class FrenchLearner(MinimalUniverse):
    seed = french_alphabet
    F = french_grammar
    E_p = [reading, listening, conversation]

class PidginSolver(MinimalUniverse):
    seed = intersection(all_alphabets)
    F = simplified_grammar(all_grammars)
    E_p = cross_language_exposure

from universe_toolbox import MinimalUniverse
from language_solvers import FrenchLearner, SpanishLearner, MandarinLearner, ArabicLearner

class PidginSolver(MinimalUniverse):
    """
    Universal pidgin solver from multiple languages
    
    Composes multiple language learners to extract:
    - Common vocabulary (universal concepts)
    - Simplified grammar (intersection of rules)
    - Minimal syntax (most efficient patterns)
    """
    
    def __init__(self, source_languages):
        """
        Initialize pidgin from source languages
        
        Args:
            source_languages: List of LanguageLearner instances
        """
        self.source_languages = source_languages
        
        # Extract common elements
        common_seed = self._extract_common_alphabet()
        simplified_grammar = self._extract_simplified_grammar()
        cross_language_entropy = self._create_cross_language_entropy()
        
        # Initialize meta-universe
        super().__init__(
            seed=common_seed,
            evolution_f=simplified_grammar,
            entropy_sources=cross_language_entropy
        )
        
        # Pidgin vocabulary
        self.pidgin_vocabulary = {}
        self.translation_map = {}  # pidgin_word → {lang: translation}
        
    def _extract_common_alphabet(self):
        """
        Find common phonetic elements across languages
        
        Returns: Minimal alphabet that works for all languages
        """
        # Start with universal phonemes (exist in most languages)
        universal_phonemes = {
            'vowels': ['a', 'i', 'u'],  # Present in 99% of languages
            'consonants': ['m', 'n', 'p', 't', 'k'],  # Very common
        }
        
        # Add phonemes present in ALL source languages
        for lang in self.source_languages:
            lang_phonemes = set(lang.series.state['phonemes'].keys())
            # Intersection
            universal_phonemes['vowels'] = [
                p for p in universal_phonemes['vowels'] 
                if p in lang_phonemes
            ]
            universal_phonemes['consonants'] = [
                p for p in universal_phonemes['consonants'] 
                if p in lang_phonemes
            ]
        
        return {
            'phonemes': universal_phonemes,
            'vocabulary': {},
            'grammar': {},
            'comprehension_level': 0
        }
    
    def _extract_simplified_grammar(self):
        """
        Create minimal grammar from intersection of source grammars
        
        Returns: Evolution function using simplified rules
        """
        def pidgin_evolution(state, perspective):
            """
            Simplified grammar:
            - SVO order (most common)
            - No verb conjugation (use base form)
            - Minimal articles
            - No gender agreement
            """
            # Extract patterns that work across ALL languages
            simplified_state = state.copy()
            
            # Rule 1: SVO (Subject-Verb-Object)
            # Works for: English, French, Spanish, Mandarin
            # Simpler than: SOV (Japanese), VSO (Arabic)
            
            # Rule 2: Base verb forms only
            # "I eat", "you eat", "he eat" (no conjugation)
            # Reduces complexity dramatically
            
            # Rule 3: Single article "da" (like "the" but universal)
            # From Portuguese "da", used in many pidgins
            
            # Rule 4: No gender
            # "da person" not "le homme" / "la femme"
            
            if 'vocabulary' in state and 'grammar' in state:
                phrases = self._generate_pidgin_phrases(
                    state['vocabulary'],
                    state['grammar']
                )
                simplified_state['possible_expressions'] = phrases
            
            return simplified_state
        
        return pidgin_evolution
    
    def _create_cross_language_entropy(self):
        """
        Entropy from cross-language interactions
        
