import atexit
import glob
import logging
import numpy as np
import os
import subprocess
from typing import *

from mlagents.envs.base_unity_environment import BaseUnityEnvironment
from .brain import AllBrainInfo, BrainInfo, BrainParameters
from .exception import (
    UnityEnvironmentException,
    UnityActionException,
    UnityTimeOutException,
)

from .communicator_objects import (
    UnityRLInput,
    UnityRLOutput,
    AgentActionProto,
    EnvironmentParametersProto,
    UnityRLInitializationInput,
    UnityRLInitializationOutput,
    UnityInput,
    UnityOutput,
    CustomResetParameters,
    CustomAction,
)

from .rpc_communicator import RpcCommunicator
from sys import platform

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("mlagents.envs")


class UnityEnvironment(BaseUnityEnvironment):
    SCALAR_ACTION_TYPES = (int, np.int32, np.int64, float, np.float32, np.float64)
    SINGLE_BRAIN_ACTION_TYPES = SCALAR_ACTION_TYPES + (list, np.ndarray)
    SINGLE_BRAIN_TEXT_TYPES = (str, list, np.ndarray)

    def __init__(
        self,
        file_name: Optional[str] = None,
        worker_id: int = 0,
        base_port: int = 5005,
        seed: int = 0,
        docker_training: bool = False,
        no_graphics: bool = False,
        timeout_wait: int = 30,
    ):
        """
        Starts a new unity environment and establishes a connection with the environment.
        Notice: Currently communication between Unity and Python takes place over an open socket without authentication.
        Ensure that the network where training takes place is secure.

        :string file_name: Name of Unity environment binary.
        :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this.
        :int worker_id: Number to add to communication port (5005) [0]. Used for asynchronous agent scenarios.
        :bool docker_training: Informs this class whether the process is being run within a container.
        :bool no_graphics: Whether to run the Unity simulator in no-graphics mode
        :int timeout_wait: Time (in seconds) to wait for connection from environment.
        :bool train_mode: Whether to run in training mode, speeding up the simulation, by default.
        """

        atexit.register(self._close)
        self.port = base_port + worker_id
        self._buffer_size = 12000
        self._version_ = "API-8"
        self._loaded = (
            False
        )  # If true, this means the environment was successfully loaded
        self.proc1 = (
            None
        )  # The process that is started. If None, no process was started
        self.communicator = self.get_communicator(worker_id, base_port, timeout_wait)

        # If the environment name is None, a new environment will not be launched
        # and the communicator will directly try to connect to an existing unity environment.
        # If the worker-id is not 0 and the environment name is None, an error is thrown
        if file_name is None and worker_id != 0:
            raise UnityEnvironmentException(
                "If the environment name is None, "
                "the worker-id must be 0 in order to connect with the Editor."
            )
        if file_name is not None:
            self.executable_launcher(file_name, docker_training, no_graphics)
        else:
            logger.info(
                "Start training by pressing the Play button in the Unity Editor."
            )
        self._loaded = True

        rl_init_parameters_in = UnityRLInitializationInput(seed=seed)
        try:
            aca_params = self.send_academy_parameters(rl_init_parameters_in)
        except UnityTimeOutException:
            self._close()
            raise
        # TODO : think of a better way to expose the academyParameters
        self._unity_version = aca_params.version
        if self._unity_version != self._version_:
            self._close()
            raise UnityEnvironmentException(
                "The API number is not compatible between Unity and python. Python API : {0}, Unity API : "
                "{1}.\nPlease go to https://github.com/Unity-Technologies/ml-agents to download the latest version "
                "of ML-Agents.".format(self._version_, self._unity_version)
            )
        self._n_agents: Dict[str, int] = {}
        self._global_done: Optional[bool] = None
        self._academy_name = aca_params.name
        self._log_path = aca_params.log_path
        self._brains: Dict[str, BrainParameters] = {}
        self._brain_names: List[str] = []
        self._external_brain_names: List[str] = []
        for brain_param in aca_params.brain_parameters:
            self._brain_names += [brain_param.brain_name]
            self._brains[brain_param.brain_name] = BrainParameters.from_proto(
                brain_param
            )
            if brain_param.is_training:
                self._external_brain_names += [brain_param.brain_name]
        self._num_brains = len(self._brain_names)
        self._num_external_brains = len(self._external_brain_names)
        self._resetParameters = dict(aca_params.environment_parameters.float_parameters)
        logger.info(
            "\n'{0}' started successfully!\n{1}".format(self._academy_name, str(self))
        )
        if self._num_external_brains == 0:
            logger.warning(
                " No Learning Brains set to train found in the Unity Environment. "
                "You will not be able to pass actions to your agent(s)."
            )

