






"""simple docstring"""



from typing import TYPE_CHECKING

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


_lowerCAmelCase     :     Tuple            =	{"""configuration_gptj""": ["""GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTJConfig""", """GPTJOnnxConfig"""]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase     :     Dict            =	[
     """GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """GPTJForCausalLM""",
     """GPTJForQuestionAnswering""",
     """GPTJForSequenceClassification""",
     """GPTJModel""",
     """GPTJPreTrainedModel""",
 ]

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

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase     :     str            =	[
     """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

 _lowerCAmelCase     :     Union[str, Any]            =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""simple docstring"""



from transformers import PretrainedConfig




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      ='''custom'''
   def __init__(						self	:     Any				,       snake_case__	:     Any=1				,       **snake_case__	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				attribute
        super().__init__(**snake_case__				)




class        lowerCAmelCase__       (  __magic_name__					):
   SCREAMING_SNAKE_CASE_					      ='''custom'''
   def __init__(						self	:     List[str]				,       snake_case__	:     Tuple=1				,       **snake_case__	:     str				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[str]				=				attribute


