





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__":
      __lowercase													=			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)

      __lowercase						,       __lowercase													=			parser.parse_known_args()

      # overwrite batch size until we have tf_glue.py
      __lowercase													=			16
      __lowercase													=			16

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

      # Load dataset
      __lowercase						,       __lowercase													=			load_dataset("""imdb""", split=["""train""", """test"""])
      __lowercase													=			train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
      __lowercase													=			test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

      # Preprocess train dataset
      __lowercase													=			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"""])

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

      # Preprocess test dataset
      __lowercase													=			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"""])

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

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

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







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment

class   __snake_case				(			unittest.TestCase   ):






    '''simple docstring'''





    def 	A		(   self						:		int							):

      """simple docstring"""





      __lowerCAmelCase			=				inspect.getfile(accelerate.test_utils							)
      __lowerCAmelCase			=				os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['''scripts''', '''test_sync.py''']							)





    @require_cpu
    def 	A		(   self						:		str							):

      """simple docstring"""





      debug_launcher(test_sync.main			,						num_processes=1							)





    @require_cpu
    def 	A		(   self						:		List[str]							):

      """simple docstring"""





      debug_launcher(test_sync.main							)





    @require_single_gpu
    def 	A		(   self						:		Optional[Any]							):

      """simple docstring"""





      test_sync.main()





    @require_multi_gpu
    def 	A		(   self						:		Union[str, Any]							):

      """simple docstring"""





      print(f'''Found {torch.cuda.device_count()} devices.'''							)
      __lowerCAmelCase			=				['''torchrun''', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
      with patch_environment(omp_num_threads=1							):
        execute_subprocess_async(snake_case			,						env=os.environ.copy()							)
