"""
This module provides convenient functions to transform sympy expressions to
lambda functions which can be used to calculate numerical values very fast.
"""

from __future__ import print_function, division

from functools import wraps
import inspect
import keyword
import re
import textwrap
import linecache

from sympy.core.compatibility import (exec_, is_sequence, iterable,
    NotIterable, string_types, range, builtins, integer_types, PY3)
from sympy.utilities.decorator import doctest_depends_on

# These are the namespaces the lambda functions will use.
MATH = {}
MPMATH = {}
NUMPY = {}
SCIPY = {}
TENSORFLOW = {}
SYMPY = {}
NUMEXPR = {}

# Default namespaces, letting us define translations that can't be defined
# by simple variable maps, like I => 1j
# These are separate from the names above because the above names are modified
# throughout this file, whereas these should remain unmodified.
MATH_DEFAULT = {}
MPMATH_DEFAULT = {}
NUMPY_DEFAULT = {"I": 1j}
SCIPY_DEFAULT = {"I": 1j}
TENSORFLOW_DEFAULT = {}
SYMPY_DEFAULT = {}
NUMEXPR_DEFAULT = {}

# Mappings between sympy and other modules function names.
MATH_TRANSLATIONS = {
    "ceiling": "ceil",
    "E": "e",
    "ln": "log",
}

MPMATH_TRANSLATIONS = {
    "Abs": "fabs",
    "elliptic_k": "ellipk",
    "elliptic_f": "ellipf",
    "elliptic_e": "ellipe",
    "elliptic_pi": "ellippi",
    "ceiling": "ceil",
    "chebyshevt": "chebyt",
    "chebyshevu": "chebyu",
    "E": "e",
    "I": "j",
    "ln": "log",
    #"lowergamma":"lower_gamma",
    "oo": "inf",
    #"uppergamma":"upper_gamma",
    "LambertW": "lambertw",
    "MutableDenseMatrix": "matrix",
    "ImmutableDenseMatrix": "matrix",
    "conjugate": "conj",
    "dirichlet_eta": "altzeta",
    "Ei": "ei",
    "Shi": "shi",
    "Chi": "chi",
    "Si": "si",
    "Ci": "ci",
    "RisingFactorial": "rf",
    "FallingFactorial": "ff",
}

NUMPY_TRANSLATIONS = {}
SCIPY_TRANSLATIONS = {}

TENSORFLOW_TRANSLATIONS = {
    "Abs": "abs",
    "ceiling": "ceil",
    "im": "imag",
    "ln": "log",
    "Mod": "mod",
    "conjugate": "conj",
    "re": "real",
}

NUMEXPR_TRANSLATIONS = {}

# Available modules:
MODULES = {
    "math": (MATH, MATH_DEFAULT, MATH_TRANSLATIONS, ("from math import *",)),
    "mpmath": (MPMATH, MPMATH_DEFAULT, MPMATH_TRANSLATIONS, ("from mpmath import *",)),
    "numpy": (NUMPY, NUMPY_DEFAULT, NUMPY_TRANSLATIONS, ("import numpy; from numpy import *",)),
    "scipy": (SCIPY, SCIPY_DEFAULT, SCIPY_TRANSLATIONS, ("import numpy; import scipy; from scipy import *; from scipy.special import *",)),
    "tensorflow": (TENSORFLOW, TENSORFLOW_DEFAULT, TENSORFLOW_TRANSLATIONS, ("import_module('tensorflow')",)),
    "sympy": (SYMPY, SYMPY_DEFAULT, {}, (
        "from sympy.functions import *",
        "from sympy.matrices import *",
        "from sympy import Integral, pi, oo, nan, zoo, E, I",)),
    "numexpr" : (NUMEXPR, NUMEXPR_DEFAULT, NUMEXPR_TRANSLATIONS,
                 ("import_module('numexpr')", )),
}


def _import(module, reload="False"):
    """
    Creates a global translation dictionary for module.

