import torch._C
from torch import Tensor
from torch.autograd import Variable, function
from torch.nn import Module, ModuleList, ParameterList, Parameter, Sequential
from torch.jit.frontend import get_jit_ast, get_default_args
import torch.jit.annotations
from torch._six import raise_from, with_metaclass, get_function_from_type
from .._jit_internal import createResolutionCallback, _compiled_weak_fns, \
    _weak_script_methods, _weak_modules, _weak_types, COMPILED, \
    COMPILATION_PENDING
import torch.testing
from collections import defaultdict, OrderedDict, namedtuple
import sys
import warnings
import itertools
import weakref
import types
import contextlib
import os
import functools
import copy
import numbers
import collections
import re
if sys.version_info[0] > 2:
    import pathlib


def _parse_env(name, default, true_message, false_message):
    value = os.environ.get(name)
    if value is None:
        return default
    if value.lower() in {'1', 'true', 'yes'}:
        return True
    elif value.lower() in {'0', 'false', 'no'}:
        return False
    if value == '1v':
        print(true_message)
        return True
    elif value == '0v':
        print(false_message)
        return False
    raise ValueError('Unknown setting of {}. Try using 0 or 1.'.format(name))


_enabled = _parse_env('PYTORCH_JIT', True, "> Using PyTorch JIT", "> PyTorch JIT DISABLED")
_flatten = torch._C._jit_flatten
_unflatten = torch._C._jit_unflatten
_jit_script_compile = torch._C._jit_script_compile
BatchTensor = torch._C._jit.BatchTensor

Future = torch._C.Future
_fork = torch._C.fork
_wait = torch._C.wait


@contextlib.contextmanager
def scope(scope_name):
    tracing_state = torch._C._get_tracing_state()
    if tracing_state:
        tracing_state.push_scope(scope_name)
    try:
        yield
    finally:
        if tracing_state:
            tracing_state.pop_scope()


def load(f):
    r"""
        Load a ``ScriptModule`` previously saved with :func:`save <torch.jit.save>`

        .. DANGER::
           All previously saved modules, no matter their device, are always loaded onto the CPU.
           This is different from :func:`torch.load`'s semantics and may change in the future.

        Arguments:
            f: a file-like object (has to implement read, readline, tell, and seek),
                or a string containing a file name

        Returns:
            A ``ScriptModule`` object.

        Example:
            >>> torch.jit.load('scriptmodule.pt')
            # Load ScriptModule from io.BytesIO object
            >>> with open('scriptmodule.pt', 'rb') as f:
                    buffer = io.BytesIO(f.read())
            >>> torch.jit.load(buffer)
    """
    m = ScriptModule()

    def module_lookup(names):
        curr = m
        for name in names:
            if not hasattr(curr, name):
                setattr(curr, name, ScriptModule())
            curr = getattr(curr, name)
        return curr

    if isinstance(f, str) or \
            (sys.version_info[0] == 2 and isinstance(f, unicode)) or \
            (sys.version_info[0] == 3 and isinstance(f, pathlib.Path)):
        torch._C.import_ir_module(module_lookup, f)
    else:
        torch._C.import_ir_module_from_buffer(module_lookup, f.read())
    return m


def save(m, f):
    """
        Saves a ScriptModule to a file.

        Args:
            m: a ScriptModule to save
            f: a file-like object (has to implement write and flush) or a string
               containing a file name

        .. warning::
            If you are using Python 2, torch.save does NOT support StringIO.StringIO
            as a valid file-like object. This is because the write method should return
            the number of bytes written; StringIO.write() does not do this.

            Please use something like io.BytesIO instead.

        Example:
            >>> m = torch.jit.ScriptModule()
            >>> # Save to file
            >>> torch.jit.save(m, 'scriptmodule.pt')
            >>> # Save to io.BytesIO buffer
            >>> buffer = io.BytesIO()
            >>> torch.jit.save(m, buffer)
    """
    if isinstance(f, str) or \
            (sys.version_info[0] == 2 and isinstance(f, unicode)) or \
            (sys.version_info[0] == 3 and isinstance(f, pathlib.Path)):
        m.save(f)
    else:
        ret = m.save_to_buffer()
        f.write(ret)


def get_trace_graph(f, args=(), kwargs=None):
    """
    Trace a function or model, returning a tuple consisting of the both the
    *trace* of an execution, as well as the original return value.

    Tracing is guaranteed not to change the semantics of the function/module
    that is traced.

    Arguments:
        f (torch.nn.Module or function): the function or module
            to be traced.
        args (tuple or Tensor): the positional arguments to pass to the
            function/module to be traced.  A non-tuple is assumed to
            be a single positional argument to be passed to the model.
        kwargs (dict): the keyword arguments to pass to the function/module
            to be traced.

    Example: Trace a cell.

        >>> trace, out = jit.trace(nn.LSTMCell(), (input, hidden))
        >>> print(trace)
    """
    if kwargs is None:
        kwargs = {}
    if not isinstance(args, tuple):
        args = (args,)
    return LegacyTracedModule(f)(*args, **kwargs)


def _unique_state_dict(module, keep_vars=False):
    state_dict = module.state_dict(keep_vars=keep_vars)
    filtered_dict = type(state_dict)()
    seen_ids = set()
    for k, v in state_dict.items():
        if id(v) in seen_ids:
            continue
        seen_ids.add(id(v))
        filtered_dict[k] = v
    return filtered_dict


class LegacyTracedModule(Module):
    def __init__(self, inner):
        super(LegacyTracedModule, self).__init__()
        # inner may be a Module, or it may be an arbitrary callable
        # If it's a Module, we get its parameters automatically, which lets
        # us avoid a special casing functions versus modules.
        self.inner = inner

