# Initialize mpact python extension.
import mpact._mlir_libs._mpact

import abc
import ctypes
from enum import Enum
from io import StringIO
import numpy as np
import os
import sys
import tempfile
import torch

from typing import Any, Callable, Optional, Tuple, Dict, TypeVar, Union

from mpact import ir
from mpact.ir import Module
from mpact.dialects import torch as torch_d
from mpact.execution_engine import *
from mpact.extras.fx_importer import FxImporter, SparsityMeta
from mpact.ir import *
from mpact.passmanager import *
from mpact.runtime import *

# One time set up of support library.
SUPPORT_LIB = os.getenv("SUPPORT_LIB", default=None)
SHARED_LIBS = [] if SUPPORT_LIB is None else [SUPPORT_LIB]

# A type shared between the result of `LinalgOnTensorsBackend.compile` and the
# input to `LinalgOnTensorsBackend.load`. Each backend will likely have a
# different definition of this type.
CompiledArtifact = TypeVar("CompiledArtifact")

# A wrapper around a backend-specific loaded program representation
# that uniformly translates the `x.method(...)` interface expected of
# Torch modules into appropriate lower-level operations.
Invoker = TypeVar("Invoker")


class LinalgOnTensorsBackend(abc.ABC):
    """The interface to an linalg-on-tensors backend.

    Backends are recommended to raise meaningful exceptions in case of error,
    ideally with easy reproduction instructions.
    """

    @abc.abstractmethod
    def compile(self, module: Module) -> CompiledArtifact:
        """Compile the provided MLIR module into a compiled artifact.

        The module adheres to the linalg-on-tensors backend contract
        (see the VerifyLinalgOnTensorsBackendContract pass).

        The compiled artifact can be any type, but must be correctly
        interpreted by the `load` method.
        """

    @abc.abstractmethod
    def load(self, artifact: CompiledArtifact) -> Invoker:
        """Load the compiled artifact into a uniformly invokable form.

        The compiled artifact is the result of a previous call to `compile`.

        See the description of `Invoker` for the requirements on the returned
        type.
        """


def get_module_name_for_debug_dump(module):
    """Gets a name suitable for a debug dump.

    The name is not guaranteed to be unique.
    """
    if not "torch.debug_module_name" in module.operation.attributes:
        return "UnnammedModule"
    return StringAttr(module.operation.attributes["torch.debug_module_name"]).value


class MPACTCompilerError(Exception):
    pass


def run_pipeline_with_repro_report(
    module, pipeline: str, description: str, enable_ir_printing: bool = False
):
    """Runs `pipeline` on `module`, with a nice repro report if it fails."""
    module_name = get_module_name_for_debug_dump(module)
    original_stderr = sys.stderr
    try:
        sys.stderr = StringIO()
        asm_for_error_report = module.operation.get_asm(
            large_elements_limit=10, enable_debug_info=True
        )
        # Lower module in place to make it ready for compiler backends.
        with module.context as ctx:
            pm = PassManager.parse(pipeline)
            if enable_ir_printing:
                ctx.enable_multithreading(False)
                pm.enable_ir_printing()
            pm.run(module.operation)
    except Exception as e:
        # TODO: More robust.
        # - don't arbitrarily clutter up /tmp. When a test suite has many
        #   tests, this can be a big disk cost (also, /tmp/ is frequently a
        #   RAM fs, which increases worries about capacity).
        # - don't have colliding filenames (hard to do without cluttering
        #   up /tmp)
        # - if we do have have colliding filenames, writes should at least
        #   avoid being racy.
        filename = os.path.join(tempfile.gettempdir(), module_name + ".mlir")
        with open(filename, "w") as f:
            f.write(asm_for_error_report)
        debug_options = "-mlir-print-ir-after-all -mlir-disable-threading"
        # Put something descriptive here even if description is empty.
        description = description or f"{module_name} compile"

        message = f"""\
            {description} failed with the following diagnostics:
            {sys.stderr.getvalue()}

            python exception: {e}

            The error can be reproduced with:
            $ mpact-opt -pass-pipeline='{pipeline}' {filename}
            Add '{debug_options}' to get the IR dump for debugging purpose.
            """
        trimmed_message = "\n".join([m.lstrip() for m in message.split("\n")])
        raise MPACTCompilerError(trimmed_message) from None
    finally:
        sys.stderr = original_stderr


def assert_arg_type_is_supported(ty):
    SUPPORTED = [
        np.float16,
        np.float32,
        np.float64,
        np.uint8,
        np.int8,
        np.int32,
        np.int64,
        np.bool_,
        np.complex64,
        np.complex128,
    ]
    assert (
        ty in SUPPORTED
    ), f"Only numpy arrays with dtypes in {SUPPORTED} are supported, but got {ty}"


memref_type_to_np_dtype = {
    "mrf16": np.float16,
    "mrf32": np.float32,
    "mrf64": np.float64,
    "mri1": np.bool_,
    "mri8": np.int8,
    "mri32": np.int32,
    "mri64": np.int64,
    "mrc32": np.complex64,
    "mrc64": np.complex128,
}
elemental_type_to_ctype = {
    "i1": ctypes.c_bool,
    "i8": ctypes.c_byte,
    "i64": ctypes.c_int,
    "f32": ctypes.c_float,
    "f64": ctypes.c_double,
}

