import itertools
from abc import ABC
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import torchgen.api.dispatcher as dispatcher
from torchgen.api.lazy import (
    getValueT,
    isValueType,
    LazyArgument,
    LazyIrProperties,
    LazyIrSchema,
    tensorListValueT,
)
from torchgen.api.translate import translate
from torchgen.api.types import (
    BaseCType,
    Binding,
    deviceT,
    DispatcherSignature,
    kernel_signature,
    NativeSignature,
    OptionalCType,
    VectorCType,
)
from torchgen.context import method_with_native_function
from torchgen.dest.lazy_ts_lowering import ts_lowering_body
from torchgen.model import (
    Argument,
    BackendIndex,
    BackendMetadata,
    BaseTy,
    BaseType,
    FunctionSchema,
    ListType,
    NativeFunction,
    NativeFunctionsGroup,
)


def node_ctor_arg_rvalue_string(arg: LazyArgument) -> str:
    """
    Given a LazyArgument,
    generate a c++ string for materializing an rvalue of that arg for passing into
    a lazy Node constructor.
    """

    # TODO: Matching on CType seems wrong; should be matching on Type
    if isValueType(arg.lazy_type):
        if isinstance(arg.lazy_type, BaseCType):
            if arg.is_wrapped_scalar:
                return f"node_{arg.name}"
            elif arg.lazy_type.type is tensorListValueT:
                return f"lazy_{arg.name}_tensorlist"
            elif arg.is_symint_or_list:
                return f"GetSymIntValue({arg.name})"
            return f"lazy_{arg.name}->GetIrValue()"
        elif isinstance(arg.lazy_type, OptionalCType):
            if arg.is_symint_or_list:
                # TODO: I don't understand when you should put lazy_ in the name
                # or not
                return f"{arg.name} ? c10::make_optional(GetSymIntValue(*{arg.name})) : c10::nullopt"
            elif arg.is_wrapped_scalar:
                return f"node_{arg.name}"
            return (
                f"lazy_{arg.name} ? "
                f"c10::make_optional(lazy_{arg.name}->GetIrValue()) : "
                "c10::nullopt"
            )
        else:
            raise AssertionError(
                f"TODO not sure if there are other valid types to handle here ({arg.lazy_type})"
            )
    else:
        # NB: this is here because right now we aren't treating SymInt[] as a
        # value type; when we do this needs to move above
        # NB: we cannot test arg.lazy_type as we've already specified it is an
        # int64_t and so we cannot distinguish between SymInt and int64_t
        if isinstance(arg.orig_type, ListType) and arg.orig_type.elem == BaseType(
            BaseTy.SymInt
        ):
            if arg.symint:
                return f"GetSymIntArrayRefValue({arg.name})"
            else:
                return f"std::vector<int64_t>({arg.name}.begin(), {arg.name}.end())"
        elif isinstance(arg.lazy_type, VectorCType) and isinstance(
            arg.lazy_type.elem, BaseCType
        ):
            return f"std::vector<{arg.lazy_type.elem.type}>({arg.name}.begin(), {arg.name}.end())"
        elif (
            isinstance(arg.lazy_type, OptionalCType)
            and isinstance(arg.lazy_type.elem, VectorCType)
            and isinstance(arg.lazy_type.elem.elem, BaseCType)
        ):
            return f"torch::lazy::ToOptionalVector<{arg.lazy_type.elem.elem.type}>({arg.name})"
        else:
            return f"{arg.name}"


def node_ctor_inputs(schema: LazyIrSchema) -> str:
    """
    Produce a formatted string with the arguments as passed into the constructor of a node class.
    """
    node_ctor_values = [
        node_ctor_arg_rvalue_string(arg) for arg in schema.filtered_args()
    ]
    return ", ".join(node_ctor_values)


