






"""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_						:					Union[str, Any]      				=			True
    UpperCamelCase_						:					int      				=			"""p3dn.24xlarge"""
    import smdistributed.dataparallel.tensorflow as sdp
else:
    UpperCamelCase_						:					Optional[int]      				=			False



def      __lowercase       (     a			:					Optional[Any]							,		a			:					Optional[Any]							,		a			:					Dict							,		a			:					Any							,		a			:					Optional[int]							,		a			:					Tuple							,		a			:					Union[str, Any]=None			)       ->       Dict:
      __snake_case   :     Union[str, Any]	      =tqdm(lowerCAmelCase_			)
      for i, batch in enumerate(lowerCAmelCase_			):
            with tf.GradientTape() as tape:
                  __snake_case   :     List[str]	      =batch
                  __snake_case   :     Tuple	      =model(lowerCAmelCase_			)
                  __snake_case   :     List[Any]	      =loss(lowerCAmelCase_							,		outputs.logits			)

            if SDP_ENABLED:
                  __snake_case   :     int	      =sdp.DistributedGradientTape(lowerCAmelCase_							,		sparse_as_dense=lowerCAmelCase_			)

            __snake_case   :     str	      =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

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



def      __lowercase       (     a			:					Dict							,		a			:					List[str]							,		a			:					str			)       ->       str:
      __snake_case   :     Tuple	      =load_dataset('''imdb'''							,		split=['''train''', '''test''']			)

      # Preprocess train dataset
      __snake_case   :     Union[str, Any]	      =train_dataset.map(
          lambda a			: tokenizer(e['''text''']							,		truncation=lowerCAmelCase_							,		padding='''max_length'''			)							,		batched=lowerCAmelCase_			)
      train_dataset.set_format(type='''tensorflow'''							,		columns=['''input_ids''', '''attention_mask''', '''label''']			)

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

      # Preprocess test dataset
      __snake_case   :     int	      =test_dataset.map(
          lambda a			: tokenizer(e['''text''']							,		truncation=lowerCAmelCase_							,		padding='''max_length'''			)							,		batched=lowerCAmelCase_			)
      test_dataset.set_format(type='''tensorflow'''							,		columns=['''input_ids''', '''attention_mask''', '''label''']			)

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

      if SDP_ENABLED:
            __snake_case   :     str	      =tf_train_dataset.shard(sdp.size()							,		sdp.rank()			)
            __snake_case   :     Optional[int]	      =tf_test_dataset.shard(sdp.size()							,		sdp.rank()			)
      __snake_case   :     Dict	      =tf_train_dataset.batch(lowerCAmelCase_							,		drop_remainder=lowerCAmelCase_			)
      __snake_case   :     Union[str, Any]	      =tf_test_dataset.batch(lowerCAmelCase_							,		drop_remainder=lowerCAmelCase_			)

      return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
    UpperCamelCase_						:					int      				=			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_						:					List[str]      				=			parser.parse_known_args()

    # Set up logging
    UpperCamelCase_						:					Tuple      				=			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_						:					Union[str, Any]      				=			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_						:					int      				=			TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    UpperCamelCase_						:					int      				=			AutoTokenizer.from_pretrained(args.model_name_or_path)

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

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

        UpperCamelCase_						:					List[str]      				=			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'''






import numpy as np



class 				lowercase			:


 """simple docstring"""


 def __init__(				self    ,a_=5    ,a_=5				)						->  Tuple:
        _UpperCAmelCase    :		Dict     	=					depth
        _UpperCAmelCase    :		List[str]     	=					0
        _UpperCAmelCase    :		List[str]     	=					None
        _UpperCAmelCase    :		Optional[int]     	=					None
        _UpperCAmelCase    :		Tuple     	=					min_leaf_size
        _UpperCAmelCase    :		int     	=					None







 def _snake_case					(				self    ,a_    ,a_				)						->  Optional[int]:
        if labels.ndim != 1:
               print("""Error: Input labels must be one dimensional"""				)

        return np.mean((labels - prediction) ** 2				)







 def _snake_case					(				self    ,a_    ,a_				)						->  int:
        if x.ndim != 1:
               print("""Error: Input data set must be one dimensional"""				)
               return
        if len(a_				) != len(a_				):
               print("""Error: X and y have different lengths"""				)
               return
        if y.ndim != 1:
               print("""Error: Data set labels must be one dimensional"""				)
               return

        if len(a_				) < 2 * self.min_leaf_size:
               _UpperCAmelCase    :		Union[str, Any]     	=					np.mean(a_				)
               return

        if self.depth == 1:
               _UpperCAmelCase    :		Dict     	=					np.mean(a_				)
               return

