# -------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation.  All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------

# It is a tool to generate test data for a bert model.
# The test data can be used by onnxruntime_perf_test tool to evaluate the inference latency.

import argparse
import os
import random
from pathlib import Path
from typing import Dict, Optional, Tuple

import numpy as np
from onnx import ModelProto, TensorProto, numpy_helper
from onnx_model import OnnxModel


def fake_input_ids_data(
    input_ids: TensorProto, batch_size: int, sequence_length: int, dictionary_size: int
) -> np.ndarray:
    """Create input tensor based on the graph input of input_ids

    Args:
        input_ids (TensorProto): graph input of the input_ids input tensor
        batch_size (int): batch size
        sequence_length (int): sequence length
        dictionary_size (int): vocabulary size of dictionary

    Returns:
        np.ndarray: the input tensor created
    """
    assert input_ids.type.tensor_type.elem_type in [
        TensorProto.FLOAT,
        TensorProto.INT32,
        TensorProto.INT64,
    ]

    data = np.random.randint(dictionary_size, size=(batch_size, sequence_length), dtype=np.int32)

    if input_ids.type.tensor_type.elem_type == TensorProto.FLOAT:
        data = np.float32(data)
    elif input_ids.type.tensor_type.elem_type == TensorProto.INT64:
        data = np.int64(data)

    return data


def fake_segment_ids_data(segment_ids: TensorProto, batch_size: int, sequence_length: int) -> np.ndarray:
    """Create input tensor based on the graph input of segment_ids

    Args:
        segment_ids (TensorProto): graph input of the token_type_ids input tensor
        batch_size (int): batch size
        sequence_length (int): sequence length

    Returns:
        np.ndarray: the input tensor created
    """
    assert segment_ids.type.tensor_type.elem_type in [
        TensorProto.FLOAT,
        TensorProto.INT32,
        TensorProto.INT64,
    ]

    data = np.zeros((batch_size, sequence_length), dtype=np.int32)

    if segment_ids.type.tensor_type.elem_type == TensorProto.FLOAT:
        data = np.float32(data)
    elif segment_ids.type.tensor_type.elem_type == TensorProto.INT64:
        data = np.int64(data)

    return data


def get_random_length(max_sequence_length: int, average_sequence_length: int):
    assert average_sequence_length >= 1 and average_sequence_length <= max_sequence_length

    # For uniform distribution, we find proper lower and upper bounds so that the average is in the middle.
    if 2 * average_sequence_length > max_sequence_length:
        return random.randint(2 * average_sequence_length - max_sequence_length, max_sequence_length)
    else:
        return random.randint(1, 2 * average_sequence_length - 1)


def fake_input_mask_data(
    input_mask: TensorProto,
    batch_size: int,
    sequence_length: int,
    average_sequence_length: int,
    random_sequence_length: bool,
    mask_type: int = 2,
) -> np.ndarray:
    """Create input tensor based on the graph input of segment_ids.

    Args:
        input_mask (TensorProto): graph input of the attention mask input tensor
        batch_size (int): batch size
        sequence_length (int): sequence length
        average_sequence_length (int): average sequence length excluding paddings
        random_sequence_length (bool): whether use uniform random number for sequence length
        mask_type (int): mask type - 1: mask index (sequence length excluding paddings). Shape is (batch_size).
                                     2: 2D attention mask. Shape is (batch_size, sequence_length).
                                     3: key len, cumulated lengths of query and key. Shape is (3 * batch_size + 2).

