text: import asyncio
  import logging
  import os
  import tempfile
  import textwrap
  import uuid
  from functools import partial
  from multiprocessing import Process
  from typing import Any, Callable, Dict, List, Optional, Text, Tuple, Union
  
  import numpy as np
  from aiohttp import ClientError
  from colorclass import Color
  from rasa.nlu.training_data.loading import MARKDOWN, RASA
  from sanic import Sanic, response
  from sanic.exceptions import NotFound
  from terminaltables import AsciiTable, SingleTable
  
  import questionary
  import rasa.cli.utils
  from questionary import Choice, Form, Question
  
  from rasa.cli import utils as cliutils
  from rasa.core import constants, run, train, utils
  from rasa.core.actions.action import ACTION_LISTEN_NAME, default_action_names
  from rasa.core.channels.channel import UserMessage
  from rasa.core.constants import (
      DEFAULT_SERVER_FORMAT,
      DEFAULT_SERVER_PORT,
      DEFAULT_SERVER_URL,
      REQUESTED_SLOT,
      UTTER_PREFIX,
  )
  from rasa.core.domain import Domain
  import rasa.core.events
  from rasa.core.events import (
      ActionExecuted,
      ActionReverted,
      BotUttered,
      Event,
      Restarted,
      UserUttered,
      UserUtteranceReverted,
  )
  from rasa.core.interpreter import INTENT_MESSAGE_PREFIX, NaturalLanguageInterpreter
  from rasa.core.trackers import EventVerbosity, DialogueStateTracker
  from rasa.core.training import visualization
  from rasa.core.training.visualization import (
      VISUALIZATION_TEMPLATE_PATH,
      visualize_neighborhood,
  )
  from rasa.core.utils import AvailableEndpoints
  from rasa.utils.common import update_sanic_log_level
  from rasa.utils.endpoints import EndpointConfig
  
  # noinspection PyProtectedMember
  from rasa.nlu.training_data import loading
  from rasa.nlu.training_data.message import Message
  
  # WARNING: This command line UI is using an external library
  # communicating with the shell - these functions are hard to test
  # automatically. If you change anything in here, please make sure to
  # run the interactive learning and check if your part of the "ui"
  # still works.
  import rasa.utils.io as io_utils
  
  logger = logging.getLogger(__name__)
  
  MAX_VISUAL_HISTORY = 3
  
  PATHS = {
      "stories": "data/stories.md",
      "nlu": "data/nlu.md",
      "backup": "data/nlu_interactive.md",
      "domain": "domain.yml",
  }
  
  SAVE_IN_E2E = False
  
  # choose other intent, making sure this doesn't clash with an existing intent
  OTHER_INTENT = uuid.uuid4().hex
  OTHER_ACTION = uuid.uuid4().hex
  NEW_ACTION = uuid.uuid4().hex
  
  NEW_TEMPLATES = {}
  
  
  class RestartConversation(Exception):
      """Exception used to break out the flow and restart the conversation."""
  
      pass
  
  
  class ForkTracker(Exception):
      """Exception used to break out the flow and fork at a previous step.
  
      The tracker will be reset to the selected point in the past and the
      conversation will continue from there."""
  
      pass
  
  
  class UndoLastStep(Exception):
      """Exception used to break out the flow and undo the last step.
  
      The last step is either the most recent user message or the most
      recent action run by the bot."""
  
      pass
  
  
  class Abort(Exception):
      """Exception used to abort the interactive learning and exit."""
  
      pass
  
  
  async def send_message(
      endpoint: EndpointConfig,
      sender_id: Text,
      message: Text,
      parse_data: Optional[Dict[Text, Any]] = None,
  ) -> Dict[Text, Any]:
      """Send a user message to a conversation."""
  
      payload = {
          "sender": UserUttered.type_name,
          "text": message,
          "parse_data": parse_data,
      }
  
      return await endpoint.request(
          json=payload, method="post", subpath=f"/conversations/{sender_id}/messages"
      )
  
  
  async def request_prediction(
      endpoint: EndpointConfig, sender_id: Text
  ) -> Dict[Text, Any]:
      """Request the next action prediction from core."""
  
      return await endpoint.request(
          method="post", subpath=f"/conversations/{sender_id}/predict"
      )
  
  
  async def retrieve_domain(endpoint: EndpointConfig) -> Dict[Text, Any]:
      """Retrieve the domain from core."""
  
      return await endpoint.request(
          method="get", subpath="/domain", headers={"Accept": "application/json"}
      )
  
  
  async def retrieve_status(endpoint: EndpointConfig) -> Dict[Text, Any]:
      """Retrieve the status from core."""
  
      return await endpoint.request(method="get", subpath="/status")
  
  
  async def retrieve_tracker(
      endpoint: EndpointConfig,
      sender_id: Text,
      verbosity: EventVerbosity = EventVerbosity.ALL,
  ) -> Dict[Text, Any]:
      """Retrieve a tracker from core."""
  
      path = "/conversations/{}/tracker?include_events={}".format(
          sender_id, verbosity.name
      )
      return await endpoint.request(
          method="get", subpath=path, headers={"Accept": "application/json"}
      )
  
  
  async def send_action(
      endpoint: EndpointConfig,
      sender_id: Text,
      action_name: Text,
      policy: Optional[Text] = None,
      confidence: Optional[float] = None,
      is_new_action: bool = False,
  ) -> Dict[Text, Any]:
      """Log an action to a conversation."""
  
      payload = ActionExecuted(action_name, policy, confidence).as_dict()
  
      subpath = f"/conversations/{sender_id}/execute"
  
      try:
          return await endpoint.request(json=payload, method="post", subpath=subpath)
      except ClientError:
          if is_new_action:
              if action_name in NEW_TEMPLATES:
                  warning_questions = questionary.confirm(
                      "WARNING: You have created a new action: '{}', "
                      "with matching template: '{}'. "
                      "This action will not return its message in this session, "
                      "but the new utterance will be saved to your domain file "
                      "when you exit and save this session. "
                      "You do not need to do anything further. "
                      "".format(action_name, [*NEW_TEMPLATES[action_name]][0])
                  )
                  await _ask_questions(warning_questions, sender_id, endpoint)
              else:
                  warning_questions = questionary.confirm(
                      "WARNING: You have created a new action: '{}', "
                      "which was not successfully executed. "
                      "If this action does not return any events, "
                      "you do not need to do anything. "
                      "If this is a custom action which returns events, "
                      "you are recommended to implement this action "
                      "in your action server and try again."
                      "".format(action_name)
                  )
                  await _ask_questions(warning_questions, sender_id, endpoint)
  
              payload = ActionExecuted(action_name).as_dict()
              return await send_event(endpoint, sender_id, payload)
          else:
              logger.error("failed to execute action!")
              raise
  
  
  async def send_event(
      endpoint: EndpointConfig,
      sender_id: Text,
      evt: Union[List[Dict[Text, Any]], Dict[Text, Any]],
  ) -> Dict[Text, Any]:
      """Log an event to a conversation."""
  
      subpath = f"/conversations/{sender_id}/tracker/events"
  
      return await endpoint.request(json=evt, method="post", subpath=subpath)
  
  
  def format_bot_output(message: BotUttered) -> Text:
      """Format a bot response to be displayed in the history table."""
  
