# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved.
#
# SPDX-License-Identifier: Apache-2.0
#
# 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
#
# 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.


# Description:
# Neural network graph classes and enums.
# Pass - A packed pass containing one or more Operations.
# CascadedPass - A scheduled pass containing one or more Passes, as well as a scheduling strategy and block
#                configurations.
# Subgraph - Holds a neural network subgraph, pointing at Tensors, Operations, Passes, and CascadedPasses.
# Graph - A full neural network graph with one or more Subgraphs.

import enum
from .data_type import BaseType, DataType
from .tensor import MemArea, TensorPurpose, TensorSubPurpose, TensorFormat, Tensor
from .operation import Operation, NpuBlockType


class PassPlacement(enum.Enum):
    Unknown = 0
    Cpu = 1
    Npu = 2
    MemoryOnly = 3
    StartupInit = 4


class TensorAllocator(enum.Enum):
    LinearAlloc = 1
    Greedy = 2

    def __str__(self):
        return self.name


class Pass:
    def __init__(self, name, placement, is_element_wise, npu_block_type):
        self.inputs = []
        self.intermediates = []
        self.outputs = []
        self.ops = []
        self.primary_op = None
        self.ifm_tensor = None
        self.ifm2_tensor = None
        self.ofm_tensor = None
        self.weight_tensor = None
        self.scale_tensor = None
        self.name = name
        self.cascade = None
        self.placement = placement

        # TODO: rename is_element_wise because it is not the same as an ElementWise operator. It is used by the tensor
        # allocation and requires that the OFM and IFM has the exact same address. Essentially complete overlap.
        self.is_element_wise = is_element_wise
        self.npu_block_type = npu_block_type
        self.block_config = None  # will be filled in by scheduler
        self.shared_buffer = None  # will be filled in by scheduler

        self.predecessors = []
        self.successors = []

    def __str__(self):
        return "<nng.Pass '%s', %s, ops=%s>" % (self.name, self.placement, [op.type for op in self.ops])

    __repr__ = __str__

    def get_primary_op_ifm_weights(self):
        if not self.primary_op:
            return None, None
        return self.primary_op.get_ifm_ifm2_weights_ofm()[::2]

    def get_primary_op_ifm_ifm2_weights_ofm(self):
        if not self.primary_op:
            return None, None, None, None
        return self.primary_op.get_ifm_ifm2_weights_ofm()

    def get_primary_op_ifm_weights_biases_ofm(self):
        if not self.primary_op:
            return None, None, None, None
        return self.primary_op.get_ifm_weights_biases_ofm()


class SchedulingStrategy(enum.Enum):
    Unknown = -1
    IfmStream = 0
    WeightStream = 1


class SchedulerRewrite(enum.Enum):
    Nop = 0
    ChangeTensorSubPurpose = 1


class CascadedPass:
    def __init__(self, name, strat, inputs, intermediates, outputs, passes, placement, is_element_wise):
        self.name = name
        self.strategy = strat
        self.inputs = inputs
        self.intermediates = intermediates
        self.outputs = outputs
        self.passes = passes
        self.placement = placement
        self.is_element_wise = is_element_wise

        self.predecessors = []
        self.successors = []

    def __str__(self):
        return "<nng.CascadedPass strategy=%s x %s '%s',  passes=%s, block_configs=%s>" % (
            self.strategy,
            len(self.passes),
            self.name,
            [ps.name for ps in self.passes],
            [ps.block_config for ps in self.passes],
        )

    __repr__ = __str__


class Subgraph:
    def __init__(self, name="<unnamed>", placement=PassPlacement.Cpu):
        self.output_tensors = []
        self.input_tensors = []
        self.original_inputs = []  # Preserve the original input order
        self.passes = []
        self.cascaded_passes = []
        self.name = name
        self.high_level_command_stream = []
        self.placement = placement
        self.command_stream_tensor = None
        self.flash_tensor = None

        self.memory_used = {}

    def __str__(self):
        return "<nng.Subgraph '%s',  n_passes=%d, n_cascaded_passes=%d>" % (
            self.name,
            len(self.passes),
            len(self.cascaded_passes),
        )

