################################################################################
# Copyright 2020-2023 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################


class LogParser:
    """
    Parses a log file with oneDNN verbose and converts it into internal
    representation.
    """

    def __init__(self, writer, input=""):
        # each data entry is a dictionary that consists of:
        # engine(str),
        # primitive(str),
        # implementation(str),
        # prop_kind(str),
        # aux({field(str) : value(str)}),
        # mds(
        #     {
        #         arg(str): {
        #             data_type(str),
        #             properties(str),
        #             format_kind(str),
        #             tag(str),
        #             strides(str),
        #             flags(str),
        #         }
        #     }
        # )
        # shapes(str)
        # extensions(str)
        # time(float)
        self.__raw_data = []
        self.__data = {}
        self.__writer = writer
        self.__input = input

    def process(self):
        """
        Adds data from the last log file.

        Parameters:
        -----------
        None

        Returns:
        --------
        None
        """

        def convert_primitive(log_entry, template):
            """
            Converts oneDNN verbose primitive entry into the internal
            representation.
            """

            def convert_mds(log_mds):
                mds = []
                for md in log_mds.split(" "):
                    # arg_dt:properties:format_kind:tag:strides:flags
                    fields = md.split(":")
                    arg_dt = fields[0]
                    properties = fields[1]
                    format_kind = fields[2]
                    tag = fields[3]

                    # Add compatibility for v3.1 verbose and below,
                    # when strides delimeter is absent.
                    # TODO: remove eventually.
                    idx = 4
                    strides = ""
                    if "f" not in fields[idx] and format_kind != "undef":
                        strides = fields[4]
                        idx += 1

                    flags = {}
                    flags["value"] = fields[idx]
                    idx += 1
                    if len(fields) > idx:
                        flag_fields = fields[idx:]
                        for f in flag_fields:
                            if f[:3] == "s8m":
                                flags["s8_comp_mask"] = f[3:]
                            if f[:3] == "zpm":
                                flags["zp_comp_mask"] = f[3:]

                    data_type = arg_dt.split("_")[-1]
                    arg = arg_dt[: -len(data_type) - 1]
                    mds.append(
                        {
                            "arg": arg,
                            "data_type": data_type,
                            "properties": properties,
                            "format_kind": format_kind,
                            "tag": tag,
                            "strides": strides,
                            "flags": flags,
                        }
                    )
                return mds

            def convert_aux(log_aux):
                aux = {}
                if log_aux == "":
                    return aux
                for log_aux_l in log_aux.split(" "):
                    # Handle strings like NAME:VAL1[:VAL2[:VAL3...]]
                    res = log_aux_l.split(":")
                    field = res[0]
                    value = ""
                    last_idx = len(res) - 1
                    for i in range(1, last_idx):
                        val_i = res[i]
                        value += f"{val_i}:"
                    val_n = res[last_idx]
                    value += f"{val_n}"
                    aux[field] = value
                return aux

            def convert_prim_kind(prim_kind):
                return prim_kind

            def convert_exts(exts):
                def extract_attr(attrs, type):
                    start_idx = attrs.find(type)
                    if start_idx == -1:
                        return ""

                    start_idx += len(type) + 1
                    end_symbol = " "
                    end_idx = attrs.find(end_symbol, start_idx)
                    return attrs[start_idx:end_idx]

                def convert_structure_to_ir_seq(ir, value):
                    params = value.split(":")
                    fields = list(ir.keys())
                    ir.update(
                        (fields[i], params[i])
                        for i in range(0, min(len(params), len(fields)))
                    )
                    return ir

                def convert_post_ops(value):
                    def convert_binary_post_op(value):
                        p_op = {"alg": "", "dt": "f32", "mask": "0", "tag": None}
                        p_op = convert_structure_to_ir_seq(p_op, value)
                        p_op["prim_kind"] = "binary"
                        return p_op

                    def convert_dw_post_op(value):
                        p_op = {
                            "alg": "",
                            "ksp": "",
                            "dst_dt": "f32",
                            "wei_dt": "f32",
                            "scales": {"mask": "0", "value": None},
                        }
                        params = value.split(":")
                        len_params = len(params)
                        p_op["alg"] = params[0]
                        p_op["ksp"] = params[1]
                        if len_params > 2:
                            p_op["dst_dt"] = params[2]
                        if len_params > 3:
                            p_op["wei_dt"] = "s8"
                            p_op["scales"]["mask"] = params[3]
                        if len_params > 4:
                            p_op["scales"]["value"] = params[4]
                        return p_op

                    def convert_eltwise_post_op(value):
                        p_op = {
                            "alg": "",
                            "alpha": "1.0",
                            "beta": "0.0",
                            "scale": "1.0",
                        }
                        return convert_structure_to_ir_seq(p_op, value)

                    def convert_sum_post_op(value):
                        p_op = {"alg": "", "scale": "1.0", "zp": "0", "dt": ""}
                        return convert_structure_to_ir_seq(p_op, value)

