





"""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__       :  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=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__       :  List[str]       		=					parser.parse_known_args()

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

   # Set up logging
   a__       :  Union[str, 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
   a__       :  List[Any]       		=					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   a__       :  Optional[Any]       		=					AutoTokenizer.from_pretrained(args.model_name_or_path)

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

   # Preprocess train dataset
   a__       :  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"""])

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

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

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

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


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)     ->	float:

 '''simple docstring'''






 snake_case__     				=					[1 / first_term]
 snake_case__     				=					1 / first_term
 for _ in range(number_of_terms - 1						):
  first_term += common_difference
  arithmetic_progression.append(_UpperCAmelCase						)
 snake_case__     				=					[1 / step for step in arithmetic_progression]
 return sum(_UpperCAmelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

