# Copyright 2018 Xiaomi, Inc.  All rights reserved.
#
# 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.

import sys
import operator

import six

from mace.proto import mace_pb2

from mace.python.tools.converter_tool import base_converter as cvt
from mace.python.tools.converter_tool.base_converter import DeviceType
from mace.python.tools.converter_tool.base_converter import ConverterUtil
from mace.python.tools.converter_tool.base_converter import MaceKeyword
from mace.python.tools.convert_util import calculate_image_shape
from mace.python.tools.convert_util import OpenCLBufferType


def MemoryTypeToStr(mem_type):
    if mem_type == mace_pb2.CPU_BUFFER:
        return 'CPU_BUFFER'
    elif mem_type == mace_pb2.GPU_BUFFER:
        return 'GPU_BUFFER'
    elif mem_type == mace_pb2.GPU_IMAGE:
        return 'GPU_IMAGE'
    else:
        return 'UNKNOWN'


class MemoryBlock(object):
    def __init__(self, mem_type, block):
        self._mem_type = mem_type
        self._block = block

    @property
    def mem_type(self):
        return self._mem_type

    @property
    def block(self):
        return self._block


class MemoryOptimizer(object):
    def __init__(self, net_def):
        self.net_def = net_def
        self.idle_mem = set()
        self.op_mem = {}  # op_name->mem_id
        self.mem_block = {}  # mem_id->[size] or mem_id->[x, y]
        self.total_mem_count = 0
        self.input_ref_counter = {}
        self.mem_ref_counter = {}
        ocl_mem_type_arg = ConverterUtil.get_arg(
            net_def, MaceKeyword.mace_opencl_mem_type)
        self.cl_mem_type = ocl_mem_type_arg.i if ocl_mem_type_arg is not None \
            else None

        consumers = {}
        for op in net_def.op:
            if not self.op_need_optimize_memory(op):
                continue
            for ipt in op.input:
                if ipt not in consumers:
                    consumers[ipt] = []
                consumers[ipt].append(op)
        # only ref op's output tensor
        for op in net_def.op:
            if not self.op_need_optimize_memory(op):
                continue
            for output in op.output:
                tensor_name = output
                if tensor_name in consumers:
                    self.input_ref_counter[tensor_name] = \
                        len(consumers[tensor_name])
                else:
                    self.input_ref_counter[tensor_name] = 0

    def op_need_optimize_memory(self, op):
        return True

    def get_op_mem_block(self, op_type, output_shape, output_type):
        data_type_size = 4
        if output_type == mace_pb2.DT_UINT8:
            data_type_size = 1
        return MemoryBlock(mace_pb2.CPU_BUFFER,
                           [six.moves.reduce(operator.mul, output_shape, 1) *
                            data_type_size])

    def mem_size(self, memory_block):
        return memory_block.block[0]

    def sub_mem_block(self, mem_block1, mem_block2):
        return self.mem_size(mem_block1) - self.mem_size(mem_block2)

    def resize_mem_block(self, old_mem_block, op_mem_block):
        return MemoryBlock(
            old_mem_block.mem_type,
            [max(old_mem_block.block[0], op_mem_block.block[0])])

    def add_net_mem_blocks(self):
        for mem in self.mem_block:
            arena = self.net_def.mem_arena
            block = arena.mem_block.add()
            block.mem_id = mem
            block.device_type = DeviceType.CPU.value
            block.mem_type = self.mem_block[mem].mem_type
            block.x = self.mem_block[mem].block[0]
            block.y = 1

    def get_total_origin_mem_size(self):
        origin_mem_size = 0
        for op in self.net_def.op:
            if not self.op_need_optimize_memory(op):
                continue
            origin_mem_size += six.moves.reduce(operator.mul,
                                                op.output_shape[0].dims,
                                                1)
        return origin_mem_size

    def get_total_optimized_mem_size(self):
        optimized_mem_size = 0
        for mem in self.mem_block:
            print(mem, MemoryTypeToStr(self.mem_block[mem].mem_type),
                  self.mem_block[mem].block)
            optimized_mem_size += self.mem_size(self.mem_block[mem])
        return optimized_mem_size

