


"""simple docstring"""







import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("""SDP_ENABLED""") or is_sagemaker_dp_enabled():
       lowerCamelCase_     :     List[str]    		=						True
       lowerCamelCase_     :     Dict    		=						"""p3dn.24xlarge"""
       import smdistributed.dataparallel.tensorflow as sdp
else:
       lowerCamelCase_     :     Dict    		=						False
def _A		(		lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase ,					lowercase=None	):







    """simple docstring"""
    a					=tqdm(lowercase	)
    for i, batch in enumerate(lowercase	):
        with tf.GradientTape() as tape:
            a	,   a					=batch
            a					=model(lowercase	)
            a					=loss(lowercase ,					outputs.logits	)

        if SDP_ENABLED:
            a					=sdp.DistributedGradientTape(lowercase ,					sparse_as_dense=lowercase	)

        a					=tape.gradient(lowercase ,					model.trainable_variables	)
        opt.apply_gradients(zip(lowercase ,					model.trainable_variables	)	)

        pbar.set_description(f'''Loss: {loss_value:.4f}'''	)

        if SDP_ENABLED and i == 0:
            sdp.broadcast_variables(model.variables ,					root_rank=0	)
            sdp.broadcast_variables(opt.variables() ,					root_rank=0	)

        if max_steps and i >= max_steps:
            break

    a					={'''loss''': loss_value.numpy()}
    return train_results
def _A		(		lowercase ,					lowercase ,					lowercase	):







    """simple docstring"""
    # Load dataset
    a	,   a					=load_dataset('''imdb''' ,					split=['''train''', '''test''']	)

    # Preprocess train dataset
    a					=train_dataset.map(
        lambda lowercase	: tokenizer(e['''text'''] ,					truncation=lowercase ,					padding='''max_length'''	) ,					batched=lowercase	)
    train_dataset.set_format(type='''tensorflow''' ,					columns=['''input_ids''', '''attention_mask''', '''label''']	)

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

    # Preprocess test dataset
    a					=test_dataset.map(
        lambda lowercase	: tokenizer(e['''text'''] ,					truncation=lowercase ,					padding='''max_length'''	) ,					batched=lowercase	)
    test_dataset.set_format(type='''tensorflow''' ,					columns=['''input_ids''', '''attention_mask''', '''label''']	)

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

    if SDP_ENABLED:
        a					=tf_train_dataset.shard(sdp.size() ,					sdp.rank()	)
        a					=tf_test_dataset.shard(sdp.size() ,					sdp.rank()	)
    a					=tf_train_dataset.batch(lowercase ,					drop_remainder=lowercase	)
    a					=tf_test_dataset.batch(lowercase ,					drop_remainder=lowercase	)

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()

       # Hyperparameters sent by the client are passed as command-line arguments to the script.
       parser.add_argument("""--epochs""", type=int, default=3)
       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)
       parser.add_argument("""--max_steps""", type=int, default=None)

       # Data, model, and output directories
       parser.add_argument("""--output_data_dir""", type=str, default=os.environ["""SM_OUTPUT_DATA_DIR"""])
       parser.add_argument("""--model_dir""", type=str, default=os.environ["""SM_MODEL_DIR"""])
       parser.add_argument("""--n_gpus""", type=str, default=os.environ["""SM_NUM_GPUS"""])

       lowerCamelCase_		,						lowerCamelCase_     :     str    		=						parser.parse_known_args()

       # Set up logging
       lowerCamelCase_     :     str    		=						logging.getLogger(__name__)

       logging.basicConfig(
           level=logging.getLevelName("""INFO"""),
           handlers=[logging.StreamHandler(sys.stdout)],
           format="""%(asctime)s - %(name)s - %(levelname)s - %(message)s""",
       )

       if SDP_ENABLED:
              sdp.init()

              lowerCamelCase_     :     Dict    		=						tf.config.experimental.list_physical_devices("""GPU""")
              for gpu in gpus:
                     tf.config.experimental.set_memory_growth(gpu, True)
              if gpus:
                     tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], """GPU""")

    # Load model and tokenizer
       lowerCamelCase_     :     Any    		=						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       lowerCamelCase_     :     List[Any]    		=						AutoTokenizer.from_pretrained(args.model_name_or_path)

       # get datasets
       lowerCamelCase_		,						lowerCamelCase_     :     Union[str, Any]    		=						get_datasets(
           tokenizer=tokenizer,
           train_batch_size=args.per_device_train_batch_size,
           eval_batch_size=args.per_device_eval_batch_size,
       )

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

       # Training
       if args.do_train:
              # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
              lowerCamelCase_     :     List[str]    		=						time.time()
              lowerCamelCase_     :     List[str]    		=						fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              lowerCamelCase_     :     Union[str, Any]    		=						time.time() - start_train_time
              logger.info("""*** Train ***""")
              logger.info(F'train_runtime = {end_train_time}')

              lowerCamelCase_     :     Any    		=						os.path.join(args.output_dir, """train_results.txt""")

              if not SDP_ENABLED or sdp.rank() == 0:
                     with open(output_eval_file, """w""") as writer:
                            logger.info("""***** Train results *****""")
                            logger.info(train_results)
                            for key, value in train_results.items():
                                   logger.info(F'  {key} = {value}')
                                   writer.write(F'{key} = {value}\n')

    # Evaluation
       if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
              lowerCamelCase_     :     List[str]    		=						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info("""*** Evaluate ***""")

              lowerCamelCase_     :     Tuple    		=						os.path.join(args.output_dir, """eval_results.txt""")

              with open(output_eval_file, """w""") as writer:
                     logger.info("""***** Eval results *****""")
                     logger.info(result)
                     for key, value in result.items():
                            logger.info(F'  {key} = {value}')
                            writer.write(F'{key} = {value}\n')

    # Save result
       if SDP_ENABLED:
              if sdp.rank() == 0:
                     model.save_pretrained(args.output_dir)
                     tokenizer.save_pretrained(args.output_dir)
       else:
              model.save_pretrained(args.output_dir)
              tokenizer.save_pretrained(args.output_dir)



"""simple docstring"""







from __future__ import annotations

from random import randrange
def _A		(		lowercase	):







    """simple docstring"""
    if len(lowercase	) < 2:
        return collection
    a					=randrange(len(lowercase	)	)  # Use random element as pivot
    a					=collection[pivot_index]
    a					=[]  # All elements greater than pivot
    a					=[]  # All elements less than or equal to pivot

    for element in collection[:pivot_index]:
        (greater if element > pivot else lesser).append(lowercase	)

    for element in collection[pivot_index + 1 :]:
        (greater if element > pivot else lesser).append(lowercase	)

    return [*quick_sort(lowercase	), pivot, *quick_sort(lowercase	)]


if __name__ == "__main__":
       lowerCamelCase_     :     Union[str, Any]    		=						input("""Enter numbers separated by a comma:\n""").strip()
       lowerCamelCase_     :     Union[str, Any]    		=						[int(item) for item in user_input.split(""",""")]
       print(quick_sort(unsorted))