# -*- coding: utf-8 -*-
# Copyright (C) 2018-2026 Intel Corporation
# SPDX-License-Identifier: Apache-2.0

# mypy: ignore-errors

import inspect
import logging
import torch
import numpy as np
from contextlib import contextmanager

from openvino import op, Type as OVType, Shape, Tensor, OVAny
from openvino import opset11 as ops
from openvino.frontend.pytorch.py_pytorch_frontend import _Type as DecoderType

log = logging.getLogger(__name__)


def make_constant(*args, **kwargs):
    return op.Constant(*args, **kwargs)


def fetch_attr(self_module, target: str):
    """Fetch an attribute from the `Module` hierarchy of `self.module`.

    Args:
        self_module (torch.nn.Module): The module to fetch the attribute from
        target (str): The fully-qualified name of the attribute to fetch

    Returns:
        Any: The value of the attribute.
    """
    target_atoms = target.split(".")
    attr_itr = self_module
    for i, atom in enumerate(target_atoms):
        if not hasattr(attr_itr, atom):
            raise RuntimeError(
                f"Node referenced nonexistent target {'.'.join(target_atoms[:i])}")
        attr_itr = getattr(attr_itr, atom)
    return attr_itr


def get_type_from_py_type(value):
    if isinstance(value, float):
        return OVType.f32
    if isinstance(value, bool):
        return OVType.boolean
    if isinstance(value, int):
        return OVType.i64
    if isinstance(value, complex):
        return OVType.f32
    return OVType.dynamic


F8_DTYPE_MAP = {
    torch.float8_e4m3fn: OVType.f8e4m3,
    torch.float8_e5m2: OVType.f8e5m2,
}


def torch_tensor_to_ov_const(torch_t: torch.Tensor, shared_memory=True):
    try:
        from torch._prims import FakeTensor
        if isinstance(torch_t, FakeTensor):
            raise AssertionError("`FakeTensor` detected. Infer the "
                                 "model before exporting to avoid this.")
    except ImportError:
        log.debug("Failed to import FakeTensor")

    dtype = torch_t.dtype
    torch_t = torch_t.contiguous()
    if dtype == torch.bfloat16:
        # reinterpret bfloat16 data as float16 to allow conversion to numpy
        torch_t = torch_t.view(torch.float16)
        narr = torch_t.numpy(force=True)
        tensor = Tensor(narr, torch_t.shape, OVType.bf16)
        ov_const = op.Constant(tensor, shared_memory=shared_memory)
    elif dtype in F8_DTYPE_MAP:
        # reinterpret f8 data as u8 to allow conversion to numpy
        torch_t = torch_t.view(torch.uint8)
        narr = torch_t.numpy(force=True)
        tensor = Tensor(narr, torch_t.shape, F8_DTYPE_MAP[dtype])
        ov_const = op.Constant(tensor, shared_memory=shared_memory)
    elif torch_t.is_complex():
        narr = torch.view_as_real(torch_t).numpy(force=True)
        # we rely on frontend to mark the constant as complex internally
        ov_const = op.Constant(narr, shared_memory=shared_memory)
    else:
        narr = torch_t.numpy(force=True)
        ov_const = op.Constant(narr, shared_memory=shared_memory)
    return ov_const


def ivalue_to_constant(ivalue, shared_memory=True):
    ov_type = get_type_from_py_type(ivalue)
    if ov_type.is_static():
        if isinstance(ivalue, complex):
            return op.Constant(ov_type, Shape([2]), [ivalue.real, ivalue.imag]).outputs()
        else:
            return op.Constant(ov_type, Shape([]), [ivalue]).outputs()

    if isinstance(ivalue, (list, tuple)):
        assert len(ivalue) > 0, "Can't deduce type for empty list"
        ov_type = get_type_from_py_type(ivalue[0])
        assert ov_type.is_static(), "Can't deduce type for list"
        return op.Constant(ov_type, Shape([len(ivalue)]), ivalue).outputs()

    if isinstance(ivalue, torch.Tensor):
        return torch_tensor_to_ov_const(ivalue, shared_memory=shared_memory).outputs()
    return None


def get_value_from_getattr(getattr_node, self_module):
    assert getattr_node.kind() == "prim::GetAttr", "Got node of kind not equal to prim::GetAttr"
    # GetAttr nodes can be nested
    stack = []
    while getattr_node.kind() == "prim::GetAttr":
        stack.append(getattr_node)
        inputs = list(getattr_node.inputs())
        if len(inputs) == 0:
            break
        getattr_node = inputs[0].node()
    module = self_module
    path_name = "self"
    while len(stack) > 0:
        node = stack.pop()
        attr_name = node.s("name")
        assert hasattr(
            module, attr_name), f'No attribute with name "{attr_name}" found in module.'
        path_name = ".".join([path_name, attr_name])
        module = getattr(module, attr_name)
    return module, path_name


def graph_has_ops(graph, op_types: list) -> bool:
    res = False
    for node in graph.nodes():
        if any(kind in node.kind() for kind in op_types):
            return True
        for block in node.blocks():
            res = graph_has_ops(block, op_types)
        if res:
            return res
    return res