    @staticmethod
    def is_memory_reuse_op(op):
        return op.type == 'Reshape' or op.type == 'Identity' \
               or op.type == 'Squeeze' or op.type == 'ExpandDims'

    def optimize(self):
        for op in self.net_def.op:
            if not self.op_need_optimize_memory(op):
                continue
            if not op.output_shape:
                six.print_("WARNING: There is no output shape information to "
                           "do memory optimization. %s (%s)" %
                           (op.name, op.type), file=sys.stderr)
                return
            if len(op.output_shape) != len(op.output):
                six.print_('WARNING: the number of output shape is '
                           'not equal to the number of output.',
                           file=sys.stderr)
                return
            for i in range(len(op.output)):
                if self.is_memory_reuse_op(op):
                    # make these ops reuse memory of input tensor
                    mem_id = self.op_mem.get(op.input[0], -1)
                else:
                    output_type = mace_pb2.DT_FLOAT
                    for arg in op.arg:
                        if arg.name == 'T':
                            output_type = arg.i
                    if len(op.output_type) > i:
                        output_type = op.output_type[i]
                    op_mem_block = self.get_op_mem_block(
                        op.type,
                        op.output_shape[i].dims,
                        output_type)
                    mem_id = -1
                    if len(self.idle_mem) > 0:
                        best_mem_add_size = six.MAXSIZE
                        best_mem_waste_size = six.MAXSIZE
                        for mid in self.idle_mem:
                            old_mem_block = self.mem_block[mid]
                            if old_mem_block.mem_type != op_mem_block.mem_type:
                                continue
                            new_mem_block = self.resize_mem_block(
                                old_mem_block, op_mem_block)
                            add_mem_size = self.sub_mem_block(new_mem_block,
                                                              old_mem_block)
                            waste_mem_size = self.sub_mem_block(new_mem_block,
                                                                op_mem_block)

                            # minimize add_mem_size; if best_mem_add_size is 0,
                            # then minimize waste_mem_size
                            if (best_mem_add_size > 0 and
                                add_mem_size < best_mem_add_size) \
                                    or (best_mem_add_size == 0 and
                                        waste_mem_size < best_mem_waste_size):
                                best_mem_id = mid
                                best_mem_add_size = add_mem_size
                                best_mem_waste_size = waste_mem_size
                                best_mem_block = new_mem_block

                        # if add mem size < op mem size, then reuse it
                        if best_mem_add_size <= self.mem_size(op_mem_block):
                            self.mem_block[best_mem_id] = best_mem_block
                            mem_id = best_mem_id
                            self.idle_mem.remove(mem_id)

                    if mem_id == -1:
                        mem_id = self.total_mem_count
                        self.total_mem_count += 1
                        self.mem_block[mem_id] = op_mem_block

                if mem_id != -1:
                    op.mem_id.extend([mem_id])
                    self.op_mem[op.output[i]] = mem_id
                    if mem_id not in self.mem_ref_counter:
                        self.mem_ref_counter[mem_id] = 1
                    else:
                        self.mem_ref_counter[mem_id] += 1

            # de-ref input tensor mem
            for idx in six.moves.range(len(op.input)):
                ipt = op.input[idx]
                if ipt in self.input_ref_counter:
                    self.input_ref_counter[ipt] -= 1
                    if self.input_ref_counter[ipt] == 0 \
                            and ipt in self.op_mem:
                        mem_id = self.op_mem[ipt]
                        self.mem_ref_counter[mem_id] -= 1
                        if self.mem_ref_counter[mem_id] == 0:
                            self.idle_mem.add(self.op_mem[ipt])
                    elif self.input_ref_counter[ipt] < 0:
                        raise Exception('ref count is less than 0')

        self.add_net_mem_blocks()

        print("total op: %d" % len(self.net_def.op))
        print("origin mem: %d, optimized mem: %d" % (
            self.get_total_origin_mem_size(),
            self.get_total_optimized_mem_size()))


class GPUMemoryOptimizer(MemoryOptimizer):
    def op_need_optimize_memory(self, op):
        if op.type == MaceKeyword.mace_buffer_transform:
            for arg in op.arg:
                if arg.name == 'mode' and arg.i == 0:
                    return False
        return op.type != MaceKeyword.mace_buffer_inverse_transform

