



'''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_				:					Union[str, Any]							=      True
   A_				:					int							=      """p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
else:
   A_				:					Optional[int]							=      False




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_=None						)->						Dict:





       '''simple docstring'''


       _UpperCAmelCase    :		Union[str, Any]     	=					tqdm(lowerCAmelCase_						)
       for i, batch in enumerate(lowerCAmelCase_						):
              with tf.GradientTape() as tape:
                     _UpperCAmelCase	,_UpperCAmelCase    :		List[str]     	=					batch
                     _UpperCAmelCase    :		Tuple     	=					model(lowerCAmelCase_						)
                     _UpperCAmelCase    :		List[Any]     	=					loss(lowerCAmelCase_			,  outputs.logits						)

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

              _UpperCAmelCase    :		str     	=					tape.gradient(lowerCAmelCase_			,  model.trainable_variables						)
              opt.apply_gradients(zip(lowerCAmelCase_			,  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

       _UpperCAmelCase    :		Optional[Any]     	=					{"""loss""": loss_value.numpy()}
       return train_results




def        snake_case_   (	lowerCAmelCase_			,  lowerCAmelCase_			,  lowerCAmelCase_						)->						str:





       '''simple docstring'''


       _UpperCAmelCase	,_UpperCAmelCase    :		Tuple     	=					load_dataset("""imdb"""			,  split=["""train""", """test"""]						)

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

       _UpperCAmelCase    :		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"""]
       }
       _UpperCAmelCase    :		Optional[Any]     	=					tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])						)

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

       _UpperCAmelCase    :		Optional[Any]     	=					{
           x: test_dataset[x].to_tensor(default_value=0			,  shape=[None, tokenizer.model_max_length]						)
           for x in ["""input_ids""", """attention_mask"""]
       }
       _UpperCAmelCase    :		List[str]     	=					tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])						)

       if SDP_ENABLED:
              _UpperCAmelCase    :		str     	=					tf_train_dataset.shard(sdp.size()			,  sdp.rank()						)
              _UpperCAmelCase    :		Optional[int]     	=					tf_test_dataset.shard(sdp.size()			,  sdp.rank()						)
       _UpperCAmelCase    :		Dict     	=					tf_train_dataset.batch(lowerCAmelCase_			,  drop_remainder=lowerCAmelCase_						)
       _UpperCAmelCase    :		Union[str, Any]     	=					tf_test_dataset.batch(lowerCAmelCase_			,  drop_remainder=lowerCAmelCase_						)

       return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   A_				:					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_				:					List[str]							=      parser.parse_known_args()

   # Set up logging
   A_				:					Tuple							=      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_				:					Union[str, 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
   A_				:					int							=      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A_				:					int							=      AutoTokenizer.from_pretrained(args.model_name_or_path)

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

      A_				:					Optional[int]							=      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_				:					List[str]							=      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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

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