"""
Flint-1.2B Data Pipeline — Thought-Action Pretraining (TAP)
============================================================

Streams real data from HuggingFace Hub, applies TAP formatting,
tokenizes, packs into fixed-length sequences, and batches.

CRITICAL: This is what actually feeds the model. If this breaks,
the model trains on garbage (or nothing).
"""

import os
import json
import random as pyrandom
import traceback
from typing import Iterator, List, Dict, Any, Optional, Tuple
from dataclasses import dataclass

import numpy as np
from datasets import load_dataset, IterableDataset
from transformers import AutoTokenizer, PreTrainedTokenizer


# ============================================================
# SPECIAL TOKENS
# ============================================================

TAP_SPECIAL_TOKENS = [
    "<think>", "</think>",
    "<tool_call>", "</tool_call>",
    "<tool_response>", "</tool_response>",
    "<observe>", "</observe>",
    "<act>", "</act>",
    "<|pad|>",
]


def create_tokenizer(base_tokenizer: str = "HuggingFaceTB/cosmo2-tokenizer") -> PreTrainedTokenizer:
    """Load tokenizer and add TAP special tokens."""
    tokenizer = AutoTokenizer.from_pretrained(base_tokenizer)
    special_tokens_dict = {
        "additional_special_tokens": TAP_SPECIAL_TOKENS[:-1],
        "pad_token": "<|pad|>",
    }
    num_added = tokenizer.add_special_tokens(special_tokens_dict)
    print(f"[Tokenizer] Base vocab: 49152 + {num_added} special = {len(tokenizer)} total")
    print(f"[Tokenizer] Pad token: '{tokenizer.pad_token}' (id={tokenizer.pad_token_id})")
    return tokenizer


# ============================================================
# FORMATTERS
# ============================================================

def format_raw_text(sample: Dict[str, Any]) -> str:
    """Raw text (FineWeb, DCLM, FineMath, code, etc.)."""
    text = sample.get("text", "")
    if isinstance(text, str):
        return text
    return ""


def format_reasoning_trace(sample: Dict[str, Any]) -> str:
    """OpenThoughts-114k: wrap assistant response in <think> tags."""
    conversations = sample.get("conversations", [])
    if not conversations or not isinstance(conversations, list):
        return ""

    parts = []
    for msg in conversations:
        if not isinstance(msg, dict):
            continue
        role = msg.get("from", "")
        content = msg.get("value", "")
        if not content:
            continue

        if role == "user":
            parts.append(content.strip())
            parts.append("")
        elif role == "assistant":
            if "<think>" in content:
                parts.append(content.strip())
            else:
                paragraphs = content.strip().split("\n\n")
                if len(paragraphs) > 1:
                    reasoning = "\n\n".join(paragraphs[:-1])
                    answer = paragraphs[-1]
                    parts.append(f"<think>\n{reasoning}\n</think>\n\n{answer}")
                else:
                    parts.append(f"<think>\n{content.strip()}\n</think>")

    return "\n".join(parts)


def format_tool_call(sample: Dict[str, Any]) -> str:
    """SmolTalk apigen-80k: messages with tool calls."""
    messages = sample.get("messages", [])
    if not messages or not isinstance(messages, list):
        return ""

    parts = []
    for msg in messages:
        if not isinstance(msg, dict):
            continue
        role = msg.get("role", "")
        content = msg.get("content", "")
        if not content:
            continue

        if role == "system":
            parts.append(f"System: {content.strip()}\n")
        elif role == "user":
            parts.append(f"User: {content.strip()}\n")
        elif role == "assistant":
            if "<tool_call>" in content or '"name"' in content:
                parts.append("<think>\nI need to call a tool to answer this.\n</think>\n")
            parts.append(f"{content.strip()}\n")

    return "\n".join(parts)


def format_agent_instruct(sample: Dict[str, Any]) -> str:
    """Orca-AgentInstruct: messages field is a JSON string."""
    messages_raw = sample.get("messages", "")

    # Parse JSON string
    if isinstance(messages_raw, str):
        try:
            messages = json.loads(messages_raw)
        except (json.JSONDecodeError, TypeError):
            # If it's not JSON, just return as raw text
            return messages_raw if len(messages_raw) > 20 else ""
    elif isinstance(messages_raw, list):
        messages = messages_raw
    else:
        return ""

    if not messages:
        return ""

    parts = []
    for msg in messages:
        if not isinstance(msg, dict):
            continue
        role = msg.get("role", "")
        content = msg.get("content", "")
        if not content or not isinstance(content, str):
            continue

        if role == "system" and content.strip():
            parts.append(f"System: {content.strip()}\n")
        elif role == "user":
            parts.append(f"User: {content.strip()}\n")
        elif role == "assistant":
            if len(content) > 500:
                sentences = content.split(". ")
                if len(sentences) > 4:
                    cut = max(len(sentences) // 4, 2)
                    thinking = ". ".join(sentences[:cut]) + "."
                    response = ". ".join(sentences[cut:])
                    parts.append(f"<think>\n{thinking}\n</think>\n\n{response}\n")
                else:
                    parts.append(f"{content.strip()}\n")
            else:
                parts.append(f"{content.strip()}\n")

    return "\n".join(parts)


