# 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 namedtuple, OrderedDict
from typing import List

import numpy as np
import pytest
import torch

from pytorch_lightning.utilities.apply_func import apply_to_collection, apply_to_collections


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

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

    @dataclasses.dataclass
    class ModelExample:
        example_ids: List[str]
        feature: Feature
        label: torch.Tensor

    to_reduce = {
        'a': torch.tensor([1.]),  # Tensor
        'b': [torch.tensor([2.])],  # list
        'c': (torch.tensor([100.]), ),  # tuple
        'd': ntc(bar=5.),  # named tuple
        'e': np.array([10.]),  # numpy array
        'f': 'this_is_a_dummy_str',  # string
        'g': 12.,  # number
        'h': Feature(input_ids=torch.tensor([1., 2., 3.]), segment_ids=np.array([4., 5., 6.])),  # dataclass
        'i': ModelExample(
            example_ids=['i-1', 'i-2', 'i-3'],
            feature=Feature(input_ids=torch.tensor([1., 2., 3.]), segment_ids=np.array([4., 5., 6.])),
            label=torch.tensor([7., 8., 9.])
        )  # nested dataclass
    }

    expected_result = {
        'a': torch.tensor([2.]),
        'b': [torch.tensor([4.])],
        'c': (torch.tensor([200.]), ),
        'd': ntc(bar=torch.tensor([10.])),
        'e': np.array([20.]),
        'f': 'this_is_a_dummy_str',
        'g': 24.,
        'h': Feature(input_ids=torch.tensor([2., 4., 6.]), segment_ids=np.array([8., 10., 12.])),
        'i': ModelExample(
            example_ids=['i-1', 'i-2', 'i-3'],
            feature=Feature(input_ids=torch.tensor([2., 4., 6.]), segment_ids=np.array([8., 10., 12.])),
            label=torch.tensor([14., 16., 18.])
        )
    }

    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.allclose(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.allclose(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.allclose(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'

    assert dataclasses.is_dataclass(reduced['h']) and not isinstance(reduced['h'], type), \
        'Reduction of a dataclass should result in a dataclass'
    assert torch.allclose(reduced['h'].input_ids, expected_result['h'].input_ids), \
        'Reduction of a dataclass did not yield the desired result'
    assert np.allclose(reduced['h'].segment_ids, expected_result['h'].segment_ids), \
        'Reduction of a dataclass did not yield the desired result'

    assert dataclasses.is_dataclass(reduced['i']) and not isinstance(reduced['i'], type), \
        'Reduction of a dataclass should result in a dataclass'
    assert dataclasses.is_dataclass(reduced['i'].feature) and not isinstance(reduced['i'].feature, type), \
        'Reduction of a nested dataclass should result in a nested dataclass'
    assert reduced['i'].example_ids == expected_result['i'].example_ids, \
        'Reduction of a nested dataclass did not yield the desired result'
    assert torch.allclose(reduced['i'].label, expected_result['i'].label), \
        'Reduction of a nested dataclass did not yield the desired result'
    assert torch.allclose(reduced['i'].feature.input_ids, expected_result['i'].feature.input_ids), \
        'Reduction of a nested dataclass did not yield the desired result'
    assert np.allclose(reduced['i'].feature.segment_ids, expected_result['i'].feature.segment_ids), \
        'Reduction of a nested dataclass did not yield the desired result'

    # 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'})


def test_apply_to_collection_include_none():
    to_reduce = [1, 2, 3.4, 5.6, 7]

    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]

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


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