    The argument module has to be one of the following strings: "math",
    "mpmath", "numpy", "sympy", "tensorflow".
    These dictionaries map names of python functions to their equivalent in
    other modules.
    """
    from sympy.external import import_module
    try:
        namespace, namespace_default, translations, import_commands = MODULES[
            module]
    except KeyError:
        raise NameError(
            "'%s' module can't be used for lambdification" % module)

    # Clear namespace or exit
    if namespace != namespace_default:
        # The namespace was already generated, don't do it again if not forced.
        if reload:
            namespace.clear()
            namespace.update(namespace_default)
        else:
            return

    for import_command in import_commands:
        if import_command.startswith('import_module'):
            module = eval(import_command)

            if module is not None:
                namespace.update(module.__dict__)
                continue
        else:
            try:
                exec_(import_command, {}, namespace)
                continue
            except ImportError:
                pass

        raise ImportError(
            "can't import '%s' with '%s' command" % (module, import_command))

    # Add translated names to namespace
    for sympyname, translation in translations.items():
        namespace[sympyname] = namespace[translation]

    # For computing the modulus of a sympy expression we use the builtin abs
    # function, instead of the previously used fabs function for all
    # translation modules. This is because the fabs function in the math
    # module does not accept complex valued arguments. (see issue 9474). The
    # only exception, where we don't use the builtin abs function is the
    # mpmath translation module, because mpmath.fabs returns mpf objects in
    # contrast to abs().
    if 'Abs' not in namespace:
        namespace['Abs'] = abs


# Used for dynamically generated filenames that are inserted into the
# linecache.
_lambdify_generated_counter = 1

@doctest_depends_on(modules=('numpy'))
def lambdify(args, expr, modules=None, printer=None, use_imps=True,
             dummify=False):
    """
    Returns an anonymous function for fast calculation of numerical values.

    If not specified differently by the user, ``modules`` defaults to
    ``["numpy"]`` if NumPy is installed, and ``["math", "mpmath", "sympy"]``
    if it isn't, that is, SymPy functions are replaced as far as possible by
    either ``numpy`` functions if available, and Python's standard library
    ``math``, or ``mpmath`` functions otherwise. To change this behavior, the
    "modules" argument can be used. It accepts:

     - the strings "math", "mpmath", "numpy", "numexpr", "sympy", "tensorflow"
     - any modules (e.g. math)
     - dictionaries that map names of sympy functions to arbitrary functions
     - lists that contain a mix of the arguments above, with higher priority
       given to entries appearing first.

    .. warning::
        Note that this function uses ``eval``, and thus shouldn't be used on
        unsanitized input.

    Arguments in the provided expression that are not valid Python identifiers
    are substitued with dummy symbols. This allows for applied functions
    (e.g. f(t)) to be supplied as arguments. Call the function with
    dummify=True to replace all arguments with dummy symbols (if `args` is
    not a string) - for example, to ensure that the arguments do not
    redefine any built-in names.

    For functions involving large array calculations, numexpr can provide a
    significant speedup over numpy.  Please note that the available functions
    for numexpr are more limited than numpy but can be expanded with
    implemented_function and user defined subclasses of Function.  If specified,
    numexpr may be the only option in modules. The official list of numexpr
    functions can be found at:
    https://github.com/pydata/numexpr#supported-functions

    In previous releases ``lambdify`` replaced ``Matrix`` with ``numpy.matrix``
    by default. As of release 1.0 ``numpy.array`` is the default.
    To get the old default behavior you must pass in ``[{'ImmutableDenseMatrix':
    numpy.matrix}, 'numpy']`` to the ``modules`` kwarg.

