
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__":
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     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)

		__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[str]         =     parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		__SCREAMING_SNAKE_CASE			:   Dict         =     16
		__SCREAMING_SNAKE_CASE			:   int         =     16

		# Set up logging
		__SCREAMING_SNAKE_CASE			:   Dict         =     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
		__SCREAMING_SNAKE_CASE			:   Tuple         =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		__SCREAMING_SNAKE_CASE			:   List[str]         =     AutoTokenizer.from_pretrained(args.model_name_or_path)

		# Load dataset
		__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   List[Any]         =     load_dataset('imdb', split=['train', 'test'])
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
		__SCREAMING_SNAKE_CASE			:   int         =     test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

		# Preprocess train dataset
		__SCREAMING_SNAKE_CASE			:   List[str]         =     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'])

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

		# Preprocess test dataset
		__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     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'])

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

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

		__SCREAMING_SNAKE_CASE			:   List[Any]         =     time.time()
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		__SCREAMING_SNAKE_CASE			:   Any         =     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}""")


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__SCREAMING_SNAKE_CASE			:   List[str]         =     {'configuration_vilt': ['VILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViltConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Any         =     ['ViltFeatureExtractor']
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['ViltImageProcessor']
		__SCREAMING_SNAKE_CASE			:   str         =     ['ViltProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Dict         =     [
		    'VILT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViltForImageAndTextRetrieval',
		    'ViltForImagesAndTextClassification',
		    'ViltForTokenClassification',
		    'ViltForMaskedLM',
		    'ViltForQuestionAnswering',
		    'ViltLayer',
		    'ViltModel',
		    'ViltPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     _LazyModule(__name__, globals()['__file__'], _import_structure)
