if inspect.iscoroutinefunction(func):
            return await func(*args, **kwargs)
        return func(*args, **kwargs)

    return wrapper


def add_tenant_id_to_kwargs(func):
    @wraps(func)
    async def wrapper(**kwargs):
        from api.apps import current_user

        kwargs["tenant_id"] = current_user.id
        if inspect.iscoroutinefunction(func):
            return await func(**kwargs)
        return func(**kwargs)

    return wrapper


def get_json_result(code: RetCode = RetCode.SUCCESS, message="success", data=None):
    response = {"code": code, "message": message, "data": data}
    return _safe_jsonify(response)


def build_error_result(code=RetCode.FORBIDDEN, message="success"):
    response = {"code": code, "message": message}
    response = _safe_jsonify(response)
    if hasattr(response, "status_code"):
        response.status_code = code
    return response


def construct_json_result(code: RetCode = RetCode.SUCCESS, message="success", data=None):
    if data is None:
        return _safe_jsonify({"code": code, "message": message})
    return _safe_jsonify({"code": code, "message": message, "data": data})


def get_result(code=RetCode.SUCCESS, message="", data=None, total=None):
    """
    Standard API response format:
    {
        "code": 0,
        "data": [...],        # List or object, backward compatible
        "total": 47,          # Optional field for pagination
        "message": "..."      # Error or status message
    }
    """
    response = {"code": code}

    if code == RetCode.SUCCESS:
        if data is not None:
            response["data"] = data
        if total is not None:
            response["total_datasets"] = total
    else:
        response["message"] = message or "Error"

    return _safe_jsonify(response)


def get_error_data_result(
    message="Sorry! Data missing!",
    code=RetCode.DATA_ERROR,
):
    result_dict = {"code": code, "message": message}
    response = {}
    for key, value in result_dict.items():
        if value is None and key != "code":
            continue
        else:
            response[key] = value
    return _safe_jsonify(response)


def get_error_argument_result(message="Invalid arguments"):
    return get_result(code=RetCode.ARGUMENT_ERROR, message=message)


def get_error_permission_result(message="Permission error"):
    return get_result(code=RetCode.PERMISSION_ERROR, message=message)


def get_error_operating_result(message="Operating error"):
    return get_result(code=RetCode.OPERATING_ERROR, message=message)


def generate_confirmation_token():
    import secrets

    return "ragflow-" + secrets.token_urlsafe(32)


def get_parser_config(chunk_method, parser_config):
    if not chunk_method:
        chunk_method = "naive"

    # Define default configurations for each chunking method
    base_defaults = {
        "table_context_size": 0,
        "image_context_size": 0,
    }
    key_mapping = {
        "naive": {
            "layout_recognize": "DeepDOC",
            "chunk_token_num": 512,
            "delimiter": "\n",
            "auto_keywords": 0,
            "auto_questions": 0,
            "html4excel": False,
            "topn_tags": 3,
            "raptor": {
                "use_raptor": True,
                "prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n      {cluster_content}\nThe above is the content you need to summarize.",
                "max_token": 256,
                "threshold": 0.1,
                "max_cluster": 64,
                "random_seed": 0,
            },
            "graphrag": {
                "use_graphrag": True,
                "entity_types": [
                    "organization",
                    "person",
                    "geo",
                    "event",
                    "category",
                ],
                "method": "light",
                "batch_chunk_token_size": 4096,
                "retry_attempts": 2,
                "retry_backoff_seconds": 2.0,
                "retry_backoff_max_seconds": 60.0,
                "build_subgraph_timeout_per_chunk_seconds": 300,
                "build_subgraph_min_timeout_seconds": 600,
                "merge_timeout_seconds": 180,
                "resolution_timeout_seconds": 1800,
                "community_timeout_seconds": 1800,
                "lock_acquire_timeout_seconds": 600,
            },
            "parent_child": {
                "use_parent_child": False,
                "children_delimiter": "\n",
            },
        },
        "qa": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
        "tag": None,
        "resume": None,
        "manual": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
        "table": None,
        "paper": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
        "book": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
        "laws": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
        "presentation": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
        "one": None,
        "knowledge_graph": {
            "chunk_token_num": 8192,
            "delimiter": r"\n",
            "entity_types": ["organization", "person", "location", "event", "time"],
            "raptor": {"use_raptor": False},
            "graphrag": {"use_graphrag": False},
        },
        "email": None,
        "picture": None,
    }

    default_config = key_mapping[chunk_method]

    # If no parser_config provided, return default merged with base defaults
    if not parser_config:
        if default_config is None:
            merged_config = deep_merge(base_defaults, {})
        else:
            merged_config = deep_merge(base_defaults, default_config)
    elif default_config is None:
        # If parser_config is provided but no defaults for this method
        merged_config = deep_merge(base_defaults, parser_config)
    else:
        # Ensure raptor and graph_rag fields have default values if not provided
        merged_config = deep_merge(base_defaults, default_config)
        merged_config = deep_merge(merged_config, parser_config)