    >>> from sympy import lambdify, Matrix
    >>> from sympy.abc import x, y
    >>> import numpy
    >>> array2mat = [{'ImmutableDenseMatrix': numpy.matrix}, 'numpy']
    >>> f = lambdify((x, y), Matrix([x, y]), modules=array2mat)
    >>> f(1, 2)
    matrix([[1],
            [2]])

    Usage
    =====

    (1) Use one of the provided modules:

        >>> from sympy import sin, tan, gamma
        >>> from sympy.abc import x, y
        >>> f = lambdify(x, sin(x), "math")

        Attention: Functions that are not in the math module will throw a name
                   error when the function definition is evaluated! So this
                   would be better:

        >>> f = lambdify(x, sin(x)*gamma(x), ("math", "mpmath", "sympy"))

    (2) Use some other module:

        >>> import numpy
        >>> f = lambdify((x,y), tan(x*y), numpy)

        Attention: There are naming differences between numpy and sympy. So if
                   you simply take the numpy module, e.g. sympy.atan will not be
                   translated to numpy.arctan. Use the modified module instead
                   by passing the string "numpy":

        >>> f = lambdify((x,y), tan(x*y), "numpy")
        >>> f(1, 2)
        -2.18503986326
        >>> from numpy import array
        >>> f(array([1, 2, 3]), array([2, 3, 5]))
        [-2.18503986 -0.29100619 -0.8559934 ]

        In the above examples, the generated functions can accept scalar
        values or numpy arrays as arguments.  However, in some cases
        the generated function relies on the input being a numpy array:

        >>> from sympy import Piecewise
        >>> f = lambdify(x, Piecewise((x, x <= 1), (1/x, x > 1)), "numpy")
        >>> f(array([-1, 0, 1, 2]))
        [-1.   0.   1.   0.5]
        >>> f(0)
        Traceback (most recent call last):
            ...
        ZeroDivisionError: division by zero

        In such cases, the input should be wrapped in a numpy array:
        >>> float(f(array([0])))
        0.0

        Or if numpy functionality is not required another module can be used:
        >>> f = lambdify(x, Piecewise((x, x <= 1), (1/x, x > 1)), "math")
        >>> f(0)
        0

    (3) Use a dictionary defining custom functions:

        >>> def my_cool_function(x): return 'sin(%s) is cool' % x
        >>> myfuncs = {"sin" : my_cool_function}
        >>> f = lambdify(x, sin(x), myfuncs); f(1)
        'sin(1) is cool'

    Examples
    ========

    >>> from sympy.utilities.lambdify import implemented_function
    >>> from sympy import sqrt, sin, Matrix
    >>> from sympy import Function
    >>> from sympy.abc import w, x, y, z

    >>> f = lambdify(x, x**2)
    >>> f(2)
    4
    >>> f = lambdify((x, y, z), [z, y, x])
    >>> f(1,2,3)
    [3, 2, 1]
    >>> f = lambdify(x, sqrt(x))
    >>> f(4)
    2.0
    >>> f = lambdify((x, y), sin(x*y)**2)
    >>> f(0, 5)
    0.0
    >>> row = lambdify((x, y), Matrix((x, x + y)).T, modules='sympy')
    >>> row(1, 2)
    Matrix([[1, 3]])

    Tuple arguments are handled and the lambdified function should
    be called with the same type of arguments as were used to create
    the function.:

    >>> f = lambdify((x, (y, z)), x + y)
    >>> f(1, (2, 4))
    3

    A more robust way of handling this is to always work with flattened
    arguments:

    >>> from sympy.utilities.iterables import flatten
    >>> args = w, (x, (y, z))
    >>> vals = 1, (2, (3, 4))
    >>> f = lambdify(flatten(args), w + x + y + z)
    >>> f(*flatten(vals))
    10

    Functions present in `expr` can also carry their own numerical
    implementations, in a callable attached to the ``_imp_``
    attribute.  Usually you attach this using the
    ``implemented_function`` factory:

    >>> f = implemented_function(Function('f'), lambda x: x+1)
    >>> func = lambdify(x, f(x))
    >>> func(4)
    5

    ``lambdify`` always prefers ``_imp_`` implementations to implementations
    in other namespaces, unless the ``use_imps`` input parameter is False.

