# 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.

import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


logger = logging.get_logger(__name__)


def list_field(default=None, metadata=None):
    return field(default_factory=lambda: default, metadata=metadata)


@dataclass
class BenchmarkArguments:
    """
    BenchMarkArguments are arguments we use in our benchmark scripts **which relate to the training loop itself**.

    Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
    line.
    """

    models: List[str] = list_field(
        default=[],
        metadata={
            "help": (
                "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
                " of all available models"
            )
        },
    )

    batch_sizes: List[int] = list_field(
        default=[8], metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}
    )

    sequence_lengths: List[int] = list_field(
        default=[8, 32, 128, 512],
        metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"},
    )

    inference: bool = field(
        default=True,
        metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."},
    )
    cuda: bool = field(
        default=True,
        metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."},
    )
    tpu: bool = field(
        default=True, metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}
    )
    fp16: bool = field(default=False, metadata={"help": "Use FP16 to accelerate inference."})
    training: bool = field(default=False, metadata={"help": "Benchmark training of model"})
    verbose: bool = field(default=False, metadata={"help": "Verbose memory tracing"})
    speed: bool = field(
        default=True,
        metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."},
    )
    memory: bool = field(
        default=True,
        metadata={
            "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
        },
    )
    trace_memory_line_by_line: bool = field(default=False, metadata={"help": "Trace memory line by line"})
    save_to_csv: bool = field(default=False, metadata={"help": "Save result to a CSV file"})
    log_print: bool = field(default=False, metadata={"help": "Save all print statements in a log file"})
    env_print: bool = field(default=False, metadata={"help": "Whether to print environment information"})
    multi_process: bool = field(
        default=True,
        metadata={
            "help": (
                "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
                " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
                " for debugging / testing and on TPU."
            )
        },
    )
    inference_time_csv_file: str = field(
        default=f"inference_time_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving time results to csv."},
    )
    inference_memory_csv_file: str = field(
        default=f"inference_memory_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving memory results to csv."},
    )
    train_time_csv_file: str = field(
        default=f"train_time_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving time results to csv for training."},
    )
    train_memory_csv_file: str = field(
        default=f"train_memory_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving memory results to csv for training."},
    )
    env_info_csv_file: str = field(
        default=f"env_info_{round(time())}.csv",
        metadata={"help": "CSV filename used if saving environment information."},
    )
    log_filename: str = field(
        default=f"log_{round(time())}.csv",
        metadata={"help": "Log filename used if print statements are saved in log."},
    )
    repeat: int = field(default=3, metadata={"help": "Times an experiment will be run."})
    only_pretrain_model: bool = field(
        default=False,
        metadata={
            "help": (
                "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
                " model weights."
            )
        },
    )

    def __post_init__(self):
        warnings.warn(
            f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
            " are deprecated in general and it is advised to use external Benchmarking libraries "
            " to benchmark Transformer models.",
            FutureWarning,
        )

    def to_json_string(self):
        """
        Serializes this instance to a JSON string.
        """
        return json.dumps(dataclasses.asdict(self), indent=2)

    @property
    def model_names(self) -> List[str]:
        if len(self.models) <= 0:
            raise ValueError(
                "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
                " bert-base-cased` or `args.models = ['bert-base-cased']."
            )
        return self.models

    @property
    def do_multi_processing(self):
        if not self.multi_process:
            return False
        elif self.is_tpu:
            logger.info("Multiprocessing is currently not possible on TPU.")
            return False
        else:
            return True