



'''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__":
				UpperCAmelCase			=    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)

				UpperCAmelCase		,   UpperCAmelCase			=    parser.parse_known_args()

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

				# Set up logging
				UpperCAmelCase			=    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
				UpperCAmelCase			=    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
				UpperCAmelCase			=    AutoTokenizer.from_pretrained(args.model_name_or_path)

				# Load dataset
				UpperCAmelCase		,   UpperCAmelCase			=    load_dataset('imdb', split=['train', 'test'])
				UpperCAmelCase			=    train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
				UpperCAmelCase			=    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

				# Preprocess train dataset
				UpperCAmelCase			=    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'])

				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'])).batch(
				    args.per_device_train_batch_size
				)

				# Preprocess test dataset
				UpperCAmelCase			=    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'])

				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'])).batch(
				    args.per_device_eval_batch_size
				)

				# fine optimizer and loss
				UpperCAmelCase			=    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
				UpperCAmelCase			=    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
				UpperCAmelCase			=    [tf.keras.metrics.SparseCategoricalAccuracy()]
				model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

				UpperCAmelCase			=    time.time()
				UpperCAmelCase			=    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
				UpperCAmelCase			=    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'''

from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCAmelCase			=    TypeVar('T', bound=bool)

class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	None:
							lowerCAmelCase    =	value
							lowerCAmelCase    =	None
							lowerCAmelCase    =	None


						@property
						def 				__snake_case    (					self      )	->	T:
							return self._value




						@staticmethod
						def 				__snake_case    (					A_	,       A_      )	->	RandomizedHeapNode[T] | None:
							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								lowerCAmelCase,   lowerCAmelCase    =	roota, roota

							if random.choice([True, False]      ):
								lowerCAmelCase,   lowerCAmelCase    =	roota.right, roota.left

							lowerCAmelCase    =	RandomizedHeapNode.merge(roota.left	,       A_      )

							return roota







class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_ = ()      )	->	None:
							lowerCAmelCase    =	None

							if data:
								for item in data:
									self.insert(A_      )


						def 				__snake_case    (					self	,       A_      )	->	None:
							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root	,       RandomizedHeapNode(A_      )      )


						def 				__snake_case    (					self      )	->	T | None:
							lowerCAmelCase    =	self.top()

							if self._root is None:
								return None

							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root.left	,       self._root.right      )

							return result


						def 				__snake_case    (					self      )	->	T:
							if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""      )
							return self._root.value


						def 				__snake_case    (					self      )	->	None:
							lowerCAmelCase    =	None


						def 				__snake_case    (					self      )	->	list[Any]:
							lowerCAmelCase    =	[]
							while self:
								result.append(self.pop()      )

							return result




						def __bool__(					self      )	->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()