# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
# Copyright 2020 The HuggingFace Team. 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 os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
USE_XLA = False
USE_AMP = False


def train_command_factory(args: Namespace):
    """
    Factory function used to instantiate training command from provided command line arguments.

    Returns: TrainCommand
    """
    return TrainCommand(args)


class TrainCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        """
        Register this command to argparse so it's available for the transformer-cli

        Args:
            parser: Root parser to register command-specific arguments
        """
        train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")

        train_parser.add_argument(
            "--train_data",
            type=str,
            required=True,
            help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.",
        )
        train_parser.add_argument(
            "--column_label", type=int, default=0, help="Column of the dataset csv file with example labels."
        )
        train_parser.add_argument(
            "--column_text", type=int, default=1, help="Column of the dataset csv file with example texts."
        )
        train_parser.add_argument(
            "--column_id", type=int, default=2, help="Column of the dataset csv file with example ids."
        )
        train_parser.add_argument(
            "--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers)."
        )

        train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
        train_parser.add_argument(
            "--validation_split",
            type=float,
            default=0.1,
            help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.",
        )

        train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.")

        train_parser.add_argument(
            "--task", type=str, default="text_classification", help="Task to train the model on."
        )
        train_parser.add_argument(
            "--model", type=str, default="bert-base-uncased", help="Model's name or path to stored model."
        )
        train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.")
        train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.")
        train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.")
        train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.")
        train_parser.set_defaults(func=train_command_factory)

    def __init__(self, args: Namespace):
        self.logger = logging.get_logger("transformers-cli/training")

        self.framework = "tf" if is_tf_available() else "torch"

        os.makedirs(args.output, exist_ok=True)
        self.output = args.output

        self.column_label = args.column_label
        self.column_text = args.column_text
        self.column_id = args.column_id

        self.logger.info(f"Loading {args.task} pipeline for {args.model}")
        if args.task == "text_classification":
            self.pipeline = TextClassificationPipeline.from_pretrained(args.model)
        elif args.task == "token_classification":
            raise NotImplementedError
        elif args.task == "question_answering":
            raise NotImplementedError

        self.logger.info(f"Loading dataset from {args.train_data}")
        self.train_dataset = Processor.create_from_csv(
            args.train_data,
            column_label=args.column_label,
            column_text=args.column_text,
            column_id=args.column_id,
            skip_first_row=args.skip_first_row,
        )
        self.valid_dataset = None
        if args.validation_data:
            self.logger.info(f"Loading validation dataset from {args.validation_data}")
            self.valid_dataset = Processor.create_from_csv(
                args.validation_data,
                column_label=args.column_label,
                column_text=args.column_text,
                column_id=args.column_id,
                skip_first_row=args.skip_first_row,
            )

        self.validation_split = args.validation_split
        self.train_batch_size = args.train_batch_size
        self.valid_batch_size = args.valid_batch_size
        self.learning_rate = args.learning_rate
        self.adam_epsilon = args.adam_epsilon

    def run(self):
        if self.framework == "tf":
            return self.run_tf()
        return self.run_torch()

    def run_torch(self):
        raise NotImplementedError

    def run_tf(self):
        self.pipeline.fit(
            self.train_dataset,
            validation_data=self.valid_dataset,
            validation_split=self.validation_split,
            learning_rate=self.learning_rate,
            adam_epsilon=self.adam_epsilon,
            train_batch_size=self.train_batch_size,
            valid_batch_size=self.valid_batch_size,
        )

        # Save trained pipeline
        self.pipeline.save_pretrained(self.output)

# Copyright 2020 The HuggingFace Team. 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 os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
    raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
USE_XLA = False
USE_AMP = False


def train_command_factory(args: Namespace):
    """
    Factory function used to instantiate training command from provided command line arguments.

    Returns: TrainCommand
    """
    return TrainCommand(args)


class TrainCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        """
        Register this command to argparse so it's available for the transformer-cli

        Args:
            parser: Root parser to register command-specific arguments
        """
        train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")

        train_parser.add_argument(
            "--train_data",
            type=str,
            required=True,
            help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.",
        )
        train_parser.add_argument(
            "--column_label", type=int, default=0, help="Column of the dataset csv file with example labels."
        )
        train_parser.add_argument(
            "--column_text", type=int, default=1, help="Column of the dataset csv file with example texts."
        )
        train_parser.add_argument(
            "--column_id", type=int, default=2, help="Column of the dataset csv file with example ids."
        )
        train_parser.add_argument(
            "--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers)."
        )

        train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
        train_parser.add_argument(
            "--validation_split",
            type=float,
            default=0.1,
            help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.",
        )

        train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.")

        train_parser.add_argument(
            "--task", type=str, default="text_classification", help="Task to train the model on."
        )
        train_parser.add_argument(
            "--model", type=str, default="bert-base-uncased", help="Model's name or path to stored model."
        )
        train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.")
        train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.")
        train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.")
        train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.")
        train_parser.set_defaults(func=train_command_factory)

    def __init__(self, args: Namespace):
        self.logger = logging.get_logger("transformers-cli/training")

        self.framework = "tf" if is_tf_available() else "torch"

        os.makedirs(args.output, exist_ok=True)
        self.output = args.output

        self.column_label = args.column_label
        self.column_text = args.column_text
        self.column_id = args.column_id

        self.logger.info(f"Loading {args.task} pipeline for {args.model}")
        if args.task == "text_classification":
            self.pipeline = TextClassificationPipeline.from_pretrained(args.model)
        elif args.task == "token_classification":
            raise NotImplementedError
        elif args.task == "question_answering":
            raise NotImplementedError

        self.logger.info(f"Loading dataset from {args.train_data}")
        self.train_dataset = Processor.create_from_csv(
            args.train_data,
            column_label=args.column_label,
            column_text=args.column_text,
            column_id=args.column_id,
            skip_first_row=args.skip_first_row,
        )
        self.valid_dataset = None
        if args.validation_data:
            self.logger.info(f"Loading validation dataset from {args.validation_data}")
            self.valid_dataset = Processor.create_from_csv(
                args.validation_data,
                column_label=args.column_label,
                column_text=args.column_text,
                column_id=args.column_id,
                skip_first_row=args.skip_first_row,
            )

        self.validation_split = args.validation_split
        self.train_batch_size = args.train_batch_size
        self.valid_batch_size = args.valid_batch_size
        self.learning_rate = args.learning_rate
        self.adam_epsilon = args.adam_epsilon

    def run(self):
        if self.framework == "tf":
            return self.run_tf()
        return self.run_torch()

    def run_torch(self):
        raise NotImplementedError

    def run_tf(self):
        self.pipeline.fit(
            self.train_dataset,
            validation_data=self.valid_dataset,
            validation_split=self.validation_split,
            learning_rate=self.learning_rate,
            adam_epsilon=self.adam_epsilon,
            train_batch_size=self.train_batch_size,
            valid_batch_size=self.valid_batch_size,
        )

        # Save trained pipeline
        self.pipeline.save_pretrained(self.output)
