"""Driver for gradient calculations."""

__authors__   = "James Bergstra, Razvan Pascanu"
__copyright__ = "(c) 2011, Universite de Montreal"
__license__   = "3-clause BSD License"
__contact__   = "theano-dev <theano-dev@googlegroups.com>"

__docformat__ = "restructuredtext en"

import __builtin__
import logging
import warnings

import numpy #for numeric_grad

import theano
from theano.tensor import TensorType, TensorVariable, ones_like, \
                zeros_like, as_tensor_variable, cast
from theano import gradient
from theano import gof, shared
from theano import compile

_logger = logging.getLogger('theano.tensor.tensor_grad')

########################
# R Operator
########################

def Rop(f, wrt, eval_points):
    """
    Computes the R operation on `f` wrt to `wrt` evaluated at points given
    in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
    to `wrt` right muliplied by the eval points.

    :type f: `Variable` or list of `Variable`s
        `f` stands for the output of the computational graph to which you
        want to apply the R operator
    :type wrt: `Variable` or list of `Variables`s
        variables for which you compute the R operator of the expression
        described by `f`
    :type eval_points: `Variable` or list of `Variable`s
        evalutation points for each of the variables in `wrt`

    :rtype: `Variable` or list/tuple of `Variable`s depending on type of f
    :return: symbolic expression such that
        R_op[i] = sum_j ( d f[i] / d wrt[j]) eval_point[j]
        where the indices in that expression are magic multidimensional
        indices that specify both the position within a list and all
        coordinates of the tensor element in the last.
        If `wrt` is a list/tuple, then return a list/tuple with the results.
        """

    using_list = isinstance(wrt, list)
    using_tuple = isinstance(wrt, tuple)

    if not (using_list or using_tuple):
        wrt = [ wrt ]

    if not isinstance(eval_points, (list, tuple)):
        eval_points = [ eval_points ]

    if not isinstance(f, (list,tuple)):
        f = [f]

    assert len(wrt) == len(eval_points)

    for pack in enumerate(zip(wrt, eval_points)):
        i = pack[0]
        wrt_elem, eval_point = pack[1]

        wrt_elem = as_tensor_variable(wrt_elem)
        eval_point = as_tensor_variable(eval_point)

        wrt_dim = len(wrt_elem.type.broadcastable)
        eval_dim = len(eval_point.type.broadcastable)

        if wrt_dim != eval_dim:
            raise ValueError('Element '+str(i)+' of wrt/eval_point have mismatched '
                    'dimensionality: '+str(wrt_dim)+' versus '+str(eval_dim))

    seen_nodes = {}

    def _traverse(node):
        if node is None:
            return None
        else:
            op     = node.op
            inputs = node.inputs
            if not hasattr(op, 'R_op'):
                raise Exception((' R_op was not implemented for %s'
                                      ' operation. Email the mailing list'
                                      ' for help') % op.__class__.__name__)
            # Compute the evaluation points corresponding to each of the
            # inputs of the node
            local_eval_points = []
            for inp in inputs:
                if inp in wrt:
                    local_eval_points.append( eval_points[wrt.index(inp)] )
                elif inp.owner is None:
                    local_eval_points.append( zeros_like(inp) )
                elif inp.owner in seen_nodes:

                    local_eval_points.append(
                        seen_nodes[inp.owner][inp.owner.outputs.index(inp) ] )

                else:
                    # We actually need to compute the R_op for this node

                    _traverse(inp.owner)
                    local_eval_points.append(
                        seen_nodes[inp.owner][inp.owner.outputs.index(inp) ])
            for x,y in zip(inputs, local_eval_points):
                if y is not None:
                    assert (as_tensor_variable(x).type == as_tensor_variable(y).type)

            seen_nodes[node] = op.R_op(node.inputs, local_eval_points)
            return None

