# coding=utf-8

import collections
import functools
import csv
import difflib
import hashlib
import inspect
import itertools
import json
import operator
import time
from copy import deepcopy


# ########################################################################### #
# ## The Motor                                                             ## #
# ########################################################################### #

def regression_test(argsrc, tests, driver_settings=None, cleanup_hack=None,
                    apply_hacks=None, on_next=None):
    """Perform regression test with argsets from `argsrc`.

    For each argset pulled from source, performs one comparison
    per driver pair in `tests`, which is list of tuples with
    comparison function and pair of test driver classes: `(operator,
    oracle_class, result_class)`.  (The classes are assumed to
    be sub-classes of `hoover.BaseTestDriver`.)

    `driver_settings` is a dictionary supposed to hold environmental
    values for all the drivers, the keys having form "DriverName.
    settingName".  Each driver is then instantiated with this
    dict, and gets a copy of the dict with settings only intended
    for itself (and the "DriverName" part stripped).

    If comparison fails, report is generated using `hoover.jsDiff()`,
    and along with affected arguments stored in `hoover.Tracker`
    instance, which is finally used as a return value.  This instance
    then contains method for basic stats as well as method to format
    the final report and a helper method to export argument sets
    as a CSV files.

    Supports hacks, which are a data transformations performed by
    `hoover.TinyCase` class and are intended to avoid known bugs
    and anomalies (`apply_hacks`) or clean up data structures of
    irrelevant data (`cleanup_hack`, performed only if the comparison
    function provided along with driver pair is not "equals").

    A function can be provided as `on_next` argument, that will be
    called after pulling each argument set, with last argument set
    (or `None`) as first argument and current one as second argument.
    """

    # TODO: do not parse driver_settings thousands of times (use a view class?)

    on_next = on_next if on_next else lambda a, b: None
    apply_hacks = apply_hacks if apply_hacks else []
    driver_settings = driver_settings if driver_settings else {}

    tracker = Tracker()
    last_argset = None

    all_classes = set(functools.reduce(
        lambda a, b: a+b,
        [triple[1:] for triple in tests]
    ))

    counter = StatCounter()

    for argset in argsrc:

        on_start = time.time()
        on_next(argset, last_argset)
        counter.add('on_next', time.time() - on_start)

        # # load the data first, only once for each driver
        #
        data = {}
        for aclass in all_classes:
            try:
                aclass.check_values(argset)
            except NotImplementedError:         # let them bail out
                counter.count_for(aclass, 'bailouts')
            else:
                data[aclass], duration, overhead = get_data_and_stats(
                    aclass, argset, driver_settings)
                counter.count_for(aclass, 'calls')
                counter.add_for(aclass, 'duration', duration)
                counter.add_for(aclass, 'overhead', overhead)

        for match_op, oclass, rclass in tests:

            # skip test if one of classes bailed out on the argset
            if oclass not in data or rclass not in data:
                continue

            diff = None

            case = TinyCase({
                'argset': argset,
                'oracle': deepcopy(data[oclass]),
                'result': deepcopy(data[rclass]),
                'oname': oclass.__name__,
                'rname': rclass.__name__
            })

            hacks_done = sum([case.hack(h) for h in apply_hacks])
            counter.add_for(oclass, 'ohacks', hacks_done)
            counter.add_for(rclass, 'rhacks', hacks_done)
            counter.add('hacks', hacks_done)
            counter.add('hacked_cases', (1 if hacks_done else 0))

            if not match_op(case['oracle'], case['result']):

                # try to clean up so that normally ignored items
                # do not clutter up the report
                if not match_op == operator.eq:
                    case.hack(cleanup_hack)
                    # but panic if that "removed" the error condition
                    if match_op(case['oracle'], case['result']):
                        raise RuntimeError("cleanup ate error")

                diff = jsDiff(dira=case['oracle'],
                              dirb=case['result'],
                              namea=case['oname'],
                              nameb=case['rname'],
                              chara='o',
                              charb='r')

            tracker.update(diff, argset)

            counter.count('cases')

        tracker.argsets_done += 1
        last_argset = argset

        counter.count('argsets')

    tracker.driver_stats = counter.all_stats()
    return tracker


def get_data_and_stats(driverClass, argset, driver_settings, only_own=False):
    """
    Run single test, return data and stats.
    """
    start = time.time()
    d = driverClass()
    d.setup(driver_settings, only_own=only_own)
    d.run(argset)
    return (d.data, d.duration, time.time() - d.duration - start)


def get_data(driverClass, argset, driver_settings, only_own=False):
    """
    Run single test, return data only.
    """
    d = driverClass()
    d.setup(driver_settings, only_own=only_own)
    d.run(argset)
    return d.data


# ########################################################################### #
# ## The Pattern                                                           ## #
# ########################################################################### #

class _BaseRuleOp:

    def __init__(self, items, item_ok):
        self._items = items
        self._item_ok = item_ok

    def _eval(self, item):
        try:                                    # it's a pattern! (recurse)
            return RuleOp.Match(item, self._item_ok)
        except ValueError:                      # no, it's something else...
            return self._item_ok(item)

    def __bool__(self):
        try:
            return self._match()
        except TypeError:
            raise ValueError("items must be an iterable: %r" % self._items)


class RuleOp:

    class ALL(_BaseRuleOp):

        def _match(self):
            return all(self._eval(item) for item in self._items)

    class ANY(_BaseRuleOp):

        def _match(self):
            return any(self._eval(item) for item in self._items)

    @staticmethod
    def Match(pattern, item_ok):
        """
        Evaluate set of logically structured patterns using passed function.

