# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import dataclasses
import numbers
from collections import defaultdict, namedtuple, OrderedDict
from dataclasses import InitVar
from typing import Any, ClassVar, List, Optional

import numpy as np
import pytest
import torch

from pytorch_lightning.utilities.apply_func import apply_to_collection, apply_to_collections, move_data_to_device
from pytorch_lightning.utilities.exceptions import MisconfigurationException


@dataclasses.dataclass
class Feature:
    input_ids: torch.Tensor
    segment_ids: np.ndarray

    def __eq__(self, o: object) -> bool:
        if not isinstance(o, Feature):
            return NotImplemented

        return torch.equal(self.input_ids, o.input_ids) and np.equal(self.segment_ids, o.segment_ids).all()


@dataclasses.dataclass
class ModelExample:
    example_ids: List[str]
    feature: Feature
    label: torch.Tensor
    some_constant: int = dataclasses.field(init=False)

    def __post_init__(self):
        self.some_constant = 7

    def __eq__(self, o: object) -> bool:
        if not isinstance(o, ModelExample):
            return NotImplemented

        return (
            self.example_ids == o.example_ids
            and self.feature == o.feature
            and torch.equal(self.label, o.label)
            and self.some_constant == o.some_constant
        )


@dataclasses.dataclass
class WithClassVar:
    class_var: ClassVar[int] = 0
    dummy: Any

    def __eq__(self, o: object) -> bool:
        if not isinstance(o, WithClassVar):
            return NotImplemented
        elif isinstance(self.dummy, torch.Tensor):
            return torch.equal(self.dummy, o.dummy)

        return self.dummy == o.dummy


@dataclasses.dataclass
class WithInitVar:
    dummy: Any
    override: InitVar[Optional[Any]] = None

    def __post_init__(self, override: Optional[Any]):
        if override is not None:
            self.dummy = override

    def __eq__(self, o: object) -> bool:
        if not isinstance(o, WithInitVar):
            return NotImplemented
        elif isinstance(self.dummy, torch.Tensor):
            return torch.equal(self.dummy, o.dummy)

        return self.dummy == o.dummy


@dataclasses.dataclass
class WithClassAndInitVar:
    class_var: ClassVar[torch.Tensor] = torch.tensor(0)
    dummy: Any
    override: InitVar[Optional[Any]] = torch.tensor(1)

    def __post_init__(self, override: Optional[Any]):
        if override is not None:
            self.dummy = override

    def __eq__(self, o: object) -> bool:
        if not isinstance(o, WithClassAndInitVar):
            return NotImplemented
        elif isinstance(self.dummy, torch.Tensor):
            return torch.equal(self.dummy, o.dummy)

        return self.dummy == o.dummy


def test_recursive_application_to_collection():
    ntc = namedtuple("Foo", ["bar"])

    model_example = ModelExample(
        example_ids=["i-1", "i-2", "i-3"],
        feature=Feature(input_ids=torch.tensor([1.0, 2.0, 3.0]), segment_ids=np.array([4.0, 5.0, 6.0])),
        label=torch.tensor([7.0, 8.0, 9.0]),
    )

    to_reduce = {
        "a": torch.tensor([1.0]),  # Tensor
        "b": [torch.tensor([2.0])],  # list
        "c": (torch.tensor([100.0]),),  # tuple
        "d": ntc(bar=5.0),  # named tuple
        "e": np.array([10.0]),  # numpy array
        "f": "this_is_a_dummy_str",  # string
        "g": 12.0,  # number
        "h": Feature(input_ids=torch.tensor([1.0, 2.0, 3.0]), segment_ids=np.array([4.0, 5.0, 6.0])),  # dataclass
        "i": model_example,  # nested dataclass
        "j": WithClassVar(torch.arange(3)),  # dataclass with class variable
        "k": WithInitVar("this_gets_overridden", torch.tensor([2.0])),  # dataclass with init-only variable
        "l": WithClassAndInitVar(model_example, None),  # nested dataclass with class and init-only variables
    }

    model_example_result = ModelExample(
        example_ids=["i-1", "i-2", "i-3"],
        feature=Feature(input_ids=torch.tensor([2.0, 4.0, 6.0]), segment_ids=np.array([8.0, 10.0, 12.0])),
        label=torch.tensor([14.0, 16.0, 18.0]),
    )

    expected_result = {
        "a": torch.tensor([2.0]),
        "b": [torch.tensor([4.0])],
        "c": (torch.tensor([200.0]),),
        "d": ntc(bar=torch.tensor([10.0])),
        "e": np.array([20.0]),
        "f": "this_is_a_dummy_str",
        "g": 24.0,
        "h": Feature(input_ids=torch.tensor([2.0, 4.0, 6.0]), segment_ids=np.array([8.0, 10.0, 12.0])),
        "i": model_example_result,
        "j": WithClassVar(torch.arange(0, 6, 2)),
        "k": WithInitVar(torch.tensor([4.0])),
        "l": WithClassAndInitVar(model_example_result, None),
    }