    # Populate the dictionary
    for out in f:
        _traverse(out.owner)

    rval = []
    for out in f:
        if out in wrt:
            rval.append( eval_points[wrt.index(out)])
        elif seen_nodes[out.owner][out.owner.outputs.index(out)] is None:
            raise ValueError(( 'The function is not differentiable with '
                              'respect to the provided inputs !'))
        else:
            rval.append(seen_nodes[out.owner][out.owner.outputs.index(out)] )

    if len(rval) == 1:
        if using_list:
            return rval
        if using_tuple:
            return tuple(rval)
        return rval[0]
    else:
        if using_tuple:
            return tuple(rval)
        return rval


def Lop(f, wrt, eval_points, consider_constant=None, warn_type=False,
         disconnected_inputs='raise'):
    """
    Computes the L operation on `f` wrt to `wrt` evaluated at points given
    in `eval_points`. Mathematically this stands for the jacobian of `f` wrt
    to `wrt` left muliplied by the eval points.

    :type f: `Variable` or list of `Variable`s
        `f` stands for the output of the computational graph to which you
        want to apply the L operator
    :type wrt: `Variable` or list of `Variables`s
        variables for which you compute the L operator of the expression
        described by `f`
    :type eval_points: `Variable` or list of `Variable`s
        evalutation points for each of the variables in `f`

    :rtype: `Variable` or list/tuple of `Variable`s depending on type of f
    :return: symbolic expression such that
        L_op[i] = sum_i ( d f[i] / d wrt[j]) eval_point[i]
        where the indices in that expression are magic multidimensional
        indices that specify both the position within a list and all
        coordinates of the tensor element in the last
        If `f` is a list/tuple, then return a list/tuple with the results.
    """
    if consider_constant is None:
        consider_constant = []

    if not isinstance(f, TensorVariable):
        raise TypeError('In tensor.Lop(), cost argument should be a TensorVariable.', f)

    if type(eval_points) not in (list, tuple):
        eval_points = [eval_points]

    using_list = isinstance(f, list)
    using_tuple = isinstance(f, tuple)
    if not (using_list or using_tuple):
        f = [f]

    inputs = gof.graph.inputs(f)
    gmap = gradient.grad_sources_inputs(
            zip(f,eval_points),
            list(inputs) + list(consider_constant),
            warn_type=warn_type)

    # Note : If p is not in gmap there can be several reasons, among which
    # is the fact that p might not be part of the computational graph. A
    # simple example is that for a+b for e.g. a[0] is not part of the graph,
    # so Theano does not know how to compute TT.grad(TT.sum(a+b), a[0])
    # such subtle cases can be fixed by a more careful implementation of the
    # gradient, but for now Theano needs to throw an exception, and make the
    # user aware that it does not know how to compute that gradient
    if not isinstance(wrt, (list, tuple)):
        wrt = [wrt]
    ret = []
    for p in wrt:
        if p in gmap:
            ret.append(gmap[p])
        else:
            message = ("Lop method was asked to compute the gradient "
                    "with respect to a variable that is not part of "
                    "the computational graph of the cost, or is used "
                    "only by a non-differentiable operator: %s" % p)
            if disconnected_inputs == 'ignore':
                pass
            elif disconnected_inputs == 'warn':
                warnings.warn(message, stacklevel=1)
            elif disconnected_inputs == 'raise':
                raise ValueError(message)
            else:
                raise ValueError("Invalid value for keyword "
                        "'disconnected_inputs', valid values are "
                        "'ignore', 'warn' and 'raise'.")
            ret.append(zeros_like(p))

    if len(ret) == 1:
        if using_list:
            return ret
        elif using_tuple:
            return tuple(ret)
        else:
            return ret[0]
    else:
        if using_tuple:
            return tuple(ret)
        return ret


#########################
# Gradient
#########################

def grad(cost, wrt, g_cost=None, consider_constant=None, warn_type=False,
         disconnected_inputs='raise'):
    """
    :type cost: Scalar (0-dimensional) `Variable`
    :type wrt: `Variable` or list of `Variable`s.
    :type g_cost: Scalar `Variable`, or None
    :param g_cost: an expression for the gradient through cost.  The default is
        ``ones_like(cost)``.
    :param consider_constant: a list of expressions not to backpropagate through

    :param warn_type: a value of True will cause warnings to be logged for any Op that emits a
        gradient that does not match its input type.