def gen_fallback_code(
    schema: LazyIrSchema,
    sig: Union[DispatcherSignature, NativeSignature],
    overload_name: str,
) -> str:
    """
    Generate code that falls back to eager conditioned on a predicate
    """
    dispatcher_sig = DispatcherSignature.from_schema(schema.func)
    exprs = translate(sig.arguments(), dispatcher_sig.arguments())
    fallback_args = ",\n                ".join([a.expr for a in exprs])
    if len(overload_name):
        aten_op_str = f"ATEN_OP2({schema.aten_name}, {overload_name})"
    else:
        aten_op_str = f"ATEN_OP({schema.aten_name})"
    or_has_generator = ""
    if schema.generator_arg:
        # generators are always optional and there is never more than one, at least currently
        or_has_generator = f" || ({schema.generator_arg.name}.has_value() && {schema.generator_arg.name}->defined())"
    return f"""
        if (force_eager_fallback({aten_symbol(schema)}){or_has_generator}) {{
            return at::native::call_fallback_fn_symint<&ltc_eager_fallback, {aten_op_str}>::call(
                {fallback_args}
            );
        }}
"""


def aten_symbol(schema: LazyIrSchema) -> str:
    missing_interned_strings = {
        "sigmoid_backward",
    }
    if schema.aten_name in missing_interned_strings:
        return f'c10::Symbol::fromQualString("aten::{schema.aten_name}")'

    if not schema.aten_name.startswith("at::"):
        return f"at::aten::{schema.aten_name}"
    else:
        return schema.aten_name


# converts  all tensor-like arguments to meta tensors. Returns:
# (1) a string containing all of the logic that does the conversions.
# (2) a context, to be used by translate(), with all of the relevant bindings.
def convert_to_meta_tensors(sig: DispatcherSignature) -> Tuple[str, List[Binding]]:
    context: List[Binding] = []
    unwrapped_tensor_args: List[str] = []
    for arg in sig.arguments():
        if isinstance(arg.argument, Argument) and arg.argument.type.is_tensor_like():
            unwrapped_name = f"{arg.name}_meta"
            unwrapped_tensor_args.append(
                f"auto {unwrapped_name} = to_meta({arg.name});"
            )
            context.append(arg.with_name(unwrapped_name))
        else:
            context.append(arg)
    unwrap_tensor_args_str = "\n        ".join(unwrapped_tensor_args)
    return unwrap_tensor_args_str, context


@dataclass(frozen=True)
class GenLazyIR(ABC):
    backend_index: BackendIndex
    backend_name: str
    node_base: str
    use_lazy_shape: bool

    @method_with_native_function
    def __call__(self, f: Union[NativeFunctionsGroup, NativeFunction]) -> List[str]:
        func = f.functional.func if isinstance(f, NativeFunctionsGroup) else f.func
        metadata = self.backend_index.get_kernel(
            f.functional if isinstance(f, NativeFunctionsGroup) else f
        )
        schema = LazyIrSchema(
            func, symint=metadata is not None and metadata.supports_symint()
        )
        return self.gen(schema)

    # there is no lowering functionality generated unless this IR base class is subclassed and
    # implemented as a backend-specific node
    def lowering_function(self, schema: LazyIrSchema) -> str:
        return ""

    def create_function(self, schema: LazyIrSchema, node_ctor_args: str) -> str:
        return ""

    def can_be_reused_function(self, schema: LazyIrSchema, node_ctor_args: str) -> str:
        return f"""bool CanBeReused({node_ctor_args}) const {{
    return false;
    }}"""

    def node_base_ctor_call(self, schema: LazyIrSchema) -> str:
        value_args = schema.filtered_args(values=True, scalars=False)
        # backends can customize the way the node base class constructor is called,
        # as long as all of its arguments can be generated from information available from the schema
        base_ctor_value_args_list = []
        for arg in value_args:
            if isinstance(arg.lazy_type, BaseCType) or isinstance(
                arg.lazy_type, VectorCType
            ):
                base_ctor_value_args_list.append(f"{arg.name}")
            elif isinstance(arg.lazy_type, OptionalCType):
                base_ctor_value_args_list.append(f"{arg.name}.value_or(kNullValue)")
            else:
                raise AssertionError(
                    f"Unsupported type ({arg.lazy_type}) - add support if necessary"
                )
        base_ctor_value_args = ", ".join(base_ctor_value_args_list)

        scalar_args = schema.filtered_args(values=False, scalars=True)

