"""
PyTorch / MPS / CPU Inference Engine for Parallel Constrained Decoding.
Optimized for Apple Silicon through Metal Performance Shaders and for CPU fallback.
"""

import os
import time
import json
import copy
import re
import threading
from typing import Dict, Any, Generator, Optional, List, Tuple

import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from transformers.cache_utils import DynamicCache

from core.schema import StructuredSchema
from core.prompt_builder import build_naive_json_prompt

MODEL_ID = os.environ.get("MODEL_ID", "Qwen/Qwen2.5-1.5B-Instruct")

_torch_model = None
_torch_tokenizer = None
_torch_device = None
_gpu_lock = threading.Lock()

# Support Hugging Face Spaces ZeroGPU if available
try:
    import spaces
    gpu_decorator = spaces.GPU(duration=60)
except Exception:
    def gpu_decorator(fn=None, **kwargs):
        if fn is not None:
            return fn
        return lambda f: f


def get_torch_engine():
    global _torch_model, _torch_tokenizer, _torch_device
    if _torch_model is None or _torch_tokenizer is None:
        _torch_device = ("mps" if torch.backends.mps.is_available() else "cpu")
        
        if _torch_device == "mps":
            dtype = torch.float16
        else:
            dtype = torch.float32

        print(f"Loading {MODEL_ID} on {_torch_device} ({dtype})...")
        t0 = time.perf_counter()
        _torch_tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
        
        load_kwargs = {
            "torch_dtype": dtype,
            "low_cpu_mem_usage": True
        }
        _torch_model = AutoModelForCausalLM.from_pretrained(MODEL_ID, **load_kwargs)
        _torch_model = _torch_model.to(_torch_device)
        _torch_model.eval()
        print(f"Engine loaded on {_torch_device} in {time.perf_counter() - t0:.2f}s.")
        
    return _torch_model, _torch_tokenizer, _torch_device


@gpu_decorator
def run_parallel_generation_torch(
    context: str,
    schema: StructuredSchema,
    temperature: float = 1.0
) -> Dict[str, Any]:
    """
    Parallel Constrained Decision Engine running on PyTorch (MPS / CPU).
    Evaluates all schema fields concurrently against a broadcast prefix KV-cache.
    """
    model, tokenizer, device = get_torch_engine()
    t0 = time.perf_counter()

    # 1. Compile schema metadata
    meta = schema.compile_parallel_metadata(tokenizer)
    field_items = meta["field_items"]
    suffix_lengths = meta["suffix_lengths"]
    cands_per_field = meta["cands_per_field"]
    prefixes = meta["prefixes"]
    has_collisions = meta["has_collisions"]
    suffixes_batch = meta["suffixes_batch"]
    M = len(field_items)

    # 2. High-density semantic catalog prefill
    schema_str = schema.to_parallel_schema_str()
    base_prompt = (
        f"<|im_start|>system\n"
        f"Classify JSON attributes:\n{schema_str}<|im_end|>\n"
        f"<|im_start|>user\n"
        f"{context}<|im_end|>\n"
        f"<|im_start|>assistant\n{{\n"
    )
    base_toks = tokenizer.encode(base_prompt, return_tensors="pt").to(device)

    t_pre0 = time.perf_counter()
    with torch.no_grad():
        base_out = model(base_toks, use_cache=True)
        base_cache = base_out.past_key_values
    t_prefill = (time.perf_counter() - t_pre0) * 1000

    # 3. Parallel Suffix Evaluation
    t_suf0 = time.perf_counter()
    pad_id = tokenizer.pad_token_id or tokenizer.eos_token_id or 0

    suffix_arr = torch.tensor(suffixes_batch, dtype=torch.long, device=device)
    suffix_mask = (suffix_arr != pad_id).long()

    # Broadcast KV cache to batch size M
    with torch.no_grad():
        batched_cache = copy.deepcopy(base_cache)
        if hasattr(batched_cache, "batch_repeat_interleave"):
            batched_cache.batch_repeat_interleave(M)
        elif isinstance(batched_cache, tuple):
            batched_cache = tuple(
                tuple(t.repeat(M, 1, 1, 1) for t in layer)
                for layer in batched_cache
            )

        prefix_len = base_toks.shape[1]
        prefix_mask = torch.ones((M, prefix_len), dtype=torch.long, device=device)
        full_mask = torch.cat([prefix_mask, suffix_mask], dim=1)

        out = model(suffix_arr, past_key_values=batched_cache, attention_mask=full_mask)
        suffix_out = out.logits

    t_suffix_eval = (time.perf_counter() - t_suf0) * 1000

    # 4. Slicing, Disambiguation & Softmax
    parsed_json = {}
    field_telemetry = {}

    for i, (fname, fdef) in enumerate(field_items):
        decision_idx = suffix_lengths[i] - 1
        field_logits = suffix_out[i, decision_idx, :]
        cand_tokens = cands_per_field[i]

        scores = [float(field_logits[tid].item()) for tid in cand_tokens]
        scores_t = torch.tensor(scores, dtype=torch.float32) / max(temperature, 1e-4)
        probs = F.softmax(scores_t, dim=-1).tolist()
        w_idx = int(torch.argmax(scores_t).item())
        w_prob = float(probs[w_idx])
        all_probs = probs

        if fdef.field_type == "boolean":
            val = (w_idx == 0)
        else:
            val = fdef.choices[w_idx]

        parsed_json[fname] = {
            "value": val,
            "prob": round(w_prob, 4)
        }