      # First, add text to output
      output = message.text or ""
  
      # Then, append all additional items
      data = message.data or {}
      if not data:
          return output
  
      if data.get("image"):
          output += "\nImage: " + data.get("image")
  
      if data.get("attachment"):
          output += "\nAttachment: " + data.get("attachment")
  
      if data.get("buttons"):
          output += "\nButtons:"
          choices = cliutils.button_choices_from_message_data(
              data, allow_free_text_input=True
          )
          for choice in choices:
              output += "\n" + choice
  
      if data.get("elements"):
          output += "\nElements:"
          for idx, element in enumerate(data.get("elements")):
              element_str = cliutils.element_to_string(element, idx)
              output += "\n" + element_str
  
      if data.get("quick_replies"):
          output += "\nQuick replies:"
          for idx, element in enumerate(data.get("quick_replies")):
              element_str = cliutils.element_to_string(element, idx)
              output += "\n" + element_str
      return output
  
  
  def latest_user_message(events: List[Dict[Text, Any]]) -> Optional[Dict[Text, Any]]:
      """Return most recent user message."""
  
      for i, e in enumerate(reversed(events)):
          if e.get("event") == UserUttered.type_name:
              return e
      return None
  
  
  def all_events_before_latest_user_msg(
      events: List[Dict[Text, Any]]
  ) -> List[Dict[Text, Any]]:
      """Return all events that happened before the most recent user message."""
  
      for i, e in enumerate(reversed(events)):
          if e.get("event") == UserUttered.type_name:
              return events[: -(i + 1)]
      return events
  
  
  async def _ask_questions(
      questions: Union[Form, Question],
      sender_id: Text,
      endpoint: EndpointConfig,
      is_abort: Callable[[Dict[Text, Any]], bool] = lambda x: False,
  ) -> Any:
      """Ask the user a question, if Ctrl-C is pressed provide user with menu."""
  
      should_retry = True
      answers = {}
  
      while should_retry:
          answers = questions.ask()
          if answers is None or is_abort(answers):
              should_retry = await _ask_if_quit(sender_id, endpoint)
          else:
              should_retry = False
      return answers
  
  
  def _selection_choices_from_intent_prediction(
      predictions: List[Dict[Text, Any]]
  ) -> List[Dict[Text, Any]]:
      """"Given a list of ML predictions create a UI choice list."""
  
      sorted_intents = sorted(predictions, key=lambda k: (-k["confidence"], k["name"]))
  
      choices = []
      for p in sorted_intents:
          name_with_confidence = "{:03.2f} {:40}".format(
              p.get("confidence"), p.get("name")
          )
          choice = {"name": name_with_confidence, "value": p.get("name")}
          choices.append(choice)
  
      return choices
  
  
  async def _request_free_text_intent(sender_id: Text, endpoint: EndpointConfig) -> Text:
      question = questionary.text(
          message="Please type the intent name:",
          validate=io_utils.not_empty_validator("Please enter an intent name"),
      )
      return await _ask_questions(question, sender_id, endpoint)
  
  
  async def _request_free_text_action(sender_id: Text, endpoint: EndpointConfig) -> Text:
      question = questionary.text(
          message="Please type the action name:",
          validate=io_utils.not_empty_validator("Please enter an action name"),
      )
      return await _ask_questions(question, sender_id, endpoint)
  
  
  async def _request_free_text_utterance(
      sender_id: Text, endpoint: EndpointConfig, action: Text
  ) -> Text:
  
      question = questionary.text(
          message=(
              "Please type the message for your new utterance "
              "template '{}':".format(action)
          ),
          validate=io_utils.not_empty_validator("Please enter a template message"),
      )
      return await _ask_questions(question, sender_id, endpoint)
  
  
  async def _request_selection_from_intents(
      intents: List[Dict[Text, Text]], sender_id: Text, endpoint: EndpointConfig
  ) -> Text:
      question = questionary.select("What intent is it?", choices=intents)
      return await _ask_questions(question, sender_id, endpoint)
  
  
  async def _request_fork_point_from_list(
      forks: List[Dict[Text, Text]], sender_id: Text, endpoint: EndpointConfig
  ) -> Text:
      question = questionary.select(
          "Before which user message do you want to fork?", choices=forks
      )
      return await _ask_questions(question, sender_id, endpoint)
  
  
  async def _request_fork_from_user(
      sender_id, endpoint
  ) -> Optional[List[Dict[Text, Any]]]:
      """Take in a conversation and ask at which point to fork the conversation.
  
      Returns the list of events that should be kept. Forking means, the
      conversation will be reset and continued from this previous point."""
  
      tracker = await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART)
  
      choices = []
      for i, e in enumerate(tracker.get("events", [])):
          if e.get("event") == UserUttered.type_name:
              choices.append({"name": e.get("text"), "value": i})
  
      fork_idx = await _request_fork_point_from_list(
          list(reversed(choices)), sender_id, endpoint
      )
  
      if fork_idx is not None:
          return tracker.get("events", [])[: int(fork_idx)]
      else:
          return None
  
  
  async def _request_intent_from_user(
      latest_message, intents, sender_id, endpoint
  ) -> Dict[Text, Any]:
      """Take in latest message and ask which intent it should have been.
  
