code: 
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	{
      """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json"""
      # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
  }
  
  
  class 							_lowerCAmelCase		(     UpperCamelCase__	):
  				lowerCamelCase__									=    'cpmant'
  
  
  
  
  				def __init__( self		,		snake_case_ = 30_720		,		snake_case_ = 4_096		,		snake_case_ = 32		,		snake_case_ = 128		,		snake_case_ = 10_240		,		snake_case_ = 48		,		snake_case_ = 0.0		,		snake_case_ = 512		,		snake_case_ = 2_048		,		snake_case_ = 1E-6		,		snake_case_ = 1.0		,		snake_case_ = 32		,		snake_case_ = 32		,		snake_case_ = 32		,		snake_case_ = True		,		**snake_case_		,		)    ->      Union[str, Any]:
  											super().__init__(**snake_case_							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =prompt_types
  											SCREAMING_SNAKE_CASE			: Tuple	     =prompt_length
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =segment_types
  											SCREAMING_SNAKE_CASE			: Tuple	     =hidden_size
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =num_attention_heads
  											SCREAMING_SNAKE_CASE			: str	     =dim_head
  											SCREAMING_SNAKE_CASE			: Tuple	     =dim_ff
  											SCREAMING_SNAKE_CASE			: Any	     =num_hidden_layers
  											SCREAMING_SNAKE_CASE			: Tuple	     =position_bias_num_buckets
  											SCREAMING_SNAKE_CASE			: Tuple	     =position_bias_max_distance
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =dropout_p
  											SCREAMING_SNAKE_CASE			: int	     =eps
  											SCREAMING_SNAKE_CASE			: Any	     =use_cache
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =vocab_size
  											SCREAMING_SNAKE_CASE			: List[Any]	     =init_std
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  