# pylint: disable=superfluous-parens, redefined-outer-name, redefined-outer-name,pointless-string-statement
# pylint: disable=consider-using-enumerate,invalid-name
"""Tuning record and serialization format"""

import argparse
import base64
import logging
import multiprocessing
import pickle
import json
import time
from collections import OrderedDict

from .. import build, lower, target as _target

from . import task
from .task import ConfigEntity, ApplyHistoryBest
from .measure import MeasureInput, MeasureResult

AUTOTVM_LOG_VERSION = 0.1
logger = logging.getLogger('autotvm')

try:  # convert unicode to str for python2
    _unicode = unicode
except NameError:
    _unicode = ()

try:
    _long = long
except NameError:
    _long = int


def measure_str_key(inp, include_config=True):
    """ get unique str key for MeasureInput

    Parameters
    ----------
    inp: MeasureInput
        input for the measure
    include_config: bool, optional
        whether includes config in the str key

    Returns
    -------
    key: str
        The str representation of key
    """
    config_str = str(inp.config) if include_config else ""
    return "".join([str(inp.target), inp.task.name, str(inp.task.args),
                    str(inp.task.kwargs), config_str])


def encode(inp, result, protocol='json'):
    """encode (MeasureInput, MeasureResult) pair to a string

    Parameters
    ----------
    inp: autotvm.tuner.MeasureInput
    result: autotvm.tuner.MeasureResult
        pair of input/result
    protocol: str
        log protocol, json or pickle

    Returns
    -------
    row: str
        a row in the logger file
    """

    if protocol == 'json':
        json_dict = {
            "i": (str(inp.target),
                  inp.task.name, inp.task.args, inp.task.kwargs,
                  inp.task.workload,
                  inp.config.to_json_dict()),

            "r": (result.costs if result.error_no == 0 else (1e9,),
                  result.error_no,
                  result.all_cost,
                  result.timestamp),

            "v": AUTOTVM_LOG_VERSION
        }
        return json.dumps(json_dict)
    elif protocol == 'pickle':
        row = (str(inp.target),
               str(base64.b64encode(pickle.dumps([inp.task.name,
                                                  inp.task.args,
                                                  inp.task.kwargs,
                                                  inp.task.workload])).decode()),
               str(base64.b64encode(pickle.dumps(inp.config)).decode()),
               str(base64.b64encode(pickle.dumps(tuple(result))).decode()))
        return '\t'.join(row)
    else:
        raise RuntimeError("Invalid log protocol: " + protocol)


def decode(row, protocol='json'):
    """Decode encoded record string to python object

    Parameters
    ----------
    row: str
        a row in the logger file
    protocol: str
        log protocol, json or pickle

    Returns
    -------
    input: autotvm.tuner.MeasureInput
    result: autotvm.tuner.MeasureResult
    """
    # pylint: disable=unused-variable
    if protocol == 'json':
        row = json.loads(row)
        tgt, task_name, task_args, task_kwargs, workload, config = row['i']
        tgt = _target.create(str(tgt))

        def clean_json_to_python(x):
            """1. Convert all list in x to tuple (hashable)
               2. Convert unicode to str for python2
            """
            if isinstance(x, list):
                return tuple([clean_json_to_python(a) for a in x])
            if isinstance(x, _unicode):
                return str(x)
            if isinstance(x, (_long, int)):
                return int(x)
            return x

        tsk = task.Task(clean_json_to_python(task_name), clean_json_to_python(task_args))
        tsk.workload = clean_json_to_python(workload)
        config = ConfigEntity.from_json_dict(config)
        inp = MeasureInput(tgt, tsk, config)
        result = MeasureResult(*[tuple(x) if isinstance(x, list) else x for x in row["r"]])

        return inp, result
    elif protocol == 'pickle':
        items = row.split("\t")
        tgt = _target.create(items[0])
        task_tuple = pickle.loads(base64.b64decode(items[1].encode()))
        config = pickle.loads(base64.b64decode(items[2].encode()))
        result = pickle.loads(base64.b64decode(items[3].encode()))

        tsk = task.Task(task_tuple[0], task_tuple[1])
        tsk.workload = task_tuple[3]
        return MeasureInput(tgt, tsk, config), MeasureResult(*result)
    else:
        raise RuntimeError("Invalid log protocol: " + protocol)


def load_from_file(filename):
    """Generator: load records from file.
    This is a generator that yields the records.