        *pattern* must be a tuple in form of `(op, items)` where *op* can be
        either `RuleOp.ALL` or `RuleOp.ANY` and *items* is a list of items
        to check using *item_ok* function.

        *item_ok* is a function that accepts single argument and its return
        value is evaluated for true-ness.

        Final result is True or False and is computed by combining results
        of individual *item_ok* calls: either all must be true (when `op
        == RuleOp.ALL`) or at least one must be true (when `op == RuleOp.ANY`).

        The evaluation is done recursively, that is, if an item in the pattern
        is also a pattern itself, it will be evaluated by calling RuleOp.Match
        and passing the same *item_ok* function.

        Note that there is no data to evaluate "against", you can use closure
        if you need to do that.
        """

        try:
            op, items = pattern
        except TypeError:
            raise ValueError("pattern is not a tuple: %r" % pattern)
        try:
            assert issubclass(op, _BaseRuleOp)
        except TypeError:
            raise ValueError("invalid operator: %r" % op)
        except AssertionError:
            raise ValueError("invalid operator class: %s" % op.__name__)
        return bool(op(items, item_ok))


# ########################################################################### #
# ## The Path                                                              ## #
# ########################################################################### #

class DictPath:
    """Mixin that adds "path-like" behavior to the top dict of dicts.

    Use this class as a mixin for a deep dictionary-like structure in order to
    access the elements using a Unix-like path.  For example:

        MyData(dict, DictPath):
            pass

        d = MyData({
            'name': 'Joe',
            'age': 34,
            'ssn': {
                'number': '012 345 678',
                'expires': '10-01-16',
            },
        })

        print ("%s's ssn number %s will expire on %s"
                % (d.getpath('/name'),
                   d.getpath('/ssn/number'),
                   d.getpath('/ssn/expiry')))
        # joe's ssn number 012 345 678 will expire 10-01-16
    """

    DIV = "/"

    class Path:

        def __init__(self, path, div):
            self.DIV = div
            self._path = path

        def _validate(self):
            try:
                assert self._path.startswith(self.DIV)
            except (AttributeError, AssertionError):
                raise ValueError("invalid path: %r" % self._path)

        def stripped(self):
            return self._path.lstrip(self.DIV)

    @classmethod
    def __s2path(cls, path):
        return cls.Path(path, cls.DIV)

    @classmethod
    def __err_path_not_found(cls, path):
        raise KeyError("path not found: %s" % path)

    @classmethod
    def __getitem(cls, dct, key):
        if cls.DIV in key:
            frag, rest = key.split(cls.DIV, 1)
            subdct = dct[frag]
            result = cls.__getitem(subdct, rest)
        else:
            result = dct[key]
        return result

    @classmethod
    def __setitem(cls, dct, key, value):
        if cls.DIV not in key:
            dct[key] = value
        else:
            frag, rest = key.split(cls.DIV, 1)
            subdct = dct[frag]
            cls.__setitem(subdct, rest, value)

    @classmethod
    def __delitem(cls, dct, key):
        if cls.DIV not in key:
            del dct[key]
        else:
            frag, rest = key.split(cls.DIV, 1)
            subdct = dct[frag]
            return cls.__delitem(subdct, rest)

    # # public methods
    #

    def getpath(self, path):
        try:
            return self.__getitem(self, self.__s2path(path).stripped())
        except (TypeError, KeyError):
            self.__err_path_not_found(path)

    def setpath(self, path, value):
        try:
            self.__setitem(self, self.__s2path(path).stripped(), value)
        except (TypeError, KeyError):
            self.__err_path_not_found(path)

    def delpath(self, path):
        try:
            self.__delitem(self, self.__s2path(path).stripped())
        except (TypeError, KeyError):
            self.__err_path_not_found(path)

    def ispath(self, path):
        try:
            self.getpath(path)
            return True
        except KeyError:
            return False


# ########################################################################### #
# ## The Case                                                              ## #
# ########################################################################### #

class TinyCase(dict, DictPath):
    """Test case for hoover.

    This class is used as an intermediary container for test parameters,
    oracles and test results.  This is to allow post-test transformations
    ("hacks") to happen before the result is evaluated for pass/fail.

    Instantiate TinyCase with data (self) in following format:

        {
            'argset': {},   # argset as fed into `BaseTestDriver.run`
            'oracle': {},   # data as returned from oracle driver's `run()`
            'result': {},   # data as returned from result driver's `run()`
            'oname': "",    # name of oracle driver's class
            'rname': ""     # name of result driver's class
        }

    Then call TinyCase.hack() with a set of rules which can alter oracles,
    results or both based on the data stored in TinyCase.

    Typical use cases for 'hacks' are:

     *  avoid known and tracked bugs,
     *  help normalize results (remove irrelevant details),
     *  solve certain limitations in oracle machines.