    @property
    def logfile_path(self):
        return self._log_path

    @property
    def brains(self):
        return self._brains

    @property
    def global_done(self):
        return self._global_done

    @property
    def academy_name(self):
        return self._academy_name

    @property
    def number_brains(self):
        return self._num_brains

    @property
    def number_external_brains(self):
        return self._num_external_brains

    @property
    def brain_names(self):
        return self._brain_names

    @property
    def external_brain_names(self):
        return self._external_brain_names

    @staticmethod
    def get_communicator(worker_id, base_port, timeout_wait):
        return RpcCommunicator(worker_id, base_port, timeout_wait)

    @property
    def external_brains(self):
        external_brains = {}
        for brain_name in self.external_brain_names:
            external_brains[brain_name] = self.brains[brain_name]
        return external_brains

    @property
    def reset_parameters(self):
        return self._resetParameters

    def executable_launcher(self, file_name, docker_training, no_graphics):
        cwd = os.getcwd()
        file_name = (
            file_name.strip()
            .replace(".app", "")
            .replace(".exe", "")
            .replace(".x86_64", "")
            .replace(".x86", "")
        )
        true_filename = os.path.basename(os.path.normpath(file_name))
        logger.debug("The true file name is {}".format(true_filename))
        launch_string = None
        if platform == "linux" or platform == "linux2":
            candidates = glob.glob(os.path.join(cwd, file_name) + ".x86_64")
            if len(candidates) == 0:
                candidates = glob.glob(os.path.join(cwd, file_name) + ".x86")
            if len(candidates) == 0:
                candidates = glob.glob(file_name + ".x86_64")
            if len(candidates) == 0:
                candidates = glob.glob(file_name + ".x86")
            if len(candidates) > 0:
                launch_string = candidates[0]

        elif platform == "darwin":
            candidates = glob.glob(
                os.path.join(
                    cwd, file_name + ".app", "Contents", "MacOS", true_filename
                )
            )
            if len(candidates) == 0:
                candidates = glob.glob(
                    os.path.join(file_name + ".app", "Contents", "MacOS", true_filename)
                )
            if len(candidates) == 0:
                candidates = glob.glob(
                    os.path.join(cwd, file_name + ".app", "Contents", "MacOS", "*")
                )
            if len(candidates) == 0:
                candidates = glob.glob(
                    os.path.join(file_name + ".app", "Contents", "MacOS", "*")
                )
            if len(candidates) > 0:
                launch_string = candidates[0]
        elif platform == "win32":
            candidates = glob.glob(os.path.join(cwd, file_name + ".exe"))
            if len(candidates) == 0:
                candidates = glob.glob(file_name + ".exe")
            if len(candidates) > 0:
                launch_string = candidates[0]
        if launch_string is None:
            self._close()
            raise UnityEnvironmentException(
                "Couldn't launch the {0} environment. "
                "Provided filename does not match any environments.".format(
                    true_filename
                )
            )
        else:
            logger.debug("This is the launch string {}".format(launch_string))
            # Launch Unity environment
            if not docker_training:
                if no_graphics:
                    self.proc1 = subprocess.Popen(
                        [
                            launch_string,
                            "-nographics",
                            "-batchmode",
                            "--port",
                            str(self.port),
                        ]
                    )
                else:
                    self.proc1 = subprocess.Popen(
                        [launch_string, "--port", str(self.port)]
                    )
            else:
                """
                Comments for future maintenance:
                    xvfb-run is a wrapper around Xvfb, a virtual xserver where all
                    rendering is done to virtual memory. It automatically creates a
                    new virtual server automatically picking a server number `auto-servernum`.
                    The server is passed the arguments using `server-args`, we are telling
                    Xvfb to create Screen number 0 with width 640, height 480 and depth 24 bits.
                    Note that 640 X 480 are the default width and height. The main reason for
                    us to add this is because we'd like to change the depth from the default
                    of 8 bits to 24.
                    Unfortunately, this means that we will need to pass the arguments through
                    a shell which is why we set `shell=True`. Now, this adds its own
                    complications. E.g SIGINT can bounce off the shell and not get propagated
                    to the child processes. This is why we add `exec`, so that the shell gets
                    launched, the arguments are passed to `xvfb-run`. `exec` replaces the shell
                    we created with `xvfb`.
                """
                docker_ls = (
                    "exec xvfb-run --auto-servernum"
                    " --server-args='-screen 0 640x480x24'"
                    " {0} --port {1}"
                ).format(launch_string, str(self.port))
                self.proc1 = subprocess.Popen(
                    docker_ls,
                    stdout=subprocess.PIPE,
                    stderr=subprocess.PIPE,
                    shell=True,
                )