    def forward(self, *args):
        in_vars, in_desc = _flatten(args)
        # NOTE: use full state, because we need it for BatchNorm export
        # This differs from the compiler path, which doesn't support it at the moment.
        module_state = list(_unique_state_dict(self, keep_vars=True).values())
        trace, all_trace_inputs = torch._C._tracer_enter(*(in_vars + module_state))
        try:
            trace_inputs = _unflatten(all_trace_inputs[:len(in_vars)], in_desc)
            out = self.inner(*trace_inputs)
            out_vars, _ = _flatten(out)
            torch._C._tracer_exit(tuple(out_vars))
        except Exception:
            torch._C._tracer_abandon()
            raise
        return trace, out


def _clone_inputs(args):
    def clone_input(a):
        if a is None:
            return None
        elif isinstance(a, torch.Tensor):
            # TODO: figure out one liner to .clone() and set requires_grad
            v = Variable(a.data.clone(), requires_grad=a.requires_grad)
            if a.grad is not None:
                v.grad = clone_input(v.grad)
            return v
        else:
            return a.clone()
    return function._nested_map(lambda x: isinstance(x, torch.Tensor),
                                clone_input, condition_msg="tensors")(args)


# This is purely for developer debugging.  We are not going to advertise it.
_JIT_DUMP = os.environ.get('PYTORCH_JIT_DUMP', False)
_JIT_TIME = os.environ.get('PYTORCH_JIT_TIME', False)  # CUDA-only timing
_JIT_DISABLE = os.environ.get('PYTORCH_JIT_DISABLE', False)
_JIT_STATS = os.environ.get('PYTORCH_JIT_STATS', False)


def _dump_trace(trace_name, pass_name, input_key, trace):
    if not _JIT_DUMP:
        return

    import torch.contrib._graph_vis as graph_vis

    filename = "{}_{}".format(trace_name, pass_name)
    # TODO: Also paste out the backtrace when the trace was compiled
    # (and maybe also when it was run?)
    with open(filename + ".ir", "w") as f:
        f.write("Input key: {}\n\n{}".format(input_key, str(trace)))
    graph_vis.write(trace.graph(), filename + ".html")


@contextlib.contextmanager
def _time(trace_name, name, time=True):
    if (not _JIT_TIME and not time) or not torch.cuda.is_available():
        yield
        return
    stream = torch.cuda.current_stream()
    start = torch.cuda.Event(enable_timing=True)
    end = torch.cuda.Event(enable_timing=True)
    stream.record_event(start)
    try:
        yield
    finally:
        stream.record_event(end)
        end.synchronize()
        print("{} {} time: {} ms".format(trace_name, name, start.elapsed_time(end)))


def verify(model, args, loss_fn=torch.sum, devices=None):
    """
    Verify that a JIT compiled model has the same behavior as its uncompiled
    version along with its backwards pass.  If your model returns multiple
    outputs, you must also specify a `loss_fn` to produce a loss for which
    the backwards will be computed.

    This function has side-effects (e.g., it executes your model / saves and loads
    parameters), so don't expect the model to come out exactly the same as what
    you passed in.

    Arguments:
        model (compiled torch.nn.Module or function): the module/function to be
            verified.  The module/function definition MUST have been decorated with
            `@torch.jit.compile`.
        args (tuple or Tensor): the positional arguments to pass to the
            compiled function/module to be verified.  A non-tuple is assumed to
            be a single positional argument to be passed to the model.
        loss_fn (function, optional): the loss function to be applied to
            the output of the model, before backwards is invoked.  By default,
            we assume that a model returns a single result, and we :func:`torch.sum`
            before calling backwards; if this is inappropriate, you can pass your
            own loss function.  Note that if a model returns a tuple of results,
            these are passed as separate positional arguments to `loss_fn`.
        devices (iterable of device IDs, optional): the GPU devices which the
            compiled module will be run on.  This determines the RNG state we
            must save when running both compiled and uncompiled versions of the model.
    """
    # TODO: In principle, we track device information in our trace, so it
    # should be possible to check if our execution actually obeyed the 'devices'
    # the user provided.

    # TODO: Consider adding a utility function to torch.jit to test
    # for this case
    if not isinstance(model, torch._C.CompiledFunction):
        raise TypeError("Cannot verify an uncompiled module.  Add @torch.jit.compile to compile it")
    is_module = isinstance(model, Module)

    if not isinstance(args, tuple):
        args = (args,)

    saved_args = _clone_inputs(args)
    if is_module:
        saved_state = copy.deepcopy(model.state_dict())

    def run_fwd_bwd(args, force_trace=False, assert_compiled=False):
        params = list(model.parameters()) if is_module else []
        in_vars, _ = _flatten((args, params))
        # We use a special API to reset the trace and compile it from scratch.
        compiled_fn = model
        if force_trace:
            compiled_fn.clear_cache()
        if assert_compiled:
            hits = compiled_fn.hits
        out = model(*args)
        if assert_compiled and compiled_fn.hits == hits:
            raise RuntimeError("failed to use the compiled function")
        if not isinstance(out, tuple):
            out = (out, )
        if loss_fn == torch.sum and len(out) != 1:
            raise ValueError(("Model returns {} outputs, but default loss function "
                              "(torch.sum) can only handle a single output").format(len(out)))
        out_vars, _ = _flatten(out)
        saved_outs = [v.data.clone() for v in out_vars]
        loss = loss_fn(*out)
        grads = torch.autograd.grad([loss], in_vars)
        # TODO: I'm not sure if the clone here is necessary but it is safer
        saved_grads = [v.data.clone() for v in grads]
        return (saved_outs, saved_grads)

    with torch.random.fork_rng(devices, _caller="torch.jit.verify"):
        uncompiled_outs, uncompiled_grads = run_fwd_bwd(args, force_trace=True)
        assert model.has_trace_for(*args)

    if is_module:
        model.load_state_dict(saved_state)
    compiled_outs, compiled_grads = run_fwd_bwd(args, assert_compiled=True)

