




'''simple docstring'''

from __future__ import annotations

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





class __magic_name__(						__lowerCAmelCase      ):






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



									'''simple docstring'''






									pass






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



									'''simple docstring'''






									pass







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



									'''simple docstring'''






									pass







a__         =     TypeVar('''T''', bound=Comparable)





class __magic_name__(						Generic[T]      ):






						def __init__(   self						:						Dict       ):



									'''simple docstring'''






									snake_case__          =						[]
									snake_case__          =						0






						def __repr__(   self						:						List[Any]       ):



									'''simple docstring'''






									return str(self.h       )






						def       __lowerCAmelCase(   self						:						Optional[int]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						List[str]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






									snake_case__          =						2 * parent_idx + 1
									if left_child_index < self.heap_size:
												return left_child_index
									return None






						def       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Optional[Any]					,   __UpperCamelCase						:						int       ):



									'''simple docstring'''






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






						def       __lowerCAmelCase(   self						:						Any					,   __UpperCamelCase						:						Iterable[T]       ):



									'''simple docstring'''






									snake_case__          =						list(__UpperCamelCase       )
									snake_case__          =						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(__UpperCamelCase       )






						def       __lowerCAmelCase(   self						:						List[Any]       ):



									'''simple docstring'''






									if self.heap_size >= 2:
												snake_case__          =						self.h[0]
												snake_case__          =						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       __lowerCAmelCase(   self						:						Union[str, Any]					,   __UpperCamelCase						:						T       ):



									'''simple docstring'''






									self.h.append(__UpperCamelCase       )
									snake_case__          =						(self.heap_size - 1) // 2
									self.heap_size += 1
									while idx >= 0:
												self.max_heapify(__UpperCamelCase       )
												snake_case__          =						(idx - 1) // 2







						def       __lowerCAmelCase(   self						:						Tuple       ):



									'''simple docstring'''






									snake_case__          =						self.heap_size
									for j in range(size - 1					,   0					,   -1       ):
												snake_case__    ,			snake_case__          =						self.h[j], self.h[0]
												self.heap_size -= 1
												self.max_heapify(0       )
									snake_case__          =						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],
		    [103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5],
		    [-45, -2, -5],
		]:
				print(F"unsorted array: {unsorted}")

				a__         =     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(100)
				print(F"after new value 100 inserted: {heap}")

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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a__         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTJForCausalLM''',
		    '''GPTJForQuestionAnswering''',
		    '''GPTJForSequenceClassification''',
		    '''GPTJModel''',
		    '''GPTJPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TFGPTJForCausalLM''',
		    '''TFGPTJForQuestionAnswering''',
		    '''TFGPTJForSequenceClassification''',
		    '''TFGPTJModel''',
		    '''TFGPTJPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''FlaxGPTJForCausalLM''',
		    '''FlaxGPTJModel''',
		    '''FlaxGPTJPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gptj import (
				    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTJForCausalLM,
				    GPTJForQuestionAnswering,
				    GPTJForSequenceClassification,
				    GPTJModel,
				    GPTJPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gptj import (
				    TFGPTJForCausalLM,
				    TFGPTJForQuestionAnswering,
				    TFGPTJForSequenceClassification,
				    TFGPTJModel,
				    TFGPTJPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)