    def __str__(self):
        return (
            """Unity Academy name: {0}
        Number of Brains: {1}
        Number of Training Brains : {2}
        Reset Parameters :\n\t\t{3}""".format(
                self._academy_name,
                str(self._num_brains),
                str(self._num_external_brains),
                "\n\t\t".join(
                    [
                        str(k) + " -> " + str(self._resetParameters[k])
                        for k in self._resetParameters
                    ]
                ),
            )
            + "\n"
            + "\n".join([str(self._brains[b]) for b in self._brains])
        )

    def reset(
        self, config=None, train_mode=True, custom_reset_parameters=None
    ) -> AllBrainInfo:
        """
        Sends a signal to reset the unity environment.
        :return: AllBrainInfo  : A data structure corresponding to the initial reset state of the environment.
        """
        if config is None:
            config = self._resetParameters
        elif config:
            logger.info(
                "Academy reset with parameters: {0}".format(
                    ", ".join([str(x) + " -> " + str(config[x]) for x in config])
                )
            )
        for k in config:
            if (k in self._resetParameters) and (isinstance(config[k], (int, float))):
                self._resetParameters[k] = config[k]
            elif not isinstance(config[k], (int, float)):
                raise UnityEnvironmentException(
                    "The value for parameter '{0}'' must be an Integer or a Float.".format(
                        k
                    )
                )
            else:
                raise UnityEnvironmentException(
                    "The parameter '{0}' is not a valid parameter.".format(k)
                )

        if self._loaded:
            outputs = self.communicator.exchange(
                self._generate_reset_input(train_mode, config, custom_reset_parameters)
            )
            if outputs is None:
                raise KeyboardInterrupt
            rl_output = outputs.rl_output
            s = self._get_state(rl_output)
            self._global_done = s[1]
            for _b in self._external_brain_names:
                self._n_agents[_b] = len(s[0][_b].agents)
            return s[0]
        else:
            raise UnityEnvironmentException("No Unity environment is loaded.")

    def step(
        self,
        vector_action=None,
        memory=None,
        text_action=None,
        value=None,
        custom_action=None,
    ) -> AllBrainInfo:
        """
        Provides the environment with an action, moves the environment dynamics forward accordingly,
        and returns observation, state, and reward information to the agent.
        :param value: Value estimates provided by agents.
        :param vector_action: Agent's vector action. Can be a scalar or vector of int/floats.
        :param memory: Vector corresponding to memory used for recurrent policies.
        :param text_action: Text action to send to environment for.
        :param custom_action: Optional instance of a CustomAction protobuf message.
        :return: AllBrainInfo  : A Data structure corresponding to the new state of the environment.
        """
        vector_action = {} if vector_action is None else vector_action
        memory = {} if memory is None else memory
        text_action = {} if text_action is None else text_action
        value = {} if value is None else value
        custom_action = {} if custom_action is None else custom_action