      Returns the intent dict that has been selected by the user."""
  
      predictions = latest_message.get("parse_data", {}).get("intent_ranking", [])
  
      predicted_intents = {p["name"] for p in predictions}
  
      for i in intents:
          if i not in predicted_intents:
              predictions.append({"name": i, "confidence": 0.0})
  
      # convert intents to ui list and add <other> as a free text alternative
      choices = [
          {"name": "<create_new_intent>", "value": OTHER_INTENT}
      ] + _selection_choices_from_intent_prediction(predictions)
  
      intent_name = await _request_selection_from_intents(choices, sender_id, endpoint)
  
      if intent_name == OTHER_INTENT:
          intent_name = await _request_free_text_intent(sender_id, endpoint)
          selected_intent = {"name": intent_name, "confidence": 1.0}
      else:
          # returns the selected intent with the original probability value
          selected_intent = next(
              (x for x in predictions if x["name"] == intent_name), {"name": None}
          )
  
      return selected_intent
  
  
  async def _print_history(sender_id: Text, endpoint: EndpointConfig) -> None:
      """Print information about the conversation for the user."""
  
      tracker_dump = await retrieve_tracker(
          endpoint, sender_id, EventVerbosity.AFTER_RESTART
      )
      events = tracker_dump.get("events", [])
  
      table = _chat_history_table(events)
      slot_strs = _slot_history(tracker_dump)
  
      print("------")
      print("Chat History\n")
      print(table)
  
      if slot_strs:
          print("\n")
          print("Current slots: \n\t{}\n".format(", ".join(slot_strs)))
  
      print("------")
  
  
  def _chat_history_table(events: List[Dict[Text, Any]]) -> Text:
      """Create a table containing bot and user messages.
  
      Also includes additional information, like any events and
      prediction probabilities."""
  
      def wrap(txt: Text, max_width: int) -> Text:
          return "\n".join(textwrap.wrap(txt, max_width, replace_whitespace=False))
  
      def colored(txt: Text, color: Text) -> Text:
          return "{" + color + "}" + txt + "{/" + color + "}"
  
      def format_user_msg(user_event: UserUttered, max_width: int) -> Text:
          intent = user_event.intent or {}
          intent_name = intent.get("name", "")
          _confidence = intent.get("confidence", 1.0)
          _md = _as_md_message(user_event.parse_data)
  
          _lines = [
              colored(wrap(_md, max_width), "hired"),
              f"intent: {intent_name} {_confidence:03.2f}",
          ]
          return "\n".join(_lines)
  
      def bot_width(_table: AsciiTable) -> int:
          return _table.column_max_width(1)
  
      def user_width(_table: AsciiTable) -> int:
          return _table.column_max_width(3)
  
      def add_bot_cell(data, cell):
          data.append([len(data), Color(cell), "", ""])
  
      def add_user_cell(data, cell):
          data.append([len(data), "", "", Color(cell)])
  
      # prints the historical interactions between the bot and the user,
      # to help with correctly identifying the action
      table_data = [
          [
              "#  ",
              Color(colored("Bot      ", "autoblue")),
              "  ",
              Color(colored("You       ", "hired")),
          ]
      ]
  
      table = SingleTable(table_data, "Chat History")
  
      bot_column = []
  
      tracker = DialogueStateTracker.from_dict("any", events)
      applied_events = tracker.applied_events()
  
      for idx, event in enumerate(applied_events):
          if isinstance(event, ActionExecuted):
              bot_column.append(colored(event.action_name, "autocyan"))
              if event.confidence is not None:
                  bot_column[-1] += colored(f" {event.confidence:03.2f}", "autowhite")
  
          elif isinstance(event, UserUttered):
              if bot_column:
                  text = "\n".join(bot_column)
                  add_bot_cell(table_data, text)
                  bot_column = []
  
              msg = format_user_msg(event, user_width(table))
              add_user_cell(table_data, msg)
  
          elif isinstance(event, BotUttered):
              wrapped = wrap(format_bot_output(event), bot_width(table))
              bot_column.append(colored(wrapped, "autoblue"))
  
          else:
              if event.as_story_string():
                  bot_column.append(wrap(event.as_story_string(), bot_width(table)))
  
      if bot_column:
          text = "\n".join(bot_column)
          add_bot_cell(table_data, text)
  
      table.inner_heading_row_border = False
      table.inner_row_border = True
      table.inner_column_border = False
      table.outer_border = False
      table.justify_columns = {0: "left", 1: "left", 2: "center", 3: "right"}
  
      return table.table
  
  
  def _slot_history(tracker_dump: Dict[Text, Any]) -> List[Text]:
      """Create an array of slot representations to be displayed."""
  
      slot_strs = []
      for k, s in tracker_dump.get("slots", {}).items():
          colored_value = cliutils.wrap_with_color(
              str(s), color=rasa.cli.utils.bcolors.WARNING
          )
          slot_strs.append(f"{k}: {colored_value}")
      return slot_strs
  
  
  async def _write_data_to_file(sender_id: Text, endpoint: EndpointConfig):
      """Write stories and nlu data to file."""
  
      story_path, nlu_path, domain_path = _request_export_info()
  
      tracker = await retrieve_tracker(endpoint, sender_id)
      events = tracker.get("events", [])
  
      serialised_domain = await retrieve_domain(endpoint)
      domain = Domain.from_dict(serialised_domain)
  
      await _write_stories_to_file(story_path, events, domain)
      await _write_nlu_to_file(nlu_path, events)
      await _write_domain_to_file(domain_path, events, domain)
  
      logger.info("Successfully wrote stories and NLU data")
  
  
  async def _ask_if_quit(sender_id: Text, endpoint: EndpointConfig) -> bool:
      """Display the exit menu.
  
