from __future__ import annotations

import logging
import os
import time
import re
from typing import Callable, Any, Optional, Tuple
from typing import List, Dict, Union

import gymnasium as gym

from desktop_env.controllers.python import PythonController
from desktop_env.controllers.setup import SetupController
from desktop_env.evaluators import metrics, getters
from desktop_env.providers import create_vm_manager_and_provider

logger = logging.getLogger("desktopenv.env")

Metric = Callable[[Any, Any], float]
Getter = Callable[[gym.Env, Dict[str, Any]], Any]

MAX_RETRIES = 5 # Maximum retries for environment setup
            


def _fix_pyautogui_less_than_bug(command: str) -> str:
    """
    Fix PyAutoGUI '<' character bug by converting it to hotkey("shift", ',') calls.
    
    This fixes the known PyAutoGUI issue where typing '<' produces '>' instead.
    References:
    - https://github.com/asweigart/pyautogui/issues/198
    - https://github.com/xlang-ai/OSWorld/issues/257
    
    Args:
        command (str): The original pyautogui command
        
    Returns:
        str: The fixed command with '<' characters handled properly
    """
    # Pattern to match press('<') or press('\u003c') calls  
    press_pattern = r'pyautogui\.press\(["\'](?:<|\\u003c)["\']\)'

    # Handle press('<') calls
    def replace_press_less_than(match):
        return 'pyautogui.hotkey("shift", ",")'
    
    # First handle press('<') calls
    command = re.sub(press_pattern, replace_press_less_than, command)

    # Pattern to match typewrite calls with quoted strings
    typewrite_pattern = r'pyautogui\.typewrite\((["\'])(.*?)\1\)'
    
    # Then handle typewrite calls
    def process_typewrite_match(match):
        quote_char = match.group(1)
        content = match.group(2)
        
        # Preprocess: Try to decode Unicode escapes like \u003c to actual '<'
        # This handles cases where '<' is represented as escaped Unicode
        try:
            # Attempt to decode unicode escapes
            decoded_content = content.encode('utf-8').decode('unicode_escape')
            content = decoded_content
        except UnicodeDecodeError:
            # If decoding fails, proceed with original content to avoid breaking existing logic
            pass  # English comment: Graceful degradation - fall back to original content if decoding fails
        
        # Check if content contains '<'
        if '<' not in content:
            return match.group(0)
        
        # Split by '<' and rebuild
        parts = content.split('<')
        result_parts = []
        
        for i, part in enumerate(parts):
            if i == 0:
                # First part
                if part:
                    result_parts.append(f"pyautogui.typewrite({quote_char}{part}{quote_char})")
            else:
                # Add hotkey for '<' and then typewrite for the rest
                result_parts.append('pyautogui.hotkey("shift", ",")')
                if part:
                    result_parts.append(f"pyautogui.typewrite({quote_char}{part}{quote_char})")
        
        return '; '.join(result_parts)
    
    command = re.sub(typewrite_pattern, process_typewrite_match, command)
    
    return command


class DesktopEnv(gym.Env):
    """
    DesktopEnv with OpenAI Gym interface. It provides a desktop environment for setting and evaluating desktop automation tasks.
    """
    def __init__(
            self,
            provider_name: str = "vmware",
            region: str = None,
            path_to_vm: str = None,
            snapshot_name: str = "init_state",
            action_space: str = "pyautogui",
            cache_dir: str = "cache",
            screen_size: Tuple[int] = (int(os.environ.get("SCREEN_WIDTH", 1920)), int(os.environ.get("SCREEN_HEIGHT", 1080))),
            headless: bool = False,
            require_a11y_tree: bool = True,
            require_terminal: bool = False,
            os_type: str = "Ubuntu",
            enable_proxy: bool = False,
            client_password: str = "",
    ):
        """
        Args:
            provider_name (str): virtualization provider name, default to "vmware"
            region (str): the region for allocate machines, work for cloud services, default to  "us-east-1"
            path_to_vm (str): path to .vmx file
            snapshot_name (str): snapshot name to revert to, default to "init_state"
            action_space (str): "computer_13" | "pyautogui"
            cache_dir (str): cache directory to cache task-related stuffs like
              reference file for evaluation
            screen_size (Tuple[int]): screen size of the VM
            headless (bool): whether to run the VM in headless mode
            require_a11y_tree (bool): whether to require accessibility tree
            require_terminal (bool): whether to require terminal output
            os_type (str): operating system type, default to "Ubuntu"
            enable_proxy (bool): whether to enable proxy support, default to False
        """
        # Initialize VM manager and vitualization provider
        self.region = region
        self.provider_name = provider_name
        self.enable_proxy = enable_proxy  # Store proxy enablement setting
        if client_password == "":
            if self.provider_name == "aws":
                self.client_password = "osworld-public-evaluation"
            else:
                self.client_password = "password"
        else:
            self.client_password = client_password

