





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

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Optional[Any]   ,			_UpperCAmelCase :  int   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  Optional[int]   ,			_UpperCAmelCase :  Dict   ,			_UpperCAmelCase :  Optional[int]=None       ):
 lowerCAmelCase  							=      tqdm(_UpperCAmelCase       )
 for i, batch in enumerate(_UpperCAmelCase       ):
  with tf.GradientTape() as tape:
   lowerCAmelCase							,lowerCAmelCase  							=      batch
   lowerCAmelCase  							=      model(_UpperCAmelCase       )
   lowerCAmelCase  							=      loss(_UpperCAmelCase   ,			outputs.logits       )

  if SDP_ENABLED:
   lowerCAmelCase  							=      sdp.DistributedGradientTape(_UpperCAmelCase   ,			sparse_as_dense=_UpperCAmelCase       )

  lowerCAmelCase  							=      tape.gradient(_UpperCAmelCase   ,			model.trainable_variables       )
  opt.apply_gradients(zip(_UpperCAmelCase   ,			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  							=      {'loss': loss_value.numpy()}
 return train_results

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  Union[str, Any]   ,			_UpperCAmelCase :  List[str]       ):
 # Load dataset
 lowerCAmelCase							,lowerCAmelCase  							=      load_dataset('imdb'   ,			split=['train', 'test']       )

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

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

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

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

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

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
       __UpperCamelCase			:		List[Any]		    =   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'''])

       __UpperCamelCase    ,__UpperCamelCase			:		str		    =   parser.parse_known_args()

       # Set up logging
       __UpperCamelCase			:		Union[str, Any]		    =   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()

              __UpperCamelCase			:		Any		    =   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
       __UpperCamelCase			:		List[str]		    =   TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
       __UpperCamelCase			:		Union[str, Any]		    =   AutoTokenizer.from_pretrained(args.model_name_or_path)

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

              __UpperCamelCase			:		Dict		    =   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):
              __UpperCamelCase			:		Tuple		    =   model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info('''*** Evaluate ***''')

              __UpperCamelCase			:		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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)