import argparse
import base64
import itertools
import json
import math
import os
import pickle
import time
from collections import Counter
from pathlib import Path
from typing import List, Dict, Union, Iterator, Any, IO

from fns.text import md5_hash


def flatten(x: List[List]) -> Iterator:
    """
    Flatten a list of list.

    Args:
        x: List of list of elements

    Returns:
        Iterator of flattened array.
    """
    return itertools.chain.from_iterable(x)


def array_except_element(arr: List, elem: Any) -> List:
    """
    Get copy of array without an element.

    Args:
        arr:
        elem:

    Returns:
        Array

    Example:
    ```python
    >>> array_except_element([1, 2, 3], 3)
    [1, 2]
    ```
    """
    elem_index = arr.index(elem)
    return arr[:elem_index] + arr[elem_index + 1 :]


def sort_dict_by_value(d: Dict, reverse: bool = False) -> Dict:
    """
    Sort items in dictionary by value.

    Example:
    ```python
    >>> sort_dict_by_value({'gold': 40, 'silver': 25})
    {'silver': 25, 'gold': 40}
    ```

    Args:
        d: Python Dictionary
        reverse: Sort order

    Returns:
        Sorted dictionary
    """
    return dict(sorted(d.items(), key=lambda item: item[1], reverse=reverse))


def reverse_mapping(d: Dict) -> Dict:
    """
    Swap mapping from key: value to value: key

    Args:
        d: Python Dictionary

    Returns:
        Dictionary with key and value swapped
    """
    return {v: k for k, v in d.items()}


def percent_dict(d: Dict) -> Dict:
    """
    Convert a dictionary of key-value to key:coverage-percent.

    Args:
        d: Dictionary of key and values

    Returns:
        Dictionary of key and percent-coverage
    """
    total = sum(d.values())
    return {key: value / total * 100.0 for key, value in d.items()}


def top(data, n: int = 5) -> Dict:
    """
    Get a dictionary of top-n items from a list.

    Args:
        data: Python collection
        n: Number of top-values

    Returns:
        Dictionary of top-n items and count
    """
    return dict(Counter(data).most_common(n))


def top_n_from_dict(dictionary: Dict, n: int = 10):
    """
    Get top n largest values from the dictionary.

    Args:
        dictionary: Python dictionary
        n: Number of keys to pick

    Returns:

    """
    return top(dictionary, n=n)


def read_json(json_path: Union[str, Path]) -> Dict:
    """
    Read json file from a path.

    Args:
        json_path: File path to a json file.

    Returns:
        Python dictionary
    """
    with open(json_path, "r") as fp:
        data = json.load(fp)
    return data


def write_json(item: Dict, path: Union[Path, str], mode: str = "w") -> None:
    """
    Save json to a file.

    Args:
        item: Python dictionary
        path: File path to save at
        mode: File write mode

    Returns:
        None
    """
    with open(path, mode=mode) as fp:
        json.dump(item, fp)


def read_pickle(path: Union[str, Path]) -> Any:
    """
    Read a pickle file from path.

    Args:
        path: File path

    Returns:
        Unpickled object
    """
    with open(path, "rb") as fp:
        return pickle.load(fp)


def write_pickle(item: Any, path: Union[Path, str]) -> None:
    """
    Pickle a python object.

    Args:
        item: Python object
        path: File path to save the pickle file

    Returns:
        None
    """
    with open(path, "wb") as fp:
        pickle.dump(item, fp)


def parse_manual(parser: argparse.ArgumentParser, command: str) -> argparse.Namespace:
    """
    Use argument parser in notebooks.

    Args:
        parser: ArgumentParser
        command: Command line arguments as string

    Returns:
        Parsed argument as namespace
    """
    args = command.split()
    return parser.parse_args(args=args)


def hash_file(file_object: IO):
    """
    Calculate MD5 hash of file.

    Args:
        file_object: File object

    Returns:
        MD5 hash of the file
    """
    # Calculate hash
    unique_hash = md5_hash(file_object.read())

    # Reset file pointer to start
    file_object.seek(0)

    return unique_hash


def num_files(path: Union[Path, str]) -> int:
    """
    Get the number of files in a path.

    Args:
        path: File path

    Returns:
        Number of files
    """
    return len(os.listdir(path))


def ngrams(tokens: List, n: int):
    """

    Args:
        tokens: List of elements
        n: N-gram size

    Returns:
        List of ngrams
    """
    return [tokens[i : i + n] for i in range(len(tokens) - n + 1)]


def print_json(d: Dict) -> None:
    """
    Render python dictionary as JSON with double quotes and indentation.

    Args:
        d: Python dictionary

    Returns:
        None
    """
    print(json.dumps(d, indent=4))


def read_as_base64(path: Union[str, Path]) -> str:
    """
    Convert file contents into a base64 string

    Args:
        path: File path

    Returns:
        Base64 string
    """
    content = Path(path).read_text()
    return base64.b64encode(content.encode("utf-8")).decode("utf-8")


def base64_dict(base64_str: str) -> Dict:
    """
    Parse a base64-encoded JSON as dictionary.

    Args:
        base64_str: Base-64 encoded string representation of JSON

    Returns:
        Python Dictionary
    """
    return json.loads(base64.b64decode(base64_str))


def format_as_hms(seconds: Union[int, float]) -> str:
    """
    Convert seconds to HH:MM:SS format.

    Args:
        seconds: Number of seconds

    Returns:
        String in the format HH:MM:SS
    """
    return time.strftime("%H:%M:%S", time.gmtime(seconds))


def roundup(n: float, m: int = 10) -> int:
    """
    Round up a number n to the nearest multiple of M.

    Args:
        n: Number
        m: Multiple of which number to roundup to

    Returns:
        Rounded integer number
    """
    return int(math.ceil(n / m)) * m


def minibatch(items, size):
    """
    Create mini-batches of length 'size' from a list of items.

    Original Source: `spacy` package

    Original function definition:
    https://github.com/explosion/spaCy/blob/master/spacy/util.py#L1426
    """
    if isinstance(size, int):
        size_ = itertools.repeat(size)
    else:
        size_ = size
    items = iter(items)
    while True:
        batch_size = next(size_)
        batch = list(itertools.islice(items, int(batch_size)))
        if len(batch) == 0:
            break
        yield list(batch)


def harmonic_mean(a: Union[int, float], b: Union[int, float]) -> Union[int, float]:
    """
    Compute harmonic mean of two numbers.

    Args:
        a: First number
        b: Second number

    Returns:
        Harmonic mean
    """
    return (2 * a * b) / (a + b)


def generate_edits(word: str, n: int = 1) -> List[str]:
    """
    Generate variations that are `n` edits away from word.

    Adapted from: https://norvig.com/spell-correct.html

    Args:
        word: Single word
        n: Number of edits away from word.

    Returns:
        List of edits
    """

    def edits1(word: str):
        letters = "abcdefghijklmnopqrstuvwxyz"
        splits = [(word[:i], word[i:]) for i in range(len(word) + 1)]
        deletes = [L + R[1:] for L, R in splits if R]
        transposes = [L + R[1] + R[0] + R[2:] for L, R in splits if len(R) > 1]
        replaces = [L + c + R[1:] for L, R in splits if R for c in letters]
        inserts = [L + c + R for L, R in splits for c in letters]
        return set(deletes + transposes + replaces + inserts)

    edits = edits1(word)
    for i in range(n - 1):
        edits = [e2 for e1 in edits for e2 in edits1(e1)]
    return edits