        self.screen_width = screen_size[0]
        self.screen_height = screen_size[1]

        # Default 
        self.server_port = 5000
        self.chromium_port = 9222
        self.vnc_port = 8006
        self.vlc_port = 8080
        
        # Initialize with default (no proxy) provider
        self.current_use_proxy = False
        self.manager, self.provider = create_vm_manager_and_provider(provider_name, region, use_proxy=False)

        self.os_type = os_type

        # Track whether environment has been used (step/setup) to optimize snapshot revert
        # docker, aws, gcp, azure are always unused as the emulator starts from a clean state
        # vmware, virtualbox are always used as the emulator starts from a dirty state
        if self.provider_name in {"docker", "aws", "gcp", "azure", "aliyun", "volcengine"}:
            self.is_environment_used = False
        elif self.provider_name in {"vmware", "virtualbox"}:
            self.is_environment_used = True
        else:
            raise ValueError(f"Invalid provider name: {self.provider_name}")

        # Initialize environment variables
        if path_to_vm:
            self.path_to_vm = os.path.abspath(os.path.expandvars(os.path.expanduser(path_to_vm))) \
                if provider_name in {"vmware", "virtualbox"} else path_to_vm
        else:
            self.path_to_vm = self.manager.get_vm_path(os_type=self.os_type, region=region, screen_size=(self.screen_width, self.screen_height))
        
        self.snapshot_name = snapshot_name
        self.cache_dir_base: str = cache_dir
        # todo: add the logic to get the screen size from the VM
        self.headless = headless
        self.require_a11y_tree = require_a11y_tree
        self.require_terminal = require_terminal

        # Initialize emulator and controller
        logger.info("Initializing...")
        self._start_emulator()

        # mode: human or machine
        self.instruction = None
        assert action_space in ["computer_13", "pyautogui", "claude_computer_use", "autoglm_computer_use"]
        self.action_space = action_space  # todo: refactor it to the ActType

        # episodic stuffs, like counters, will be updated or reset
        # when calling self.reset()
        self._traj_no: int = -1
        self._step_no: int = 0
        self.action_history: List[Dict[str, any]] = []


    def _start_emulator(self):
        try:
            # Power on the virtual machine
            self.provider.start_emulator(self.path_to_vm, self.headless, self.os_type)

            # Get the ip from the virtual machine, and setup the controller
            vm_ip_ports = self.provider.get_ip_address(self.path_to_vm).split(':')
            self.vm_ip = vm_ip_ports[0]
            # Get the ports from the virtual machine (for Docker provider only)
            if len(vm_ip_ports) > 1:
                self.server_port = int(vm_ip_ports[1])
                self.chromium_port = int(vm_ip_ports[2])
                self.vnc_port = int(vm_ip_ports[3])
                self.vlc_port = int(vm_ip_ports[4])
            self.controller = PythonController(vm_ip=self.vm_ip, server_port=self.server_port)
            self.setup_controller = SetupController(vm_ip=self.vm_ip, server_port=self.server_port, chromium_port=self.chromium_port, vlc_port=self.vlc_port, cache_dir=self.cache_dir_base, client_password=self.client_password, screen_width=self.screen_width, screen_height=self.screen_height)

