import os
import pickle
import time

import numpy as np

from ray.tune import result as tune_result
from ray.rllib.algorithms.algorithm import Algorithm, AlgorithmConfig
from ray.rllib.utils.annotations import override


class _MockTrainer(Algorithm):
    """Mock Algorithm for use in tests."""

    @classmethod
    @override(Algorithm)
    def get_default_config(cls) -> AlgorithmConfig:
        return (
            AlgorithmConfig()
            .framework("tf")
            .update_from_dict(
                {
                    "mock_error": False,
                    "persistent_error": False,
                    "test_variable": 1,
                    "user_checkpoint_freq": 0,
                    "sleep": 0,
                }
            )
        )

    @classmethod
    def default_resource_request(cls, config: AlgorithmConfig):
        return None

    @override(Algorithm)
    def setup(self, config):
        self.callbacks = self.config.callbacks_class()

        # Add needed properties.
        self.info = None
        self.restored = False

    @override(Algorithm)
    def step(self):
        if (
            self.config.mock_error
            and self.iteration == 1
            and (self.config.persistent_error or not self.restored)
        ):
            raise Exception("mock error")
        if self.config.sleep:
            time.sleep(self.config.sleep)
        result = dict(
            episode_reward_mean=10, episode_len_mean=10, timesteps_this_iter=10, info={}
        )
        if self.config.user_checkpoint_freq > 0 and self.iteration > 0:
            if self.iteration % self.config.user_checkpoint_freq == 0:
                result.update({tune_result.SHOULD_CHECKPOINT: True})
        return result

    @override(Algorithm)
    def save_checkpoint(self, checkpoint_dir):
        path = os.path.join(checkpoint_dir, "mock_agent.pkl")
        with open(path, "wb") as f:
            pickle.dump(self.info, f)
        return path

    @override(Algorithm)
    def load_checkpoint(self, checkpoint_path):
        with open(checkpoint_path, "rb") as f:
            info = pickle.load(f)
        self.info = info
        self.restored = True

    @staticmethod
    @override(Algorithm)
    def _get_env_id_and_creator(env_specifier, config):
        # No env to register.
        return None, None

    def set_info(self, info):
        self.info = info
        return info

    def get_info(self, sess=None):
        return self.info


class _SigmoidFakeData(_MockTrainer):
    """Algorithm that returns sigmoid learning curves.

    This can be helpful for evaluating early stopping algorithms."""

    @classmethod
    @override(Algorithm)
    def get_default_config(cls) -> AlgorithmConfig:
        return AlgorithmConfig().update_from_dict(
            {
                "width": 100,
                "height": 100,
                "offset": 0,
                "iter_time": 10,
                "iter_timesteps": 1,
            }
        )

    def step(self):
        i = max(0, self.iteration - self.config.offset)
        v = np.tanh(float(i) / self.config.width)
        v *= self.config.height
        return dict(
            episode_reward_mean=v,
            episode_len_mean=v,
            timesteps_this_iter=self.config.iter_timesteps,
            time_this_iter_s=self.config.iter_time,
            info={},
        )


class _ParameterTuningTrainer(_MockTrainer):
    @classmethod
    @override(Algorithm)
    def get_default_config(cls) -> AlgorithmConfig:
        return AlgorithmConfig().update_from_dict(
            {
                "reward_amt": 10,
                "dummy_param": 10,
                "dummy_param2": 15,
                "iter_time": 10,
                "iter_timesteps": 1,
            }
        )

    def step(self):
        return dict(
            episode_reward_mean=self.config.reward_amt * self.iteration,
            episode_len_mean=self.config.reward_amt,
            timesteps_this_iter=self.config.iter_timesteps,
            time_this_iter_s=self.config.iter_time,
            info={},
        )


def _algorithm_import_failed(trace):
    """Returns dummy Algorithm class for if PyTorch etc. is not installed."""

    class _AlgorithmImportFailed(Algorithm):
        _name = "AlgorithmImportFailed"

        def setup(self, config):
            raise ImportError(trace)

    return _AlgorithmImportFailed
