




'''simple docstring'''





def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   if not root or root not in binary_tree_mirror_dictionary:
      return
   SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Dict							    =			binary_tree_mirror_dictionary[root][:2]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			[right_child, left_child]
   binary_tree_mirror_dict(snake_case						,  snake_case )
   binary_tree_mirror_dict(snake_case						,  snake_case )


def 					UpperCamelCase						(	snake_case						,  snake_case = 1 ):




   '''simple docstring'''

   if not binary_tree:
      raise ValueError('''binary tree cannot be empty''' )
   if root not in binary_tree:
      SCREAMING_SNAKE_CASE__      :					Any							    =			f"""root {root} is not present in the binary_tree"""
      raise ValueError(snake_case )
   SCREAMING_SNAKE_CASE__      :					Dict							    =			dict(snake_case )
   binary_tree_mirror_dict(snake_case						,  snake_case )
   return binary_tree_mirror_dictionary


if __name__ == "__main__":
       lowercase__								=						{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
       print(F"Binary tree: {binary_tree}")
       lowercase__								=						binary_tree_mirror(binary_tree, 5)
       print(F"Binary tree mirror: {binary_tree_mirror_dictionary}")







'''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__)