# 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 .base_layer import BaseLayer


class BaseConv1d(BaseLayer):
    def __init__(
        self, context, name, input_size, output_size, kernels=1, strides=1, pads=0, dilations=1, groups=1, bias=True):
        super().__init__(context, name)
        self.input_size = input_size
        self.output_size = output_size
        self.kernels = [kernels]
        self.strides = [strides]
        self.pads = [pads] * 2 if isinstance(pads, int) else pads
        self.dilations = [dilations]
        self.groups = groups
        self.bias = bias
        self.collect_bind_layer_weights()

    def collect_bind_layer_weights(self):
        weight_key = '.'.join([self.context, 'weight'])
        weight_shape = [self.output_size, self.input_size // self.groups] + self.kernels
        weight_np = self.get_param_from_state_dict(weight_key, weight_shape)
        self.weight_id = self.add_initialized_input_tensor(weight_np, weight_key)

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

    def __call__(self, graph, x):
        x = ops.conv(
            graph=graph,
            x=x,
            weight=self.weight_id,
            bias=self.bias_id if self.bias else None,
            kernel_shape=self.kernels,
            strides=self.strides,
            pads=self.pads,
            dilations=self.dilations,
            group=self.groups
        )
        return x


class TPConv1d(BaseConv1d):
    pass


class BaseConv2d(BaseLayer):
    def __init__(
        self, context, name, input_size, output_size, kernels=1, strides=1, pads=0, dilations=1, groups=1, bias=True):
        super().__init__(context, name)
        self.input_size = input_size
        self.output_size = output_size
        self.kernels = [kernels] * 2 if isinstance(kernels, int) else kernels
        self.strides = [strides] * 2 if isinstance(strides, int) else strides
        self.pads = [pads] * 4 if isinstance(pads, int) else pads
        self.dilations = [dilations] * 2 if isinstance(dilations, int) else dilations
        self.groups = groups
        self.bias = bias
        self.collect_bind_layer_weights()

    def collect_bind_layer_weights(self):
        weight_key = '.'.join([self.context, 'weight'])
        weight_shape = [self.output_size, self.input_size // self.groups] + self.kernels
        weight_np = self.get_param_from_state_dict(weight_key, weight_shape)
        self.weight_id = self.add_initialized_input_tensor(weight_np, weight_key)

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

    def __call__(self, graph, x):
        x = ops.conv(
            graph=graph,
            x=x,
            weight=self.weight_id,
            bias=self.bias_id if self.bias else None,
            kernel_shape=self.kernels,
            strides=self.strides,
            pads=self.pads,
            dilations=self.dilations,
            group=self.groups
        )
        return x

class TPConv2d(BaseConv2d):
    pass


class Conv1d(TPConv1d, BaseConv1d):

    layer_class_map = {
        'tp': TPConv1d,
        'shard': BaseConv1d
    }

    def __init__(
        self, context, name, input_size, output_size, kernels=1, strides=1, pads=0, dilations=1, groups=1, bias=True):
        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, kernels, strides, pads, dilations, groups, bias)

    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)


class Conv2d(TPConv2d, BaseConv2d):

    layer_class_map = {
        'tp': TPConv2d,
        'shard': BaseConv2d
    }

    def __init__(
        self, context, name, input_size, output_size, kernels=1, strides=1, pads=0, dilations=1, groups=1, bias=True):
        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, kernels, strides, pads, dilations, groups, bias)

    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)