    Note that while for most tests, you should strive for zero hacks,
    sometimes they are inevitable.  In such cases, number of hacks can
    be a useful quality metric.   For that reason, 'hoover.regression_test'
    will count the applied hacks and return it in the test report.
    """

    def a_exchange(self, action):
        """Exchange value A for value B.

        Expects a dict, where key is a tuple of two values `(a, b)` and
        value is a list of paths.  For each key, it goes through the
        paths and if the value equals `a` it is set to `b`.
        """
        for (oldv, newv), paths in action.items():
            for path in paths:
                try:
                    curv = self.getpath(path)
                except KeyError:
                    continue
                else:
                    if curv == oldv:
                        self.setpath(path, newv)

    def a_format_str(self, action):
        """Convert value to a string using format string.

        Expects a dict, where key is a format string, and value is a list
        of paths.  For each record, the paths are traversed, and value is
        converted to string using the format string and the `%` operator.

        This is especially useful for floats which you may want to trim
        before comparison, since direct comparison of floats is unreliable
        on some architectures.
        """
        for fmt, paths in action.items():
            for path in paths:
                if self.ispath(path):
                    new = fmt % self.getpath(path)
                    self.setpath(path, new)

    def a_even_up(self, action):
        """Even up structure of both dictionaries.

        Expects a list of two-element tuples `('/dict/a', '/dict/b')`
        containing pairs of path do simple dictionaries.

        Then the two dicts are altered to have same structure: if a key
        in dict "a" is missing in dict "b", it is set to `None` in "b" and
        vice-versa,
        """
        for patha, pathb in action:
            try:
                a = self.getpath(patha)
                b = self.getpath(pathb)
            except KeyError:
                continue
            else:
                for key in set(a.keys()) | set(b.keys()):
                    if key in a and key in b:
                        pass    # nothing to do here
                    elif key in a and a[key] is None:
                        b[key] = None
                    elif key in b and b[key] is None:
                        a[key] = None
                    else:
                        pass    # bailout: odd key but value is *not* None

    def a_remove(self, action):
        """Remove elements from structure.

        Expects a simple list of paths that are simply deleted fro, the
        structure.
        """
        for path in action:
            if self.ispath(path):
                self.delpath(path)

    def a_round(self, action):
        """Round a (presumably) float using tha `float()` built-in.

        Expects dict with precision (ndigits, after the dot) as a key and
        list of paths as value.
        """
        for ndigits, paths in action.items():
            for path in paths:
                try:
                    f = self.getpath(path)
                except KeyError:
                    pass
                else:
                    self.setpath(path, round(f, ndigits))

    known_actions = {'remove': a_remove,
                     'even_up': a_even_up,
                     'format_str': a_format_str,
                     'exchange': a_exchange,
                     'round': a_round}

    def hack(self, ruleset):
        """
        Run any matching actions in the *ruleset*.

        Each rule must be in in a following form:

            {
                'drivers': [{}],        # list of structures to match
                                        # against self
                'argsets': [{}],        # -ditto-
                <action_name>: <Arg>    # an action name with argument
                <action_name>: <Arg>    # another action...
            }

        Each of the rules is first evaluated for match (does it apply to this
        TinyCase?), and if the rule applies, transformation is done.  The
        transformation is defined by `<action_name>: <Arg>` pairs and it can
        alter 'oracle', 'result' or both.

        The match evaluation is done using `hoover.dataMatch()` -- this is
        basically a recursive pattern match against 'drivers' and 'argsets'.
        Both 'drivers' and 'argsets' are optional, but when specified, all
        items must must match in order for the rule to apply.  (If 'drivers'
        and 'argsets' are both missing or empty, rule will apply to each and
        all test cases.)

        If rule does not match, `TinyCase.hack()` moves on to next one.

        If a rule does match, `TinyCase.hack()` will look for actions defined
        in it.  Action consists of action name (key of the rule dictionary,
        <action_name>) and an argument (<Arg>).

        Action name must be one of: 'remove', 'even_up', 'format_str',
        'exchange' or 'round'.  Each action corresponds to a TinyCase method
        prefixed by 'a_'; for example 'even_up' action corresponds to
        TinyCase.a_even_up method.  Each action expects different argument
        so see the corresponding method docstrings.

        Because 'oracle' and 'result' can be relatively complex structures,
        actions accept Unix-like paths to specify elements inside them.
        The "root" of the path is the TinyCase instance, and "directories"
        are keys under it.  For example, following would be valid paths
        if test drivers work with dictionaries such as `{'temperature': 50,
        'stats': {'word_count': 15}}`:

            /oracle/temperature
            /result/stats/word_count

        Warning: All actions will silently ignore any paths that are invalid
                 or leading to non-existent data!
                 (This does not apply to a path leading to `None`.)
        """

        def driver_matches(rule):
            if 'drivers' not in rule:
                return True
            else:
                return any(dataMatch(p, self)
                           for p in rule['drivers'])

        def argset_matches(rule):
            if 'argsets' not in rule:
                return True
            else:
                return any(dataMatch(p, self)
                           for p in rule['argsets'])

        matched = False
        cls = self.__class__
        for rule in ruleset:
            if driver_matches(rule) and argset_matches(rule):
                matched = True
                for action_name in cls.known_actions:
                    if action_name in rule:
                        cls.known_actions[action_name](self, rule[action_name])
        return matched


