code: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  A_:      Any        =			{'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Optional[int]        =			[
  				    'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'GPTJForCausalLM',
  				    'GPTJForQuestionAnswering',
  				    'GPTJForSequenceClassification',
  				    'GPTJModel',
  				    'GPTJPreTrainedModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Optional[int]        =			[
  				    'TFGPTJForCausalLM',
  				    'TFGPTJForQuestionAnswering',
  				    'TFGPTJForSequenceClassification',
  				    'TFGPTJModel',
  				    'TFGPTJPreTrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[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
  
  				A_:      List[str]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
style_context: from ctypes import c_float, sizeof
  from enum import Enum
  from typing import TYPE_CHECKING, Optional, Union
  
  
  if TYPE_CHECKING:
  				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  
  class _lowercase					(       _UpperCAmelCase						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							lowerCAmelCase__       =					c_float
  
  
  
  							@property
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return sizeof(self.value  )
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A ,_A ,_A = 0					):
  
  
  
  							"""simple docstring"""
  
  
  							if dimension <= 0:
  														_lowercase				=	fixed_dimension
  
  							dimension -= num_token_to_add
  							return dimension
  
  
  def 					__lowerCAmelCase			(  _A ,_A					):
  
  
  
  							"""simple docstring"""
  
  
  							return num_parameters * dtype.size
  
  
  
  
  
  
  
  def 					__lowerCAmelCase			(  _A					):
  
  
  
  							"""simple docstring"""
  
  
  							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
  
  							try:
  														return AutoProcessor.from_pretrained(_A					)
  							except (ValueError, OSError, KeyError):
  														_lowercase				,       _lowercase				=	None, None
  														try:
  																					_lowercase				=	AutoTokenizer.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  														try:
  																					_lowercase				=	AutoFeatureExtractor.from_pretrained(_A					)
  														except (OSError, KeyError):
  																					pass
  
  														if tokenizer is not None and feature_extractor is not None:
  																					raise ValueError(
  																					    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''					)
  														elif tokenizer is None and feature_extractor is None:
  																					return None
  														elif tokenizer is not None:
  																					return tokenizer
  														else:
  																					return feature_extractor
  
  
  