text: from copy import copy
  from dataclasses import dataclass
  from typing import Callable, Optional, Sequence
  import json
  import logging
  import time
  
  from talon import Module, resource, events
  from talon.debug import log_exception
  from talon.experimental.parrot import ParrotSystem, ParrotDelegate, ParrotFrame
  from talon_init import TALON_HOME
  
  PARROT_HOME = TALON_HOME / 'parrot'
  pattern_path = str(PARROT_HOME / 'patterns.json')
  model_path = str(PARROT_HOME / 'model.pkl')
  
  ## START PARROT CLASSES ##
  class PatternMatcher:
      def detect_all(self, frame: ParrotFrame, detection_functions: list[Callable]) -> bool:
          """Matcher for all detection functions"""
          for detect_function in detection_functions:
              if detect_function(self, frame) == False:
                  return False
          return True
  
  @dataclass
  class NoiseTimestamps:
      last_detected_at:  float = 0.0
      duration_start:    float = 0.0
      detection_after:   float = 0.0
      graceperiod_until: float = 0.0
      throttled_at:      float = 0.0
      throttled_until:   float = 0.0
  
  MatchPatternCallable = Callable[['NoisePattern', ParrotFrame, float], bool]
  
  class NoisePattern(PatternMatcher):
      """Class containing all the thresholds and timestamps for a noise"""
      name: str
      # match_pattern: MatchPatternCallable # mypy bug
      duration: float
      throttles: dict[str, float]
      lowest_power_thresholds: list[float]
      labels: frozenset[str]
  
      # This graceperiod is purely used to dampen the effects of a noises probability getting more unstable over time with the current model
      graceperiod_length: float
      detection_after:    float
      timestamps: NoiseTimestamps
  
      def __init__(self, name: str, labels: list[str], match_pattern: MatchPatternCallable, lowest_power_thresholds: list[float], throttles: dict[str, float], detection_after: float, graceperiod_length: float):
          self.name = name
          self.match_pattern = match_pattern
          self.lowest_power_thresholds = lowest_power_thresholds
          self.detection_after = detection_after
          self.graceperiod_length = graceperiod_length
          self.throttles = throttles
          self.duration = 0
          self.labels = frozenset(labels)
          self.timestamps = NoiseTimestamps()
  
      def is_active(self, time) -> bool:
          """Check if a current noise is active"""
          return self.timestamps.throttled_until < time
  
      def detect(self, frame: ParrotFrame) -> bool:
          """Matches the pattern against the last N raw frames of the model classifier"""
          grace_detected = False
          detected = False
          if self.is_active(frame.ts):
              if self.match_pattern(self, frame, self.timestamps.graceperiod_until):
                  self.timestamps.duration_start = self.timestamps.duration_start if self.timestamps.duration_start > 0 else frame.ts
                  grace_detected = True
  
                  # If there is a duration threshold, wait to activate until the duration has passed before marking the pattern as detected
                  if (self.timestamps.duration_start + self.detection_after) <= frame.ts:
                      detected = True
                      self.timestamps.last_detected_at = frame.ts
                      self.timestamps.graceperiod_until = frame.ts + self.graceperiod_length
                      self.duration = frame.ts - self.timestamps.duration_start
  
          # Reset graceperiod if the detection did not match
          if grace_detected == False:
              self.timestamps.graceperiod_until = 0
              self.duration = 0
  
          # Reset the duration if the graceperiod has ended
          if self.timestamps.duration_start > 0 and detected == False and grace_detected == False and self.timestamps.graceperiod_until < (frame.ts + self.graceperiod_length):
              self.timestamps.duration_start = 0
              self.duration = 0
  
          return detected
  
      def reset_timestamps(self):
          """Reset all the activation timeouts"""
          self.timestamps.graceperiod_until = 0
          self.timestamps.duration_start = 0
          self.duration = 0
  
      def throttle(self, throttle_until: float, throttle_at: float):
          """Throttle the detection of this pattern until the timestamp is passed if the throttle is higher than the highest current throttle"""
          if throttle_until > self.timestamps.throttled_until:
              self.timestamps.throttled_at = throttle_at
              self.timestamps.throttled_until = throttle_until
              self.timestamps.graceperiod_until = 0
  
      def get_throttles(self) -> dict[str, float]:
          """Returns a dict of all the patterns to throttle after this pattern has been detected"""
          return self.throttles
  
