# What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
Fix last models for common tests that are too big.. # What does this PR do?

This PR fixes the last batch of models that are too big for common tests. After it, only two classes override the common test to skip it:
- timm-backbone
- layoutlmv2
In the first case, it's because there is no timm model small enough to work (I did switch to the smallest resnet though) and in the second detectron2 does not let us configure a smaller backbone (I did try with the constant exposed by they don't seem to have any effect).
import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    # Hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument("--epochs", type=int, default=1)
    parser.add_argument("--per_device_train_batch_size", type=int, default=16)
    parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
    parser.add_argument("--model_name_or_path", type=str)
    parser.add_argument("--learning_rate", type=str, default=5e-5)
    parser.add_argument("--do_train", type=bool, default=True)
    parser.add_argument("--do_eval", type=bool, default=True)
    parser.add_argument("--output_dir", type=str)

    args, _ = parser.parse_known_args()

    # overwrite batch size until we have tf_glue.py
    args.per_device_train_batch_size = 16
    args.per_device_eval_batch_size = 16

    # Set up logging
    logger = logging.getLogger(__name__)

    logging.basicConfig(
        level=logging.getLevelName("INFO"),
        handlers=[logging.StreamHandler(sys.stdout)],
        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    )

    # Load model and tokenizer
    model = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)

    # Load dataset
    train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
    train_dataset = train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
    test_dataset = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

    # Preprocess train dataset
    train_dataset = train_dataset.map(
        lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
    )
    train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

    train_features = {
        x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
        for x in ["input_ids", "attention_mask"]
    }
    tf_train_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
        args.per_device_train_batch_size
    )

    # Preprocess test dataset
    test_dataset = test_dataset.map(
        lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
    )
    test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

    test_features = {
        x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
        for x in ["input_ids", "attention_mask"]
    }
    tf_test_dataset = tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])).batch(
        args.per_device_eval_batch_size
    )

    # fine optimizer and loss
    optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    metrics = [tf.keras.metrics.SparseCategoricalAccuracy()]
    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

    start_train_time = time.time()
    train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
    end_train_time = time.time() - start_train_time

    logger.info("*** Train ***")
    logger.info(f"train_runtime = {end_train_time}")
    for key, value in train_results.history.items():
        logger.info(f"  {key} = {value}")

import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
    parser = argparse.ArgumentParser()

    # Hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument("--epochs", type=int, default=1)
    parser.add_argument("--per_device_train_batch_size", type=int, default=16)
    parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
    parser.add_argument("--model_name_or_path", type=str)
    parser.add_argument("--learning_rate", type=str, default=5e-5)
    parser.add_argument("--do_train", type=bool, default=True)
    parser.add_argument("--do_eval", type=bool, default=True)
    parser.add_argument("--output_dir", type=str)

    args, _ = parser.parse_known_args()

    # overwrite batch size until we have tf_glue.py
    args.per_device_train_batch_size = 16
    args.per_device_eval_batch_size = 16

    # Set up logging
    logger = logging.getLogger(__name__)

    logging.basicConfig(
        level=logging.getLevelName("INFO"),
        handlers=[logging.StreamHandler(sys.stdout)],
        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    )

    # Load model and tokenizer
    model = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)

    # Load dataset
    train_dataset, test_dataset = load_dataset("imdb", split=["train", "test"])
    train_dataset = train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
    test_dataset = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

    # Preprocess train dataset
    train_dataset = train_dataset.map(
        lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
    )
    train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

    train_features = {
        x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
        for x in ["input_ids", "attention_mask"]
    }
    tf_train_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
        args.per_device_train_batch_size
    )

    # Preprocess test dataset
    test_dataset = test_dataset.map(
        lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
    )
    test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

    test_features = {
        x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
        for x in ["input_ids", "attention_mask"]
    }
    tf_test_dataset = tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])).batch(
        args.per_device_eval_batch_size
    )

    # fine optimizer and loss
    optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    metrics = [tf.keras.metrics.SparseCategoricalAccuracy()]
    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

    start_train_time = time.time()
    train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
    end_train_time = time.time() - start_train_time

    logger.info("*** Train ***")
    logger.info(f"train_runtime = {end_train_time}")
    for key, value in train_results.history.items():
        logger.info(f"  {key} = {value}")