pt_to_ov_type_map = {
    "float": OVType.f32,
    "int": OVType.i64,
    "bool": OVType.boolean,
    "torch.float8_e4m3fn": OVType.f8e4m3,
    "torch.float8_e5m2": OVType.f8e5m2,
    "torch.bfloat16": OVType.bf16,
    "torch.float16": OVType.f16,
    "torch.float32": OVType.f32,
    "torch.float64": OVType.f64,
    "torch.complex32": DecoderType.Complex(OVAny(OVType.f16)),
    "torch.complex64": DecoderType.Complex(OVAny(OVType.f32)),
    "torch.complex128": DecoderType.Complex(OVAny(OVType.f64)),
    "torch.uint8": OVType.u8,
    "torch.int8": OVType.i8,
    "torch.int16": OVType.i16,
    "torch.int32": OVType.i32,
    "torch.int64": OVType.i64,
    "torch.bool": OVType.boolean,
    "torch.DoubleTensor": OVType.f64,
    "torch.FloatTensor": OVType.f32,
    "torch.HalfTensor": OVType.f16,
    "torch.BFloat16Tensor": OVType.bf16,
    "torch.IntTensor": OVType.i32,
    "torch.LongTensor": OVType.i64,
    "torch.ShortTensor": OVType.i16,
    "torch.CharTensor": OVType.i8,
    "torch.ByteTensor": OVType.u8,
    "torch.BoolTensor": OVType.boolean,
    "torch.ComplexHalfTensor": DecoderType.Complex(OVAny(OVType.f16)),
    "torch.ComplexFloatTensor": DecoderType.Complex(OVAny(OVType.f32)),
    "torch.ComplexDoubleTensor": DecoderType.Complex(OVAny(OVType.f64)),
    "torch.quint8": OVType.u8,
    "torch.qint8": OVType.i8,
    "torch.qint32": OVType.i32,
}


wrapper_template = """
import torch
from typing import *

class ModelWrapper(torch.nn.Module):
    def __init__(self, model):
        super().__init__()
        self.model = model

    def forward(self, {input_sign}):
        return self.model({example_input})
"""


def build_wrapper(template, model):
    """Builds a wrapper around the given model using the provided template."""
    result = {}
    try:
        exec(template, result)

        wrapped_model = result["ModelWrapper"](model)
        wrapped_model.eval()
    # if wrapping failed, it is better to return original model for avoid user confusion regarding error message
    except Exception:
        log.error("Failed to build model wrapper.")
        wrapped_model = model
    return wrapped_model


def process_dict_inputs(inputs, input_params, model):
    ordered_inputs = []
    for input_name in input_params:
        if input_name in inputs:
            ordered_inputs.append(input_name)

    input_signature = list(input_params)
    if ordered_inputs == input_signature[: len(ordered_inputs)]:
        example_inputs = [inputs[input_name] for input_name in ordered_inputs]
        if all(isinstance(inp, torch.Tensor) for inp in example_inputs):
            return {"example_inputs": [inputs[name] for name in ordered_inputs]}, ordered_inputs, model
        return {"example_inputs": example_inputs}, ordered_inputs, model

    # PyTorch has some difficulties to trace models with named unordered parameters:
    # torch < 2.0.0 supports only positional arguments for tracing
    # pytorch == 2.0.0 supports input kwargs tracing,
    # but does not support complex nested objects (e. g. tuple of tuples of tensors)
    # We will use wrapper for making them positional as workaround.

    input_sign_str = []
    input_params_str = []

    for input_name in ordered_inputs:
        if str(input_params[input_name].annotation).startswith("typing.Union"):
            filter_custom_args = []
            for arg in input_params[input_name].annotation.__args__:
                str_arg = str(arg)
                is_typing = str_arg.startswith("typing.")
                is_torch = "torch." in str_arg
                is_builten = str_arg in (str(int), str(float), str(type(None)))
                if not (is_typing or is_torch or is_builten):
                    continue
                filter_custom_args.append(arg)
            input_params[input_name].annotation.__args__ = tuple(
                filter_custom_args)
        input_sign_str.append(
            str(input_params[input_name]).replace("NoneType", "None"))
        input_params_str.append(f"{input_name}={input_name}")

    wrapper_class = wrapper_template.format(input_sign=", ".join(
        input_sign_str), example_input=", ".join(input_params_str))

    wrapped_model = build_wrapper(wrapper_class, model)

    return {"example_inputs": [inputs[name] for name in ordered_inputs]}, ordered_inputs, wrapped_model


def prepare_example_inputs_and_model(inputs, input_params, model):
    input_is_list = False
    input_signature = list(input_params)
    if isinstance(inputs, dict):
        examples, ordered, wrapped = process_dict_inputs(
            inputs, input_params, model)
        return examples, ordered, wrapped, input_is_list
    if isinstance(inputs, list) and len(inputs) == 1 and isinstance(inputs[0], torch.Tensor):
        if "list" in str(input_params[input_signature[0]].annotation):
            inputs = inputs[0].unsqueeze(0)
            input_is_list = True

