"""
Copyright 2013 Steven Diamond

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 typing import Optional, Tuple

import numpy as np
import scipy.sparse as sp

import cvxpy.lin_ops.lin_op as lo
import cvxpy.lin_ops.lin_utils as lu
from cvxpy.atoms.affine.affine_atom import AffAtom
from cvxpy.atoms.affine.reshape import reshape
from cvxpy.atoms.affine.vec import vec
from cvxpy.constraints.constraint import Constraint
from cvxpy.expressions.expression import Expression
from cvxpy.utilities import key_utils as ku


class index(AffAtom):
    """Indexing/slicing into an Expression.

    CVXPY supports NumPy-like indexing semantics via the Expression
    class' overloading of the ``[]`` operator. This is a low-level class
    constructed by that operator, and it should not be instantiated directly.

    Parameters
    ----------
    expr : Expression
        The expression indexed/sliced into.
    key :
        The index/slicing key (i.e. expr[key[0],key[1]])
    
    Examples
    --------
    >>> import cvxpy as cp
    >>> import numpy as np
    >>> x = cp.Variable((2, 3, 4))
    >>> x[..., 2].shape
    (2, 3)
    >>> x[..., np.newaxis].shape
    (2, 3, 4, 1)
    >>> x[1, 2].shape
    (4,)
    """

    def __init__(self, expr, key, orig_key=None) -> None:
        # Format and validate key.
        if orig_key is None:
            self._orig_key = key
            self.key = ku.validate_key(key, expr.shape)
        else:
            self._orig_key = orig_key
            self.key = key
        super(index, self).__init__(expr)

    def is_atom_log_log_convex(self) -> bool:
        """Is the atom log-log convex?"""
        return True

    def is_atom_log_log_concave(self) -> bool:
        """Is the atom log-log concave?"""
        return True

    def name(self):
        """String representation of the index expression."""
        inner_str = "[%s" + ", %s"*(len(self.key)-1) + "]"
        return self.args[0].name() + inner_str % ku.to_str(self.key)

    def numeric(self, values):
        """Returns the index/slice into the given value."""
        return values[0][self._orig_key]

    def shape_from_args(self) -> Tuple[int, ...]:
        """Returns the shape of the index expression."""
        return ku.shape(self.key, self._orig_key, self.args[0].shape)

    def get_data(self) -> list:
        """Returns the (row slice, column slice)."""
        return [self.key, self._orig_key]

    def graph_implementation(
        self, arg_objs, shape: Tuple[int, ...], data=None
    ) -> Tuple[lo.LinOp, list[Constraint]]:
        """Index/slice into the expression.

        Parameters
        ----------
        arg_objs : list
            LinExpr for each argument.
        shape : tuple
            The shape of the resulting expression.
        data : tuple
            A tuple of slices.
        """
        obj = lu.index(arg_objs[0], shape, data[0])
        return (obj, [])


class special_index(AffAtom):
    """Indexing using logical indexing or a list of indices.

    Parameters
    ----------
    expr : Expression
        The expression being indexed/sliced into.
    key : tuple
        ndarrays or lists.
    """

    def __init__(self, expr: Expression, key) -> None:
        self.key = key
        # Order the entries of expr and select them using key.
        expr = index.cast_to_const(expr)
        idx_mat = np.arange(expr.size)
        idx_mat = np.reshape(idx_mat, expr.shape, order='F')
        self._select_mat = idx_mat[key]
        self._shape = self._select_mat.shape
        super(special_index, self).__init__(expr)

    def is_atom_log_log_convex(self) -> bool:
        """Is the atom log-log convex?
        """
        return True

    def is_atom_log_log_concave(self) -> bool:
        """Is the atom log-log concave?
        """
        return True

    def name(self) -> str:
        """String representation of the special index expression."""
        key_str = ku.special_key_to_str(self.key)
        return f"{self.args[0].name()}[{key_str}]"

    def numeric(self, values):
        """Returns the index/slice into the given value.
        """
        return values[0][self.key]

    def shape_from_args(self) -> Tuple[int, ...]:
        """Returns the shape of the index expression."""
        return self._shape

    def get_data(self) -> list:
        """Returns the key."""
        return [self.key]

    @property
    def grad(self) -> Optional[list[sp.csc_array]]:
        """Gives the (sub/super)gradient of the expression w.r.t. each variable.

        Matrix expressions are vectorized, so the gradient is a matrix.
        None indicates variable values unknown or outside domain.
        """
        select_vec = np.reshape(self._select_mat, self._select_mat.size, order='F')
        identity = sp.eye_array(self.args[0].size, format='csc')
        lowered = reshape(
            identity[select_vec] @ vec(self.args[0], order='F'),
            self._shape,
            order='F'
        )
        return lowered.grad

    def graph_implementation(self,
                            arg_objs: list,
                            shape: Tuple[int, ...],
                            data=None) -> Tuple[lo.LinOp, list[Constraint]]:
        """Index/slice into the expression.

        Parameters
        ----------
        arg_objs : list
            LinExpr for each argument.
        shape : tuple
            The shape of the resulting expression.
        data : tuple
            A tuple of slices.
        """
        select_mat = self._select_mat
        final_shape = self._select_mat.shape
        select_vec = np.reshape(select_mat, select_mat.size, order='F')
        # Select the chosen entries from expr.
        arg = arg_objs[0]
        identity = sp.eye_array(self.args[0].size, format='csc')
        vec_arg = lu.reshape(arg, (self.args[0].size,))
        mul_mat = identity[select_vec]
        mul_const = lu.create_const(mul_mat, mul_mat.shape, sparse=True)
        mul_expr = lu.mul_expr(mul_const, vec_arg, (mul_mat.shape[0],))
        obj = lu.reshape(mul_expr, final_shape)
        return (obj, [])
