import bz2
from typing import Iterator, Dict, Any
import pandas as pd
import os
import hashlib
import json
from sklearn.model_selection import StratifiedKFold
import numpy as np
from multiprocessing import Pool, cpu_count
from functools import partial
import subprocess


def get_cache_path(file_path: str, required_fields: list[str]) -> str:
    """
    Generate a unique cache file path based on input file and fields.

    Args:
        file_path: Path to the input JSONL file
        required_fields: List of field names to extract

    Returns:
        Path to the cache file
    """
    # Create a unique hash based on the file path and fields
    fields_str = ",".join(sorted(required_fields))
    hash_input = f"{fields_str}"
    hash_str = hashlib.md5(hash_input.encode()).hexdigest()[:10]

    # Get the directory of the input file
    base_dir = os.path.dirname(file_path)
    # Get filename from file path
    file_name = os.path.basename(file_path).split(".")[0]
    cache_name = f"{file_name}_cache_{hash_str}.parquet"
    return os.path.join(base_dir, cache_name)


def read_jsonl_fields_fast(
    file_path: str, required_fields: list[str], use_cache: bool = True
) -> pd.DataFrame:
    """
    Quickly extract specific fields from a compressed JSONL file using string operations.
    Results are cached in parquet format for faster subsequent reads.

    Args:
        file_path: Path to the JSONL file (can be bz2 compressed)
        required_fields: List of field names to extract from each JSON object
        use_cache: Whether to use/create cache file (default: True)

    Returns:
        DataFrame containing the requested fields
    """
    cache_path = get_cache_path(file_path, required_fields)
    print(f"Cache path: {cache_path}")
    # Try to load from cache first
    if use_cache and os.path.exists(cache_path):
        return pd.read_parquet(cache_path)

    # If no cache exists, process the file
    records = []
    patterns = [f'"{field}":' for field in required_fields]

    with bz2.open(file_path, "rt") as file:
        for line in file:
            if not line.strip():
                continue

            result = {}
            for field, pattern in zip(required_fields, patterns):
                try:
                    # Find the field in the line
                    start_idx = line.find(pattern)
                    if start_idx == -1:
                        continue

                    # Move to the start of the value
                    start_idx += len(pattern)
                    while start_idx < len(line) and line[start_idx].isspace():
                        start_idx += 1

                    # Handle different value types
                    if start_idx >= len(line):
                        continue

                    if line[start_idx] == '"':
                        # String value
                        start_idx += 1
                        end_idx = line.find('"', start_idx)
                        value = line[start_idx:end_idx]
                    elif line[start_idx] == "{" or line[start_idx] == "[":
                        # Skip nested objects/arrays
                        continue
                    else:
                        # Number, boolean, or null
                        end_idx = line.find(",", start_idx)
                        if end_idx == -1:
                            end_idx = line.find("}", start_idx)
                        value = line[start_idx:end_idx].strip()
                        # Convert to appropriate type
                        if value == "true":
                            value = True
                        elif value == "false":
                            value = False
                        elif value == "null":
                            value = None
                        else:
                            try:
                                value = float(value) if "." in value else int(value)
                            except ValueError:
                                continue

                    result[field] = value
                except Exception:
                    continue

            if result:
                records.append(result)

    # Convert to DataFrame
    df = pd.DataFrame.from_records(records)

    # Convert columns to appropriate types
    for col in df.columns:
        # If the column contains any strings, convert the whole column to strings
        if (
            df[col].dtype == object
            and df[col].apply(lambda x: isinstance(x, str)).any()
        ):
            df[col] = df[col].astype(str)
        # You can add more type conversions here if needed

    # Save cache if enabled
    if use_cache:
        df.to_parquet(cache_path)

    return df


def process_answer_types(df: pd.DataFrame) -> pd.DataFrame:
    """
    Process the answer field to create a new answer_type field.

    Args:
        df: Input DataFrame with 'answer' column

    Returns:
        DataFrame with new 'answer_type' column
    """
    # Create a copy to avoid modifying the original
    df = df.copy()

    # Print unique answers to debug
    print("Unique answers in dataset:")
    print(df["answer"].unique())

    # Create answer_type column with case-insensitive matching
    conditions = [
        df["answer"].str.lower() == "invalid question",
        df["answer"].str.lower() == "i don't know",  # Try exact match
    ]
    choices = ["invalid", "no_answer"]
    df["answer_type"] = np.select(conditions, choices, default="valid")

    # Print distribution to verify
    print("\nAnswer type distribution:")
    print(df["answer_type"].value_counts())

    return df


def create_stratified_subsamples(
    df: pd.DataFrame,
    n_subsamples: int,
    stratify_columns: list[str] = [
        "domain",
        "answer_type",
        "question_type",
        "static_or_dynamic",
    ],
    output_path: str = "subsamples.json",
    force_compute: bool = False,
) -> dict:
    """
    Create stratified subsamples of the dataset and save them to a JSON file.
    Each subsample gets a unique ID based on its indices.