    Usage with Tensorflow module:

    >>> import tensorflow as tf
    >>> f = Max(x, sin(x))
    >>> func = lambdify(x, f, 'tensorflow')
    >>> result = func(tf.constant(1.0))
    >>> result # a tf.Tensor representing the result of the calculation
    <tf.Tensor 'Maximum:0' shape=() dtype=float32>
    >>> sess = tf.Session()
    >>> sess.run(result) # compute result
    1.0
    >>> var = tf.Variable(1.0)
    >>> sess.run(tf.global_variables_initializer())
    >>> sess.run(func(var)) # also works for tf.Variable and tf.Placeholder
    1.0
    >>> tensor = tf.constant([[1.0, 2.0], [3.0, 4.0]]) # works with any shape tensor
    >>> sess.run(func(tensor))
    array([[ 1.,  2.],
           [ 3.,  4.]], dtype=float32)

    """
    from sympy.core.symbol import Symbol
    from sympy.utilities.iterables import flatten

    # If the user hasn't specified any modules, use what is available.
    module_provided = True
    if modules is None:
        module_provided = False

        try:
            _import("scipy")
        except ImportError:
            try:
                _import("numpy")
            except ImportError:
                # Use either numpy (if available) or python.math where possible.
                # XXX: This leads to different behaviour on different systems and
                #      might be the reason for irreproducible errors.
                modules = ["math", "mpmath", "sympy"]
            else:
                modules = ["numpy"]
        else:
            modules = ["scipy", "numpy"]

    # Get the needed namespaces.
    namespaces = []
    # First find any function implementations
    if use_imps:
        namespaces.append(_imp_namespace(expr))
    # Check for dict before iterating
    if isinstance(modules, (dict, str)) or not hasattr(modules, '__iter__'):
        namespaces.append(modules)
    else:
        # consistency check
        if _module_present('numexpr', modules) and len(modules) > 1:
            raise TypeError("numexpr must be the only item in 'modules'")
        namespaces += list(modules)
    # fill namespace with first having highest priority
    namespace = {}
    for m in namespaces[::-1]:
        buf = _get_namespace(m)
        namespace.update(buf)

    if hasattr(expr, "atoms"):
        #Try if you can extract symbols from the expression.
        #Move on if expr.atoms in not implemented.
        syms = expr.atoms(Symbol)
        for term in syms:
            namespace.update({str(term): term})

    if printer is None:
        if _module_present('mpmath', namespaces):
            from sympy.printing.pycode import MpmathPrinter as Printer
        elif _module_present('scipy', namespaces):
            from sympy.printing.pycode import SciPyPrinter as Printer
        elif _module_present('numpy', namespaces):
            from sympy.printing.pycode import NumPyPrinter as Printer
        elif _module_present('numexpr', namespaces):
            from sympy.printing.lambdarepr import NumExprPrinter as Printer
        elif _module_present('tensorflow', namespaces):
            from sympy.printing.lambdarepr import TensorflowPrinter as Printer
        elif _module_present('sympy', namespaces):
            from sympy.printing.pycode import SymPyPrinter as Printer
        else:
            from sympy.printing.pycode import PythonCodePrinter as Printer
        user_functions = {}
        for m in namespaces[::-1]:
            if isinstance(m, dict):
                for k in m:
                    user_functions[k] = k
        printer = Printer({'fully_qualified_modules': False, 'inline': True,
                           'allow_unknown_functions': True,
                           'user_functions': user_functions})

    # Get the names of the args, for creating a docstring
    if not iterable(args):
        args = (args,)
    names = []
    # Grab the callers frame, for getting the names by inspection (if needed)
    callers_local_vars = inspect.currentframe().f_back.f_locals.items()
    for n, var in enumerate(args):
        if hasattr(var, 'name'):
            names.append(var.name)
        else:
            # It's an iterable. Try to get name by inspection of calling frame.
            name_list = [var_name for var_name, var_val in callers_local_vars
                    if var_val is var]
            if len(name_list) == 1:
                names.append(name_list[0])
            else:
                # Cannot infer name with certainty. arg_# will have to do.
                names.append('arg_' + str(n))

    imp_mod_lines = []
    for mod, keys in (getattr(printer, 'module_imports', None) or {}).items():
        for k in keys:
            if k not in namespace:
                imp_mod_lines.append("from %s import %s" % (mod, k))
    for ln in imp_mod_lines:
        exec_(ln, {}, namespace)

