# mypy: allow-untyped-defs
import re
from collections import defaultdict, OrderedDict
from typing import Any, Callable, Dict, List, Set, Tuple, Union

import torch
from torch.ao.nn.intrinsic import _FusedModule
from torch.ao.quantization import QConfig
from torch.ao.quantization.backend_config import BackendConfig, DTypeConfig
from torch.ao.quantization.backend_config.utils import get_module_to_qat_module
from torch.ao.quantization.observer import _is_activation_post_process
from torch.ao.quantization.qconfig import (
    _add_module_to_qconfig_obs_ctr,
    qconfig_equals,
    QConfigAny,
)
from torch.ao.quantization.qconfig_mapping import (
    _MODULE_NAME_DICT_KEY,
    _MODULE_NAME_REGEX_DICT_KEY,
    _OBJECT_TYPE_DICT_KEY,
    QConfigMapping,
)
from torch.ao.quantization.utils import _parent_name, get_qconfig_dtypes
from torch.fx import GraphModule
from torch.fx.graph import Graph


__all__: List[str] = []


def _maybe_adjust_qconfig_for_module_name_object_type_order(
    qconfig_mapping: QConfigMapping,
    cur_module_path: str,
    cur_object_type: Callable,
    cur_object_type_idx: int,
    fallback_qconfig: QConfigAny,
) -> QConfigAny:
    for (
        module_name,
        object_type,
        index,
    ), qconfig in qconfig_mapping.module_name_object_type_order_qconfigs.items():
        if (
            (module_name == cur_module_path)
            and (object_type == cur_object_type)
            and (index == cur_object_type_idx)
        ):
            return qconfig
    return fallback_qconfig


def _update_qconfig_for_fusion(model: GraphModule, qconfig_mapping: QConfigMapping):
    """
    Update the QConfigMapping to account for fused modules such as LinearReLU.
    This assumes the QConfigMapping's attributes have already been converted to OrderedDicts.
    """
    object_type_dict = qconfig_mapping.object_type_qconfigs
    if len(object_type_dict) == 0:
        return qconfig_mapping

    modules = dict(model.named_modules())

    for node in model.graph.nodes:
        if node.op == "call_module" and node.target in modules:
            maybe_fused_module = modules[str(node.target)]
            if not isinstance(maybe_fused_module, _FusedModule):
                continue

            ops = list(maybe_fused_module._modules.values())
            fused_qconfig = object_type_dict.get(type(ops[0]), None)

            # Raise an error if the modules in the fused module have
            # different qconfigs specified in the qconfig_dict
            # TODO: currently it only works for modules,
            # need to make this work for torch.nn.functional.relu
            # TODO: currently it only works for object_type configurations,
            # ideally it should work for different types of configurations,
            # maybe we want to redesign this part
            for op in ops[1:]:
                if not qconfig_equals(
                    object_type_dict.get(type(op), None), fused_qconfig
                ):
                    raise LookupError(
                        "During fusion, we need to specify the same "
                        + f"qconfigs for all module types in {type(maybe_fused_module)} "
                        + f"offending type: {type(op)}"
                    )

            if fused_qconfig is not None:
                object_type_dict[type(maybe_fused_module)] = fused_qconfig


def _generate_node_name_to_qconfig(
    root: torch.nn.Module,
    modules: Dict[str, torch.nn.Module],
    input_graph: Graph,
    qconfig_mapping: QConfigMapping,
    node_name_to_scope: Dict[str, Tuple[str, type]],
) -> Dict[str, QConfigAny]:
    global_qconfig = qconfig_mapping.global_qconfig
    node_name_to_qconfig = {}

    # example:
    #
    #   {'foo.bar': {F.linear: 0, F.conv2d: 1, ...}, ...}
    #
    # meaning in submodule 'foo.bar', we have seen 0 F.linear and
    # 1 F.conv2d invocations so far.
    submodule_to_object_type_to_cur_idx: Dict[str, Dict[Callable, int]] = defaultdict(
        lambda: defaultdict(int)
    )
    for node in input_graph.nodes:
        qconfig = None
        if node.op == "get_attr":
            module_name, _ = _parent_name(node.target)
            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
                qconfig_mapping, type(modules[module_name]), module_name, global_qconfig
            )
            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
                qconfig, modules.get(node.target, None)
            )
        elif node.op == "call_function":
            # precedence: module_name_qconfig
            # > function_qconfig > global_qconfig
            # module_name takes precedence over function qconfig
            function_qconfig = _get_object_type_qconfig(
                qconfig_mapping, node.target, global_qconfig
            )
            module_path, module_type = node_name_to_scope[node.name]
            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
                qconfig_mapping, module_type, module_path, function_qconfig
            )