    Args:
        df: Input DataFrame
        n_subsamples: Number of subsamples to create
        stratify_columns: Columns to use for stratification
        output_path: Path to save/load the JSON output
        force_compute: If True, always compute subsamples even if file exists

    Returns:
        Dictionary containing the subsamples information
    """
    # Check if file exists and we can use it
    if not force_compute and os.path.exists(output_path):
        try:
            with open(output_path, "r") as f:
                subsamples_data = json.load(f)

            # Validate the loaded data has the expected structure
            if (
                subsamples_data.get("metadata", {}).get("n_subsamples") == n_subsamples
                and subsamples_data.get("metadata", {}).get("stratify_columns")
                == stratify_columns
            ):
                print(f"Loading existing subsamples from {output_path}")
                return subsamples_data
            else:
                print(
                    "Existing subsamples file has different parameters, recomputing..."
                )
        except Exception as e:
            print(f"Error loading existing subsamples file: {e}, recomputing...")

    # Create a combined category for stratification
    df["strat_category"] = df[stratify_columns].astype(str).agg("_".join, axis=1)

    # Initialize the subsampleter
    skf = StratifiedKFold(n_splits=n_subsamples, shuffle=True, random_state=42)

    # Create subsamples
    subsamples_info = []
    for subsample_idx, (_, subsample_indices) in enumerate(
        skf.split(df, df["strat_category"])
    ):
        # Sort indices for consistent hashing
        sorted_indices = sorted(subsample_indices.tolist())

        # Create a deterministic ID from the indices
        subsample_id = hashlib.md5(str(sorted_indices).encode()).hexdigest()[:8]

        # Calculate statistics for this subsample
        stats = {}
        subsample_df = df.iloc[subsample_indices]
        for col in stratify_columns:
            stats[col] = subsample_df[col].value_counts().to_dict()

        subsamples_info.append(
            {
                "id": subsample_id,
                "statistics": stats,
                "indices": sorted_indices,
                "size": len(subsample_indices),
            }
        )

    # Add global statistics
    global_stats = {}
    for col in stratify_columns:
        global_stats[col] = df[col].value_counts().to_dict()

    output_data = {
        "metadata": {
            "n_subsamples": n_subsamples,
            "total_samples": len(df),
            "stratify_columns": stratify_columns,
            "global_statistics": global_stats,
        },
        "subsamples": subsamples_info,
    }

    # Save to JSON
    with open(output_path, "w") as f:
        json.dump(output_data, f, indent=2)

    return output_data


def write_subsample(
    input_file: str, indices: list[int], output_file: str, compress: bool = True
) -> None:
    """
    Write a single subsample to a file using awk.

    Args:
        input_file: Path to input JSONL file
        indices: List of indices to extract
        output_file: Path to output file
        compress: Whether to compress output
    """
    # Convert indices to awk condition
    # NR is the current line number in awk
    indices_set = set(i + 1 for i in indices)  # Convert to 1-based indexing
    indices_str = ",".join(str(i) for i in sorted(indices_set))

    # Create awk script with escaped curly braces
    awk_script = (
        f'BEGIN {{subsample("{indices_str}",a,","); for(i in a) n[a[i]];}} NR in n'
    )

    if input_file.endswith(".bz2"):
        if compress:
            cmd = f"bzcat '{input_file}' | awk '{awk_script}' | bzip2 > '{output_file}'"
        else:
            cmd = f"bzcat '{input_file}' | awk '{awk_script}' > '{output_file}'"
    else:
        if compress:
            cmd = f"awk '{awk_script}' '{input_file}' | bzip2 > '{output_file}'"
        else:
            cmd = f"awk '{awk_script}' '{input_file}' > '{output_file}'"

    print(f"Process {os.getpid()} - Starting subsample to {output_file}")
    try:
        result = subprocess.run(
            cmd,
            shell=True,
            check=True,
            stderr=subprocess.PIPE,
            stdout=subprocess.PIPE,
            text=True,
        )
        print(f"Process {os.getpid()} - Finished subsample to {output_file}")