    # Provide lambda expression with builtins, and compatible implementation of range
    namespace.update({'builtins':builtins, 'range':range})

    # Create the function definition code and execute it

    funcname = '_lambdifygenerated'

    if _module_present('tensorflow', namespaces):
        funcprinter = _TensorflowEvaluatorPrinter(printer, dummify)
    else:
        funcprinter = _EvaluatorPrinter(printer, dummify)

    funcstr = funcprinter.doprint(funcname, args, expr)

    funclocals = {}
    global _lambdify_generated_counter
    filename = '<lambdifygenerated-%s>' % _lambdify_generated_counter
    _lambdify_generated_counter += 1
    c = compile(funcstr, filename, 'exec')
    exec_(c, namespace, funclocals)
    # mtime has to be None or else linecache.checkcache will remove it
    linecache.cache[filename] = (len(funcstr), None, funcstr.splitlines(True), filename)

    func = funclocals[funcname]

    # Apply the docstring
    sig = "func({0})".format(", ".join(str(i) for i in names))
    sig = textwrap.fill(sig, subsequent_indent=' '*8)
    expr_str = str(expr)
    if len(expr_str) > 78:
        expr_str = textwrap.wrap(expr_str, 75)[0] + '...'
    func.__doc__ = (
        "Created with lambdify. Signature:\n\n"
        "{sig}\n\n"
        "Expression:\n\n"
        "{expr}\n\n"
        "Source code:\n\n"
        "{src}\n\n"
        "Imported modules:\n\n"
        "{imp_mods}"
        ).format(sig=sig, expr=expr_str, src=funcstr, imp_mods='\n'.join(imp_mod_lines))
    return func

def _module_present(modname, modlist):
    if modname in modlist:
        return True
    for m in modlist:
        if hasattr(m, '__name__') and m.__name__ == modname:
            return True
    return False


def _get_namespace(m):
    """
    This is used by _lambdify to parse its arguments.
    """
    if isinstance(m, string_types):
        _import(m)
        return MODULES[m][0]
    elif isinstance(m, dict):
        return m
    elif hasattr(m, "__dict__"):
        return m.__dict__
    else:
        raise TypeError("Argument must be either a string, dict or module but it is: %s" % m)

def lambdastr(args, expr, printer=None, dummify=False):
    """
    Returns a string that can be evaluated to a lambda function.

    Examples
    ========

    >>> from sympy.abc import x, y, z
    >>> from sympy.utilities.lambdify import lambdastr
    >>> lambdastr(x, x**2)
    'lambda x: (x**2)'
    >>> lambdastr((x,y,z), [z,y,x])
    'lambda x,y,z: ([z, y, x])'

    Although tuples may not appear as arguments to lambda in Python 3,
    lambdastr will create a lambda function that will unpack the original
    arguments so that nested arguments can be handled:

    >>> lambdastr((x, (y, z)), x + y)
    'lambda _0,_1: (lambda x,y,z: (x + y))(_0,_1[0],_1[1])'
    """
    # Transforming everything to strings.
    from sympy.matrices import DeferredVector
    from sympy import Dummy, sympify, Symbol, Function, flatten

    if printer is not None:
        if inspect.isfunction(printer):
            lambdarepr = printer
        else:
            if inspect.isclass(printer):
                lambdarepr = lambda expr: printer().doprint(expr)
            else:
                lambdarepr = lambda expr: printer.doprint(expr)
    else:
        #XXX: This has to be done here because of circular imports
        from sympy.printing.lambdarepr import lambdarepr

    def sub_args(args, dummies_dict):
        if isinstance(args, str):
            return args
        elif isinstance(args, DeferredVector):
            return str(args)
        elif iterable(args):
            dummies = flatten([sub_args(a, dummies_dict) for a in args])
            return ",".join(str(a) for a in dummies)
        else:
            #Sub in dummy variables for functions or symbols
            if isinstance(args, (Function, Symbol)):
                dummies = Dummy()
                dummies_dict.update({args : dummies})
                return str(dummies)
            else:
                return str(args)

