#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2021 Intel Corporation
#
# 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.
"""Concat Operator."""

import onnx
from neural_compressor.adaptor.ox_utils.operators.ops import op_registry, Operator, QOperator, qop_registry
from neural_compressor.adaptor.ox_utils.util import attribute_to_kwarg, ms_domain

@op_registry(op_types="Concat")
class ConcatOperator(Operator):
    """Concat Operator."""

    def __init__(self, onnx_quantizer, onnx_node):
        """Initialization."""
        super(ConcatOperator, self).__init__(onnx_quantizer, onnx_node)

    def quantize_check(self):
        """Check if quantizaion can be done."""
        node = self.node
        if len(node.input) == 1: # pragma: no cover
            return False
        inits = [i.name for i in self.quantizer.model.initializer()]
        if all([inp not in self.quantizer.quantized_value_map and inp not in inits \
            for inp in node.input]) or \
            not all([inp in self.quantizer.quantized_value_map or inp in inits \
            for inp in node.input]):
            return False
        return True

    def quantize(self):
        """Do quantizaion."""
        node = self.node
        inits = [i.name for i in self.quantizer.model.initializer()] 
        for idx, inp in enumerate(node.input):
            initializer_use_weight_qType = inp not in inits
            self.quantizer.quantize_inputs(node, [idx], initializer_use_weight_qType)
        if not self.disable_qdq_for_node_output or self.quantizer.mode != 'qdq':
            self.quantizer.quantize_outputs(node)
        node.name = node.name + "_quant"

    def convert_check(self, convert_format):
        """Check if conversion can be done."""
        node = self.node
        assert convert_format in ['static'], \
            "convert format for {} should be in ['static']".format(node.op_type)
            
        parents = self.quantizer.model.get_parents(node)
        children = self.quantizer.model.get_children(node)
        if len(children) == 0 or len(parents) == 0 or not node.name.endswith('_quant'):
            return False
        return True

    def convert(self, convert_format):
        """Convert to QOperator format."""
        node = self.node
        
        parents = self.quantizer.model.get_parents(node)
        children = self.quantizer.model.get_children(node)
        
        if all([i.op_type == 'DequantizeLinear' for i in parents]) and \
            any([i.op_type == 'QuantizeLinear' for i in children]):
            inputs = []

            inputs.extend([i for i in children if i.op_type == 'QuantizeLinear'][0].input[1:])
            for parent in parents:
                inputs.extend(parent.input)
                self.quantizer.remove_nodes.append(parent)
            for child in children:
                if child.op_type == 'QuantizeLinear':
                    self.quantizer.remove_nodes.append(child)
                    self.quantizer.model.replace_input_of_all_nodes(
                        child.output[0], node.output[0] + '_quantized')
            
            kwargs = {}
            for attribute in node.attribute:
                kwargs.update(attribute_to_kwarg(attribute))
            kwargs["domain"] = ms_domain
            qlconcat_node = onnx.helper.make_node("QLinearConcat", 
                                                  inputs, 
                                                  [node.output[0] + "_quantized"], 
                                                  node.name, 
                                                  **kwargs)        

            self.quantizer.new_nodes += [qlconcat_node]
            self.quantizer.remove_nodes.append(node)
    
    def cast(self): # pragma: no cover
        """Cast node."""
        node = self.node
        if node.input[0] not in [i.tensor_name for i in self.quantizer.new_value_info.values()]:
            return
        self.quantizer.dtype_cast(self.node, self.dtype)

@qop_registry(op_types="QLinearConcat")
class QConcatOperator(QOperator):
    """QConcat Operator."""

    def __init__(self, onnx_node, children, initializers):
        """Initialization."""
        super().__init__(onnx_node, children, initializers)

    def convert(self):
        """Convert to QDQ format."""
        node = self.node
        add_nodes = []
        inputs = []
        inits = []
        # input dq
        for i in range(int((len(node.input) - 2) / 3)):
            in_dq = onnx.helper.make_node(
                'DequantizeLinear',
                node.input[2 + i*3 : 2 + (i+1)*3],
                [node.name + '_in_dequant_' + str(i)],
                node.name + '_in_dequant_' + str(i))
            inputs.append(node.name + '_in_dequant_' + str(i))
            add_nodes.append(in_dq)

        # output q
        out_q = onnx.helper.make_node(
            'QuantizeLinear',
            [node.name + '_out', node.input[0], node.input[1]],
            node.output,
            node.name + '_out_quant')
        outputs = [node.name + '_out']
        add_nodes.append(out_q)

        kwargs = {}
        for attribute in node.attribute: # pragma: no cover
            kwargs.update(attribute_to_kwarg(attribute))

        concat_node = onnx.helper.make_node(
            'Concat', inputs,
            outputs, node.name + '_convert', **kwargs)
        add_nodes.append(concat_node)
        return True, add_nodes, inits