      def get_current_lowest_power_threshold(self, time: float) -> float:
          """Retrieve the lowest power threshold to aid in determining when to skip forward passes"""
          return self.lowest_power_thresholds[1] if time < self.timestamps.graceperiod_until else self.lowest_power_thresholds[0]
  
  class PatternBuilder:
      """Class that builds and validates the patterns to make sure they have the right syntax"""
      possible_thresholds = frozenset(['>power', '>f0', '>f1', '>f2', '>probability', '>ratio', '<power', '<f0', '<f1', '<f2', '<probability', '<ratio'])
      possible_keys = frozenset(['sounds', 'detect_after', 'threshold', 'graceperiod', 'grace_threshold', 'throttle'])
  
      def build(self, name: str, pattern: dict) -> Optional[NoisePattern]:
          """Builds a valid pattern or skips it in case the pattern is invalid"""
          for key in pattern:
              if key not in self.possible_keys:
                  logging.warning(f"[parrot] unknown key {repr(key)}")
  
          if 'sounds' not in pattern or len(pattern['sounds']) == 0:
              logging.error(f"[parrot] skipping noise {repr(name)}: No sounds defined.")
              return None
  
          for key in pattern['threshold']:
              if key not in self.possible_thresholds:
                  logging.warning(f"[parrot] unknown threshold key: {repr(key)}")
          detection_calls = self.generate_matching_functions(pattern['threshold'], pattern['sounds'])
  
          if 'grace_threshold' in pattern:
              for key in pattern['grace_threshold']:
                  if key not in self.possible_thresholds:
                      logging.warning("[parrot] unknown grace threshold key: {repr(key)}")
              graceperiod_detection_calls = self.generate_matching_functions(pattern['grace_threshold'], pattern['sounds'])
          else:
              graceperiod_detection_calls = copy(detection_calls)
  
          # Calculate the lowest power thresholds - These are used for optimisation purposes
          # By default set the threshold at 20 to prevent continuous recognition on silence
          lowest_power_thresholds: list[float] = [20, 0]
          if '>power' in pattern['threshold']:
              lowest_power_thresholds[0] = pattern['threshold']['>power']
          if 'grace_threshold' in pattern and '>power' in pattern['grace_threshold']:
              lowest_power_thresholds[1] = pattern['grace_threshold']['>power']
  
          grace_period = pattern['graceperiod'] if 'graceperiod' in pattern else 0
          detection_after = pattern['detect_after'] if 'detect_after' in pattern else 0
  
          def match_pattern(self, frame: ParrotFrame, graceperiod_until: float):
              return self.detect_all(frame, graceperiod_detection_calls) if frame.ts < graceperiod_until else self.detect_all(frame, detection_calls)
  
          throttles = {}
          if 'throttle' in pattern:
              if name not in pattern['throttle']:
                  pattern['throttle'][name] = 0
              throttles = pattern['throttle']
  
          return NoisePattern(name, pattern['sounds'], match_pattern, lowest_power_thresholds, throttles, detection_after, grace_period)
  
      def generate_matching_functions(self, thresholds, sounds):
          """Generate noise matching functions"""
          detection_calls = []
  
          if '>probability' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['>probability'], sounds=sounds: sum( frame.classes[sound] for sound in sounds) >= threshold )
          if '>power' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['>power']: frame.power >= threshold )
          if '>ratio' in thresholds and len(sounds) > 1:
              detection_calls.append( lambda self, frame, threshold=thresholds['>ratio'], sounds=sounds: ( frame.classes[sounds[0]] / frame.classes[sounds[1]] >= threshold ) )
          if '>f0' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['>f0']: frame.f0 >= threshold )
          if '>f1' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['>f1']: frame.f1 >= threshold )
          if '>f2' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['>f2']: frame.f2 >= threshold )
  
