"""Base logger and its helper handlers."""
import numbers
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union

import torch
import torch.nn as nn
from torch.optim import Optimizer

from ignite.engine import Engine, Events, EventsList, State
from ignite.engine.events import CallableEventWithFilter, RemovableEventHandle


class BaseHandler(metaclass=ABCMeta):
    """Base handler for defining various useful handlers."""

    @abstractmethod
    def __call__(self, engine: Engine, logger: Any, event_name: Union[str, Events]) -> None:
        pass


class BaseWeightsHandler(BaseHandler):
    """
    Base handler for logging weights or their gradients.
    """

    def __init__(
        self,
        model: nn.Module,
        tag: Optional[str] = None,
        whitelist: Optional[Union[List[str], Callable[[str, nn.Parameter], bool]]] = None,
    ):

        if not isinstance(model, torch.nn.Module):
            raise TypeError(f"Argument model should be of type torch.nn.Module, but given {type(model)}")

        self.model = model
        self.tag = tag

        weights = {}
        if whitelist is None:

            weights = dict(model.named_parameters())
        elif callable(whitelist):

            for n, p in model.named_parameters():
                if whitelist(n, p):
                    weights[n] = p
        else:

            for n, p in model.named_parameters():
                for item in whitelist:
                    if n.startswith(item):
                        weights[n] = p

        self.weights = weights.items()


class BaseOptimizerParamsHandler(BaseHandler):
    """
    Base handler for logging optimizer parameters
    """

    def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None):
        if not (
            isinstance(optimizer, Optimizer)
            or (hasattr(optimizer, "param_groups") and isinstance(optimizer.param_groups, Sequence))
        ):
            raise TypeError(
                "Argument optimizer should be torch.optim.Optimizer or has attribute 'param_groups' as list/tuple, "
                f"but given {type(optimizer)}"
            )

        self.optimizer = optimizer
        self.param_name = param_name
        self.tag = tag


class BaseOutputHandler(BaseHandler):
    """
    Helper handler to log engine's output and/or metrics
    """

    def __init__(
        self,
        tag: str,
        metric_names: Optional[Union[str, List[str]]] = None,
        output_transform: Optional[Callable] = None,
        global_step_transform: Optional[Callable] = None,
        state_attributes: Optional[List[str]] = None,
    ):

        if metric_names is not None:
            if not (isinstance(metric_names, list) or (isinstance(metric_names, str) and metric_names == "all")):
                raise TypeError(
                    f"metric_names should be either a list or equal 'all', got {type(metric_names)} instead."
                )

        if output_transform is not None and not callable(output_transform):
            raise TypeError(f"output_transform should be a function, got {type(output_transform)} instead.")

        if output_transform is None and metric_names is None and state_attributes is None:
            raise ValueError("Either metric_names, output_transform or state_attributes should be defined")

        if global_step_transform is not None and not callable(global_step_transform):
            raise TypeError(f"global_step_transform should be a function, got {type(global_step_transform)} instead.")

        if global_step_transform is None:

            def global_step_transform(engine: Engine, event_name: Union[str, Events]) -> int:
                return engine.state.get_event_attrib_value(event_name)

        self.tag = tag
        self.metric_names = metric_names
        self.output_transform = output_transform
        self.global_step_transform = global_step_transform
        self.state_attributes = state_attributes

    def _setup_output_metrics_state_attrs(
        self, engine: Engine, log_text: Optional[bool] = False, key_tuple: Optional[bool] = True
    ) -> Dict[Any, Any]:
        """Helper method to setup metrics and state attributes to log"""
        metrics_state_attrs = OrderedDict()
        if self.metric_names is not None:
            if isinstance(self.metric_names, str) and self.metric_names == "all":
                metrics_state_attrs = OrderedDict(engine.state.metrics)
            else:
                for name in self.metric_names:
                    if name not in engine.state.metrics:
                        warnings.warn(
                            f"Provided metric name '{name}' is missing "
                            f"in engine's state metrics: {list(engine.state.metrics.keys())}"
                        )
                        continue
                    metrics_state_attrs[name] = engine.state.metrics[name]

        if self.output_transform is not None:
            output_dict = self.output_transform(engine.state.output)

            if not isinstance(output_dict, dict):
                output_dict = {"output": output_dict}

            metrics_state_attrs.update(output_dict)

        if self.state_attributes is not None:
            metrics_state_attrs.update({name: getattr(engine.state, name, None) for name in self.state_attributes})

        metrics_state_attrs_dict = OrderedDict()  # type: Dict[Any, Union[str, float, numbers.Number]]

        def key_tuple_tf(tag: str, name: str, *args: str) -> Tuple[str, ...]:
            return (tag, name) + args

        def key_str_tf(tag: str, name: str, *args: str) -> str:
            return "/".join((tag, name) + args)

