from pathlib import Path
from typing import Optional, List

import click
import logging
import operator
import os
import shutil
import subprocess
from datetime import datetime

import pandas as pd
from pandas.api.types import is_string_dtype, is_numeric_dtype
from ray.air.constants import EXPR_RESULT_FILE
from ray.tune.result import (
    DEFAULT_EXPERIMENT_INFO_KEYS,
    DEFAULT_RESULT_KEYS,
    CONFIG_PREFIX,
)
from ray.tune.analysis import ExperimentAnalysis
from ray.tune import TuneError
from ray._private.thirdparty.tabulate.tabulate import tabulate

logger = logging.getLogger(__name__)

EDITOR = os.getenv("EDITOR", "vim")

TIMESTAMP_FORMAT = "%Y-%m-%d %H:%M:%S (%A)"

DEFAULT_CLI_KEYS = DEFAULT_EXPERIMENT_INFO_KEYS + DEFAULT_RESULT_KEYS

DEFAULT_PROJECT_INFO_KEYS = (
    "name",
    "total_trials",
    "last_updated",
)

TERM_WIDTH, TERM_HEIGHT = shutil.get_terminal_size(fallback=(100, 100))

OPERATORS = {
    "<": operator.lt,
    "<=": operator.le,
    "==": operator.eq,
    "!=": operator.ne,
    ">=": operator.ge,
    ">": operator.gt,
}


def _check_tabulate():
    """Checks whether tabulate is installed."""
    if tabulate is None:
        raise ImportError("Tabulate not installed. Please run `pip install tabulate`.")


def print_format_output(dataframe):
    """Prints output of given dataframe to fit into terminal.

    Returns:
        table: Final outputted dataframe.
        dropped_cols: Columns dropped due to terminal size.
        empty_cols: Empty columns (dropped on default).
    """
    print_df = pd.DataFrame()
    dropped_cols = []
    empty_cols = []
    # column display priority is based on the info_keys passed in
    for i, col in enumerate(dataframe):
        if dataframe[col].isnull().all():
            # Don't add col to print_df if is fully empty
            empty_cols += [col]
            continue

        print_df[col] = dataframe[col]
        test_table = tabulate(print_df, headers="keys", tablefmt="psql")
        if str(test_table).index("\n") > TERM_WIDTH:
            # Drop all columns beyond terminal width
            print_df.drop(col, axis=1, inplace=True)
            dropped_cols += list(dataframe.columns)[i:]
            break

    table = tabulate(print_df, headers="keys", tablefmt="psql", showindex="never")

    print(table)
    if dropped_cols:
        click.secho("Dropped columns: {}".format(dropped_cols), fg="yellow")
        click.secho("Please increase your terminal size to view remaining columns.")
    if empty_cols:
        click.secho("Empty columns: {}".format(empty_cols), fg="yellow")

    return table, dropped_cols, empty_cols


def list_trials(
    experiment_path: str,
    sort: Optional[List[str]] = None,
    output: Optional[str] = None,
    filter_op: Optional[str] = None,
    info_keys: Optional[List[str]] = None,
    limit: int = None,
    desc: bool = False,
):
    """Lists trials in the directory subtree starting at the given path.

    Args:
        experiment_path: Directory where trials are located.
            Like Experiment.local_dir/Experiment.name/experiment*.json.
        sort: Keys to sort by.
        output: Name of file where output is saved.
        filter_op: Filter operation in the format
            "<column> <operator> <value>".
        info_keys: Keys that are displayed.
        limit: Number of rows to display.
        desc: Sort ascending vs. descending.
    """
    _check_tabulate()

    try:
        checkpoints_df = ExperimentAnalysis(experiment_path).dataframe()  # last result
    except TuneError as e:
        raise click.ClickException("No trial data found!") from e

    config_prefix = CONFIG_PREFIX + "/"

    def key_filter(k):
        return k in DEFAULT_CLI_KEYS or k.startswith(config_prefix)

    col_keys = [k for k in checkpoints_df.columns if key_filter(k)]

    if info_keys:
        for k in info_keys:
            if k not in checkpoints_df.columns:
                raise click.ClickException(
                    "Provided key invalid: {}. "
                    "Available keys: {}.".format(k, checkpoints_df.columns)
                )
        col_keys = [k for k in checkpoints_df.columns if k in info_keys]

    if not col_keys:
        raise click.ClickException("No columns to output.")

