# Copyright 2023 Flower Labs GmbH. 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.
# ==============================================================================
"""Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021].

Paper: arxiv.org/abs/1803.01498
"""
from logging import WARNING
from typing import Callable, Dict, List, Optional, Tuple, Union

from flwr.common import (
    FitRes,
    MetricsAggregationFn,
    NDArrays,
    Parameters,
    Scalar,
    ndarrays_to_parameters,
    parameters_to_ndarrays,
)
from flwr.common.logger import log
from flwr.server.client_proxy import ClientProxy

from .aggregate import aggregate_trimmed_avg
from .fedavg import FedAvg


# pylint: disable=line-too-long
class FedTrimmedAvg(FedAvg):
    """Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021].

    Implemented based on: https://arxiv.org/abs/1803.01498

    Parameters
    ----------
    fraction_fit : float, optional
        Fraction of clients used during training. Defaults to 1.0.
    fraction_evaluate : float, optional
        Fraction of clients used during validation. Defaults to 1.0.
    min_fit_clients : int, optional
        Minimum number of clients used during training. Defaults to 2.
    min_evaluate_clients : int, optional
        Minimum number of clients used during validation. Defaults to 2.
    min_available_clients : int, optional
        Minimum number of total clients in the system. Defaults to 2.
    evaluate_fn : Optional[Callable[[int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]]]
        Optional function used for validation. Defaults to None.
    on_fit_config_fn : Callable[[int], Dict[str, Scalar]], optional
        Function used to configure training. Defaults to None.
    on_evaluate_config_fn : Callable[[int], Dict[str, Scalar]], optional
        Function used to configure validation. Defaults to None.
    accept_failures : bool, optional
        Whether or not accept rounds containing failures. Defaults to True.
    initial_parameters : Parameters, optional
        Initial global model parameters.
    beta : float, optional
        Fraction to cut off of both tails of the distribution. Defaults to 0.2.
    """

    # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
    def __init__(
        self,
        *,
        fraction_fit: float = 1.0,
        fraction_evaluate: float = 1.0,
        min_fit_clients: int = 2,
        min_evaluate_clients: int = 2,
        min_available_clients: int = 2,
        evaluate_fn: Optional[
            Callable[
                [int, NDArrays, Dict[str, Scalar]],
                Optional[Tuple[float, Dict[str, Scalar]]],
            ]
        ] = None,
        on_fit_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
        on_evaluate_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
        accept_failures: bool = True,
        initial_parameters: Optional[Parameters] = None,
        fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
        beta: float = 0.2,
    ) -> None:
        super().__init__(
            fraction_fit=fraction_fit,
            fraction_evaluate=fraction_evaluate,
            min_fit_clients=min_fit_clients,
            min_evaluate_clients=min_evaluate_clients,
            min_available_clients=min_available_clients,
            evaluate_fn=evaluate_fn,
            on_fit_config_fn=on_fit_config_fn,
            on_evaluate_config_fn=on_evaluate_config_fn,
            accept_failures=accept_failures,
            initial_parameters=initial_parameters,
            fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
        )
        self.beta = beta

    def __repr__(self) -> str:
        """Compute a string representation of the strategy."""
        rep = f"FedTrimmedAvg(accept_failures={self.accept_failures})"
        return rep

    def aggregate_fit(
        self,
        server_round: int,
        results: List[Tuple[ClientProxy, FitRes]],
        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
        """Aggregate fit results using trimmed average."""
        if not results:
            return None, {}
        # Do not aggregate if there are failures and failures are not accepted
        if not self.accept_failures and failures:
            return None, {}

        # Convert results
        weights_results = [
            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
            for _, fit_res in results
        ]
        parameters_aggregated = ndarrays_to_parameters(
            aggregate_trimmed_avg(weights_results, self.beta)
        )

        # Aggregate custom metrics if aggregation fn was provided
        metrics_aggregated = {}
        if self.fit_metrics_aggregation_fn:
            fit_metrics = [(res.num_examples, res.metrics) for _, res in results]
            metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)
        elif server_round == 1:  # Only log this warning once
            log(WARNING, "No fit_metrics_aggregation_fn provided")

        return parameters_aggregated, metrics_aggregated