        key_tf = key_tuple_tf if key_tuple else key_str_tf

        for name, value in metrics_state_attrs.items():
            if isinstance(value, numbers.Number):
                metrics_state_attrs_dict[key_tf(self.tag, name)] = value
            elif isinstance(value, torch.Tensor) and value.ndimension() == 0:
                metrics_state_attrs_dict[key_tf(self.tag, name)] = value.item()
            elif isinstance(value, torch.Tensor) and value.ndimension() == 1:
                for i, v in enumerate(value):
                    metrics_state_attrs_dict[key_tf(self.tag, name, str(i))] = v.item()
            else:
                if isinstance(value, str) and log_text:
                    metrics_state_attrs_dict[key_tf(self.tag, name)] = value
                else:
                    warnings.warn(f"Logger output_handler can not log metrics value type {type(value)}")
        return metrics_state_attrs_dict


class BaseWeightsScalarHandler(BaseWeightsHandler):
    """
    Helper handler to log model's weights or gradients as scalars.
    """

    def __init__(
        self,
        model: nn.Module,
        reduction: Callable[[torch.Tensor], Union[float, torch.Tensor]] = torch.norm,
        tag: Optional[str] = None,
        whitelist: Optional[Union[List[str], Callable[[str, nn.Parameter], bool]]] = None,
    ):

        super(BaseWeightsScalarHandler, self).__init__(model, tag=tag, whitelist=whitelist)

        if not callable(reduction):
            raise TypeError(f"Argument reduction should be callable, but given {type(reduction)}")

        def _is_0D_tensor(t: Any) -> bool:
            return isinstance(t, torch.Tensor) and t.ndimension() == 0

        # Test reduction function on a tensor
        o = reduction(torch.ones(4, 2))
        if not (isinstance(o, numbers.Number) or _is_0D_tensor(o)):
            raise TypeError(f"Output of the reduction function should be a scalar, but got {type(o)}")

        self.reduction = reduction


class BaseLogger(metaclass=ABCMeta):
    """
    Base logger handler. See implementations: TensorboardLogger, VisdomLogger, PolyaxonLogger, MLflowLogger, ...

    """

    def attach(
        self,
        engine: Engine,
        log_handler: Callable,
        event_name: Union[str, Events, CallableEventWithFilter, EventsList],
        *args: Any,
        **kwargs: Any,
    ) -> RemovableEventHandle:
        """Attach the logger to the engine and execute `log_handler` function at `event_name` events.

        Args:
            engine: engine object.
            log_handler: a logging handler to execute
            event_name: event to attach the logging handler to. Valid events are from
                :class:`~ignite.engine.events.Events` or :class:`~ignite.engine.events.EventsList` or any `event_name`
                added by :meth:`~ignite.engine.engine.Engine.register_events`.
            args: args forwarded to the `log_handler` method
            kwargs: kwargs forwarded to the  `log_handler` method

        Returns:
            :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
        """
        if isinstance(event_name, EventsList):
            for name in event_name:
                if name not in State.event_to_attr:
                    raise RuntimeError(f"Unknown event name '{name}'")
                engine.add_event_handler(name, log_handler, self, name)

            return RemovableEventHandle(event_name, log_handler, engine)

        else:

            if event_name not in State.event_to_attr:
                raise RuntimeError(f"Unknown event name '{event_name}'")

            return engine.add_event_handler(event_name, log_handler, self, event_name, *args, **kwargs)

    def attach_output_handler(self, engine: Engine, event_name: Any, *args: Any, **kwargs: Any) -> RemovableEventHandle:
        """Shortcut method to attach `OutputHandler` to the logger.

        Args:
            engine: engine object.
            event_name: event to attach the logging handler to. Valid events are from
                :class:`~ignite.engine.events.Events` or any `event_name` added by
                :meth:`~ignite.engine.engine.Engine.register_events`.
            args: args to initialize `OutputHandler`
            kwargs: kwargs to initialize `OutputHandler`

        Returns:
            :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.
        """
        return self.attach(engine, self._create_output_handler(*args, **kwargs), event_name=event_name)

    def attach_opt_params_handler(
        self, engine: Engine, event_name: Any, *args: Any, **kwargs: Any
    ) -> RemovableEventHandle:
        """Shortcut method to attach `OptimizerParamsHandler` to the logger.

        Args:
            engine: engine object.
            event_name: event to attach the logging handler to. Valid events are from
                :class:`~ignite.engine.events.Events` or any `event_name` added by
                :meth:`~ignite.engine.engine.Engine.register_events`.
            args: args to initialize `OptimizerParamsHandler`
            kwargs: kwargs to initialize `OptimizerParamsHandler`

        Returns:
            :class:`~ignite.engine.events.RemovableEventHandle`, which can be used to remove the handler.

        .. versionchanged:: 0.4.3
            Added missing return statement.
        """
        return self.attach(engine, self._create_opt_params_handler(*args, **kwargs), event_name=event_name)

    @abstractmethod
    def _create_output_handler(self, engine: Engine, *args: Any, **kwargs: Any) -> Callable:
        pass

    @abstractmethod
    def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> Callable:
        pass

    def __enter__(self) -> "BaseLogger":
        return self

    def __exit__(self, type: Any, value: Any, traceback: Any) -> None:
        self.close()

    def close(self) -> None:
        pass
