






'''simple docstring'''
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__":
						__lowerCAmelCase							=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)

						__lowerCAmelCase							,				__lowerCAmelCase							=parser.parse_known_args()

						# overwrite batch size until we have tf_glue.py
						__lowerCAmelCase							=16
						__lowerCAmelCase							=16

						# Set up logging
						__lowerCAmelCase							=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
						__lowerCAmelCase							=TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
						__lowerCAmelCase							=AutoTokenizer.from_pretrained(args.model_name_or_path)

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

						# Preprocess train dataset
						__lowerCAmelCase							=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"])

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

						# Preprocess test dataset
						__lowerCAmelCase							=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"])

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

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

						__lowerCAmelCase							=time.time()
						__lowerCAmelCase							=model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
						__lowerCAmelCase							=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}''')









'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase							={"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RegNetForImageClassification",
						    "RegNetModel",
						    "RegNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRegNetForImageClassification",
						    "TFRegNetModel",
						    "TFRegNetPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCAmelCase							=[
						    "FlaxRegNetForImageClassification",
						    "FlaxRegNetModel",
						    "FlaxRegNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_regnet import (
												    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RegNetForImageClassification,
												    RegNetModel,
												    RegNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_regnet import (
												    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRegNetForImageClassification,
												    TFRegNetModel,
												    TFRegNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_regnet import (
												    FlaxRegNetForImageClassification,
												    FlaxRegNetModel,
												    FlaxRegNetPreTrainedModel,
												)


else:
						import sys

						__lowerCAmelCase							=_LazyModule(__name__, globals()["__file__"], _import_structure)