# ########################################################################### #
# ## Drivers                                                               ## #
# ########################################################################### #

class DriverError(Exception):
    """Error encountered when obtaining driver data"""

    def __init__(self, message, driver):
        self.message = message
        self.driver = driver

    def __str__(self):

        result = ("\n\n"
                  "  type: %s\n"
                  "  message: %s\n"
                  "  driver: %s\n"
                  "  args: %s\n"
                  "  settings: %s\n"
                  % (self.message.__class__.__name__,
                     self.message,
                     self.driver.__class__.__name__,
                     self.driver._args,
                     self.driver._settings))

        return result


class DriverDataError(Exception):
    """Error encountered when decoding or normalizing driver data"""

    def __init__(self, exception, driver):
        self.exception = exception
        self.driver = driver

    def __str__(self):

        result = ("%s: %s\n"
                  "  class: %s\n"
                  "  args: %s\n"
                  "  data: %s\n"
                  % (self.exception.__class__.__name__, self.exception,
                     self.driver.__class__.__name__,
                     json.dumps(self.driver._args, sort_keys=True, indent=4),
                     json.dumps(self.driver.data, sort_keys=True, indent=4)))
        return result


class BaseTestDriver:
    """Base class for test drivers used by `hoover.regression_test` and others.

    This class tepresents test driver and can be used to:

     *  Wrap system under test (SUT).

        Provide simple interface to set up, sandbox and activate the system
        and collect any relevant results.  This can be merely return value
        (purely functional test) but also other characteristics such as
        time to complete.

     *  Mimic ("mock") the system under test.

        Also called as oracle machine, this can be used to predict expected
        behavior of SUT under given parameters.

     *  Wrap an alternative implementation of SUT.

        As a special case of the previous role, sometimes it's desirable to
        use an alternative implementation of SUT as oracle machine.  This
        can be a legacy implementation, reference implementation or other
        platform implementation.

    In either case, the driver makes sure that any input arguments are
    interpreted (and passed on) correctly and any results are returned in
    a consistent way.

    To use this class, sub-class it and implement `_get_data()` method.
    Tge `_get_data()` method must:

     *  Accept single argument; this contains arguments to the SUT.

        If using `hoover.regression_test()`, this value will be retrieved
        from the *argsrc* iterator.

     *  Implement the test case defined by the argument set.

        The implementation can either be a wrapper to real SUT, alternative
        one, or can be an oracle machine -- i.e. it can figure out the result
        on its own.  Note that this can be much easier as it sounds, given
        that you can "cheat" by crafting the set of test cases so that the
        prediction is easy (but still effective at hitting bugs), or you
        can "hide the answer" in the *args* itself, and define set of
        test cases statically in form of "question, answer" pairs.

     *  Collect any relevant data and set it to `data` property.

        Optionally, you can also set `duration` property (in fractional
        seconds, as returned by standard time module).  If you don't
        it will be automatically measured.

    Any exception from the *_get_data* method will be re-raised as
    DriverError.

    Optionally, you can:

    *   Implement *__init__* method calling base __init__ and setting more
        properties:

        *   `self._mandatory_args`, a list of keys that need to be present
            in `args` argument to `run()`

        *   and `self._mandatory_settings`, a list of keys that need to be
            present in the `settings` argument to `__init__`

    *   Implement methods

        *   `_decode_data` and `_normalize_data`, which are intended to decode
             the data from any raw format it is received, and to prepare it
             for comparison in test,

        *   and `_check_data`, to allow for early detection of failure,

        from which any exception is re-raised as a DriverDataError with
        some additional info

    *   set "bailouts", a list of functions which, when passed "args"
        argument, return true to indicate that driver is not able to
        process these values (see below for explanation).  If any of
        these functions returns true, NotImplementedError is raised.

    The expected workflow when using the driver is:

        # 1. sub-class hoover.BaseTestDriver
        # 2. prepare settings and args
        MyDriver.check_values(args)     # optional, to force bailouts ASAP
        d = MyDriver()
        d.setup(settings)
        d.run(args)
        assert d.data, "no data"        # evaluate the result...
        assert d.duration < 1           # duration of _get_data in seconds

    Note on bailouts:  Typical strategy for which the driver is intended is
    that each possible combination of `args` is exhausted, and results from
    multiple drivers are compared to evaluate if driver, i.e. system in
    question is O.K.

    The bailouts mechanism is useful in cases, where for a certain system,
    a valid combination of arguments would bring the same result as another,
    so there is basically no value in testing both of them.

    Example might be a system that does not support a binary flag and
    behaves as if it was "on": you can simply make the test driver
    accept the option but "bail out" any time it is "off", therefore
    skipping the time-and-resource-consuming test.
    """

    bailouts = []

    ##
    #  internal methods
    #

    def __init__(self):
        self.data = {}
        self.duration = None
        self._args = {}
        self._mandatory_args = []
        self._mandatory_settings = []
        self._settings = {}
        self._setup_ok = False

    def __check_mandatory(self):
        """validate before run()"""
        for key in self._mandatory_args:
            assert key in self._args, "missing arg: '%s'" % key
        for key in self._mandatory_settings:
            assert key in self._settings, "missing setting: '%s'" % key

    def __cleanup_data(self):
        """remove hidden data; e.g. what was only there for _check_data"""
        for key in self.data:
            if key.startswith("_"):
                del self.data[key]

    ##
    #  virtual methods
    #

    def _check_data(self):
        """Early check for failure"""
        pass

    def _decode_data(self):
        """Decode from raw data as brought by _get_data"""
        pass

    def _normalize_data(self):
        """Preare data for comparison (e.g. sort, split, trim...)"""
        pass

    ##
    #  public methods
    #

    @classmethod
    def check_values(cls, args=None):
        """
        Check args in advance before running or setting up anything.
        """
        for fn in cls.bailouts:
            if fn(args):
                raise NotImplementedError(inspect.getsource(fn))

    def setup(self, settings, only_own=False):
        """
        Load settings.