    checkpoints_df = checkpoints_df[col_keys]
    if "last_update_time" in checkpoints_df:
        with pd.option_context("mode.use_inf_as_null", True):
            datetime_series = checkpoints_df["last_update_time"].dropna()

        datetime_series = datetime_series.apply(
            lambda t: datetime.fromtimestamp(t).strftime(TIMESTAMP_FORMAT)
        )
        checkpoints_df["last_update_time"] = datetime_series

    if "logdir" in checkpoints_df:
        # logdir often too long to view in table, so drop experiment_path
        checkpoints_df["logdir"] = checkpoints_df["logdir"].str.replace(
            experiment_path, ""
        )

    if filter_op:
        col, op, val = filter_op.split(" ")
        col_type = checkpoints_df[col].dtype
        if is_numeric_dtype(col_type):
            val = float(val)
        elif is_string_dtype(col_type):
            val = str(val)
        # TODO(Andrew): add support for datetime and boolean
        else:
            raise click.ClickException(
                "Unsupported dtype for {}: {}".format(val, col_type)
            )
        op = OPERATORS[op]
        filtered_index = op(checkpoints_df[col], val)
        checkpoints_df = checkpoints_df[filtered_index]

    if sort:
        for key in sort:
            if key not in checkpoints_df:
                raise click.ClickException(
                    "{} not in: {}".format(key, list(checkpoints_df))
                )
        ascending = not desc
        checkpoints_df = checkpoints_df.sort_values(by=sort, ascending=ascending)

    if limit:
        checkpoints_df = checkpoints_df[:limit]

    print_format_output(checkpoints_df)

    if output:
        file_extension = os.path.splitext(output)[1].lower()
        if file_extension in (".p", ".pkl", ".pickle"):
            checkpoints_df.to_pickle(output)
        elif file_extension == ".csv":
            checkpoints_df.to_csv(output, index=False)
        else:
            raise click.ClickException("Unsupported filetype: {}".format(output))
        click.secho("Output saved at {}".format(output), fg="green")


def list_experiments(
    project_path: str,
    sort: Optional[List[str]] = None,
    output: str = None,
    filter_op: str = None,
    info_keys: Optional[List[str]] = None,
    limit: int = None,
    desc: bool = False,
):
    """Lists experiments in the directory subtree.

    Args:
        project_path: Directory where experiments are located.
            Corresponds to Experiment.local_dir.
        sort: Keys to sort by.
        output: Name of file where output is saved.
        filter_op: Filter operation in the format
            "<column> <operator> <value>".
        info_keys: Keys that are displayed.
        limit: Number of rows to display.
        desc: Sort ascending vs. descending.
    """
    _check_tabulate()
    base, experiment_folders, _ = next(os.walk(project_path))

    experiment_data_collection = []

    for experiment_dir in experiment_folders:
        num_trials = sum(
            EXPR_RESULT_FILE in files
            for _, _, files in os.walk(os.path.join(base, experiment_dir))
        )

        experiment_data = {"name": experiment_dir, "total_trials": num_trials}
        experiment_data_collection.append(experiment_data)

    if not experiment_data_collection:
        raise click.ClickException("No experiments found!")

    info_df = pd.DataFrame(experiment_data_collection)
    if not info_keys:
        info_keys = DEFAULT_PROJECT_INFO_KEYS
    col_keys = [k for k in list(info_keys) if k in info_df]
    if not col_keys:
        raise click.ClickException(
            "None of keys {} in experiment data!".format(info_keys)
        )
    info_df = info_df[col_keys]

    if filter_op:
        col, op, val = filter_op.split(" ")
        col_type = info_df[col].dtype
        if is_numeric_dtype(col_type):
            val = float(val)
        elif is_string_dtype(col_type):
            val = str(val)
        # TODO(Andrew): add support for datetime and boolean
        else:
            raise click.ClickException(
                "Unsupported dtype for {}: {}".format(val, col_type)
            )
        op = OPERATORS[op]
        filtered_index = op(info_df[col], val)
        info_df = info_df[filtered_index]

    if sort:
        for key in sort:
            if key not in info_df:
                raise click.ClickException("{} not in: {}".format(key, list(info_df)))
        ascending = not desc
        info_df = info_df.sort_values(by=sort, ascending=ascending)

    if limit:
        info_df = info_df[:limit]

    print_format_output(info_df)

    if output:
        file_extension = os.path.splitext(output)[1].lower()
        if file_extension in (".p", ".pkl", ".pickle"):
            info_df.to_pickle(output)
        elif file_extension == ".csv":
            info_df.to_csv(output, index=False)
        else:
            raise click.ClickException("Unsupported filetype: {}".format(output))
        click.secho("Output saved at {}".format(output), fg="green")


def add_note(path: str, filename: str = "note.txt"):
    """Opens a txt file at the given path where user can add and save notes.

    Args:
        path: Directory where note will be saved.
        filename: Name of note. Defaults to "note.txt"
    """
    path = Path(path).expanduser()
    assert path.is_dir(), "{} is not a valid directory.".format(path)

    filepath = path / filename

    try:
        subprocess.call([EDITOR, filepath.as_posix()])
    except Exception as exc:
        click.secho("Editing note failed: {}".format(str(exc)), fg="red")
    if filepath.exists():
        print("Note updated at:", filepath.as_posix())
    else:
        print("Note created at:", filepath.as_posix())