        choices_list = ["true", "false"] if fdef.field_type == "boolean" else fdef.choices
        scored_choices = []
        for c, p in zip(choices_list, all_probs):
            scored_choices.append({"choice": c, "probability": round(p, 4)})
        scored_choices.sort(key=lambda x: x["probability"], reverse=True)

        field_telemetry[fname] = {
            "value": val,
            "type": fdef.field_type,
            "confidence": round(w_prob, 4),
            "cardinality": fdef.cardinality,
            "top_choices": scored_choices[:5]
        }

    total_elapsed_ms = (time.perf_counter() - t0) * 1000

    return {
        "mode": "parallel_constrained_calibrated",
        "elapsed_ms": round(total_elapsed_ms, 2),
        "prefill_ms": round(t_prefill, 2),
        "suffix_eval_ms": round(t_suffix_eval, 2),
        "total_tokens_generated": 0,
        "sequential_forward_passes": 1,
        "is_valid_json": True,
        "schema_match": True,
        "parsed_json": parsed_json,
        "field_telemetry": field_telemetry,
        "has_calibrated_probabilities": True,
        "num_fields": len(schema),
        "device": device
    }


@gpu_decorator
def run_naive_generation_torch(
    context: str,
    schema: StructuredSchema,
    temperature: float = 0.2,
    max_new_tokens: int = 512
) -> Dict[str, Any]:
    """
    Standard autoregressive baseline using PyTorch.
    """
    model, tokenizer, device = get_torch_engine()
    t0 = time.perf_counter()

    prompt = build_naive_json_prompt(context, schema)
    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
    prompt_tokens = input_ids.shape[1]

    with torch.no_grad():
        output_ids = model.generate(
            input_ids,
            max_new_tokens=max_new_tokens,
            do_sample=(temperature > 0.0),
            temperature=max(temperature, 1e-4),
            pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id
        )

    elapsed_ms = (time.perf_counter() - t0) * 1000
    gen_tokens = output_ids.shape[1] - prompt_tokens
    tok_per_sec = (gen_tokens / (elapsed_ms / 1000.0)) if elapsed_ms > 0 else 0.0

    raw_text = tokenizer.decode(output_ids[0][prompt_tokens:], skip_special_tokens=True)

    # Parse JSON
    parsed_json = None
    is_valid = False
    try:
        first_brace = raw_text.find("{")
        last_brace = raw_text.rfind("}")
        if first_brace != -1 and last_brace != -1:
            cleaned = raw_text[first_brace:last_brace + 1]
            parsed_json = json.loads(cleaned)
            is_valid = True
    except Exception:
        pass

    schema_match = False
    if is_valid and isinstance(parsed_json, dict):
        expected_keys = set(schema.get_field_names())
        schema_match = (set(parsed_json.keys()) == expected_keys)

    return {
        "mode": "autoregressive_naive",
        "elapsed_ms": round(elapsed_ms, 2),
        "total_tokens": gen_tokens,
        "tokens_per_second": round(tok_per_sec, 1),
        "sequential_forward_passes": gen_tokens,
        "is_valid_json": is_valid,
        "schema_match": schema_match,
        "raw_text": raw_text,
        "parsed_json": parsed_json,
        "device": device
    }


def stream_naive_generation_torch(
    context: str,
    schema: StructuredSchema,
    temperature: float = 0.2,
    max_new_tokens: int = 512
) -> Generator[Dict[str, Any], None, None]:
    """
    Generator streaming individual tokens for side-by-side comparison visualizer.
    """
    model, tokenizer, device = get_torch_engine()
    t0 = time.perf_counter()

    prompt = build_naive_json_prompt(context, schema)
    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
    prompt_tokens = input_ids.shape[1]

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    
    gen_kwargs = {
        "input_ids": input_ids,
        "max_new_tokens": max_new_tokens,
        "do_sample": (temperature > 0.0),
        "temperature": max(temperature, 1e-4),
        "pad_token_id": tokenizer.pad_token_id or tokenizer.eos_token_id,
        "streamer": streamer
    }

    thread = threading.Thread(target=model.generate, kwargs=gen_kwargs)
    thread.start()

    full_text = ""
    tok_count = 0

    for token_str in streamer:
        tok_count += 1
        full_text += token_str
        yield {
            "type": "token",
            "token": token_str,
            "token_count": tok_count
        }

    thread.join()
    elapsed_ms = (time.perf_counter() - t0) * 1000

    parsed_json = None
    is_valid = False
    try:
        first_brace = full_text.find("{")
        last_brace = full_text.rfind("}")
        if first_brace != -1 and last_brace != -1:
            cleaned = full_text[first_brace:last_brace + 1]
            parsed_json = json.loads(cleaned)
            is_valid = True
    except Exception:
        pass

    schema_match = False
    if is_valid and isinstance(parsed_json, dict):
        expected_keys = set(schema.get_field_names())
        schema_match = (set(parsed_json.keys()) == expected_keys)

    result = {
        "mode": "autoregressive_naive",
        "elapsed_ms": round(elapsed_ms, 2),
        "total_tokens": tok_count,
        "tokens_per_second": round((tok_count / (elapsed_ms / 1000.0)) if elapsed_ms > 0 else 0.0, 1),
        "sequential_forward_passes": tok_count,
        "is_valid_json": is_valid,
        "schema_match": schema_match,
        "raw_text": full_text,
        "parsed_json": parsed_json,
        "device": device
    }

    yield {
        "type": "done",
        "result": result
    }