        If *only_own* is false, *settings* are merely assigned to
        settings attribute.

        if *only_own* is true, settings are filtered:  Any keys that don't
        begin with the prefix of driver class name and period are ignored.
        Settings that do start with this prefix are assigned to settings
        attribute with the prefix removed.
        """
        if only_own:
            for ckey in settings:
                driver_class_name, setting_name = ckey.split(".", 2)
                if self.__class__.__name__ == driver_class_name:
                    self._settings[setting_name] = settings[ckey]
        else:
            self._settings = settings
        self._setup_ok = True

    def run(self, args):
        """
        Validate args, run SUT and store data.
        """

        self._args = args
        assert self._setup_ok, "run() before setup()?"
        self.__class__.check_values(self._args)
        self.__check_mandatory()
        start = time.time()
        try:
            self._get_data()        # run the test, i.e. obtain raw data
        except Exception as e:
            raise DriverError(e, self)
        self.duration = (time.time() - start if self.duration is None
                         else self.duration)
        try:
            self._decode_data()     # decode raw data
            self._normalize_data()  # normalize decoded data
            self._check_data()      # perform arbitrarty checking
        except Exception as e:
            raise DriverDataError(e, self)
        self.__cleanup_data()   # cleanup (remove data['_*'])


class MockDriverTrue(BaseTestDriver):
    """A simple mock driver, always returning True"""

    def _get_data(self, args):
        self.data = True


# ########################################################################### #
# ## Helpers                                                               ## #
# ########################################################################### #

class StatCounter:
    """
    A simple counter with support for custom formulas.
    """

    def __init__(self):
        self.generic_stats = {}
        self.driver_stats = {}
        self.formulas = {}
        self._born = time.time()

    def _register(self, dname):
        self.driver_stats[dname] = {
            'calls': 0,
            'rhacks': 0,
            'ohacks': 0,
            'duration': 0,
            'overhead': 0
        }

        ##
        # Formulas
        #

        # cumulative duration/overhead; just round to ms
        self.add_formula(dname + '_overhead',
                         lambda g, d: int(1000 * d[dname]['overhead']))
        self.add_formula(dname + '_duration',
                         lambda g, d: int(1000 * d[dname]['duration']))

        # average (per driver call) overhead/duration
        self.add_formula(
            dname + '_overhead_per_call',
            lambda g, d: int(1000 * d[dname]['overhead'] / d[dname]['calls'])
        )
        self.add_formula(
            dname + '_duration_per_call',
            lambda g, d: int(1000 * d[dname]['duration'] / d[dname]['calls'])
        )

        def gtotal_drivertime(g, d):
            driver_time = (sum(s['overhead'] for s in d.values())
                           + sum(s['duration'] for s in d.values()))
            return int(1000 * driver_time)

        def gtotal_loop_overhead(g, d):
            driver_time = gtotal_drivertime(g, d)
            onnext_time = int(1000 * g['on_next'])
            age = int(1000 * (time.time() - self._born))
            return age - driver_time - onnext_time

        # grand totals in times: driver time, loop overhead
        self.add_formula('gtotal_drivertime', gtotal_drivertime)
        self.add_formula('gtotal_loop_overhead', gtotal_loop_overhead)
        self.add_formula('gtotal_loop_onnext',
                         lambda g, d: int(1000 * g['on_next']))

        # average (per driver call) overhead/duration
        self.add_formula(
            'cases_hacked',
            lambda g, d: round(100 * float(g['hacked_cases']) / g['cases'], 2)
        )

    def _computed_stats(self):
        computed = dict.fromkeys(self.formulas)
        for fname, fml in self.formulas.items():
            try:
                v = fml(self.generic_stats, self.driver_stats)
            except ZeroDivisionError:
                v = None
            computed[fname] = v
        return computed

    def add_formula(self, vname, formula):
        """
        Add a function to work with generic_stats, driver_stats.
        """
        self.formulas[vname] = formula

    def add(self, vname, value):
        """
        Add a value to generic stat counter.
        """
        if vname in self.generic_stats:
            self.generic_stats[vname] += value
        else:
            self.generic_stats[vname] = value

    def add_for(self, dclass, vname, value):
        """
        Add a value to driver stat counter.
        """
        dname = dclass.__name__
        if dname not in self.driver_stats:
            self._register(dname)
        if vname in self.driver_stats[dname]:
            self.driver_stats[dname][vname] += value
        else:
            self.driver_stats[dname][vname] = value

    def count(self, vname):
        """
        Alias to add(vname, 1)
        """
        self.add(vname, 1)

    def count_for(self, dclass, vname):
        """
        Alias to add_for(vname, 1)
        """
        self.add_for(dclass, vname, 1)

    def all_stats(self):
        """
        Compute stats from formulas and add them to colledted data.
        """
        stats = self.generic_stats
        for dname, dstats in self.driver_stats.items():
            for key, value in dstats.items():
                stats[dname + "_" + key] = value
        stats.update(self._computed_stats())
        return stats


class Tracker(dict):
    """
    Error tracker to allow for usable reports from huge regression tests.

