# Copyright (c) 2022 Graphcore Ltd. All rights reserved.
# Copyright 2020 Google Research. All Rights Reserved.
#
# 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.
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# ==============================================================================
"""Model Builder for EfficientNet.

efficientnet-bx (x=0,1,2,3,4,5,6,7) checkpoints are located in:
  https://storage.googleapis.com/cloud-tpu-checkpoints/efficientnet/ckptsaug/efficientnet-bx.tar.gz
"""
import functools
import os
import re
from absl import logging
import numpy as np
import tensorflow as tf

import utils
from backbone import efficientnet_model


def efficientnet_params(model_name):
    """Get efficientnet params based on model name."""
    params_dict = {
        # (width_coefficient, depth_coefficient, resolution, dropout_rate)
        "efficientnet-b0": (1.0, 1.0, 224, 0.2),
        "efficientnet-b1": (1.0, 1.1, 240, 0.2),
        "efficientnet-b2": (1.1, 1.2, 260, 0.3),
        "efficientnet-b3": (1.2, 1.4, 300, 0.3),
        "efficientnet-b4": (1.4, 1.8, 380, 0.4),
        "efficientnet-b5": (1.6, 2.2, 456, 0.4),
        "efficientnet-b6": (1.8, 2.6, 528, 0.5),
        "efficientnet-b7": (2.0, 3.1, 600, 0.5),
        "efficientnet-b8": (2.2, 3.6, 672, 0.5),
        "efficientnet-l2": (4.3, 5.3, 800, 0.5),
    }
    return params_dict[model_name]


class BlockDecoder(object):
    """Block Decoder for readability."""

    def _decode_block_string(self, block_string):
        """Gets a block through a string notation of arguments."""
        assert isinstance(block_string, str)
        ops = block_string.split("_")
        options = {}
        for op in ops:
            splits = re.split(r"(\d.*)", op)
            if len(splits) >= 2:
                key, value = splits[:2]
                options[key] = value

        if "s" not in options or len(options["s"]) != 2:
            raise ValueError("Strides options should be a pair of integers.")

        return efficientnet_model.BlockArgs(
            kernel_size=int(options["k"]),
            num_repeat=int(options["r"]),
            input_filters=int(options["i"]),
            output_filters=int(options["o"]),
            expand_ratio=int(options["e"]),
            id_skip=("noskip" not in block_string),
            se_ratio=float(options["se"]) if "se" in options else None,
            strides=[int(options["s"][0]), int(options["s"][1])],
            conv_type=int(options["c"]) if "c" in options else 0,
            fused_conv=int(options["f"]) if "f" in options else 0,
            super_pixel=int(options["p"]) if "p" in options else 0,
            condconv=("cc" in block_string),
        )

    def _encode_block_string(self, block):
        """Encodes a block to a string."""
        args = [
            "r%d" % block.num_repeat,
            "k%d" % block.kernel_size,
            "s%d%d" % (block.strides[0], block.strides[1]),
            "e%s" % block.expand_ratio,
            "i%d" % block.input_filters,
            "o%d" % block.output_filters,
            "c%d" % block.conv_type,
            "f%d" % block.fused_conv,
            "p%d" % block.super_pixel,
        ]
        if block.se_ratio > 0 and block.se_ratio <= 1:
            args.append("se%s" % block.se_ratio)
        if block.id_skip is False:  # pylint: disable=g-bool-id-comparison
            args.append("noskip")
        if block.condconv:
            args.append("cc")
        return "_".join(args)

    def decode(self, string_list):
        """Decodes a list of string notations to specify blocks inside the network.

        Args:
          string_list: a list of strings, each string is a notation of block.

        Returns:
          A list of namedtuples to represent blocks arguments.
        """
        assert isinstance(string_list, list)
        blocks_args = []
        for block_string in string_list:
            blocks_args.append(self._decode_block_string(block_string))
        return blocks_args

    def encode(self, blocks_args):
        """Encodes a list of Blocks to a list of strings.

        Args:
          blocks_args: A list of namedtuples to represent blocks arguments.
        Returns:
          a list of strings, each string is a notation of block.
        """
        block_strings = []
        for block in blocks_args:
            block_strings.append(self._encode_block_string(block))
        return block_strings


def swish(features, use_native=True, use_hard=False):
    """Computes the Swish activation function.

    We provide three alternatives:
      - Native tf.nn.swish, use less memory during training than composable swish.
      - Quantization friendly hard swish.
      - A composable swish, equivalent to tf.nn.swish, but more general for
        finetuning and TF-Hub.

    Args:
      features: A `Tensor` representing preactivation values.
      use_native: Whether to use the native swish from tf.nn that uses a custom
        gradient to reduce memory usage, or to use customized swish that uses
        default TensorFlow gradient computation.
      use_hard: Whether to use quantization-friendly hard swish.

