import logging
import numpy as np
import io

from typing import Dict, List, Optional
from PIL import Image

logger = logging.getLogger("mlagentsdev.envs")


class BrainInfo:
    def __init__(self, visual_observation, vector_observation, text_observations, memory=None,
                 reward=None, agents=None, local_done=None,
                 vector_action=None, text_action=None, max_reached=None, action_mask=None,
                 custom_observations=None):
        """
        Describes experience at current step of all agents linked to a brain.
        """
        self.visual_observations = visual_observation
        self.vector_observations = vector_observation
        self.text_observations = text_observations
        self.memories = memory
        self.rewards = reward
        self.local_done = local_done
        self.max_reached = max_reached
        self.agents = agents
        self.previous_vector_actions = vector_action
        self.previous_text_actions = text_action
        self.action_masks = action_mask
        self.custom_observations = custom_observations

    def merge(self, other):
        for i in range(len(self.visual_observations)):
            self.visual_observations[i].extend(other.visual_observations[i])
        self.vector_observations = np.append(self.vector_observations, other.vector_observations, axis=0)
        self.text_observations.extend(other.text_observations)
        self.memories = self.merge_memories(self.memories, other.memories, self.agents, other.agents)
        self.rewards = safe_concat_lists(self.rewards, other.rewards)
        self.local_done = safe_concat_lists(self.local_done, other.local_done)
        self.max_reached = safe_concat_lists(self.max_reached, other.max_reached)
        self.agents = safe_concat_lists(self.agents, other.agents)
        self.previous_vector_actions = safe_concat_np_ndarray(
            self.previous_vector_actions, other.previous_vector_actions
        )
        self.previous_text_actions = safe_concat_lists(
            self.previous_text_actions, other.previous_text_actions
        )
        self.action_masks = safe_concat_np_ndarray(self.action_masks, other.action_masks)
        self.custom_observations = safe_concat_lists(self.custom_observations, other.custom_observations)

    @staticmethod
    def merge_memories(m1, m2, agents1, agents2):
        if len(m1) == 0 and len(m2) != 0:
            m1 = np.zeros((len(agents1), m2.shape[1]))
        elif len(m2) == 0 and len(m1) != 0:
            m2 = np.zeros((len(agents2), m1.shape[1]))
        elif m2.shape[1] > m1.shape[1]:
            new_m1 = np.zeros((m1.shape[0], m2.shape[1]))
            new_m1[0:m1.shape[0], 0:m1.shape[1]] = m1
            return np.append(new_m1, m2, axis=0)
        elif m1.shape[1] > m2.shape[1]:
            new_m2 = np.zeros((m2.shape[0], m1.shape[1]))
            new_m2[0:m2.shape[0], 0:m2.shape[1]] = m2
            return np.append(m1, new_m2, axis=0)
        return np.append(m1, m2, axis=0)

    @staticmethod
    def process_pixels(image_bytes, gray_scale):
        """
        Converts byte array observation image into numpy array, re-sizes it,
        and optionally converts it to grey scale
        :param gray_scale: Whether to convert the image to grayscale.
        :param image_bytes: input byte array corresponding to image
        :return: processed numpy array of observation from environment
        """
        s = bytearray(image_bytes)
        image = Image.open(io.BytesIO(s))
        s = np.array(image) / 255.0
        if gray_scale:
            s = np.mean(s, axis=2)
            s = np.reshape(s, [s.shape[0], s.shape[1], 1])
        return s

    @staticmethod
    def from_agent_proto(agent_info_list, brain_params):
        """
        Converts list of agent infos to BrainInfo.
        """
        vis_obs = []
        for i in range(brain_params.number_visual_observations):
            obs = [BrainInfo.process_pixels(x.visual_observations[i],
                                            brain_params.camera_resolutions[i]['blackAndWhite'])
                   for x in agent_info_list]
            vis_obs += [obs]
        if len(agent_info_list) == 0:
            memory_size = 0
        else:
            memory_size = max([len(x.memories) for x in agent_info_list])
        if memory_size == 0:
            memory = np.zeros((0, 0))
        else:
            [x.memories.extend([0] * (memory_size - len(x.memories))) for x in agent_info_list]
            memory = np.array([list(x.memories) for x in agent_info_list])
        total_num_actions = sum(brain_params.vector_action_space_size)
        mask_actions = np.ones((len(agent_info_list), total_num_actions))
        for agent_index, agent_info in enumerate(agent_info_list):
            if agent_info.action_mask is not None:
                if len(agent_info.action_mask) == total_num_actions:
                    mask_actions[agent_index, :] = [
                        0 if agent_info.action_mask[k] else 1 for k in range(total_num_actions)]
        if any([np.isnan(x.reward) for x in agent_info_list]):
            logger.warning("An agent had a NaN reward for brain " + brain_params.brain_name)
        if any([np.isnan(x.stacked_vector_observation).any() for x in agent_info_list]):
            logger.warning("An agent had a NaN observation for brain " + brain_params.brain_name)