CONSUME_RETURN_FUNC_PREFIX = "refbackend_consume_func_return_"

SPARSE_LAYOUTS = [
    torch.sparse_coo,
    torch.sparse_csr,
    torch.sparse_csc,
    torch.sparse_bsr,
    torch.sparse_bsc,
]


def get_return_funcs(module):
    return_prefix_len = len(CONSUME_RETURN_FUNC_PREFIX)
    return_funcs = []
    with module.context:
        for func in module.body:
            # Returns strings of the form `"refbackend.."` so `"` is deleted.
            func_name = str(func.attributes["sym_name"]).replace('"', "")
            if func_name[:return_prefix_len] == CONSUME_RETURN_FUNC_PREFIX:
                return_funcs.append(func_name)

    return return_funcs


def get_ctype_func(func_name):
    return_prefix_len = len(CONSUME_RETURN_FUNC_PREFIX)
    ret_types = func_name[return_prefix_len:].split("_")
    ctypes_arg = [None]
    for type in ret_types:
        if type in elemental_type_to_ctype:
            ctypes_arg.append(elemental_type_to_ctype[type])
        elif type in memref_type_to_np_dtype:
            ctypes_arg.append(ctypes.POINTER(UnrankedMemRefDescriptor))
        else:
            assert False, f"Not supported type: {type}"

    return ctypes.CFUNCTYPE(*ctypes_arg), ret_types


class MpactBackendInvoker:
    def __init__(self, module, opt_level):
        self.ee = ExecutionEngine(module, opt_level=opt_level, shared_libs=SHARED_LIBS)
        self.result = None

        return_funcs = get_return_funcs(module)

        for ret_func in return_funcs:
            ctype_wrapper, ret_types = get_ctype_func(ret_func)

            def consume_return_funcs(*args):
                self.result = tuple(
                    [
                        (
                            arg
                            if type in elemental_type_to_ctype
                            else unranked_memref_to_numpy(
                                arg, memref_type_to_np_dtype[type]
                            )
                        )
                        for arg, type in zip(args, ret_types)
                    ]
                )
                if len(self.result) == 1:
                    self.result = self.result[0]

            self.ee.register_runtime(ret_func, ctype_wrapper(consume_return_funcs))

    def __getattr__(self, function_name: str):
        def invoke(*args):
            ffi_args = []
            for arg in args:
                assert_arg_type_is_supported(arg.dtype)
                ffi_args.append(
                    ctypes.pointer(ctypes.pointer(get_unranked_memref_descriptor(arg)))
                )

            self.ee.invoke(function_name, *ffi_args)
            result = self.result
            assert result is not None, "Invocation didn't produce a result"
            self.result = None
            return result

        return invoke


LOWERING_PIPELINE = (
    "builtin.module("
    + ",".join(
        [
            "func.func(linalg-generalize-named-ops)",
            "func.func(linalg-fuse-elementwise-ops)",
            "convert-shape-to-std",
            # Propagate sparse encodings before sparsifier mini-pipeline.
            # TODO: the following pass currently contains no pattern. Will be
            # added as needed.
            "func.func(sparse-encoding-propagation)",
            # MLIR Sparsifier mini-pipeline.
            "sparse-assembler{direct-out}",
            "sparsification-and-bufferization",
            "sparse-storage-specifier-to-llvm",
            # Buffer deallocation pass does not know how to handle realloc.
            "func.func(expand-realloc)",
            # Generalize pad and concat after sparse compiler, as they are handled
            # differently when the operations involve sparse operands.
            "func.func(refback-generalize-tensor-pad)",
            "func.func(refback-generalize-tensor-concat)",
            # Bufferize.
            "func.func(tm-tensor-bufferize)",
            "one-shot-bufferize{copy-before-write bufferize-function-boundaries function-boundary-type-conversion=identity-layout-map}",
            "refback-mlprogram-bufferize",
            "func.func(finalizing-bufferize)",
            "func.func(buffer-deallocation)",
            # Inline sparse helper methods where useful (but after dealloc).
            "inline",
            "refback-munge-calling-conventions",
            "func.func(tm-tensor-to-loops)",
            "func.func(refback-munge-memref-copy)",
            "func.func(convert-linalg-to-loops)",
            "func.func(lower-affine)",
            "convert-scf-to-cf",
            "func.func(refback-expand-ops-for-llvm)",
            "func.func(arith-expand)",
            "func.func(convert-math-to-llvm)",
            "convert-math-to-libm",
            "expand-strided-metadata",
            "finalize-memref-to-llvm",
            "lower-affine",
            "convert-bufferization-to-memref",
            "finalize-memref-to-llvm",
            "func.func(convert-arith-to-llvm)",
            "convert-vector-to-llvm",
            "convert-func-to-llvm",
            "convert-cf-to-llvm",
            "convert-complex-to-llvm",
            "reconcile-unrealized-casts",
        ]
    )
    + ")"
)