      Return `True` if the previous question should be retried."""
  
      answer = questionary.select(
          message="Do you want to stop?",
          choices=[
              Choice("Continue", "continue"),
              Choice("Undo Last", "undo"),
              Choice("Fork", "fork"),
              Choice("Start Fresh", "restart"),
              Choice("Export & Quit", "quit"),
          ],
      ).ask()
  
      if not answer or answer == "quit":
          # this is also the default answer if the user presses Ctrl-C
          await _write_data_to_file(sender_id, endpoint)
          raise Abort()
      elif answer == "continue":
          # in this case we will just return, and the original
          # question will get asked again
          return True
      elif answer == "undo":
          raise UndoLastStep()
      elif answer == "fork":
          raise ForkTracker()
      elif answer == "restart":
          raise RestartConversation()
  
  
  async def _request_action_from_user(
      predictions: List[Dict[Text, Any]], sender_id: Text, endpoint: EndpointConfig
  ) -> Tuple[Text, bool]:
      """Ask the user to correct an action prediction."""
  
      await _print_history(sender_id, endpoint)
  
      choices = [
          {
              "name": "{:03.2f} {:40}".format(a.get("score"), a.get("action")),
              "value": a.get("action"),
          }
          for a in predictions
      ]
  
      tracker = await retrieve_tracker(endpoint, sender_id)
      events = tracker.get("events", [])
  
      session_actions_all = [a["name"] for a in _collect_actions(events)]
      session_actions_unique = list(set(session_actions_all))
      old_actions = [action["value"] for action in choices]
      new_actions = [
          {"name": action, "value": OTHER_ACTION + action}
          for action in session_actions_unique
          if action not in old_actions
      ]
      choices = (
          [{"name": "<create new action>", "value": NEW_ACTION}] + new_actions + choices
      )
      question = questionary.select("What is the next action of the bot?", choices)
  
      action_name = await _ask_questions(question, sender_id, endpoint)
      is_new_action = action_name == NEW_ACTION
  
      if is_new_action:
          # create new action
          action_name = await _request_free_text_action(sender_id, endpoint)
          if action_name.startswith(UTTER_PREFIX):
              utter_message = await _request_free_text_utterance(
                  sender_id, endpoint, action_name
              )
              NEW_TEMPLATES[action_name] = {utter_message: ""}
  
      elif action_name[:32] == OTHER_ACTION:
          # action was newly created in the session, but not this turn
          is_new_action = True
          action_name = action_name[32:]
  
      print(f"Thanks! The bot will now run {action_name}.\n")
      return action_name, is_new_action
  
  
  def _request_export_info() -> Tuple[Text, Text, Text]:
      """Request file path and export stories & nlu data to that path"""
  
      # export training data and quit
      questions = questionary.form(
          export_stories=questionary.text(
              message="Export stories to (if file exists, this "
              "will append the stories)",
              default=PATHS["stories"],
              validate=io_utils.file_type_validator(
                  [".md"],
                  "Please provide a valid export path for the stories, e.g. 'stories.md'.",
              ),
          ),
          export_nlu=questionary.text(
              message="Export NLU data to (if file exists, this will "
              "merge learned data with previous training examples)",
              default=PATHS["nlu"],
              validate=io_utils.file_type_validator(
                  [".md", ".json"],
                  "Please provide a valid export path for the NLU data, e.g. 'nlu.md'.",
              ),
          ),
          export_domain=questionary.text(
              message="Export domain file to (if file exists, this "
              "will be overwritten)",
              default=PATHS["domain"],
              validate=io_utils.file_type_validator(
                  [".yml", ".yaml"],
                  "Please provide a valid export path for the domain file, e.g. 'domain.yml'.",
              ),
          ),
      )
  
      answers = questions.ask()
      if not answers:
          raise Abort()
  
      return (answers["export_stories"], answers["export_nlu"], answers["export_domain"])
  
  
  def _split_conversation_at_restarts(
      events: List[Dict[Text, Any]]
  ) -> List[List[Dict[Text, Any]]]:
      """Split a conversation at restart events.
  
      Returns an array of event lists, without the restart events."""
  
      sub_conversations = []
      current = []
      for e in events:
          if e.get("event") == "restart":
              if current:
                  sub_conversations.append(current)
              current = []
          else:
              current.append(e)
  
      if current:
          sub_conversations.append(current)
  
      return sub_conversations
  
  
  def _collect_messages(events: List[Dict[Text, Any]]) -> List[Message]:
      """Collect the message text and parsed data from the UserMessage events
      into a list"""
      from rasa.nlu.extractors.duckling_http_extractor import DucklingHTTPExtractor
      from rasa.nlu.extractors.mitie_entity_extractor import MitieEntityExtractor
      from rasa.nlu.extractors.spacy_entity_extractor import SpacyEntityExtractor
  
      msgs = []
  
      for event in events:
          if event.get("event") == UserUttered.type_name:
              data = event.get("parse_data", {})
  
              for entity in data.get("entities", []):
  
                  excluded_extractors = [
                      DucklingHTTPExtractor.__name__,
                      SpacyEntityExtractor.__name__,
                      MitieEntityExtractor.__name__,
                  ]
                  logger.debug(
                      "Exclude entity marking of following extractors"
                      " {} when writing nlu data "
                      "to file.".format(excluded_extractors)
                  )
  
                  if entity.get("extractor") in excluded_extractors:
                      data["entities"].remove(entity)
  
              msg = Message.build(data["text"], data["intent"]["name"], data["entities"])
              msgs.append(msg)
  
          elif event.get("event") == UserUtteranceReverted.type_name and msgs:
              msgs.pop()  # user corrected the nlu, remove incorrect example
  
      return msgs
  
  
  def _collect_actions(events: List[Dict[Text, Any]]) -> List[Dict[Text, Any]]:
      """Collect all the `ActionExecuted` events into a list."""
  
      return [evt for evt in events if evt.get("event") == ActionExecuted.type_name]
  
  
  async def _write_stories_to_file(
      export_story_path: Text, events: List[Dict[Text, Any]], domain: Domain
  ) -> None:
      """Write the conversation of the sender_id to the file paths."""
  
      sub_conversations = _split_conversation_at_restarts(events)
  
      io_utils.create_path(export_story_path)
  
      if os.path.exists(export_story_path):
          append_write = "a"  # append if already exists
      else:
          append_write = "w"  # make a new file if not
  
      with open(export_story_path, append_write, encoding=io_utils.DEFAULT_ENCODING) as f:
          i = 1
          for conversation in sub_conversations:
              parsed_events = rasa.core.events.deserialise_events(conversation)
              tracker = DialogueStateTracker.from_events(
                  f"interactive_story_{i}", evts=parsed_events, slots=domain.slots
              )
  
              if any(
                  isinstance(event, UserUttered) for event in tracker.applied_events()
              ):
                  i += 1
                  f.write("\n" + tracker.export_stories(SAVE_IN_E2E))
  
  
  def _filter_messages(msgs: List[Message]) -> List[Message]:
      """Filter messages removing those that start with INTENT_MESSAGE_PREFIX"""
  
      filtered_messages = []
      for msg in msgs:
          if not msg.text.startswith(INTENT_MESSAGE_PREFIX):
              filtered_messages.append(msg)
      return filtered_messages
  
  
  async def _write_nlu_to_file(
      export_nlu_path: Text, events: List[Dict[Text, Any]]
  ) -> None:
      """Write the nlu data of the sender_id to the file paths."""
      from rasa.nlu.training_data import TrainingData
  
      msgs = _collect_messages(events)
      msgs = _filter_messages(msgs)
  
