




'''simple docstring'''




from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						@abstractmethod
						def __lt__(     self      :       T							, _lowerCamelCase      :       T    ):





											"""simple docstring"""







											pass







						@abstractmethod
						def __gt__(     self      :       T							, _lowerCamelCase      :       T    ):





											"""simple docstring"""







											pass







						@abstractmethod
						def __eq__(     self      :       T							, _lowerCamelCase      :       object    ):





											"""simple docstring"""







											pass






snake_case__          =						TypeVar("""T""", bound=Comparable)






class     UpperCamelCase_     (Generic[T]     ):







						"""simple docstring"""


						def __init__(     self      :       int    ):





											"""simple docstring"""







											A_ :     list[T] =							[]
											A_ :     int =							0







						def __repr__(     self      :       Dict    ):





											"""simple docstring"""







											return str(self.h    )







						def       _a    (     self      :       Union[str, Any]							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if child_idx > 0:
																return (child_idx - 1) // 2
											return None







						def       _a    (     self      :       Optional[int]							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     Optional[Any] =							2 * parent_idx + 1
											if left_child_index < self.heap_size:
																return left_child_index
											return None







						def       _a    (     self      :       Any							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											A_ :     Any =							2 * parent_idx + 2
											if right_child_index < self.heap_size:
																return right_child_index
											return None







						def       _a    (     self      :       int							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if index < self.heap_size:
																A_ :     int =							index
																A_ :     Any =							self.left_child_idx(_lowerCamelCase    )
																A_ :     List[str] =							self.right_child_idx(_lowerCamelCase    )
																# check which child is larger than its parent
																if left_child is not None and self.h[left_child] > self.h[violation]:
																					A_ :     Dict =							left_child
																if right_child is not None and self.h[right_child] > self.h[violation]:
																					A_ :     Dict =							right_child
																# if violation indeed exists
																if violation != index:
																					# swap to fix the violation
																					A_ :     List[str] =							self.h[index], self.h[violation]
																					# fix the subsequent violation recursively if any
																					self.max_heapify(_lowerCamelCase    )







						def       _a    (     self      :       Union[str, Any]							, _lowerCamelCase      :       Iterable[T]    ):





											"""simple docstring"""







											A_ :     str =							list(_lowerCamelCase    )
											A_ :     str =							len(self.h    )
											if self.heap_size > 1:
																# max_heapify from right to left but exclude leaves (last level)
																for i in range(self.heap_size // 2 - 1							, -1							, -1    ):
																					self.max_heapify(_lowerCamelCase    )







						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											if self.heap_size >= 2:
																A_ :     Union[str, Any] =							self.h[0]
																A_ :     List[str] =							self.h.pop(-1    )
																self.heap_size -= 1
																self.max_heapify(0    )
																return me
											elif self.heap_size == 1:
																self.heap_size -= 1
																return self.h.pop(-1    )
											else:
																raise Exception('''Empty heap'''    )







						def       _a    (     self      :       int							, _lowerCamelCase      :       T    ):





											"""simple docstring"""







											self.h.append(_lowerCamelCase    )
											A_ :     Optional[int] =							(self.heap_size - 1) // 2
											self.heap_size += 1
											while idx >= 0:
																self.max_heapify(_lowerCamelCase    )
																A_ :     Union[str, Any] =							(idx - 1) // 2







						def       _a    (     self      :       int    ):





											"""simple docstring"""







											A_ :     Tuple =							self.heap_size
											for j in range(size - 1							, 0							, -1    ):
																A_ :     Union[str, Any] =							self.h[j], self.h[0]
																self.heap_size -= 1
																self.max_heapify(0    )
											A_ :     Dict =							size


if __name__ == "__main__":
							import doctest

							# run doc test
							doctest.testmod()

							# demo
							for unsorted in [
							    [0],
							    [2],
							    [3, 5],
							    [5, 3],
							    [5, 5],
							    [0, 0, 0, 0],
							    [1, 1, 1, 1],
							    [2, 2, 3, 5],
							    [0, 2, 2, 3, 5],
							    [2, 5, 3, 0, 2, 3, 0, 3],
							    [6, 1, 2, 7, 9, 3, 4, 5, 10, 8],
							    [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
							    [-45, -2, -5],
							]:
														print(F'unsorted array: {unsorted}')

														snake_case__          =						Heap()
														heap.build_max_heap(unsorted)
														print(F'after build heap: {heap}')

														print(F'max value: {heap.extract_max()}')
														print(F'after max value removed: {heap}')

														heap.insert(1_00)
														print(F'after new value 100 inserted: {heap}')

														heap.heap_sort()
														print(F'heap-sorted array: {heap}\n')









'''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__":
							snake_case__          =						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)

							snake_case__       ,						snake_case__          =						parser.parse_known_args()

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

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

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

							# Preprocess train dataset
							snake_case__          =						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"""])

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

							# Preprocess test dataset
							snake_case__          =						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"""])

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

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

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