    _verify_equal(uncompiled_outs, compiled_outs)
    _verify_equal(uncompiled_grads, compiled_grads)


def _verify_equal(xs, ys):
    for x, y in zip(xs, ys):
        if x.sub(y).abs().max() > 1e-6:
            raise RuntimeError("JIT and real computation mismatch")


def indent(s):
    return '\n'.join(['\t' + line for line in s.splitlines()])


class TracingCheckError(Exception):
    def __init__(self, graph_diff_error, tensor_compare_error, extra_msg=None):
        self.message = 'Tracing failed sanity checks!\n'
        if extra_msg is not None:
            self.message += extra_msg + '\n'
        if graph_diff_error is not None:
            self.message += 'ERROR: Graphs differed across invocations!\n'
            self.message += indent(graph_diff_error) + '\n'
        if tensor_compare_error is not None:
            self.message += 'ERROR: Tensor-valued Constant nodes differed in value ' \
                            'across invocations. This often indicates that the tracer has' \
                            ' encountered untraceable code.\n'
            self.message += indent(tensor_compare_error) + '\n'
        super(TracingCheckError, self).__init__(self.message)


# Check the traced module against a set of user-provided validation inputs
@torch.no_grad()
def _check_trace(check_inputs, func, executor_options, module, check_tolerance):
    # Note: tracing is independent of optimizations, which consume the trace
    executor_options['optimize'] = False
    for inputs in check_inputs:
        if isinstance(inputs, torch.Tensor):
            inputs = (inputs,)
        check_mod = torch.jit.trace(func, _clone_inputs(inputs), check_trace=False, **executor_options)

        def graph_diagnostic_info():
            mod_canonicalized = torch._C._jit_pass_canonicalize(module.graph)
            torch._C._jit_pass_erase_shape_information(mod_canonicalized)
            check_canonicalized = torch._C._jit_pass_canonicalize(check_mod.graph)
            torch._C._jit_pass_erase_shape_information(check_canonicalized)

            graph_diff_errors = None
            if str(mod_canonicalized) != str(check_canonicalized):
                import difflib
                graph_diff = difflib.ndiff(str(mod_canonicalized).splitlines(True),
                                           str(check_canonicalized).splitlines(True))
                graph_diff_errors = 'Graph diff:\n' + indent(''.join(graph_diff)) + '\n'

                for n_mod, n_check in zip(mod_canonicalized.nodes(), check_canonicalized.nodes()):
                    if str(n_mod) != str(n_check):
                        graph_diff_errors += 'First diverging operator:\n'
                        node_diff = difflib.ndiff(str(n_mod).splitlines(True),
                                                  str(n_check).splitlines(True))
                        source_printout = 'Node diff:\n' + indent(''.join(node_diff)) + '\n'
                        mod_stack = n_mod.getSourceLocation()
                        if mod_stack:
                            source_printout += 'Trace source location:\n' + indent(mod_stack) + '\n'
                        check_stack = n_check.getSourceLocation()
                        if check_stack:
                            source_printout += 'Check source location:\n' + indent(check_stack) + '\n'
                        graph_diff_errors += source_printout

                        break  # For now, only print out the first pair of nodes that diverges

            tensor_compare_errors = None
            # Check Tensor-valued constant nodes
            for n_mod, n_check in zip(mod_canonicalized.nodes(), check_canonicalized.nodes()):
                if n_mod.kind() != n_check.kind():
                    break  # Graphs have already diverged

                if n_mod.kind() == n_check.kind() and n_mod.kind() == 'prim::Constant':
                    if n_mod.kindOf('value') != 't' or n_check.kindOf('value') != 't':
                        continue

                    mod_tensor_val = n_mod.t('value')
                    check_tensor_val = n_check.t('value')

                    try:
                        torch.testing.assert_allclose(mod_tensor_val, check_tensor_val)
                    except (RuntimeError, AssertionError) as e:
                        if tensor_compare_errors is None:
                            tensor_compare_errors = ''
                        tensor_compare_errors += 'Node:\n' + indent(str(n_mod)) + '\n'
                        compare_stack = n_mod.getSourceLocation()
                        if compare_stack:
                            tensor_compare_errors += 'Source Location:\n' + indent(compare_stack) + '\n'
                        tensor_compare_errors += 'Comparison exception: ' + indent(str(e))

                        break  # For now, only print the first diverging pair

            return graph_diff_errors, tensor_compare_errors

        def wrap_retval(x):
            return x if isinstance(x, tuple) else (x,)

        def run_mod_and_filter_tensor_outputs(mod, inputs, running_what):
            try:
                outs = wrap_retval(mod(*_clone_inputs(inputs)))
                outs = [out for out in outs if isinstance(out, torch.Tensor)]
                return outs
            except Exception as e:
                raise TracingCheckError(*graph_diagnostic_info(),
                                        extra_msg='Encountered an exception while running the ' + running_what +
                                                  ' with test inputs.\nException:\n' + indent(str(e)))

        has_warned = [False]

        def maybe_warn_nondeterministic():
            if has_warned[0]:
                return
            has_warned[0] = True
            nondeterm_ops = [op for op in module.graph.nodes() if op.isNondeterministic()]
            if len(nondeterm_ops) > 0:
                nondeterministic_ops_warning = "Trace had nondeterministic nodes. Nodes:\n"
                nondeterministic_ops_warning += "\n".join([indent(str(op)) for op in nondeterm_ops][:20])
                nondeterministic_ops_warning += "\nThis may cause errors in trace checking. To disable trace checking,"\
                                                " pass check_trace=False to torch.jit.trace()"
                warnings.warn(nondeterministic_ops_warning, category=TracerWarning, stacklevel=5)

        def compare_outputs(original, reference, match_what):
            all_ok = True
            for i, (orig, ref) in enumerate(zip(original, reference)):
                try:
                    torch.testing.assert_allclose(orig.double(), ref.double(), rtol=check_tolerance,
                                                  atol=torch.testing._get_default_tolerance(orig, ref)[1])
                except AssertionError as e:
                    maybe_warn_nondeterministic()
                    warnings.warn('Output nr ' + str(i + 1) + '. of the traced function does not match '
                                  'the corresponding output of the ' + match_what + '. Detailed error:\n' + str(e),
                                  category=TracerWarning, stacklevel=4)
                    all_ok = False

