





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__":
       snake_case__      :							Dict									=  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)

       snake_case__		,					snake_case__      :							Dict									=  parser.parse_known_args()

       # overwrite batch size until we have tf_glue.py
       snake_case__      :							str									=  16
       snake_case__      :							Dict									=  16

       # Set up logging
       snake_case__      :							int									=  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
       snake_case__      :							Dict									=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       snake_case__      :							List[Any]									=  AutoTokenizer.from_pretrained(args.model_name_or_path)

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

       # Preprocess train dataset
       snake_case__      :							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'])

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

       # Preprocess test dataset
       snake_case__      :							List[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'])

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

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

       snake_case__      :							Optional[int]									=  time.time()
       snake_case__      :							List[str]									=  model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
       snake_case__      :							Dict									=  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_tf_available, is_torch_available


snake_case__      :							Optional[int]									=  {'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__      :							Union[str, Any]									=  [
           'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CvtForImageClassification',
           'CvtModel',
           'CvtPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case__      :							List[str]									=  [
           'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFCvtForImageClassification',
           'TFCvtModel',
           'TFCvtPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_cvt import (
                  CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CvtForImageClassification,
                  CvtModel,
                  CvtPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_cvt import (
                  TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFCvtForImageClassification,
                  TFCvtModel,
                  TFCvtPreTrainedModel,
              )


else:
       import sys

       snake_case__      :							Dict									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





