from universe_toolbox import MinimalUniverse, Perspective, MinimalDHT, MinimalBitTorrent

class FrenchLearner(MinimalUniverse):
    """
    Language acquisition through universe evolution
    
    Maps French learning to universal framework:
    - State = Current vocabulary + grammar knowledge
    - F = Phonetic rules + grammar construction
    - E_p = Exposure (reading, listening, conversation)
    - Perspectives = Different contexts/registers
    """
    
    def __init__(self):
        # Seed: French alphabet + basic phonemes
        alphabet = {
            'letters': 'abcdefghijklmnopqrstuvwxyz',
            'accents': 'àâéèêëïôùûüÿç',
            'phonemes': {
                # Vowels
                'a': ['a', 'ɑ'],  # IPA notation
                'e': ['ə', 'e', 'ɛ'],
                'i': ['i'],
                'o': ['o', 'ɔ'],
                'u': ['y'],
                # Consonants
                'c': ['k', 's'],
                'g': ['ɡ', 'ʒ'],
                'r': ['ʁ'],  # French R
                # ... more phonemes
            },
            'vocabulary': {},  # Empty at start
            'grammar': {},
            'comprehension_level': 0
        }
        
        # F: Grammar rules + word formation
        def french_evolution(state, perspective):
            """
            Evolve language knowledge
            
            Combines phonemes → words → phrases according to grammar
            """
            new_state = state.copy()
            
            # Apply phonetic rules
            if 'phonemes' in state:
                # Combine phonemes into syllables
                syllables = self._combine_phonemes(state['phonemes'])
                new_state['syllables'] = syllables
            
            # Apply grammar rules
            if 'vocabulary' in state and 'grammar' in state:
                # Generate possible phrases
                phrases = self._generate_phrases(
                    state['vocabulary'],
                    state['grammar'],
                    perspective
                )
                new_state['possible_expressions'] = phrases
            
            return new_state
        
        # E_p: Exposure sources
        def reading_entropy(state, perspective):
            """Exposure through reading"""
            # Encounter new words in text
            new_words = self._extract_from_text(
                text=self.current_text,
                known_vocab=state['vocabulary']
            )
            
            state['vocabulary'].update(new_words)
            return state
        
        def listening_entropy(state, perspective):
            """Exposure through audio"""
            # Reinforce phoneme recognition
            heard_phonemes = self._process_audio(
                audio=self.current_audio
            )
            
            # Match to known vocabulary
            recognized_words = self._match_phonemes_to_words(
                heard_phonemes,
                state['vocabulary']
            )
            
            return state
        
        def conversation_entropy(state, perspective):
            """Exposure through conversation"""
            # Active production + feedback
            response = self._generate_response(
                context=self.conversation_context,
                vocabulary=state['vocabulary'],
                grammar=state['grammar']
            )
            
            # Update based on feedback
            if response.get('correct'):
                state['comprehension_level'] += 0.1
            else:
                # Learn from correction
                state['grammar'].update(response['correction'])
            
            return state
        
        # Initialize universe
        super().__init__(
            seed=alphabet,
            evolution_f=french_evolution,
            entropy_sources=[
                reading_entropy,
                listening_entropy,
                conversation_entropy
            ]
        )
        
        # Add perspectives
        self.add_perspective(Perspective(
            observer_id='formal',
            position=[0, 0, 1],  # Vous form
            velocity=[0, 0, 0]
        ))
        
        self.add_perspective(Perspective(
            observer_id='informal',
            position=[0, 0, -1],  # Tu form
            velocity=[0, 0, 0]
        ))
        
        # Distributed resources
        self.dht = None  # For shared vocabulary
        self.bittorrent = None  # For audio files
        
        # Learning state
        self.current_text = ""
        self.current_audio = None
        self.conversation_context = {}
    
    def _combine_phonemes(self, phonemes):
        """
        Combine phonemes into syllables
        
        French syllable structure: (C)V(C)
        C = consonant, V = vowel
        """
        syllables = []
        
        # Simple combination rules
        # CV: ba, pa, ra
        # CVC: bac, pac, rac
        # V: a, i, o
        # VC: ac, ic, oc
        
        for vowel_letter, vowel_sounds in phonemes.items():
            if self._is_vowel(vowel_letter):
                for sound in vowel_sounds:
                    # V pattern
                    syllables.append(sound)
                    
                    # CV pattern
                    for cons_letter, cons_sounds in phonemes.items():
                        if not self._is_vowel(cons_letter):
                            for cons_sound in cons_sounds:
                                syllables.append(cons_sound + sound)
                                
                                # CVC pattern
                                for final_sound in cons_sounds:
                                    syllables.append(
                                        cons_sound + sound + final_sound
                                    )
        
        return syllables
    
    def _generate_phrases(self, vocabulary, grammar, perspective):
        """
        Generate grammatically correct phrases
        
        Uses learned grammar rules
        """
        phrases = []
        
