



'''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():
    A__  :					Optional[Any]				= True
    A__  :					str				= '''p3dn.24xlarge'''
    import smdistributed.dataparallel.tensorflow as sdp
else:
    A__  :					str				= False





def    a_					(						_UpperCAmelCase  :	List[str]      ,_UpperCAmelCase  :	Any      ,_UpperCAmelCase  :	List[Any]      ,_UpperCAmelCase  :	int      ,_UpperCAmelCase  :	Optional[int]      ,_UpperCAmelCase  :	Tuple      ,_UpperCAmelCase  :	Tuple=None							)  ->   Union[str, Any]:
      __snake_case      :					Optional[Any]						=  tqdm(_lowercase							)
      for i, batch in enumerate(_lowercase							):
            with tf.GradientTape() as tape:
                  __snake_case      :					Optional[Any]						=  batch
                  __snake_case      :					Dict						=  model(_lowercase							)
                  __snake_case      :					List[Any]						=  loss(_lowercase      ,outputs.logits							)

            if SDP_ENABLED:
                  __snake_case      :					Tuple						=  sdp.DistributedGradientTape(_lowercase      ,sparse_as_dense=_lowercase							)

            __snake_case      :					List[str]						=  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

      __snake_case      :					Dict						=  {'''loss''': loss_value.numpy()}
      return train_results





def    a_					(						_UpperCAmelCase  :	Optional[Any]      ,_UpperCAmelCase  :	Dict      ,_UpperCAmelCase  :	List[Any]							)  ->   List[Any]:
      # Load dataset
      __snake_case      :					List[Any]						=  load_dataset('imdb'      ,split=['train', 'test']							)

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

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

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

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

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

      return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
    A__  :					Optional[int]				= 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'''])

    A__			,     A__  :					Dict				= parser.parse_known_args()

    # Set up logging
    A__  :					int				= 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()

        A__  :					str				= 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
    A__  :					Dict				= TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    A__  :					Any				= AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

        A__  :					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)










"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase_           ="""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

UpperCamelCase_           ="""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


UpperCamelCase_           ="""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,				_KWARGS_DESCRIPTION				)
class  _a						(      datasets.Metric				):






				def 				snake_case	( self		:    Optional[int]							)	->      List[Any]:




										'''simple docstring'''



										return datasets.MetricInfo(
										    description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(self._get_feature_types()							), reference_urls=[
										        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'''
										    ], )






				def 				snake_case	( self		:    Dict							)	->      Union[str, Any]:




										'''simple docstring'''



										if self.config_name == "multilist":
																return {
																    "predictions": datasets.Sequence(datasets.Value('''float'''							)							),
																    "references": datasets.Sequence(datasets.Value('''float'''							)							),
																}
										else:
																return {
																    "predictions": datasets.Value('''float'''							),
																    "references": datasets.Value('''float'''							),
																}






				def 				snake_case	( self		:    List[Any], lowerCAmelCase__		:    Dict, lowerCAmelCase__		:    Union[str, Any], lowerCAmelCase__		:    Tuple=None, lowerCAmelCase__		:    Tuple="uniform_average"							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					List[str]     =    mean_absolute_error(lowerCAmelCase__, lowerCAmelCase__, sample_weight=lowerCAmelCase__, multioutput=lowerCAmelCase__							)

										return {"mae": mae_score}