    :type disconnected_inputs: string
    :param disconnected_inputs: Defines the behaviour if some of the variables
        in ``wrt`` are not part of the computational graph computing ``cost``
        (or if all links are non-differentiable). The possible values are:
        - 'ignore': considers that the gradient on these parameters is zero.
        - 'warn': consider the gradient zero, and print a warning.
        - 'raise': raise an exception.

    :rtype: `Variable` or list/tuple of `Variable`s (depending upon `wrt`)

    :return: symbolic expression of gradient of `cost` with respect to `wrt`.
             If an element of `wrt` is not differentiable with respect
             to the output, then a zero variable is returned.
             If `wrt` is a list/tuple, longer then 1, a list will be returned.
             DEPRECATION: In Theano 0.5, grad will return an object of the same
             type as `wrt`: a list/tuple or TensorVariable in all case.

    This function is a wrapper around the more general function
    `theano.gradient.grad_sources_inputs``.

    """
    if consider_constant is None:
        consider_constant = []

    if not isinstance(cost, TensorVariable):
        raise TypeError('In tensor.grad(), cost argument should be a TensorVariable.', cost)

    if cost.type.ndim:
        raise TypeError(
                'In tensor.grad, "cost" argument should be a scalar, but ndim'
                ' is %i (should be 0). If you want to compute the gradient of'
                ' the sum of cost, you should use cost.sum().'
                % cost.type.ndim)

    if g_cost is None:
        g_cost = ones_like(cost)
    inputs = gof.graph.inputs([cost])
    gmap = gradient.grad_sources_inputs(
            [(cost, g_cost)],
            list(inputs) + list(consider_constant),
            warn_type=warn_type)


    # Note : If p is not in gmap there can be several reasons, among which
    # is the fact that p might not be part of the computational graph. A
    # simple example is that for a+b for e.g. a[0] is not part of the graph,
    # so Theano does not know how to compute TT.grad(TT.sum(a+b), a[0])
    # such subtle cases can be fixed by a more careful implementation of the
    # gradient, but for now Theano needs to throw an exception, and make the
    # user aware that it does not know how to compute that gradient
    using_list = isinstance(wrt, list)
    using_tuple = isinstance(wrt, tuple)
    if not (using_list or using_tuple):
        wrt = [wrt]
    ret = []
    for p in wrt:
        if p in gmap:
            ret.append(gmap[p])
        else:
            message = ("grad method was asked to compute the gradient "
                    "with respect to a variable that is not part of "
                    "the computational graph of the cost, or is used "
                    "only by a non-differentiable operator: %s" % p)
            if disconnected_inputs == 'ignore':
                pass
            elif disconnected_inputs == 'warn':
                warnings.warn(message, stacklevel=1)
            elif disconnected_inputs == 'raise':
                raise ValueError(message)
            else:
                raise ValueError("Invalid value for keyword "
                        "'disconnected_inputs', valid values are "
                        "'ignore', 'warn' and 'raise'.")
            ret.append(zeros_like(p))

    if len(ret) == 1:
        if using_list or using_tuple:
            warnings.warn(("The return type of tensor.grad will change in this "
                           "case. In the future grad(cost, wrt) will return an "
                           "object of the same type as wrt. So if wrt is a "
                           "list/tuple, list/tuple will be returned. Idem for "
                           "TensorVariable."),
                          stacklevel=2)
        # TODO: when we release Theano 0.5, uncomment the following lines
        #       and remove the warning. Don't forget the line in the currently
        #       enabled else.
        #if using_list:
        #    return ret
        #elif using_tuple:
        #    return tuple(ret)
        #else:
        return ret[0]
    else:
        #if using_tuple:
        #    return tuple(ret)
        return ret

class numeric_grad:
    """WRITEME"""