      # noinspection PyBroadException
      try:
          previous_examples = loading.load_data(export_nlu_path)
      except Exception as e:
          logger.debug(
              "An exception occurred while trying to load the NLU data. {}".format(str(e))
          )
          # No previous file exists, use empty training data as replacement.
          previous_examples = TrainingData()
  
      nlu_data = previous_examples.merge(TrainingData(msgs))
  
      # need to guess the format of the file before opening it to avoid a read
      # in a write
      nlu_format = _get_nlu_target_format(export_nlu_path)
      if nlu_format == MARKDOWN:
          stringified_training_data = nlu_data.nlu_as_markdown()
      else:
          stringified_training_data = nlu_data.nlu_as_json()
  
      io_utils.write_text_file(stringified_training_data, export_nlu_path)
  
  
  def _get_nlu_target_format(export_path: Text) -> Text:
      guessed_format = loading.guess_format(export_path)
  
      if guessed_format not in {MARKDOWN, RASA}:
          if export_path.endswith(".json"):
              guessed_format = RASA
          else:
              guessed_format = MARKDOWN
  
      return guessed_format
  
  
  def _entities_from_messages(messages):
      """Return all entities that occur in at least one of the messages."""
      return list({e["entity"] for m in messages for e in m.data.get("entities", [])})
  
  
  def _intents_from_messages(messages):
      """Return all intents that occur in at least one of the messages."""
  
      # set of distinct intents
      distinct_intents = {m.data["intent"] for m in messages if "intent" in m.data}
  
      return distinct_intents
  
  
  async def _write_domain_to_file(
      domain_path: Text, events: List[Dict[Text, Any]], old_domain: Domain
  ) -> None:
      """Write an updated domain file to the file path."""
  
      io_utils.create_path(domain_path)
  
      messages = _collect_messages(events)
      actions = _collect_actions(events)
      templates = NEW_TEMPLATES
  
      # TODO for now there is no way to distinguish between action and form
      collected_actions = list(
          {e["name"] for e in actions if e["name"] not in default_action_names()}
      )
  
      new_domain = Domain(
          intents=_intents_from_messages(messages),
          entities=_entities_from_messages(messages),
          slots=[],
          templates=templates,
          action_names=collected_actions,
          form_names=[],
      )
  
      old_domain.merge(new_domain).persist_clean(domain_path)
  
  
  async def _predict_till_next_listen(
      endpoint: EndpointConfig,
      sender_id: Text,
      sender_ids: List[Text],
      plot_file: Optional[Text],
  ) -> None:
      """Predict and validate actions until we need to wait for a user message."""
  
      listen = False
      while not listen:
          result = await request_prediction(endpoint, sender_id)
          predictions = result.get("scores")
          probabilities = [prediction["score"] for prediction in predictions]
          pred_out = int(np.argmax(probabilities))
          action_name = predictions[pred_out].get("action")
          policy = result.get("policy")
          confidence = result.get("confidence")
  
          await _print_history(sender_id, endpoint)
          await _plot_trackers(
              sender_ids, plot_file, endpoint, unconfirmed=[ActionExecuted(action_name)]
          )
  
          listen = await _validate_action(
              action_name, policy, confidence, predictions, endpoint, sender_id
          )
  
          await _plot_trackers(sender_ids, plot_file, endpoint)
  
      tracker_dump = await retrieve_tracker(
          endpoint, sender_id, EventVerbosity.AFTER_RESTART
      )
      events = tracker_dump.get("events", [])
  
      if len(events) >= 2:
          last_event = events[-2]  # last event before action_listen
  
          # if bot message includes buttons the user will get a list choice to reply
          # the list choice is displayed in place of action listen
          if last_event.get("event") == BotUttered.type_name and last_event["data"].get(
              "buttons", None
          ):
              response = _get_button_choice(last_event)
              if response != cliutils.FREE_TEXT_INPUT_PROMPT:
                  await send_message(endpoint, sender_id, response)
  
  
  def _get_button_choice(last_event: Dict[Text, Any]) -> Text:
      data = last_event["data"]
      message = last_event.get("text", "")
  
      choices = cliutils.button_choices_from_message_data(
          data, allow_free_text_input=True
      )
      question = questionary.select(message, choices)
      response = cliutils.payload_from_button_question(question)
      return response
  
  
  async def _correct_wrong_nlu(
      corrected_nlu: Dict[Text, Any],
      events: List[Dict[Text, Any]],
      endpoint: EndpointConfig,
      sender_id: Text,
  ) -> None:
      """A wrong NLU prediction got corrected, update core's tracker."""
  
      revert_latest_user_utterance = UserUtteranceReverted().as_dict()
      # `UserUtteranceReverted` also removes the `ACTION_LISTEN` event before, hence we
      # have to replay it.
      listen_for_next_message = ActionExecuted(ACTION_LISTEN_NAME).as_dict()
      corrected_message = latest_user_message(events)
  
      if corrected_message is None:
          raise Exception("Failed to correct NLU data. User message not found.")
  
      corrected_message["parse_data"] = corrected_nlu
      await send_event(
          endpoint,
          sender_id,
          [revert_latest_user_utterance, listen_for_next_message, corrected_message],
      )
  
  
  async def _correct_wrong_action(
      corrected_action: Text,
      endpoint: EndpointConfig,
      sender_id: Text,
      is_new_action: bool = False,
  ) -> None:
      """A wrong action prediction got corrected, update core's tracker."""
  
      await send_action(
          endpoint, sender_id, corrected_action, is_new_action=is_new_action
      )
  
  
  def _form_is_rejected(action_name, tracker):
      """Check if the form got rejected with the most recent action name."""
      return (
          tracker.get("active_form", {}).get("name")
          and action_name != tracker["active_form"]["name"]
          and action_name != ACTION_LISTEN_NAME
      )
  
  
  def _form_is_restored(action_name, tracker):
      """Check whether the form is called again after it was rejected."""
      return (
          tracker.get("active_form", {}).get("rejected")
          and tracker.get("latest_action_name") == ACTION_LISTEN_NAME
          and action_name == tracker.get("active_form", {}).get("name")
      )
  
  
  async def _confirm_form_validation(action_name, tracker, endpoint, sender_id):
      """Ask a user whether an input for a form should be validated.
  