    __repr__ = __str__

    def update_consumers(self):
        visit_op_set = set()
        visit_tensor_set = set()
        self.input_tensors = []

        print_visit = False

        def visit_op(op):
            if op in visit_op_set:
                return

            visit_op_set.add(op)
            for inp in op.inputs:
                if print_visit:
                    print(inp, "adding consumer", op)
                visit_tensor(inp)
                inp.consumer_list.append(op)

            if op.type in set(("Placeholder", "SubgraphInput")):
                assert len(op.outputs) == 1
                self.input_tensors.append(op.outputs[0])

            for out in op.outputs:
                if out not in visit_tensor_set:
                    out.consumer_list = []  # reset unvisited output, just in case

        def visit_tensor(tens):
            if tens in visit_tensor_set:
                return
            visit_tensor_set.add(tens)
            tens.consumer_list = []
            for op in tens.ops:
                visit_op(op)

        for ps in self.passes:
            for tens in ps.outputs + ps.inputs:
                tens.consumer_list = []  # reset unvisited tensors to start with

        for tens in self.output_tensors:
            visit_tensor(tens)
            tens.consumer_list.append(None)  # special op to indicate that the graph consumes the result

        print_visit = True
        for ps in self.passes:
            for op in ps.ops:
                visit_op(op)
            for tens in ps.inputs:
                visit_tensor(tens)

    def build_pass_links(self):
        for idx, ps in enumerate(self.passes):
            ps.time = 2 * idx
            ps.predecessors = []
            ps.successors = []

        for ps in self.passes:
            for tens in ps.inputs:
                for op in tens.ops:
                    pred_pass = op.scheduled_pass
                    assert pred_pass.time < ps.time
                    if ps not in pred_pass.successors:
                        pred_pass.successors.append(ps)

                    if pred_pass not in ps.predecessors:
                        ps.predecessors.append(pred_pass)

                    assert tens in pred_pass.outputs

    def build_pass_dag_predecessors(self):
        for ps in self.passes:
            ps.dag_predecessors = []

        class State(enum.Enum):
            NotVisited = 0
            BeingVisited = 1
            Visited = 2

        pass_visit_dict = {}

        def visit_pass(ps):
            state = pass_visit_dict.get(ps, State.NotVisited)
            if state == State.Visited:
                return True
            elif state == State.BeingVisited:
                return False  # this is a loop, need to remove this link
            elif state == State.NotVisited:
                pass_visit_dict[ps] = State.BeingVisited

                ps.dag_predecessors = []
                for pred in ps.predecessors:
                    if visit_pass(pred):
                        ps.dag_predecessors.append(pred)

                pass_visit_dict[ps] = State.Visited
                return True

        for ps in self.passes:
            if not ps.successors:
                visit_pass(ps)

    def build_cascaded_pass_links(self):
        for cps in self.cascaded_passes:
            cps.predecessors = []
            cps.successors = []

        for cps in self.cascaded_passes:
            for tens in cps.inputs:
                for op in tens.ops:
                    pred_cpass = op.scheduled_pass.cascade
                    if cps not in pred_cpass.successors:
                        pred_cpass.successors.append(cps)

                    if pred_cpass not in cps.predecessors:
                        cps.predecessors.append(pred_cpass)

                    assert tens in pred_cpass.outputs

    def refresh_after_modification(self):
        self.update_consumers()

    def prune_startup_init_pass(self):
        assert len(self.passes) >= 1
        ps = self.passes[0]
        assert ps.placement == PassPlacement.StartupInit

        ps.outputs = [out_tens for out_tens in ps.outputs if len(out_tens.consumers()) > 0]
        ps.ops = [op for op in ps.ops if op.outputs[0] in ps.outputs]

    def get_all_ops(self):
        all_ops = []
        visit_op_set = set()
        visit_tensor_set = set()

        def visit_op(op):
            if op in visit_op_set:
                return
            visit_op_set.add(op)
            for inp in op.inputs:
                visit_tensor(inp)

            all_ops.append(op)

        def visit_tensor(tens):
            if tens in visit_tensor_set:
                return
            visit_tensor_set.add(tens)
            for op in tens.ops:
                visit_op(op)

        for tens in self.output_tensors:
            visit_tensor(tens)