class MpactBackendLinalgOnTensorsBackend(LinalgOnTensorsBackend):
    """Main entry-point for the MPACT backend."""

    def __init__(self, opt_level):
        super().__init__()
        self.opt_level = opt_level

    def compile(self, imported_module: Module):
        """Compiles an imported module, with a flat list of functions.
        The module is expected to be in linalg-on-tensors + scalar code form.

        Args:
          imported_module: The MLIR module in the torch dialect.
        Returns:
          An opaque artifact that can be passed to `load`.
        """
        run_pipeline_with_repro_report(
            imported_module,
            LOWERING_PIPELINE,
            "Lowering Linalg-on-Tensors IR to LLVM with MpactBackend",
            enable_ir_printing=False,
        )
        return imported_module

    def load(self, module) -> MpactBackendInvoker:
        """Loads a compiled artifact into the runtime."""
        return MpactBackendInvoker(module, self.opt_level)


def sparse_metadata(a: torch.Tensor) -> SparsityMeta:
    """
    Returns a meta data tuple for the given sparse tensor.

    NOTE: this will be fully replaced by fx graph SparseTensorMetadata
    """
    sparse_dim = a.sparse_dim()
    dense_dim = a.dense_dim()
    batch_dim = a.ndim - dense_dim - sparse_dim
    blocksize = None
    if a.layout is torch.sparse_coo:
        return SparsityMeta(
            a.layout,
            batch_dim,
            sparse_dim,
            dense_dim,
            blocksize,
            a._indices().dtype,
            a._indices().dtype,
        )
    elif a.layout is torch.sparse_csr or a.layout is torch.sparse_bsr:
        if a.layout is torch.sparse_bsr:
            blocksize = a.values().shape[batch_dim + 1 : batch_dim + 3]
        return SparsityMeta(
            a.layout,
            batch_dim,
            sparse_dim,
            dense_dim,
            blocksize,
            a.crow_indices().dtype,
            a.col_indices().dtype,
        )
    elif a.layout is torch.sparse_csc or a.layout is torch.sparse_bsc:
        if a.layout is torch.sparse_bsc:
            blocksize = a.values().shape[batch_dim + 1 : batch_dim + 3]
        return SparsityMeta(
            a.layout,
            batch_dim,
            sparse_dim,
            dense_dim,
            blocksize,
            a.ccol_indices().dtype,
            a.row_indices().dtype,
        )
    else:
        raise RuntimeError(f"Unsupported sparse layout for {a}")


def sparse_export(
    f: Callable, args: Tuple[Any, ...], kwargs: Optional[Dict[str, Any]] = None
) -> torch.export.ExportedProgram:
    """
    This is a ***temporary*** wrapper around `torch.export.export`
    that eventually should be removed and simply replaced by the
    standard API for exporting traced graphs.

