import collections
import contextlib
import functools
import importlib
import inspect
import random
import types
from typing import Dict, List

import torch.nn

from .. import variables
from ..exc import unimplemented
from ..guards import GuardBuilder
from ..source import AttrSource, ODictGetItemSource, RandomValueSource
from ..utils import is_namedtuple_cls, namedtuple_fields
from .base import MutableLocal, VariableTracker
from .misc import NullContextVariable


class UserDefinedVariable(VariableTracker):
    pass


class UserDefinedClassVariable(UserDefinedVariable):
    def __init__(self, value, **kwargs):
        super().__init__(**kwargs)
        self.value = value

    def as_python_constant(self):
        return self.value

    def python_type(self):
        return type(self.value)

    def var_getattr(self, tx, name: str) -> "VariableTracker":
        from . import ConstantVariable
        from .builder import VariableBuilder

        options = VariableTracker.propagate(self)
        source = AttrSource(self.source, name) if self.source is not None else None
        try:
            obj = inspect.getattr_static(self.value, name)
        except AttributeError:
            obj = None
        if isinstance(obj, staticmethod):
            return variables.UserFunctionVariable(
                obj.__get__(self.value), source=source, **options
            )
        elif isinstance(obj, classmethod):
            return variables.UserMethodVariable(
                obj.__func__, self, source=source, **options
            )

        if name in getattr(self.value, "__dict__", {}) or ConstantVariable.is_literal(
            obj
        ):
            if source:
                return VariableBuilder(tx, source)(obj).add_options(options)
            elif ConstantVariable.is_literal(obj):
                return ConstantVariable(obj, **options)

        return super().var_getattr(tx, name)

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        if (
            name == "__subclasses__"
            and len(args) == 0
            and not kwargs
            and "__subclasses__" not in self.value.__dict__
        ):
            options = VariableTracker.propagate(self, args, kwargs.values())
            options["mutable_local"] = MutableLocal()
            subs_as_vars: List[VariableTracker] = list()
            for sub in self.value.__subclasses__():
                source = AttrSource(tx.import_source(sub.__module__), sub.__name__)
                subs_as_vars.append(
                    variables.UserDefinedClassVariable(sub, source=source)
                )

            return variables.ListVariable(subs_as_vars, **options)

        return super().call_method(tx, name, args, kwargs)

    def call_function(
        self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
    ) -> "VariableTracker":
        from ..side_effects import SideEffects

        options = VariableTracker.propagate(self, args, kwargs.values())

        if self.value in (
            contextlib.nullcontext,
            torch.autograd.profiler.profile,
        ):
            return NullContextVariable(**options)
        elif is_namedtuple_cls(self.value):
            fields = namedtuple_fields(self.value)
            items = list(args)
            items.extend([None] * (len(fields) - len(items)))
            for name, value in kwargs.items():
                assert name in fields
                items[fields.index(name)] = value
            assert all(x is not None for x in items)
            return variables.NamedTupleVariable(
                items, self.value, **VariableTracker.propagate(self, items)
            )
        elif (
            inspect.getattr_static(self.value, "__new__", None) in (object.__new__,)
            and SideEffects.cls_supports_mutation_side_effects(self.value)
            and self.source
        ):
            var = tx.output.side_effects.track_object_new(
                self.source, self.value, UserDefinedObjectVariable, options
            )
            return var.add_options(var.call_method(tx, "__init__", args, kwargs))
        elif variables.DataClassVariable.is_matching_cls(self.value):
            options["mutable_local"] = MutableLocal()
            return variables.DataClassVariable.create(self.value, args, kwargs, options)

        return super().call_function(tx, args, kwargs)

    def const_getattr(self, tx, name):
        if name == "__name__":
            return self.value.__name__
        return super().const_getattr(tx, name)


class UserDefinedObjectVariable(UserDefinedVariable):
    """
    Mostly objects of defined type.  Catch-all for something where we only know the type.
    """

    def __init__(self, value, value_type=None, **kwargs):
        super().__init__(**kwargs)
        self.value = value
        self.value_type = value_type or type(value)
        assert type(value) is self.value_type

    def __str__(self):
        inner = self.value_type.__name__
        if inner in [
            "builtin_function_or_method",
            "getset_descriptor",
            "method_descriptor",
            "method",
        ]:
            inner = str(getattr(self.value, "__name__", None))
        return f"{self.__class__.__name__}({inner})"

    def python_type(self):
        return self.value_type

    @staticmethod
    @functools.lru_cache(None)
    def _supported_random_functions():
        fns = {
            random.random,
            random.randint,
            random.randrange,
            random.uniform,
        }
        return fns

    def call_method(
        self,
        tx,
        name,
        args: "List[VariableTracker]",
        kwargs: "Dict[str, VariableTracker]",
    ) -> "VariableTracker":
        from . import ConstantVariable, TupleVariable, UserMethodVariable

        options = VariableTracker.propagate(self, args, kwargs.values())

        if name not in getattr(self.value, "__dict__", {}):
            try:
                method = inspect.getattr_static(type(self.value), name)
            except AttributeError:
                method = None
            if method is object.__init__:
                return ConstantVariable(None, **options)