        return all_ops

    def print_operators(self):
        all_ops = self.get_all_ops()
        unique_ops = []
        print("print_operators")
        for op in all_ops:
            if op.type in set(("Const", "Identity", "Placeholder")):
                continue

            attrs = op.attrs
            if (
                op.type == "Conv2D"
                or op.type == "DepthwiseConv2dNative"
                or op.type == "Conv2DBiasAct"
                or op.type == "DepthwiseConv2dBiasAct"
            ):
                kshape = op.inputs[1].shape
                attrs["kshape"] = [kshape[0], kshape[1]]
            attrs["type"] = op.type
            attrs.pop("use_cudnn_on_gpu", None)
            if attrs not in unique_ops:
                unique_ops.append(attrs)
                # print attributes in human readable format
                a = attrs.copy()
                s = a.pop("type")
                data_format = a.pop("data_format", None)
                if data_format and data_format != b"NHWC":
                    s += " " + str(data_format)
                t = a.pop("T", None)
                if t:
                    s += " " + str(t)[9:-2]
                srct = a.pop("SrcT", None)
                if srct:
                    s += " " + str(srct)[9:-2]
                dstt = a.pop("DstT", None)
                if dstt:
                    s += "->" + str(dstt)[9:-2]
                print(s + " " + str(a))

    def print_graph(self):
        all_ops = self.get_all_ops()
        for idx, op in enumerate(all_ops):
            print(idx, op.type, op.name)

    def print_graph_with_tensors(self):
        all_ops = self.get_all_ops()
        for idx, op in enumerate(all_ops):
            print(idx, op.type, op.name)
            for idx, tens in enumerate(op.inputs):
                print("    Input  %02d %20s %20s %s" % (idx, tens.purpose.name, tens.mem_area.name, tens))
            for idx, tens in enumerate(op.outputs):
                print("    Output %02d %20s %20s %s" % (idx, tens.purpose.name, tens.mem_area.name, tens))
            print()

    def print_graph_with_tensor_quantization(self):
        all_ops = self.get_all_ops()
        for idx, op in enumerate(all_ops):
            print(idx, op.type, op.name)
            for idx, tens in enumerate(op.inputs):
                q = tens.quantization
                if q is None:
                    print("    Input  %02d %10s NO QUANTIZATION INFO %s" % (idx, tens.dtype, tens.name))
                else:
                    print(
                        "    Input  %02d %10s min=%s max=%s scale=%s zero_point=%s %s"
                        % (idx, tens.dtype, q.min, q.max, q.scale_f32, q.zero_point, tens.name)
                    )
            for idx, tens in enumerate(op.outputs):
                q = tens.quantization
                if q is None:
                    print("    Output %02d %10s NO QUANTIZATION INFO %s" % (idx, tens.dtype, tens.name))
                else:
                    print(
                        "    Output %02d %10s min=%s max=%s scale=%s zero_point=%s %s"
                        % (idx, tens.dtype, q.min, q.max, q.scale_f32, q.zero_point, tens.name)
                    )
            print()

    def print_passes(self):
        for idx, ps in enumerate(self.passes):
            print("%03d %s" % (idx * 2, ps))

    def print_passes_with_tensors(self):
        for idx, ps in enumerate(self.passes):
            print("%3d %s" % (idx * 2, ps))
            for idx, tens in enumerate(ps.inputs):
                print(
                    "    Input        %2d %-15s %-15s %-15s     %s"
                    % (idx, tens.purpose.name, tens.mem_area.name, tens.format.name, tens.name)
                )
            for idx, tens in enumerate(ps.intermediates):
                print(
                    "    Intermediate %2d %-15s %-15s %-15s     %s"
                    % (idx, tens.purpose.name, tens.mem_area.name, tens.format.name, tens.name)
                )
            for idx, tens in enumerate(ps.outputs):
                print(
                    "    Output       %2d %-15s %-15s %-15s     %s"
                    % (idx, tens.purpose.name, tens.mem_area.name, tens.format.name, tens.name)
                )
            print()

    def print_cascaded_passes(self):
        for idx, ps in enumerate(self.cascaded_passes):
            print("%3d %s SRAM used %.1f KB" % (idx * 2, ps, ps.sram_used / 1024))

