#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2018-2025 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import logging as log
import sys
from functools import reduce

import numpy as np
import openvino as ov
from openvino import op, opset1, opset8

from data import digits


def create_model(model_path: str) -> ov.Model:
    """Create a model on the fly from the source code using openvino."""

    def shape_and_length(shape: list) -> tuple[list, int]:
        length = reduce(lambda x, y: x * y, shape)
        return shape, length

    weights = np.fromfile(model_path, dtype=np.float32)
    weights_offset = 0
    padding_begin = padding_end = [0, 0]

    # input
    input_shape = [64, 1, 28, 28]
    param_node = op.Parameter(ov.Type.f32, ov.Shape(input_shape))

    # convolution 1
    conv_1_kernel_shape, conv_1_kernel_length = shape_and_length([20, 1, 5, 5])
    conv_1_kernel = op.Constant(ov.Type.f32, ov.Shape(conv_1_kernel_shape), weights[0:conv_1_kernel_length].tolist())
    weights_offset += conv_1_kernel_length
    conv_1_node = opset8.convolution(param_node, conv_1_kernel, [1, 1], padding_begin, padding_end, [1, 1])

    # add 1
    add_1_kernel_shape, add_1_kernel_length = shape_and_length([1, 20, 1, 1])
    add_1_kernel = op.Constant(ov.Type.f32, ov.Shape(add_1_kernel_shape),
                               weights[weights_offset : weights_offset + add_1_kernel_length])
    weights_offset += add_1_kernel_length
    add_1_node = opset8.add(conv_1_node, add_1_kernel)

    # maxpool 1
    maxpool_1_node = opset1.max_pool(add_1_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')

    # convolution 2
    conv_2_kernel_shape, conv_2_kernel_length = shape_and_length([50, 20, 5, 5])
    conv_2_kernel = op.Constant(ov.Type.f32, ov.Shape(conv_2_kernel_shape),
                                weights[weights_offset : weights_offset + conv_2_kernel_length],
                                )
    weights_offset += conv_2_kernel_length
    conv_2_node = opset8.convolution(maxpool_1_node, conv_2_kernel, [1, 1], padding_begin, padding_end, [1, 1])

    # add 2
    add_2_kernel_shape, add_2_kernel_length = shape_and_length([1, 50, 1, 1])
    add_2_kernel = op.Constant(ov.Type.f32, ov.Shape(add_2_kernel_shape),
                               weights[weights_offset : weights_offset + add_2_kernel_length],
                               )
    weights_offset += add_2_kernel_length
    add_2_node = opset8.add(conv_2_node, add_2_kernel)

    # maxpool 2
    maxpool_2_node = opset1.max_pool(add_2_node, [2, 2], padding_begin, padding_end, [2, 2], 'ceil')

    # reshape 1
    reshape_1_dims, reshape_1_length = shape_and_length([2])
    # workaround to get int64 weights from float32 ndarray w/o unnecessary copying
    dtype_weights = np.frombuffer(
        weights[weights_offset : weights_offset + 2 * reshape_1_length],
        dtype=np.int64,
    )
    reshape_1_kernel = op.Constant(ov.Type.i64, ov.Shape(list(dtype_weights.shape)), dtype_weights)
    weights_offset += 2 * reshape_1_length
    reshape_1_node = opset8.reshape(maxpool_2_node, reshape_1_kernel, True)

    # matmul 1
    matmul_1_kernel_shape, matmul_1_kernel_length = shape_and_length([500, 800])
    matmul_1_kernel = op.Constant(ov.Type.f32, ov.Shape(matmul_1_kernel_shape),
                                  weights[weights_offset : weights_offset + matmul_1_kernel_length],
                                  )
    weights_offset += matmul_1_kernel_length
    matmul_1_node = opset8.matmul(reshape_1_node, matmul_1_kernel, False, True)

    # add 3
    add_3_kernel_shape, add_3_kernel_length = shape_and_length([1, 500])
    add_3_kernel = op.Constant(ov.Type.f32, ov.Shape(add_3_kernel_shape),
                               weights[weights_offset : weights_offset + add_3_kernel_length],
                               )
    weights_offset += add_3_kernel_length
    add_3_node = opset8.add(matmul_1_node, add_3_kernel)

    # ReLU
    relu_node = opset8.relu(add_3_node)

    # reshape 2
    reshape_2_kernel = op.Constant(ov.Type.i64, ov.Shape(list(dtype_weights.shape)), dtype_weights)
    reshape_2_node = opset8.reshape(relu_node, reshape_2_kernel, True)