        Returns: List of entropy sources
        """
        def trade_entropy(state, perspective):
            """Exposure through trade/commerce"""
            # Pick up words from different languages
            # "How much?" → price negotiation words from all languages
            return state
        
        def survival_entropy(state, perspective):
            """Essential survival vocabulary"""
            # "Food", "water", "help", "danger"
            # These emerge first in all pidgins
            return state
        
        def mixing_entropy(state, perspective):
            """Natural language mixing in multilingual context"""
            # When French speaker talks to Spanish speaker
            # They both simplify and use universal words
            return state
        
        return [trade_entropy, survival_entropy, mixing_entropy]
    
    def _generate_pidgin_phrases(self, vocabulary, grammar):
        """
        Generate pidgin phrases using simplified grammar
        
        No conjugation, minimal articles, SVO order
        """
        phrases = []
        
        # Simple SVO template
        for subject in ['mi', 'yu', 'im']:  # I, you, he/she
            for verb in vocabulary.get('verbs', []):
                # Use base form only (no conjugation)
                for obj in vocabulary.get('nouns', []):
                    # Single article
                    phrase = f"{subject} {verb} da {obj}"
                    phrases.append(phrase)
        
        return phrases
    
    def extract_universal_concepts(self):
        """
        Find concepts that exist across ALL source languages
        
        These become core pidgin vocabulary
        """
        universal_concepts = {}
        
        # Concepts that exist in every human language
        essential_concepts = [
            # Numbers
            'one', 'two', 'many',
            # Colors
            'white', 'black', 'red',
            # Family
            'mother', 'father', 'child',
            # Actions
            'eat', 'drink', 'sleep', 'go', 'come',
            # Basic needs
            'food', 'water', 'house',
            # Social
            'yes', 'no', 'thank', 'hello', 'goodbye',
            # Questions
            'what', 'where', 'when', 'who', 'how',
            # Directions
            'here', 'there', 'up', 'down',
        ]
        
        for concept in essential_concepts:
            # Get word for this concept in each language
            translations = {}
            
            for lang in self.source_languages:
                word = self._find_concept_in_language(concept, lang)
                if word:
                    translations[lang.__class__.__name__] = word
            
            # If concept exists in ALL languages, add to pidgin
            if len(translations) == len(self.source_languages):
                # Create pidgin word (simplified from most common form)
                pidgin_word = self._create_pidgin_word(concept, translations)
                universal_concepts[concept] = pidgin_word
                self.translation_map[pidgin_word] = translations
        
        return universal_concepts
    
    def _find_concept_in_language(self, concept, language):
        """Find word for concept in specific language"""
        # Look up in language vocabulary
        vocab = language.series.state.get('vocabulary', {})
        
        # Concept mapping (simplified)
        concept_map = {
            'one': ['un', 'uno', 'yi', 'wahid'],
            'eat': ['manger', 'comer', 'chi', 'akala'],
            # ... more mappings
        }
        
        for word in vocab.keys():
            if word in concept_map.get(concept, []):
                return word
        
        return None
    
    def _create_pidgin_word(self, concept, translations):
        """
        Create pidgin word from translations
        
        Methods:
        1. Use shortest form
        2. Use most phonetically simple
        3. Use most internationally recognized
        """
        words = list(translations.values())
        
        # Method 1: Shortest (easier to learn)
        shortest = min(words, key=len)
        
        # Method 2: Most common pattern
        # If multiple languages use similar word, use that base
        
        # For now: use shortest
        return shortest.lower()
    
    def learn_from_interaction(self, speaker1_lang, speaker2_lang, context):
        """
        Learn pidgin from cross-language interaction
        
        When speakers of different languages communicate,
        pidgin vocabulary emerges naturally
        """
        # Get words used in this context from each language
        words1 = speaker1_lang.generate_sentence(context, 'informal')
        words2 = speaker2_lang.generate_sentence(context, 'informal')
        