            return all_ok

        traced_outs = run_mod_and_filter_tensor_outputs(module, inputs, 'trace')
        fn_outs = run_mod_and_filter_tensor_outputs(func, inputs, 'Python function')
        if compare_outputs(traced_outs, fn_outs, 'Python function'):
            check_outs = run_mod_and_filter_tensor_outputs(check_mod, inputs, 'repeated trace')
            compare_outputs(traced_outs, check_outs, 'repeated trace')

        diag_info = graph_diagnostic_info()
        if any(info is not None for info in diag_info):
            raise TracingCheckError(*diag_info)


class TracerWarning(Warning):
    @staticmethod
    def ignore_lib_warnings():
        # We ignore warnings from all submodules excluding the JIT, because we need them e.g. for _check_trace
        warnings.filterwarnings('ignore', category=TracerWarning, module='torch.(?!jit)')


# We ignore the tracer warnings coming form inside the library, because all our shape
# checks in nn will trigger them.
TracerWarning.ignore_lib_warnings()
torch._C._tracer_warn_use_python()


def trace(func, example_inputs, optimize=True, check_trace=True, check_inputs=None, check_tolerance=1e-5):
    """
    Trace a function and return an executable trace that will be optimized
    using just-in-time compilation.

    .. warning::

        Tracing only correctly records functions and modules which are not data
        dependent (e.g., have conditionals on data in tensors) and do not have
        any untracked external dependencies (e.g., perform input/output or
        access global variables). If you trace such models, you may silently get
        incorrect results on subsequent invocations of the model. The tracer
        will try to emit warnings when doing something that may cause an
        incorrect trace to be produced.

    Arguments:
        func (callable or torch.nn.Module):  a python function or torch.nn.Module
                                             that will be run with example_inputs.
                                             arguments and returns to func must be Tensors
                                             or (possibly nested) tuples that
                                             contain tensors.
        example_inputs (tuple):  a tuple of example inputs that will be passed to the function
                                 while tracing. The resulting trace can be run with
                                 inputs of different types and shapes assuming the traced operations
                                 support those types and shapes. example_inputs may also be a single
                                 Tensor in which case it is automatically wrapped in a tuple

    Keyword arguments:
        optimize (bool, optional): whether or not to apply optimizations.  Default: ``True``.
        check_trace (bool, optional): check if the same inputs run through
                                      traced code produce the same outputs. Default: ``True``. You might want
                                      to disable this if, for example, your network contains non-
                                      deterministic ops or if you are sure that the network is correct despite
                                      a checker failure.

        check_inputs (list of tuples, optional): A list of tuples of input arguments that should be used
                                                 to check the trace against what is expected. Each tuple
                                                 is equivalent to a seet of input arguments that would
                                                 be specified in ``args``. For best results, pass in a
                                                 set of checking inputs representative of the space of
                                                 shapes and types of inputs you expect the network to see.
                                                 If not specified, the original ``args`` is used for checking
        check_tolerance (float, optional): Floating-point comparison tolerance to use in the checker procedure.
                                           This can be used to relax the checker strictness in the event that
                                           results diverge numerically for a known reason, such as operator fusion.

    Returns:
        A ``ScriptModule`` object with a single ``forward()`` method containing the traced code.
        When func is a ``torch.nn.Module``, the returned ``ScriptModule`` will have the same set of
        sub-modules and parameters as func.

    Example:
       >>> def f(x):
       ...     return x * 2
       >>> traced_f = torch.jit.trace(f, torch.rand(1))

    """
    if not _enabled:
        return func
    executor_options = {'optimize': bool(optimize)}
    # Special case for common case of passing a single Tensor
    if isinstance(example_inputs, torch.Tensor):
        example_inputs = (example_inputs,)
    # done primarily so that weird iterables fail here and not pybind11 code
    elif not isinstance(example_inputs, tuple):
        example_inputs = tuple(example_inputs)
    module = TopLevelTracedModule(func, **executor_options)
    module._create_method_from_trace('forward', func, example_inputs)

    # Check the trace against new traces created from user-specified inputs
    if check_trace:
        if check_inputs is not None:
            _check_trace(check_inputs, func, executor_options, module, check_tolerance)
        else:
            _check_trace([example_inputs], func, executor_options, module, check_tolerance)

    return module


class CompilationUnit(object):
    def __init__(self, lang=None, optimize=True, _frames_up=0):
        self.module = torch._C.ScriptModule()
        self.module._set_optimized(optimize)
        if lang is not None:
            self.define(lang, _frames_up=_frames_up + 1)
        self.optimize = optimize

    def define(self, lang, rcb=None, _frames_up=0):
        if not rcb:
            rcb = createResolutionCallback(_frames_up + 1)
        self.module._define(lang, rcb, False)

    def __getattr__(self, attr):
        return self.module._get_method(attr)


def _try_compile_weak_script(fn):
    entry = _compiled_weak_fns.get(fn)
    if entry is None:
        return None
    if entry["status"] == COMPILATION_PENDING:
        compiled_fn = torch.jit.script(fn, True, 0, entry["rcb"])
        del entry["rcb"]
        _compiled_weak_fns[fn]["compiled_fn"] = compiled_fn
        entry["status"] = COMPILED
        return compiled_fn
    else:
        return entry["compiled_fn"]


def script(fn, optimize=True, _frames_up=0, _rcb=None):
    if not _enabled:
        return fn
    if _rcb is None:
        _rcb = createResolutionCallback(_frames_up + 1)
    ast = get_jit_ast(fn, is_method=False)
    graph = _jit_script_compile(ast, _rcb)
    mod = ScriptModule()
    mod._create_method_from_graph('forward', graph)
    # TODO: refactor everything so we're not 1) creating a ScriptModule
    # 2) Throwing everything away except for the graph 3) Creating a new
    # ScriptModule and dumping that graph in 4) Re-populating the schema
    # because it was lost doing the previous
    mod.__getattr__('forward').forward_schema(ast, get_default_args(fn), False)
    # Forward docstrings
    mod.__doc__ = fn.__doc__
    return mod


