# coding=utf-8
#
# This file is part of Hypothesis, which may be found at
# https://github.com/HypothesisWorks/hypothesis/
#
# Most of this work is copyright (C) 2013-2019 David R. MacIver
# (david@drmaciver.com), but it contains contributions by others. See
# CONTRIBUTING.rst for a full list of people who may hold copyright, and
# consult the git log if you need to determine who owns an individual
# contribution.
#
# This Source Code Form is subject to the terms of the Mozilla Public License,
# v. 2.0. If a copy of the MPL was not distributed with this file, You can
# obtain one at https://mozilla.org/MPL/2.0/.
#
# END HEADER

from __future__ import absolute_import, division, print_function

import hypothesis.internal.conjecture.utils as cu
from hypothesis.errors import InvalidArgument
from hypothesis.internal.compat import OrderedDict
from hypothesis.internal.conjecture.junkdrawer import LazySequenceCopy
from hypothesis.internal.conjecture.utils import combine_labels
from hypothesis.searchstrategy.strategies import (
    MappedSearchStrategy,
    SearchStrategy,
    filter_not_satisfied,
)


class TupleStrategy(SearchStrategy):
    """A strategy responsible for fixed length tuples based on heterogenous
    strategies for each of their elements."""

    def __init__(self, strategies):
        SearchStrategy.__init__(self)
        self.element_strategies = tuple(strategies)

    def do_validate(self):
        for s in self.element_strategies:
            s.validate()

    def calc_label(self):
        return combine_labels(
            self.class_label, *[s.label for s in self.element_strategies]
        )

    def __repr__(self):
        if len(self.element_strategies) == 1:
            tuple_string = "%s," % (repr(self.element_strategies[0]),)
        else:
            tuple_string = ", ".join(map(repr, self.element_strategies))
        return "TupleStrategy((%s))" % (tuple_string,)

    def calc_has_reusable_values(self, recur):
        return all(recur(e) for e in self.element_strategies)

    def do_draw(self, data):
        return tuple(data.draw(e) for e in self.element_strategies)

    def calc_is_empty(self, recur):
        return any(recur(e) for e in self.element_strategies)


class ListStrategy(SearchStrategy):
    """A strategy for lists which takes a strategy for its elements and the
    allowed lengths, and generates lists with the correct size and contents."""

    def __init__(self, elements, min_size=0, max_size=float("inf")):
        SearchStrategy.__init__(self)
        self.min_size = min_size or 0
        self.max_size = max_size if max_size is not None else float("inf")
        assert 0 <= self.min_size <= self.max_size
        self.average_size = min(
            max(self.min_size * 2, self.min_size + 5),
            0.5 * (self.min_size + self.max_size),
        )
        self.element_strategy = elements

    def calc_label(self):
        return combine_labels(self.class_label, self.element_strategy.label)

    def do_validate(self):
        self.element_strategy.validate()
        if self.is_empty:
            raise InvalidArgument(
                (
                    "Cannot create non-empty lists with elements drawn from "
                    "strategy %r because it has no values."
                )
                % (self.element_strategy,)
            )
        if self.element_strategy.is_empty and 0 < self.max_size < float("inf"):
            raise InvalidArgument(
                "Cannot create a collection of max_size=%r, because no "
                "elements can be drawn from the element strategy %r"
                % (self.max_size, self.element_strategy)
            )

    def calc_is_empty(self, recur):
        if self.min_size == 0:
            return False
        else:
            return recur(self.element_strategy)

    def do_draw(self, data):
        if self.element_strategy.is_empty:
            assert self.min_size == 0
            return []

        elements = cu.many(
            data,
            min_size=self.min_size,
            max_size=self.max_size,
            average_size=self.average_size,
        )
        result = []
        while elements.more():
            result.append(data.draw(self.element_strategy))
        return result

    def __repr__(self):
        return "%s(%r, min_size=%r, max_size=%r)" % (
            self.__class__.__name__,
            self.element_strategy,
            self.min_size,
            self.max_size,
        )


class UniqueListStrategy(ListStrategy):
    def __init__(self, elements, min_size, max_size, keys):
        super(UniqueListStrategy, self).__init__(elements, min_size, max_size)
        self.keys = keys

    def do_draw(self, data):
        if self.element_strategy.is_empty:
            assert self.min_size == 0
            return []

        elements = cu.many(
            data,
            min_size=self.min_size,
            max_size=self.max_size,
            average_size=self.average_size,
        )
        seen_sets = tuple(set() for _ in self.keys)
        result = []

        # We construct a filtered strategy here rather than using a check-and-reject
        # approach because some strategies have special logic for generation under a
        # filter, and FilteredStrategy can consolidate multiple filters.
        filtered = self.element_strategy.filter(
            lambda val: all(
                key(val) not in seen for (key, seen) in zip(self.keys, seen_sets)
            )
        )
        while elements.more():
            value = filtered.filtered_strategy.do_filtered_draw(
                data=data, filter_strategy=filtered
            )
            if value is filter_not_satisfied:
                elements.reject()
            else:
                for key, seen in zip(self.keys, seen_sets):
                    seen.add(key(value))
                result.append(value)
        assert self.max_size >= len(result) >= self.min_size
        return result


class UniqueSampledListStrategy(ListStrategy):
    def __init__(self, elements, min_size, max_size, keys):
        super(UniqueSampledListStrategy, self).__init__(elements, min_size, max_size)
        self.keys = keys

    def do_draw(self, data):
        should_draw = cu.many(
            data,
            min_size=self.min_size,
            max_size=self.max_size,
            average_size=self.average_size,
        )
        seen_sets = tuple(set() for _ in self.keys)
        result = []

        remaining = LazySequenceCopy(self.element_strategy.elements)

        while should_draw.more():
            i = len(remaining) - 1
            j = cu.integer_range(data, 0, i)
            if j != i:
                remaining[i], remaining[j] = remaining[j], remaining[i]
            value = remaining.pop()

            if all(key(value) not in seen for (key, seen) in zip(self.keys, seen_sets)):
                for key, seen in zip(self.keys, seen_sets):
                    seen.add(key(value))
                result.append(value)
            else:
                should_draw.reject()
        assert self.max_size >= len(result) >= self.min_size
        return result


class FixedKeysDictStrategy(MappedSearchStrategy):
    """A strategy which produces dicts with a fixed set of keys, given a
    strategy for each of their equivalent values.

    e.g. {'foo' : some_int_strategy} would generate dicts with the single
    key 'foo' mapping to some integer.
    """

    def __init__(self, strategy_dict):
        self.dict_type = type(strategy_dict)

        if isinstance(strategy_dict, OrderedDict):
            self.keys = tuple(strategy_dict.keys())
        else:
            try:
                self.keys = tuple(sorted(strategy_dict.keys()))
            except TypeError:
                self.keys = tuple(sorted(strategy_dict.keys(), key=repr))
        super(FixedKeysDictStrategy, self).__init__(
            strategy=TupleStrategy(strategy_dict[k] for k in self.keys)
        )

    def calc_is_empty(self, recur):
        return recur(self.mapped_strategy)

    def __repr__(self):
        return "FixedKeysDictStrategy(%r, %r)" % (self.keys, self.mapped_strategy)

    def pack(self, value):
        return self.dict_type(zip(self.keys, value))