    But until issue

      https://github.com/pytorch/pytorch/pull/117907

    is addressed, this wrapper provides support for the sparse
    tensor types by first converting all operands to dense tensors,
    building the traced graph as for the dense case, then annotating
    sparse parameters with their actual sparse layout attributes,
    followed by some simple propagation rules. This temporary solution
    accelerates testing torch-mlir with PyTorch sparse tensors until
    the issue is resolved upstream.
    """
    # Convert all arguments to dense.
    dargs = tuple(a.to_dense() if a.layout in SPARSE_LAYOUTS else a for a in args)
    mask = [a.layout in SPARSE_LAYOUTS for a in args]
    # Build the regular FX traced graph with only dense arguments
    # (the current version would crash otherwise, see issue above).
    prog = torch.export.export(f, dargs, kwargs)
    # Annotate sparse arguments in the graph and apply some very
    # basic propagation rules for sparsity.
    specs = prog.graph_signature.input_specs
    alen = len(specs)
    k = 0
    for i, node in enumerate(prog.graph.nodes):
        if node.op == "placeholder":
            # Argument.
            spec = specs[i]
            if spec.kind is torch.export.graph_signature.InputKind.USER_INPUT:
                if mask[k]:
                    node.meta["sparsity"] = sparse_metadata(args[k])
                k = k + 1
        elif node.op == "call_function":
            # TODO: use upstream _opname implementation when available
            opname = node.target._schema.name.split("::")[1]
            # Zero preserving elt-wise unary op.
            if opname in {"abs", "neg", "relu", "sin"}:
                node.meta["sparsity"] = node.args[0].meta.get("sparsity", None)
            elif opname == "_to_sparse" or opname == "to_sparse":
                dim = len(node.meta.get("val").shape)
                node.meta["sparsity"] = SparsityMeta(
                    torch.sparse_coo, 0, dim, 0, None, torch.int64, torch.int64
                )
            # TODO: Uncomment this to hack sparsity into the network.
            # elif opname == "_to_dense" or opname == "to_dense":
            #     # hack (assumes we never really want the to_dense for now)
            #     node.meta["sparsity"] = node.args[0].meta.get("sparsity", None)
            elif opname == "select" and node.args[0].meta.get("sparsity", None):
                dim = len(node.meta.get("val").shape)
                node.meta["sparsity"] = SparsityMeta(
                    torch.sparse_coo, 0, dim, 0, None, torch.int64, torch.int64
                )
            elif opname == "stack" and node.args[0][0].meta.get("sparsity", None):
                dim = len(node.meta.get("val").shape)
                node.meta["sparsity"] = SparsityMeta(
                    torch.sparse_coo, 0, dim - 1, 1, None, torch.int64, torch.int64
                )
    return prog


def export_and_import(f, *args, **kwargs):
    """This method implements Stella's importer, stripped down to essentials."""
    context = ir.Context()
    torch_d.register_dialect(context)
    fx_importer = FxImporter(context=context)
    prog = sparse_export(f, args, kwargs)
    fx_importer.import_frozen_program(prog)
    return fx_importer.module


def mpact_jit_compile(f, *args, opt_level=2, **kwargs):
    """This method compiles the given callable using the MPACT backend."""
    # Import module and lower into Linalg IR.
    module = export_and_import(f, *args, **kwargs)
    run_pipeline_with_repro_report(
        module,
        (
            "builtin.module("
            "func.func(torch-decompose-complex-ops),"
            "torch-backend-to-linalg-on-tensors-backend-pipeline)"
        ),
        "Lowering TorchFX IR -> Linalg IR",
        enable_ir_printing=False,
    )
    # Compile with MPACT backend.
    backend = MpactBackendLinalgOnTensorsBackend(opt_level=opt_level)
    compiled = backend.compile(module)
    invoker = backend.load(compiled)
    return invoker, f


def mpact_jit_run(invoker, f, *args, **kwargs):
    """This method runs the given callable using the given MPACT invoker."""
    xargs = []
    # Prepare the buffer parameters (assume all dense).
    # TODO: filters out scalar arguments, anything else?
    params = dict(f.named_buffers(remove_duplicate=True))
    params_flat, params_spec = torch.utils._pytree.tree_flatten(params)
    for p in params_flat:
        if len(p.shape) > 0:
            xargs.append(p.numpy())
    # Prepare input parameters. Sparse input tensors are split into
    # their composite tensors. All PyTorch tensors are converted
    # to their backing numpy arrays. Note that the output consists
    # of numpy arrays as well, which can trivially be reconstructed
    # into PyTorch tensors (dense and sparse).
    for a in args:
        if a.layout is torch.sparse_coo:
            # Construct the additional position array required by MLIR with data
            # array([0, nnz]). The COO format always uses int64 indices.
            xargs.append(np.array([0, a._nnz()], dtype=np.int64))
            # Transform a tensor<ndim x nnz> into ndim x tensor<nnz> to conform
            # to the MLIR SoA COO representation.
            for idx in a._indices():
                xargs.append(idx.numpy())
            xargs.append(a._values().numpy())
        elif a.layout is torch.sparse_csr or a.layout is torch.sparse_bsr:
            xargs.append(a.crow_indices().numpy())
            xargs.append(a.col_indices().numpy())
            xargs.append(a.values().numpy())
        elif a.layout is torch.sparse_csc or a.layout is torch.sparse_bsc:
            xargs.append(a.ccol_indices().numpy())
            xargs.append(a.row_indices().numpy())
            xargs.append(a.values().numpy())
        else:
            xargs.append(a.numpy())
    # Invoke.
    return invoker.main(*xargs)


# Convenience wrapper.
def mpact_jit(f, *args, **kwargs):
    """This method compiles and runs the given callable using the MPACT backend."""
    invoker, fn = mpact_jit_compile(f, *args, **kwargs)
    return mpact_jit_run(invoker, fn, *args, **kwargs)