ScriptMethodStub = namedtuple('ScriptMethodStub', ('resolution_callback', 'def_', 'original_method'))


def script_method(fn, _rcb=None):
    if not _enabled:
        return fn
    # NOTE: we need to traverse two frames here because the meta-class frame
    # for ScriptModule will be present, as opposed to invoking @script on a
    # a function or invoking define() on a CompilationUnit.
    # The stack will look like:
    #
    # 0. createResolutionCallback()
    # 1. script_method()
    # 2. ScriptModule metaclass frame
    # 3. Surrounding scope
    #
    # createResolutionCallback internally adds 1 to get us to the scope of this
    # function (the calling function). Adding 2 gets us to the proper surrounding scope.
    if _rcb is None:
        _rcb = createResolutionCallback(frames_up=2)
    ast = get_jit_ast(fn, is_method=True)
    return ScriptMethodStub(_rcb, ast, fn)


def _try_get_weak_module(mod):
    """
    Get the WeakScriptModuleProxy corresponding to mod if it exists
    """
    if not isinstance(mod, Module):
        return None
    return _weak_modules.get(mod)


def _is_weak_type(cls):
    """
    Check if a type has been annotated with `weak_module`
    """
    return cls in _weak_types


def batch(batch_size=1, optimize=True, _frames_up=0):
    def decorator(fn):
        if not _enabled:
            return fn
        import torch.jit.batchop
        mod = script(fn, optimize, _frames_up)
        res_graph = torch.to_batch_graph(mod.graph)
        res_mod = ScriptModule()
        res_mod._create_method_from_graph('forward', res_graph)

        def wrapper(*args):
            new_args = []
            for arg in args:
                if isinstance(arg, torch.Tensor):
                    arg = BatchTensor(arg, batch_size)
                if isinstance(arg, BatchTensor):
                    new_args.extend([arg.get_data(), arg.get_mask(), arg.get_dims()])
                else:
                    new_args.append(arg)
            res = res_mod(*new_args)
            assert len(res) % 3 == 0
            if len(res) % 3 != 0:
                raise "non-batched-tensor output is not supported yet"
            result = [BatchTensor(*res[i * 3: i * 3 + 3]) for i in range(len(res) // 3)]
            if len(result) == 1:
                return result[0]
            return result
        wrapper.__doc__ = fn.__doc__
        return wrapper
    return decorator


# These OrderedDictWrapper classes replace the actual OrderedDicts in
# module with versions that get/set properties inside of script::Module.
# This allows us to reuse most of nn.Module while still storing the
# data in C++.
# Each OrderedDict needs to support:
#  x not in view
#  x in view
#  view[name] = ...
#  view.values()
#  del view[name]
#  view.items()
#  view.keys()
#  len(view)

class OrderedDictWrapper(object):
    def __init__(self, module):
        self.module_ref = weakref.ref(module)

    @property
    def module(self):
        r = self.module_ref()
        if r is None:
            raise RuntimeError("_parameters or _modules alive after module is dead")
        return r

    def keys(self):
        return [k for k, v in self.items()]

    def values(self):
        return [v for k, v in self.items()]

    def __delitem__(self, k):
        raise RuntimeError("cannot delete methods or parameters of a script module")

    def items(self):
        raise NotImplementedError

    def __contains__(self, k):
        raise NotImplementedError

    def __getitem__(self, k):
        raise NotImplementedError

    def __setitem__(self, k, v):
        raise NotImplementedError


class OrderedModuleDict(OrderedDictWrapper):
    def __init__(self, module):
        super(OrderedModuleDict, self).__init__(module)
        # contains _both_ script modules and non-script python-only modules

        # because script modules are subclassed in python and the
        # C++ script::Module class will not hold references to them,
        # to ensure that you always get the same python value here
        # we store it in the python dict as well
        self._python_modules = OrderedDict()

    def items(self):
        r = self._python_modules.items()
        return r

    def __contains__(self, k):
        return k in self._python_modules

    def __setitem__(self, k, v):
        if k in self._python_modules:
            raise RuntimeError("cannot re-assign modules in a ScriptModule")
        if isinstance(v, ScriptModule):
            self.module._register_module(k, v)

        self._python_modules[k] = v

    def __getitem__(self, k):
        return self._python_modules[k]


class OrderedParameterDict(OrderedDictWrapper):
    def __init__(self, module):
        super(OrderedParameterDict, self).__init__(module)

    def items(self):
        return [(name, param) for name, param, is_buffer
                in self.module._get_parameters()
                if not is_buffer]

    def __setitem__(self, k, v):
        self.module._register_parameter(k, v, False)

    def __contains__(self, k):
        return self.module._has_parameter(k)

    def __getitem__(self, k):
        if k not in self:
            raise KeyError(k)
        return self.module._get_parameter(k)


class OrderedBufferDict(OrderedDictWrapper):
    def __init__(self, module):
        super(OrderedBufferDict, self).__init__(module)

    def items(self):
        return [(name, param) for name, param, is_buffer
                in self.module._get_parameters()
                if is_buffer]

    def __setitem__(self, k, v):
        self.module._register_parameter(k, v, True)

    def __contains__(self, k):
        return self.module._has_buffer(k)

    def __getitem__(self, k):
        if k not in self:
            raise KeyError(k)
        return self.module._get_parameter(k)