        _UpperCAmelCase    :		Union[str, Any]     	=					0
        _UpperCAmelCase    :		Optional[Any]     	=					self.mean_squared_error(a_    ,np.mean(a_				)				) * 2
        for i in range(len(a_				)				):
               if len(x[:i]				) < self.min_leaf_size:
                      continue
               elif len(x[i:]				) < self.min_leaf_size:
                      continue
               else:
                      _UpperCAmelCase    :		Dict     	=					self.mean_squared_error(x[:i]    ,np.mean(y[:i]				)				)
                      _UpperCAmelCase    :		Any     	=					self.mean_squared_error(x[i:]    ,np.mean(y[i:]				)				)
                      _UpperCAmelCase    :		Optional[Any]     	=					error_left + error_right
                      if error < min_error:
                             _UpperCAmelCase    :		Union[str, Any]     	=					i
                             _UpperCAmelCase    :		Optional[int]     	=					error

        if best_split != 0:
               _UpperCAmelCase    :		int     	=					x[:best_split]
               _UpperCAmelCase    :		Optional[Any]     	=					y[:best_split]
               _UpperCAmelCase    :		Optional[int]     	=					x[best_split:]
               _UpperCAmelCase    :		Dict     	=					y[best_split:]

               _UpperCAmelCase    :		List[str]     	=					x[best_split]
               _UpperCAmelCase    :		List[Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               _UpperCAmelCase    :		Union[str, Any]     	=					DecisionTree(
                   depth=self.depth - 1    ,min_leaf_size=self.min_leaf_size				)
               self.left.train(a_    ,a_				)
               self.right.train(a_    ,a_				)
        else:
               _UpperCAmelCase    :		Any     	=					np.mean(a_				)

        return







 def _snake_case					(				self    ,a_				)						->  List[Any]:
        if self.prediction is not None:
               return self.prediction
        elif self.left or self.right is not None:
               if x >= self.decision_boundary:
                      return self.right.predict(a_				)
               else:
                      return self.left.predict(a_				)
        else:
               print("""Error: Decision tree not yet trained"""				)
               return None







class 				lowercase			:


 """simple docstring"""


 @staticmethod
 def _snake_case					(				a_    ,a_				)						->  int:
        _UpperCAmelCase    :		Union[str, Any]     	=					float(0				)
        for label in labels:
               squared_error_sum += (label - prediction) ** 2

        return float(squared_error_sum / labels.size				)







def        snake_case_   (	)->						Optional[int]:





       '''simple docstring'''


       _UpperCAmelCase    :		Tuple     	=					np.arange(-1.0			,  1.0			,  0.0_0_5						)
       _UpperCAmelCase    :		Optional[int]     	=					np.sin(lowerCAmelCase_						)

       _UpperCAmelCase    :		int     	=					DecisionTree(depth=10			,  min_leaf_size=10						)
       tree.train(lowerCAmelCase_			,  lowerCAmelCase_						)

       _UpperCAmelCase    :		Any     	=					(np.random.rand(10						) * 2) - 1
       _UpperCAmelCase    :		Dict     	=					np.array([tree.predict(lowerCAmelCase_						) for x in test_cases]						)
       _UpperCAmelCase    :		Optional[Any]     	=					np.mean((predictions - test_cases) ** 2						)

       print("""Test values: """ + str(lowerCAmelCase_						)						)
       print("""Predictions: """ + str(lowerCAmelCase_						)						)
       print("""Average error: """ + str(lowerCAmelCase_						)						)


if __name__ == "__main__":
   main()
   import doctest

   doctest.testmod(name="""mean_squarred_error""", verbose=True)