          # Lower than matches
          if '<probability' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['<probability'], sounds=sounds: sum( frame.classes[sound] for sound in sounds) < threshold )
          if '<power' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['<power']: frame.power >= threshold )
          if '<ratio' in thresholds and len(sounds) > 1:
              detection_calls.append( lambda self, frame, threshold=thresholds['<ratio'], sounds=sounds: ( frame.classes[sounds[0]] / frame.classes[sounds[1]] < threshold ) )
          if '<f0' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['<f0']: frame.f0 < threshold )
          if '<f1' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['<f1']: frame.f1 < threshold )
          if '<f2' in thresholds:
              detection_calls.append( lambda self, frame, threshold=thresholds['<f2']: frame.f2 < threshold )
  
          return detection_calls
  
  # END PARROT CLASSES #
  
  class Delegate(ParrotDelegate):
      patterns: dict[str, NoisePattern]
      debug: bool
      last_frame_was_forwardpass: bool
  
      def __init__(self, pattern_path: str, *, debug: bool=False):
          self.pattern_path = pattern_path
          self.debug = debug
          self.patterns = {}
          self.last_frame_was_forwardpass = False
  
      def set_class_names(self, classes: set[str]) -> None:
          patterns_to_validate = self.load_patterns(self.pattern_path, classes)
          # discard invalid patterns
          invalid_patterns: set[str] = set()
          patterns: dict[str, NoisePattern] = {}
          for pattern in patterns_to_validate:
              # NOTE: could make a note of the actual invalid labels, from the result of this set subtraction
              if len(pattern.labels - classes) > 0:
                  invalid_patterns.add(pattern.name)
                  events.write("parrot", f"invalid: {pattern.name}")
              else:
                  patterns[pattern.name] = pattern
                  events.write("parrot", f"added: {pattern.name}")
          self.patterns = patterns
          for name in invalid_patterns:
              logging.warning(f"[parrot] pattern {repr(name)} contains invalid labels and will not be used.")
          if invalid_patterns:
              class_names = ', '.join(classes)
              logging.warning(f"[parrot] use one of the following labels: {class_names}")
  
      def load_patterns(self, pattern_path: str, classes: set[str]) -> list[NoisePattern]:
          """Load the patterns"""
          try:
              json_patterns = json.loads(resource.read(pattern_path))
              pattern_builder = PatternBuilder()
              patterns: list[NoisePattern] = []
              for key, config in json_patterns.items():
                  pattern = pattern_builder.build(key, config)
                  if pattern is not None:
                      patterns.append(pattern)
              return patterns
          except Exception:
              log_exception(f"[parrot] invalid pattern file: {pattern_path}")
              return []
  
      def calculate_silence_threshold(self) -> float:
          """Calculate the power threshold needed before we need to do noise recognition given the currently active noises"""
          current_time = time.perf_counter()
          inactive_power_threshold = 10000000
          thresholds = []
          for pattern in self.patterns.values():
              if pattern.is_active(current_time):
                  thresholds.append(pattern.get_current_lowest_power_threshold(current_time))
  
          # If the debug is on, drop the silence threshold to make it easier to view model output for debugging
          if self.debug:
              thresholds.append(10)
  
          # If no noises are active - skip all noise recognition and save some CPU
          if len(thresholds) == 0:
              return inactive_power_threshold
          else:
              return min(thresholds)
  
      def throttle_patterns(self, throttles_s: dict[str, float], current_time: float):
          """Throttles the given pattern names until the matching milliseconds are passed"""
          for pattern_name in throttles_s:
              pattern = self.patterns.get(pattern_name)
              if pattern is not None:
                  pattern.throttle(current_time + throttles_s[pattern_name], current_time)
  
      def power_threshold(self, power: float) -> bool:
          silence_power_threshold = self.calculate_silence_threshold()
  
          # Reset all the duration timeouts if we are transitioning to silence
          should_forwardpass = power > silence_power_threshold
          if self.last_frame_was_forwardpass and not should_forwardpass:
              for pattern in self.patterns.values():
                  pattern.reset_timestamps()
  
          self.last_frame_was_forwardpass = should_forwardpass
          return should_forwardpass
  
      def pattern_match(self, frame: ParrotFrame) -> set[str]:
          if self.debug:
              winner_label, winner_prob = next(iter(frame.classes.items()))
              events.write('parrot', f"predict {winner_label} {winner_prob * 100:.2f}% pow={frame.power:.2f} f0={frame.f0:.3f} f1={frame.f1:.3f} f2={frame.f2:.3f}")
  
          active: set[str] = set()
          for pattern in self.patterns.values():
              if pattern.detect(frame):
                  active.add(pattern.name)
                  self.throttle_patterns(pattern.get_throttles(), frame.ts)
  
          return active
  
  parrot_delegate = Delegate(pattern_path, debug=False)
  system = ParrotSystem(model_path, parrot_delegate)
  