"""
===============================================================================
PRODUCTION-GRADE DATA CLEANING PIPELINE
===============================================================================

Author  : Datta Sable
Purpose : High-performance cleaning pipeline for 1M+ row datasets
Version : 1.0
Python  : 3.10+

FEATURES
--------
✓ Handles millions of records efficiently
✓ Chunk-based processing
✓ Memory optimization
✓ Automatic schema inference
✓ Null handling
✓ Duplicate detection
✓ Data type optimization
✓ Outlier handling
✓ Logging system
✓ Error recovery
✓ Validation reports
✓ Zero context-loss cleaning approach
✓ CSV / Parquet support
✓ Production-ready architecture

INSTALL
-------
pip install pandas pyarrow numpy tqdm psutil loguru openpyxl

USAGE
-----
python data_cleaner.py

===============================================================================
"""

from __future__ import annotations

import gc
import os
import re
import sys
import json
import time
import psutil
import traceback
from pathlib import Path
from typing import Dict, List, Optional

import numpy as np
import pandas as pd

from tqdm import tqdm  # type: ignore # pyright: ignore[reportMissingImports]
from loguru import logger  # type: ignore # pyright: ignore[reportMissingImports]

# =============================================================================
# CONFIGURATION
# =============================================================================

CONFIG = {
    "INPUT_FILE": "raw_dataset.csv",
    "OUTPUT_FILE": "cleaned_dataset.parquet",
    "REPORT_FILE": "cleaning_report.json",
    "CHUNK_SIZE": 100_000,
    "LOW_MEMORY": True,
    "DROP_DUPLICATES": True,
    "REMOVE_EMPTY_ROWS": True,
    "NORMALIZE_TEXT": True,
    "OPTIMIZE_MEMORY": True,
    "PARSE_DATES": True,
    "HANDLE_OUTLIERS": False,
    "SAVE_INTERMEDIATE": False,
    "LOG_FILE": "pipeline.log",
}

# =============================================================================
# LOGGING SETUP
# =============================================================================

logger.remove()
logger.add(
    sys.stdout,
    level="INFO",
    format="<green>{time}</green> | <level>{level}</level> | {message}"
)

logger.add(
    CONFIG["LOG_FILE"],
    rotation="50 MB",
    retention="10 days",
    level="DEBUG"
)

# =============================================================================
# UTILITIES
# =============================================================================

class MemoryMonitor:
    @staticmethod
    def current_memory_gb() -> float:
        process = psutil.Process(os.getpid())
        return process.memory_info().rss / (1024 ** 3)

    @staticmethod
    def log_memory():
        logger.info(f"Memory Usage: {MemoryMonitor.current_memory_gb():.2f} GB")


class DataProfiler:
    @staticmethod
    def generate_profile(df: pd.DataFrame) -> Dict:
        return {
            "rows": len(df),
            "columns": len(df.columns),
            "missing_values": df.isnull().sum().to_dict(),
            "dtypes": df.dtypes.astype(str).to_dict(),
            "duplicate_rows": int(df.duplicated().sum()),
        }


# =============================================================================
# CLEANING ENGINE
# =============================================================================

class ProductionDataCleaner:

    def __init__(self, config: Dict):
        self.config = config
        self.report = {
            "initial_rows": 0,
            "final_rows": 0,
            "duplicates_removed": 0,
            "nulls_handled": {},
            "processing_time_seconds": 0,
            "memory_usage_gb": 0,
            "errors": [],
        }

    # =========================================================================
    # TEXT NORMALIZATION
    # =========================================================================

    @staticmethod
    def normalize_text(value):
        if pd.isna(value):
            return value

        value = str(value)

        value = value.strip()
        value = re.sub(r"\s+", " ", value)
        value = value.replace("\n", " ")
        value = value.replace("\t", " ")

        return value

    # =========================================================================
    # COLUMN CLEANING
    # =========================================================================

    def clean_column_names(self, df: pd.DataFrame) -> pd.DataFrame:
        df.columns = (
            df.columns
            .str.strip()
            .str.lower()
            .str.replace(" ", "_")
            .str.replace(r"[^\w]", "", regex=True)
        )
        return df

    # =========================================================================
    # TYPE OPTIMIZATION
    # =========================================================================

    def optimize_dtypes(self, df: pd.DataFrame) -> pd.DataFrame:

        for col in df.columns:

            col_type = df[col].dtype

            try:

                if col_type == "int64":
                    df[col] = pd.to_numeric(df[col], downcast="integer")

                elif col_type == "float64":
                    df[col] = pd.to_numeric(df[col], downcast="float")

                elif col_type == "object":

                    unique_ratio = df[col].nunique() / max(len(df), 1)

                    if unique_ratio < 0.5:
                        df[col] = df[col].astype("category")

            except Exception as e:
                logger.warning(f"Could not optimize column {col}: {e}")

        return df

    # =========================================================================
    # NULL HANDLING
    # =========================================================================

    def handle_nulls(self, df: pd.DataFrame) -> pd.DataFrame:

        for col in df.columns:

            missing_count = int(df[col].isnull().sum())

            if missing_count == 0:
                continue

            self.report["nulls_handled"][col] = missing_count

            if pd.api.types.is_numeric_dtype(df[col]):
                median_value = df[col].median()
                df[col] = df[col].fillna(median_value)

            elif pd.api.types.is_datetime64_any_dtype(df[col]):
                df[col] = df[col].fillna(method="ffill")

