




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		:    List[Any]	      =		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		:    Optional[Any]	      =		parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		__lowerCAmelCase		:    List[Any]	      =		16
		__lowerCAmelCase		:    List[Any]	      =		16

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

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

		# Preprocess train dataset
		__lowerCAmelCase		:    Optional[int]	      =		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		:    Tuple	      =		{
		    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ['input_ids', 'attention_mask']
		}
		__lowerCAmelCase		:    Any	      =		tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
		    args.per_device_train_batch_size
		)

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

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

		__lowerCAmelCase		:    str	      =		time.time()
		__lowerCAmelCase		:    Any	      =		model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		__lowerCAmelCase		:    List[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}''')












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("You need to specify an `image_processor`."							)
							if tokenizer is None:
										raise ValueError("You need to specify a `tokenizer`."							)

							super().__init__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      args[1:]

							if images is None and text is None:
										raise ValueError("You need to specify either an `images` or `text` input to process."							)

							if images is not None:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							warnings.warn(
							    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
							    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
							    "your images inputs, or in a separate call."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