    def get_op_image_mem_block(self, op_type, output_shape):
        if op_type == 'WinogradTransform' or op_type == 'MatMul':
            buffer_shape = list(output_shape) + [1]
            mem_block = MemoryBlock(
                mace_pb2.GPU_IMAGE,
                calculate_image_shape(OpenCLBufferType.IN_OUT_HEIGHT,
                                      buffer_shape))
        elif op_type in ['Shape',
                         'InferConv2dShape',
                         'StridedSlice',
                         'Stack',
                         'ScalarMath']:
            if len(output_shape) == 1:
                mem_block = MemoryBlock(mace_pb2.CPU_BUFFER,
                                        [output_shape[0], 1])
            elif len(output_shape) == 0:
                mem_block = MemoryBlock(mace_pb2.CPU_BUFFER,
                                        [1, 1])
            else:
                raise Exception('%s output shape dim size is not 0 or 1.' %
                                op_type)
        else:
            if len(output_shape) == 2:  # only support fc/softmax
                buffer_shape = [output_shape[0], output_shape[1]]
            elif len(output_shape) == 4:
                buffer_shape = output_shape
            else:
                raise Exception('%s output shape dim size is not 2 or 4.' %
                                op_type)
            mem_block = MemoryBlock(
                mace_pb2.GPU_IMAGE,
                calculate_image_shape(OpenCLBufferType.IN_OUT_CHANNEL,
                                      buffer_shape))
        return mem_block

    def get_op_buffer_mem_block(self, output_shape):
        return MemoryBlock(mace_pb2.GPU_BUFFER,
                           [reduce(operator.mul, output_shape, 1), 1])

    def get_op_mem_block(self, op_type, output_shape, output_type):
        if self.cl_mem_type == mace_pb2.GPU_IMAGE:
            return self.get_op_image_mem_block(op_type, output_shape)
        else:
            return self.get_op_buffer_mem_block(output_shape)

    def mem_size(self, memory_block):
        if memory_block.mem_type == mace_pb2.GPU_IMAGE:
            return memory_block.block[0] * memory_block.block[1] * 4
        else:
            return memory_block.block[0]

    def resize_mem_block(self, old_mem_block, op_mem_block):
        resize_mem_block = MemoryBlock(
            old_mem_block.mem_type,
            [
                max(old_mem_block.block[0], op_mem_block.block[0]),
                max(old_mem_block.block[1], op_mem_block.block[1])
            ])

        return resize_mem_block

    def add_net_mem_blocks(self):
        max_image_size_x = 0
        max_image_size_y = 0
        for mem in self.mem_block:
            arena = self.net_def.mem_arena
            block = arena.mem_block.add()
            block.mem_id = mem
            block.device_type = DeviceType.GPU.value
            block.mem_type = self.mem_block[mem].mem_type
            block.x = self.mem_block[mem].block[0]
            block.y = self.mem_block[mem].block[1]
            if self.mem_block[mem].mem_type == mace_pb2.GPU_IMAGE:
                max_image_size_x = max(max_image_size_x, block.x)
                max_image_size_y = max(max_image_size_y, block.y)

        if self.cl_mem_type == mace_pb2.GPU_IMAGE:
            # Update OpenCL max image size
            net_ocl_max_img_size_arg = None
            for arg in self.net_def.arg:
                if arg.name == cvt.MaceKeyword.mace_opencl_max_image_size:
                    net_ocl_max_img_size_arg = arg
                    max_image_size_x = max(arg.ints[0], max_image_size_x)
                    max_image_size_y = max(arg.ints[1], max_image_size_y)
                    break
            if net_ocl_max_img_size_arg is None:
                net_ocl_max_img_size_arg = self.net_def.arg.add()
                net_ocl_max_img_size_arg.name = \
                    cvt.MaceKeyword.mace_opencl_max_image_size

            net_ocl_max_img_size_arg.ints[:] = [max_image_size_x,
                                                max_image_size_y]


def optimize_gpu_memory(net_def):
    mem_optimizer = GPUMemoryOptimizer(net_def)
    mem_optimizer.optimize()


def optimize_cpu_memory(net_def):
    mem_optimizer = MemoryOptimizer(net_def)
    mem_optimizer.optimize()
