import json
import math
import requests
import io
import contextlib
import re
import textwrap
from rich.console import Console
from rich.markdown import Markdown

console = Console()


# --- CONFIGURATION ---
LLM_API_URL = "http://localhost:8090/v1/chat/completions"
MAX_ITERATIONS = 3
EXPANSION_N = 2
UCT_CONSTANT = 1.41


class Node:
    _id_counter = 0

    def __init__(self, state, parent=None, action="", observation="", reflection=""):
        self.id = Node._id_counter
        Node._id_counter += 1
        self.state = state
        self.parent = parent
        self.action = action
        self.observation = observation
        self.reflection = reflection
        self.children = []
        self.visits = 0
        self.value = 0.0

    def uct_score(self):
        if self.visits == 0:
            return float('inf')
        return (self.value / self.visits) + UCT_CONSTANT * math.sqrt(math.log(self.parent.visits) / self.visits)

    def __repr__(self):
        return f"[Node {self.id} | Val: {self.value:.2f} | Visits: {self.visits}]"


def clean_code(text):
    """
    EXTRACTOR: Strips Markdown code blocks and normalizes indentation.
    """
    # Regex to find content inside ```python or ``` blocks
    pattern = r"```(?:python)?\n?(.*?)```"
    matches = re.findall(pattern, text, re.DOTALL)
    
    if matches:
        code = matches[0]
        # 1. dedent removes common leading whitespace from every line
        # 2. strip removes leading/trailing empty lines
        return textwrap.dedent(code).strip()
    
    # Fallback: if provided raw code without backticks, still clean indentation
    return textwrap.dedent(text).strip()


def call_llm(prompt, system_prompt="You are a helpful coding assistant.", temperature=0.1, json_schema=None):
    # console.print(f"\nSENT:\n{system_prompt}\n{prompt}\nTemperature: {temperature}\n")
    payload = {
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": prompt}
        ],
        "temperature": temperature,
        "max_tokens": 4096
    }
    if json_schema:
            payload["json_schema"] = json_schema
    try:
        response = requests.post(LLM_API_URL, json=payload, timeout=60)
        response_text = response.json()['choices'][0]['message']['content'].strip()
        # md = Markdown(response_text)
        # console.print("LLM Response:")
        # console.print(md)

        return response_text
    except Exception as e:
        return f"Error: {e}"


def execute_python(code, supplied_input=""):
    f = io.StringIO()
    
    def fake_input(prompt=""):
        # behave like input(): read one line from supplied_input
        if not fake_input._lines:
            raise EOFError("No input provided via supplied_input")
        return fake_input._lines.pop(0)
    fake_input._lines = supplied_input.splitlines()

    # CRITICAL CHANGE: We use one dictionary for the environment
    env = {"__builtins__": __builtins__, "input": fake_input}
    
    with contextlib.redirect_stdout(f):
        try:
            # CRITICAL CHANGE: Pass 'env' as BOTH globals and locals.
            # This mimics running a standard script at the module level.
            exec(code, env, env)
        except Exception as e:
            # We print the error so it is captured in f.getvalue() 
            # and returned to the agent as part of the observation.
            print(f"Runtime Error: {e}")
            
    return f.getvalue().strip()


# --- LATS OPERATIONS ---

def LATS_Search(task_description):
    # 1. Store the task string separately so we don't lose it in the tree
    root = Node(state="") # Root state is empty (no code yet)
    print(f"\n🚀 STARTING LATS SEARCH FOR: {task_description}")

    for i in range(MAX_ITERATIONS):
        print(f"\n{'=' * 60}\nITERATION {i + 1}\n{'=' * 60}")

        # --- 1. SELECTION ---
        print(f"🔍 [1/6 SELECTION] Choosing best path using UCT...")
        curr = root
        path = [str(curr.id)]
        while curr.children:
            curr = max(curr.children, key=lambda c: c.uct_score())
            path.append(str(curr.id))
        print(f"   Selected Node {curr.id} following path: {' -> '.join(path)}")