    Best used as a result bearer from `regression_test`, this class keeps
    a simple in-memory "database" of errors seen during the regression
    test, and implements few methods to access the data.

    The basic usage is:

         1. Instantiate (no parameters)

         2. Each time you have a result of a test, you pass it to `update()`
            method along with the argument set (as a single object, typically
            a dict) that caused the error.

            If boolean value of the result is False, the object is thrown away
            and nothing happens.  Otherwise, its string value is used as a key
            under which the argument set is saved.

            The string interpretation of the result is supposed to be
            "as deterministic as possible", i.e. it should provide only
            necessary information about the error:  do not include any
            timestamps or "volatile" values such as PID's, version numbers
            or tempfile names.

         3. At final stage, you can retrieve statistics as how many (distinct)
            errors have been recorded, what was the duration of the whole test,
            how many times `update()` was called, etc.

         4. Optionally, you can also call `format_report()` to get a nicely
            formatted report with list of arguments for each error string.

         5. Since in bigger tests, argument lists can grow really large,
            complete lists are not normally printed.  Instead, you can use
            `write_stats_csv()`, which will create one CSV per each error,
            named as first 7 chars of its SHA1 (inspired by Git).

            Note that you need to pass an existing writable folder path.
    """

    ##
    #  internal methods
    #

    def __init__(self):
        self._start = time.time()
        self._db = {}
        self.tests_done = 0
        self.tests_passed = 0
        self.argsets_done = 0
        self.driver_stats = {}

    def _csv_fname(self, errstr, prefix):
        """
        Format name of file for this error string
        """
        return '%s/%s.csv' % (prefix, self._eid(errstr))

    def _eid(self, errstr):
        """
        Return EID for the error string (first 7 chars of SHA1).
        """
        return hashlib.sha1(errstr).hexdigest()[:7]

    def _insert(self, errstr, argset):
        """
        Insert the argset into DB.
        """
        if errstr not in self._db:
            self._db[errstr] = []
        self._db[errstr].append(argset)

    def _format_error(self, errstr, max_aa=0):
        """
        Format single error for output.
        """
        argsets_affected = self._db[errstr]
        num_aa = len(argsets_affected)

        # trim if list is too long for Jenkins
        argsets_shown = argsets_affected
        if max_aa and (num_aa > max_aa):
            div = ["[...] not showing %s cases, see %s.csv for full list"
                   % (num_aa - max_aa, self._eid(errstr))]
            argsets_shown = argsets_affected[0:max_aa] + div

        # format error
        formatted_aa = "\n".join([str(arg) for arg in argsets_shown])
        return ("~~~ ERROR FOUND (%s) ~~~~~~~~~~~~~~~~~~~~~~~~~\n"
                "--- error string: -----------------------------------\n%s\n"
                "--- argsets affected (%d) ---------------------------\n%s\n"
                % (self._eid(errstr), errstr, num_aa, formatted_aa))

    ##
    #  public methods
    #

    def errors_found(self):
        """
        Return number of non-distinct errors in db.
        """
        return bool(self._db)

    def format_report(self, max_aa=0):
        """
        Return complete report formatted as string.
        """
        error_list = "\n".join([self._format_error(e, max_aa=max_aa)
                                for e in self._db])
        return ("Found %(total_errors)s (%(distinct_errors)s distinct) errors"
                " in %(tests_done)s tests with %(argsets)s argsets"
                " (duration: %(time)ss):"
                % self.getstats()
                + "\n\n" + error_list)

    def getstats(self):
        """
        Return basic and driver stats

        Returns dictionary with following values:

            'tests_done' - how many times Tracker.update() was called

            'distinct_errors' - how many distinct errors (same `str(error)`)
                           were seen by Tracker.update()

            'total_errors' - how many times `Tracker.update()` saw an
                           error, i.e. how many argsets are in DB

            'time'       - how long since init (seconds)
        """

        def total_errors():
            return functools.reduce(
                lambda x, y: x + len(y),
                self._db.values(),
                initial=0,
            )

        stats = {
            "argsets": self.argsets_done,
            "tests_done": self.tests_done,
            "distinct_errors": len(self._db),
            "total_errors": total_errors(),
            "time": int(time.time() - self._start)
        }
        stats.update(self.driver_stats)
        return stats

    def update(self, error, argset):
        """
        Update tracker with test result.

        If `bool(error)` is true, it is considered error and argset
        is inserted to DB with `str(error)` as key.  This allows for later
        sorting and analysis.
        """
        self.tests_done += 1
        if error:
            errstr = str(error)
            self._insert(errstr, argset)

    def write_stats_csv(self, fname):
        """
        Write stats to a simple one row (plus header) CSV.
        """
        stats = self.getstats()
        colnames = sorted(stats.keys())
        with open(fname, 'a') as fh:
            cw = csv.DictWriter(fh, colnames)
            cw.writerow(dict(zip(colnames, colnames)))  # header
            cw.writerow(stats)

    def write_args_csv(self, prefix=''):
        """
        Write out a set of CSV files, one per distinctive error.