    # matmul 2
    matmul_2_kernel_shape, matmul_2_kernel_length = shape_and_length([10, 500])
    matmul_2_kernel = op.Constant(ov.Type.f32, ov.Shape(matmul_2_kernel_shape),
                                  weights[weights_offset : weights_offset + matmul_2_kernel_length],
                                  )
    weights_offset += matmul_2_kernel_length
    matmul_2_node = opset8.matmul(reshape_2_node, matmul_2_kernel, False, True)

    # add 4
    add_4_kernel_shape, add_4_kernel_length = shape_and_length([1, 10])
    add_4_kernel = op.Constant(ov.Type.f32, ov.Shape(add_4_kernel_shape),
                               weights[weights_offset : weights_offset + add_4_kernel_length],
                               )
    weights_offset += add_4_kernel_length
    add_4_node = opset8.add(matmul_2_node, add_4_kernel)

    # softmax
    softmax_axis = 1
    softmax_node = opset8.softmax(add_4_node, softmax_axis)

    return ov.Model(softmax_node, [param_node], 'lenet')


def main():
    log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
    # Parsing and validation of input arguments
    if len(sys.argv) != 3:
        log.info(f'Usage: {sys.argv[0]} <path_to_model> <device_name>')
        return 1

    model_path = sys.argv[1]
    device_name = sys.argv[2]
    labels = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
    number_top = 1
    # ---------------------------Step 1. Initialize OpenVINO Runtime Core--------------------------------------------------
    log.info('Creating OpenVINO Runtime Core')

    # ---------------------------Step 2. Read a model in OpenVINO Intermediate Representation------------------------------
    log.info(f'Loading the model using openvino with weights from {model_path}')
    model = create_model(model_path)
    # ---------------------------Step 3. Apply preprocessing----------------------------------------------------------
    # Get names of input and output blobs
    ppp = ov.preprocess.PrePostProcessor(model)
    # 1) Set input tensor information:
    # - input() provides information about a single model input
    # - precision of tensor is supposed to be 'u8'
    # - layout of data is 'NHWC'
    ppp.input().tensor() \
        .set_element_type(ov.Type.u8) \
        .set_layout(ov.Layout('NHWC'))  # noqa: N400

    # 2) Here we suppose model has 'NCHW' layout for input
    ppp.input().model().set_layout(ov.Layout('NCHW'))
    # 3) Set output tensor information:
    # - precision of tensor is supposed to be 'f32'
    ppp.output().tensor().set_element_type(ov.Type.f32)

    # 4) Apply preprocessing modifying the original 'model'
    model = ppp.build()

    # Set a batch size equal to number of input images
    ov.set_batch(model, digits.shape[0])
    # ---------------------------Step 4. Loading model to the device-------------------------------------------------------
    log.info('Loading the model to the plugin')
    core = ov.Core()
    compiled_model = core.compile_model(model, device_name)

    # ---------------------------Step 5. Prepare input---------------------------------------------------------------------
    n, c, h, w = model.input().shape
    input_data = np.ndarray(shape=(n, c, h, w))
    for i in range(n):
        image = digits[i].reshape(28, 28)
        image = image[:, :, np.newaxis]
        input_data[i] = image

    # ---------------------------Step 6. Do inference----------------------------------------------------------------------
    log.info('Starting inference in synchronous mode')
    results = compiled_model.infer_new_request({0: input_data})

    # ---------------------------Step 7. Process output--------------------------------------------------------------------
    predictions = next(iter(results.values()))

    log.info(f'Top {number_top} results: ')
    for i in range(n):
        probs = predictions[i]
        # Get an array of number_top class IDs in descending order of probability
        top_n_idexes = np.argsort(probs)[-number_top :][::-1]

        header = 'classid probability'
        header = header + ' label' if labels else header

        log.info(f'Image {i}')
        log.info('')
        log.info(header)
        log.info('-' * len(header))

        for class_id in top_n_idexes:
            probability_indent = ' ' * (len('classid') - len(str(class_id)) + 1)
            label_indent = ' ' * (len('probability') - 8) if labels else ''
            label = labels[class_id] if labels else ''
            log.info(f'{class_id}{probability_indent}{probs[class_id]:.7f}{label_indent}{label}')
        log.info('')

    # ----------------------------------------------------------------------------------------------------------------------
    log.info('This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool\n')
    return 0


if __name__ == '__main__':
    sys.exit(main())
