


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



def     UpperCAmelCase__      (			UpperCAmelCase_					:       Tuple   ,					UpperCAmelCase_					:       Dict   ,					UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple=None							)  ->	Optional[Any]:
     __lowerCamelCase     :			int              =	tqdm(lowercase__							)
     for i, batch in enumerate(lowercase__							):
          with tf.GradientTape() as tape:
               __lowerCamelCase       , __lowerCamelCase     :			Tuple              =	batch
               __lowerCamelCase     :			Dict              =	model(lowercase__							)
               __lowerCamelCase     :			str              =	loss(lowercase__   ,					outputs.logits							)

          if SDP_ENABLED:
               __lowerCamelCase     :			Any              =	sdp.DistributedGradientTape(lowercase__   ,					sparse_as_dense=lowercase__							)

          __lowerCamelCase     :			Tuple              =	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

     __lowerCamelCase     :			Tuple              =	{'loss': loss_value.numpy()}
     return train_results



def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Optional[int]							)  ->	Dict:
     # Load dataset
     __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	load_dataset('imdb'   ,					split=['train', 'test']							)

     # Preprocess train dataset
     __lowerCamelCase     :			List[str]              =	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']							)

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

     # Preprocess test dataset
     __lowerCamelCase     :			List[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']							)

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

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

     return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      A__	:      str				=						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__	:      Optional[int]				=						parser.parse_known_args()

      # Set up logging
      A__	:      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()

            A__	:      Tuple				=						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__	:      str				=						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      A__	:      List[str]				=						AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      A__		,      A__	:      Optional[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
      A__	:      int				=						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      A__	:      Tuple				=						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      A__	:      Union[str, Any]				=						[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__	:      Union[str, Any]				=						time.time()
            A__	:      Any				=						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__	:      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__	:      Optional[Any]				=						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info("""*** Evaluate ***""")

            A__	:      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 typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__	:      Any				=						logging.get_logger(__name__)

A__	:      str				=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Dict							  =    'prophetnet'
    lowerCamelCase						:      List[str]							  =    ['past_key_values']
    lowerCamelCase						:      List[str]							  =    {
        'num_attention_heads': 'num_encoder_attention_heads',
    }







    def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
         __lowerCamelCase     :			List[Any]              =	vocab_size
         __lowerCamelCase     :			Union[str, Any]              =	hidden_size
         __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
         __lowerCamelCase     :			Any              =	num_encoder_layers
         __lowerCamelCase     :			int              =	num_encoder_attention_heads
         __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
         __lowerCamelCase     :			str              =	num_decoder_layers
         __lowerCamelCase     :			int              =	num_decoder_attention_heads
         __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
         __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
         __lowerCamelCase     :			Union[str, Any]              =	activation_function

         # parameters for prophetnet
         __lowerCamelCase     :			int              =	ngram
         __lowerCamelCase     :			Optional[int]              =	num_buckets
         __lowerCamelCase     :			str              =	relative_max_distance
         __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
         __lowerCamelCase     :			str              =	eps

         # 3 Types of Dropout
         __lowerCamelCase     :			Optional[Any]              =	attention_dropout
         __lowerCamelCase     :			str              =	activation_dropout
         __lowerCamelCase     :			Optional[Any]              =	dropout

         __lowerCamelCase     :			Optional[Any]              =	use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )







    @property
    def    lowercase_       (							self			)       ->  int:
         return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
         raise NotImplementedError(
             'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
             ' `num_decoder_layers`.'			)