# base types that can be constants
# in addition, tuples and lists of these base types are also considered constants
# If you edit this list, then you also need to edit the handlers in
# ConstantValue in jit/script/init.cpp
_constant_types = (bool, float, int, types.FunctionType, torch.device, torch.layout, torch.dtype)


def _get_valid_constant(attr, v):
    if isinstance(v, _constant_types):
        return v
    elif isinstance(v, tuple) or isinstance(v, list):
        return tuple(_get_valid_constant(attr, x) for x in v)
    constants = ", ".join(typ.__name__ for typ in _constant_types)
    raise TypeError(
        "'{}' object for attribute '{}' ".format(type(v).__name__, attr) +
        "is not a valid constant.\n" +
        "Valid constants are:\n" +
        "  1. a nn.ModuleList\n" +
        "  2. a value of type {{{}}}\n".format(constants) +
        "  3. a list or tuple of (2)\n")


def _create_methods_from_stubs(self, stubs):
    defs = [m.def_ for m in stubs]
    rcbs = [m.resolution_callback for m in stubs]
    defaults = [get_default_args(m.original_method) for m in stubs]
    self._create_methods(defs, rcbs, defaults)

# For each user-defined class that subclasses ScriptModule this meta-class,
# (1) finds all the methods annotated with @script_method
# in a ScriptModule and removes them from the class attributes, and
# (2) puts a wrapper around the class's __init__ method to register
# all of the script_methods with the module after the original __init__
# has run. This has to occur after the user-defined __init__ so that
# submodules and parameters are initialized _before_ the script compiler
# resolve references to `self.param` or `self.module`.


class ScriptMeta(type(torch._C.ScriptModule)):
    # this has to inherit from pybind11's metaclass otherwise we get
    # issues because ScriptModule inherits from torch._C.ScriptModule,
    # a pybind11 type
    def __init__(cls, name, bases, attrs):
        # find all the script methods
        cls._original_methods = {}
        methods = []
        for k, v in sorted(attrs.items()):
            if isinstance(v, ScriptMethodStub):
                delattr(cls, k)
                methods.append(v)
                cls._original_methods[v.original_method.__name__] = v.original_method
        # after the user's __init__ register all the script methods
        # with the module
        original_init = getattr(cls, '__init__', lambda self: None)
        super_constants = getattr(super(cls), '_constants_set', set())
        cls._constants_set = set(getattr(cls, '__constants__', ())).union(super_constants)

        @functools.wraps(original_init)
        def init_then_register(self, *args, **kwargs):
            # ensure even if the user forgets to call super that
            # the pybind object is initialized so it will not segfault
            # run this once, before the most-derived __init__ is called
            if cls is type(self):
                torch._C.ScriptModule.__init__(self)
            original_init(self, *args, **kwargs)
            _create_methods_from_stubs(self, methods)

        cls.__init__ = init_then_register
        return super(ScriptMeta, cls).__init__(name, bases, attrs)


if _enabled:
    class ScriptModule(with_metaclass(ScriptMeta, torch._C.ScriptModule, Module)):
        r"""
        The core data structure in Torch Script is the ``ScriptModule``. It is an
        analogue of torch's nn.Module and represents an entire model as a tree of
        submodules. Like normal modules, each individual module in a ScriptModule can
        have submodules, parameters, and methods. In nn.Modules methods are implemented
        as Python functions, but in ScriptModules methods typically implemented as
        *Torch Script* functions,  a statically-typed subset of Python that contains all
        of PyTorch's built-in Tensor operations. This difference allows your
        ScriptModules code to run without the need for a Python interpreter.

        ScriptModules and the Torch Script functions inside of them can be created in
        two ways:

        **Tracing:**

            Using ``torch.jit.trace``, you can take an existing module or python
            function, provide example inputs, and we run the function, recording the
            operations performed on all the tensors. We turn the resulting recording
            into a Torch Script method that is installed as the ``forward`` method of a
            ScriptModule. This module also contains any parameters that the original
            module had as well.

            Example::

                import torch
                def foo(x, y):
                    return 2*x + y
                traced_foo = torch.jit.trace(foo, (torch.rand(3), torch.rand(3)))

            .. note::
                Tracing a *function* will produce a ``ScriptModule`` with a single
                ``forward`` method that implements that function, and that contains
                no parameters.

            Example::

                import torch
                import torchvision
                traced_net = torch.jit.trace(torchvision.models.resnet18(),
                                             torch.rand(1, 3, 224, 224))

            .. note::

                Tracing only records operations done when the given function is run on the given
                tensors. Therefore, the returned ``ScriptModule`` will always run the same traced
                graph on any input. This has some important implications when your module is
                expected to run different sets of operations, depending on the input and/or the
                module state. For example,

                    + Tracing will not record any control-flow like if statements or loops. When
                      this control-flow is constant across your module, this is fine and it often
                      just inlines configuration decisions. But sometimes the control-flow is
                      actually part of the model itself. For instance, a beam search in
                      sequence-to-sequence translation is a loop over the (varying) sequence
                      length of inputs.

                    + In the returned ``ScriptModule``, operations that have different behaviors
                      in ``training`` and ``eval`` modes will always behave as if it is in the
                      mode it was in during tracing, no matter which mode the ``ScriptModule``
                      is in.

                In cases like these, tracing would not be appropriate and scripting is a better
                choice.

        **Scripting:**

            You can write Torch Script code directly using Python syntax. You do this
            using the ``torch.jit.script`` annotation (for functions) or
            ``torch.jit.script_method`` annotation (for methods) on subclasses of
            ScriptModule. With this annotation the body of the annotated function is
            directly translated into Torch Script. Torch Script itself is a subset of
            the Python language, so not all features in python work, but we provide
            enough functionality to compute on tensors and do control-dependent
            operations.

            Example::

                import torch
                @torch.jit.script
                def foo(x, y):
                    if x.max() > y.max():
                        r = x
                    else:
                        r = y
                    return r

            .. note::
                A script *function* annotation will construct a ScriptModule
                with a single ``forward`` method that implements that function,
                and that contains no parameters.