    # Note on step sizes and tolerances:
    #
    # There is a relationship between the step size and the function value and the measurement
    # error that is incurred due to rounding.  The finite difference we measure is
    # delta = f(x0) - f(x0+eps)
    #
    # For maximum precision, f should be close to zero.
    # For every power of 2 that f departs from zero, we lose a bit of precision in delta.
    #
    # Even in this case of maximum accuracy, there is a tradeoff between stepsize and
    # measurement error.
    # Taking small steps allows us to measure large derivatives accuractly, but longer steps
    # are required to measure small derivatives accurately.  However longer steps introduce
    # bias into our measurement in general for non-linear functions.
    #
    # It would be interesting to have a version of numeric grad that used an adaptive stepsize.
    #
    # For now, we use a heuristic that catches very bad gradients, but is not perfectly
    # accurate.
    type_eps = {'float64': 1e-7,
            'float32': 3e-4,
            numpy.dtype('float64'):1e-7,
            numpy.dtype('float32'):3e-4}

    def __init__(self, f, pt, eps=None):
        """Return the gradient of f at pt.

        This function computes the gradient by a one-sided finite differences of a
        fixed step size (eps).

        It is assumed that f(...) will return a scalar.
        It is assumed that all f's inputs are numpy.ndarray objects.

        :param eps: the stepsize for the finite differencing.  None means input
        dtype-dependent. See `type_eps`.
        """

        def prod(inputs):
            rval = 1
            for i in inputs:
                rval *= i
            return rval

        packed_pt = False
        if not isinstance(pt, (list, tuple)):
            pt = [pt]
            packed_pt = True

        apt = [numpy.array(p) for p in pt]

        shapes = [p.shape for p in apt]
        dtypes = [str(p.dtype) for p in apt]

        # TODO: remove this eventually (why was this here in the first place ?)
        # In the case of CSM, the arguments are a mixture of floats and integers...
        #if not dtypes == [dtypes[0]] * len(apt):
            #raise TypeError('All function arguments must have same dtype')

        total_size = __builtin__.sum(prod(sh) for sh in shapes)

        working_dtype = __builtin__.min((self.type_eps[dt], dt) for dt in dtypes)[1]

        #create un-initialized memory
        x = numpy.ndarray((total_size,), dtype=working_dtype)
        gx = numpy.ndarray((total_size,), dtype=working_dtype)

        if eps is None:
            eps = __builtin__.max(self.type_eps[dt] for dt in dtypes)


        #set up aliases so that apt[i] is backed by memory in x
        # and self.gf is backed by memory in gx
        cur_pos = 0
        self.gf = []
        for i,p in enumerate(apt):
            p_size = prod(p.shape)
            # set up alias
            apt[i] = x[cur_pos:cur_pos+p_size].reshape(p.shape)
            self.gf.append(gx[cur_pos:cur_pos+p_size].reshape(p.shape))
            # initialize with p's value
            apt[i][...] = p
            cur_pos += p_size

        f_x = f(*[p.copy() for p in apt])

        # now iterate over the elements of x, and call f on apt.
        x_copy = x.copy()
        for i in xrange(total_size):
            x[:] = x_copy

            x[i] += eps
            f_eps = f(*apt)

            gx[i] = numpy.asarray((f_eps - f_x)/eps)

        if packed_pt:
            self.gf = self.gf[0]

    @staticmethod
    def abs_rel_err(a,b):
        """Return absolute and relative error between a and b.

        The relative error is a small number when a and b are close, relative to how big they are.

        Formulas used:
            abs_err = abs(a - b)
            rel_err = abs_err / (abs(a) + abs(b))

        The tuple (abs_err, rel_err) is returned
        """
        abs_err = abs(a-b)
        rel_err = abs_err / (abs(a) + abs(b))
        return (abs_err, rel_err)

    def abs_rel_errors(self, g_pt):
        """Return the abs and rel error of gradient estimate `g_pt`

        `g_pt` must be a list of ndarrays of the same length as self.gf, otherwise a ValueError
        is raised.

        Corresponding ndarrays in `g_pt` and `self.gf` must have the same shape or ValueError
        is raised.