      Previous to this call, the active form was chosen after it was rejected."""
  
      requested_slot = tracker.get("slots", {}).get(REQUESTED_SLOT)
  
      validation_questions = questionary.confirm(
          "Should '{}' validate user input to fill "
          "the slot '{}'?".format(action_name, requested_slot)
      )
      validate_input = await _ask_questions(validation_questions, sender_id, endpoint)
  
      if not validate_input:
          # notify form action to skip validation
          await send_event(
              endpoint, sender_id, {"event": "form_validation", "validate": False}
          )
  
      elif not tracker.get("active_form", {}).get("validate"):
          # handle contradiction with learned behaviour
          warning_question = questionary.confirm(
              "ERROR: FormPolicy predicted no form validation "
              "based on previous training stories. "
              "Make sure to remove contradictory stories "
              "from training data. "
              "Otherwise predicting no form validation "
              "will not work as expected."
          )
  
          await _ask_questions(warning_question, sender_id, endpoint)
          # notify form action to validate an input
          await send_event(
              endpoint, sender_id, {"event": "form_validation", "validate": True}
          )
  
  
  async def _validate_action(
      action_name: Text,
      policy: Text,
      confidence: float,
      predictions: List[Dict[Text, Any]],
      endpoint: EndpointConfig,
      sender_id: Text,
  ) -> bool:
      """Query the user to validate if an action prediction is correct.
  
      Returns `True` if the prediction is correct, `False` otherwise."""
  
      question = questionary.confirm(f"The bot wants to run '{action_name}', correct?")
  
      is_correct = await _ask_questions(question, sender_id, endpoint)
  
      if not is_correct:
          action_name, is_new_action = await _request_action_from_user(
              predictions, sender_id, endpoint
          )
      else:
          is_new_action = False
  
      tracker = await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART)
  
      if _form_is_rejected(action_name, tracker):
          # notify the tracker that form was rejected
          await send_event(
              endpoint,
              sender_id,
              {
                  "event": "action_execution_rejected",
                  "name": tracker["active_form"]["name"],
              },
          )
  
      elif _form_is_restored(action_name, tracker):
          await _confirm_form_validation(action_name, tracker, endpoint, sender_id)
  
      if not is_correct:
          await _correct_wrong_action(
              action_name, endpoint, sender_id, is_new_action=is_new_action
          )
      else:
          await send_action(endpoint, sender_id, action_name, policy, confidence)
  
      return action_name == ACTION_LISTEN_NAME
  
  
  def _as_md_message(parse_data: Dict[Text, Any]) -> Text:
      """Display the parse data of a message in markdown format."""
      from rasa.nlu.training_data.formats import MarkdownWriter
  
      if parse_data.get("text", "").startswith(INTENT_MESSAGE_PREFIX):
          return parse_data["text"]
  
      if not parse_data.get("entities"):
          parse_data["entities"] = []
      # noinspection PyProtectedMember
      return MarkdownWriter()._generate_message_md(parse_data)
  
  
  def _validate_user_regex(latest_message: Dict[Text, Any], intents: List[Text]) -> bool:
      """Validate if a users message input is correct.
  
      This assumes the user entered an intent directly, e.g. using
      `/greet`. Return `True` if the intent is a known one."""
  
      parse_data = latest_message.get("parse_data", {})
      intent = parse_data.get("intent", {}).get("name")
  
      if intent in intents:
          return True
      else:
          return False
  
  
  async def _validate_user_text(
      latest_message: Dict[Text, Any], endpoint: EndpointConfig, sender_id: Text
  ) -> bool:
      """Validate a user message input as free text.
  
      This assumes the user message is a text message (so NOT `/greet`)."""
  
      parse_data = latest_message.get("parse_data", {})
      text = _as_md_message(parse_data)
      intent = parse_data.get("intent", {}).get("name")
      entities = parse_data.get("entities", [])
      if entities:
          message = (
              "Is the intent '{}' correct for '{}' and are "
              "all entities labeled correctly?".format(intent, text)
          )
      else:
          message = (
              "Your NLU model classified '{}' with intent '{}'"
              " and there are no entities, is this correct?".format(text, intent)
          )
  
      if intent is None:
          print(f"The NLU classification for '{text}' returned '{intent}'")
          return False
      else:
          question = questionary.confirm(message)
  
          return await _ask_questions(question, sender_id, endpoint)
  
  
  async def _validate_nlu(
      intents: List[Text], endpoint: EndpointConfig, sender_id: Text
  ) -> None:
      """Validate if a user message, either text or intent is correct.
  
      If the prediction of the latest user message is incorrect,
      the tracker will be corrected with the correct intent / entities."""
  
      tracker = await retrieve_tracker(endpoint, sender_id, EventVerbosity.AFTER_RESTART)
  
      latest_message = latest_user_message(tracker.get("events", [])) or {}
  
      if latest_message.get("text", "").startswith(  # pytype: disable=attribute-error
          INTENT_MESSAGE_PREFIX
      ):
          valid = _validate_user_regex(latest_message, intents)
      else:
          valid = await _validate_user_text(latest_message, endpoint, sender_id)
  
      if not valid:
          corrected_intent = await _request_intent_from_user(
              latest_message, intents, sender_id, endpoint
          )
          # corrected intents have confidence 1.0
          corrected_intent["confidence"] = 1.0
  
          events = tracker.get("events", [])
  
          entities = await _correct_entities(latest_message, endpoint, sender_id)
          corrected_nlu = {
              "intent": corrected_intent,
              "entities": entities,
              "text": latest_message.get("text"),
          }
  
          await _correct_wrong_nlu(corrected_nlu, events, endpoint, sender_id)
  
  
  async def _correct_entities(
      latest_message: Dict[Text, Any], endpoint: EndpointConfig, sender_id: Text
  ) -> List[Dict[Text, Any]]:
      """Validate the entities of a user message.
  
