


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():
   _SCREAMING_SNAKE_CASE :					List[str]			=	True
   _SCREAMING_SNAKE_CASE :					Dict			=	"""p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
else:
   _SCREAMING_SNAKE_CASE :					Dict			=	False







def 						_lowercase       (     __lowerCamelCase			:					str		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					Tuple		,__lowerCamelCase			:					Any		,__lowerCamelCase			:					List[Any]		,__lowerCamelCase			:					Optional[int]=None			)  ->   Union[str, Any]:





     '''simple docstring'''





     UpperCamelCase__							:  Tuple								=     tqdm(__lowerCamelCase			)
     for i, batch in enumerate(__lowerCamelCase			):
          with tf.GradientTape() as tape:
               UpperCamelCase__	,UpperCamelCase__							:  List[str]								=     batch
               UpperCamelCase__							:  Dict								=     model(__lowerCamelCase			)
               UpperCamelCase__							:  Optional[int]								=     loss(__lowerCamelCase		,outputs.logits			)

          if SDP_ENABLED:
               UpperCamelCase__							:  Tuple								=     sdp.DistributedGradientTape(__lowerCamelCase		,sparse_as_dense=__lowerCamelCase			)

          UpperCamelCase__							:  Any								=     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__							:  Dict								=     {'''loss''': loss_value.numpy()}
     return train_results







def 						_lowercase       (     __lowerCamelCase			:					Tuple		,__lowerCamelCase			:					str		,__lowerCamelCase			:					Optional[Any]			)  ->   Union[str, Any]:





     '''simple docstring'''





     UpperCamelCase__	,UpperCamelCase__							:  Tuple								=     load_dataset('''imdb'''		,split=['''train''', '''test''']			)

     # Preprocess train dataset
     UpperCamelCase__							:  Optional[int]								=     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__							:  List[str]								=     {
         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__							:  Optional[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__							:  int								=     tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])			)

     if SDP_ENABLED:
          UpperCamelCase__							:  Union[str, Any]								=     tf_train_dataset.shard(sdp.size()		,sdp.rank()			)
          UpperCamelCase__							:  List[str]								=     tf_test_dataset.shard(sdp.size()		,sdp.rank()			)
     UpperCamelCase__							:  List[str]								=     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__":
   _SCREAMING_SNAKE_CASE :					Dict			=	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"""])

   _SCREAMING_SNAKE_CASE :					str			=	parser.parse_known_args()

   # Set up logging
   _SCREAMING_SNAKE_CASE :					str			=	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()

      _SCREAMING_SNAKE_CASE :					Dict			=	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
   _SCREAMING_SNAKE_CASE :					Any			=	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   _SCREAMING_SNAKE_CASE :					List[Any]			=	AutoTokenizer.from_pretrained(args.model_name_or_path)

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

      _SCREAMING_SNAKE_CASE :					Any			=	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):
      _SCREAMING_SNAKE_CASE :					List[str]			=	model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
      logger.info("""*** Evaluate ***""")

      _SCREAMING_SNAKE_CASE :					Tuple			=	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 math







def 						_lowercase       (     __lowerCamelCase			:					int			)  ->   str:





     '''simple docstring'''





     if number == 0:
          UpperCamelCase__							:  List[str]								=     '''Zero'''
          return words
     else:
          UpperCamelCase__							:  str								=     math.logaa(__lowerCamelCase			)
          UpperCamelCase__							:  Tuple								=     digits + 1
          UpperCamelCase__							:  List[str]								=     {}
          UpperCamelCase__							:  Optional[int]								=     ''''''
          UpperCamelCase__							:  str								=     '''One'''
          UpperCamelCase__							:  int								=     '''Two'''
          UpperCamelCase__							:  str								=     '''Three'''
          UpperCamelCase__							:  Dict								=     '''Four'''
          UpperCamelCase__							:  List[str]								=     '''Five'''
          UpperCamelCase__							:  int								=     '''Six'''
          UpperCamelCase__							:  str								=     '''Seven'''
          UpperCamelCase__							:  List[str]								=     '''Eight'''
          UpperCamelCase__							:  Tuple								=     '''Nine'''