        except Exception as e:
            try:
                self.provider.stop_emulator(self.path_to_vm)
            except Exception as stop_err:
                logger.warning(f"Cleanup after interrupt failed: {stop_err}")
            raise

    def _revert_to_snapshot(self):
        # Revert to certain snapshot of the virtual machine, and refresh the path to vm and ip of vm
        # due to the fact it could be changed when implemented by cloud services
        path_to_vm = self.provider.revert_to_snapshot(self.path_to_vm, self.snapshot_name)
        if path_to_vm and not path_to_vm == self.path_to_vm:
            # path_to_vm has to be a new path 
            
            self.manager.delete_vm(self.path_to_vm, self.region)
            self.manager.add_vm(path_to_vm, self.region)
            self.manager.occupy_vm(path_to_vm, os.getpid(), self.region)
            self.path_to_vm = path_to_vm

    def _save_state(self, snapshot_name=None):
        # Save the current virtual machine state to a certain snapshot name
        self.provider.save_state(self.path_to_vm, snapshot_name)

    def close(self):
        # Close (release) the virtual machine
        self.provider.stop_emulator(self.path_to_vm)

    def reset(self, task_config: Optional[Dict[str, Any]] = None, seed=None, options=None) -> Dict[str, Any]:
        
        # Reset to certain task in OSWorld
        logger.info("Resetting environment...")
        logger.info("Switching task...")
        logger.info("Setting counters...")
        self._traj_no += 1
        self._step_no = 0
        self.action_history.clear()

        for attempt in range(MAX_RETRIES):
            # Only revert to snapshot if environment has been used (step/setup)
            # This optimization is especially important for cloud providers like AWS
            # where unnecessary snapshot operations are costly and time-consuming
            
            if task_config is not None:
                # Only consider task proxy requirement if proxy is enabled at system level
                task_use_proxy = task_config.get("proxy", False) and self.enable_proxy
                if not self.enable_proxy and task_config.get("proxy", False):
                    logger.info("Task requires proxy but proxy is disabled at system level, ignoring proxy requirement.")
                
                if task_use_proxy != self.current_use_proxy:
                    # keep because get_info_from_website depend on this
                    self.current_use_proxy = task_use_proxy
            
            if self.is_environment_used:
                logger.info("Environment has been used, reverting to snapshot {}...".format(self.snapshot_name))
                self._revert_to_snapshot()
                logger.info("Starting emulator...")
                self._start_emulator()
                logger.info("Emulator started.")
                # Reset the usage flag after reverting
                self.is_environment_used = False
            else:
                logger.info("Environment is clean, skipping snapshot revert (provider: {}).".format(self.provider_name))

            if task_config is not None:
                if task_config.get("proxy", False) and self.enable_proxy:
                    # If using proxy and proxy is enabled, set up the proxy configuration
                    self.setup_controller._proxy_setup(self.client_password)
                self._set_task_info(task_config)
                self.setup_controller.reset_cache_dir(self.cache_dir)
                logger.info("Setting up environment...")
                success = self.setup_controller.setup(self.config, task_config.get("proxy", False) and self.enable_proxy)
                if success:
                    # Mark environment as used when setup is successfully executed
                    if self.config:  # Only mark as used if there were actual setup operations
                        self.is_environment_used = True
                    break
                else:
                    logger.error(
                        "Environment setup failed, retrying (%d/%d)...",
                        attempt + 1,
                        MAX_RETRIES,
                    )
                    time.sleep(5)
            else:
                break
            
        logger.info("Environment setup complete.")

        observation = self._get_obs()
        return observation

    def _get_obs(self):
        # We provide screenshot, accessibility_tree (optional), terminal (optional), and instruction.
        # can be customized and scaled
        return {
            "screenshot": self.controller.get_screenshot(),
            "accessibility_tree": self.controller.get_accessibility_tree() if self.require_a11y_tree else None,
            "terminal": self.controller.get_terminal_output() if self.require_terminal else None,
            "instruction": self.instruction
        }

