from typing import List, Tuple
import numpy as np

from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.trajectory import Trajectory, AgentExperience
from mlagents_envs.base_env import (
    DecisionSteps,
    TerminalSteps,
    BehaviorSpec,
    ActionSpec,
)


def create_mock_steps(
    num_agents: int,
    observation_shapes: List[Tuple],
    action_spec: ActionSpec,
    done: bool = False,
) -> Tuple[DecisionSteps, TerminalSteps]:
    """
    Creates a mock Tuple[DecisionSteps, TerminalSteps] with observations.
    Imitates constant vector/visual observations, rewards, dones, and agents.

    :int num_agents: Number of "agents" to imitate.
    :List observation_shapes: A List of the observation spaces in your steps
    :int num_vector_acts: Number of actions in your action space
    :bool discrete: Whether or not action space is discrete
    :bool done: Whether all the agents in the batch are done
    """
    obs_list = []
    for _shape in observation_shapes:
        obs_list.append(np.ones((num_agents,) + _shape, dtype=np.float32))
    action_mask = None
    if action_spec.is_discrete():
        action_mask = [
            np.array(num_agents * [action_size * [False]])
            for action_size in action_spec.discrete_branches  # type: ignore
        ]  # type: ignore

    reward = np.array(num_agents * [1.0], dtype=np.float32)
    interrupted = np.array(num_agents * [False], dtype=np.bool)
    agent_id = np.arange(num_agents, dtype=np.int32)
    behavior_spec = BehaviorSpec(observation_shapes, action_spec)
    if done:
        return (
            DecisionSteps.empty(behavior_spec),
            TerminalSteps(obs_list, reward, interrupted, agent_id),
        )
    else:
        return (
            DecisionSteps(obs_list, reward, agent_id, action_mask),
            TerminalSteps.empty(behavior_spec),
        )


def create_steps_from_behavior_spec(
    behavior_spec: BehaviorSpec, num_agents: int = 1
) -> Tuple[DecisionSteps, TerminalSteps]:
    return create_mock_steps(
        num_agents=num_agents,
        observation_shapes=behavior_spec.observation_shapes,
        action_spec=behavior_spec.action_spec,
    )


def make_fake_trajectory(
    length: int,
    observation_shapes: List[Tuple],
    action_spec: ActionSpec,
    max_step_complete: bool = False,
    memory_size: int = 10,
) -> Trajectory:
    """
    Makes a fake trajectory of length length. If max_step_complete,
    the trajectory is terminated by a max step rather than a done.
    """
    steps_list = []

    action_size = action_spec.discrete_size + action_spec.continuous_size
    action_probs = {
        "action_probs": np.ones(
            int(np.sum(action_spec.discrete_branches) + action_spec.continuous_size),
            dtype=np.float32,
        )
    }
    for _i in range(length - 1):
        obs = []
        for _shape in observation_shapes:
            obs.append(np.ones(_shape, dtype=np.float32))
        reward = 1.0
        done = False
        if action_spec.is_continuous():
            action = {"continuous_action": np.zeros(action_size, dtype=np.float32)}
        else:
            action = {"discrete_action": np.zeros(action_size, dtype=np.float32)}
        action_pre = np.zeros(action_size, dtype=np.float32)
        action_mask = (
            [
                [False for _ in range(branch)]
                for branch in action_spec.discrete_branches
            ]  # type: ignore
            if action_spec.is_discrete()
            else None
        )
        prev_action = None
        if action_spec.is_discrete():
            prev_action = np.ones(action_size, dtype=np.int32)

        max_step = False
        memory = np.ones(memory_size, dtype=np.float32)
        agent_id = "test_agent"
        behavior_id = "test_brain"
        experience = AgentExperience(
            obs=obs,
            reward=reward,
            done=done,
            action=action,
            action_probs=action_probs,
            action_pre=action_pre,
            action_mask=action_mask,
            prev_action=prev_action,
            interrupted=max_step,
            memory=memory,
        )
        steps_list.append(experience)
    obs = []
    for _shape in observation_shapes:
        obs.append(np.ones(_shape, dtype=np.float32))
    last_experience = AgentExperience(
        obs=obs,
        reward=reward,
        done=not max_step_complete,
        action=action,
        action_probs=action_probs,
        action_pre=action_pre,
        action_mask=action_mask,
        prev_action=prev_action,
        interrupted=max_step_complete,
        memory=memory,
    )
    steps_list.append(last_experience)
    return Trajectory(
        steps=steps_list, agent_id=agent_id, behavior_id=behavior_id, next_obs=obs
    )


def simulate_rollout(
    length: int,
    behavior_spec: BehaviorSpec,
    memory_size: int = 10,
    exclude_key_list: List[str] = None,
) -> AgentBuffer:
    trajectory = make_fake_trajectory(
        length,
        behavior_spec.observation_shapes,
        action_spec=behavior_spec.action_spec,
        memory_size=memory_size,
    )
    buffer = trajectory.to_agentbuffer()
    # If a key_list was given, remove those keys
    if exclude_key_list:
        for key in exclude_key_list:
            if key in buffer:
                buffer.pop(key)
    return buffer


def setup_test_behavior_specs(
    use_discrete=True, use_visual=False, vector_action_space=2, vector_obs_space=8
):
    if use_discrete:
        action_spec = ActionSpec.create_discrete(tuple(vector_action_space))
    else:
        action_spec = ActionSpec.create_continuous(vector_action_space)
    behavior_spec = BehaviorSpec(
        [(84, 84, 3)] * int(use_visual) + [(vector_obs_space,)], action_spec
    )
    return behavior_spec


def create_mock_3dball_behavior_specs():
    return setup_test_behavior_specs(
        False, False, vector_action_space=2, vector_obs_space=8
    )


def create_mock_pushblock_behavior_specs():
    return setup_test_behavior_specs(
        True, False, vector_action_space=7, vector_obs_space=70
    )


def create_mock_banana_behavior_specs():
    return setup_test_behavior_specs(
        True, True, vector_action_space=[3, 3, 3, 2], vector_obs_space=0
    )