            cur_object_type_idx = submodule_to_object_type_to_cur_idx[module_path][
                node.target
            ]
            submodule_to_object_type_to_cur_idx[module_path][node.target] += 1
            qconfig = _maybe_adjust_qconfig_for_module_name_object_type_order(
                qconfig_mapping, module_path, node.target, cur_object_type_idx, qconfig
            )
            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
                qconfig, modules.get(node.target, None)
            )

        elif node.op == "call_method":
            module_path, module_type = node_name_to_scope[node.name]
            # first use node.target (string) to get the qconfig
            # this is to support configs like
            # "object_type": [("reshape", qconfig)]
            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
                qconfig_mapping, node.target, module_path, global_qconfig
            )
            # if there is no special config for the method, we'll fall back to the
            # config for the module that contains the call_method node
            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
                qconfig_mapping, module_type, module_path, qconfig
            )
            # currently call_method does not support modifying qconfig
            # by order, we can add this later if it is needed.
            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
                qconfig, modules.get(node.target, None)
            )

        elif node.op == "call_module":
            # if the node is an observer, just continue - don't add it to the qconfig_map
            if _is_activation_post_process(modules[node.target]):
                continue
            qconfig = _maybe_adjust_qconfig_for_module_type_or_name(
                qconfig_mapping, type(modules[node.target]), node.target, global_qconfig
            )

            module_path, module_type = node_name_to_scope[node.name]
            # Note: for call_module, the module_path is the current module's name.
            # to meaningfully count invocations, we need to count them in the parent
            # module.
            parent_name, _ = _parent_name(module_path)
            cur_object_type_idx = submodule_to_object_type_to_cur_idx[parent_name][
                module_type
            ]
            submodule_to_object_type_to_cur_idx[parent_name][module_type] += 1
            qconfig = _maybe_adjust_qconfig_for_module_name_object_type_order(
                qconfig_mapping, parent_name, module_type, cur_object_type_idx, qconfig
            )
            qconfig_with_device_check = _add_module_to_qconfig_obs_ctr(
                qconfig, modules.get(node.target, None)
            )

            # regex is not supported eager mode propagate_qconfig_, we'll
            # need to set the qconfig explicitly here in case regex
            # is used
            modules[node.target].qconfig = qconfig_with_device_check
        else:
            qconfig_with_device_check = None

        node_name_to_qconfig[node.name] = qconfig_with_device_check
    return node_name_to_qconfig


def _check_is_valid_config_dict(
    config_dict: Any, allowed_keys: Set[str], dict_name: str
) -> None:
    r"""Checks if the given config_dict has the correct keys

    Args:
      `config_dict`: dictionary whose keys we want to check
    """

    for k in config_dict.keys():
        if k not in allowed_keys:
            raise ValueError(
                "Expected "
                + dict_name
                + " to have the following keys: "
                + str(allowed_keys)
                + ". But found '"
                + k
                + "' instead."
            )


def _compare_prepare_convert_qconfig_mappings(
    prepare_qconfig_mapping: QConfigMapping, convert_qconfig_mapping: QConfigMapping
):
    r"""Compare the qconfig_mapping passed in convert to the one from prepare and check the values

    Args:
      `prepare_qconfig_mapping`: configuration for prepare quantization step
      `convert_qconfig_mapping`: configuration for convert quantization step
    """
    assert qconfig_equals(
        prepare_qconfig_mapping.global_qconfig, convert_qconfig_mapping.global_qconfig
    ), "Expected global qconfigs to be the same in the prepare and convert quantization configs"
    prepare_dicts: List[OrderedDict] = [
        prepare_qconfig_mapping.object_type_qconfigs,
        prepare_qconfig_mapping.module_name_qconfigs,
        prepare_qconfig_mapping.module_name_regex_qconfigs,
    ]
    convert_dicts: List[OrderedDict] = [
        convert_qconfig_mapping.object_type_qconfigs,
        convert_qconfig_mapping.module_name_qconfigs,
        convert_qconfig_mapping.module_name_regex_qconfigs,
    ]
    dict_names = [
        _OBJECT_TYPE_DICT_KEY,
        _MODULE_NAME_DICT_KEY,
        _MODULE_NAME_REGEX_DICT_KEY,
    ]
    for i in range(len(prepare_dicts)):
        for name in prepare_dicts[i].keys():
            assert (
                name in convert_dicts[i]
            ), f"Missing key {dict_names[i]} {name} in convert QConfigMapping \
                when it was present in prepare"
            assert convert_dicts[i][name] is None or qconfig_equals(
                prepare_dicts[i][name], convert_dicts[i][name]
            ), f"Expected convert QConfigMapping to have the same qconfig as prepare for key {dict_names[i]} {name}; \
                prepare: {prepare_dicts[i][name]}; convert: {convert_dicts[i][name]}"


