# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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.
"""Functions and classes related to optimization (weight updates)."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import re
import tensorflow as tf
import lamb_optimizer
from gpt_2_simple.src import memory_saving_gradients


def create_optimizer(
    loss,
    init_lr,
    num_train_steps,
    num_warmup_steps,
    use_tpu,
    optimizer="lamb",
    poly_power=1.0,
    start_warmup_step=0,
    use_memory_saving_gradients=False
):
    """Creates an optimizer training op."""
    global_step = tf.train.get_or_create_global_step()

    learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)

    # Implements linear decay of the learning rate.
    learning_rate = tf.train.polynomial_decay(
        learning_rate,
        global_step,
        num_train_steps,
        end_learning_rate=0.0,
        power=poly_power,
        cycle=False,
    )

    # Implements linear warmup. I.e., if global_step - start_warmup_step <
    # num_warmup_steps, the learning rate will be
    # `(global_step - start_warmup_step)/num_warmup_steps * init_lr`.
    if num_warmup_steps:
        tf.logging.info(
            "++++++ warmup starts at step "
            + str(start_warmup_step)
            + ", for "
            + str(num_warmup_steps)
            + " steps ++++++"
        )
        global_steps_int = tf.cast(global_step, tf.int32)
        start_warm_int = tf.constant(start_warmup_step, dtype=tf.int32)
        global_steps_int = global_steps_int - start_warm_int
        warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)

        global_steps_float = tf.cast(global_steps_int, tf.float32)
        warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)

        warmup_percent_done = global_steps_float / warmup_steps_float
        warmup_learning_rate = init_lr * warmup_percent_done

        is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
        learning_rate = (
            1.0 - is_warmup
        ) * learning_rate + is_warmup * warmup_learning_rate

    # It is OK that you use this optimizer for finetuning, since this
    # is how the model was trained (note that the Adam m/v variables are NOT
    # loaded from init_checkpoint.)
    # It is OK to use AdamW in the finetuning even the model is trained by LAMB.
    # As report in the Bert pulic github, the learning rate for SQuAD 1.1 finetune
    # is 3e-5, 4e-5 or 5e-5. For LAMB, the users can use 3e-4, 4e-4,or 5e-4 for a
    # batch size of 64 in the finetune.
    if optimizer == "adamw":
        tf.logging.info("using adamw")
        optimizer = AdamWeightDecayOptimizer(
            learning_rate=learning_rate,
            weight_decay_rate=0.01,
            beta_1=0.9,
            beta_2=0.999,
            epsilon=1e-6,
            exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
        )
    elif optimizer == "lamb":
        tf.logging.info("using lamb")
        optimizer = lamb_optimizer.LAMBOptimizer(
            learning_rate=learning_rate,
            weight_decay_rate=0.01,
            beta_1=0.9,
            beta_2=0.999,
            epsilon=1e-6,
            exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
        )
    else:
        raise ValueError("Not supported optimizer: ", optimizer)

    if use_tpu:
        optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)

    tvars = tf.trainable_variables()


    if use_memory_saving_gradients:
        grads = memory_saving_gradients.gradients(loss, tvars)
    else:
        grads = tf.gradients(ys=loss, xs=tvars)


    # This is how bert was pre-trained.
    #(grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)

    train_op = optimizer.apply_gradients(zip(grads, tvars), global_step=global_step)

    # Normally the global step update is done inside of `apply_gradients`.
    # However, neither `AdamWeightDecayOptimizer` nor `LAMBOptimizer` do this.
    # But if you use a different optimizer, you should probably take this line
    # out.
    new_global_step = global_step + 1
    train_op = tf.group(train_op, [global_step.assign(new_global_step)])
    return train_op


class AdamWeightDecayOptimizer(tf.train.Optimizer):
    """A basic Adam optimizer that includes "correct" L2 weight decay."""

    def __init__(
        self,
        learning_rate,
        weight_decay_rate=0.0,
        beta_1=0.9,
        beta_2=0.999,
        epsilon=1e-6,
        exclude_from_weight_decay=None,
        name="AdamWeightDecayOptimizer",
    ):
        """Constructs a AdamWeightDecayOptimizer."""
        super(AdamWeightDecayOptimizer, self).__init__(False, name)

        self.learning_rate = learning_rate
        self.weight_decay_rate = weight_decay_rate
        self.beta_1 = beta_1
        self.beta_2 = beta_2
        self.epsilon = epsilon
        self.exclude_from_weight_decay = exclude_from_weight_decay

    def apply_gradients(self, grads_and_vars, global_step=None, name=None):
        """See base class."""
        assignments = []
        for (grad, param) in grads_and_vars:
            if grad is None or param is None:
                continue

            param_name = self._get_variable_name(param.name)

            m = tf.get_variable(
                name=param_name + "/adam_m",
                shape=param.shape.as_list(),
                dtype=tf.float32,
                trainable=False,
                initializer=tf.zeros_initializer(),
            )
            v = tf.get_variable(
                name=param_name + "/adam_v",
                shape=param.shape.as_list(),
                dtype=tf.float32,
                trainable=False,
                initializer=tf.zeros_initializer(),
            )

            # Standard Adam update.
            next_m = tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad)
            next_v = tf.multiply(self.beta_2, v) + tf.multiply(
                1.0 - self.beta_2, tf.square(grad)
            )

            update = next_m / (tf.sqrt(next_v) + self.epsilon)

            # Just adding the square of the weights to the loss function is *not*
            # the correct way of using L2 regularization/weight decay with Adam,
            # since that will interact with the m and v parameters in strange ways.
            #
            # Instead we want ot decay the weights in a manner that doesn't interact
            # with the m/v parameters. This is equivalent to adding the square
            # of the weights to the loss with plain (non-momentum) SGD.
            if self._do_use_weight_decay(param_name):
                update += self.weight_decay_rate * param

            update_with_lr = self.learning_rate * update

            next_param = param - update_with_lr

            assignments.extend(
                [param.assign(next_param), m.assign(next_m), v.assign(next_v)]
            )
        return tf.group(*assignments, name=name)

    def _do_use_weight_decay(self, param_name):
        """Whether to use L2 weight decay for `param_name`."""
        if not self.weight_decay_rate:
            return False
        if self.exclude_from_weight_decay:
            for r in self.exclude_from_weight_decay:
                if re.search(r, param_name) is not None:
                    return False
        return True

    def _get_variable_name(self, param_name):
        """Get the variable name from the tensor name."""
        m = re.match("^(.*):\\d+$", param_name)
        if m is not None:
            param_name = m.group(1)
        return param_name