            else:
                mode_value = (
                    df[col].mode()[0]
                    if not df[col].mode().empty
                    else "UNKNOWN"
                )

                df[col] = df[col].fillna(mode_value)

        return df

    # =========================================================================
    # DATE PARSING
    # =========================================================================

    def parse_dates(self, df: pd.DataFrame) -> pd.DataFrame:

        date_keywords = ["date", "time", "created", "updated"]

        for col in df.columns:

            if any(keyword in col.lower() for keyword in date_keywords):

                try:
                    df[col] = pd.to_datetime(
                        df[col],
                        errors="coerce"
                    )

                    logger.info(f"Parsed datetime column: {col}")

                except Exception:
                    pass

        return df

    # =========================================================================
    # OUTLIER HANDLING
    # =========================================================================

    def handle_outliers(self, df: pd.DataFrame) -> pd.DataFrame:

        numeric_cols = df.select_dtypes(include=np.number).columns

        for col in numeric_cols:

            q1 = df[col].quantile(0.25)
            q3 = df[col].quantile(0.75)

            iqr = q3 - q1

            lower = q1 - 1.5 * iqr
            upper = q3 + 1.5 * iqr

            df[col] = np.where(
                df[col] < lower,
                lower,
                np.where(df[col] > upper, upper, df[col])
            )

        return df

    # =========================================================================
    # DUPLICATE HANDLING
    # =========================================================================

    def remove_duplicates(self, df: pd.DataFrame) -> pd.DataFrame:

        before = len(df)

        df = df.drop_duplicates()

        removed = before - len(df)

        self.report["duplicates_removed"] += removed

        return df

    # =========================================================================
    # MAIN CLEANING PIPELINE
    # =========================================================================

    def clean_chunk(self, df: pd.DataFrame) -> pd.DataFrame:

        df = self.clean_column_names(df)

        if self.config["NORMALIZE_TEXT"]:

            object_cols = df.select_dtypes(include=["object"]).columns

            for col in object_cols:
                df[col] = df[col].apply(self.normalize_text)

        if self.config["PARSE_DATES"]:
            df = self.parse_dates(df)

        df = self.handle_nulls(df)

        if self.config["DROP_DUPLICATES"]:
            df = self.remove_duplicates(df)

        if self.config["HANDLE_OUTLIERS"]:
            df = self.handle_outliers(df)

        if self.config["REMOVE_EMPTY_ROWS"]:
            df = df.dropna(how="all")

        if self.config["OPTIMIZE_MEMORY"]:
            df = self.optimize_dtypes(df)

        return df

    # =========================================================================
    # EXECUTION
    # =========================================================================

    def run(self):

        start_time = time.time()

        logger.info("Starting Production Cleaning Pipeline")

        input_path = Path(self.config["INPUT_FILE"])

        if not input_path.exists():
            raise FileNotFoundError(f"File not found: {input_path}")

        cleaned_chunks = []

        try:

            for chunk in tqdm(
                pd.read_csv(
                    input_path,
                    chunksize=self.config["CHUNK_SIZE"],
                    low_memory=self.config["LOW_MEMORY"]
                ),
                desc="Processing Chunks"
            ):

                self.report["initial_rows"] += len(chunk)

                cleaned_chunk = self.clean_chunk(chunk)

                cleaned_chunks.append(cleaned_chunk)

                MemoryMonitor.log_memory()

                gc.collect()

            logger.info("Merging cleaned chunks")

            final_df = pd.concat(cleaned_chunks, ignore_index=True)

            self.report["final_rows"] = len(final_df)

            logger.info("Saving cleaned dataset")

            if self.config["OUTPUT_FILE"].endswith(".parquet"):

                final_df.to_parquet(
                    self.config["OUTPUT_FILE"],
                    index=False,
                    engine="pyarrow"
                )

            else:

                final_df.to_csv(
                    self.config["OUTPUT_FILE"],
                    index=False
                )

            profile = DataProfiler.generate_profile(final_df)

            self.report["dataset_profile"] = profile

        except Exception as e:

            logger.error(traceback.format_exc())

            self.report["errors"].append(str(e))

        finally:

            self.report["processing_time_seconds"] = round(
                time.time() - start_time,
                2
            )

            self.report["memory_usage_gb"] = round(
                MemoryMonitor.current_memory_gb(),
                2
            )

            with open(self.config["REPORT_FILE"], "w") as f:
                json.dump(self.report, f, indent=4)

            logger.info("Cleaning Completed")
            logger.info(json.dumps(self.report, indent=2))


# =============================================================================
# ENTRY POINT
# =============================================================================

if __name__ == "__main__":

    cleaner = ProductionDataCleaner(CONFIG)

    cleaner.run()

"""
===============================================================================
RECOMMENDED PRODUCTION STACK
===============================================================================

FOR 1M - 10M RECORDS
--------------------
✓ Pandas + PyArrow
✓ Chunk processing
✓ Parquet output

FOR 10M - 100M RECORDS
----------------------
✓ Polars
✓ DuckDB
✓ Dask

FOR ENTERPRISE SCALE
--------------------
✓ Spark
✓ Databricks
✓ Snowflake
✓ BigQuery

===============================================================================
PERFORMANCE TIPS
===============================================================================

1. ALWAYS use Parquet instead of CSV
2. Downcast numeric types
3. Use categorical encoding
4. Process in chunks
5. Avoid object dtype
6. Use vectorized operations
7. Avoid Python loops
8. Enable PyArrow backend
9. Monitor memory continuously
10. Use SSD storage

===============================================================================
"""