          UpperCamelCase__							:  int								=     {}
          UpperCamelCase__							:  Any								=     ''''''
          UpperCamelCase__							:  Union[str, Any]								=     '''Twenty'''
          UpperCamelCase__							:  Optional[int]								=     '''Thirty'''
          UpperCamelCase__							:  Optional[int]								=     '''Forty'''
          UpperCamelCase__							:  Any								=     '''Fifty'''
          UpperCamelCase__							:  Optional[Any]								=     '''Sixty'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Seventy'''
          UpperCamelCase__							:  Tuple								=     '''Eighty'''
          UpperCamelCase__							:  Dict								=     '''Ninety'''

          UpperCamelCase__							:  Any								=     {}
          UpperCamelCase__							:  str								=     '''Ten'''
          UpperCamelCase__							:  List[Any]								=     '''Eleven'''
          UpperCamelCase__							:  List[str]								=     '''Twelve'''
          UpperCamelCase__							:  List[str]								=     '''Thirteen'''
          UpperCamelCase__							:  List[Any]								=     '''Fourteen'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Fifteen'''
          UpperCamelCase__							:  str								=     '''Sixteen'''
          UpperCamelCase__							:  int								=     '''Seventeen'''
          UpperCamelCase__							:  Tuple								=     '''Eighteen'''
          UpperCamelCase__							:  int								=     '''Nineteen'''

          UpperCamelCase__							:  Union[str, Any]								=     {}
          UpperCamelCase__							:  Dict								=     '''Hundred,'''
          UpperCamelCase__							:  Union[str, Any]								=     '''Thousand,'''
          UpperCamelCase__							:  List[Any]								=     '''Lakh,'''
          UpperCamelCase__							:  str								=     '''Crore,'''

          UpperCamelCase__							:  int								=     number
          UpperCamelCase__							:  int								=     ''''''
          UpperCamelCase__							:  int								=     0
          UpperCamelCase__							:  int								=     int(__lowerCamelCase			)
          while counter < digits:
               UpperCamelCase__							:  str								=     temp_num % 10
               if counter % 2 == 0:
                    UpperCamelCase__							:  List[Any]								=     ''''''
                    if counter in placevalue and current != 0:
                         UpperCamelCase__							:  Optional[Any]								=     placevalue[counter]
                    if counter == 2:
                         UpperCamelCase__							:  Tuple								=     singles[current] + addition + words
                    elif counter == 0:
                         if ((temp_num % 100) // 10) == 1:
                              UpperCamelCase__							:  Optional[Any]								=     teens[current] + addition + words
                              UpperCamelCase__							:  int								=     temp_num // 10
                              counter += 1
                         else:
                              UpperCamelCase__							:  Optional[Any]								=     singles[current] + addition + words

                    else:
                         UpperCamelCase__							:  Tuple								=     doubles[current] + addition + words

               else:
                    if counter == 1:
                         if current == 1:
                              UpperCamelCase__							:  Optional[Any]								=     teens[number % 10] + words
                         else:
                              UpperCamelCase__							:  Union[str, Any]								=     ''''''
                              if counter in placevalue:
                                   UpperCamelCase__							:  Optional[int]								=     placevalue[counter]
                              UpperCamelCase__							:  Optional[Any]								=     doubles[current] + addition + words
                    else:
                         UpperCamelCase__							:  List[Any]								=     ''''''
                         if counter in placevalue:
                              if current == 0 and ((temp_num % 100) // 10) == 0:
                                   UpperCamelCase__							:  Dict								=     ''''''
                              else:
                                   UpperCamelCase__							:  List[Any]								=     placevalue[counter]
                         if ((temp_num % 100) // 10) == 1:
                              UpperCamelCase__							:  Optional[int]								=     teens[current] + addition + words
                              UpperCamelCase__							:  Dict								=     temp_num // 10
                              counter += 1
                         else:
                              UpperCamelCase__							:  Optional[int]								=     singles[current] + addition + words
               counter += 1
               UpperCamelCase__							:  List[str]								=     temp_num // 10
     return words


if __name__ == "__main__":
   import doctest

   doctest.testmod()




