

'''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__":
     lowerCamelCase				:   Union[str, 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=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)

     lowerCamelCase				,					lowerCamelCase				:   Any      				=  parser.parse_known_args()

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

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

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

     # Preprocess train dataset
     lowerCamelCase				:   Dict      				=  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"])

     lowerCamelCase				:   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"]
     }
     lowerCamelCase				:   int      				=  tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
         args.per_device_train_batch_size
     )

     # Preprocess test dataset
     lowerCamelCase				:   Union[str, 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"])

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

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

     lowerCamelCase				:   str      				=  time.time()
     lowerCamelCase				:   Optional[int]      				=  model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
     lowerCamelCase				:   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'''
lowerCamelCase				:   int      				=  {
    0: "underwear",
    1: "pants",
    2: "belt",
    3: "suit",
    4: "shoe",
    5: "socks",
    6: "shirt",
    7: "tie",
    8: "watch",
}

lowerCamelCase				:   Union[str, Any]      				=  [[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

lowerCamelCase				:   int      				=  [0 for x in range(len(graph))]
lowerCamelCase				:   Optional[Any]      				=  []


def  _lowerCAmelCase      (		_UpperCamelCase		:  List[str]							,				_UpperCamelCase		:  Union[str, Any]       )   ->    Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     while stack:
          _SCREAMING_SNAKE_CASE												=stack.pop()
          print(_UpperCamelCase							,				clothes[current_clothing]       )
          order += 1


def  _lowerCAmelCase      (		_UpperCamelCase		:  str							,				_UpperCamelCase		:  Optional[Any]							,				_UpperCamelCase		:  Dict       )   ->    Optional[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE												=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )

     stack.append(_UpperCamelCase       )


def  _lowerCAmelCase      (		_UpperCamelCase		:  Dict							,				_UpperCamelCase		:  List[str]       )   ->    Optional[int]:
     """simple docstring"""




     for v in range(len(_UpperCamelCase       )       ):
          if not visited[v]:
               depth_first_search(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase       )


if __name__ == "__main__":
     topological_sort(graph, visited)
     print(stack)
     print_stack(stack, clothes)