        if len(agent_info_list) == 0:
            vector_obs = np.zeros(
                (0, brain_params.vector_observation_space_size * brain_params.num_stacked_vector_observations)
            )
        else:
            vector_obs = np.nan_to_num(
                np.array([x.stacked_vector_observation for x in agent_info_list])
            )
        brain_info = BrainInfo(
            visual_observation=vis_obs,
            vector_observation=vector_obs,
            text_observations=[x.text_observation for x in agent_info_list],
            memory=memory,
            reward=[x.reward if not np.isnan(x.reward) else 0 for x in agent_info_list],
            agents=[x.id for x in agent_info_list],
            local_done=[x.done for x in agent_info_list],
            vector_action=np.array([x.stored_vector_actions for x in agent_info_list]),
            text_action=[list(x.stored_text_actions) for x in agent_info_list],
            max_reached=[x.max_step_reached for x in agent_info_list],
            custom_observations=[x.custom_observation for x in agent_info_list],
            action_mask=mask_actions
        )
        return brain_info


def safe_concat_lists(l1: Optional[List], l2: Optional[List]):
    if l1 is None and l2 is None:
        return None
    if l1 is None and l2 is not None:
        return l2.copy()
    if l1 is not None and l2 is None:
        return l1.copy()
    else:
        copy = l1.copy()
        copy.extend(l2)
        return copy


def safe_concat_np_ndarray(a1: Optional[np.ndarray], a2: Optional[np.ndarray]):
    if a1 is not None and a1.size != 0:
        if a2 is not None and a2.size != 0:
            return np.append(a1, a2, axis=0)
        else:
            return a1.copy()
    elif a2 is not None and a2.size != 0:
        return a2.copy()
    return None


# Renaming of dictionary of brain name to BrainInfo for clarity
AllBrainInfo = Dict[str, BrainInfo]


class BrainParameters:
    def __init__(self,
                 brain_name: str,
                 vector_observation_space_size: int,
                 num_stacked_vector_observations: int,
                 camera_resolutions: List[Dict],
                 vector_action_space_size: List[int],
                 vector_action_descriptions: List[str],
                 vector_action_space_type: int):
        """
        Contains all brain-specific parameters.
        """
        self.brain_name = brain_name
        self.vector_observation_space_size = vector_observation_space_size
        self.num_stacked_vector_observations = num_stacked_vector_observations
        self.number_visual_observations = len(camera_resolutions)
        self.camera_resolutions = camera_resolutions
        self.vector_action_space_size = vector_action_space_size
        self.vector_action_descriptions = vector_action_descriptions
        self.vector_action_space_type = ["discrete", "continuous"][vector_action_space_type]

    def __str__(self):
        return '''Unity brain name: {}
        Number of Visual Observations (per agent): {}
        Vector Observation space size (per agent): {}
        Number of stacked Vector Observation: {}
        Vector Action space type: {}
        Vector Action space size (per agent): {}
        Vector Action descriptions: {}'''.format(self.brain_name,
                                                 str(self.number_visual_observations),
                                                 str(self.vector_observation_space_size),
                                                 str(self.num_stacked_vector_observations),
                                                 self.vector_action_space_type,
                                                 str(self.vector_action_space_size),
                                                 ', '.join(self.vector_action_descriptions))

    @staticmethod
    def from_proto(brain_param_proto):
        """
        Converts brain parameter proto to BrainParameter object.
        :param brain_param_proto: protobuf object.
        :return: BrainParameter object.
        """
        resolution = [{
            "height": x.height,
            "width": x.width,
            "blackAndWhite": x.gray_scale
        } for x in brain_param_proto.camera_resolutions]
        brain_params = BrainParameters(brain_param_proto.brain_name,
                                       brain_param_proto.vector_observation_size,
                                       brain_param_proto.num_stacked_vector_observations,
                                       resolution,
                                       list(brain_param_proto.vector_action_size),
                                       list(brain_param_proto.vector_action_descriptions),
                                       brain_param_proto.vector_action_space_type)
        return brain_params