                    def convert_prelu_post_op(value):
                        p_op = {"alg": "", "mask": "0"}
                        return convert_structure_to_ir_seq(p_op, value)

                    convert = {
                        "binary": convert_binary_post_op,
                        "dw": convert_dw_post_op,
                        "eltwise": convert_eltwise_post_op,
                        "sum": convert_sum_post_op,
                        "prelu": convert_prelu_post_op,
                    }

                    entries = value.split("+")
                    postops = []
                    for e in entries:
                        for k in convert.keys():
                            if k in e:
                                cvt = convert.get(k)
                                postops.append(cvt(e))
                                break
                    return postops

                def convert_scales(value):
                    res = {}
                    scales = value.split("+")
                    for s in scales:
                        arg = s[: s.find(":")]
                        s_wo_arg = s[s.find(":") + 1 :]
                        scale_dict = {"mask": "0"}
                        res[arg] = convert_structure_to_ir_seq(scale_dict, s_wo_arg)
                    return res

                def convert_zero_points(value):
                    res = {}
                    zp_value = value.split("+")
                    for zp in zp_value:
                        arg = zp[: zp.find(":")]
                        zp_value_wo_arg = zp[zp.find(":") + 1 :]
                        zp_dict = {"mask": "0"}
                        res[arg] = convert_structure_to_ir_seq(zp_dict, zp_value_wo_arg)
                    return res

                def convert_scratchpad_mode(value):
                    return value

                def convert_fpmath_mode(value):
                    return value

                converters = {
                    "attr-post-ops": convert_post_ops,
                    "attr-scales": convert_scales,
                    "attr-zero-points": convert_zero_points,
                    "attr-scratchpad": convert_scratchpad_mode,
                    "attr-fpmath": convert_fpmath_mode,
                }
                attrs = {}
                for e in converters.keys():
                    attr = extract_attr(exts, e)
                    if attr != "":
                        attrs[e] = converters[e](attr)
                return attrs

            def convert_pass(v):
                return v

            convert = {
                "prim_kind": convert_prim_kind,
                "mds": convert_mds,
                "aux": convert_aux,
                "exts": convert_exts,
            }

            dnnl_to_ir = {
                "engine": "engine",
                "prim_kind": "primitive",
                "impl": "implementation",
                "prop_kind": "prop_kind",
                "mds": "memory_descriptors",
                "exts": "attributes",
                "aux": "auxiliary",
                "shapes": "problem_desc",
                "time": "exec_time",
                "timestamp": "timestamp",
            }

            ir_req = [
                "engine",
                "prim_kind",
                "impl",
                "prop_kind",
                "mds",
                "exts",
                "aux",
                "shapes",
            ]

            entry = {}

            t = template.split(",")
            for key, value in dnnl_to_ir.items():
                notification_level = "WARN" if key in ir_req else "INFO"
                try:
                    idx = t.index(value)
                    if idx != -1:
                        cvt = convert.get(key)
                        if cvt is None:
                            cvt = convert_pass
                        field = log_entry[idx]
                        try:
                            entry[key] = cvt(field)
                        except:
                            self.__writer.print(
                                f"Parser: parsing entry error: {field}: {value}",
                                notification_level,
                            )
                    else:
                        self.__writer.print(
                            f"Parser: Unknown entry: {value}", notification_level
                        )
                except:
                    self.__writer.print(
                        f"Parser: skipping empty entry: {key}", notification_level
                    )
            return entry

        verbose_template = (
            "onednn_verbose,operation,engine,primitive,"
            + "implementation,prop_kind,memory_descriptors,attributes,"
            + "auxiliary,problem_desc"
        )

        i = len(self.__data)
        for line in self.__input:
            self.__raw_data.append(line.rstrip())
            l_raw = line.split(",")
            marker = l_raw[0]
            if marker == "onednn_verbose":
                if l_raw[1].split(".")[0].isdigit():
                    l_raw.pop(1)
                event = l_raw[1].split(":")[0]
                if event == "info":
                    opt = l_raw[2]
                    if opt == "prim_template":
                        verbose_template = "onednn_verbose," + line.split(":")[1]
                if event in ["exec", "create"]:
                    l_converted = convert_primitive(
                        l_raw, verbose_template + ",exec_time"
                    )
                    if l_converted:
                        self.__data[i] = l_converted
                        i = i + 1

    def get_data(self):
        """
        Returns information about DNN calls.

        Parameters
        ----------
        None

        Returns
        -------
        data
        """

        return self.__data

    def dump(self, converted=False):
        """
        Prints data parsed from input to stdout.

        Parameters
        ----------
        converted (default: False) -- If True dump() prints data in internal
        represenataion, otherwise prints data in the original form.

        Returns
        -------
        None
        """

        if converted:
            [
                self.__writer.print(f"{key}, {value}", "STDIO")
                for key, value in self.__data.items()
            ]
        else:
            [self.__writer.print(d, "STDIO") for d in self.__raw_data]