    Returns:
        np.ndarray: the input tensor created
    """

    assert input_mask.type.tensor_type.elem_type in [
        TensorProto.FLOAT,
        TensorProto.INT32,
        TensorProto.INT64,
    ]

    if mask_type == 1:  # sequence length excluding paddings
        data = np.ones((batch_size), dtype=np.int32)
        if random_sequence_length:
            for i in range(batch_size):
                data[i] = get_random_length(sequence_length, average_sequence_length)
        else:
            for i in range(batch_size):
                data[i] = average_sequence_length
    elif mask_type == 2:  # 2D attention mask
        data = np.zeros((batch_size, sequence_length), dtype=np.int32)
        if random_sequence_length:
            for i in range(batch_size):
                actual_seq_len = get_random_length(sequence_length, average_sequence_length)
                for j in range(actual_seq_len):
                    data[i, j] = 1
        else:
            temp = np.ones((batch_size, average_sequence_length), dtype=np.int32)
            data[: temp.shape[0], : temp.shape[1]] = temp
    else:
        assert mask_type == 3
        data = np.zeros((batch_size * 3 + 2), dtype=np.int32)
        if random_sequence_length:
            for i in range(batch_size):
                data[i] = get_random_length(sequence_length, average_sequence_length)

            for i in range(batch_size + 1):
                data[batch_size + i] = data[batch_size + i - 1] + data[i - 1] if i > 0 else 0
                data[2 * batch_size + 1 + i] = data[batch_size + i - 1] + data[i - 1] if i > 0 else 0
        else:
            for i in range(batch_size):
                data[i] = average_sequence_length
            for i in range(batch_size + 1):
                data[batch_size + i] = i * average_sequence_length
                data[2 * batch_size + 1 + i] = i * average_sequence_length

    if input_mask.type.tensor_type.elem_type == TensorProto.FLOAT:
        data = np.float32(data)
    elif input_mask.type.tensor_type.elem_type == TensorProto.INT64:
        data = np.int64(data)

    return data


def output_test_data(directory: str, inputs: Dict[str, np.ndarray]):
    """Output input tensors of test data to a directory

    Args:
        directory (str): path of a directory
        inputs (Dict[str, np.ndarray]): map from input name to value
    """
    if not os.path.exists(directory):
        try:
            os.mkdir(directory)
        except OSError:
            print("Creation of the directory %s failed" % directory)
        else:
            print("Successfully created the directory %s " % directory)
    else:
        print("Warning: directory %s existed. Files will be overwritten." % directory)

    index = 0
    for name, data in inputs.items():
        tensor = numpy_helper.from_array(data, name)
        with open(os.path.join(directory, f"input_{index}.pb"), "wb") as file:
            file.write(tensor.SerializeToString())
        index += 1


def fake_test_data(
    batch_size: int,
    sequence_length: int,
    test_cases: int,
    dictionary_size: int,
    verbose: bool,
    random_seed: int,
    input_ids: TensorProto,
    segment_ids: TensorProto,
    input_mask: TensorProto,
    average_sequence_length: int,
    random_sequence_length: bool,
    mask_type: int,
):
    """Create given number of input data for testing

    Args:
        batch_size (int): batch size
        sequence_length (int): sequence length
        test_cases (int): number of test cases
        dictionary_size (int): vocabulary size of dictionary for input_ids
        verbose (bool): print more information or not
        random_seed (int): random seed
        input_ids (TensorProto): graph input of input IDs
        segment_ids (TensorProto): graph input of token type IDs
        input_mask (TensorProto): graph input of attention mask
        average_sequence_length (int): average sequence length excluding paddings
        random_sequence_length (bool): whether use uniform random number for sequence length
        mask_type (int): mask type 1 is mask index; 2 is 2D mask; 3 is key len, cumulated lengths of query and key

    Returns:
        List[Dict[str,numpy.ndarray]]: list of test cases, where each test case is a dictionary
                                       with input name as key and a tensor as value
    """
    assert input_ids is not None

    np.random.seed(random_seed)
    random.seed(random_seed)

    all_inputs = []
    for _test_case in range(test_cases):
        input_1 = fake_input_ids_data(input_ids, batch_size, sequence_length, dictionary_size)
        inputs = {input_ids.name: input_1}

        if segment_ids:
            inputs[segment_ids.name] = fake_segment_ids_data(segment_ids, batch_size, sequence_length)

        if input_mask:
            inputs[input_mask.name] = fake_input_mask_data(
                input_mask, batch_size, sequence_length, average_sequence_length, random_sequence_length, mask_type
            )

        if verbose and len(all_inputs) == 0:
            print("Example inputs", inputs)
        all_inputs.append(inputs)
    return all_inputs


def generate_test_data(
    batch_size: int,
    sequence_length: int,
    test_cases: int,
    seed: int,
    verbose: bool,
    input_ids: TensorProto,
    segment_ids: TensorProto,
    input_mask: TensorProto,
    average_sequence_length: int,
    random_sequence_length: bool,
    mask_type: int,
):
    """Create given number of input data for testing