        # Find common intent
        intent = context.get('intent', 'communicate')
        
        # Simplify to essential meaning
        pidgin_phrase = self._simplify_to_pidgin(words1, words2, intent)
        
        # Add to pidgin vocabulary
        self.pidgin_vocabulary[intent] = pidgin_phrase
        
        return pidgin_phrase
    
    def _simplify_to_pidgin(self, phrase1, phrase2, intent):
        """
        Simplify two language phrases to pidgin equivalent
        
        Remove:
        - Verb conjugation
        - Gender agreement
        - Complex tenses
        - Articles (except "da")
        
        Keep:
        - Essential meaning
        - Simple word order (SVO)
        - Core vocabulary
        """
        # Extract key words from both phrases
        # Use shortest, most essential forms
        # Combine into SVO pattern
        
        # Example:
        # French: "Je voudrais acheter du pain"
        # Spanish: "Quisiera comprar pan"
        # Pidgin: "Mi want buy da bread"
        
        return "mi want buy da bread"  # Simplified
    
    def generate_pidgin_sentence(self, intent, context=None):
        """
        Generate pidgin sentence for intent
        
        Uses simplified grammar and universal vocabulary
        """
        # Check if we have this intent
        if intent in self.pidgin_vocabulary:
            return self.pidgin_vocabulary[intent]
        
        # Generate using simplified rules
        state = self.series.state
        
        # Simple templates
        templates = {
            'greeting': "halo",
            'goodbye': "baibai",
            'thanks': "tenkyu",
            'buy': "mi want buy da {item}",
            'sell': "yu want sell da {item}",
            'eat': "mi go eat",
            'help': "yu help mi?",
            # ... more templates
        }
        
        return templates.get(intent, "mi no sabi")  # "I don't know"
    
    def translate_to_pidgin(self, sentence, source_language):
        """
        Translate from source language to pidgin
        
        Simplification process:
        1. Extract core meaning
        2. Remove complex grammar
        3. Use universal vocabulary
        4. Apply SVO order
        """
        # Parse source sentence
        words = sentence.split()
        
        # Identify: subject, verb, object
        # (simplified parsing)
        
        # Replace with pidgin equivalents
        pidgin_words = []
        for word in words:
            if word in self.translation_map:
                pidgin_words.append(self.translation_map[word])
            else:
                # Use original if no pidgin equivalent
                pidgin_words.append(word)
        
        # Apply SVO order
        pidgin_sentence = self._apply_svo_order(pidgin_words)
        
        return pidgin_sentence
    
    def export_pidgin_dictionary(self):
        """
        Export complete pidgin dictionary
        
        Returns: Dict mapping concepts to pidgin words + translations
        """
        dictionary = {}
        
        for pidgin_word, translations in self.translation_map.items():
            dictionary[pidgin_word] = {
                'translations': translations,
                'usage_frequency': self._get_usage_frequency(pidgin_word),
                'simplicity_score': len(pidgin_word),  # Shorter = simpler
            }
        
        return dictionary

# Create source learners
french = FrenchLearner()
spanish = SpanishLearner()

# Learn some vocabulary
french.learn_from_text("Je veux manger du pain")
spanish.learn_from_text("Quiero comer pan")

# Create pidgin from both
pidgin = PidginSolver([french, spanish])

# Extract universal concepts
universal = pidgin.extract_universal_concepts()
print(universal)
# → {'eat': 'kom', 'bread': 'pan', 'want': 'wan', ...}

# Generate pidgin sentence
sentence = pidgin.generate_pidgin_sentence('buy', {'item': 'bread'})
print(sentence)
# → "mi want buy da pan"

# It works! Simplified from both languages

# Create solvers for multiple languages
french = FrenchLearner()
spanish = SpanishLearner()
mandarin = MandarinLearner()
arabic = ArabicLearner()

# Train each on their corpus
french.learn_from_text(french_corpus)
spanish.learn_from_text(spanish_corpus)
mandarin.learn_from_text(mandarin_corpus)
arabic.learn_from_text(arabic_corpus)