            if method is collections.OrderedDict.keys and self.source:
                # subclass of OrderedDict
                assert not (args or kwargs)
                keys = list(self.value.keys())
                assert all(map(ConstantVariable.is_literal, keys))
                return TupleVariable(
                    [ConstantVariable(k, **options) for k in keys], **options
                ).add_guard(self.source.make_guard(GuardBuilder.ODICT_KEYS))

            if (
                method is collections.OrderedDict.items
                and isinstance(self.value, collections.OrderedDict)
                and self.source
            ):
                assert not (args or kwargs)
                items = []
                keys = self.call_method(tx, "keys", [], {})
                options = VariableTracker.propagate(self, args, kwargs.values(), keys)
                for key in keys.unpack_var_sequence(tx):
                    items.append(
                        TupleVariable(
                            [key, self.odict_getitem(tx, key)],
                            **options,
                        )
                    )
                return TupleVariable(items, **options)

            if method is collections.OrderedDict.__getitem__ and len(args) == 1:
                assert not kwargs
                return self.odict_getitem(tx, args[0])

            # check for methods implemented in C++
            if isinstance(method, types.FunctionType):
                source = (
                    None
                    if self.source is None
                    else AttrSource(AttrSource(self.source, "__class__"), name)
                )
                # TODO(jansel): add a guard to check for monkey patching?
                return UserMethodVariable(
                    method, self, source=source, **options
                ).call_function(tx, args, kwargs)

        return super().call_method(tx, name, args, kwargs)

    def is_supported_random(self):
        try:
            return self.value in self._supported_random_functions()
        except TypeError:
            # TypeError: unhashable type
            return False

    def call_function(
        self, tx, args: "List[VariableTracker]", kwargs: "Dict[str, VariableTracker]"
    ) -> "VariableTracker":
        from .builder import VariableBuilder

        if (
            self.is_supported_random()
            and all(k.is_python_constant() for k in args)
            and all(v.is_python_constant() for v in kwargs.values())
        ):
            args = [x.as_python_constant() for x in args]
            kwargs = {k: v.as_python_constant() for k, v in kwargs.items()}
            random_call_index = len(tx.random_calls)
            if random_call_index == 0:
                tx.output.initial_random_state = random.getstate()
            example_value = self.value(*args, **kwargs)
            source = RandomValueSource(random_call_index)
            tx.random_calls.append((self.value, args, kwargs))
            return VariableBuilder(tx, source).wrap_unspecialized_primitive(
                example_value
            )

        return super().call_function(tx, args, kwargs)

    def _check_for_getattribute(self):
        try:
            if isinstance(
                inspect.getattr_static(type(self.value), "__getattribute__"),
                types.FunctionType,
            ):
                unimplemented("UserDefinedObjectVariable with custom __getattribute__")
        except AttributeError:
            pass

    def _check_for_getattr(self):
        try:
            getattr_fn = inspect.getattr_static(type(self.value), "__getattr__")
        except AttributeError:
            getattr_fn = None
        if getattr_fn is torch.nn.Module.__getattr__:
            # ignore this case of getattr
            getattr_fn = None
        return getattr_fn

    def _getattr_static(self, name):
        if (
            isinstance(self.value, torch.nn.Module)
            or "__slots__" in self.value.__class__.__dict__
        ):
            # getattr_static doesn't work on these
            subobj = getattr(self.value, name)
        else:
            subobj = inspect.getattr_static(self.value, name)
        return subobj

    def var_getattr(self, tx, name):
        from . import ConstantVariable
        from .builder import VariableBuilder

        options = VariableTracker.propagate(self)
        value = self.value
        source = AttrSource(self.source, name) if self.source else None
        self._check_for_getattribute()
        getattr_fn = self._check_for_getattr()

        try:
            subobj = self._getattr_static(name)
        except AttributeError:
            subobj = None
            if isinstance(getattr_fn, types.FunctionType):
                return variables.UserMethodVariable(
                    getattr_fn, self, source=source, **options
                ).call_function(tx, [ConstantVariable(name)], {})
            elif getattr_fn is not None:
                unimplemented("UserDefined with non-function __getattr__")

        if isinstance(subobj, property):
            return variables.UserMethodVariable(
                subobj.fget, self, source=source, **options
            ).call_function(tx, [], {})
        elif isinstance(subobj, staticmethod):
            return variables.UserFunctionVariable(
                subobj.__get__(self.value), source=source, **options
            )
        elif isinstance(subobj, classmethod):
            return variables.UserMethodVariable(
                subobj.__func__, self, source=source, **options
            )
        elif isinstance(subobj, types.FunctionType):
            return variables.UserMethodVariable(subobj, self, source=source, **options)

        if (
            name in getattr(value, "__dict__", {})
            or ConstantVariable.is_literal(subobj)
            or isinstance(
                subobj,
                (
                    torch.Tensor,
                    torch.nn.Module,
                ),
            )
        ):
            if source:
                return VariableBuilder(tx, source)(subobj).add_options(options)