# Copyright (c) 2023 Graphcore Ltd.
# 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 numpy as np
from poptransformer import ops
from poptransformer.layers import BaseLayer
from poptransformer.layers import Linear


class BaseRWKVFeedforward(BaseLayer):
    def __init__(self, context, name, hidden_size, intermediate_size, layer_id):
        super().__init__(context, name)
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.layer_id = layer_id
        self.collect_bind_layer_weights()

    def collect_bind_layer_weights(self):
        self.key_linear = Linear(self.context, 'key', self.hidden_size, self.intermediate_size, use_bias=False)
        self.receptance_linear = Linear(self.context, 'receptance', self.hidden_size, self.hidden_size, use_bias=False)
        self.value_linear = Linear(self.context, 'value', self.intermediate_size, self.hidden_size, use_bias=False)

        time_mix_key_name = '.'.join([self.context, 'time_mix_key'])
        time_mix_key_np = self.get_param_from_state_dict(time_mix_key_name, [1, 1, self.hidden_size])
        self.time_mix_key = self.add_initialized_input_tensor(time_mix_key_np, time_mix_key_name)

        time_mix_receptance_name = '.'.join([self.context, 'time_mix_receptance'])
        time_mix_receptance_np = self.get_param_from_state_dict(time_mix_receptance_name, [1, 1, self.hidden_size])
        self.time_mix_receptance = self.add_initialized_input_tensor(time_mix_receptance_np, time_mix_receptance_name)

    def gate(self, graph, a, w, b):
        y1 = ops.mul(graph, a, w)
        y2 = ops.sub(graph, ops.constant(graph, np.array(1).astype(self.np_float_type)), w)
        y2 = ops.mul(graph, y2, b)
        y = ops.add(graph, y1, y2)
        return y

    def __call__(self, graph, hidden, layer_state):
        temp_layer_state = layer_state[0]
        key = self.gate(graph, hidden, self.time_mix_key, temp_layer_state)
        receptance = self.gate(graph, hidden, self.time_mix_receptance, temp_layer_state)
        layer_state[0] = hidden
        key = self.key_linear(graph, key)
        key = ops.relu(graph, key)
        key = ops.mul(graph, key, key)
        value = self.value_linear(graph, key)
        receptance = self.receptance_linear(graph, receptance)
        receptance = ops.sigmoid(graph, receptance)
        output = ops.mul(graph, receptance, value)
        return output, layer_state


class TPRWKVFeedforward(BaseRWKVFeedforward):
    def collect_bind_layer_weights(self):
        key_tp_setting = {
            'strategy_name': 'start',
        }
        value_tp_setting = {
            'strategy_name': 'end',
        }
        self.key_linear = Linear(
            self.context, 'key', self.hidden_size, self.intermediate_size, use_bias=False, **key_tp_setting)
        self.receptance_linear = Linear(
            self.context, 'receptance', self.hidden_size, self.hidden_size, use_bias=False, **key_tp_setting)
        self.value_linear = Linear(
            self.context, 'value', self.intermediate_size, self.hidden_size, use_bias=False, **value_tp_setting)

        time_mix_key_name = '.'.join([self.context, 'time_mix_key'])
        time_mix_key_np = self.get_param_from_state_dict(time_mix_key_name, [1, 1, self.hidden_size])
        self.time_mix_key = self.add_initialized_input_tensor(time_mix_key_np, time_mix_key_name)

        time_mix_receptance_name = '.'.join([self.context, 'time_mix_receptance'])
        time_mix_receptance_np = self.get_param_from_state_dict(time_mix_receptance_name, [1, 1, self.hidden_size])
        self.time_mix_receptance = self.add_initialized_input_tensor(time_mix_receptance_np, time_mix_receptance_name)

    def __call__(self, graph, hidden, layer_state):
        temp_layer_state = layer_state[0]
        key = self.gate(graph, hidden, self.time_mix_key, temp_layer_state)
        receptance = self.gate(graph, hidden, self.time_mix_receptance, temp_layer_state)
        layer_state[0] = hidden
        key = self.key_linear(graph, key)
        key = ops.relu(graph, key)
        key = ops.mul(graph, key, key)
        value = self.value_linear(graph, key)
        receptance = self.receptance_linear(graph, receptance)
        receptance = ops.replicated_allgather(graph, receptance)
        receptance = ops.sigmoid(graph, receptance)
        output = ops.mul(graph, receptance, value)
        return output, layer_state


class RWKVFeedforward(TPRWKVFeedforward, BaseRWKVFeedforward):
    layer_class_map = {
        'tp': TPRWKVFeedforward,
        'shard': BaseRWKVFeedforward}

    def __init__(self, context, name, hidden_size, intermediate_size, layer_id):
        model_type = self.model_type
        self.layer_class = self.layer_class_map.get(model_type, None)
        if not self.layer_class:
            raise ValueError(f"Invalid model_type {model_type}, options: {self.layer_class_map.keys()}")
        self.logger.debug(f'initializing model type: {self.layer_class.__name__}')
        super().__init__(context, name, hidden_size, intermediate_size, layer_id)

    def __call__(self, graph, hidden, layer_state):
        return self.layer_class.__call__(self, graph, hidden, layer_state)

    def collect_bind_layer_weights(self):
        return self.layer_class.collect_bind_layer_weights(self)