        Each CSV is named with error EID (first 7 chars of SHA1) and lists
        all argument sets affected by this error.  This is supposed to make
        easier to further analyse impact and trigerring values of errors,
        perhaps using a table processor software.
        """

        def get_all_colnames():
            cn = {}
            for affected in self._db.values():
                for argset in affected:
                    cn.update(dict.fromkeys(argset))
                return sorted(cn.keys())

        all_colnames = get_all_colnames()

        for errstr in self._db:
            with open(self._csv_fname(errstr, prefix), 'a') as fh:
                cw = csv.DictWriter(fh, all_colnames)
                cw.writerow(dict(zip(all_colnames, all_colnames)))  # header
                for argset in self._db[errstr]:
                    cw.writerow(argset)


def dataMatch(pattern, data):
    """Check if data structure matches a pattern data structure.

    Supports lists, dictionaries and scalars (int, float, string).

    For scalars, simple `==` is used.

    Lists are converted to sets and "to match" means "to have a matching
    subset (e.g. `[1, 2, 3, 4]` matches `[3, 2]`).

    Both lists and dictionaries are matched recursively.
    """

    def listMatch(pattern, data):
        """
        Match list-like objects
        """
        assert all([hasattr(o, 'append') for o in [pattern, data]])
        results = []
        for pv in pattern:
            if any([dataMatch(pv, dv) for dv in data]):
                results.append(True)
            else:
                results.append(False)
        return all(results)

    def dictMatch(pattern, data):
        """
        Match dict-like objects
        """
        assert all([hasattr(o, 'iteritems') for o in [pattern, data]])
        results = []
        try:
            for pk, pv in pattern.items():
                results.append(dataMatch(pv, data[pk]))
        except KeyError:
            results.append(False)
        return all(results)

    result = None
    if pattern == data:
        result = True
    else:
        for handler in [dictMatch, listMatch]:
            try:
                result = handler(pattern, data)
            except AssertionError:
                continue
    return result


def jsDump(data):
    """
    A human-readable JSON dump.
    """
    return json.dumps(data, sort_keys=True, indent=4,
                      separators=(',', ': '))


def jsDiff(dira, dirb, namea="A", nameb="B", chara="a", charb="b"):
    """
    JSON-based human-readable diff of two data structures.

    '''BETA''' version.

    jsDiff is based on unified diff of two human-readable JSON dumps except
    that instead of showing line numbers and context based on proximity to
    the changed lines, it prints only context important from the data
    structure point.

    The goal is to be able to quickly tell the story of what has changed
    where in the structure, no matter size and complexity of the data set.

    For example:

        a = {
            'w': {1: 2, 3: 4},
            'x': [1, 2, 3],
            'y': [3, 1, 2]
        }
        b = {
            'w': {1: 2, 3: 4},
            'x': [1, 1, 3],
            'y': [3, 1, 3]
        }
        print jsDiff(a, b)

    will output:

        aaa ~/A
             "x": [
        a        2,
             "y": [
        a        2
        bbb ~/B
             "x": [
        b        1,
             "y": [
        b        3

    Notice that the final output somehow resembles the traditional unified
    diff, so to avoid confusion, +/- is changed to a/b (the characters can
    be provided as well as the names A/B).
    """

    def compress(lines):

        def is_body(line):
            return line.startswith(("-", "+", " "))

        def is_diff(line):
            return line.startswith(("-", "+"))

        def is_diffA(line):
            return line.startswith("-")

        def is_diffB(line):
            return line.startswith("+")

        def is_context(line):
            return line.startswith(" ")

        def is_hdr(line):
            return line.startswith(("@@", "---", "+++"))

        def is_hdr_hunk(line):
            return line.startswith("@@")

        def is_hdr_A(line):
            return line.startswith("---")

        def is_hdr_B(line):
            return line.startswith("+++")

        class Level:

            def __init__(self, hint):
                self.hint = hint
                self.hinted = False

            def __str__(self):
                return str(self.hint)

            def get_hint(self):
                if not self.hinted:
                    self.hinted = True
                    return self.hint

        class ContextTracker:

            def __init__(self):
                self.trace = []
                self.last_line = None
                self.last_indent = -1

            def indent_of(self, line):
                meat = line[1:].lstrip(" ")
                ind = len(line) - len(meat) - 1
                return ind

            def check(self, line):
                indent = self.indent_of(line)
                if indent > self.last_indent:
                    self.trace.append(Level(self.last_line))
                elif indent < self.last_indent:
                    self.trace.pop()
                self.last_line = line
                self.last_indent = indent

            def get_hint(self):
                return self.trace[-1].get_hint()

        buffa = []
        buffb = []
        ct = ContextTracker()

        for line in lines:

            if is_hdr_hunk(line):
                continue
            elif is_hdr_A(line):
                line = line.replace("---", chara * 3, 1)
                buffa.insert(0, line)
            elif is_hdr_B(line):
                line = line.replace("+++", charb * 3, 1)
                buffb.insert(0, line)

            elif is_body(line):

                ct.check(line)