    def sub_expr(expr, dummies_dict):
        try:
            expr = sympify(expr).xreplace(dummies_dict)
        except Exception:
            if isinstance(expr, DeferredVector):
                pass
            elif isinstance(expr, dict):
                k = [sub_expr(sympify(a), dummies_dict) for a in expr.keys()]
                v = [sub_expr(sympify(a), dummies_dict) for a in expr.values()]
                expr = dict(zip(k, v))
            elif isinstance(expr, tuple):
                expr = tuple(sub_expr(sympify(a), dummies_dict) for a in expr)
            elif isinstance(expr, list):
                expr = [sub_expr(sympify(a), dummies_dict) for a in expr]
        return expr

    # Transform args
    def isiter(l):
        return iterable(l, exclude=(str, DeferredVector, NotIterable))

    def flat_indexes(iterable):
        n = 0

        for el in iterable:
            if isiter(el):
                for ndeep in flat_indexes(el):
                    yield (n,) + ndeep
            else:
                yield (n,)

            n += 1

    if isiter(args) and any(isiter(i) for i in args):
        dum_args = [str(Dummy(str(i))) for i in range(len(args))]

        indexed_args = ','.join([
            dum_args[ind[0]] + ''.join(["[%s]" % k for k in ind[1:]])
                    for ind in flat_indexes(args)])

        lstr = lambdastr(flatten(args), expr, printer=printer, dummify=dummify)

        return 'lambda %s: (%s)(%s)' % (','.join(dum_args), lstr, indexed_args)

    dummies_dict = {}
    if dummify:
        args = sub_args(args, dummies_dict)
    else:
        if isinstance(args, str):
            pass
        elif iterable(args, exclude=DeferredVector):
            args = ",".join(str(a) for a in args)

    # Transform expr
    if dummify:
        if isinstance(expr, str):
            pass
        else:
            expr = sub_expr(expr, dummies_dict)
    expr = lambdarepr(expr)
    return "lambda %s: (%s)" % (args, expr)

class _EvaluatorPrinter(object):
    def __init__(self, printer=None, dummify=False):
        self._dummify = dummify

        #XXX: This has to be done here because of circular imports
        from sympy.printing.lambdarepr import LambdaPrinter

        if printer is None:
            printer = LambdaPrinter()

        if inspect.isfunction(printer):
            self._exprrepr = printer
        else:
            if inspect.isclass(printer):
                printer = printer()

            self._exprrepr = printer.doprint

            if hasattr(printer, '_print_Symbol'):
                symbolrepr = printer._print_Symbol

            if hasattr(printer, '_print_Dummy'):
                dummyrepr = printer._print_Dummy

        # Used to print the generated function arguments in a standard way
        self._argrepr = LambdaPrinter().doprint

    def doprint(self, funcname, args, expr):
        """Returns the function definition code as a string."""
        from sympy import Dummy

        funcbody = []

        if not iterable(args):
            args = [args]

        argstrs, expr = self._preprocess(args, expr)

        # Generate argument unpacking and final argument list
        funcargs = []
        unpackings = []

        for argstr in argstrs:
            if iterable(argstr):
                funcargs.append(self._argrepr(Dummy()))
                unpackings.extend(self._print_unpacking(argstr, funcargs[-1]))
            else:
                funcargs.append(argstr)

        funcsig = 'def {}({}):'.format(funcname, ', '.join(funcargs))

        # Wrap input arguments before unpacking
        funcbody.extend(self._print_funcargwrapping(funcargs))

        funcbody.extend(unpackings)

        funcbody.append('return ({})'.format(self._exprrepr(expr)))

        funclines = [funcsig]
        funclines.extend('    ' + line for line in funcbody)

        return '\n'.join(funclines) + '\n'

    if PY3:
        @classmethod
        def _is_safe_ident(cls, ident):
            return isinstance(ident, str) and ident.isidentifier() \
                    and not keyword.iskeyword(ident)
    else:
        _safe_ident_re = re.compile('^[a-zA-Z_][a-zA-Z0-9_]*$')

        @classmethod
        def _is_safe_ident(cls, ident):
            return isinstance(ident, str) and cls._safe_ident_re.match(ident) \
                and not (keyword.iskeyword(ident) or ident == 'None')

    def _preprocess(self, args, expr):
        """Preprocess args, expr to replace arguments that do not map
        to valid Python identifiers.

