# Copyright (C) 2021 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:
# Groups Operators in a schedule together to form Cascades.
from .numeric_util import round_up
from .operation import NpuBlockType
from .shape4d import Shape4D

non_cascadable_blocks = (
    NpuBlockType.Default,
    NpuBlockType.VectorProduct,
    NpuBlockType.ElementWise,
    NpuBlockType.ReduceSum,
)


class CascadeInfo:
    """Contains metadata about a cascade"""

    def __init__(self, start, end, buffers, mem_usage: int):
        self.start = start
        self.end = end
        self.buffers = buffers
        self.mem_usage = mem_usage


class BufferMap:
    """Caches the buffers seen"""

    def __init__(self):
        self.buffer_map = {}

    def get_buffer(self, producer, consumer, cost):
        assert producer or consumer
        key = (producer, consumer)
        if key not in self.buffer_map:
            # No cached buffer between these two SchedulerOperations
            if consumer is None:
                # There are either no consumers or multiple consumers - FeatureMap needs to be stored in full
                buffer_shape = producer.ofm.shape
                buffer_size = producer.ofm_size_in_bytes()
            elif producer is None:
                # First Op in subgraph or cascade - FeatureMap needs to be stored in full
                buffer_shape = consumer.ifm.shape
                buffer_size = consumer.ifm_size_in_bytes()
            elif producer.requires_full_ofm or consumer.requires_full_ifm:
                # FeatureMap needs to be stored in full
                buffer_shape = max(producer.ofm.shape, consumer.ifm.shape)
                buffer_size = max(producer.ofm_size_in_bytes(), consumer.ifm_size_in_bytes())
            else:
                # Use a rolling buffer
                buffer_shape = rolling_buffer_shape(cost[producer].stripe, cost[consumer].stripe_input)
                buffer_size = buffer_shape.elements() * producer.ofm.dtype.size_in_bytes()

            self.buffer_map[key] = (buffer_shape, buffer_size)

        return self.buffer_map[key]


def rolling_buffer_shape(producer_stripe: Shape4D, consumer_stripe_input: Shape4D) -> Shape4D:
    """Calculates the storage shape of the rolling buffer between two SchedulerOperations in a Cascade"""
    buffer_height = round_up(producer_stripe.height + consumer_stripe_input.height, consumer_stripe_input.height)
    # Rolling buffers have to conform to NHCWB16 format
    return consumer_stripe_input.with_height(buffer_height).with_depth(round_up(producer_stripe.depth, 16))


class CascadeBuilder:
    """Class for grouping SchedulerOperations into cascades"""

    def __init__(self, sched_ops, spilling, non_local_mem_usage=None):
        self.sched_ops = sched_ops
        self.no_cascade = 0
        self.non_local_mem_usage = non_local_mem_usage if non_local_mem_usage else {}
        self.spilling = spilling

    def _is_cascadable(self, sched_op, cost) -> bool:
        """Checks if 'sched_op' can be cascaded"""
        return (
            sched_op.op_type.npu_block_type not in non_cascadable_blocks
            and cost.stripe.height < sched_op.ofm.shape.height
            and sched_op.parent_op.read_offsets[0] is None
            and sched_op.parent_op.read_offsets[1] is None
        )

    def _estimate_sram_usage(self, sched_op, cost) -> int:
        """Estimate the SRAM required for the Op if all FeatureMaps are in SRAM"""
        ifm2_size = sched_op.ifm2_size_in_bytes()
        if sched_op.requires_full_ifm:
            ifm_size = sched_op.ifm_size_in_bytes()
        else:
            ifm_size = (
                cost.stripe_input.with_depth(round_up(cost.stripe_input.depth, 16)).elements()
                * sched_op.ifm.dtype.size_in_bytes()
            )
        if sched_op.requires_full_ofm:
            ofm_size = sched_op.ofm_size_in_bytes()
        else:
            ofm_size = (
                cost.stripe.with_depth(round_up(cost.stripe.depth, 16)).elements() * sched_op.ofm.dtype.size_in_bytes()
            )

        return ifm_size + ifm2_size + ofm_size + self.non_local_mem_usage.get(sched_op, 0)

    def build_cascades(self, ref_schedule, fallback_schedule, guiding_mem_limit):
        ref_cost = ref_schedule.cost_map
        fallback_cost = fallback_schedule.cost_map
        cost = {}
        cascade_map = {}
        buffers = BufferMap()

        # Peak memory usage so far - updated continously, unless dedicated SRAM where this is a hard limit
        peak_sram_usage = guiding_mem_limit

        idx = 0
        while idx < len(self.sched_ops):
            op = self.sched_ops[idx]
            if op in cost:
                # Already processed this Op
                idx += 1
                continue

            if not self._is_cascadable(op, ref_cost[op]):
                # Op is not a candidate for cascading - assign fallback cost
                cost[op] = fallback_cost[op]
                if not self.spilling:
                    peak_sram_usage = max(self._estimate_sram_usage(op, fallback_cost[op]), peak_sram_usage)
                idx += 1
                continue