    @property
    def vm_platform(self):
        return self.controller.get_vm_platform()

    @property
    def vm_machine(self):
        return self.controller.get_vm_machine()

    @property
    def vm_screen_size(self):
        return self.controller.get_vm_screen_size()

    def _set_task_info(self, task_config: Dict[str, Any]):
        """Set task info (proxy logic is handled in reset method)"""
        self.task_id: str = task_config["id"]
        self.cache_dir: str = os.path.join(self.cache_dir_base, self.task_id)
        os.makedirs(self.cache_dir, exist_ok=True)
        self.instruction = task_config["instruction"]
        self.config = task_config["config"] if "config" in task_config else []
        
        self._set_evaluator_info(task_config)

    def _set_evaluator_info(self, task_config: Dict[str, Any]):
        """Set evaluator information from task config"""
        # evaluator dict
        # func -> metric function string, or list of metric function strings
        # conj -> conjunction of multiple metrics if func is a list with length > 1, "and"/"or"
        # result -> result getter config, or list of result getter configs
        # expected (optional) -> expected getter config, or list of expected getter configs
        # options (optional) -> metric options, or list of metric options
        # if func is a str list, then result, expected (if exists), options (if exists) should also be lists of the same length
        # even if one of the metrics does not need expected or options field, it should be included in the list with None
        self.evaluator = task_config["evaluator"]
        self.metric: Metric = [getattr(metrics, func) for func in self.evaluator["func"]] \
            if isinstance(self.evaluator["func"], list) \
            else getattr(metrics, self.evaluator["func"])
        self.metric_conj: str = self.evaluator.get("conj", "and")  # take conjunction of multiple metrics
        if "result" in self.evaluator and len(self.evaluator["result"]) > 0:
            self.result_getter: Getter = [getattr(getters, "get_{:}".format(res["type"])) for res in
                                          self.evaluator["result"]] \
                if isinstance(self.evaluator["result"], list) \
                else getattr(getters, "get_{:}".format(self.evaluator["result"]["type"]))
        else:
            self.result_getter = [None] * len(self.metric) \
                if isinstance(self.metric, list) \
                else None

        if "expected" in self.evaluator and len(self.evaluator["expected"]) > 0:
            self.expected_getter: Getter = [getattr(getters, "get_{:}".format(exp["type"])) if exp else None for exp in
                                            self.evaluator["expected"]] \
                if isinstance(self.evaluator["expected"], list) \
                else getattr(getters, "get_{:}".format(self.evaluator["expected"]["type"]))
        else:
            self.expected_getter = [None] * len(self.metric) \
                if isinstance(self.metric, list) \
                else None
        self.metric_options: Union[List[Dict[str, Any]], Dict[str, Any]] = [opt if opt else {} for opt in
                                                                            self.evaluator["options"]] \
            if isinstance(self.evaluator.get("options", {}), list) \
            else self.evaluator["options"] \
            if "options" in self.evaluator \
            else [{}] * len(self.metric) \
            if isinstance(self.metric, list) \
            else {}

        assert (not isinstance(self.evaluator["func"], list)
                or (len(self.metric) == len(self.result_getter) == len(self.expected_getter) == len(
                    self.metric_options)))

    def step(self, action, pause=2):
        self._step_no += 1
        self.action_history.append(action)
        
        # Mark environment as used when step is called
        self.is_environment_used = True

        reward = 0  # todo: Define reward calculation for each example
        done = False  # todo: Define episode termination condition for each example
        info = {}
        logger.info(f"Step {self._step_no} in trajectory {self._traj_no} with action: {action}")
        # handle the special actions
        if action in ['WAIT', 'FAIL', 'DONE'] or (type(action) == dict and action['action_type'] in ['WAIT', 'FAIL', 'DONE']):
            if action == 'WAIT' or (type(action) == dict and action.get('action_type') == 'WAIT'):
                time.sleep(pause)
            elif action == 'FAIL' or (type(action) == dict and action.get('action_type') == 'FAIL'):
                done = True
                info = {"fail": True}
            elif action == 'DONE' or (type(action) == dict and action.get('action_type') == 'DONE'):
                done = True
                info = {"done": True}