    Parameters
    ----------
    filename: str

    Yields
    ------
    input: autotvm.tuner.MeasureInput
    result: autotvm.tuner.MeasureResult
    """
    for row in open(filename):
        if row and not row.startswith('#'):
            yield decode(row)


def split_workload(in_file, clean=True):
    """Split a log file into separate files, each of which contains only a single workload
    This function can also delete duplicated records in log file

    Parameters
    ----------
    in_file: str
        input filename
    clean: bool
        whether delete duplicated items
    """
    tic = time.time()
    lines = list(open(in_file).readlines())

    logger.info("start converting...")
    pool = multiprocessing.Pool()
    lines = pool.map(decode, lines)
    logger.info("map done %.2f", time.time() - tic)

    wkl_dict = OrderedDict()
    for inp, res in lines:
        wkl = measure_str_key(inp, False)
        if wkl not in wkl_dict:
            wkl_dict[wkl] = []
        wkl_dict[wkl].append([inp, res])

    if clean:
        for i, (k, v) in enumerate(wkl_dict.items()):
            # clean duplicated items
            added = set()
            cleaned = []
            for inp, res in v:
                str_key = measure_str_key(inp)
                if str_key in added:
                    continue
                added.add(str_key)
                cleaned.append([inp, res])

            # write to file
            logger.info("Key: %s\tValid: %d\tDup: %d\t", k, len(cleaned), len(v) - len(cleaned))
            with open(args.i + ".%03d.wkl" % i, 'w') as fout:
                for inp, res in cleaned:
                    fout.write(encode(inp, res) + '\n')
    else:
        for i, (k, v) in enumerate(wkl_dict.items()):
            logger.info("Key: %s\tNum: %d", k, len(v))
            with open(args.i + ".%03d.wkl" % i, 'w') as fout:
                for inp, res in v:
                    fout.write(encode(inp, res) + '\n')

def pick_best(in_file, out_file):
    """
    Pick best entries from a file and store it to another file.
    This distill the useful log entries from a large log file.

    Parameters
    ----------
    in_file: str
        The filename of input
    out_file: str or file
        The filename of output
    """
    best_context = ApplyHistoryBest(load_from_file(in_file))
    best_set = set()

    for v in best_context.best_by_model.values():
        best_set.add(measure_str_key(v[0]))

    for v in best_context.best_by_targetkey.values():
        best_set.add(measure_str_key(v[0]))

    logger.info("Extract %d best records from the %s", len(best_set), in_file)
    fout = open(out_file, 'w') if isinstance(out_file, str) else out_file

    for inp, res in load_from_file(in_file):
        if measure_str_key(inp) in best_set:
            fout.write(encode(inp, res) + "\n")
            best_set.remove(measure_str_key(inp))

"""
Usage:
This record executable module has three modes.

* Print log file in readable format
e.g. python -m tvm.autotvm.record --mode read --i collect_conv.log --begin 0 --end 5 --ir --code

* Extract history best from a large log file
e.g. python -m tvm.autotvm.record --mode pick --i collect.log

* Split a log file into separate files, each of which contains only a single wkl
e.g. python -m tvm.autotvm.record --mode split --i collect.log
"""
if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--mode", choices=['read', 'pick', 'split'], default='read')
    parser.add_argument("--i", type=str, help="input file")
    parser.add_argument("--o", type=str, default=None, help='output file')
    parser.add_argument("--begin", type=int, default=0)
    parser.add_argument("--end", type=int, default=5)
    parser.add_argument("--ir", action='store_true')
    parser.add_argument("--code", action='store_true')

    args = parser.parse_args()
    logging.basicConfig(level=logging.INFO)

    if args.mode == 'pick':
        args.o = args.o or args.i + ".best.log"
        pick_best(args.i, args.o)
    elif args.mode == 'read':
        for i, (inp, result) in enumerate(load_from_file(args.i)):
            if args.begin <= i < args.end:
                with inp.target:
                    s, arg_bufs = inp.task.instantiate(inp.config)

                print("")
                print(inp.target, inp.task, inp.config)
                print(result)

                if args.ir:
                    with inp.target:
                        print(lower(s, arg_bufs, simple_mode=True))

                if args.code:
                    with inp.target:
                        func = build(s, arg_bufs)
                        print(func.imported_modules[0].get_source())
    elif args.mode == 'split':
        split_workload(args.i)
