"""simple docstring"""







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 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	c_float







				@property
				def 				__UpperCAmelCase       (			self     :							Optional[Any]    ):

					"""simple docstring"""






					return sizeof(self.value    )



def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = 0      ):






	"""simple docstring"""





	if dimension <= 0:
		_lowercase			:     Dict      					=				fixed_dimension

	dimension -= num_token_to_add
	return dimension


def   __lowerCAmelCase(		__UpperCAmelCase  ,__UpperCAmelCase      ):






	"""simple docstring"""





	return num_parameters * dtype.size







def   __lowerCAmelCase(		__UpperCAmelCase      ):






	"""simple docstring"""





	from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

	try:
		return AutoProcessor.from_pretrained(__UpperCAmelCase      )
	except (ValueError, OSError, KeyError):
		_lowercase					,   _lowercase			:     Dict      					=				None, None
		try:
			_lowercase			:     List[Any]      					=				AutoTokenizer.from_pretrained(__UpperCAmelCase      )
		except (OSError, KeyError):
			pass
		try:
			_lowercase			:     Any      					=				AutoFeatureExtractor.from_pretrained(__UpperCAmelCase      )
		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




"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE						=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=    {
    'facebook/regnet-y-040': 'https://huggingface.co/facebook/regnet-y-040/blob/main/config.json',
}



class 			_lowerCamelCase      (__lowerCamelCase  ):
				_snake_case  =	"regnet"
				_snake_case  =	["x", "y"]







				def __init__(			self     :							Any      ,       lowerCamelCase_     :							Optional[Any]=3      ,       lowerCamelCase_     :							List[str]=3_2      ,       lowerCamelCase_     :							str=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]      ,       lowerCamelCase_     :							int=[2, 6, 1_2, 2]      ,       lowerCamelCase_     :							Union[str, Any]=6_4      ,       lowerCamelCase_     :							int="y"      ,       lowerCamelCase_     :							Optional[Any]="relu"      ,       **lowerCamelCase_     :							Optional[Any]      ,       ):

					"""simple docstring"""






					super().__init__(**lowerCamelCase_    )
					if layer_type not in self.layer_types:
						raise ValueError(F'''layer_type={layer_type} is not one of {','.join(self.layer_types    )}'''    )
					_lowercase			:     str      					=				num_channels
					_lowercase			:     Optional[Any]      					=				embedding_size
					_lowercase			:     Union[str, Any]      					=				hidden_sizes
					_lowercase			:     Optional[int]      					=				depths
					_lowercase			:     Optional[int]      					=				groups_width
					_lowercase			:     Tuple      					=				layer_type
					_lowercase			:     Union[str, Any]      					=				hidden_act
					# always downsample in the first stage
					_lowercase			:     Optional[int]      					=				True



