# 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.

from poptransformer import ops
from poptransformer.utils import ParamHandler
from .base_layer import BaseLayer


class BaseLinear(BaseLayer):
    def __init__(self, context, name, input_size, output_size, use_bias=True, **kwargs):
        super().__init__(context, name)
        self.input_size = input_size
        self.output_size = output_size
        self.use_bias = use_bias
        self.kwargs = kwargs
        self.collect_bind_layer_weights()

    def collect_bind_layer_weights(self):
        self.param_handler = ParamHandler(host_layer=self)
        weight_key = '.'.join([self.context, 'weight'])
        weight_np = self.get_param_from_state_dict(weight_key, [self.output_size, self.input_size])
        weight_np = weight_np.transpose(1, 0)
        weight_np = self.param_handler.process_linear_weight(weight_np, weight_key)
        self.weight_id = self.add_initialized_input_tensor(weight_np, weight_key)

        if self.use_bias:
            bias_key = '.'.join([self.context, 'bias'])
            bias_np = self.get_param_from_state_dict(bias_key, [self.output_size])
            bias_np = self.param_handler.process_linear_bias(bias_np)
            self.bias_id = self.add_initialized_input_tensor(bias_np, bias_key)

    def __call__(self, graph, x):
        with graph.nameScope(self.context):
            x = self.param_handler.matmul(graph, x, self.weight_id)
            x = ops.add(graph, x, self.bias_id) if self.use_bias else x
        return x


class TPLinear(BaseLinear):

    def collect_bind_layer_weights(self):
        vs_setting = {'vs_type': 'consecutive', 'group_size': 1}
        self.param_handler = ParamHandler(
            host_layer=self,
            tp_strategy_name=self.kwargs.get('strategy_name'),
            **vs_setting
        )
        weight_key = '.'.join([self.context, 'weight'])
        weight_np = self.get_param_from_state_dict(weight_key, [self.output_size, self.input_size])
        weight_np = weight_np.transpose(1, 0)
        weight_np = self.param_handler.process_linear_weight(weight_np, weight_key)
        self.weight_id = self.add_initialized_input_tensor(weight_np, weight_key, **vs_setting)

        if self.use_bias:
            bias_key = '.'.join([self.context, 'bias'])
            bias_np = self.get_param_from_state_dict(bias_key, [self.output_size])
            bias_np = self.param_handler.process_linear_bias(bias_np)
            self.bias_id = self.add_initialized_input_tensor(bias_np, bias_key, **vs_setting)


class Linear(TPLinear, BaseLinear):

    layer_class_map = {
        'tp': TPLinear,
        'shard': BaseLinear
    }

    def __init__(self, context, name, input_size, output_size, use_bias=True, **kwargs):
        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, input_size, output_size, use_bias, **kwargs)

    def __call__(self, graph, x):
        return self.layer_class.__call__(self, graph, x)

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