# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  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.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
    Benchmarking the library on inference and training in PyTorch.
"""


import logging
import random
import timeit
from functools import wraps
from typing import Callable, Optional

from transformers import (
    TF_MODEL_MAPPING,
    TF_MODEL_WITH_LM_HEAD_MAPPING,
    PretrainedConfig,
    is_py3nvml_available,
    is_tf_available,
)

from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
    import tensorflow as tf
    from .benchmark_args_tf import TensorFlowBenchmarkArguments
    from tensorflow.python.framework.errors_impl import ResourceExhaustedError

if is_py3nvml_available():
    import py3nvml.py3nvml as nvml

logger = logging.getLogger(__name__)


def run_with_tf_optimizations(do_eager_mode: bool, use_xla: bool):
    def run_func(func):
        @wraps(func)
        def run_in_eager_mode(*args, **kwargs):
            return func(*args, **kwargs)

        @wraps(func)
        @tf.function(experimental_compile=use_xla)
        def run_in_graph_mode(*args, **kwargs):
            return func(*args, **kwargs)

        if do_eager_mode is True:
            assert (
                use_xla is False
            ), "Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`."
            return run_in_eager_mode
        else:
            return run_in_graph_mode

    return run_func


def random_input_ids(batch_size: int, sequence_length: int, vocab_size: int) -> ["tf.Tensor"]:
    rng = random.Random()
    values = [rng.randint(0, vocab_size - 1) for i in range(batch_size * sequence_length)]
    return tf.constant(values, shape=(batch_size, sequence_length), dtype=tf.int32)


class TensorFlowBenchmark(Benchmark):

    args: TensorFlowBenchmarkArguments
    configs: PretrainedConfig
    framework: str = "TensorFlow"

    @property
    def framework_version(self):
        return tf.__version__

    def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
        # initialize GPU on separate process
        strategy = self.args.strategy
        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
        _inference = self._prepare_inference_func(model_name, batch_size, sequence_length)
        return self._measure_speed(_inference)

    def _train_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
        strategy = self.args.strategy
        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
        _train = self._prepare_train_func(model_name, batch_size, sequence_length)
        return self._measure_speed(_train)

    def _inference_memory(
        self, model_name: str, batch_size: int, sequence_length: int
    ) -> [Memory, Optional[MemorySummary]]:
        # initialize GPU on separate process
        if self.args.is_gpu:
            tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], True)
        strategy = self.args.strategy
        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."
        _inference = self._prepare_inference_func(model_name, batch_size, sequence_length)
        return self._measure_memory(_inference)

    def _train_memory(
        self, model_name: str, batch_size: int, sequence_length: int
    ) -> [Memory, Optional[MemorySummary]]:
        if self.args.is_gpu:
            tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx], True)
        strategy = self.args.strategy
        assert strategy is not None, "A device strategy has to be initialized before using TensorFlow."

        _train = self._prepare_train_func(model_name, batch_size, sequence_length)
        return self._measure_memory(_train)

    def _prepare_inference_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]:
        config = self.config_dict[model_name]

        if self.args.fp16:
            raise NotImplementedError("Mixed precision is currently not supported.")

        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
        if not self.args.only_pretrain_model and has_model_class_in_config:
            try:
                model_class = "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
                transformers_module = __import__("transformers", fromlist=[model_class])
                model_cls = getattr(transformers_module, model_class)
                model = model_cls(config)
            except ImportError:
                raise ImportError(
                    f"{model_class} does not exist. If you just want to test the pretrained model, you might want to set `--only_pretrain_model` or `args.only_pretrain_model=True`."
                )
        else:
            model = TF_MODEL_MAPPING[config.__class__](config)

        # encoder-decoder has vocab size saved differently
        vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size
        input_ids = random_input_ids(batch_size, sequence_length, vocab_size)

        @run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
        def encoder_decoder_forward():
            return model(input_ids, decoder_input_ids=input_ids, training=False)

        @run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
        def encoder_forward():
            return model(input_ids, training=False)

        _inference = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

        return _inference

    def _prepare_train_func(self, model_name: str, batch_size: int, sequence_length: int) -> Callable[[], None]:
        config = self.config_dict[model_name]

        assert (
            self.args.eager_mode is False
        ), "Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`."

