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      :				Optional[int]     			=True
    lowerCAmelCase      :				int     			="p3dn.24xlarge"
    import smdistributed.dataparallel.tensorflow as sdp
else:
    lowerCAmelCase      :				int     			=False







def     A__	(       __A ,			__A ,			__A ,			__A ,			__A ,			__A ,			__A=None						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[Any]			    =    tqdm(__A						)
    for i, batch in enumerate(__A						):
        with tf.GradientTape() as tape:
            _lowerCamelCase    ,				_lowerCamelCase  :		int			    =    batch
            _lowerCamelCase  :		str			    =    model(__A						)
            _lowerCamelCase  :		List[str]			    =    loss(__A ,			outputs.logits						)

        if SDP_ENABLED:
            _lowerCamelCase  :		str			    =    sdp.DistributedGradientTape(__A ,			sparse_as_dense=__A						)

        _lowerCamelCase  :		List[Any]			    =    tape.gradient(__A ,			model.trainable_variables						)
        opt.apply_gradients(zip(__A ,			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

    _lowerCamelCase  :		int			    =    {"""loss""": loss_value.numpy()}
    return train_results







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    # Load dataset
    _lowerCamelCase    ,				_lowerCamelCase  :		Optional[int]			    =    load_dataset("""imdb""" ,			split=["""train""", """test"""]						)

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

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

    # Preprocess test dataset
    _lowerCamelCase  :		List[str]			    =    test_dataset.map(
        lambda __A						: tokenizer(e["""text"""] ,			truncation=__A ,			padding="""max_length"""						) ,			batched=__A						)
    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  :		List[Any]			    =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])						)

    if SDP_ENABLED:
        _lowerCamelCase  :		int			    =    tf_train_dataset.shard(sdp.size() ,			sdp.rank()						)
        _lowerCamelCase  :		List[Any]			    =    tf_test_dataset.shard(sdp.size() ,			sdp.rank()						)
    _lowerCamelCase  :		Tuple			    =    tf_train_dataset.batch(__A ,			drop_remainder=__A						)
    _lowerCamelCase  :		Optional[int]			    =    tf_test_dataset.batch(__A ,			drop_remainder=__A						)

    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=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)
    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      :				Union[str, Any]     			=parser.parse_known_args()

    # Set up logging
    lowerCAmelCase      :				List[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      :				int     			=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      :				Tuple     			=AutoTokenizer.from_pretrained(args.model_name_or_path)

    # get datasets
    lowerCAmelCase			,			lowerCAmelCase      :				str     			=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      :				List[Any]     			=tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
    lowerCAmelCase      :				Union[str, Any]     			=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    lowerCAmelCase      :				List[Any]     			=[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      :				Tuple     			=time.time()
        lowerCAmelCase      :				str     			=fit(
            model,
            loss,
            optimizer,
            tf_train_dataset,
            args.epochs,
            args.per_device_train_batch_size,
            max_steps=args.max_steps,
        )
        lowerCAmelCase      :				Dict     			=time.time() - start_train_time
        logger.info("*** Train ***")
        logger.info(F"""train_runtime = {end_train_time}""")

        lowerCAmelCase      :				List[str]     			=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[Any]     			=model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
        logger.info("*** Evaluate ***")

        lowerCAmelCase      :				Union[str, Any]     			=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)





from __future__ import annotations

import sys







def     A__	(       __A						):







    '''simple docstring'''
    if n < 0:
        raise ValueError("""Negative arguments are not supported"""						)
    return _fib(__A						)[0]







def     A__	(       __A						):







    '''simple docstring'''
    if n == 0:  # (F(0), F(1))
        return (0, 1)

    # F(2n) = F(n)[2F(n+1) − F(n)]
    # F(2n+1) = F(n+1)^2+F(n)^2
    _lowerCamelCase    ,				_lowerCamelCase  :		List[Any]			    =    _fib(n // 2						)
    _lowerCamelCase  :		List[Any]			    =    a * (b * 2 - a)
    _lowerCamelCase  :		Optional[int]			    =    a * a + b * b
    return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=int(sys.argv[1])
    print(F"""fibonacci({n}) is {fibonacci(n)}""")