        # Check that environment is loaded, and episode is currently running.
        if not self._loaded:
            raise UnityEnvironmentException("No Unity environment is loaded.")
        elif self._global_done:
            raise UnityActionException(
                "The episode is completed. Reset the environment with 'reset()'"
            )
        elif self.global_done is None:
            raise UnityActionException(
                "You cannot conduct step without first calling reset. "
                "Reset the environment with 'reset()'"
            )
        else:
            if isinstance(vector_action, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    vector_action = {self._external_brain_names[0]: vector_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names a keys, "
                        "and vector_actions as values".format(self._num_brains)
                    )
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a vector_action input"
                    )

            if isinstance(memory, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    memory = {self._external_brain_names[0]: memory}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and memories as values".format(self._num_brains)
                    )
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a memory input"
                    )

            if isinstance(text_action, self.SINGLE_BRAIN_TEXT_TYPES):
                if self._num_external_brains == 1:
                    text_action = {self._external_brain_names[0]: text_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and text_actions as values".format(self._num_brains)
                    )
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a value input"
                    )

            if isinstance(value, self.SINGLE_BRAIN_ACTION_TYPES):
                if self._num_external_brains == 1:
                    value = {self._external_brain_names[0]: value}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and state/action value estimates as values".format(
                            self._num_brains
                        )
                    )
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a value input"
                    )

            if isinstance(custom_action, CustomAction):
                if self._num_external_brains == 1:
                    custom_action = {self._external_brain_names[0]: custom_action}
                elif self._num_external_brains > 1:
                    raise UnityActionException(
                        "You have {0} brains, you need to feed a dictionary of brain names as keys "
                        "and CustomAction instances as values".format(self._num_brains)
                    )
                else:
                    raise UnityActionException(
                        "There are no external brains in the environment, "
                        "step cannot take a custom_action input"
                    )

            for brain_name in (
                list(vector_action.keys())
                + list(memory.keys())
                + list(text_action.keys())
            ):
                if brain_name not in self._external_brain_names:
                    raise UnityActionException(
                        "The name {0} does not correspond to an external brain "
                        "in the environment".format(brain_name)
                    )

            for brain_name in self._external_brain_names:
                n_agent = self._n_agents[brain_name]
                if brain_name not in vector_action:
                    if self._brains[brain_name].vector_action_space_type == "discrete":
                        vector_action[brain_name] = (
                            [0.0]
                            * n_agent
                            * len(self._brains[brain_name].vector_action_space_size)
                        )
                    else:
                        vector_action[brain_name] = (
                            [0.0]
                            * n_agent
                            * self._brains[brain_name].vector_action_space_size[0]
                        )
                else:
                    vector_action[brain_name] = self._flatten(vector_action[brain_name])
                if brain_name not in memory:
                    memory[brain_name] = []
                else:
                    if memory[brain_name] is None:
                        memory[brain_name] = []
                    else:
                        memory[brain_name] = self._flatten(memory[brain_name])
                if brain_name not in text_action:
                    text_action[brain_name] = [""] * n_agent
                else:
                    if text_action[brain_name] is None:
                        text_action[brain_name] = [""] * n_agent
                    if isinstance(text_action[brain_name], str):
                        text_action[brain_name] = [text_action[brain_name]] * n_agent
                if brain_name not in custom_action:
                    custom_action[brain_name] = [None] * n_agent
                else:
                    if custom_action[brain_name] is None:
                        custom_action[brain_name] = [None] * n_agent
                    if isinstance(custom_action[brain_name], CustomAction):
                        custom_action[brain_name] = [
                            custom_action[brain_name]
                        ] * n_agent

                number_text_actions = len(text_action[brain_name])
                if not ((number_text_actions == n_agent) or number_text_actions == 0):
                    raise UnityActionException(
                        "There was a mismatch between the provided text_action and "
                        "the environment's expectation: "
                        "The brain {0} expected {1} text_action but was given {2}".format(
                            brain_name, n_agent, number_text_actions
                        )
                    )

                discrete_check = (
                    self._brains[brain_name].vector_action_space_type == "discrete"
                )

                expected_discrete_size = n_agent * len(
                    self._brains[brain_name].vector_action_space_size
                )

                continuous_check = (
                    self._brains[brain_name].vector_action_space_type == "continuous"
                )

                expected_continuous_size = (
                    self._brains[brain_name].vector_action_space_size[0] * n_agent
                )