      Returns the corrected entities"""
      from rasa.nlu.training_data.formats import MarkdownReader
  
      parse_original = latest_message.get("parse_data", {})
      entity_str = _as_md_message(parse_original)
      question = questionary.text(
          "Please mark the entities using [value](type) notation", default=entity_str
      )
  
      annotation = await _ask_questions(question, sender_id, endpoint)
      # noinspection PyProtectedMember
      parse_annotated = MarkdownReader()._parse_training_example(annotation)
  
      corrected_entities = _merge_annotated_and_original_entities(
          parse_annotated, parse_original
      )
  
      return corrected_entities
  
  
  def _merge_annotated_and_original_entities(parse_annotated, parse_original):
      # overwrite entities which have already been
      # annotated in the original annotation to preserve
      # additional entity parser information
      entities = parse_annotated.get("entities", [])[:]
      for i, entity in enumerate(entities):
          for original_entity in parse_original.get("entities", []):
              if _is_same_entity_annotation(entity, original_entity):
                  entities[i] = original_entity
                  break
      return entities
  
  
  def _is_same_entity_annotation(entity, other):
      return entity["value"] == other["value"] and entity["entity"] == other["entity"]
  
  
  async def _enter_user_message(sender_id: Text, endpoint: EndpointConfig) -> None:
      """Request a new message from the user."""
  
      question = questionary.text("Your input ->")
  
      message = await _ask_questions(question, sender_id, endpoint, lambda a: not a)
  
      if message == (INTENT_MESSAGE_PREFIX + constants.USER_INTENT_RESTART):
          raise RestartConversation()
  
      await send_message(endpoint, sender_id, message)
  
  
  async def is_listening_for_message(sender_id: Text, endpoint: EndpointConfig) -> bool:
      """Check if the conversation is in need for a user message."""
  
      tracker = await retrieve_tracker(endpoint, sender_id, EventVerbosity.APPLIED)
  
      for i, e in enumerate(reversed(tracker.get("events", []))):
          if e.get("event") == UserUttered.type_name:
              return False
          elif e.get("event") == ActionExecuted.type_name:
              return e.get("name") == ACTION_LISTEN_NAME
      return False
  
  
  async def _undo_latest(sender_id: Text, endpoint: EndpointConfig) -> None:
      """Undo either the latest bot action or user message, whatever is last."""
  
      tracker = await retrieve_tracker(endpoint, sender_id, EventVerbosity.ALL)
  
      # Get latest `UserUtterance` or `ActionExecuted` event.
      last_event_type = None
      for i, e in enumerate(reversed(tracker.get("events", []))):
          last_event_type = e.get("event")
          if last_event_type in {ActionExecuted.type_name, UserUttered.type_name}:
              break
          elif last_event_type == Restarted.type_name:
              break
  
      if last_event_type == ActionExecuted.type_name:
          undo_action = ActionReverted().as_dict()
          await send_event(endpoint, sender_id, undo_action)
      elif last_event_type == UserUttered.type_name:
          undo_user_message = UserUtteranceReverted().as_dict()
          listen_for_next_message = ActionExecuted(ACTION_LISTEN_NAME).as_dict()
  
          await send_event(
              endpoint, sender_id, [undo_user_message, listen_for_next_message]
          )
  
  
  async def _fetch_events(
      sender_ids: List[Union[Text, List[Event]]], endpoint: EndpointConfig
  ) -> List[List[Event]]:
      """Retrieve all event trackers from the endpoint for all sender ids."""
  
      event_sequences = []
      for sender_id in sender_ids:
          if isinstance(sender_id, str):
              tracker = await retrieve_tracker(endpoint, sender_id)
              events = tracker.get("events", [])
  
              for conversation in _split_conversation_at_restarts(events):
                  parsed_events = rasa.core.events.deserialise_events(conversation)
                  event_sequences.append(parsed_events)
          else:
              event_sequences.append(sender_id)
      return event_sequences
  
  
  async def _plot_trackers(
      sender_ids: List[Union[Text, List[Event]]],
      output_file: Optional[Text],
      endpoint: EndpointConfig,
      unconfirmed: Optional[List[Event]] = None,
  ):
      """Create a plot of the trackers of the passed sender ids.
  
      This assumes that the last sender id is the conversation we are currently
      working on. If there are events that are not part of this active tracker
      yet, they can be passed as part of `unconfirmed`. They will be appended
      to the currently active conversation."""
  
      if not output_file or not sender_ids:
          # if there is no output file provided, we are going to skip plotting
          # same happens if there are no sender ids
          return None
  
      event_sequences = await _fetch_events(sender_ids, endpoint)
  
      if unconfirmed:
          event_sequences[-1].extend(unconfirmed)
  
      graph = await visualize_neighborhood(
          event_sequences[-1], event_sequences, output_file=None, max_history=2
      )
  
      from networkx.drawing.nx_pydot import write_dot
  
      write_dot(graph, output_file)
  
  
  def _print_help(skip_visualization: bool) -> None:
      """Print some initial help message for the user."""
  
      if not skip_visualization:
          visualization_url = DEFAULT_SERVER_FORMAT.format(
              "http", DEFAULT_SERVER_PORT + 1
          )
          visualization_help = "Visualisation at {}/visualization.html.".format(
              visualization_url
          )
      else:
          visualization_help = ""
  
      rasa.cli.utils.print_success(
          "Bot loaded. {}\n"
          "Type a message and press enter "
          "(press 'Ctr-c' to exit). "
          "".format(visualization_help)
      )
  
  
  async def record_messages(
      endpoint: EndpointConfig,
      sender_id: Text = UserMessage.DEFAULT_SENDER_ID,
      max_message_limit: Optional[int] = None,
      stories: Optional[Text] = None,
      skip_visualization: bool = False,
  ):
      """Read messages from the command line and print bot responses."""
  