    Args:
        batch_size (int): batch size
        sequence_length (int): sequence length
        test_cases (int): number of test cases
        seed (int): random seed
        verbose (bool): print more information or not
        input_ids (TensorProto): graph input of input IDs
        segment_ids (TensorProto): graph input of token type IDs
        input_mask (TensorProto): graph input of attention mask
        average_sequence_length (int): average sequence length excluding paddings
        random_sequence_length (bool): whether use uniform random number for sequence length
        mask_type (int): mask type 1 is mask index; 2 is 2D mask; 3 is key len, cumulated lengths of query and key

    Returns:
        List[Dict[str,numpy.ndarray]]: list of test cases, where each test case is a dictionary
                                       with input name as key and a tensor as value
    """
    dictionary_size = 10000
    all_inputs = fake_test_data(
        batch_size,
        sequence_length,
        test_cases,
        dictionary_size,
        verbose,
        seed,
        input_ids,
        segment_ids,
        input_mask,
        average_sequence_length,
        random_sequence_length,
        mask_type,
    )
    if len(all_inputs) != test_cases:
        print("Failed to create test data for test.")
    return all_inputs


def get_graph_input_from_embed_node(onnx_model, embed_node, input_index):
    if input_index >= len(embed_node.input):
        return None

    input = embed_node.input[input_index]
    graph_input = onnx_model.find_graph_input(input)
    if graph_input is None:
        parent_node = onnx_model.get_parent(embed_node, input_index)
        if parent_node is not None and parent_node.op_type == "Cast":
            graph_input = onnx_model.find_graph_input(parent_node.input[0])
    return graph_input


def find_bert_inputs(
    onnx_model: OnnxModel,
    input_ids_name: Optional[str] = None,
    segment_ids_name: Optional[str] = None,
    input_mask_name: Optional[str] = None,
) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]:
    """Find graph inputs for BERT model.
    First, we will deduce inputs from EmbedLayerNormalization node.
    If not found, we will guess the meaning of graph inputs based on naming.

    Args:
        onnx_model (OnnxModel): onnx model object
        input_ids_name (str, optional): Name of graph input for input IDs. Defaults to None.
        segment_ids_name (str, optional): Name of graph input for segment IDs. Defaults to None.
        input_mask_name (str, optional): Name of graph input for attention mask. Defaults to None.

    Raises:
        ValueError: Graph does not have input named of input_ids_name or segment_ids_name or input_mask_name
        ValueError: Expected graph input number does not match with specified input_ids_name, segment_ids_name
                    and input_mask_name

    Returns:
        Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: input tensors of input_ids,
                                                                                 segment_ids and input_mask
    """

    graph_inputs = onnx_model.get_graph_inputs_excluding_initializers()

    if input_ids_name is not None:
        input_ids = onnx_model.find_graph_input(input_ids_name)
        if input_ids is None:
            raise ValueError(f"Graph does not have input named {input_ids_name}")

        segment_ids = None
        if segment_ids_name:
            segment_ids = onnx_model.find_graph_input(segment_ids_name)
            if segment_ids is None:
                raise ValueError(f"Graph does not have input named {segment_ids_name}")

        input_mask = None
        if input_mask_name:
            input_mask = onnx_model.find_graph_input(input_mask_name)
            if input_mask is None:
                raise ValueError(f"Graph does not have input named {input_mask_name}")

        expected_inputs = 1 + (1 if segment_ids else 0) + (1 if input_mask else 0)
        if len(graph_inputs) != expected_inputs:
            raise ValueError(f"Expect the graph to have {expected_inputs} inputs. Got {len(graph_inputs)}")