    # Flatten parent_child config into children_delimiter for the execution layer
    pc = merged_config.get("parent_child", {})
    if pc.get("use_parent_child"):
        merged_config["children_delimiter"] = pc.get("children_delimiter", "\n")
    elif pc:
        merged_config["children_delimiter"] = ""

    return merged_config


def get_data_openai(id=None, created=None, model=None, prompt_tokens=0, completion_tokens=0, content=None, finish_reason=None, object="chat.completion", param=None, stream=False):
    total_tokens = prompt_tokens + completion_tokens

    if stream:
        usage = None
        if finish_reason is not None:
            usage = {
                "prompt_tokens": prompt_tokens,
                "completion_tokens": completion_tokens,
                "total_tokens": total_tokens,
                "completion_tokens_details": {
                    "reasoning_tokens": 0,
                    "accepted_prediction_tokens": 0,
                    "rejected_prediction_tokens": 0,
                },
            }
        return {
            "id": f"{id}",
            "object": "chat.completion.chunk",
            "created": created if created is not None else int(time.time()),
            "model": model,
            "system_fingerprint": "",
            "usage": usage,
            "choices": [
                {
                    "delta": {
                        "content": content,
                        "role": "assistant",
                        "function_call": None,
                        "tool_calls": None,
                    },
                    "finish_reason": finish_reason,
                    "index": 0,
                    "logprobs": None,
                }
            ],
        }

    return {
        "id": f"{id}",
        "object": object,
        "created": created if created is not None else int(time.time()),
        "model": model,
        "param": param,
        "usage": {
            "prompt_tokens": prompt_tokens,
            "completion_tokens": completion_tokens,
            "total_tokens": total_tokens,
            "completion_tokens_details": {
                "reasoning_tokens": 0,
                "accepted_prediction_tokens": 0,
                "rejected_prediction_tokens": 0,
            },
        },
        "choices": [
            {
                "message": {"role": "assistant", "content": content},
                "logprobs": None,
                "finish_reason": finish_reason,
                "index": 0,
            }
        ],
    }


def check_duplicate_ids(ids, id_type="item"):
    """
    Check for duplicate IDs in a list and return unique IDs and error messages.

    Args:
        ids (list): List of IDs to check for duplicates
        id_type (str): Type of ID for error messages (e.g., 'document', 'dataset', 'chunk')

    Returns:
        tuple: (unique_ids, error_messages)
            - unique_ids (list): List of unique IDs
            - error_messages (list): List of error messages for duplicate IDs
    """
    id_count = {}
    duplicate_messages = []

    # Count occurrences of each ID
    for id_value in ids:
        id_count[id_value] = id_count.get(id_value, 0) + 1

    # Check for duplicates
    for id_value, count in id_count.items():
        if count > 1:
            duplicate_messages.append(f"Duplicate {id_type} ids: {id_value}")

    # Return unique IDs and error messages
    return list(set(ids)), duplicate_messages


def verify_embedding_availability(embd_id: str, tenant_id: str) -> tuple[bool, str | None]:
    from api.db.joint_services.tenant_model_service import resolve_model_config

    """
    Verifies availability of an embedding model for a specific tenant.

    Performs comprehensive verification through:
    1. Identifier Parsing: Decomposes embd_id into name and factory components
    2. System Verification: Checks model registration in LLMService
    3. Tenant Authorization: Validates tenant-specific model assignments
    4. Built-in Model Check: Confirms inclusion in predefined system models

    Args:
        embd_id (str): Unique identifier for the embedding model in format "model_name@factory"
        tenant_id (str): Tenant identifier for access control

    Returns:
        tuple[bool, Response | None]:
        - First element (bool):
            - True: Model is available and authorized
            - False: Validation failed
        - Second element contains:
            - None on success
            - Error detail dict on failure

    Raises:
        ValueError: When model identifier format is invalid
        OperationalError: When database connection fails (auto-handled)

    Examples:
        >>> verify_embedding_availability("text-embedding@openai", "tenant_123")
        (True, None)

        >>> verify_embedding_availability("invalid_model", "tenant_123")
        (False, {'code': 101, 'message': "Unsupported model: <invalid_model>"})
    """
    try:
        resolve_model_config(tenant_id, LLMType.EMBEDDING, embd_id)
    except LookupError as e:
        return False, str(e)
    except OperationalError as e:
        logging.exception(e)
        return False, "Database operation failed"
    except Exception as e:
        logging.exception(e)
        return False, "Internal server error"

    return True, None


def deep_merge(default: dict, custom: dict) -> dict:
    """
    Recursively merges two dictionaries with priority given to `custom` values.

    Creates a deep copy of the `default` dictionary and iteratively merges nested
    dictionaries using a stack-based approach. Non-dict values in `custom` will
    completely override corresponding entries in `default`.

    Args:
        default (dict): Base dictionary containing default values.
        custom (dict): Dictionary containing overriding values.