        if self.args.fp16:
            raise NotImplementedError("Mixed precision is currently not supported.")

        has_model_class_in_config = hasattr(config, "architecture") and len(config.architectures) > 1
        if not self.args.only_pretrain_model and has_model_class_in_config:
            try:
                model_class = "TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
                transformers_module = __import__("transformers", fromlist=[model_class])
                model_cls = getattr(transformers_module, model_class)
                model = model_cls(config)
            except ImportError:
                raise ImportError(
                    f"{model_class} does not exist. If you just want to test the pretrained model, you might want to set `--only_pretrain_model` or `args.only_pretrain_model=True`."
                )
        else:
            model = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](config)

        # encoder-decoder has vocab size saved differently
        vocab_size = config.vocab_size if hasattr(config, "vocab_size") else config.encoder.vocab_size
        input_ids = random_input_ids(batch_size, sequence_length, vocab_size)

        @run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
        def encoder_decoder_train():
            loss = model(input_ids, decoder_input_ids=input_ids, labels=input_ids, training=True)[0]
            gradients = tf.gradients(loss, model.trainable_variables)
            return gradients

        @run_with_tf_optimizations(self.args.eager_mode, self.args.use_xla)
        def encoder_train():
            loss = model(input_ids, labels=input_ids, training=True)[0]
            gradients = tf.gradients(loss, model.trainable_variables)
            return gradients

        _train = encoder_decoder_train if config.is_encoder_decoder else encoder_train

        return _train

    def _measure_speed(self, func) -> float:
        with self.args.strategy.scope():
            try:
                if self.args.is_tpu or self.args.use_xla:
                    # run additional 10 times to stabilize compilation for tpu
                    logger.info("Do inference on TPU. Running model 5 times to stabilize compilation")
                    timeit.repeat(func, repeat=1, number=5)

                # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                runtimes = timeit.repeat(func, repeat=self.args.repeat, number=10,)

                return min(runtimes) / 10.0
            except ResourceExhaustedError as e:
                self.print_fn("Doesn't fit on GPU. {}".format(e))

    def _measure_memory(self, func: Callable[[], None]) -> [Memory, MemorySummary]:
        logger.info(
            "Note that TensorFlow allocates more memory than"
            "it might need to speed up computation."
            "The memory reported here corresponds to the memory"
            "reported by `nvidia-smi`, which can vary depending"
            "on total available memory on the GPU that is used."
        )
        with self.args.strategy.scope():
            try:
                if self.args.trace_memory_line_by_line:
                    assert (
                        self.args.eager_mode
                    ), "`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory consumption line by line."
                    trace = start_memory_tracing("transformers")

                if self.args.is_tpu:
                    # tpu
                    raise NotImplementedError(
                        "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with `args.no_memory=True`"
                    )
                elif self.args.is_gpu:
                    # gpu
                    if not is_py3nvml_available():
                        logger.warning(
                            "py3nvml not installed, we won't log GPU memory usage. "
                            "Install py3nvml (pip install py3nvml) to log information about GPU."
                        )
                        memory = "N/A"
                    else:
                        logger.info(
                            "Measuring total GPU usage on GPU device. Make sure to not have additional processes running on the same GPU."
                        )
                        # init nvml
                        nvml.nvmlInit()
                        func()
                        handle = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                        meminfo = nvml.nvmlDeviceGetMemoryInfo(handle)
                        max_bytes_in_use = meminfo.used
                        memory = Memory(max_bytes_in_use)
                        # shutdown nvml
                        nvml.nvmlShutdown()
                else:
                    # cpu
                    if self.args.trace_memory_line_by_line:
                        logger.info(
                            "When enabling line by line tracing, the max peak memory for CPU is inaccurate in TensorFlow."
                        )
                        memory = None
                    else:
                        memory_bytes = measure_peak_memory_cpu(func)
                        memory = Memory(memory_bytes) if isinstance(memory_bytes, int) else memory_bytes
                if self.args.trace_memory_line_by_line:
                    summary = stop_memory_tracing(trace)
                    if memory is None:
                        memory = summary.total
                else:
                    summary = None

                return memory, summary
            except ResourceExhaustedError as e:
                self.print_fn("Doesn't fit on GPU. {}".format(e))
                return "N/A", None