        # Subject-Verb-Object construction
        if 'verbs' in vocabulary and 'nouns' in vocabulary:
            for subject in vocabulary.get('pronouns', []):
                for verb in vocabulary.get('verbs', []):
                    # Conjugate verb based on subject
                    conjugated = self._conjugate(verb, subject, grammar)
                    
                    for obj in vocabulary.get('nouns', []):
                        # Apply article
                        article = self._get_article(obj, grammar)
                        
                        # Adjust for perspective (tu vs vous)
                        if perspective and perspective.id == 'formal':
                            if subject == 'tu':
                                subject = 'vous'
                                conjugated = self._conjugate(
                                    verb, 'vous', grammar
                                )
                        
                        phrase = f"{subject} {conjugated} {article} {obj}"
                        phrases.append(phrase)
        
        return phrases
    
    def _extract_from_text(self, text, known_vocab):
        """
        Extract new vocabulary from text
        
        Returns: dict of new words with context
        """
        import re
        
        # Tokenize
        words = re.findall(r'\b\w+\b', text.lower())
        
        new_vocab = {}
        
        for word in words:
            if word not in known_vocab:
                # Try to infer meaning from context
                context = self._get_context(word, text)
                
                # Classify word type (noun, verb, adj, etc)
                word_type = self._classify_word(word)
                
                new_vocab[word] = {
                    'type': word_type,
                    'context': context,
                    'frequency': 1
                }
            else:
                # Reinforce known word
                known_vocab[word]['frequency'] += 1
        
        return new_vocab
    
    def learn_from_text(self, french_text):
        """
        Process French text for learning
        
        Extracts vocabulary, patterns, grammar
        """
        self.current_text = french_text
        
        # Evolve state with reading entropy
        new_state = self.series.step(self.perspectives.get('formal'))
        
        # Update
        self.series.state = new_state
        self.iteration += 1
        
        return new_state
    
    def learn_from_audio(self, audio_file):
        """
        Process French audio for learning
        
        Reinforces phoneme recognition
        """
        self.current_audio = audio_file
        
        # Evolve with listening entropy
        new_state = self.series.step(None)
        
        return new_state
    
    def practice_conversation(self, context):
        """
        Practice conversation in context
        
        Active production + feedback
        """
        self.conversation_context = context
        
        # Evolve with conversation entropy
        new_state = self.series.step(
            self.perspectives.get(context.get('formality', 'informal'))
        )
        
        return new_state
    
    def get_vocabulary_size(self):
        """Current vocabulary count"""
        return len(self.series.state.get('vocabulary', {}))
    
    def get_comprehension_level(self):
        """Estimated comprehension level"""
        return self.series.state.get('comprehension_level', 0)
    
    def generate_sentence(self, intent, perspective='formal'):
        """
        Generate French sentence for intent
        
        Uses current grammar + vocabulary
        """
        state = self.series.state
        perspective_obj = self.perspectives.get(perspective)
        
        phrases = self._generate_phrases(
            state['vocabulary'],
            state['grammar'],
            perspective_obj
        )
        
        # Select phrase matching intent
        best_match = self._match_intent(intent, phrases)
        
        return best_match
    
    def export_progress(self):
        """
        Export learning progress
        
        Returns: Stats on vocabulary, comprehension, etc
        """
        state = self.series.state
        
        return {
            'vocabulary_size': len(state.get('vocabulary', {})),
            'comprehension_level': state.get('comprehension_level', 0),
            'grammar_rules_learned': len(state.get('grammar', {})),
            'iterations': self.iteration,
            'fluency_estimate': self._estimate_fluency(state)
        }
    
    def _estimate_fluency(self, state):
        """
        Estimate fluency level
        
        Based on vocab size + comprehension + grammar
        """
        vocab_size = len(state.get('vocabulary', {}))
        comprehension = state.get('comprehension_level', 0)
        grammar_rules = len(state.get('grammar', {}))
        
        # Rough CEFR estimation
        if vocab_size < 500:
            return 'A1' # Beginner
        elif vocab_size < 1000:
            return 'A2' # Elementary
        elif vocab_size < 2000:
            return 'B1' # Intermediate
        elif vocab_size < 4000:
            return 'B2' # Upper Intermediate
        elif vocab_size < 8000:
            return 'C1' # Advanced
        else:
            return 'C2' # Mastery

# Evolution function builds grammar
def french_grammar_evolution(state, perspective):
    # Discover patterns from examples
    
    # Pattern 1: Article-noun agreement
    if "le chat" in examples and "la maison" in examples:
        state['grammar']['article_agreement'] = {
            'masculine': 'le',
            'feminine': 'la',
            'plural': 'les'
        }
    
    # Pattern 2: Verb conjugation
    if "je parle" in examples and "tu parles" in examples:
        state['grammar']['present_tense'] = {
            'je': '-e',
            'tu': '-es',
            'il/elle': '-e',
            'nous': '-ons',
            'vous': '-ez',
            'ils/elles': '-ent'
        }
    
    return state

# Prove comprehension without revealing answer

# Test: "Le chat noir mange la souris"
question = "What is the cat doing?"

# Student generates proof of understanding
proof = learner.prove_comprehension(question, context)

# Teacher verifies without seeing student's internal process
is_understood = learner.verify_comprehension(proof)
# → True (student understands "mange" = "eats")

# Use case: Adaptive testing that verifies understanding
# without multiple choice (which gives away answers)

# Same sentence, different registers

# Informal (tu)
informal = learner.generate_sentence(
    intent="ask_how_are_you",
    perspective='informal'
)
# → "Comment vas-tu?" (How are you?)