        if self.action_space == "computer_13":
            # the set of all possible actions defined in the action representation
            self.controller.execute_action(action)
        elif self.action_space == "pyautogui" or self.action_space == "claude_computer_use":
            if action in ['WAIT', 'FAIL', 'DONE'] or (type(action) == dict and action.get('action_type') in ['WAIT', 'FAIL', 'DONE']):
                self.controller.execute_action(action)
            else:
                # the set of all possible python commands insides `pyautogui`
                if type(action) == str:
                    # Fix PyAutoGUI '<' character bug before execution
                    fixed_command = _fix_pyautogui_less_than_bug(action)
                    self.controller.execute_python_command(fixed_command)
                elif type(action) == dict:
                    # Fix PyAutoGUI '<' character bug before execution
                    fixed_command = _fix_pyautogui_less_than_bug(action['command'])
                    self.controller.execute_python_command(fixed_command)

        time.sleep(pause)
        observation = self._get_obs()

        return observation, reward, done, info

    def evaluate(self):
        """
        Evaluate whether the task is successfully completed.
        """

        postconfig = self.evaluator.get("postconfig", [])
        self.setup_controller.setup(postconfig, self.enable_proxy)
        # Mark environment as used if there were postconfig setup operations
        if postconfig:
            self.is_environment_used = True

        if self.evaluator['func'] == "infeasible":
            if len(self.action_history) > 0:
                last_action = self.action_history[-1]
                if last_action == "FAIL" or (type(last_action) == dict and last_action.get('action_type') == 'FAIL'):
                    return 1
            return 0
        else:
            if len(self.action_history) > 0:
                last_action = self.action_history[-1]
                if last_action == "FAIL" or (type(last_action) == dict and last_action.get('action_type') == 'FAIL'):
                    return 0

        if type(self.metric) == list:
            # Multiple metrics to evaluate whether the task is successfully completed
            results = []
            assert len(self.metric) == len(self.result_getter), "The number of metrics and result getters must be the same"
            if "expected" in self.evaluator:
                assert len(self.metric) == len(self.expected_getter), "The number of metrics and expected getters must be the same"
            for idx, metric in enumerate(self.metric):
                try:
                    config = self.evaluator["result"][idx]
                    result_state = self.result_getter[idx](self, config)
                except FileNotFoundError:
                    logger.error("File not found!")
                    if self.metric_conj == 'and':
                        return 0

                if "expected" in self.evaluator and self.expected_getter and self.evaluator["expected"]:
                    expected_state = self.expected_getter[idx](self, self.evaluator["expected"][idx])
                    metric: int = metric(result_state, expected_state, **self.metric_options[idx])
                else:
                    metric: int = metric(result_state, **self.metric_options[idx])

                if self.metric_conj == 'and' and float(metric) == 0.0:
                    return 0
                elif self.metric_conj == 'or' and float(metric) == 1.0:
                    return 1
                else:
                    results.append(metric)

            return sum(results) / len(results) if self.metric_conj == 'and' else max(results)
        else:
            # Single metric to evaluate whether the task is successfully completed
            try:
                result_state = self.result_getter(self, self.evaluator["result"])
            except FileNotFoundError:
                logger.error("File not found!")
                return 0

            if "expected" in self.evaluator and self.expected_getter and self.evaluator["expected"]:
                expected_state = self.expected_getter(self, self.evaluator["expected"])
                metric: float = self.metric(result_state, expected_state, **self.metric_options)
            else:
                metric: float = self.metric(result_state, **self.metric_options)

        return metric

    def render(self, mode='rgb_array'):
        if mode == 'rgb_array':
            return self.controller.get_screenshot()
        else:
            raise ValueError('Unsupported render mode: {}'.format(mode))