FORMATTERS = {
    "raw": format_raw_text,
    "reasoning": format_reasoning_trace,
    "tool_call": format_tool_call,
    "agent": format_agent_instruct,
}


# ============================================================
# SEQUENCE PACKING
# ============================================================

class SequencePacker:
    """Pack documents into fixed-length sequences. Zero padding waste."""

    def __init__(self, max_length: int, pad_token_id: int, eos_token_id: int):
        self.max_length = max_length
        self.pad_token_id = pad_token_id
        self.eos_token_id = eos_token_id
        self.buffer = []

    def add_document(self, token_ids: List[int]) -> Optional[np.ndarray]:
        """Add tokens. Returns packed sequence when buffer is full."""
        if not token_ids:
            return None

        # Truncate long documents
        if len(token_ids) > self.max_length - 1:
            token_ids = token_ids[:self.max_length - 1]

        needed = len(token_ids) + 1  # +1 for EOS separator

        if len(self.buffer) + needed > self.max_length:
            result = self._emit()
            self.buffer = token_ids + [self.eos_token_id]
            return result
        else:
            self.buffer.extend(token_ids)
            self.buffer.append(self.eos_token_id)
            if len(self.buffer) >= self.max_length:
                return self._emit()
            return None

    def _emit(self) -> np.ndarray:
        seq = self.buffer[:self.max_length]
        if len(seq) < self.max_length:
            seq = seq + [self.pad_token_id] * (self.max_length - len(seq))
        self.buffer = []
        return np.array(seq, dtype=np.int32)

    def flush(self) -> Optional[np.ndarray]:
        if self.buffer:
            return self._emit()
        return None


# ============================================================
# DATA SOURCES
# ============================================================

@dataclass
class DataSource:
    dataset_id: str
    config_name: Optional[str]
    weight: float
    text_column: str
    formatter: str
    split: str = "train"


def get_stage_sources(stage: int) -> List[DataSource]:
    """Data sources per curriculum stage. Ordered by reliability."""
    if stage == 1:
        return [
            # High-reliability sources first
            DataSource("HuggingFaceFW/fineweb-edu", "sample-10BT", 0.50, "text", "raw"),
            DataSource("HuggingFaceTB/smollm-corpus", "cosmopedia-v2", 0.15, "text", "raw"),
            DataSource("open-thoughts/OpenThoughts-114k", None, 0.15, "conversations", "reasoning"),
            DataSource("HuggingFaceTB/smoltalk", "apigen-80k", 0.08, "messages", "tool_call"),
            DataSource("HuggingFaceTB/smollm-corpus", "python-edu", 0.05, "text", "raw"),
            # Lower priority (may be slow to stream)
            DataSource("mlfoundations/dclm-baseline-1.0-parquet", None, 0.07, "text", "raw"),
        ]
    elif stage == 2:
        return [
            DataSource("HuggingFaceFW/fineweb-edu", "sample-10BT", 0.25, "text", "raw"),
            DataSource("open-thoughts/OpenThoughts-114k", None, 0.20, "conversations", "reasoning"),
            DataSource("HuggingFaceTB/finemath", "finemath-3plus", 0.15, "text", "raw"),
            DataSource("HuggingFaceTB/smoltalk", "apigen-80k", 0.10, "messages", "tool_call"),
            DataSource("HuggingFaceTB/smollm-corpus", "cosmopedia-v2", 0.10, "text", "raw"),
            DataSource("open-web-math/open-web-math", None, 0.08, "text", "raw"),
            DataSource("HuggingFaceTB/smollm-corpus", "python-edu", 0.07, "text", "raw"),
            DataSource("microsoft/orca-agentinstruct-1M-v1", "creative_content", 0.05, "messages", "agent"),
        ]
    else:  # stage 3 (annealing)
        return [
            DataSource("HuggingFaceTB/finemath", "finemath-4plus", 0.25, "text", "raw"),
            DataSource("open-thoughts/OpenThoughts-114k", None, 0.25, "conversations", "reasoning"),
            DataSource("HuggingFaceFW/fineweb-edu", "sample-10BT", 0.20, "text", "raw"),
            DataSource("HuggingFaceTB/smoltalk", "apigen-80k", 0.12, "messages", "tool_call"),
            DataSource("HuggingFaceTB/smollm-corpus", "python-edu", 0.08, "text", "raw"),
            DataSource("microsoft/orca-agentinstruct-1M-v1", "analytical_reasoning", 0.10, "messages", "agent"),
        ]


# ============================================================
# PIPELINE
# ============================================================

def get_current_stage(step: int, config) -> int:
    """Determine curriculum stage from step number."""
    stage1_end = int(config.total_steps * config.stage1_end_frac)
    stage2_end = int(config.total_steps * config.stage2_end_frac)
    if step < stage1_end:
        return 1
    elif step < stage2_end:
        return 2
    return 3


class TAPDataPipeline:
    """
    Streams real data from HF Hub, formats with TAP tags, packs sequences.

