code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  UpperCAmelCase_       :				Any		   =      {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				Optional[Any]		   =      [
  			    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''GPTJForCausalLM''',
  			    '''GPTJForQuestionAnswering''',
  			    '''GPTJForSequenceClassification''',
  			    '''GPTJModel''',
  			    '''GPTJPreTrainedModel''',
  			]
  
  try:
  			if not is_tf_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				Tuple		   =      [
  			    '''TFGPTJForCausalLM''',
  			    '''TFGPTJForQuestionAnswering''',
  			    '''TFGPTJForSequenceClassification''',
  			    '''TFGPTJModel''',
  			    '''TFGPTJPreTrainedModel''',
  			]
  
  try:
  			if not is_flax_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			UpperCAmelCase_       :				Tuple		   =      [
  			    '''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_       :				Union[str, Any]		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
style_context: 
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  UpperCAmelCase_       :				Optional[Any]		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				List[str]		   =      {
      '''uw-madison/nystromformer-512''': '''https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json''',
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      Optional[Any]    				=							'''nystromformer'''
  
  
  
  
  	def __init__(    self	:    Optional[Any]				,			__A	:    Optional[Any]=3_0000				,			__A	:    Optional[int]=768				,			__A	:    List[str]=12				,			__A	:    str=12				,			__A	:    Tuple=3072				,			__A	:    List[Any]="gelu_new"				,			__A	:    Dict=0.1				,			__A	:    Optional[int]=0.1				,			__A	:    int=510				,			__A	:    Union[str, Any]=2				,			__A	:    Optional[int]=64				,			__A	:    str=64				,			__A	:    List[str]=65				,			__A	:    Tuple=False				,			__A	:    List[str]=0.0_2				,			__A	:    Tuple=1e-5				,			__A	:    List[str]=1				,			__A	:    int=0				,			__A	:    Tuple=2				,			**__A	:    Dict				,			):
  		__A   : str								= vocab_size
  		__A   : Union[str, Any]								= max_position_embeddings
  		__A   : Optional[int]								= hidden_size
  		__A   : Optional[int]								= num_hidden_layers
  		__A   : List[Any]								= num_attention_heads
  		__A   : Dict								= intermediate_size
  		__A   : Union[str, Any]								= hidden_act
  		__A   : int								= hidden_dropout_prob
  		__A   : Tuple								= attention_probs_dropout_prob
  		__A   : Optional[Any]								= initializer_range
  		__A   : int								= type_vocab_size
  		__A   : Optional[int]								= segment_means_seq_len
  		__A   : List[str]								= num_landmarks
  		__A   : Dict								= conv_kernel_size
  		__A   : Union[str, Any]								= inv_coeff_init_option
  		__A   : Union[str, Any]								= layer_norm_eps
  		super().__init__(pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			**__A       )
  
  
  
  
  
  