                if not (
                    (
                        discrete_check
                        and len(vector_action[brain_name]) == expected_discrete_size
                    )
                    or (
                        continuous_check
                        and len(vector_action[brain_name]) == expected_continuous_size
                    )
                ):
                    raise UnityActionException(
                        "There was a mismatch between the provided action and "
                        "the environment's expectation: "
                        "The brain {0} expected {1} {2} action(s), but was provided: {3}".format(
                            brain_name,
                            str(expected_discrete_size)
                            if discrete_check
                            else str(expected_continuous_size),
                            self._brains[brain_name].vector_action_space_type,
                            str(vector_action[brain_name]),
                        )
                    )

            outputs = self.communicator.exchange(
                self._generate_step_input(
                    vector_action, memory, text_action, value, custom_action
                )
            )
            if outputs is None:
                raise KeyboardInterrupt
            rl_output = outputs.rl_output
            state = self._get_state(rl_output)
            self._global_done = state[1]
            for _b in self._external_brain_names:
                self._n_agents[_b] = len(state[0][_b].agents)
            return state[0]

    def close(self):
        """
        Sends a shutdown signal to the unity environment, and closes the socket connection.
        """
        if self._loaded:
            self._close()
        else:
            raise UnityEnvironmentException("No Unity environment is loaded.")

    def _close(self):
        self._loaded = False
        self.communicator.close()
        if self.proc1 is not None:
            self.proc1.kill()

    @classmethod
    def _flatten(cls, arr) -> List[float]:
        """
        Converts arrays to list.
        :param arr: numpy vector.
        :return: flattened list.
        """
        if isinstance(arr, cls.SCALAR_ACTION_TYPES):
            arr = [float(arr)]
        if isinstance(arr, np.ndarray):
            arr = arr.tolist()
        if len(arr) == 0:
            return arr
        if isinstance(arr[0], np.ndarray):
            arr = [item for sublist in arr for item in sublist.tolist()]
        if isinstance(arr[0], list):
            arr = [item for sublist in arr for item in sublist]
        arr = [float(x) for x in arr]
        return arr

    def _get_state(self, output: UnityRLOutput) -> Tuple[AllBrainInfo, bool]:
        """
        Collects experience information from all external brains in environment at current step.
        :return: a dictionary of BrainInfo objects.
        """
        _data = {}
        global_done = output.global_done
        for brain_name in output.agentInfos:
            agent_info_list = output.agentInfos[brain_name].value
            _data[brain_name] = BrainInfo.from_agent_proto(
                agent_info_list, self.brains[brain_name]
            )
        return _data, global_done

    def _generate_step_input(
        self, vector_action, memory, text_action, value, custom_action
    ) -> UnityInput:
        rl_in = UnityRLInput()
        for b in vector_action:
            n_agents = self._n_agents[b]
            if n_agents == 0:
                continue
            _a_s = len(vector_action[b]) // n_agents
            _m_s = len(memory[b]) // n_agents
            for i in range(n_agents):
                action = AgentActionProto(
                    vector_actions=vector_action[b][i * _a_s : (i + 1) * _a_s],
                    memories=memory[b][i * _m_s : (i + 1) * _m_s],
                    text_actions=text_action[b][i],
                    custom_action=custom_action[b][i],
                )
                if b in value:
                    if value[b] is not None:
                        action.value = float(value[b][i])
                rl_in.agent_actions[b].value.extend([action])
                rl_in.command = 0
        return self.wrap_unity_input(rl_in)

    def _generate_reset_input(
        self, training, config, custom_reset_parameters
    ) -> UnityInput:
        rl_in = UnityRLInput()
        rl_in.is_training = training
        rl_in.environment_parameters.CopyFrom(EnvironmentParametersProto())
        for key in config:
            rl_in.environment_parameters.float_parameters[key] = config[key]
        if custom_reset_parameters is not None:
            rl_in.environment_parameters.custom_reset_parameters.CopyFrom(
                custom_reset_parameters
            )
        rl_in.command = 1
        return self.wrap_unity_input(rl_in)

    def send_academy_parameters(
        self, init_parameters: UnityRLInitializationInput
    ) -> UnityRLInitializationOutput:
        inputs = UnityInput()
        inputs.rl_initialization_input.CopyFrom(init_parameters)
        return self.communicator.initialize(inputs).rl_initialization_output

    @staticmethod
    def wrap_unity_input(rl_input: UnityRLInput) -> UnityInput:
        result = UnityInput()
        result.rl_input.CopyFrom(rl_input)
        return result