# Formal (vous)
formal = learner.generate_sentence(
    intent="ask_how_are_you",
    perspective='formal'
)
# → "Comment allez-vous?" (How are you? - formal)

# Perspective changes the language!

# Distributed vocabulary learning

dht = MinimalDHT(node_id='learner_1', port=5000)
learner.dht = dht

# Contribute learned words
learner.dht.put('word:bonjour', {
    'meaning': 'hello',
    'usage': 'greeting',
    'frequency': 'very_common',
    'examples': ['Bonjour! Comment allez-vous?'],
    'audio_hash': 'abc123...'
})

# Retrieve from collective knowledge
word_data = learner.dht.get('word:merci')
# → {'meaning': 'thank you', 'usage': 'gratitude', ...}

# Network effect: Everyone's learning helps everyone

# Distributed pronunciation database

bittorrent = MinimalBitTorrent(node_id='learner_1')
learner.bittorrent = bittorrent

# Store pronunciation audio
with open('bonjour.mp3', 'rb') as f:
    audio_data = f.read()
    manifest = bittorrent.store(audio_data)

# Share manifest in DHT
dht.put('audio:bonjour', manifest)

# Other learners can retrieve
manifest = dht.get('audio:bonjour')
audio = bittorrent.retrieve(manifest)

# Save and play
with open('downloaded_bonjour.mp3', 'wb') as f:
    f.write(audio)

# No central audio server needed!

# Initialize learner
learner = FrenchLearner()

# Day 1-7: Learn phonemes
phoneme_text = """
A comme dans "chat" - [ʃa]
B comme dans "bébé" - [bebe]
C comme dans "café" - [kafe]
...
"""

for day in range(7):
    learner.learn_from_text(phoneme_text)
    print(f"Day {day+1}: {learner.get_comprehension_level():.1%}")

# Output:
# Day 1: 1.0%
# Day 2: 2.1%
# Day 3: 3.2%
# ...
# Day 7: 7.0%

# Month 1: Common words through exposure

beginner_texts = [
    "Le chat est noir.",
    "La maison est grande.",
    "Je parle français.",
    "Tu aimes le café?",
    "Il mange le pain.",
    # ... 100+ simple sentences
]

for text in beginner_texts:
    learner.learn_from_text(text)
    
vocab_size = learner.get_vocabulary_size()
print(f"Vocabulary: {vocab_size} words")
# → Vocabulary: 247 words

fluency = learner.export_progress()['fluency_estimate']
print(f"Level: {fluency}")
# → Level: A1

# Practice conversation with context

contexts = [
    {
        'situation': 'restaurant',
        'formality': 'formal',
        'intent': 'order_food'
    },
    {
        'situation': 'friend',
        'formality': 'informal',
        'intent': 'make_plans'
    }
]

for context in contexts:
    learner.practice_conversation(context)
    
    sentence = learner.generate_sentence(
        context['intent'],
        context['formality']
    )
    
    print(f"{context['situation']}: {sentence}")

# Output:
# restaurant: "Je voudrais un café, s'il vous plaît."
# friend: "Tu veux aller au cinéma?"

# Combine all entropy sources

# Reading
with open('le_petit_prince.txt', 'r') as f:
    text = f.read()
    learner.learn_from_text(text)

# Listening
audio_files = ['podcast_1.mp3', 'podcast_2.mp3', ...]
for audio in audio_files:
    learner.learn_from_audio(audio)

# Conversation
for _ in range(30):  # 30 conversations
    context = {'formality': random.choice(['formal', 'informal'])}
    learner.practice_conversation(context)

# Check progress
progress = learner.export_progress()
print(f"Fluency: {progress['fluency_estimate']}")
print(f"Vocabulary: {progress['vocabulary_size']} words")
print(f"Comprehension: {progress['comprehension_level']:.0%}")

# Output after 6 months:
# Fluency: B1
# Vocabulary: 2,341 words
# Comprehension: 68%

class SpanishLearner(MinimalUniverse):
    seed = spanish_alphabet  # ñ, accents
    F = spanish_grammar  # SVO order, gender
    E_p = [reading, listening, conversation]

class MandarinLearner(MinimalUniverse):
    seed = pinyin + tones  # 4 tones + neutral
    F = character_composition  # Radicals → characters
    E_p = [reading, listening, tone_practice]

class ArabicLearner(MinimalUniverse):
    seed = arabic_alphabet  # 28 letters, right-to-left
    F = root_system  # Trilateral roots
    E_p = [reading, listening, calligraphy]