from iree import runtime as ireert
from iree.tf.support import module_utils
from iree.compiler import tf as tfc
from iree.compiler import compile_str
from absl import app
import time

import numpy as np
import os
import tensorflow as tf

from official.nlp.modeling import layers
from official.nlp.modeling import networks
from official.nlp.modeling.models import bert_classifier

vocab_size = 100
NUM_CLASSES = 5
SEQUENCE_LENGTH = 512
BATCH_SIZE = 1
# Create a set of 2-dimensional inputs
bert_input = [
    tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
    tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
    tf.TensorSpec(shape=[BATCH_SIZE, SEQUENCE_LENGTH], dtype=tf.int32),
]


class BertModule(tf.Module):
    def __init__(self):
        super(BertModule, self).__init__()
        dict_outputs = False
        test_network = networks.BertEncoder(
            vocab_size=vocab_size,
            num_layers=24,
            hidden_size=1024,
            num_attention_heads=16,
            dict_outputs=dict_outputs,
        )

        # Create a BERT trainer with the created network.
        bert_trainer_model = bert_classifier.BertClassifier(
            test_network, num_classes=NUM_CLASSES
        )
        bert_trainer_model.summary()

        # Invoke the trainer model on the inputs. This causes the layer to be built.
        self.m = bert_trainer_model
        self.m.predict = lambda x: self.m.call(x, training=False)
        self.predict = tf.function(input_signature=[bert_input])(
            self.m.predict
        )
        self.m.learn = lambda x, y: self.m.call(x, training=False)
        self.loss = tf.keras.losses.SparseCategoricalCrossentropy()
        self.optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)

    @tf.function(
        input_signature=[
            bert_input,  # inputs
            tf.TensorSpec(shape=[BATCH_SIZE], dtype=tf.int32),  # labels
        ],
        jit_compile=True,
    )
    def learn(self, inputs, labels):
        with tf.GradientTape() as tape:
            # Capture the gradients from forward prop...
            probs = self.m(inputs, training=True)
            loss = self.loss(labels, probs)

        # ...and use them to update the model's weights.
        variables = self.m.trainable_variables
        gradients = tape.gradient(loss, variables)
        self.optimizer.apply_gradients(zip(gradients, variables))
        return loss


if __name__ == "__main__":
    # BertModule()
    # Compile the model using IREE
    compiler_module = tfc.compile_module(
        BertModule(), exported_names=["learn"], import_only=True
    )

    # Compile the model using IREE
    backend = "dylib-llvm-aot"
    args = [
        "--iree-llvmcpu-target-cpu-features=host",
        "--iree-mhlo-demote-i64-to-i32=false",
    ]
    backend_config = "dylib"
    # backend = "cuda"
    # backend_config = "cuda"
    # args = ["--iree-cuda-llvm-target-arch=sm_80", "--iree-enable-fusion-with-reduction-ops"]
    flatbuffer_blob = compile_str(
        compiler_module,
        target_backends=[backend],
        extra_args=args,
        input_type="auto",
    )
    # flatbuffer_blob = compile_str(compiler_module, target_backends=["dylib-llvm-aot"])

    # Save module as MLIR file in a directory
    vm_module = ireert.VmModule.from_flatbuffer(flatbuffer_blob)
    tracer = ireert.Tracer(os.getcwd())
    config = ireert.Config("dylib", tracer)
    ctx = ireert.SystemContext(config=config)
    ctx.add_vm_module(vm_module)
    BertCompiled = ctx.modules.module
    predict_sample_input = [
        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
        np.random.randint(5, size=(BATCH_SIZE, SEQUENCE_LENGTH)),
    ]
    learn_sample_input = [
        predict_sample_input,
        np.random.randint(5, size=(BATCH_SIZE)),
    ]
    warmup = 5
    total_iter = 10
    num_iter = total_iter - warmup
    for i in range(10):
        if i == warmup - 1:
            start = time.time()
        print(
            BertCompiled.learn(
                predict_sample_input, np.random.randint(5, size=(BATCH_SIZE))
            )
        )
    end = time.time()
    total_time = end - start
    print("time: " + str(total_time))
    print("time/iter: " + str(total_time / num_iter))