            Example::

              import torch
              class MyModule(torch.jit.ScriptModule):
                  def __init__(self, N, M):
                      super(MyModule, self).__init__()
                      self.weight = torch.nn.Parameter(torch.rand(N, M))

                  @torch.jit.script_method
                  def forward(self, input):
                      return self.weight.mv(input)

            Example::

                import torch
                import torch.nn as nn
                import torch.nn.functional as F
                from torch.jit import ScriptModule, script_method, trace

                class MyScriptModule(ScriptModule):
                    def __init__(self):
                        super(MyScriptModule, self).__init__()
                        # trace produces a ScriptModule's conv1 and conv2
                        self.conv1 = trace(nn.Conv2d(1, 20, 5), torch.rand(1, 1, 16, 16))
                        self.conv2 = trace(nn.Conv2d(20, 20, 5), torch.rand(1, 20, 16, 16))

                    @script_method
                    def forward(self, input):
                      input = F.relu(self.conv1(input))
                      input = F.relu(self.conv2(input))
                      return input
        """

        def __init__(self, optimize=True):
            # must be before Module.init since the field is used in __getattr__
            Module.__init__(self)
            self._set_optimized(optimize)
            self._parameters = OrderedParameterDict(self)
            self._buffers = OrderedBufferDict(self)
            self._modules = OrderedModuleDict(self)

        def __getattr__(self, attr):
            if self._has_method(attr):
                if attr in self.__class__._original_methods:
                    original_method = self.__class__._original_methods[attr]
                    script_method = self._get_method(attr)
                    return functools.wraps(original_method)(script_method)
                else:
                    return self._get_method(attr)
            if attr == 'graph' and self._has_method('forward'):
                return self.__getattr__('forward').graph
            return Module.__getattr__(self, attr)

        def __setattr__(self, attr, value):
            if attr not in self._constants_set:
                if isinstance(value, Module) and _is_weak_type(type(value)):
                    # Compile weak script module
                    value = _make_strong(value)
                return super(ScriptModule, self).__setattr__(attr, value)
            if hasattr(self, attr):
                raise RuntimeError("attempting to re-assign constant '{}'".format(attr))
            if isinstance(value, ModuleList):
                # special case for list of modules. Modules need to be registered with their
                # parent module. To do this, we create a ConstModuleList, which is itself a module, that
                # contains each of these modules as submodules. The ConstModuleList then
                # is set as an attribute of the parent module.
                super(ScriptModule, self).__setattr__(attr, _ConstModuleList(value))
            elif isinstance(value, Sequential):
                super(ScriptModule, self).__setattr__(attr, _ConstSequential(value))
            else:
                super(ScriptModule, self).__setattr__(attr, _get_valid_constant(attr, value))

        def __dir__(self):
            return sorted(Module.__dir__(self) + self._method_names())

        def define(self, lang):
            # We use frames_up=1 to get to the proper surrounding scope. The stack
            # will look like:
            # 0. createResolutionCallback
            # 1. define()
            # 2. surrounding scope.
            #
            # createResolutionCallback internally adds 1 to get us to our frame, then
            # we add 1 to get to the proper surrounding scope.
            rcb = createResolutionCallback(frames_up=1)
            self._define(lang, rcb, True)

    class WeakScriptModuleProxy(ScriptModule):
        def __init__(self, original, stubs):
            # Guards behavior of __setattr__ and __getattr__ so ScriptModule
            # __init__ can run correctly
            self.__dict__['_initialized'] = False
            super(WeakScriptModuleProxy, self).__init__()

            # Copy constants
            self.__dict__["_original"] = weakref.ref(original)
            self.__dict__["_constants_set"] = set(getattr(original, "__constants__", []))

            # Copy Parameters / Modules / Buffers
            for name in dir(original):
                item = getattr(original, name)
                if isinstance(item, Parameter) or (isinstance(item, Module) and item is not self):
                    ScriptModule.__setattr__(self, name, item)
            for name in original._buffers:
                self.register_buffer(name, original._buffers[name])

            self.__dict__["_initialized"] = True
            _create_methods_from_stubs(self, stubs)

        def __getattr__(self, attr):
            # Try to get the attribute directly, if that fails, fall back to the
            # weak module itself
            try:
                return ScriptModule.__getattr__(self, attr)
            except AttributeError:
                if self.__dict__["_initialized"]:
                    return getattr(self.__dict__["_original"](), attr)
                else:
                    # Only fall back to original once __init__() is done
                    raise AttributeError("Weak module has no attribute '{}'"
                                         .format(attr))

        def __setattr__(self, attr, value):
            # Once constructed, no new properties can be set

            if not self.__dict__["_initialized"]:
                # If constructing, don't fall back to original module
                return ScriptModule.__setattr__(self, attr, value)

            if hasattr(self, attr):
                return ScriptModule.__setattr__(self, attr, value)
            else:
                raise AttributeError("Cannot set new attribute '{}' on "
                                     "weak script module once it has been "
                                     "created".format(attr))

else:
    ScriptModule = torch.nn.Module


def _get_weak_stubs(cls):
    """
    Calls script_method for each method on the type of the object passed in and
    returns the generated ScriptMethodStubs
    """
    stubs = []
    for name in dir(cls):
        func = get_function_from_type(cls, name)
        if func in _weak_script_methods:
            entry = _weak_script_methods[func]
            stub = script_method(entry["original_method"], entry["rcb"])
            stubs.append(stub)
    return stubs


def _make_strong(mod):
    """
    Converts a weak module into a subclass of ScriptModule
    """
    if mod in _weak_modules:
        return _weak_modules[mod]

    stubs = _weak_types.get(type(mod))["method_stubs"]

    if stubs is None:
        # Generate stubs and and store on _weak_types in case this type is
        # used again
        stubs = _get_weak_stubs(type(mod))
        _weak_types[type(mod)]["method_stubs"] = stubs

    # Create proxy with stubs
    proxy = WeakScriptModuleProxy(mod, stubs)

    _weak_modules[mod] = proxy

    return proxy


def _get_methods(cls):
    import inspect
    # In Python 3 unbound methods are functions, but in Python 2 they are methods
    return inspect.getmembers(cls, predicate=lambda x: inspect.isfunction(x) or inspect.ismethod(x))