        # Shape constuction.
        # Conditionally build shape depending on specified shape property
        if schema.properties.ShapePrecompute:
            shape_ctor_arg = "std::move(shapes),"
        elif schema.properties.ShapeCompute:
            shape_args = [a.name for a in value_args]
            shape_args.extend(a.name for a in scalar_args)
            shape_ctor_arg = f"compute_shape_{schema.name}({', '.join(shape_args)}),"
        elif schema.properties.ShapeCache:
            shape_args = [f"operand({i})" for i in range(len(value_args))]
            shape_args.extend(a.name for a in scalar_args)
            shape_ctor_arg = f"[&](){{ return compute_shape_{schema.name}({', '.join(shape_args)})[0]; }},"
        else:
            shape_ctor_arg = ""

        scalar_hashes = ", ".join(f"{a.name}" for a in scalar_args)

        return f"""{self.node_base}(
              {schema.node_name}::ClassOpKind(),
              OpList{{{base_ctor_value_args}}},
              {shape_ctor_arg}
              /* num_outputs */ {len(schema.returns)},
              torch::lazy::MHash({scalar_hashes}))"""

    def gen(self, schema: LazyIrSchema) -> List[str]:
        opkind = schema.opkind or aten_symbol(schema)

        # for now, we just want one IR class decl and soon after also the method defs
        # and we use the functional version not out/inplace.
        all_args = schema.filtered_args()
        value_args = schema.filtered_args(values=True, scalars=False)
        scalar_args = schema.filtered_args(values=False, scalars=True)

        ctor_args = [f"const {i.lazy_type.cpp_type()}& {i.name}" for i in all_args]
        reuse_ctor_args = ", ".join(ctor_args)
        if self.use_lazy_shape and schema.properties.ShapePrecompute:
            ctor_args.append("std::vector<torch::lazy::Shape>&& shapes")
        node_ctor_args = ", ".join(ctor_args)

        scalar_initializers = ",\n        ".join(
            [
                # This code is just special casing the mapping from string_view -> strings
                f"{a.name}({a.name}.has_value() ? c10::make_optional(std::string(*{a.name})) : c10::nullopt)"
                if a.lazy_type.cpp_type() == "c10::optional<c10::string_view>"
                else f"{a.name}({a.name})"
                for a in scalar_args
            ]
        )
        if len(scalar_initializers):
            scalar_initializers = f",\n        {scalar_initializers}"
        scalar_decls = "\n  ".join(
            [
                f"std::string {a.name};"
                if a.lazy_type.cpp_type() == "c10::string_view"
                else f"c10::optional<std::string> {a.name};"
                if a.lazy_type.cpp_type() == "c10::optional<c10::string_view>"
                else f"{a.lazy_type.cpp_type()} {a.name};"
                for a in scalar_args
            ]
        )
        optional_values = [
            arg.name
            for arg in schema.filtered_args(values=True, scalars=False)
            if isinstance(arg.lazy_type, OptionalCType)
        ]
        has_optional_decls = "\n  ".join(
            [f"bool has_{value}: 1;" for value in optional_values]
        )
        has_optional_defs = "\n    ".join(
            [f"has_{value} = !!{value};" for value in optional_values]
        )
        members_to_string = []
        for arg in scalar_args:
            if isinstance(arg.lazy_type, OptionalCType):
                members_to_string.append(
                    f"""if ({arg.name}.has_value()) {{
      ss << ", {arg.name}=" << {arg.name}.value();
    }} else {{
      ss << ", {arg.name}=null";
    }}"""
                )
            else:
                members_to_string.append(f'ss << ", {arg.name}=" << {arg.name};')
        members_to_string_str = "\n    ".join(members_to_string)

        return [
            f"""\
class {schema.node_name} : public {self.node_base} {{
 public:
  static torch::lazy::OpKind ClassOpKind() {{
    return torch::lazy::OpKind({opkind});
  }}

  {schema.node_name}({node_ctor_args})
      : {self.node_base_ctor_call(schema)}{scalar_initializers}
  {{
    {has_optional_defs}
  }}

  std::string ToString() const override {{
    std::stringstream ss;
    ss << {self.node_base}::ToString();
    {members_to_string_str}
    return ss.str();
  }}

  {self.create_function(schema, reuse_ctor_args)}

  {self.can_be_reused_function(schema, reuse_ctor_args)}

  {self.lowering_function(schema)}

  {scalar_decls}
  {has_optional_decls}

}};

""",
        ]


@dataclass(frozen=True)
class GenTSLazyIR(GenLazyIR):
    def lowering_function(self, schema: LazyIrSchema) -> str:
        signature = """