    def print_cascaded_passes_with_tensors(self):
        for idx, ps in enumerate(self.cascaded_passes):
            print("%3d %s SRAM used %.1f KB" % (idx * 2, ps, ps.sram_used / 1024))
            for idx, tens in enumerate(ps.inputs):
                print(
                    "    Input        %2d %-15s %-15s %-15s     %s"
                    % (idx, tens.purpose.name, tens.mem_area.name, tens.format.name, tens.name)
                )
            for idx, tens in enumerate(ps.intermediates):
                print(
                    "    Intermediate %2d %-15s %-15s %-15s     %s"
                    % (idx, tens.purpose.name, tens.mem_area.name, tens.format.name, tens.name)
                )
            for idx, tens in enumerate(ps.outputs):
                print(
                    "    Output       %2d %-15s %-15s %-15s     %s"
                    % (idx, tens.purpose.name, tens.mem_area.name, tens.format.name, tens.name)
                )
            print()

    def print_cascaded_passes_with_tensor_sizes(self):
        for idx, ps in enumerate(self.cascaded_passes):
            print("%3d %s SRAM used %.1f KB" % (idx * 2, ps, ps.sram_used / 1024))
            for idx, tens in enumerate(ps.inputs):
                print(
                    "    Input        %2d %7.1f KB %-24s %-15s %-15s %-20s  %s"
                    % (
                        idx,
                        tens.storage_size() / 1024,
                        tens.storage_shape,
                        tens.mem_area.name,
                        tens.purpose.name,
                        tens.format.name,
                        tens.name,
                    )
                )
            for idx, tens in enumerate(ps.intermediates):
                print(
                    "    Intermediate %2d %7.1f KB %-24s %-15s %-15s %-20s  %s"
                    % (
                        idx,
                        tens.storage_size() / 1024,
                        tens.storage_shape,
                        tens.mem_area.name,
                        tens.purpose.name,
                        tens.format.name,
                        tens.name,
                    )
                )
            for idx, tens in enumerate(ps.outputs):
                print(
                    "    Output       %2d %7.1f KB %-24s %-15s %-15s %-20s  %s"
                    % (
                        idx,
                        tens.storage_size() / 1024,
                        tens.storage_shape,
                        tens.mem_area.name,
                        tens.purpose.name,
                        tens.format.name,
                        tens.name,
                    )
                )
            print()

    def print_high_level_command_stream(self):
        for idx, cmd in enumerate(self.high_level_command_stream):
            print("%3d %s" % (idx, cmd))


class Graph:
    def __init__(self, name="<unnamed>", batch_size=1):
        self.name = name
        self.batch_size = batch_size
        self.subgraphs = []

        self.memory_used = {}
        self.bits_per_element = {}
        self.total_size = {}
        self.total_elements = {}

    def get_root_subgraph(self):
        return self.subgraphs[0]

    def prune_startup_init_pass(self):
        for sg in self.subgraphs:
            sg.prune_startup_init_pass()

    def update_consumers(self):
        for sg in self.subgraphs:
            sg.update_consumers()

    def refresh_after_modification(self):
        for sg in self.subgraphs:
            sg.refresh_after_modification()

    def print_operators(self):
        for sg in self.subgraphs:
            sg.print_operators()

    def print_graph(self):
        for sg in self.subgraphs:
            sg.print_graph()

    def print_graph_with_tensors(self):
        for sg in self.subgraphs:
            sg.print_graph_with_tensors()

    def print_graph_with_tensor_quantization(self):
        for sg in self.subgraphs:
            sg.print_graph_with_tensor_quantization()

    def print_passes(self):
        for sg in self.subgraphs:
            sg.print_passes()

    def print_passes_with_tensors(self):
        for sg in self.subgraphs:
            sg.print_passes_with_tensors()

    def print_cascaded_passes(self):
        for sg in self.subgraphs:
            sg.print_cascaded_passes()

    def print_cascaded_passes_with_tensors(self):
        for sg in self.subgraphs:
            sg.print_cascaded_passes_with_tensors()

    def print_cascaded_passes_with_tensor_sizes(self):
        for sg in self.subgraphs:
            sg.print_cascaded_passes_with_tensor_sizes()

    def print_high_level_command_stream(self):
        for sg in self.subgraphs:
            sg.print_high_level_command_stream()