        """
        if len(g_pt) != len(self.gf):
            raise ValueError('argument has wrong number of elements', len(g_pt))
        errs = []
        for i, (a, b) in enumerate(zip(g_pt, self.gf)):
            if a.shape != b.shape:
                raise ValueError('argument element %i has wrong shape %s' %(i,str((a.shape,
                    b.shape))))
            errs.append(numeric_grad.abs_rel_err(a,b))
        return errs

    def max_err(self, g_pt, abs_tol, rel_tol):
        """Find the biggest error between g_pt and self.gf.

        What is measured is the violation of relative and absolute errors,
        wrt the provided tolerances (abs_tol, rel_tol).
        A value > 1 means both tolerances are exceeded.

        Return the argmax of min(abs_err / abs_tol, rel_err / rel_tol) over g_pt,
        as well as abs_err and rel_err at this point.
        """
        pos = []
        errs = []
        abs_errs = []
        rel_errs = []

        abs_rel_errs = self.abs_rel_errors(g_pt)
        for abs_err, rel_err in abs_rel_errs:
            scaled_err = numpy.minimum(abs_err/abs_tol, rel_err/rel_tol)
            max_i = scaled_err.argmax()

            pos.append(max_i)
            errs.append(scaled_err.flatten()[max_i])
            abs_errs.append(abs_err.flatten()[max_i])
            rel_errs.append(rel_err.flatten()[max_i])

        # max over the arrays in g_pt
        max_arg = numpy.argmax(errs)
        max_pos = pos[max_arg]
        return (max_arg, pos[max_arg], abs_errs[max_arg], rel_errs[max_arg])


def verify_grad(fun, pt, n_tests=2, rng=None, eps=None, abs_tol=None, rel_tol=None,
        mode=None, cast_to_output_type=False):
    """ Test a gradient by Finite Difference Method. Raise error on failure.

    Example:
    >>> verify_grad(theano.tensor.tanh,
                    (numpy.asarray([[2,3,4], [-1, 3.3, 9.9]]),),
                    rng=numpy.random)

    Raises an Exception if the difference between the analytic gradient and
    numerical gradient (computed through the Finite Difference Method) of a random
    projection of the fun's output to a scalar exceeds
    the given tolerance.

    :param fun: a Python function that takes Theano variables as inputs,
        and returns a Theano variable. For instance, an Op instance with
        a single output.
    :param pt: the list of numpy.ndarrays to use as input values.
        These arrays must be either float32 or float64 arrays.
    :param n_tests: number of times to run the test
    :param rng: random number generator used to sample u, we test gradient of sum(u * fun) at pt
    :param eps: stepsize used in the Finite Difference Method (Default None is type-dependent)
    :param abs_tol: absolute tolerance used as threshold for gradient comparison
    :param rel_tol: relative tolerance used as threshold for gradient comparison

    :note: WARNING to unit-test writers: if `op` is a function that builds a graph,
           try to make it a SMALL graph.  Often verify grad is run in
           debug mode, which can be very slow if it has to verify a lot
           of intermediate computations.

    :note: This op does not support multiple outputs. In tests/test_scan.py there is
           an experimental verify_grad that covers that case as well by using random
           projections.
    """
    assert isinstance(pt, (list,tuple))
    pt = [numpy.array(p) for p in pt]

    for i, p in enumerate(pt):
        if p.dtype not in ('float32', 'float64'):
            raise TypeError(('verify_grad can work only with floating point '
                'inputs, but input %i has dtype "%s".') % (i, p.dtype))

    _type_tol = dict( # relativ error tolerances for different types
            float32=1e-2,
            float64=1e-4)

    if abs_tol is None:
        abs_tol = __builtin__.max(_type_tol[str(p.dtype)] for p in pt)
    if rel_tol is None:
        rel_tol = __builtin__.max(_type_tol[str(p.dtype)] for p in pt)

    if rng is None:
        raise TypeError('rng should be a valid instance of numpy.random.RandomState.',
                'You may want to use theano.tests.unittest_tools.verify_grad instead of theano.tensor.verify_grad.')