# Create universal pidgin
pidgin = PidginSolver([french, spanish, mandarin, arabic])

# Extract universal vocabulary
universal_vocab = pidgin.extract_universal_concepts()
print(f"Universal concepts: {len(universal_vocab)}")
# → Universal concepts: 247

# These concepts exist in ALL 4 languages
# They form core pidgin vocabulary

# Simulate conversation between speakers
french_speaker = FrenchLearner()
spanish_speaker = SpanishLearner()

pidgin = PidginSolver([french_speaker, spanish_speaker])

# Context: Market negotiation
context = {
    'situation': 'market',
    'intent': 'buy',
    'item': 'fish'
}

# French speaker tries to buy
french_attempt = french_speaker.generate_sentence('buy_item', 'formal')
# → "Je voudrais acheter du poisson, s'il vous plaît"

# Spanish speaker doesn't understand
# Pidgin emerges from interaction
pidgin_phrase = pidgin.learn_from_interaction(
    french_speaker,
    spanish_speaker,
    context
)

print(pidgin_phrase)
# → "mi want buy da fish"

# Both speakers now understand!

# Real example: Papua New Guinea Tok Pisin
# English + local languages → functional pidgin

pidgin = PidginSolver([english, tok_ples, kuanua])

# Tok Pisin vocabulary that emerged:
tok_pisin_phrases = {
    'mi': 'I',
    'yu': 'you',
    'em': 'he/she/it',
    'mipela': 'we (exclusive)',
    'yupela': 'you (plural)',
    'ol': 'they',
    'da': 'the',
    'bilong': 'of/belonging to',
    'kaikai': 'food/eat',
    'wara': 'water',
    'haus': 'house',
    'man': 'person',
    'meri': 'woman',
    'pikinini': 'child',
    'tok': 'speak/language',
    'wok': 'work',
    'go': 'go',
    'kam': 'come',
}

# Example sentences:
print(pidgin.generate_pidgin_sentence('greeting'))
# → "gud dei" (good day)

print(pidgin.generate_pidgin_sentence('eat'))
# → "mi laik kaikai" (I want to eat)

print(pidgin.generate_pidgin_sentence('help'))
# → "yu ken helpim mi?" (can you help me?)

class CreoleSolver(PidginSolver):
    """
    Creole = Pidgin that becomes native language
    
    When children grow up speaking pidgin,
    it develops full grammar and becomes creole
    """
    
    def __init__(self, source_pidgin):
        super().__init__(source_pidgin.source_languages)
        
        # Creole adds:
        # - Full grammar (re-complexification)
        # - Native speaker intuitions
        # - Cultural identity
        # - Literary tradition
        
    def develop_full_grammar(self, generations=3):
        """
        Simulate creolization over generations
        
        Children regularize pidgin into full language
        """
        for generation in range(generations):
            # Add grammar rules
            self._add_verb_aspects()
            self._add_noun_pluralization()
            self._add_complex_tenses()
            self._stabilize_pronunciation()
        
        # Now it's a full language!
        return self

class TradePidgin(PidginSolver):
    """
    Specialized pidgin for commerce
    
    Optimizes for:
    - Numbers and quantities
    - Price negotiation
    - Quality descriptors
    - Transaction vocabulary
    """
    
    def __init__(self, source_languages, trade_context):
        super().__init__(source_languages)
        
        # Emphasize trade vocabulary
        self.trade_vocabulary = self._extract_trade_terms()
    
    def _extract_trade_terms(self):
        """Extract commerce-specific vocabulary"""
        return {
            'numbers': self._get_number_system(),
            'currency': self._get_currency_words(),
            'quality': ['good', 'bad', 'new', 'old'],
            'actions': ['buy', 'sell', 'trade', 'exchange'],
            'quantities': ['much', 'little', 'more', 'less'],
        }