


'''simple docstring'''







import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


A__:   Tuple    =	threading.Lock()
A__:   Optional[logging.Handler]    =	None

A__:   Union[str, Any]    =	{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

A__:   List[str]    =	logging.WARNING

A__:   Tuple    =	True


def      SCREAMING_SNAKE_CASE_ (  )  ->      int:
       _a   :			Optional[int]					 =os.getenv("""DIFFUSERS_VERBOSITY"""   ,_UpperCAmelCase  )
       if env_level_str:
              if env_level_str in log_levels:
                     return log_levels[env_level_str]
              else:
                     logging.getLogger().warning(
                         F"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
                         F"has to be one of: { ', '.join(log_levels.keys()  ) }"  )
       return _default_log_level


def      SCREAMING_SNAKE_CASE_ (  )  ->      str:
       return __name__.split("""."""  )[0]


def      SCREAMING_SNAKE_CASE_ (  )  ->      logging.Logger:
       return logging.getLogger(_get_library_name()  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       global _default_handler

       with _lock:
              if _default_handler:
                     # This library has already configured the library root logger.
                     return
              _a   :			int					 =logging.StreamHandler()  # Set sys.stderr as stream.
              _a   :			Optional[int]					 =sys.stderr.flush

              # Apply our default configuration to the library root logger.
              _a   :			List[str]					 =_get_library_root_logger()
              library_root_logger.addHandler(_default_handler  )
              library_root_logger.setLevel(_get_default_logging_level()  )
              _a   :			List[str]					 =False


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       global _default_handler

       with _lock:
              if not _default_handler:
                     return

              _a   :			Optional[Any]					 =_get_library_root_logger()
              library_root_logger.removeHandler(_default_handler  )
              library_root_logger.setLevel(logging.NOTSET  )
              _a   :			List[Any]					 =None


def      SCREAMING_SNAKE_CASE_ (  )  ->      Any:
       return log_levels


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     Optional[str] = None  )  ->      logging.Logger:

       if name is None:
              _a   :			int					 =_get_library_name()

       _configure_library_root_logger()
       return logging.getLogger(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      int:
       _configure_library_root_logger()
       return _get_library_root_logger().getEffectiveLevel()


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int  )  ->      None:
       _configure_library_root_logger()
       _get_library_root_logger().setLevel(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      Optional[int]:
       return set_verbosity(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      Union[str, Any]:
       return set_verbosity(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      List[Any]:
       return set_verbosity(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      Optional[Any]:
       return set_verbosity(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _configure_library_root_logger()

       assert _default_handler is not None
       _get_library_root_logger().removeHandler(_default_handler  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _configure_library_root_logger()

       assert _default_handler is not None
       _get_library_root_logger().addHandler(_default_handler  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     logging.Handler  )  ->      None:
       _configure_library_root_logger()

       assert handler is not None
       _get_library_root_logger().addHandler(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     logging.Handler  )  ->      None:
       _configure_library_root_logger()

       assert handler is not None and handler not in _get_library_root_logger().handlers
       _get_library_root_logger().removeHandler(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _configure_library_root_logger()
       _a   :			Union[str, Any]					 =False


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _configure_library_root_logger()
       _a   :			Union[str, Any]					 =True


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _a   :			str					 =_get_library_root_logger().handlers

       for handler in handlers:
              _a   :			Dict					 =logging.Formatter("""[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"""  )
              handler.setFormatter(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      None:
       _a   :			List[Any]					 =_get_library_root_logger().handlers

       for handler in handlers:
              handler.setFormatter(_UpperCAmelCase  )


def      SCREAMING_SNAKE_CASE_ (  self     :     Union[str, Any]   ,*_UpperCAmelCase     :     Optional[Any]   ,**_UpperCAmelCase     :     Tuple  )  ->      List[Any]:
       _a   :			Optional[Any]					 =os.getenv("""DIFFUSERS_NO_ADVISORY_WARNINGS"""   ,_UpperCAmelCase  )
       if no_advisory_warnings:
              return
       self.warning(*_UpperCAmelCase   ,**_UpperCAmelCase  )


A__:   Union[str, Any]    =	warning_advice







class A__	:






      def __init__(			self      :Any					,						*SCREAMING_SNAKE_CASE      :int					,						**SCREAMING_SNAKE_CASE      :int     )	-> Any:  # pylint: disable=unused-argument




             '''simple docstring'''
             _a   :			Optional[int]					 =args[0] if args else None






      def __iter__(			self      :str     )	-> Tuple:




             '''simple docstring'''
             return iter(self._iterator     )






      def __getattr__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Optional[int]     )	-> Any:




             '''simple docstring'''

             def empty_fn(*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :int     ):  # pylint: disable=unused-argument
                    return

             return empty_fn






      def __enter__(			self      :Optional[Any]     )	-> str:




             '''simple docstring'''
             return self






      def __exit__(			self      :List[Any]					,						SCREAMING_SNAKE_CASE      :Dict					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Any     )	-> Optional[int]:




             '''simple docstring'''
             return







class A__	:






      def __call__(			self      :Any					,						*SCREAMING_SNAKE_CASE      :int					,						**SCREAMING_SNAKE_CASE      :int     )	-> Optional[int]:




             '''simple docstring'''
             if _tqdm_active:
                    return tqdm_lib.tqdm(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )
             else:
                    return EmptyTqdm(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Optional[Any]					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Any:




             '''simple docstring'''
             _a   :			Dict					 =None
             if _tqdm_active:
                    return tqdm_lib.tqdm.set_lock(*SCREAMING_SNAKE_CASE					,						**SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :int     )	-> Tuple:




             '''simple docstring'''
             if _tqdm_active:
                    return tqdm_lib.tqdm.get_lock()

A__:   str    =	_tqdm_cls()


def      SCREAMING_SNAKE_CASE_ (  )  ->      bool:
       global _tqdm_active
       return bool(_tqdm_active  )


def      SCREAMING_SNAKE_CASE_ (  )  ->      Optional[Any]:
       global _tqdm_active
       _a   :			Optional[int]					 =True


def      SCREAMING_SNAKE_CASE_ (  )  ->      Tuple:
       global _tqdm_active
       _a   :			int					 =False






'''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__:   Optional[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)

   A__   ,	A__:   List[Any]    =	parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A__:   Tuple    =	16
   A__:   List[str]    =	16

   # Set up logging
   A__:   str    =	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__:   int    =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A__:   Any    =	AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A__   ,	A__:   Optional[int]    =	load_dataset('''imdb''', split=['''train''', '''test'''])
   A__:   int    =	train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
   A__:   int    =	test_dataset.shuffle().select(range(500))  # 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[Any]    =	{
       x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
       for x in ['''input_ids''', '''attention_mask''']
   }
   A__:   str    =	tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
       args.per_device_train_batch_size
   )

   # Preprocess test dataset
   A__:   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'''])

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

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

   A__:   str    =	time.time()
   A__:   Dict    =	model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A__:   Tuple    =	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}")


