code: '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __lowercase:		Optional[int]							=     logging.get_logger(__name__)
  
  __lowercase:		List[str]							=     {
      "facebook/regnet-y-040": "https://huggingface.co/facebook/regnet-y-040/blob/main/config.json",
  }
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					'regnet'
   _lowerCamelCase :					List[str]    					=					['x', 'y']
  
   def __init__( self :	Optional[int],					a_ :	Union[str, Any]=3,					a_ :	Optional[Any]=32,					a_ :	Union[str, Any]=[128, 192, 512, 1088],					a_ :	Union[str, Any]=[2, 6, 12, 2],					a_ :	Optional[Any]=64,					a_ :	Tuple="y",					a_ :	Any="relu",					**a_ :	List[str],					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__(**a_					)
      if layer_type not in self.layer_types:
         raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types					)}'					)
      UpperCamelCase__ 						=						num_channels
      UpperCamelCase__ 						=						embedding_size
      UpperCamelCase__ 						=						hidden_sizes
      UpperCamelCase__ 						=						depths
      UpperCamelCase__ 						=						groups_width
      UpperCamelCase__ 						=						layer_type
      UpperCamelCase__ 						=						hidden_act
      # always downsample in the first stage
      UpperCamelCase__ 						=						True
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowercase:		int							=     {
      "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
  }
  
  try:
         if not is_sentencepiece_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		str							=     ["LlamaTokenizer"]
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		List[Any]							=     ["LlamaTokenizerFast"]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         __lowercase:		Union[str, Any]							=     [
             "OpenLlamaForCausalLM",
             "OpenLlamaModel",
             "OpenLlamaPreTrainedModel",
             "OpenLlamaForSequenceClassification",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig
  
         try:
                if not is_sentencepiece_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from transformers import LlamaTokenizerFast
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_open_llama import (
                    OpenLlamaForCausalLM,
                    OpenLlamaForSequenceClassification,
                    OpenLlamaModel,
                    OpenLlamaPreTrainedModel,
                )
  
  
  else:
         import sys
  
         __lowercase:		Tuple							=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)