    reduced = apply_to_collection(to_reduce, (torch.Tensor, numbers.Number, np.ndarray), lambda x: x * 2)

    assert isinstance(reduced, dict), "Type Consistency of dict not preserved"
    assert all(x in reduced for x in to_reduce), "Not all entries of the dict were preserved"
    assert all(
        isinstance(reduced[k], type(expected_result[k])) for k in to_reduce
    ), "At least one type was not correctly preserved"

    assert isinstance(reduced["a"], torch.Tensor), "Reduction Result of a Tensor should be a Tensor"
    assert torch.equal(expected_result["a"], reduced["a"]), "Reduction of a tensor does not yield the expected value"

    assert isinstance(reduced["b"], list), "Reduction Result of a list should be a list"
    assert all(
        torch.equal(x, y) for x, y in zip(reduced["b"], expected_result["b"])
    ), "At least one value of list reduction did not come out as expected"

    assert isinstance(reduced["c"], tuple), "Reduction Result of a tuple should be a tuple"
    assert all(
        torch.equal(x, y) for x, y in zip(reduced["c"], expected_result["c"])
    ), "At least one value of tuple reduction did not come out as expected"

    assert isinstance(reduced["d"], ntc), "Type Consistency for named tuple not given"
    assert isinstance(
        reduced["d"].bar, numbers.Number
    ), "Failure in type promotion while reducing fields of named tuples"
    assert reduced["d"].bar == expected_result["d"].bar

    assert isinstance(reduced["e"], np.ndarray), "Type Promotion in reduction of numpy arrays failed"
    assert reduced["e"] == expected_result["e"], "Reduction of numpy array did not yield the expected result"

    assert isinstance(reduced["f"], str), "A string should not be reduced"
    assert reduced["f"] == expected_result["f"], "String not preserved during reduction"

    assert isinstance(reduced["g"], numbers.Number), "Reduction of a number should result in a number"
    assert reduced["g"] == expected_result["g"], "Reduction of a number did not yield the desired result"

    def _assert_dataclass_reduction(actual, expected, dataclass_type: str = ""):
        assert dataclasses.is_dataclass(actual) and not isinstance(
            actual, type
        ), f"Reduction of a {dataclass_type} dataclass should result in a dataclass"
        for field in dataclasses.fields(actual):
            if dataclasses.is_dataclass(field.type):
                _assert_dataclass_reduction(getattr(actual, field.name), getattr(expected, field.name), "nested")
        assert actual == expected, f"Reduction of a {dataclass_type} dataclass did not yield the desired result"

    _assert_dataclass_reduction(reduced["h"], expected_result["h"])

    _assert_dataclass_reduction(reduced["i"], expected_result["i"])

    dataclass_type = "ClassVar-containing"
    _assert_dataclass_reduction(reduced["j"], expected_result["j"], dataclass_type)
    assert WithClassVar.class_var == 0, f"Reduction of a {dataclass_type} dataclass should not change the class var"

    _assert_dataclass_reduction(reduced["k"], expected_result["k"], "InitVar-containing")

    dataclass_type = "Class-and-InitVar-containing"
    _assert_dataclass_reduction(reduced["l"], expected_result["l"], dataclass_type)
    assert torch.equal(
        WithClassAndInitVar.class_var, torch.tensor(0)
    ), f"Reduction of a {dataclass_type} dataclass should not change the class var"

    # mapping support
    reduced = apply_to_collection({"a": 1, "b": 2}, int, lambda x: str(x))
    assert reduced == {"a": "1", "b": "2"}
    reduced = apply_to_collection(OrderedDict([("b", 2), ("a", 1)]), int, lambda x: str(x))
    assert reduced == OrderedDict([("b", "2"), ("a", "1")])

    # custom mappings
    class _CustomCollection(dict):
        def __init__(self, initial_dict):
            super().__init__(initial_dict)

    to_reduce = _CustomCollection({"a": 1, "b": 2, "c": 3})
    reduced = apply_to_collection(to_reduce, int, lambda x: str(x))
    assert reduced == _CustomCollection({"a": "1", "b": "2", "c": "3"})

    # defaultdict
    to_reduce = defaultdict(int, {"a": 1, "b": 2, "c": 3})
    reduced = apply_to_collection(to_reduce, int, lambda x: str(x))
    assert reduced == defaultdict(int, {"a": "1", "b": "2", "c": "3"})


def test_apply_to_collection_include_none():
    to_reduce = [1, 2, 3.4, 5.6, 7, (8, 9.1, {10: 10})]

    def fn(x):
        if isinstance(x, float):
            return x

    reduced = apply_to_collection(to_reduce, (int, float), fn)
    assert reduced == [None, None, 3.4, 5.6, None, (None, 9.1, {10: None})]

    reduced = apply_to_collection(to_reduce, (int, float), fn, include_none=False)
    assert reduced == [3.4, 5.6, (9.1, {})]


def test_apply_to_collections():
    to_reduce_1 = {"a": {"b": [1, 2]}, "c": 5}
    to_reduce_2 = {"a": {"b": [3, 4]}, "c": 6}

    def fn(a, b):
        return a + b

    # basic test
    reduced = apply_to_collections(to_reduce_1, to_reduce_2, int, fn)
    assert reduced == {"a": {"b": [4, 6]}, "c": 11}

    with pytest.raises(KeyError):
        # strict mode - if a key does not exist in both we fail
        apply_to_collections({**to_reduce_2, "d": "foo"}, to_reduce_1, float, fn)