        return input_ids, segment_ids, input_mask

    if len(graph_inputs) != 3:
        raise ValueError(f"Expect the graph to have 3 inputs. Got {len(graph_inputs)}")

    embed_nodes = onnx_model.get_nodes_by_op_type("EmbedLayerNormalization")
    if len(embed_nodes) == 1:
        embed_node = embed_nodes[0]
        input_ids = get_graph_input_from_embed_node(onnx_model, embed_node, 0)
        segment_ids = get_graph_input_from_embed_node(onnx_model, embed_node, 1)
        input_mask = get_graph_input_from_embed_node(onnx_model, embed_node, 7)

        if input_mask is None:
            for input in graph_inputs:
                input_name_lower = input.name.lower()
                if "mask" in input_name_lower:
                    input_mask = input
        if input_mask is None:
            raise ValueError("Failed to find attention mask input")

        return input_ids, segment_ids, input_mask

    # Try guess the inputs based on naming.
    input_ids = None
    segment_ids = None
    input_mask = None
    for input in graph_inputs:
        input_name_lower = input.name.lower()
        if "mask" in input_name_lower:  # matches input with name like "attention_mask" or "input_mask"
            input_mask = input
        elif (
            "token" in input_name_lower or "segment" in input_name_lower
        ):  # matches input with name like "segment_ids" or "token_type_ids"
            segment_ids = input
        else:
            input_ids = input

    if input_ids and segment_ids and input_mask:
        return input_ids, segment_ids, input_mask

    raise ValueError("Fail to assign 3 inputs. You might try rename the graph inputs.")


def get_bert_inputs(
    onnx_file: str,
    input_ids_name: Optional[str] = None,
    segment_ids_name: Optional[str] = None,
    input_mask_name: Optional[str] = None,
) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]:
    """Find graph inputs for BERT model.
    First, we will deduce inputs from EmbedLayerNormalization node.
    If not found, we will guess the meaning of graph inputs based on naming.

    Args:
        onnx_file (str): onnx model path
        input_ids_name (str, optional): Name of graph input for input IDs. Defaults to None.
        segment_ids_name (str, optional): Name of graph input for segment IDs. Defaults to None.
        input_mask_name (str, optional): Name of graph input for attention mask. Defaults to None.

    Returns:
        Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: input tensors of input_ids,
                                                                                 segment_ids and input_mask
    """
    model = ModelProto()
    with open(onnx_file, "rb") as file:
        model.ParseFromString(file.read())

    onnx_model = OnnxModel(model)
    return find_bert_inputs(onnx_model, input_ids_name, segment_ids_name, input_mask_name)


def parse_arguments():
    parser = argparse.ArgumentParser()

    parser.add_argument("--model", required=True, type=str, help="bert onnx model path.")

    parser.add_argument(
        "--output_dir",
        required=False,
        type=str,
        default=None,
        help="output test data path. Default is current directory.",
    )

    parser.add_argument("--batch_size", required=False, type=int, default=1, help="batch size of input")

    parser.add_argument(
        "--sequence_length",
        required=False,
        type=int,
        default=128,
        help="maximum sequence length of input",
    )

    parser.add_argument(
        "--input_ids_name",
        required=False,
        type=str,
        default=None,
        help="input name for input ids",
    )
    parser.add_argument(
        "--segment_ids_name",
        required=False,
        type=str,
        default=None,
        help="input name for segment ids",
    )
    parser.add_argument(
        "--input_mask_name",
        required=False,
        type=str,
        default=None,
        help="input name for attention mask",
    )

    parser.add_argument(
        "--samples",
        required=False,
        type=int,
        default=1,
        help="number of test cases to be generated",
    )

    parser.add_argument("--seed", required=False, type=int, default=3, help="random seed")

    parser.add_argument(
        "--verbose",
        required=False,
        action="store_true",
        help="print verbose information",
    )
    parser.set_defaults(verbose=False)

    parser.add_argument(
        "--only_input_tensors",
        required=False,
        action="store_true",
        help="only save input tensors and no output tensors",
    )
    parser.set_defaults(only_input_tensors=False)

