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


lowerCAmelCase			:							str     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={
    "microsoft/cvt-13": "https://huggingface.co/microsoft/cvt-13/resolve/main/config.json",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'cvt'



      def __init__(					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Any=3    ,  _UpperCamelCase     :				List[str]=[7, 3, 3]    ,  _UpperCamelCase     :				Dict=[4, 2, 2]    ,  _UpperCamelCase     :				List[Any]=[2, 1, 1]    ,  _UpperCamelCase     :				Union[str, Any]=[64, 192, 384]    ,  _UpperCamelCase     :				Optional[int]=[1, 3, 6]    ,  _UpperCamelCase     :				Dict=[1, 2, 10]    ,  _UpperCamelCase     :				List[Any]=[4.0, 4.0, 4.0]    ,  _UpperCamelCase     :				str=[0.0, 0.0, 0.0]    ,  _UpperCamelCase     :				Any=[0.0, 0.0, 0.0]    ,  _UpperCamelCase     :				List[str]=[0.0, 0.0, 0.1]    ,  _UpperCamelCase     :				int=[True, True, True]    ,  _UpperCamelCase     :				Tuple=[False, False, True]    ,  _UpperCamelCase     :				str=["dw_bn", "dw_bn", "dw_bn"]    ,  _UpperCamelCase     :				List[Any]=[3, 3, 3]    ,  _UpperCamelCase     :				Tuple=[1, 1, 1]    ,  _UpperCamelCase     :				List[str]=[2, 2, 2]    ,  _UpperCamelCase     :				List[str]=[1, 1, 1]    ,  _UpperCamelCase     :				int=[1, 1, 1]    ,  _UpperCamelCase     :				Optional[Any]=0.0_2    ,  _UpperCamelCase     :				int=1E-1_2    ,  **_UpperCamelCase     :				List[str]    ,  )				->List[Any]:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)
          _lowerCamelCase     :		int 						=						num_channels
          _lowerCamelCase     :		Tuple 						=						patch_sizes
          _lowerCamelCase     :		List[Any] 						=						patch_stride
          _lowerCamelCase     :		List[Any] 						=						patch_padding
          _lowerCamelCase     :		int 						=						embed_dim
          _lowerCamelCase     :		Dict 						=						num_heads
          _lowerCamelCase     :		Tuple 						=						depth
          _lowerCamelCase     :		Any 						=						mlp_ratio
          _lowerCamelCase     :		Tuple 						=						attention_drop_rate
          _lowerCamelCase     :		Dict 						=						drop_rate
          _lowerCamelCase     :		Any 						=						drop_path_rate
          _lowerCamelCase     :		Dict 						=						qkv_bias
          _lowerCamelCase     :		int 						=						cls_token
          _lowerCamelCase     :		int 						=						qkv_projection_method
          _lowerCamelCase     :		List[Any] 						=						kernel_qkv
          _lowerCamelCase     :		List[str] 						=						padding_kv
          _lowerCamelCase     :		Dict 						=						stride_kv
          _lowerCamelCase     :		Any 						=						padding_q
          _lowerCamelCase     :		int 						=						stride_q
          _lowerCamelCase     :		Tuple 						=						initializer_range
          _lowerCamelCase     :		Union[str, Any] 						=						layer_norm_eps





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




