from collections import Counter
import spacy

def build_domain_vocabulary(texts, min_freq=5):
    nlp = spacy.load('en_core_web_sm')
    word_counts = Counter()
    
    for text in texts:
        doc = nlp(text)
        words = [token.text.lower() for token in doc 
                if not token.is_stop and not token.is_punct]
        word_counts.update(words)
    
    return {word: count for word, count in word_counts.items() 
            if count >= min_freq}

from spacy.tokens import Doc, Span
from spacy.language import Language

@Language.component("domain_entities")
def domain_entity_recognizer(doc):
    # Custom entity patterns
    patterns = {
        "MEDICAL_CONDITION": ["diabetes", "hypertension", "asthma"],
        "MEDICATION": ["aspirin", "ibuprofen", "paracetamol"],
        "DOSAGE": [r"\d+mg", r"\d+ml"]
    }
    
    entities = []
    for label, terms in patterns.items():
        for term in terms:
            for match in re.finditer(term, doc.text.lower()):
                start, end = match.span()
                span = doc.char_span(start, end, label=label)
                if span:
                    entities.append(span)
    
    doc.ents = entities
    return doc

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from transformers import TrainingArguments, Trainer

def fine_tune_domain_model(train_texts, train_labels, model_name="bert-base-uncased"):
    # Load pre-trained model and tokenizer
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForSequenceClassification.from_pretrained(
        model_name, 
        num_labels=len(set(train_labels))
    )
    
    # Prepare training arguments
    training_args = TrainingArguments(
        output_dir="./domain_model",
        num_train_epochs=3,
        per_device_train_batch_size=16,
        learning_rate=2e-5
    )
    
    # Create trainer and train
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=create_dataset(train_texts, train_labels)
    )
    
    trainer.train()
    return model, tokenizer

from gensim.models import Word2Vec

def train_domain_embeddings(texts, vector_size=100):
    # Tokenize texts
    tokenized_texts = [text.split() for text in texts]
    
    # Train Word2Vec model
    model = Word2Vec(
        sentences=tokenized_texts,
        vector_size=vector_size,
        window=5,
        min_count=1,
        workers=4
    )
    
    return model

def classify_technical_document(text, model, tokenizer):
    # Preprocess technical text
    cleaned_text = clean_technical_text(text)
    
    # Classify
    inputs = tokenizer(cleaned_text, return_tensors="pt", truncation=True)
    outputs = model(**inputs)
    predicted_label = outputs.logits.argmax(-1).item()
    
    return predicted_label

class LegalDocumentAnalyzer:
    def __init__(self):
        self.nlp = spacy.load("en_core_web_sm")
        self.legal_terms = set(load_legal_vocabulary())
        
    def extract_legal_entities(self, text):
        doc = self.nlp(text)
        legal_entities = []
        
        for ent in doc.ents:
            if ent.label_ in ["ORG", "PERSON", "DATE"]:
                legal_entities.append({
                    "text": ent.text,
                    "label": ent.label_,
                    "start": ent.start_char,
                    "end": ent.end_char
                })
                
        return legal_entities

def clean_domain_text(text, domain_patterns):
    # Remove domain-specific artifacts
    for pattern in domain_patterns:
        text = re.sub(pattern, "", text)
    
    # Normalize domain-specific terms
    text = normalize_domain_terms(text)
    
    return text

class DomainTokenizer:
    def __init__(self, vocab_file, special_tokens):
        self.vocab = load_vocabulary(vocab_file)
        self.special_tokens = special_tokens
        
    def tokenize(self, text):
        # Handle special tokens
        for token in self.special_tokens:
            text = text.replace(token, f" {token} ")
            
        # Split on whitespace
        tokens = text.split()
        
        # Apply domain-specific rules
        tokens = self.apply_domain_rules(tokens)
        
        return tokens

def evaluate_domain_performance(predictions, ground_truth, domain_weights):
    metrics = {
        'accuracy': calculate_accuracy(predictions, ground_truth),
        'domain_precision': calculate_domain_precision(
            predictions, 
            ground_truth, 
            domain_weights
        ),
        'domain_recall': calculate_domain_recall(
            predictions, 
            ground_truth, 
            domain_weights
        )
    }
    return metrics

def validate_with_experts(model_outputs, expert_feedback):
    agreement_scores = []
    
    for output, feedback in zip(model_outputs, expert_feedback):
        agreement = calculate_agreement(output, feedback)
        agreement_scores.append(agreement)
        
    return {
        'mean_agreement': np.mean(agreement_scores),
        'std_agreement': np.std(agreement_scores)
    }