    Returns:
        dict: New merged dictionary combining values from both inputs.

    Example:
        >>> from copy import deepcopy
        >>> default = {"a": 1, "nested": {"x": 10, "y": 20}}
        >>> custom = {"b": 2, "nested": {"y": 99, "z": 30}}
        >>> deep_merge(default, custom)
        {'a': 1, 'b': 2, 'nested': {'x': 10, 'y': 99, 'z': 30}}

        >>> deep_merge({"config": {"mode": "auto"}}, {"config": "manual"})
        {'config': 'manual'}

    Notes:
        1. Merge priority is always given to `custom` values at all nesting levels
        2. Non-dict values (e.g. list, str) in `custom` will replace entire values
           in `default`, even if the original value was a dictionary
        3. Time complexity: O(N) where N is total key-value pairs in `custom`
        4. Recommended for configuration merging and nested data updates
    """
    merged = deepcopy(default)
    stack = [(merged, custom)]

    while stack:
        base_dict, override_dict = stack.pop()

        for key, val in override_dict.items():
            if key in base_dict and isinstance(val, dict) and isinstance(base_dict[key], dict):
                stack.append((base_dict[key], val))
            else:
                base_dict[key] = val

    return merged


def remap_dictionary_keys(source_data: dict, key_aliases: dict = None) -> dict:
    """
    Transform dictionary keys using a configurable mapping schema.

    Args:
        source_data: Original dictionary to process
        key_aliases: Custom key transformation rules (Optional)
            When provided, overrides default key mapping
            Format: {<original_key>: <new_key>, ...}

    Returns:
        dict: New dictionary with transformed keys preserving original values

    Example:
        >>> input_data = {"old_key": "value", "another_field": 42}
        >>> remap_dictionary_keys(input_data, {"old_key": "new_key"})
        {'new_key': 'value', 'another_field': 42}
    """
    DEFAULT_KEY_MAP = {
        "chunk_num": "chunk_count",
        "doc_num": "document_count",
        "parser_id": "chunk_method",
        "embd_id": "embedding_model",
    }

    transformed_data = {}
    mapping = key_aliases or DEFAULT_KEY_MAP

    for original_key, value in source_data.items():
        mapped_key = mapping.get(original_key, original_key)
        transformed_data[mapped_key] = value

    return transformed_data


def group_by(list_of_dict, key):
    res = {}
    for item in list_of_dict:
        if item[key] in res.keys():
            res[item[key]].append(item)
        else:
            res[item[key]] = [item]
    return res


def get_mcp_tools(mcp_servers: list, timeout: float | int = 10) -> tuple[dict, str]:
    results = {}
    tool_call_sessions = []
    try:
        for mcp_server in mcp_servers:
            server_key = mcp_server.id

            cached_tools = mcp_server.variables.get("tools", {})

            tool_call_session = MCPToolCallSession(mcp_server, mcp_server.variables)
            tool_call_sessions.append(tool_call_session)

            try:
                tools = tool_call_session.get_tools(timeout)
            except Exception:
                tools = []

            results[server_key] = []
            for tool in tools:
                tool_dict = tool.model_dump()
                cached_tool = cached_tools.get(tool_dict["name"], {})

                tool_dict["enabled"] = cached_tool.get("enabled", True)
                results[server_key].append(tool_dict)

        return results, ""
    except Exception as e:
        return {}, str(e)
    finally:
        # PERF: blocking call to close sessions — consider moving to background thread or task queue
        close_multiple_mcp_toolcall_sessions(tool_call_sessions)


async def is_strong_enough(chat_model, embedding_model):
    count = settings.STRONG_TEST_COUNT
    if not chat_model or not embedding_model:
        return
    if isinstance(count, int) and count <= 0:
        return

    @timeout(60, 2)
    async def _is_strong_enough():
        nonlocal chat_model, embedding_model
        if embedding_model:
            await asyncio.wait_for(thread_pool_exec(embedding_model.encode, ["Are you strong enough!?"]), timeout=10)

        if chat_model:
            res = await asyncio.wait_for(chat_model.async_chat("Nothing special.", [{"role": "user", "content": "Are you strong enough!?"}]), timeout=30)
            if "**ERROR**" in res:
                raise Exception(res)

    # Pressure test for GraphRAG task
    tasks = [asyncio.create_task(_is_strong_enough()) for _ in range(count)]
    try:
        await asyncio.gather(*tasks, return_exceptions=False)
    except Exception as e:
        logging.error(f"Pressure test failed: {e}")
        for t in tasks:
            t.cancel()
        await asyncio.gather(*tasks, return_exceptions=True)
        raise


def get_allowed_llm_factories() -> list:
    factories = list(LLMFactoriesService.get_all(reverse=True, order_by="rank"))
    if settings.ALLOWED_LLM_FACTORIES is None:
        return factories

    return [factory for factory in factories if factory.name in settings.ALLOWED_LLM_FACTORIES]
