text: # 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, move_data_to_device
  
  
  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
          some_constant: int = dataclasses.field(init=False)
  
          def __post_init__(self):
              self.some_constant = 7
  
      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": 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]),
          ),  # nested dataclass
      }
  
      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": 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]),
          ),
      }
  
      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, (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
  
  
  @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"))
  