




"""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__":
		a								=							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=1_6)
		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)

		a				,    a								=							parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		a								=							1_6
		a								=							1_6

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

		# Load dataset
		a				,    a								=							load_dataset('imdb', split=['train', 'test'])
		a								=							train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
		a								=							test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

		# Preprocess train dataset
		a								=							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'])

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

		# Preprocess test dataset
		a								=							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'])

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

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

		a								=							time.time()
		a								=							model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		a								=							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"""







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  ['image_processor', 'tokenizer']
						_a        =  'VideoMAEImageProcessor'
						_a        =  ('CLIPTokenizer', 'CLIPTokenizerFast')


						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     Optional[Any]=None  ,  lowerCAmelCase			:     Tuple=None  ,  **lowerCAmelCase			:     List[str]   ):
										lowerCAmelCase		=			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  ,  )
														lowerCAmelCase		=			kwargs.pop("""feature_extractor"""   )

										lowerCAmelCase		=			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   )
										lowerCAmelCase		=			self.image_processor


						def __call__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     str=None  ,  lowerCAmelCase			:     Tuple=None  ,  lowerCAmelCase			:     int=None  ,  **lowerCAmelCase			:     str   ):

										if text is None and videos is None:
														raise ValueError("""You have to specify either text or videos. Both cannot be none."""   )

										if text is not None:
														lowerCAmelCase		=			self.tokenizer(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if videos is not None:
														lowerCAmelCase		=			self.image_processor(lowerCAmelCase  ,  return_tensors=lowerCAmelCase  ,  **lowerCAmelCase   )

										if text is not None and videos is not None:
														lowerCAmelCase		=			image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**lowerCAmelCase   )  ,  tensor_type=lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  *lowerCAmelCase			:     List[str]  ,  **lowerCAmelCase			:     int   ):
										return self.tokenizer.batch_decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						def 					__lowercase	(							self			:     List[str]  ,  *lowerCAmelCase			:     Any  ,  **lowerCAmelCase			:     List[Any]   ):
										return self.tokenizer.decode(*lowerCAmelCase  ,  **lowerCAmelCase   )


						@property
						def 					__lowercase	(							self			:     Dict   ):
										return ["input_ids", "attention_mask", "position_ids", "pixel_values"]


						@property
						def 					__lowercase	(							self			:     List[str]   ):
										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 					__lowercase	(							self			:     Tuple   ):
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,  lowerCAmelCase  ,  )
										return self.image_processor





