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# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
from typing import Optional, Dict, List
import numpy as np
import pytest
import torch
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
def test_from_to_json_doclist():
da = DocList[MyDoc](
[
MyDoc(
embedding=[1, 2, 3, 4, 5], text='hello', image=ImageDoc(url='aux.png')
),
MyDoc(embedding=[5, 4, 3, 2, 1], text='hello world', image=ImageDoc()),
]
)
json_da = da.to_json()
da2 = DocList[MyDoc].from_json(json_da)
assert len(da2) == 2
assert len(da) == len(da2)
for d1, d2 in zip(da, da2):
assert d1.embedding.tolist() == d2.embedding.tolist()
assert d1.text == d2.text
assert d1.image.url == d2.image.url
assert da[1].image.url is None
assert da2[1].image.url is None
@pytest.mark.parametrize('tensor_type', [TorchTensor, NdArray])
def test_from_to_json_docvec(tensor_type):
def generate_docs(tensor_type):
class InnerDoc(BaseDoc):
tens: tensor_type
class MyDoc(BaseDoc):
text: str
num: Optional[int] = None
tens: tensor_type
tens_none: Optional[tensor_type] = None
inner: InnerDoc
inner_none: Optional[InnerDoc] = None
inner_vec: DocVec[InnerDoc]
inner_vec_none: Optional[DocVec[InnerDoc]] = None
def _rand_vec_gen(tensor_type):
arr = np.random.rand(5)
if tensor_type == TorchTensor:
arr = torch.from_numpy(arr).to(torch.float32)
return arr
inner = InnerDoc(tens=_rand_vec_gen(tensor_type))
inner_vec = DocVec[InnerDoc]([inner, inner], tensor_type=tensor_type)
vec = DocVec[MyDoc](
[
MyDoc(
text=str(i),
num=None,
tens=_rand_vec_gen(tensor_type),
inner=inner,
inner_none=None,
inner_vec=inner_vec,
inner_vec_none=None,
)
for i in range(5)
],
tensor_type=tensor_type,
)
return vec
v = generate_docs(tensor_type)
json_str = v.to_json()
v_after = DocVec[v.doc_type].from_json(json_str, tensor_type=tensor_type)
assert v_after.tensor_type == v.tensor_type
assert set(v_after._storage.columns.keys()) == set(v._storage.columns.keys())
assert v_after._storage == v._storage
@pytest.mark.tensorflow
def test_from_to_json_docvec_tf():
from docarray.typing import TensorFlowTensor
def generate_docs():
class InnerDoc(BaseDoc):
tens: TensorFlowTensor
class MyDoc(BaseDoc):
text: str
num: Optional[int] = None
tens: TensorFlowTensor
tens_none: Optional[TensorFlowTensor] = None
inner: InnerDoc
inner_none: Optional[InnerDoc] = None
inner_vec: DocVec[InnerDoc]
inner_vec_none: Optional[DocVec[InnerDoc]] = None
inner = InnerDoc(tens=np.random.rand(5))
inner_vec = DocVec[InnerDoc]([inner, inner], tensor_type=TensorFlowTensor)
vec = DocVec[MyDoc](
[
MyDoc(
text=str(i),
num=None,
tens=np.random.rand(5),
inner=inner,
inner_none=None,
inner_vec=inner_vec,
inner_vec_none=None,
)
for i in range(5)
],
tensor_type=TensorFlowTensor,
)
return vec
v = generate_docs()
json_str = v.to_json()
v_after = DocVec[v.doc_type].from_json(json_str, tensor_type=TensorFlowTensor)
assert v_after.tensor_type == v.tensor_type
assert set(v_after._storage.columns.keys()) == set(v._storage.columns.keys())
assert v_after._storage == v._storage
def test_union_type():
from typing import Union
from docarray.documents import TextDoc
class CustomDoc(BaseDoc):
ud: Union[TextDoc, ImageDoc] = TextDoc(text='union type')
docs = DocList[CustomDoc]([CustomDoc(ud=TextDoc(text='union type'))])
docs_copy = docs.from_json(docs.to_json())
assert docs == docs_copy
@pytest.mark.parametrize('tensor_type', [NdArray, TorchTensor])
def test_from_to_json_tensor_type(tensor_type):
da = DocVec[MyDoc](
[
MyDoc(
embedding=[1, 2, 3, 4, 5], text='hello', image=ImageDoc(url='aux.png')
),
MyDoc(embedding=[5, 4, 3, 2, 1], text='hello world', image=ImageDoc()),
],
tensor_type=tensor_type,
)
json_da = da.to_json()
da2 = DocVec[MyDoc].from_json(json_da, tensor_type=tensor_type)
assert da2.tensor_type == tensor_type
assert isinstance(da2.embedding, tensor_type)