





"""simple docstring"""





class 							_lowerCamelCase   :


						def __init__(self		)			-> Tuple:
								UpperCamelCase =						[0]
								UpperCamelCase =						0


						def      snake_case_ (self ,				__a		)			-> None:
								UpperCamelCase =						self.__heap[i]
								while i // 2 > 0:
										if self.__heap[i] > self.__heap[i // 2]:
												UpperCamelCase =						self.__heap[i // 2]
												UpperCamelCase =						temporary
										i //= 2


						def      snake_case_ (self ,				__a		)			-> None:
								self.__heap.append(__a		)
								self.__size += 1
								self.__swap_up(self.__size		)


						def      snake_case_ (self ,				__a		)			-> None:
								while self.__size >= 2 * i:
										if 2 * i + 1 > self.__size:
												UpperCamelCase =						2 * i
										else:
												if self.__heap[2 * i] > self.__heap[2 * i + 1]:
														UpperCamelCase =						2 * i
												else:
														UpperCamelCase =						2 * i + 1
										UpperCamelCase =						self.__heap[i]
										if self.__heap[i] < self.__heap[bigger_child]:
												UpperCamelCase =						self.__heap[bigger_child]
												UpperCamelCase =						temporary
										UpperCamelCase =						bigger_child


						def      snake_case_ (self		)			-> int:
								UpperCamelCase =						self.__heap[1]
								UpperCamelCase =						self.__heap[self.__size]
								self.__size -= 1
								self.__heap.pop()
								self.__swap_down(1		)
								return max_value


						@property
						def      snake_case_ (self		)			-> Tuple:
								return self.__heap[1:]




						def __len__(self		)			-> str:
								return self.__size


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# create an instance of BinaryHeap
						lowerCAmelCase__        =      BinaryHeap()
						binary_heap.insert(6)
						binary_heap.insert(10)
						binary_heap.insert(15)
						binary_heap.insert(12)
						# pop root(max-values because it is max heap)
						print(binary_heap.pop())  # 15
						print(binary_heap.pop())  # 12
						# get the list and size after operations
						print(binary_heap.get_list)
						print(len(binary_heap))













"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase__        =      {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''OpenAIGPTTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''OpenAIGPTDoubleHeadsModel''',
						    '''OpenAIGPTForSequenceClassification''',
						    '''OpenAIGPTLMHeadModel''',
						    '''OpenAIGPTModel''',
						    '''OpenAIGPTPreTrainedModel''',
						    '''load_tf_weights_in_openai_gpt''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFOpenAIGPTDoubleHeadsModel''',
						    '''TFOpenAIGPTForSequenceClassification''',
						    '''TFOpenAIGPTLMHeadModel''',
						    '''TFOpenAIGPTMainLayer''',
						    '''TFOpenAIGPTModel''',
						    '''TFOpenAIGPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
						from .tokenization_openai import OpenAIGPTTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_openai_fast import OpenAIGPTTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_openai import (
												    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OpenAIGPTDoubleHeadsModel,
												    OpenAIGPTForSequenceClassification,
												    OpenAIGPTLMHeadModel,
												    OpenAIGPTModel,
												    OpenAIGPTPreTrainedModel,
												    load_tf_weights_in_openai_gpt,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_openai import (
												    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFOpenAIGPTDoubleHeadsModel,
												    TFOpenAIGPTForSequenceClassification,
												    TFOpenAIGPTLMHeadModel,
												    TFOpenAIGPTMainLayer,
												    TFOpenAIGPTModel,
												    TFOpenAIGPTPreTrainedModel,
												)

else:
						import sys

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