    Returns:
      The activation value.
    """
    if use_native and use_hard:
        raise ValueError("Cannot specify both use_native and use_hard.")

    if use_native:
        return tf.nn.swish(features)

    if use_hard:
        return features * tf.nn.relu6(features + np.float32(3)) * (1.0 / 6.0)

    features = tf.convert_to_tensor(features, name="features")
    return features * tf.nn.sigmoid(features)


_DEFAULT_BLOCKS_ARGS = [
    "r1_k3_s11_e1_i32_o16_se0.25",
    "r2_k3_s22_e6_i16_o24_se0.25",
    "r2_k5_s22_e6_i24_o40_se0.25",
    "r3_k3_s22_e6_i40_o80_se0.25",
    "r3_k5_s11_e6_i80_o112_se0.25",
    "r4_k5_s22_e6_i112_o192_se0.25",
    "r1_k3_s11_e6_i192_o320_se0.25",
]


def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2, survival_prob=0.8):
    """Creates a efficientnet model."""
    global_params = efficientnet_model.GlobalParams(
        blocks_args=_DEFAULT_BLOCKS_ARGS,
        batch_norm_momentum=0.99,
        batch_norm_epsilon=1e-3,
        dropout_rate=dropout_rate,
        survival_prob=survival_prob,
        data_format="channels_last",
        num_classes=1000,
        width_coefficient=width_coefficient,
        depth_coefficient=depth_coefficient,
        depth_divisor=8,
        min_depth=None,
        relu_fn=tf.nn.swish,
        # The default is TPU-specific batch norm.
        # The alternative is tf.layers.BatchNormalization.
        batch_norm=utils.TpuBatchNormalization,  # TPU-specific requirement.
        use_se=True,
        clip_projection_output=False,
    )
    return global_params


def get_model_params(model_name, override_params):
    """Get the block args and global params for a given model."""
    if model_name.startswith("efficientnet"):
        width_coefficient, depth_coefficient, _, dropout_rate = efficientnet_params(model_name)
        global_params = efficientnet(width_coefficient, depth_coefficient, dropout_rate)
    else:
        raise NotImplementedError("model name is not pre-defined: %s" % model_name)

    if override_params:
        # ValueError will be raised here if override_params has fields not included
        # in global_params.
        global_params = global_params._replace(**override_params)

    decoder = BlockDecoder()
    blocks_args = decoder.decode(global_params.blocks_args)

    logging.info("global_params= %s", global_params)
    return blocks_args, global_params


def build_model(
    images,
    model_name,
    training,
    override_params=None,
    model_dir=None,
    fine_tuning=False,
    features_only=False,
    pooled_features_only=False,
):
    """A helper function to create a model and return predicted logits.

    Args:
      images: input images tensor.
      model_name: string, the predefined model name.
      training: boolean, whether the model is constructed for training.
      override_params: A dictionary of params for overriding. Fields must exist in
        efficientnet_model.GlobalParams.
      model_dir: string, optional model dir for saving configs.
      fine_tuning: boolean, whether the model is used for finetuning.
      features_only: build the base feature network only (excluding final
        1x1 conv layer, global pooling, dropout and fc head).
      pooled_features_only: build the base network for features extraction (after
        1x1 conv layer and global pooling, but before dropout and fc head).

    Returns:
      logits: the logits tensor of classes.
      endpoints: the endpoints for each layer.

    Raises:
      When model_name specified an undefined model, raises NotImplementedError.
      When override_params has invalid fields, raises ValueError.
    """
    assert tf.is_tensor(images)
    assert not (features_only and pooled_features_only)

    # For backward compatibility.
    if override_params and override_params.get("drop_connect_rate", None):
        override_params["survival_prob"] = 1 - override_params["drop_connect_rate"]

    if not training or fine_tuning:
        if not override_params:
            override_params = {}
        if "batch_norm" not in override_params:
            override_params["batch_norm"] = utils.BatchNormalization
        if fine_tuning:
            override_params["relu_fn"] = functools.partial(swish, use_native=False)
    blocks_args, global_params = get_model_params(model_name, override_params)

    if model_dir:
        param_file = os.path.join(model_dir, "model_params.txt")
        if not tf.io.gfile.exists(param_file):
            if not tf.io.gfile.exists(model_dir):
                tf.io.gfile.makedirs(model_dir)
            with tf.io.gfile.GFile(param_file, "w") as f:
                logging.info("writing to %s", param_file)
                f.write("model_name= %s\n\n" % model_name)
                f.write("global_params= %s\n\n" % str(global_params))
                f.write("blocks_args= %s\n\n" % str(blocks_args))

    model = efficientnet_model.Model(blocks_args, global_params, model_name)
    outputs = model(images, training=training, features_only=features_only, pooled_features_only=pooled_features_only)
    features, endpoints = outputs[0], outputs[1:]
    if features_only:
        features = tf.identity(features, "features")
    elif pooled_features_only:
        features = tf.identity(features, "pooled_features")
    else:
        features = tf.identity(features, "logits")
    return features, endpoints


def build_model_base(images, model_name, training, override_params=None):
    """Create a base feature network and return the features before pooling.

    Args:
      images: input images tensor.
      model_name: string, the predefined model name.
      training: boolean, whether the model is constructed for training.
      override_params: A dictionary of params for overriding. Fields must exist in
        efficientnet_model.GlobalParams.

    Returns:
      features: base features before pooling.
      endpoints: the endpoints for each layer.

    Raises:
      When model_name specified an undefined model, raises NotImplementedError.
      When override_params has invalid fields, raises ValueError.
    """
    assert isinstance(images, tf.Tensor)
    # For backward compatibility.
    if override_params and override_params.get("drop_connect_rate", None):
        override_params["survival_prob"] = 1 - override_params["drop_connect_rate"]

    blocks_args, global_params = get_model_params(model_name, override_params)

    model = efficientnet_model.Model(blocks_args, global_params, model_name)
    outputs = model(images, training=training, features_only=True)
    return outputs[0], outputs[1:]