




'''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__":
     UpperCAmelCase_       :  str      =		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)

     UpperCAmelCase_       :  Union[str, Any]      =		parser.parse_known_args()

     # overwrite batch size until we have tf_glue.py
     UpperCAmelCase_       :  str      =		1_6
     UpperCAmelCase_       :  List[Any]      =		1_6

     # Set up logging
     UpperCAmelCase_       :  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
     UpperCAmelCase_       :  Tuple      =		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     UpperCAmelCase_       :  Optional[Any]      =		AutoTokenizer.from_pretrained(args.model_name_or_path)

     # Load dataset
     UpperCAmelCase_       :  List[Any]      =		load_dataset("imdb", split=["train", "test"])
     UpperCAmelCase_       :  Tuple      =		train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
     UpperCAmelCase_       :  List[str]      =		test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

     # Preprocess train dataset
     UpperCAmelCase_       :  Any      =		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"])

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

     # Preprocess test dataset
     UpperCAmelCase_       :  List[str]      =		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"])

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

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

     UpperCAmelCase_       :  Dict      =		time.time()
     UpperCAmelCase_       :  Dict      =		model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
     UpperCAmelCase_       :  Optional[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}""")












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


UpperCAmelCase_       :  int      =		logging.get_logger(__name__)





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  None:



            '''simple docstring'''




            warnings.warn(
                '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MaskFormerImageProcessor instead.'''    ,  __SCREAMING_SNAKE_CASE    ,  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