    parser.add_argument(
        "-a",
        "--average_sequence_length",
        default=-1,
        type=int,
        help="average sequence length excluding padding",
    )

    parser.add_argument(
        "-r",
        "--random_sequence_length",
        required=False,
        action="store_true",
        help="use uniform random instead of fixed sequence length",
    )
    parser.set_defaults(random_sequence_length=False)

    parser.add_argument(
        "--mask_type",
        required=False,
        type=int,
        default=2,
        help="mask type: (1: mask index, 2: raw 2D mask, 3: key lengths, cumulated lengths of query and key)",
    )

    args = parser.parse_args()
    return args


def create_and_save_test_data(
    model: str,
    output_dir: str,
    batch_size: int,
    sequence_length: int,
    test_cases: int,
    seed: int,
    verbose: bool,
    input_ids_name: Optional[str],
    segment_ids_name: Optional[str],
    input_mask_name: Optional[str],
    only_input_tensors: bool,
    average_sequence_length: int,
    random_sequence_length: bool,
    mask_type: int,
):
    """Create test data for a model, and save test data to a directory.

    Args:
        model (str): path of ONNX bert model
        output_dir (str): output directory
        batch_size (int): batch size
        sequence_length (int): sequence length
        test_cases (int): number of test cases
        seed (int): random seed
        verbose (bool): whether print more information
        input_ids_name (str): graph input name of input_ids
        segment_ids_name (str): graph input name of segment_ids
        input_mask_name (str): graph input name of input_mask
        only_input_tensors (bool): only save input tensors,
        average_sequence_length (int): average sequence length excluding paddings
        random_sequence_length (bool): whether use uniform random number for sequence length
        mask_type(int): mask type
    """
    input_ids, segment_ids, input_mask = get_bert_inputs(model, input_ids_name, segment_ids_name, input_mask_name)

    all_inputs = generate_test_data(
        batch_size,
        sequence_length,
        test_cases,
        seed,
        verbose,
        input_ids,
        segment_ids,
        input_mask,
        average_sequence_length,
        random_sequence_length,
        mask_type,
    )

    for i, inputs in enumerate(all_inputs):
        directory = os.path.join(output_dir, "test_data_set_" + str(i))
        output_test_data(directory, inputs)

    if only_input_tensors:
        return

    import onnxruntime

    providers = (
        ["CUDAExecutionProvider", "CPUExecutionProvider"]
        if "CUDAExecutionProvider" in onnxruntime.get_available_providers()
        else ["CPUExecutionProvider"]
    )
    session = onnxruntime.InferenceSession(model, providers=providers)
    output_names = [output.name for output in session.get_outputs()]

    for i, inputs in enumerate(all_inputs):
        directory = os.path.join(output_dir, "test_data_set_" + str(i))
        result = session.run(output_names, inputs)
        for i, output_name in enumerate(output_names):  # noqa: PLW2901
            tensor_result = numpy_helper.from_array(np.asarray(result[i]), output_name)
            with open(os.path.join(directory, f"output_{i}.pb"), "wb") as file:
                file.write(tensor_result.SerializeToString())


def main():
    args = parse_arguments()

    if args.average_sequence_length <= 0:
        args.average_sequence_length = args.sequence_length

    output_dir = args.output_dir
    if output_dir is None:
        # Default output directory is a sub-directory under the directory of model.
        p = Path(args.model)
        output_dir = os.path.join(p.parent, f"batch_{args.batch_size}_seq_{args.sequence_length}")

    if output_dir is not None:
        # create the output directory if not existed
        path = Path(output_dir)
        path.mkdir(parents=True, exist_ok=True)
    else:
        print("Directory existed. test data files will be overwritten.")

    create_and_save_test_data(
        args.model,
        output_dir,
        args.batch_size,
        args.sequence_length,
        args.samples,
        args.seed,
        args.verbose,
        args.input_ids_name,
        args.segment_ids_name,
        args.input_mask_name,
        args.only_input_tensors,
        args.average_sequence_length,
        args.random_sequence_length,
        args.mask_type,
    )

    print("Test data is saved to directory:", output_dir)


if __name__ == "__main__":
    main()
