code: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase:		Any							=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __lowercase:		str							=     ["""GPT2TokenizerFast"""]
  
  try:
     if not is_torch_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __lowercase:		Union[str, Any]							=     [
         """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """GPT2DoubleHeadsModel""",
         """GPT2ForQuestionAnswering""",
         """GPT2ForSequenceClassification""",
         """GPT2ForTokenClassification""",
         """GPT2LMHeadModel""",
         """GPT2Model""",
         """GPT2PreTrainedModel""",
         """load_tf_weights_in_gpt2""",
     ]
  
  try:
     if not is_tf_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __lowercase:		Dict							=     [
         """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFGPT2DoubleHeadsModel""",
         """TFGPT2ForSequenceClassification""",
         """TFGPT2LMHeadModel""",
         """TFGPT2MainLayer""",
         """TFGPT2Model""",
         """TFGPT2PreTrainedModel""",
     ]
  
  try:
     if not is_keras_nlp_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __lowercase:		Dict							=     ["""TFGPT2Tokenizer"""]
  
  try:
     if not is_flax_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     __lowercase:		List[Any]							=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
     from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
     from .tokenization_gpta import GPTaTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_gpta_fast import GPTaTokenizerFast
  
     try:
        if not is_torch_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_gpta import (
            GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTaDoubleHeadsModel,
            GPTaForQuestionAnswering,
            GPTaForSequenceClassification,
            GPTaForTokenClassification,
            GPTaLMHeadModel,
            GPTaModel,
            GPTaPreTrainedModel,
            load_tf_weights_in_gpta,
        )
  
     try:
        if not is_tf_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_tf_gpta import (
            TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFGPTaDoubleHeadsModel,
            TFGPTaForSequenceClassification,
            TFGPTaLMHeadModel,
            TFGPTaMainLayer,
            TFGPTaModel,
            TFGPTaPreTrainedModel,
        )
  
     try:
        if not is_keras_nlp_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_gpta_tf import TFGPTaTokenizer
  
     try:
        if not is_flax_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
     import sys
  
     __lowercase:		Dict							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  from typing import Any, Generic, TypeVar
  
  __lowercase:		Optional[Any]							=     TypeVar("T", bound=bool)
  
  
  
  
  
  
  
  class  UpperCAmelCase (       Generic[T]):
  
   def __init__( self :	int,					a_ :	T					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						value
      UpperCamelCase__ 						=						None
      UpperCamelCase__ 						=						None
  
   @property
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self._value
  
   @staticmethod
   def 		lowercase_						( a_ :	RandomizedHeapNode[T] | None,					a_ :	RandomizedHeapNode[T] | None					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not roota:
         return roota
  
      if not roota:
         return roota
  
      if roota.value > roota.value:
         UpperCamelCase__						,		UpperCamelCase__ 						=						roota, roota
  
      if random.choice([True, False]					):
         UpperCamelCase__						,		UpperCamelCase__ 						=						roota.right, roota.left
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(roota.left,					a_					)
  
      return roota
  
  
  
  
  
  
  
  class  UpperCAmelCase (       Generic[T]):
  
   def __init__( self :	List[Any],					a_ :	Iterable[T] | None = ()					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						None
  
      if data:
         for item in data:
            self.insert(a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	T					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root,					RandomizedHeapNode(a_					)					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.top()
  
      if self._root is None:
         return None
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root.left,					self._root.right					)
  
      return result
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not self._root:
         raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value
  
   def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						None
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      while self:
         result.append(self.pop()					)
  
      return result
  
   def __bool__( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self._root is not None
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()