




'''simple docstring'''







import numpy as np





def 		snake_case_				(_a      :				Dict       ,  _a      :				Union[str, Any]	):
    UpperCAmelCase     			=						np.array(_a	)
    UpperCAmelCase     			=						np.array(_a	)

    UpperCAmelCase     			=						abs(predict - actual	)
    UpperCAmelCase     			=						difference.mean()

    return score





def 		snake_case_				(_a      :				List[str]       ,  _a      :				Any	):
    UpperCAmelCase     			=						np.array(_a	)
    UpperCAmelCase     			=						np.array(_a	)

    UpperCAmelCase     			=						predict - actual
    UpperCAmelCase     			=						np.square(_a	)

    UpperCAmelCase     			=						square_diff.mean()
    return score





def 		snake_case_				(_a      :				Optional[int]       ,  _a      :				Optional[Any]	):
    UpperCAmelCase     			=						np.array(_a	)
    UpperCAmelCase     			=						np.array(_a	)

    UpperCAmelCase     			=						predict - actual
    UpperCAmelCase     			=						np.square(_a	)
    UpperCAmelCase     			=						square_diff.mean()
    UpperCAmelCase     			=						np.sqrt(_a	)
    return score





def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				Any	):
    UpperCAmelCase     			=						np.array(_a	)
    UpperCAmelCase     			=						np.array(_a	)

    UpperCAmelCase     			=						np.log(predict + 1	)
    UpperCAmelCase     			=						np.log(actual + 1	)

    UpperCAmelCase     			=						log_predict - log_actual
    UpperCAmelCase     			=						np.square(_a	)
    UpperCAmelCase     			=						square_diff.mean()

    UpperCAmelCase     			=						np.sqrt(_a	)

    return score





def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				Dict	):
    UpperCAmelCase     			=						np.array(_a	)
    UpperCAmelCase     			=						np.array(_a	)

    UpperCAmelCase     			=						predict - actual
    UpperCAmelCase     			=						np.sum(_a	) / len(_a	)
    UpperCAmelCase     			=						np.sum(_a	) / len(_a	)
    # print(numerator, denumerator)
    UpperCAmelCase     			=						float(_a	) / denumerator * 1_0_0

    return score







def 		snake_case_				(_a      :				Optional[Any]       ,  _a      :				List[Any]	):
    return np.mean(np.array(_a	) == np.array(_a	)	)










'''simple docstring'''







import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


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=1)
   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)

   A       , A					       =parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A					       =16
   A					       =16

   # 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',
   )

   # Load model and tokenizer
   A					       =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A					       =AutoTokenizer.from_pretrained(args.model_name_or_path)

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

   # Preprocess train dataset
   A					       =train_dataset.map(
       lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
   )
   train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

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

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

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

   A					       =time.time()
   A					       =model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
   A					       =time.time() - start_train_time

   logger.info('*** Train ***')
   logger.info(f"""train_runtime = {end_train_time}""")
   for key, value in train_results.history.items():
      logger.info(f"""  {key} = {value}""")