    # multiple dtypes
    reduced = apply_to_collections(to_reduce_1, to_reduce_2, (list, int), fn)
    assert reduced == {"a": {"b": [1, 2, 3, 4]}, "c": 11}

    # wrong dtype
    reduced = apply_to_collections(to_reduce_1, to_reduce_2, (list, int), fn, wrong_dtype=int)
    assert reduced == {"a": {"b": [1, 2, 3, 4]}, "c": 5}

    # list takes precedence because it is the type of data1
    reduced = apply_to_collections([1, 2, 3], [4], (int, list), fn)
    assert reduced == [1, 2, 3, 4]

    # different sizes
    with pytest.raises(AssertionError, match="Sequence collections have different sizes"):
        apply_to_collections([[1, 2], [3]], [4], int, fn)

    def fn(a, b):
        return a.keys() | b.keys()

    # base case
    reduced = apply_to_collections(to_reduce_1, to_reduce_2, dict, fn)
    assert reduced == {"a", "c"}

    # type conversion
    to_reduce = [(1, 2), (3, 4)]
    reduced = apply_to_collections(to_reduce, to_reduce, int, lambda *x: sum(x))
    assert reduced == [(2, 4), (6, 8)]

    # named tuple
    foo = namedtuple("Foo", ["bar"])
    to_reduce = [foo(1), foo(2), foo(3)]
    reduced = apply_to_collections(to_reduce, to_reduce, int, lambda *x: sum(x))
    assert reduced == [foo(2), foo(4), foo(6)]

    # passing none
    reduced1 = apply_to_collections([1, 2, 3], None, int, lambda x: x * x)
    reduced2 = apply_to_collections(None, [1, 2, 3], int, lambda x: x * x)
    assert reduced1 == reduced2 == [1, 4, 9]
    reduced = apply_to_collections(None, None, int, lambda x: x * x)
    assert reduced is None


def test_apply_to_collections_dataclass():
    to_reduce_1 = Feature(input_ids=torch.tensor([1.0, 2.0, 3.0]), segment_ids=np.array([4.0, 5.0, 6.0]))
    to_reduce_2 = Feature(input_ids=torch.tensor([1.0, 2.0, 3.0]), segment_ids=np.array([4.0, 5.0, 6.0]))

    def fn(a, b):
        return a + b

    reduced = apply_to_collections(to_reduce_1, to_reduce_2, (torch.Tensor, numbers.Number, np.ndarray), fn)

    assert reduced == Feature(input_ids=torch.tensor([2.0, 4.0, 6.0]), segment_ids=np.array([8.0, 10.0, 12.0]))

    model_example = ModelExample(
        example_ids=["i-1", "i-2", "i-3"],
        feature=to_reduce_1,
        label=torch.tensor([7.0, 8.0, 9.0]),
    )

    # different types
    with pytest.raises(TypeError, match="Expected inputs to be dataclasses of the same type"):
        apply_to_collections(to_reduce_1, [1, 2], (torch.Tensor, numbers.Number, np.ndarray), fn)

    # unmatched fields
    with pytest.raises(TypeError, match="Dataclasses fields do not match"):
        apply_to_collections(to_reduce_1, model_example, (torch.Tensor, numbers.Number, np.ndarray), fn)

    classvar = WithClassVar(torch.arange(3))  # dataclass with same number but different type of fields
    with pytest.raises(TypeError, match="Dataclasses fields do not match"):
        apply_to_collections(to_reduce_1, classvar, (torch.Tensor, numbers.Number, np.ndarray), fn)


def test_apply_to_collection_frozen_dataclass():
    @dataclasses.dataclass(frozen=True)
    class Foo:
        input: torch.Tensor

    foo = Foo(torch.tensor(0))

    with pytest.raises(MisconfigurationException, match="frozen dataclass was passed"):
        apply_to_collection(foo, torch.Tensor, lambda t: t.to(torch.int))


@pytest.mark.parametrize("should_return", [False, True])
def test_wrongly_implemented_transferable_data_type(should_return):
    class TensorObject:
        def __init__(self, tensor: torch.Tensor, should_return: bool = True):
            self.tensor = tensor
            self.should_return = should_return

        def to(self, device):
            self.tensor.to(device)
            # simulate a user forgets to return self
            if self.should_return:
                return self

    tensor = torch.tensor(0.1)
    obj = TensorObject(tensor, should_return)
    assert obj == move_data_to_device(obj, torch.device("cpu"))