    Robust to individual dataset failures — skips broken sources and
    redistributes weight to working ones.
    """

    def __init__(self, config, tokenizer, start_step=0, start_position=0):
        self.config = config
        self.tokenizer = tokenizer
        self.current_stage = get_current_stage(start_step, config)
        self.position = start_position
        self.step = start_step
        self.samples_processed = 0
        self.samples_failed = 0

        self.packer = SequencePacker(
            max_length=config.max_seq_len,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id if tokenizer.eos_token_id else 0,
        )

        self._load_stage_datasets()

    def _load_stage_datasets(self):
        """Load datasets for current stage. Skip any that fail."""
        sources = get_stage_sources(self.current_stage)
        self.datasets = []
        self.weights = []

        print(f"[Data] Loading stage {self.current_stage} datasets...")
        for source in sources:
            try:
                kwargs = {"path": source.dataset_id, "split": source.split, "streaming": True}
                if source.config_name:
                    kwargs["name"] = source.config_name
                ds = load_dataset(**kwargs)
                self.datasets.append((ds, source))
                self.weights.append(source.weight)
                print(f"  ✓ {source.dataset_id}/{source.config_name or ''} ({source.weight:.0%}) [{source.formatter}]")
            except Exception as e:
                print(f"  ✗ {source.dataset_id}/{source.config_name or ''}: {e}")

        if not self.datasets:
            raise RuntimeError("[Data] FATAL: No datasets loaded! Check network/auth.")

        # Normalize weights
        total_w = sum(self.weights)
        self.weights = [w / total_w for w in self.weights]

        # Create iterators
        self.iterators = [iter(ds) for ds, _ in self.datasets]
        print(f"[Data] ✓ {len(self.datasets)} sources ready for stage {self.current_stage}\n")

    def _get_sample(self) -> Tuple[Optional[Dict], Optional[DataSource]]:
        """Get one sample from weighted random source. Handles StopIteration."""
        if not self.datasets:
            return None, None

        idx = pyrandom.choices(range(len(self.datasets)), weights=self.weights, k=1)[0]
        _, source = self.datasets[idx]

        try:
            sample = next(self.iterators[idx])
            return sample, source
        except StopIteration:
            # Restart iterator (epoch boundary)
            ds, _ = self.datasets[idx]
            self.iterators[idx] = iter(ds)
            try:
                sample = next(self.iterators[idx])
                return sample, source
            except StopIteration:
                return None, None
        except Exception as e:
            # Network glitch, rate limit, etc. — skip this sample
            self.samples_failed += 1
            if self.samples_failed % 100 == 0:
                print(f"[Data] Warning: {self.samples_failed} samples failed ({e})")
            return None, None

    def _tokenize(self, sample: Dict, source: DataSource) -> List[int]:
        """Format + tokenize. Returns empty list on failure."""
        try:
            formatter = FORMATTERS[source.formatter]
            text = formatter(sample)
            if not text or len(text.strip()) < 20:
                return []
            tokens = self.tokenizer.encode(text, add_special_tokens=False)
            if len(tokens) < 5:
                return []
            self.samples_processed += 1
            return tokens
        except Exception:
            self.samples_failed += 1
            return []

    def get_batch(self, batch_size: int, step: int) -> np.ndarray:
        """Get one batch of packed sequences."""
        # Check stage switch
        new_stage = get_current_stage(step, self.config)
        if new_stage != self.current_stage:
            print(f"\n[Data] ═══ Stage switch: {self.current_stage} → {new_stage} ═══")
            self.current_stage = new_stage
            self._load_stage_datasets()

        sequences = []
        max_attempts = batch_size * 200

        for _ in range(max_attempts):
            if len(sequences) >= batch_size:
                break

            sample, source = self._get_sample()
            if sample is None:
                continue

            tokens = self._tokenize(sample, source)
            if not tokens:
                continue

            result = self.packer.add_document(tokens)
            if result is not None:
                sequences.append(result)

        # Flush if needed
        while len(sequences) < batch_size:
            result = self.packer.flush()
            if result is not None:
                sequences.append(result)
            else:
                # Absolute last resort — should never happen with working data
                print("[Data] ⚠️  Had to insert padding sequence!")
                sequences.append(
                    np.full(self.config.max_seq_len, self.tokenizer.pad_token_id, dtype=np.int32)
                )

        return np.stack(sequences[:batch_size])

    def __iter__(self):
        step = self.step
        while True:
            yield self.get_batch(self.config.global_batch_size, step)
            step += 1


def create_data_pipeline(config, tokenizer, start_step=0, start_position=0) -> Iterator:
    """Create the streaming data pipeline. Returns infinite batch iterator."""
    pipeline = TAPDataPipeline(config, tokenizer, start_step, start_position)
    return iter(pipeline)