                if is_diff(line):
                    hint = ct.get_hint()
                    if hint:
                        buffa.append(hint)
                        buffb.append(hint)

                if is_diffA(line):
                    line = line.replace("-", chara, 1)
                    buffa.append(line)

                elif is_diffB(line):
                    line = line.replace("+", charb, 1)
                    buffb.append(line)

            else:
                raise AssertionError("difflib.unified_diff emitted"
                                     " unknown format (%s chars):\n%s"
                                     % (len(line), line))

        return buffa + buffb

    dumpa = jsDump(dira)
    dumpb = jsDump(dirb)
    udiff = difflib.unified_diff(dumpa.split("\n"), dumpb.split("\n"),
                                 "~/" + namea, "~/" + nameb,
                                 n=10000, lineterm='')

    return "\n".join(compress([line for line in udiff]))


class Cartman:
    """
    Create argument sets from ranges (or ay iterators) of values.

    This class is to enable easy definition and generation of dictionary
    argument sets using Cartesian product.

    To use Cartman iterator, you need to define structure of an argument
    set.  Argument set--typically a dictionary--is a set of values that
    together constitute a test case.  Within the argument set, values
    will change from test case to test case, so for each changing value,
    you will also need to define range of values you want to test on.

    Cartman initiator expects following arguments:

     *  *scheme*, which is a "prototype" of a final argument set, except
        that values are replaced by either `Cartman.Iterable` if the
        value is changing from test case to another, and `Cartman.Scalar`
        if the value is constant.

     *  *source*, which has the same structure, except that where in scheme
        is `Cartman.Iterable`, the source has an iterable.  Where scheme has
        `Cartman.Scalar`, the source can have any value.

    Finally, when Cartman instance is used in loop, it uses Cartesian product
    in order to generate argument sets.

    Consider this example:

        You have a system (wrapped up in test driver) that takes ''size''
        argument, that is supposed to be ''width'', ''height'' and ''depth'',
        each an integer ranging from 1 to 100, and ''color'' that can
        be "white", "black" or "yellow".

        For a test using all-combinations strategy, you will need to generate
        100 * 100 * 100 * 3 argument sets, i.e. 3M tests.

        All you need to do is:

            scheme = {
                'size': {
                    'width': Cartman.Iterable,
                    'height': Cartman.Iterable,
                    'depth': Cartman.Iterable,
                }
                'color': Cartman.Iterable,
            }

            source = {
                'size': {
                    'width': range(1, 100),
                    'height': range(1, 100),
                    'depth': range(1, 100),
                }
                'color': ['white', 'black', 'yellow'],
            }

            c = Cartman(source, scheme)

            for argset in c:
                result = my_test(argset)
                # assert ...

    The main advantage is that you can separate the definition from
    the code, and you can keep yor iterators as big or as small as
    needed, and add / remove values.

    Also in case your parameters vary in structure over time, or from
    one test to another, it gets much easier to keep up with changes
    without much jumping through hoops.

    Note: `Cartman.Scalar` is provided mainly to make your definitions
    more readable.  Following constructions are functionally equal:

        c = Cartman({'a': 1}, {'a': Cartman.Scalar})
        c = Cartman({'a': [1]}, {'a': Cartman.Iterable})

    In future, however, this might change, though, mainly in case
    optimization became possible based on what was used.
    """

    # TODO: support for arbitrary ordering (profile / nginx)
    # TODO: implement getstats and fmtstats
    # TODO: N-wise

    class _BaseMark:
        pass

    class Scalar(_BaseMark):
        pass

    class Iterable(_BaseMark):
        pass

    def __init__(self, source, scheme):
        self.source = source
        self.scheme = scheme

        # validate scheme + source and throw useful error
        scheme_ok = isinstance(self.scheme, collections.Mapping)
        source_ok = isinstance(self.source, collections.Mapping)
        if not scheme_ok:
            raise ValueError("scheme must be a mapping (e.g. dict)")
        elif scheme_ok and not source_ok:
            raise ValueError("scheme vs. source mismatch")

    def __deepcopy__(self, memo):
        return Cartman(deepcopy(self.source, memo),
                       deepcopy(self.scheme, memo))

    def _is_mark(self, subscheme):
        try:
            return issubclass(subscheme, Cartman._BaseMark)
        except TypeError:
            return False

    def _means_scalar(self, subscheme):
        if self._is_mark(subscheme):
            return issubclass(subscheme, Cartman.Scalar)

    def _means_iterable(self, subscheme):
        if self._is_mark(subscheme):
            return issubclass(subscheme, Cartman.Iterable)

    def _get_iterable_for(self, key):
        subscheme = self.scheme[key]
        subsource = self.source[key]
        if self._means_scalar(subscheme):
            return [subsource]
        elif self._means_iterable(subscheme):
            return subsource
        else:   # try to use it as scheme
            return iter(Cartman(subsource, subscheme))

    def __iter__(self):

        names = []
        iterables = []

        for key in self.scheme:
            try:
                iterables.append(self._get_iterable_for(key))
            except KeyError:
                pass    # ignore that subsource mentioned by scheme is missing
            else:
                names.append(key)

        for values in itertools.product(*iterables):
            yield dict(zip(names, values))

    def getstats(self):
        return {}

    def fmtstats(self):
        return ""