






from typing import TYPE_CHECKING

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


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

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

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A          = [
				    '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:
				__A          = [
				    '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

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









from __future__ import annotations

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

lowerCamelCase_  						=   TypeVar("T", bound=bool)
class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=value
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None




     @property
     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





         return self._value




     @staticmethod
     def   __A				(	_UpperCamelCase			: RandomizedHeapNode[T] | None				,_UpperCamelCase			: RandomizedHeapNode[T] | None						)		->   RandomizedHeapNode[T] | None:


         '''simple docstring'''





         if not roota:
             return roota

         if not roota:
             return roota

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

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

         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(roota.left				,_UpperCamelCase						)

         return roota






class       __a					( Generic[T]     ):


     """simple docstring"""


     def __init__(	self			: Any				,_UpperCamelCase			: Iterable[T] | None = ()						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None

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




     def   __A				(	self			: Union[str, Any]				,_UpperCamelCase			: T						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RandomizedHeapNode.merge(self._root				,RandomizedHeapNode(_UpperCamelCase						)						)




     def   __A				(	self			: Dict						)		->   T | None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.top()

         if self._root is None:
             return None

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

         return result




     def   __A				(	self			: Optional[Any]						)		->   T:


         '''simple docstring'''





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




     def   __A				(	self			: Optional[int]						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None




     def   __A				(	self			: List[Any]						)		->   list[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         while self:
             result.append(self.pop()						)

         return result




     def __bool__(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()






