



from transformers import PretrainedConfig



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''custom'''

   def __init__(   self      :							int				, __lowerCAmelCase      :							Dict=1				, **__lowerCAmelCase      :							int				)							->					str:


    """simple docstring"""






    a							=		attribute
    super().__init__(**__lowerCAmelCase				)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      '''custom'''

   def __init__(   self      :							Optional[Any]				, __lowerCAmelCase      :							int=1				, **__lowerCAmelCase      :							List[str]				)							->					int:


    """simple docstring"""






    a							=		attribute








'''simple docstring'''






from typing import TYPE_CHECKING

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


__UpperCamelCase      :   List[str]								=     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

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

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

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase      :   Union[str, Any]								=     [
	    '''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

	__UpperCamelCase      :   List[str]								=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