    # We allow input downcast in function, because numeric_grad works in the
    # most precise dtype used among the inputs, so we may need to cast some.
    def function(inputs, output):
        if mode is None:
            f = compile.function(inputs, output, accept_inplace=True,
                    allow_input_downcast=True)
        else:
            f = compile.function(inputs, output, accept_inplace=True,
                    allow_input_downcast=True, mode=mode)
        return f

    tensor_pt = [TensorType(as_tensor_variable(p).dtype, as_tensor_variable(p).broadcastable)(name='input %i'%i) for i,p in enumerate(pt)]

    #fun can be either a function or an actual Op instance
    o_output = fun(*tensor_pt)

    if isinstance(o_output,list):
        raise NotImplementedError('cant (yet) autotest gradient of fun with multiple outputs')
        # we could make loop over outputs making random projections R for each,
        # but this doesn't handle the case where not all the outputs are
        # differentiable... so I leave this as TODO for now -JB.

    o_fn = function(tensor_pt, o_output)
    o_fn_out = o_fn(*[p.copy() for p in pt])

    if isinstance(o_fn_out, tuple) or isinstance(o_fn_out, list):
        raise TypeError('It seems like you are trying to use verify_grad '
                'on an op or a function which outputs a list: there should'
                ' be a single (array-like) output instead')

    # random_projection should not have elements too small,
    # otherwise too much precision is lost in numerical gradient
    def random_projection():
        plain =  rng.rand(*o_fn_out.shape) + 0.5
        if cast_to_output_type:
            return numpy.array(plain,o_output.dtype)
        return plain

    t_r = shared(random_projection())

    #random projection of o onto t_r
    cost = theano.tensor.sum(t_r * o_output)  #This sum() is defined above, it's not the builtin sum.
    cost_fn = function(tensor_pt, cost)

    #todo-- determine if this is actually needed
    g_cost = as_tensor_variable(1.0,name='g_cost')
    if cast_to_output_type:
        g_cost = cast(g_cost, o_output.dtype)

    symbolic_grad = grad(cost, tensor_pt, g_cost,
                         disconnected_inputs='ignore')
    #if o_output.dtype in ['float32','float64']:
    #    assert all([x.dtype == o_output.dtype for x in symbolic_grad]),("Expected grad of type %s, got %s "%( symbolic_grad.dtype, o_output.dtyp))

    grad_fn = function(tensor_pt, symbolic_grad)

    for test_num in xrange(n_tests):
        num_grad = numeric_grad(cost_fn, [p.copy() for p in pt], eps)


        analytic_grad = grad_fn(*[p.copy() for p in pt])

        if not isinstance(analytic_grad, (list, tuple)):
            analytic_grad = [analytic_grad]

        max_arg, max_err_pos, max_abs_err, max_rel_err =\
                num_grad.max_err(analytic_grad, abs_tol, rel_tol)

        if max_abs_err > abs_tol and max_rel_err > rel_tol:
            raise verify_grad.E_grad(max_arg, max_err_pos,
                    max_abs_err, max_rel_err, abs_tol, rel_tol)

        #get new random projection for next test
        if test_num < n_tests - 1:
            t_r.set_value(random_projection(), borrow=True)


class GradientError(Exception):
    """This error is raised when a gradient is calculated, but incorrect."""
    def __init__(self, arg, err_pos, abs_err, rel_err, abs_tol, rel_tol):
        self.arg = arg
        self.err_pos = err_pos
        self.abs_err = abs_err
        self.rel_err = rel_err
        self.abs_tol = abs_tol
        self.rel_tol = rel_tol


    def __str__(self):
        return """GradientError: numeric gradient and analytic gradient exceed tolerance:
        At position %i of argument %i,
            abs. error = %f,  abs. tolerance = %f
            rel. error = %f,  rel. tolerance = %f
        """ %(self.err_pos, self.arg,
              self.abs_err, self.abs_tol,
              self.rel_err, self.rel_tol)

verify_grad.E_grad = GradientError
