




'''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		:	int       =      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=1_6)
  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		:	Any       =      parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  a		:	List[Any]       =      1_6
  a		:	List[Any]       =      1_6

  # Set up logging
  a		:	Dict       =      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		:	Optional[int]       =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  a		:	Optional[Any]       =      AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  a      ,				a		:	str       =      load_dataset('''imdb''', split=['''train''', '''test'''])
  a		:	int       =      train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
  a		:	List[Any]       =      test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  a		:	Optional[Any]       =      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		:	Optional[Any]       =      {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['''input_ids''', '''attention_mask''']
  }
  a		:	str       =      tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  a		:	Union[str, Any]       =      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		:	List[str]       =      {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['''input_ids''', '''attention_mask''']
  }
  a		:	Tuple       =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
      args.per_device_eval_batch_size
  )

  # fine optimizer and loss
  a		:	str       =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  a		:	Dict       =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  a		:	Optional[Any]       =      [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  a		:	str       =      time.time()
  a		:	Dict       =      model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  a		:	Any       =      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}""")







'''simple docstring'''







import matplotlib.pyplot as plt
import numpy as np







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:str										=      len(SCREAMING_SNAKE_CASE     )  # Number of training samples
		SCREAMING_SNAKE_CASE_						:List[Any]										=      np.eye(SCREAMING_SNAKE_CASE     )  # Initialize weights as identity matrix
		for j in range(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Tuple										=      point - x_train[j]
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.exp(diff @ diff.T / (-2.0 * tau**2)     )

		return weights







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      weight_matrix(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      np.zeros(len(SCREAMING_SNAKE_CASE     )     )  # Initialize array of predictions
		for i, item in enumerate(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      item @ local_weight(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		return y_pred







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		import seaborn as sns

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sns.load_dataset(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.array(data[x_name]     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array(data[y_name]     )

		SCREAMING_SNAKE_CASE_						:str										=      np.ones(len(SCREAMING_SNAKE_CASE     )     )

		# pairing elements of one and x_data
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.column_stack((one, x_data)     )

		return x_train, x_data, y_data







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		):
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.sort(SCREAMING_SNAKE_CASE ,		axis=0     )
		plt.scatter(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		color='blue'     )
		plt.plot(
		    x_train_sorted[:, 1] ,		preds[x_train[:, 1].argsort(0     )] ,		color='yellow' ,		linewidth=5 ,		)
		plt.title('Local Weighted Regression'     )
		plt.xlabel(SCREAMING_SNAKE_CASE     )
		plt.ylabel(SCREAMING_SNAKE_CASE     )
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : str				=   load_data("""tips""", """total_bill""", """tip""")
							SCREAMING_SNAKE_CASE__     : str				=   local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")
