code: 
  
  
  
  
  
  
  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 			lowercase__  (				A_:	List[Any]     ,			A_:	str     ,			A_:	int     ,			A_:	str     ,			A_:	Optional[Any]     ,			A_:	List[Any]     ,			A_:	Tuple=None		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=tqdm(A_		)
         for i, batch in enumerate(A_		):
                with tf.GradientTape() as tape:
                       __UpperCAmelCase							,   __UpperCAmelCase    							=batch
                       __UpperCAmelCase    							=model(A_		)
                       __UpperCAmelCase    							=loss(A_     ,			outputs.logits		)
  
                if SDP_ENABLED:
                       __UpperCAmelCase    							=sdp.DistributedGradientTape(A_     ,			sparse_as_dense=A_		)
  
                __UpperCAmelCase    							=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
  
         __UpperCAmelCase    							={"""loss""": loss_value.numpy()}
         return train_results
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	List[str]     ,			A_:	str     ,			A_:	List[Any]		)  ->							Tuple:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase							,   __UpperCAmelCase    							=load_dataset("""imdb"""     ,			split=["""train""", """test"""]		)
  
         # Preprocess train dataset
         __UpperCAmelCase    							=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"""]		)
  
         __UpperCAmelCase    							={
             x: train_dataset[x].to_tensor(default_value=0     ,			shape=[None, tokenizer.model_max_length]		)
             for x in ["""input_ids""", """attention_mask"""]
         }
         __UpperCAmelCase    							=tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])		)
  
         # Preprocess test dataset
         __UpperCAmelCase    							=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"""]		)
  
         __UpperCAmelCase    							={
             x: test_dataset[x].to_tensor(default_value=0     ,			shape=[None, tokenizer.model_max_length]		)
             for x in ["""input_ids""", """attention_mask"""]
         }
         __UpperCAmelCase    							=tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])		)
  
         if SDP_ENABLED:
                __UpperCAmelCase    							=tf_train_dataset.shard(sdp.size()     ,			sdp.rank()		)
                __UpperCAmelCase    							=tf_test_dataset.shard(sdp.size()     ,			sdp.rank()		)
         __UpperCAmelCase    							=tf_train_dataset.batch(A_     ,			drop_remainder=A_		)
         __UpperCAmelCase    							=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)
  
  
style_context: 
  
  
  
  
  
  
  import math as m
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         return (f(a + h		) - f(a - h		)) / (2 * h)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=xa  # set the initial guess
         __UpperCAmelCase    							=[a]
         __UpperCAmelCase    							=abs(f(A_		)		)
         __UpperCAmelCase    							=lambda A_		: calc_derivative(A_     ,			A_     ,			h=A_		)  # noqa: E731  Derivative of f(x)
         for _ in range(A_		):
                if fa(A_		) == 0:
                       raise ValueError("""No converging solution found"""		)
                __UpperCAmelCase    							=a - f(A_		) / fa(A_		)  # Calculate the next estimate
                if logsteps:
                       steps.append(A_		)
                if error < maxerror:
                       break
         else:
                raise ValueError("""Iteration limit reached, no converging solution found"""		)
         if logsteps:
                # If logstep is true, then log intermediate steps
                return a, error, steps
         return a, error
  
  
  if __name__ == "__main__":
     from matplotlib import pyplot as plt
  
     __A  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
     __A							,    __A							,    __A  							=  newton_raphson(
         f, xa=10, maxiter=10_00, step=1E-6, logsteps=True
     )
     plt.plot([abs(f(x)) for x in steps])
     plt.xlabel("step")
     plt.ylabel("error")
     plt.show()
     print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")
  
  
  
  
  