def _is_qconfig_supported_by_dtype_configs(
    qconfig: QConfig, dtype_configs: List[DTypeConfig]
):
    for dtype_config in dtype_configs:
        is_dynamic = dtype_config.is_dynamic
        if is_dynamic is None:
            is_dynamic = False
        input_dtype = dtype_config.input_dtype or torch.float
        weight_dtype = dtype_config.weight_dtype or torch.float
        bias_dtype = dtype_config.bias_dtype or torch.float
        output_dtype = dtype_config.output_dtype or torch.float
        (
            qconfig_activation_dtype,
            qconfig_weight_dtype,
            qconfig_input_act_is_dynamic,
        ) = get_qconfig_dtypes(qconfig)
        qconfig_bias_dtype = (
            torch.float16
            if (
                qconfig_activation_dtype == torch.float16
                and qconfig_weight_dtype == torch.float16
                and not is_dynamic
            )
            else torch.float
        )

        if is_dynamic:
            is_match = (
                qconfig_input_act_is_dynamic
                and input_dtype == qconfig_activation_dtype
                and output_dtype == torch.float
                and weight_dtype == qconfig_weight_dtype
            )
        else:
            is_match = (
                input_dtype == qconfig_activation_dtype
                and output_dtype == qconfig_activation_dtype
                and weight_dtype == qconfig_weight_dtype
                and bias_dtype == qconfig_bias_dtype
            )
        if is_match:
            return True
    return False


def _get_object_type_qconfig(
    qconfig_mapping: QConfigMapping,
    object_type: Union[Callable, str],
    fallback_qconfig: QConfigAny,
) -> QConfigAny:
    return qconfig_mapping.object_type_qconfigs.get(object_type, fallback_qconfig)


def _get_module_name_regex_qconfig(qconfig_mapping, module_name, fallback_qconfig):
    for regex_pattern, qconfig in qconfig_mapping.module_name_regex_qconfigs.items():
        if re.match(regex_pattern, module_name):
            # first match wins
            return qconfig
    return fallback_qconfig


def _get_module_name_qconfig(qconfig_mapping, module_name, fallback_qconfig):
    if module_name == "":
        # module name qconfig not found
        return fallback_qconfig
    if module_name in qconfig_mapping.module_name_qconfigs:
        return qconfig_mapping.module_name_qconfigs[module_name]
    else:
        parent, _ = _parent_name(module_name)
        return _get_module_name_qconfig(qconfig_mapping, parent, fallback_qconfig)


def _maybe_adjust_qconfig_for_module_type_or_name(
    qconfig_mapping, module_type, module_name, global_qconfig
):
    # get qconfig for module_name,
    # fallback to module_name_regex_qconfig, module_type_qconfig,
    # global_qconfig if necessary
    module_type_qconfig = _get_object_type_qconfig(
        qconfig_mapping, module_type, global_qconfig
    )
    module_name_regex_qconfig = _get_module_name_regex_qconfig(
        qconfig_mapping, module_name, module_type_qconfig
    )
    module_name_qconfig = _get_module_name_qconfig(
        qconfig_mapping, module_name, module_name_regex_qconfig
    )
    return module_name_qconfig


def _get_flattened_qconfig_dict(
    qconfig_mapping: QConfigMapping,
) -> Dict[Union[Callable, str], QConfigAny]:
    """flatten the global, object_type and module_name qconfig
    to the same qconfig_dict so that it can be used by
    propagate_qconfig_ function.
    "module_name_regex" is ignored for now since it's not supported
    in propagate_qconfig_, but it can be fixed later.

    For example:
    Input: {
      "": qconfig,
      "object_type": [
        (torch.add, qconfig)
      ],
      "module_name": [
        ("conv", qconfig)
      ]
    }

    Output: {
      "": qconfig,
      torch.add: qconfig,
      "conv": qconfig
    }
    """
    flattened: Dict[Union[Callable, str], QConfigAny] = {
        "": qconfig_mapping.global_qconfig
    }
    for obj, qconfig in qconfig_mapping.object_type_qconfigs.items():
        flattened[obj] = qconfig
    for obj, qconfig in qconfig_mapping.module_name_qconfigs.items():
        flattened[obj] = qconfig
    return flattened


def _update_qconfig_for_qat(
    qconfig_mapping: QConfigMapping, backend_config: BackendConfig
):
    """
    Update the qconfig_mapping to account for module swaps during QAT.
    During QAT we perform a module swap on the nn.Module types to the corresponding nn.qat.modules types.
    """
    module_to_qat_module_class = get_module_to_qat_module(backend_config)
    object_type_dict = qconfig_mapping.object_type_qconfigs
    new_object_type_dict = object_type_dict.copy()
    for k, v in new_object_type_dict.items():
        if k in module_to_qat_module_class:
            object_type_dict[module_to_qat_module_class[k]] = v