        # Verify the output file exists and has content
        if os.path.exists(output_file) and os.path.getsize(output_file) > 0:
            print(
                f"Process {os.getpid()} - Successfully created {output_file} ({os.path.getsize(output_file)} bytes)"
            )
        else:
            raise Exception(f"Output file {output_file} is empty or doesn't exist")

    except subprocess.CalledProcessError as e:
        print(f"Error executing command: {e.stderr}")
        print(f"Command output: {e.stdout}")
        raise
    except Exception as e:
        print(f"Error: {str(e)}")
        raise


def subsample_jsonl_file(
    input_file: str,
    subsamples_file: str,
    output_dir: str = None,
    compress: bool = True,
    n_processes: int = None,
    overwrite: bool = False,
) -> None:
    """
    subsample a large JSONL file into multiple files using sed for maximum performance.

    Args:
        input_file: Path to input JSONL file (can be bz2 compressed)
        subsamples_file: Path to JSON file containing subsample indices
        output_dir: Directory to save subsample files (defaults to input file directory)
        compress: Whether to compress output files with bz2
        n_processes: Number of processes to use (defaults to min(n_subsamples, cpu_count))
        overwrite: If False, skip existing output files (default: False)
    """
    # Load subsamples information
    with open(subsamples_file, "r") as f:
        subsamples_data = json.load(f)

    # Determine optimal number of processes
    n_subsamples = len(subsamples_data["subsamples"])
    if n_processes is None:
        n_processes = min(n_subsamples, cpu_count())

    if output_dir is None:
        output_dir = os.path.dirname(input_file)
    os.makedirs(output_dir, exist_ok=True)

    base_name = os.path.splitext(os.path.basename(input_file))[0]
    if base_name.endswith(".jsonl"):
        base_name = os.path.splitext(base_name)[0]

    # Prepare arguments for parallel processing
    write_args = []
    skipped_files = []
    for subsample in subsamples_data["subsamples"]:
        subsample_id = subsample["id"]
        output_name = f"{base_name}_subsample_{subsample_id}.jsonl"
        if compress:
            output_name += ".bz2"
        output_path = os.path.join(output_dir, output_name)

        # Skip if file exists and overwrite is False
        if not overwrite and os.path.exists(output_path):
            skipped_files.append(output_path)
            continue

        write_args.append((input_file, subsample["indices"], output_path, compress))

    if skipped_files:
        print(f"Skipping {len(skipped_files)} existing files:")
        for file in skipped_files:
            print(f"  - {file}")

    if write_args:
        print(f"Processing {len(write_args)} subsamples using {n_processes} processes")
        with Pool(processes=n_processes) as pool:
            pool.starmap(write_subsample, write_args)
    else:
        print("No files to process - all files exist and overwrite=False")


def run_crag_task_1_and_2(
    file_path: str,
    fields_to_extract: list[str],
    n_subsamples: int = 5,
    output_dir: str = None,
    compress: bool = True,
    n_processes: int = None,
    overwrite: bool = False,
):
    # Load and process data
    df = read_jsonl_fields_fast(file_path, fields_to_extract)
    df = process_answer_types(df)
    print(df.head())

    output_path = os.path.join(
        os.path.dirname(file_path),
        os.path.basename(file_path).split(".")[0] + "_subsamples.json",
    )

    # This will load from file if it exists and parameters match
    subsamples_data = create_stratified_subsamples(
        df, n_subsamples=5, output_path=output_path
    )

    # Example of how to read and use the subsamples
    with open(output_path, "r") as f:
        subsamples_data = json.load(f)

    # Print some information about the subsamples
    print(f"Created {subsamples_data['metadata']['n_subsamples']} subsamples")
    print("\nGlobal statistics:")
    print(json.dumps(subsamples_data["metadata"]["global_statistics"], indent=2))

    # Print statistics for first subsample
    print("\nFirst subsample statistics:")
    print(json.dumps(subsamples_data["subsamples"][0]["statistics"], indent=2))

    # This will use all available CPU cores
    subsample_jsonl_file(file_path, output_path, compress=True)


# Example usage
if __name__ == "__main__":
    file_path = "./local_data/crag_task_1_and_2_dev_v4.jsonl.bz2"
    fields_to_extract = ["domain", "answer", "question_type", "static_or_dynamic"]

    run_crag_task_1_and_2(file_path, fields_to_extract)