        Returns string form of args, and updated expr.
        """
        from sympy import Dummy, Symbol, MatrixSymbol, Function, flatten
        from sympy.matrices import DeferredVector

        dummify = self._dummify

        # Args of type Dummy can cause name collisions with args
        # of type Symbol.  Force dummify of everything in this
        # situation.
        if not dummify:
            dummify = any(isinstance(arg, Dummy) for arg in flatten(args))

        argstrs = []
        for arg in args:
            if iterable(arg):
                nested_argstrs, expr = self._preprocess(arg, expr)
                argstrs.append(nested_argstrs)
            elif isinstance(arg, DeferredVector):
                argstrs.append(str(arg))
            elif isinstance(arg, Symbol) or isinstance(arg, MatrixSymbol):
                argrep = self._argrepr(arg)

                if dummify or not self._is_safe_ident(argrep):
                    dummy = Dummy()
                    argstrs.append(self._argrepr(dummy))
                    expr = self._subexpr(expr, {arg: dummy})
                else:
                    argstrs.append(argrep)
            elif isinstance(arg, Function):
                dummy = Dummy()
                argstrs.append(self._argrepr(dummy))
                expr = self._subexpr(expr, {arg: dummy})
            elif dummify:
                dummy = Dummy()
                argstrs.append(self._argrepr(dummy))
                expr = self._subexpr(expr, {arg: dummy})
            else:
                argstrs.append(str(arg))

        return argstrs, expr

    def _subexpr(self, expr, dummies_dict):
        from sympy.matrices import DeferredVector
        from sympy import sympify

        try:
            expr = sympify(expr).xreplace(dummies_dict)
        except Exception:
            if isinstance(expr, DeferredVector):
                pass
            elif isinstance(expr, dict):
                k = [self._subexpr(sympify(a), dummies_dict) for a in expr.keys()]
                v = [self._subexpr(sympify(a), dummies_dict) for a in expr.values()]
                expr = dict(zip(k, v))
            elif isinstance(expr, tuple):
                expr = tuple(self._subexpr(sympify(a), dummies_dict) for a in expr)
            elif isinstance(expr, list):
                expr = [self._subexpr(sympify(a), dummies_dict) for a in expr]
        return expr

    def _print_funcargwrapping(self, args):
        """Generate argument wrapping code.

        args is the argument list of the generated function (strings).

        Return value is a list of lines of code that will be inserted  at
        the beginning of the function definition.
        """
        return []

    def _print_unpacking(self, unpackto, arg):
        """Generate argument unpacking code.

        arg is the function argument to be unpacked (a string), and
        unpackto is a list or nested lists of the variable names (strings) to
        unpack to.
        """
        def unpack_lhs(lvalues):
            return '[{}]'.format(', '.join(
                unpack_lhs(val) if iterable(val) else val for val in lvalues))

        return ['{} = {}'.format(unpack_lhs(unpackto), arg)]

class _TensorflowEvaluatorPrinter(_EvaluatorPrinter):
    def _print_unpacking(self, lvalues, rvalue):
        """Generate argument unpacking code.

        This method is used when the input value is not interable,
        but can be indexed (see issue #14655).
        """
        from sympy import flatten

        def flat_indexes(elems):
            n = 0

            for el in elems:
                if iterable(el):
                    for ndeep in flat_indexes(el):
                        yield (n,) + ndeep
                else:
                    yield (n,)

                n += 1

        indexed = ', '.join('{}[{}]'.format(rvalue, ']['.join(map(str, ind)))
                                for ind in flat_indexes(lvalues))

        return ['[{}] = [{}]'.format(', '.join(flatten(lvalues)), indexed)]

def _imp_namespace(expr, namespace=None):
    """ Return namespace dict with function implementations

    We need to search for functions in anything that can be thrown at
    us - that is - anything that could be passed as `expr`.  Examples
    include sympy expressions, as well as tuples, lists and dicts that may
    contain sympy expressions.

