# Copyright The PyTorch Lightning team.
#
# 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.

"""
CSV logger
----------

CSV logger for basic experiment logging that does not require opening ports

"""
import csv
import io
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Union

import torch

from pytorch_lightning import _logger as log
from pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.base import LightningLoggerBase
from pytorch_lightning.utilities.distributed import rank_zero_only, rank_zero_warn


class ExperimentWriter(object):
    r"""
    Experiment writer for CSVLogger.

    Currently supports to log hyperparameters and metrics in YAML and CSV
    format, respectively.

    Args:
        log_dir: Directory for the experiment logs
    """

    NAME_HPARAMS_FILE = 'hparams.yaml'
    NAME_METRICS_FILE = 'metrics.csv'

    def __init__(self, log_dir: str) -> None:
        self.hparams = {}
        self.metrics = []

        self.log_dir = log_dir
        if os.path.exists(self.log_dir) and os.listdir(self.log_dir):
            rank_zero_warn(
                f"Experiment logs directory {self.log_dir} exists and is not empty."
                " Previous log files in this directory will be deleted when the new ones are saved!"
            )
        os.makedirs(self.log_dir, exist_ok=True)

        self.metrics_file_path = os.path.join(self.log_dir, self.NAME_METRICS_FILE)

    def log_hparams(self, params: Dict[str, Any]) -> None:
        """Record hparams"""
        self.hparams.update(params)

    def log_metrics(self, metrics_dict: Dict[str, float], step: Optional[int] = None) -> None:
        """Record metrics"""
        def _handle_value(value):
            if isinstance(value, torch.Tensor):
                return value.item()
            return value

        if step is None:
            step = len(self.metrics)

        metrics = {k: _handle_value(v) for k, v in metrics_dict.items()}
        metrics['step'] = step
        self.metrics.append(metrics)

    def save(self) -> None:
        """Save recorded hparams and metrics into files"""
        hparams_file = os.path.join(self.log_dir, self.NAME_HPARAMS_FILE)
        save_hparams_to_yaml(hparams_file, self.hparams)

        if not self.metrics:
            return

        last_m = {}
        for m in self.metrics:
            last_m.update(m)
        metrics_keys = list(last_m.keys())

        with io.open(self.metrics_file_path, 'w', newline='') as f:
            self.writer = csv.DictWriter(f, fieldnames=metrics_keys)
            self.writer.writeheader()
            self.writer.writerows(self.metrics)


class CSVLogger(LightningLoggerBase):
    r"""
    Log to local file system in yaml and CSV format.

    Logs are saved to ``os.path.join(save_dir, name, version)``.

    Example:
        >>> from pytorch_lightning import Trainer
        >>> from pytorch_lightning.loggers import CSVLogger
        >>> logger = CSVLogger("logs", name="my_exp_name")
        >>> trainer = Trainer(logger=logger)

    Args:
        save_dir: Save directory
        name: Experiment name. Defaults to ``'default'``.
        version: Experiment version. If version is not specified the logger inspects the save
            directory for existing versions, then automatically assigns the next available version.
        prefix: A string to put at the beginning of metric keys.
    """

    LOGGER_JOIN_CHAR = '-'

    def __init__(
        self,
        save_dir: str,
        name: Optional[str] = "default",
        version: Optional[Union[int, str]] = None,
        prefix: str = '',
    ):
        super().__init__()
        self._save_dir = save_dir
        self._name = name or ''
        self._version = version
        self._prefix = prefix
        self._experiment = None

    @property
    def root_dir(self) -> str:
        """
        Parent directory for all checkpoint subdirectories.
        If the experiment name parameter is ``None`` or the empty string, no experiment subdirectory is used
        and the checkpoint will be saved in "save_dir/version_dir"
        """
        if not self.name:
            return self.save_dir
        return os.path.join(self.save_dir, self.name)

    @property
    def log_dir(self) -> str:
        """
        The log directory for this run. By default, it is named
        ``'version_${self.version}'`` but it can be overridden by passing a string value
        for the constructor's version parameter instead of ``None`` or an int.
        """
        # create a pseudo standard path ala test-tube
        version = self.version if isinstance(self.version, str) else f"version_{self.version}"
        log_dir = os.path.join(self.root_dir, version)
        return log_dir

    @property
    def save_dir(self) -> Optional[str]:
        return self._save_dir

    @property
    def experiment(self) -> ExperimentWriter:
        r"""

        Actual ExperimentWriter object. To use ExperimentWriter features in your
        :class:`~pytorch_lightning.core.lightning.LightningModule` do the following.

        Example::

            self.logger.experiment.some_experiment_writer_function()

        """
        if self._experiment:
            return self._experiment

        os.makedirs(self.root_dir, exist_ok=True)
        self._experiment = ExperimentWriter(log_dir=self.log_dir)
        return self._experiment

    @rank_zero_only
    def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
        params = self._convert_params(params)
        self.experiment.log_hparams(params)

    @rank_zero_only
    def log_metrics(self, metrics: Dict[str, float], step: Optional[int] = None) -> None:
        metrics = self._add_prefix(metrics)
        self.experiment.log_metrics(metrics, step)

    @rank_zero_only
    def save(self) -> None:
        super().save()
        self.experiment.save()

    @rank_zero_only
    def finalize(self, status: str) -> None:
        self.save()

    @property
    def name(self) -> str:
        return self._name

    @property
    def version(self) -> int:
        if self._version is None:
            self._version = self._get_next_version()
        return self._version

    def _get_next_version(self):
        root_dir = os.path.join(self._save_dir, self.name)

        if not os.path.isdir(root_dir):
            log.warning('Missing logger folder: %s', root_dir)
            return 0

        existing_versions = []
        for d in os.listdir(root_dir):
            if os.path.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
                existing_versions.append(int(d.split("_")[1]))

        if len(existing_versions) == 0:
            return 0

        return max(existing_versions) + 1