import sys
import os
import time
from tqdm import tqdm
from tools import MechEventName
from typing import List
import pandas as pd
import gc
from pathlib import Path

REDUCE_FACTOR = 0.25
SLEEP = 0.5
REQUEST_ID_FIELD = "request_id"
SCRIPTS_DIR = Path(__file__).parent
ROOT_DIR = SCRIPTS_DIR.parent
DATA_DIR = ROOT_DIR / "data"
BLOCK_FIELD = "block"


def parse_args() -> str:
    """Parse the arguments and return the RPC."""
    if len(sys.argv) != 2:
        raise ValueError("Expected the RPC as a positional argument.")
    return sys.argv[1]


def read_abi(abi_path: str) -> str:
    """Read and return the wxDAI contract's ABI."""
    with open(abi_path) as abi_file:
        return abi_file.read()


def reduce_window(contract_instance, event, from_block, batch_size, latest_block):
    """Dynamically reduce the batch size window."""
    keep_fraction = 1 - REDUCE_FACTOR
    events_filter = contract_instance.events[event].build_filter()
    events_filter.fromBlock = from_block
    batch_size = int(batch_size * keep_fraction)
    events_filter.toBlock = min(from_block + batch_size, latest_block)
    tqdm.write(f"RPC timed out! Resizing batch size to {batch_size}.")
    time.sleep(SLEEP)
    return events_filter, batch_size


def limit_text(text: str, limit: int = 200) -> str:
    """Limit the given text"""
    if len(text) > limit:
        return f"{text[:limit]}..."
    return text


def check_for_dicts(df: pd.DataFrame) -> List[str]:
    """Check for columns that contain dictionaries."""
    dict_columns = []
    for column in df.columns:
        if df[column].apply(lambda x: isinstance(x, dict)).any():
            dict_columns.append(column)
    return dict_columns


def drop_dict_rows(df: pd.DataFrame, dict_columns: List[str]) -> pd.DataFrame:
    """Drop rows that contain dictionaries."""
    for column in dict_columns:
        df = df[~df[column].apply(lambda x: isinstance(x, dict))]
    return df


def clean(df: pd.DataFrame) -> pd.DataFrame:
    """Clean the dataframe."""
    dict_columns = check_for_dicts(df)
    df = drop_dict_rows(df, dict_columns)
    cleaned = df.drop_duplicates()
    cleaned[REQUEST_ID_FIELD] = cleaned[REQUEST_ID_FIELD].astype("str")
    return cleaned


def gen_event_filename(event_name: MechEventName) -> str:
    """Generate the filename of an event."""
    return f"{event_name.value.lower()}s.parquet"


def read_n_last_lines(filename: str, n: int = 1) -> str:
    """Return the `n` last lines' content of a file."""
    num_newlines = 0
    with open(filename, "rb") as f:
        try:
            f.seek(-2, os.SEEK_END)
            while num_newlines < n:
                f.seek(-2, os.SEEK_CUR)
                if f.read(1) == b"\n":
                    num_newlines += 1
        except OSError:
            f.seek(0)
        last_line = f.readline().decode()
    return last_line


def get_earliest_block(event_name: MechEventName) -> int:
    """Get the earliest block number to use when filtering for events."""
    filename = gen_event_filename(event_name)
    if not os.path.exists(DATA_DIR / filename):
        return 0

    df = pd.read_parquet(DATA_DIR / filename)
    block_field = f"{event_name.value.lower()}_{BLOCK_FIELD}"
    earliest_block = int(df[block_field].max())
    # clean and release all memory
    del df
    gc.collect()
    return earliest_block