    Parameters
    ----------
    expr : object
       Something passed to lambdify, that will generate valid code from
       ``str(expr)``.
    namespace : None or mapping
       Namespace to fill.  None results in new empty dict

    Returns
    -------
    namespace : dict
       dict with keys of implemented function names within `expr` and
       corresponding values being the numerical implementation of
       function

    Examples
    ========

    >>> from sympy.abc import x
    >>> from sympy.utilities.lambdify import implemented_function, _imp_namespace
    >>> from sympy import Function
    >>> f = implemented_function(Function('f'), lambda x: x+1)
    >>> g = implemented_function(Function('g'), lambda x: x*10)
    >>> namespace = _imp_namespace(f(g(x)))
    >>> sorted(namespace.keys())
    ['f', 'g']
    """
    # Delayed import to avoid circular imports
    from sympy.core.function import FunctionClass
    if namespace is None:
        namespace = {}
    # tuples, lists, dicts are valid expressions
    if is_sequence(expr):
        for arg in expr:
            _imp_namespace(arg, namespace)
        return namespace
    elif isinstance(expr, dict):
        for key, val in expr.items():
            # functions can be in dictionary keys
            _imp_namespace(key, namespace)
            _imp_namespace(val, namespace)
        return namespace
    # sympy expressions may be Functions themselves
    func = getattr(expr, 'func', None)
    if isinstance(func, FunctionClass):
        imp = getattr(func, '_imp_', None)
        if imp is not None:
            name = expr.func.__name__
            if name in namespace and namespace[name] != imp:
                raise ValueError('We found more than one '
                                 'implementation with name '
                                 '"%s"' % name)
            namespace[name] = imp
    # and / or they may take Functions as arguments
    if hasattr(expr, 'args'):
        for arg in expr.args:
            _imp_namespace(arg, namespace)
    return namespace


def implemented_function(symfunc, implementation):
    """ Add numerical ``implementation`` to function ``symfunc``.

    ``symfunc`` can be an ``UndefinedFunction`` instance, or a name string.
    In the latter case we create an ``UndefinedFunction`` instance with that
    name.

    Be aware that this is a quick workaround, not a general method to create
    special symbolic functions. If you want to create a symbolic function to be
    used by all the machinery of SymPy you should subclass the ``Function``
    class.

    Parameters
    ----------
    symfunc : ``str`` or ``UndefinedFunction`` instance
       If ``str``, then create new ``UndefinedFunction`` with this as
       name.  If `symfunc` is an Undefined function, create a new function
       with the same name and the implemented function attached.
    implementation : callable
       numerical implementation to be called by ``evalf()`` or ``lambdify``

    Returns
    -------
    afunc : sympy.FunctionClass instance
       function with attached implementation

    Examples
    ========

    >>> from sympy.abc import x
    >>> from sympy.utilities.lambdify import lambdify, implemented_function
    >>> from sympy import Function
    >>> f = implemented_function('f', lambda x: x+1)
    >>> lam_f = lambdify(x, f(x))
    >>> lam_f(4)
    5
    """
    # Delayed import to avoid circular imports
    from sympy.core.function import UndefinedFunction
    # if name, create function to hold implementation
    _extra_kwargs = {}
    if isinstance(symfunc, UndefinedFunction):
        _extra_kwargs = symfunc._extra_kwargs
        symfunc = symfunc.__name__
    if isinstance(symfunc, string_types):
        # Keyword arguments to UndefinedFunction are added as attributes to
        # the created class.
        symfunc = UndefinedFunction(symfunc, _imp_=staticmethod(implementation), **_extra_kwargs)
    elif not isinstance(symfunc, UndefinedFunction):
        raise ValueError('symfunc should be either a string or'
                         ' an UndefinedFunction instance.')
    return symfunc