






'''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__	=     True
 UpperCamelCase__	=     'p3dn.24xlarge'
 import smdistributed.dataparallel.tensorflow as sdp
else:
 UpperCamelCase__	=     False




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase=None							):


   """simple docstring"""




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

      if SDP_ENABLED:
         lowercase_		:      Union[str, Any]          =       sdp.DistributedGradientTape(_UpperCamelCase  ,						sparse_as_dense=_UpperCamelCase							)

      lowercase_		:      Optional[int]          =       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

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




def 						__SCREAMING_SNAKE_CASE   (					_UpperCamelCase  ,						_UpperCamelCase  ,						_UpperCamelCase							):


   """simple docstring"""




   lowercase_		:      List[str]          =       load_dataset("imdb"  ,						split=["train", "test"]							)

   # Preprocess train dataset
   lowercase_		:      Dict          =       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"]							)

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

   # Preprocess test dataset
   lowercase_		:      Union[str, Any]          =       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"]							)

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

   if SDP_ENABLED:
      lowercase_		:      Optional[Any]          =       tf_train_dataset.shard(sdp.size()  ,						sdp.rank()							)
      lowercase_		:      Any          =       tf_test_dataset.shard(sdp.size()  ,						sdp.rank()							)
   lowercase_		:      Dict          =       tf_train_dataset.batch(_UpperCamelCase  ,						drop_remainder=_UpperCamelCase							)
   lowercase_		:      Optional[int]          =       tf_test_dataset.batch(_UpperCamelCase  ,						drop_remainder=_UpperCamelCase							)

   return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 UpperCamelCase__	=     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__	=     parser.parse_known_args()

 # Set up logging
 UpperCamelCase__	=     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__	=     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__	=     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 UpperCamelCase__	=     AutoTokenizer.from_pretrained(args.model_name_or_path)

 # get datasets
 UpperCamelCase__,       UpperCamelCase__	=     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__	=     tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 UpperCamelCase__	=     tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 UpperCamelCase__	=     [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__	=     time.time()
  UpperCamelCase__	=     fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  UpperCamelCase__	=     time.time() - start_train_time
  logger.info('*** Train ***')
  logger.info(f"""train_runtime = {end_train_time}""")

  UpperCamelCase__	=     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__	=     model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('*** Evaluate ***')

  UpperCamelCase__	=     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_flax_available,
    is_tf_available,
    is_torch_available,
)


UpperCamelCase__	=     {
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__	=     [
     'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'UniSpeechSatForAudioFrameClassification',
     'UniSpeechSatForCTC',
     'UniSpeechSatForPreTraining',
     'UniSpeechSatForSequenceClassification',
     'UniSpeechSatForXVector',
     'UniSpeechSatModel',
     'UniSpeechSatPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_unispeech_sat import (
      UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
      UniSpeechSatForAudioFrameClassification,
      UniSpeechSatForCTC,
      UniSpeechSatForPreTraining,
      UniSpeechSatForSequenceClassification,
      UniSpeechSatForXVector,
      UniSpeechSatModel,
      UniSpeechSatPreTrainedModel,
  )

else:
 import sys

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