_compiled_methods_whitelist = {
    'forward', 'register_buffer', 'register_parameter', 'add_module',
    '_apply', 'apply', 'cuda', 'cpu', 'type', 'float', 'double', 'half',
    'state_dict', 'load_state_dict', '_load_from_state_dict',
    '_named_members', 'parameters', 'named_parameters',
    'buffers', 'named_buffers', 'children', 'named_children', 'modules',
    'named_modules', 'zero_grad', 'share_memory', '_get_name', 'extra_repr',
    '_slow_forward', '_tracing_name', 'eval', 'train',
}


def _make_fail(name):
    def fail(self, *args, **kwargs):
        raise RuntimeError(name + " is not supported on TracedModules")
    return fail


for name, method in _get_methods(torch.nn.Module):
    if name.startswith('__'):
        continue
    if name not in ScriptModule.__dict__ and name not in _compiled_methods_whitelist:
        setattr(ScriptModule, method.__name__, _make_fail(name))


class TracedModule(ScriptModule):
    __frozen = False

    def __init__(self, orig, id_set=None, optimize=True):
        # XXX: orig can be a nn.Module or a function!
        super(TracedModule, self).__init__(optimize=optimize)
        if id_set is None:
            id_set = set()

        if not isinstance(orig, torch.nn.Module):
            self._name = orig.__name__
            orig = torch.nn.Module()
        else:
            self._name = 'TracedModule[' + type(orig).__name__ + ']'

        def check_unique(param):
            if param in id_set:
                raise ValueError("TracedModules don't support parameter sharing between modules")
            id_set.add(param)

        self.training = orig.training

        for name, param in orig._parameters.items():
            if param is not None:
                self._parameters[name] = param
                check_unique(param)
        for name, buf in orig._buffers.items():
            if buf is not None:
                self._buffers[name] = buf
                check_unique(buf)

        if orig._backward_hooks or orig._forward_hooks or orig._forward_pre_hooks:
            raise ValueError("Modules that have hooks assigned can't be compiled")

        for name, submodule in orig._modules.items():
            self._modules[name] = TracedModule(submodule, id_set, optimize=optimize)

        self._freeze()

    def forward(self, *args, **kwargs):
        raise RuntimeError('Trace submodules cannot be called.')

    def _freeze(self):
        self.__frozen = True

    def _get_name(self):
        return self._name

    def __setattr__(self, attr, value):
        if not self.__frozen or hasattr(self, attr):
            return super(TracedModule, self).__setattr__(attr, value)
        raise RuntimeError("Cannot set new properties on a traced module.")


class TopLevelTracedModule(TracedModule):
    def forward(self, *args, **kwargs):
        return self._get_method('forward')(*args, **kwargs)


class _ConstModuleList(ScriptModule):
    def __init__(self, modules):
        super(_ConstModuleList, self).__init__()
        for i, module in enumerate(modules):
            self.add_module(str(i), module)

    def __getitem__(self, idx):
        if isinstance(idx, slice):
            return _ConstModuleList(list(self._modules.values())[idx])
        else:
            if not (-len(self) <= idx < len(self)):
                raise IndexError('index {} is out of range'.format(idx))
            if idx < 0:
                idx += len(self)
            return self._modules[str(idx)]

    def __len__(self):
        return len(self._modules)

    def __iter__(self):
        return iter(self._modules.values())

    def __dir__(self):
        keys = super(_ConstModuleList, self).__dir__()
        keys = [key for key in keys if not key.isdigit()]
        return keys


class _ConstSequential(_ConstModuleList):
    __constants__ = ['mods']

    def __init__(self, mods):
        super(_ConstSequential, self).__init__(mods._modules.values())

        # we define the forward method via self.define rather than
        # making it a direct class member (with a @script) annotation
        # because, in optimized runtime environments where only .pyc files
        # are shipped, we cant retrieve the source code.
        # TODO: find a workaround for this and remove this hack
        self.define("""
        def forward(self, input):
            for m in self:
                input = m(input)
            return input
        """)


_builtin_table = None

_modules_containing_builtins = (torch, torch.nn.functional)

# These functions don't have aten ops but have been converted to weak script, so
# don't add them as builtins
# TODO: delete this list, _should_skip(), and remove torch.nn.functional from
# builtins list once everything in it has been converted to weak script
_builtin_blacklist = {
    'tanhshrink',
    'softsign',
    'pairwise_distance',
    'prelu',
    'hardshrink',
}


def _should_skip(mod, name):
    return mod is torch.nn.functional and name in _builtin_blacklist


# lazily built to ensure the correct initialization order
def _get_builtin_table():
    global _builtin_table
    if _builtin_table is not None:
        return _builtin_table
    _builtin_table = {}

    def register_all(mod):
        for name in dir(mod):
            v = getattr(mod, name)
            if callable(v) and not _should_skip(mod, name):
                _builtin_table[id(v)] = "aten::" + name
    for mod in _modules_containing_builtins:
        register_all(mod)
    _builtin_table[id(warnings.warn)] = "aten::warn"

    return _builtin_table


def _register_builtin(fn, op):
    _get_builtin_table()[id(fn)] = op


def _find_builtin(fn):
    return _get_builtin_table().get(id(fn))


# Python equivalents for the empty list construction builtins. We need
# these otherwise the tests won't execute in regular Python mode.
def _construct_empty_int_list():
    return []


_register_builtin(_construct_empty_int_list, 'aten::_construct_empty_int_list')


def _construct_empty_float_list():
    return []


_register_builtin(_construct_empty_float_list, 'aten::_construct_empty_float_list')


def _construct_empty_tensor_list():
    return []


_register_builtin(_construct_empty_tensor_list, 'aten::_construct_empty_tensor_list')


_register_builtin(len, 'aten::len')


_register_builtin(_wait, 'aten::wait')


class _disable_tracing(object):
    def __enter__(self):
        self.state = torch._C._get_tracing_state()
        torch._C._set_tracing_state(None)

    def __exit__(self, *args):
        torch._C._set_tracing_state(self.state)
        self.state = None


if not torch._C._jit_init():
    raise RuntimeError("JIT initialization failed")