            # Propose a cascade starting with this Op
            cascade_start = op.index
            # Keep track of which Ops are in the proposed cascade as well as the best cascade so far
            ops_in_cascade = [op]
            ops_in_best_cascade = [op]
            # Get the size of the weight buffer
            weight_buffer = 0
            if ref_cost[op].buffered_weight_tensor:
                weight_buffer = ref_cost[op].buffered_weight_tensor.storage_size()

            # The first IFM needs to be stored in full
            cascade_ifm_size = op.ifm_size_in_bytes() if not self.spilling else 0

            # Add non-local memory usage
            cascade_ifm_size += self.non_local_mem_usage.get(op, 0)

            # Sum of all intermediate cascade buffers (including weight buffers)
            cascade_buffers = weight_buffer
            # Best cascade size - Initially it's the fallback cost of the first Op in the cascade
            best_cascade_size = self._estimate_sram_usage(op, fallback_cost[op])

            # Op is the producer of the OFM consumed by the next Op to consider
            producer = op
            while True:
                dependants = producer.get_dependants()
                if len(dependants) != 1:
                    # producer is either the last Op in the schedule or the start of a branch
                    break

                current_op = dependants[0]
                if (
                    current_op in cost
                    or current_op not in ref_cost
                    or not self._is_cascadable(current_op, ref_cost[current_op])
                    or producer.ofm.shape != current_op.ifm.shape
                    or current_op.requires_full_ifm
                    or producer.requires_full_ofm
                ):
                    # Current op has already been processed or cannot be cascaded
                    break

                if producer.index + 1 != current_op.index:
                    # Cascading is possible, but requires reordering of operations in the schedule,
                    # this is currently not supported
                    break

                # Get the size of the FeatureMap buffers between current and neighbouring Ops
                op_full_ifm = current_op.ifm_size_in_bytes()
                op_full_ofm = current_op.ofm_size_in_bytes()
                _, op_ifm_buffer = buffers.get_buffer(producer, current_op, ref_cost)

                # Get the size of the weight buffer
                op_weight_buffer = 0
                if ref_cost[current_op].buffered_weight_tensor:
                    op_weight_buffer = ref_cost[current_op].buffered_weight_tensor.storage_size()

                # Calculate the uncascaded memory requirement for current Op
                uncascaded_sram_usage = op_full_ifm + op_full_ofm + self.non_local_mem_usage.get(current_op, 0)

                # Add current Op to cascade
                ops_in_cascade.append(current_op)

                # Increase the accumulated intermediate buffers in the cascade
                cascade_buffers += op_ifm_buffer + op_weight_buffer

                if self.spilling:
                    # For Dedicated SRAM only the intermediate buffers are in SRAM
                    if uncascaded_sram_usage < peak_sram_usage or cascade_buffers > peak_sram_usage:
                        # Cascade until an Op fits in its entirety or the accumulated buffers no longer fit
                        break
                    else:
                        # Any addition to the cascade that fits is the new best cascade for Dedicated SRAM
                        ops_in_best_cascade = [op for op in ops_in_cascade]
                        best_cascade_size = cascade_buffers

                else:
                    # Calculate the total size of the current cascade
                    cascade_size = cascade_ifm_size + cascade_buffers + op_full_ofm

                    # Determine if cascading search should stop
                    if (
                        uncascaded_sram_usage < peak_sram_usage
                        and best_cascade_size < peak_sram_usage
                        or (cascade_ifm_size + cascade_buffers) > best_cascade_size
                    ):
                        # Both the existing cascade and current Op fits
                        break

                    # Determine if current cascade is the best so far
                    if cascade_size < best_cascade_size:
                        best_cascade_size = cascade_ifm_size + cascade_buffers + op_full_ofm
                        ops_in_best_cascade = [op for op in ops_in_cascade]

                producer = current_op

            if len(ops_in_best_cascade) > 1:
                # A cascade was created - assign cascade and ref_cost to all of the Ops
                cascade_end = cascade_start + (len(ops_in_best_cascade) - 1)
                buffers_in_cascade = {}
                prev_op = None
                for cascaded_op in ops_in_best_cascade:
                    assert cascade_start <= cascaded_op.index <= cascade_end
                    cost[cascaded_op] = ref_cost[cascaded_op]
                    cost[cascaded_op].cascade = cascade_end
                    if prev_op:
                        rolling_buffer_shape, _ = buffers.get_buffer(prev_op, cascaded_op, ref_cost)
                        buffers_in_cascade[cascaded_op] = rolling_buffer_shape

                    prev_op = cascaded_op

                # Create a CascadeInfo for the cascade
                cascade_map[cascade_end] = CascadeInfo(
                    cascade_start, cascade_end, buffers_in_cascade, best_cascade_size
                )
                if not self.spilling:
                    # Update peak memory usage
                    peak_sram_usage = max(best_cascade_size, peak_sram_usage)
            else:
                # Assign fallback cost to the initial Op
                cost[op] = fallback_cost[op]
                if not self.spilling:
                    peak_sram_usage = max(self._estimate_sram_usage(op, fallback_cost[op]), peak_sram_usage)

        # Update costing and cascde information for the ref_schedule
        ref_schedule.cost_map = cost
        ref_schedule.cascades = cascade_map
        return ref_schedule