    if isinstance(inputs, torch.Tensor):
        inputs = [inputs]
    input_signature = input_signature[: len(inputs)]
    return {"example_inputs": inputs}, input_signature, model, input_is_list


def convert_quantized_tensor(qtensor: torch.Tensor, shared_memory: bool):
    # represents torch quantized tensor as
    # Constant(u8) -> Convert(f32) -> Subtract(zero_point) -> Multiply(scale)
    qscheme = qtensor.qscheme()
    if qscheme == torch.per_channel_affine:
        int8_tensor = qtensor.int_repr()
        scale = qtensor.q_per_channel_scales().numpy().astype(np.float32)
        zero_point = qtensor.q_per_channel_zero_points().numpy().astype(np.float32)
        axis = np.int32(qtensor.q_per_channel_axis())

        new_shape = np.ones(len(int8_tensor.shape), dtype=np.int32)
        new_shape[axis] = -1
        zero_point_bc = np.reshape(zero_point, new_shape)
        scale_bc = np.reshape(scale, new_shape)

        int8_const = torch_tensor_to_ov_const(
            int8_tensor, shared_memory=shared_memory)
        convert = ops.convert(int8_const, np.float32)
        sub = ops.subtract(convert, zero_point_bc)
        return ops.multiply(sub, scale_bc).outputs()
    elif qscheme == torch.per_tensor_affine:
        int8_tensor = qtensor.int_repr()
        scale = np.float32(qtensor.q_scale())
        zero_point = np.float32(qtensor.q_zero_point())

        int8_const = torch_tensor_to_ov_const(
            int8_tensor, shared_memory=shared_memory)
        convert = ops.convert(int8_const, np.float32)
        sub = ops.subtract(convert, zero_point)
        return ops.multiply(sub, scale).outputs()
    raise AssertionError(f"Unsupported qscheme: {qscheme}")


def process_individual_input(arg, arg_name):
    """Generate signature, param string, example, and wrap flag from input.

    Args:
        arg: The input value to process.
        arg_name: The name of the input.

    Returns:
        tuple: (signature, param string, example entry, wrap flag).
    """
    sign = None
    param = None
    example_entry = None
    to_wrap = False
    if isinstance(arg, tuple):
        internal_input = []
        new_tuple = []
        index = 0
        for value in arg:
            if value is None:
                to_wrap = True
                internal_input.append("None")
            else:
                internal_input.append(f"{arg_name}[{index}]")
                new_tuple.append(value)
                index += 1
        param = f"({', '.join(internal_input)},)"
        if len(new_tuple) > 0:
            example_entry = tuple(new_tuple)
            sign = arg_name
    elif arg is None:
        to_wrap = True
        param = "None"
    else:
        sign = arg_name
        param = arg_name
        example_entry = arg
    return sign, param, example_entry, to_wrap


def patch_none_example(model: torch.nn.Module, example):
    """Patch a PyTorch model to handle None values in the input example."""
    callable_func = getattr(model, "forward", model.__call__)
    input_params = inspect.signature(callable_func).parameters
    input_signature = list(input_params)
    input_sign_str = []
    input_params_str = []
    to_wrap = False
    if isinstance(example, tuple) and len(input_signature) >= len(example):
        new_example = []
        for i, arg in enumerate(example):
            arg_name = input_signature[i]
            sign, param, example_entry, _to_wrap = process_individual_input(arg, arg_name)
            to_wrap = to_wrap or _to_wrap
            if sign is not None:
                input_sign_str.append(str(input_params[sign]))
            input_params_str.append(param)
            if example_entry is not None:
                new_example.append(example_entry)
        if to_wrap:
            wrapper_class = wrapper_template.format(input_sign=", ".join(input_sign_str),
                                                    example_input=", ".join(input_params_str))
            wrapped_model = build_wrapper(wrapper_class, model)
            log.warning("Model has None in the example input. The input "
                        "with None will be removed from the resulting model.")
            return wrapped_model, tuple(new_example)
    elif isinstance(example, dict) and len(input_signature) >= len(example):
        new_example = {}
        input_signature = [s for s in input_signature if s in example]
        for arg_name in input_signature:
            arg = example[arg_name]
            sign, param, example_entry, _to_wrap = process_individual_input(arg, arg_name)
            to_wrap = to_wrap or _to_wrap
            if sign is not None:
                input_sign_str.append(str(input_params[sign]))
            input_params_str.append(f"{arg_name}={param}")
            if example_entry is not None:
                new_example[arg_name] = example_entry
        if to_wrap:
            wrapper_class = wrapper_template.format(input_sign=", ".join(input_sign_str),
                                                    example_input=", ".join(input_params_str))
            wrapped_model = build_wrapper(wrapper_class, model)
            log.warning("Model has None in the example input. The input "
                        "with None will be removed from the resulting model.")
            return wrapped_model, new_example
    return model, example


@contextmanager
def no_jit_trace():
    """Context manager to disable JIT tracing.

    Note: using this function on large models consume a lot of memory.
    """
    state = torch._C._get_tracing_state()
    torch._C._set_tracing_state(None)
    try:
        yield
    finally:
        torch._C._set_tracing_state(state)
