





"""simple docstring"""






import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
		pass

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


A 			=  logging.get_logger(__name__)

A 			=  {
    '''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''align_text_model'''







					def __init__(						self	,			_UpperCAmelCase=30522	,			_UpperCAmelCase=768	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=512	,			_UpperCAmelCase=2	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-1_2	,			_UpperCAmelCase=0	,			_UpperCAmelCase="absolute"	,			_UpperCAmelCase=True	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

									__a :						Any			       =  vocab_size
									__a :						Union[str, Any]			       =  hidden_size
									__a :						Optional[Any]			       =  num_hidden_layers
									__a :						str			       =  num_attention_heads
									__a :						Optional[Any]			       =  hidden_act
									__a :						Optional[int]			       =  intermediate_size
									__a :						Any			       =  hidden_dropout_prob
									__a :						Any			       =  attention_probs_dropout_prob
									__a :						Union[str, Any]			       =  max_position_embeddings
									__a :						Tuple			       =  type_vocab_size
									__a :						Dict			       =  initializer_range
									__a :						str			       =  layer_norm_eps
									__a :						List[Any]			       =  position_embedding_type
									__a :						Optional[Any]			       =  use_cache
									__a :						Any			       =  pad_token_id







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									cls._set_token_in_kwargs(_UpperCAmelCase     )

									__a       ,						__a :						Any			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )

									# get the text config dict if we are loading from AlignConfig
									if config_dict.get('''model_type'''     ) == "align":
													__a :						Any			       =  config_dict['''text_config''']

									if "model_type" in config_dict and hasattr(cls	,			'''model_type'''     ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

									return cls.from_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''align_vision_model'''







					def __init__(						self	,			_UpperCAmelCase = 3	,			_UpperCAmelCase = 600	,			_UpperCAmelCase = 2.0	,			_UpperCAmelCase = 3.1	,			_UpperCAmelCase = 8	,			_UpperCAmelCase = [3, 3, 5, 3, 5, 5, 3]	,			_UpperCAmelCase = [32, 16, 24, 40, 80, 112, 192]	,			_UpperCAmelCase = [16, 24, 40, 80, 112, 192, 320]	,			_UpperCAmelCase = []	,			_UpperCAmelCase = [1, 2, 2, 2, 1, 2, 1]	,			_UpperCAmelCase = [1, 2, 2, 3, 3, 4, 1]	,			_UpperCAmelCase = [1, 6, 6, 6, 6, 6, 6]	,			_UpperCAmelCase = 0.2_5	,			_UpperCAmelCase = "swish"	,			_UpperCAmelCase = 2560	,			_UpperCAmelCase = "mean"	,			_UpperCAmelCase = 0.0_2	,			_UpperCAmelCase = 0.0_0_1	,			_UpperCAmelCase = 0.9_9	,			_UpperCAmelCase = 0.2	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

									__a :						Dict			       =  num_channels
									__a :						Dict			       =  image_size
									__a :						List[Any]			       =  width_coefficient
									__a :						Optional[int]			       =  depth_coefficient
									__a :						Optional[int]			       =  depth_divisor
									__a :						Any			       =  kernel_sizes
									__a :						List[str]			       =  in_channels
									__a :						Optional[Any]			       =  out_channels
									__a :						Union[str, Any]			       =  depthwise_padding
									__a :						Dict			       =  strides
									__a :						Union[str, Any]			       =  num_block_repeats
									__a :						Optional[Any]			       =  expand_ratios
									__a :						str			       =  squeeze_expansion_ratio
									__a :						Dict			       =  hidden_act
									__a :						List[Any]			       =  hidden_dim
									__a :						Optional[int]			       =  pooling_type
									__a :						Tuple			       =  initializer_range
									__a :						Dict			       =  batch_norm_eps
									__a :						Dict			       =  batch_norm_momentum
									__a :						Dict			       =  drop_connect_rate
									__a :						int			       =  sum(_UpperCAmelCase     ) * 4







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									cls._set_token_in_kwargs(_UpperCAmelCase     )

									__a       ,						__a :						int			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )

									# get the vision config dict if we are loading from AlignConfig
									if config_dict.get('''model_type'''     ) == "align":
													__a :						Optional[int]			       =  config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls	,			'''model_type'''     ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

									return cls.from_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''align'''
					__lowerCAmelCase  =  True







					def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=640	,			_UpperCAmelCase=1.0	,			_UpperCAmelCase=0.0_2	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

									if text_config is None:
													__a :						List[str]			       =  {}
													logger.info('''text_config is None. Initializing the AlignTextConfig with default values.'''     )

									if vision_config is None:
													__a :						Tuple			       =  {}
													logger.info('''vision_config is None. Initializing the AlignVisionConfig with default values.'''     )

									__a :						Union[str, Any]			       =  AlignTextConfig(**_UpperCAmelCase     )
									__a :						Any			       =  AlignVisionConfig(**_UpperCAmelCase     )

									__a :						int			       =  projection_dim
									__a :						Union[str, Any]			       =  temperature_init_value
									__a :						Dict			       =  initializer_range







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return cls(text_config=text_config.to_dict()	,			vision_config=vision_config.to_dict()	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  copy.deepcopy(self.__dict__     )
									__a :						str			       =  self.text_config.to_dict()
									__a :						List[str]			       =  self.vision_config.to_dict()
									__a :						List[Any]			       =  self.__class__.model_type
									return output






"""simple docstring"""






from math import log

from scipy.constants import Boltzmann, physical_constants

A 			=  300  # TEMPERATURE (unit = K)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float   ,       )		->		float:

				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()