'''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



'''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__)