# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.

from paddle import _C_ops
from ..framework import core, dygraph_only
from ..tensor import to_tensor
from ..tensor import max
from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype

__all__ = [
 'sparse_coo_tensor',
 'sparse_csr_tensor',
]


def _handle_dtype(data, dtype):
 if dtype:
 if convert_dtype(dtype) != convert_dtype(data.dtype):
 return data.astype(convert_dtype(dtype))
 return data


def _infer_dense_shape(indices):
 assert len(indices.shape) == 2
 lens = max(indices, axis=1)
 lens = lens + 1
 return list(lens.numpy())


@dygraph_only
def sparse_coo_tensor(indices,
 values,
 shape=None,
 dtype=None,
 place=None,
 stop_gradient=True):
 r"""
 Constructs a sparse ``paddle.Tensor`` in coordinate format according to the indices 
 and values of the specified non-zero elements.

 Args:
 indices(list|tuple|ndarray|Tensor): the indices of non-zero elements.
 Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. The indices must be 2-D.
 values(list|tuple|ndarray|Tensor): Initial values for the tensor.
 Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
 shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of
 original dense tensor. If not provided the smallest shape will be inferred to 
 hold all elements.
 dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 
 'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
 'complex64' , 'complex128'. Default: None, infers dtype from ``data`` 
 except for python float number which gets dtype from ``get_default_type`` .
 place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be 
 CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is 
 string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. 
 stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.

 Returns:
 Tensor: A Tensor constructed from ``indices`` and ``values`` .

 Raises:
 TypeError: If the data type of ``values`` is not list, tuple, numpy.ndarray, paddle.Tensor
 ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``indices`` is not a 2-D. 
 TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
 ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string. 

 Examples:

 .. code-block:: python

 import paddle
 from paddle.fluid.framework import _test_eager_guard

 with _test_eager_guard():
 indices = [[0, 1, 2], [1, 2, 0]]
 values = [1.0, 2.0, 3.0]
 dense_shape = [2, 3]
 coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape)
 # print(coo)
 # Tensor(shape=[2, 3], dtype=paddle.float32, place=Place(gpu:0), stop_gradient=True,
 # indices=[[0, 1, 2],
 # [1, 2, 0]],
 # values=[1., 2., 3.])
 """

 if not isinstance(indices, core.eager.Tensor):
 indices = to_tensor(
 indices, dtype=None, place=place, stop_gradient=True)
 if not isinstance(values, core.eager.Tensor):
 values = to_tensor(values, dtype, place, stop_gradient)
 if len(indices.shape) != 2:
 raise ValueError("'indices' must be 2-D.")
 if place is not None:
 indices = indices._copy_to(place, False)
 values = values._copy_to(place, False)
 values = _handle_dtype(values, dtype)
 if shape is None:
 shape = _infer_dense_shape(indices)
 return core.eager.sparse_coo_tensor(indices, values, shape, stop_gradient)


#TODO: need to support shape is None
@dygraph_only
def sparse_csr_tensor(crows,
 cols,
 values,
 shape,
 dtype=None,
 place=None,
 stop_gradient=True):
 r"""
 Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the 
 ``crows``, ``cols`` and ``values``.

 Args:
 crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the 
 starting position of the first non-zero element of each row in values. 
 Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. 
 cols(list|tuple|ndarray|Tensor): 1-D array, the column of non-zero elements.
 Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. 
 values(list|tuple|ndarray|Tensor): 1-D array, the non-zero elements.
 Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
 shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of
 original dense tensor. 
 hold all elements.
 dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 
 'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
 'complex64' , 'complex128'. Default: None, infers dtype from ``data`` 
 except for python float number which gets dtype from ``get_default_type`` .
 place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be 
 CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is 
 string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. 
 stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.

 Returns:
 Tensor: A Tensor constructed from ``crows``, ``cols`` and ``values`` .

 Raises:
 TypeError: If the data type of ``values`` is not list, tuple, numpy.ndarray, paddle.Tensor
 ValueError: If ``values`` is tuple|list, it can't contain nested tuple|list with different lengths , such as: [[1, 2], [3, 4, 5]]. If the ``crow``, ``cols`` and ``values`` is not a 2-D. 
 TypeError: If ``dtype`` is not bool, float16, float32, float64, int8, int16, int32, int64, uint8, complex64, complex128
 ValueError: If ``place`` is not paddle.CPUPlace, paddle.CUDAPinnedPlace, paddle.CUDAPlace or specified pattern string. 

 Examples:

 .. code-block:: python

 import paddle
 from paddle.fluid.framework import _test_eager_guard

 with _test_eager_guard():
 crows = [0, 2, 3, 5]
 cols = [1, 3, 2, 0, 1]
 values = [1, 2, 3, 4, 5]
 dense_shape = [3, 4]
 csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape)
 # print(csr)
 # Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(gpu:0), stop_gradient=True,
 # crows=[0, 2, 3, 5],
 # cols=[1, 3, 2, 0, 1],
 # values=[1, 2, 3, 4, 5])
 """
 if not isinstance(crows, core.eager.Tensor):
 crows = to_tensor(crows, dtype=None, place=place, stop_gradient=True)
 if not isinstance(cols, core.eager.Tensor):
 cols = to_tensor(cols, dtype=None, place=place, stop_gradient=True)
 if not isinstance(values, core.eager.Tensor):
 values = to_tensor(values, dtype, place, stop_gradient)
 if len(crows.shape) != 1 or len(cols.shape) != 1 or len(values.shape) != 1:
 raise ValueError(
 "SparseCsrTensor only support 2-D or 3-D matrix. The 'crows', 'cols' and 'values' must be 1-D."
 )

 if place is not None:
 crows = crows._copy_to(place, False)
 cols = cols._copy_to(place, False)
 values = values._copy_to(place, False)
 values = _handle_dtype(values, dtype)
 return core.eager.sparse_csr_tensor(crows, cols, values, shape,
 stop_gradient)