






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 			=							True
 a 			=							'p3dn.24xlarge'
 import smdistributed.dataparallel.tensorflow as sdp
else:
 a 			=							False
def    a__    (	A		,					A		,					A		,					A		,					A		,					A		,					A=None       ):
   lowercase__    :						int       =			tqdm(A       )
   for i, batch in enumerate(A       ):
      with tf.GradientTape() as tape:
         lowercase__    :						Optional[Any]       =			batch
         lowercase__    :						str       =			model(A       )
         lowercase__    :						Optional[Any]       =			loss(A		,					outputs.logits       )

      if SDP_ENABLED:
         lowercase__    :						List[Any]       =			sdp.DistributedGradientTape(A		,					sparse_as_dense=A       )

      lowercase__    :						Optional[int]       =			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

   lowercase__    :						List[Any]       =			{'''loss''': loss_value.numpy()}
   return train_results
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						Tuple       =			load_dataset("""imdb"""		,					split=["""train""", """test"""]       )

   # Preprocess train dataset
   lowercase__    :						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"""]       )

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

   # Preprocess test dataset
   lowercase__    :						Tuple       =			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"""]       )

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

   if SDP_ENABLED:
      lowercase__    :						Dict       =			tf_train_dataset.shard(sdp.size()		,					sdp.rank()       )
      lowercase__    :						Union[str, Any]       =			tf_test_dataset.shard(sdp.size()		,					sdp.rank()       )
   lowercase__    :						int       =			tf_train_dataset.batch(A		,					drop_remainder=A       )
   lowercase__    :						Tuple       =			tf_test_dataset.batch(A		,					drop_remainder=A       )

   return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 a 			=							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'])

 a			,   a 			=							parser.parse_known_args()

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

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

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

  a 			=							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)










import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
						import librosa  # noqa: E402

						__lowerCamelCase						= True
						__lowerCamelCase						= ''
except Exception as e:
						__lowerCamelCase						= False
						__lowerCamelCase						= (
						    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
						)


from PIL import Image  # noqa: E402

class      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase       ):
						lowercase									=  '''mel_config.json'''




						@register_to_config
						def __init__(    self		,	lowercase = 256		,	lowercase = 256		,	lowercase = 22_050		,	lowercase = 2_048		,	lowercase = 512		,	lowercase = 80		,	lowercase = 32		,	)			->						Optional[Any]:
													_a  :    Tuple			=						hop_length
													_a  :    Dict			=						sample_rate
													_a  :    Dict			=						n_fft
													_a  :    List[str]			=						top_db
													_a  :    Optional[int]			=						n_iter
													self.set_resolution(lowercase		,	lowercase       )
													_a  :    Optional[int]			=						None

													if not _librosa_can_be_imported:
																				raise ValueError(_import_error       )




						def      snake_case__(    self		,	lowercase		,	lowercase       )			->						Any:
													_a  :    Any			=						x_res
													_a  :    Optional[Any]			=						y_res
													_a  :    List[Any]			=						self.y_res
													_a  :    Tuple			=						self.x_res * self.hop_length - 1




						def      snake_case__(    self		,	lowercase = None		,	lowercase = None       )			->						Union[str, Any]:
													if audio_file is not None:
																				_a		,					_a  :    str			=						librosa.load(lowercase		,	mono=lowercase		,	sr=self.sr       )
													else:
																				_a  :    Any			=						raw_audio

													# Pad with silence if necessary.
													if len(self.audio       ) < self.x_res * self.hop_length:
																				_a  :    str			=						np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio       ),)       )]       )




						def      snake_case__(    self       )			->						int:
													return len(self.audio       ) // self.slice_size




						def      snake_case__(    self		,	lowercase = 0       )			->						np.ndarray:
													return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]




						def      snake_case__(    self       )			->						int:
													return self.sr




						def      snake_case__(    self		,	lowercase       )			->						Image.Image:
													_a  :    int			=						librosa.feature.melspectrogram(
													    y=self.get_audio_slice(lowercase       )		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_mels=self.n_mels       )
													_a  :    List[str]			=						librosa.power_to_db(lowercase		,	ref=np.max		,	top_db=self.top_db       )
													_a  :    List[str]			=						(((log_S + self.top_db) * 255 / self.top_db).clip(0		,	255       ) + 0.5).astype(np.uinta       )
													_a  :    Any			=						Image.fromarray(lowercase       )
													return image







						def      snake_case__(    self		,	lowercase       )			->						np.ndarray:
													_a  :    Any			=						np.frombuffer(image.tobytes()		,	dtype='''uint8'''       ).reshape((image.height, image.width)       )
													_a  :    Any			=						bytedata.astype('''float'''       ) * self.top_db / 255 - self.top_db
													_a  :    Dict			=						librosa.db_to_power(lowercase       )
													_a  :    List[str]			=						librosa.feature.inverse.mel_to_audio(
													    lowercase		,	sr=self.sr		,	n_fft=self.n_fft		,	hop_length=self.hop_length		,	n_iter=self.n_iter       )
													return audio