        # --- 2. EXPANSION ---
        print(f"🌱 [2/6 EXPANSION] Generating {EXPANSION_N} options...")
        
        # DYNAMIC PROMPTING
        if curr.id == root.id:
            # We are at the start: Generate first draft
            system_msg = "You are a Python coding assistant."
            prompt = (
                f"# Task: {task_description}\n\n"
                f"Write the complete Python code to solve this task."
            )
        else:
            # We are deep in the tree: Fix/Refine the code
            system_msg = "You are a debugging assistant. Rewrite the code to fix the error."
            prompt = (
                f"# Task: {task_description}\n\n"
                f"Current Code:\n```python\n{curr.state}\n```\n\n"
                f"Previous Output/Error:\n{curr.observation}\n\n"
                f"Insight/Feedback: {curr.reflection}\n\n"
                f"Action: Generate the FULL corrected Python code."
            )

        for n in range(EXPANSION_N):
            print(f"   Generating option {n + 1}/{EXPANSION_N}...")
            
            # Use 'clean_code' from previous turn to handle markdown/indentation
            raw_response = call_llm(prompt, system_prompt=system_msg, temperature=1.0)
            generated_code = clean_code(raw_response)
            
            # FIX: The state is the NEW code, not appended code.
            # (If generated_code is empty due to parse error, fall back to parent state to avoid crash)
            full_code = generated_code if generated_code else curr.state

            # --- 3. SIMULATION ---
            print(f"🧪 [3/6 SIMULATION] Executing code...")
            # Use fixed 'execute_python' from previous turn
            observation = execute_python(full_code) 
            
            # Truncate long observations for the console (keep full for LLM)
            display_obs = (observation[:100] + '...') if len(observation) > 100 else observation
            print(f"      REPL Output: {display_obs}")
            if not observation: observation = "(No output)"

            # --- 4. EVALUATION ---
            print(f"📊 [4/6 EVALUATION] Scoring...")
            
            json_schema = {
                "type": "object",
                "properties": {
                    "reward": {"type": "number", "minimum": 0.0, "maximum": 1.0},
                    "reasoning": {"type": "string"}
                },
                "required": ["reward", "reasoning"]
            }

            eval_prompt = (
                f"Task: {task_description}\n"
                f"Code:\n```python\n{full_code}\n```\n"
                f"Execution Result: {observation}\n\n"
                "Evaluate if the code solves the task and runs without errors."
            )
            
            res = call_llm(eval_prompt, system_prompt="You are a code judge.", temperature=0.1, json_schema=json_schema)
            
            try:
                data = json.loads(res)
                lm_score = data["reward"]
                reasoning = data["reasoning"]
            except:
                lm_score = 0.0
                reasoning = "JSON Parsing Failed"

            # Create Child
            child = Node(state=full_code, parent=curr, action=generated_code, observation=observation)
            child.value = lm_score
            child.visits = 1
            
            # --- 5. REFLECTION ---
            # Store the reasoning as reflection immediately (Efficiency boost)
            child.reflection = reasoning 
            curr.children.append(child)
            
            print(f"      Node {child.id} Score: {lm_score} | {reasoning[:60]}...")

            if lm_score >= 1.0:
                print(f"\n🎯 PERFECT SCORE FOUND IN ITERATION {i + 1}!")
                return child.state

        # --- 6. BACKPROPAGATION ---
        print(f"⬆️ [6/6 BACKPROPAGATION]...")
        # LATS Standard: Backpropagate the best immediate child's value
        best_child_val = max(c.value for c in curr.children) if curr.children else 0
        
        temp = curr
        while temp:
            temp.visits += 1
            # Update value (Running Average or Max, depending on preference. LATS often uses Max for coding)
            temp.value = max(temp.value, best_child_val)
            temp = temp.parent

    # Final Select
    all_nodes = []
    def collect(n):
        all_nodes.append(n)
        for c in n.children: collect(c)
    collect(root)
    
    # Filter out empty root
    candidates = [n for n in all_nodes if n.state]
    if not candidates: return "No code generated."
    
    best = max(candidates, key=lambda n: n.value)
    return best.state


# --- TEST EXECUTION ---
if __name__ == "__main__":
    # Using Task 2 from the previous list: Date extraction/sorting
    task = "Given strings S and T, find the shortest substring of S which has T as a subsequence. Return the substring or empty string if none. Use DP or two-pointer scan with backward trace. Input: ('abcdebdde', 'bde')."

    final_code = LATS_Search(task)
    print("\n" + "=" * 60)
    print("FINAL RESULTING CODE:")
    print("=" * 60)
    print(final_code)