      from rasa.core import training
  
      try:
          _print_help(skip_visualization)
  
          try:
              domain = await retrieve_domain(endpoint)
          except ClientError:
              logger.exception(
                  "Failed to connect to Rasa Core server at '{}'. "
                  "Is the server running?".format(endpoint.url)
              )
              return
  
          trackers = await training.load_data(
              stories,
              Domain.from_dict(domain),
              augmentation_factor=0,
              use_story_concatenation=False,
          )
  
          intents = [next(iter(i)) for i in (domain.get("intents") or [])]
  
          num_messages = 0
          sender_ids = [t.events for t in trackers] + [sender_id]
  
          if not skip_visualization:
              plot_file = "story_graph.dot"
              await _plot_trackers(sender_ids, plot_file, endpoint)
          else:
              plot_file = None
  
          while not utils.is_limit_reached(num_messages, max_message_limit):
              try:
                  if await is_listening_for_message(sender_id, endpoint):
                      await _enter_user_message(sender_id, endpoint)
                      await _validate_nlu(intents, endpoint, sender_id)
  
                  await _predict_till_next_listen(
                      endpoint, sender_id, sender_ids, plot_file
                  )
  
                  num_messages += 1
              except RestartConversation:
                  await send_event(endpoint, sender_id, Restarted().as_dict())
  
                  await send_event(
                      endpoint, sender_id, ActionExecuted(ACTION_LISTEN_NAME).as_dict()
                  )
  
                  logger.info("Restarted conversation, starting a new one.")
              except UndoLastStep:
                  await _undo_latest(sender_id, endpoint)
                  await _print_history(sender_id, endpoint)
              except ForkTracker:
                  await _print_history(sender_id, endpoint)
  
                  events_fork = await _request_fork_from_user(sender_id, endpoint)
  
                  await send_event(endpoint, sender_id, Restarted().as_dict())
  
                  if events_fork:
                      for evt in events_fork:
                          await send_event(endpoint, sender_id, evt)
                  logger.info("Restarted conversation at fork.")
  
                  await _print_history(sender_id, endpoint)
                  await _plot_trackers(sender_ids, plot_file, endpoint)
  
      except Abort:
          return
      except Exception:
          logger.exception("An exception occurred while recording messages.")
          raise
  
  
  def _serve_application(app, stories, skip_visualization, server_args):
      """Start a core server and attach the interactive learning IO."""
  
      if server_args.get("port"):
          port = server_args.get("port")
          DEFAULT_SERVER_URL = f"http://localhost:{port}"
  
      endpoint = EndpointConfig(url=DEFAULT_SERVER_URL)
  
      async def run_interactive_io(running_app: Sanic):
          """Small wrapper to shut down the server once cmd io is done."""
  
          await record_messages(
              endpoint=endpoint,
              stories=stories,
              skip_visualization=skip_visualization,
              sender_id=uuid.uuid4().hex,
          )
  
          logger.info("Killing Sanic server now.")
  
          running_app.stop()  # kill the sanic server
  
      app.add_task(run_interactive_io)
  
      update_sanic_log_level()
  
      if port:
          app.run(host="0.0.0.0", port=port)
      else:
          app.run(host="0.0.0.0", port=DEFAULT_SERVER_PORT)
  
      return app
  
  
  def start_visualization(image_path: Text = None) -> None:
      """Add routes to serve the conversation visualization files."""
  
      app = Sanic(__name__)
  
      # noinspection PyUnusedLocal
      @app.exception(NotFound)
      async def ignore_404s(request, exception):
          return response.text("Not found", status=404)
  
      # noinspection PyUnusedLocal
      @app.route(VISUALIZATION_TEMPLATE_PATH, methods=["GET"])
      def visualisation_html(request):
          return response.file(visualization.visualization_html_path())
  
      # noinspection PyUnusedLocal
      @app.route("/visualization.dot", methods=["GET"])
      def visualisation_png(request):
          try:
              headers = {"Cache-Control": "no-cache"}
              return response.file(os.path.abspath(image_path), headers=headers)
          except FileNotFoundError:
              return response.text("", 404)
  
      update_sanic_log_level()
  
      app.run(host="0.0.0.0", port=DEFAULT_SERVER_PORT + 1, access_log=False)
  
  
  # noinspection PyUnusedLocal
  async def train_agent_on_start(args, endpoints, additional_arguments, app, loop):
      _interpreter = NaturalLanguageInterpreter.create(args.get("nlu"), endpoints.nlu)
  
      model_directory = args.get("out", tempfile.mkdtemp(suffix="_core_model"))
  
      _agent = await train(
          args.get("domain"),
          args.get("stories"),
          model_directory,
          _interpreter,
          endpoints,
          args.get("dump_stories"),
          args.get("config")[0],
          None,
          additional_arguments,
      )
      app.agent = _agent
  
  
  async def wait_til_server_is_running(endpoint, max_retries=30, sleep_between_retries=1):
      """Try to reach the server, retry a couple of times and sleep in between."""
  
      while max_retries:
          try:
              r = await retrieve_status(endpoint)
              logger.info(f"Reached core: {r}")
              if not r.get("is_ready"):
                  # server did not finish loading the agent yet
                  # in this case, we need to wait till the model trained
                  # so we might be sleeping for a while...
                  await asyncio.sleep(sleep_between_retries)
                  continue
              else:
                  # server is ready to go
                  return True
          except ClientError:
              max_retries -= 1
              if max_retries:
                  await asyncio.sleep(sleep_between_retries)
  
      return False
  
  
  def run_interactive_learning(
      stories: Text = None,
      skip_visualization: bool = False,
      server_args: Dict[Text, Any] = None,
      additional_arguments: Dict[Text, Any] = None,
  ):
      """Start the interactive learning with the model of the agent."""
      global SAVE_IN_E2E
      server_args = server_args or {}
  
      if server_args.get("nlu_data"):
          PATHS["nlu"] = server_args["nlu_data"]
  
      if server_args.get("stories"):
          PATHS["stories"] = server_args["stories"]
  
      if server_args.get("domain"):
          PATHS["domain"] = server_args["domain"]
  
      if server_args.get("port"):
          port = server_args.get("port")
  
      SAVE_IN_E2E = server_args["e2e"]
  
      if not skip_visualization:
          p = Process(target=start_visualization, args=("story_graph.dot",))
          p.daemon = True
          p.start()
      else:
          p = None
  
      app = run.configure_app(port=port, enable_api=True)
      endpoints = AvailableEndpoints.read_endpoints(server_args.get("endpoints"))
  
      # before_server_start handlers make sure the agent is loaded before the
      # interactive learning IO starts
      if server_args.get("model"):
          app.register_listener(
              partial(run.load_agent_on_start, server_args.get("model"), endpoints, None),
              "before_server_start",
          )
      else:
          app.register_listener(
              partial(train_agent_on_start, server_args, endpoints, additional_arguments),
              "before_server_start",
          )
  
      _serve_application(app, stories, skip_visualization, server_args)
  
      if not skip_visualization and p is not None:
          p.terminate()  # pytype: disable=attribute-error
          p.join()  # pytype: disable=attribute-error
  