
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


SCREAMING_SNAKE_CASE							:Tuple										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:int										=    {
    't5-small': 'https://huggingface.co/t5-small/resolve/main/config.json',
    't5-base': 'https://huggingface.co/t5-base/resolve/main/config.json',
    't5-large': 'https://huggingface.co/t5-large/resolve/main/config.json',
    't5-3b': 'https://huggingface.co/t5-3b/resolve/main/config.json',
    't5-11b': 'https://huggingface.co/t5-11b/resolve/main/config.json',
}





class 							UpperCAmelCase				(  a__  ):




			'''simple docstring'''





			snake_case_    =					"t5"
			snake_case_    =					["past_key_values"]
			snake_case_    =					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}






			def __init__(	self :			List[str]							,A :			Optional[int]=3_21_28							,A :			Union[str, Any]=5_12							,A :			Optional[int]=64							,A :			List[str]=20_48							,A :			Optional[int]=6							,A :			List[Any]=None							,A :			List[Any]=8							,A :			Dict=32							,A :			int=1_28							,A :			Optional[int]=0.1							,A :			List[Any]=1E-6							,A :			int=1.0							,A :			int="relu"							,A :			Tuple=True							,A :			Optional[Any]=True							,A :			str=0							,A :			str=1							,**A :			List[Any]							,):
							__A             =			vocab_size
							__A             =			d_model
							__A             =			d_kv
							__A             =			d_ff
							__A             =			num_layers
							__A             =			(
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							__A             =			num_heads
							__A             =			relative_attention_num_buckets
							__A             =			relative_attention_max_distance
							__A             =			dropout_rate
							__A             =			layer_norm_epsilon
							__A             =			initializer_factor
							__A             =			feed_forward_proj
							__A             =			use_cache

							__A             =			self.feed_forward_proj.split("-"			)
							__A             =			act_info[-1]
							__A             =			act_info[0] == '''gated'''

							if len(_lowerCamelCase			) > 1 and act_info[0] != "gated" or len(_lowerCamelCase			) > 2:
											raise ValueError(
											    f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
											    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
											    "\'gated-gelu\' or \'relu\'"			)

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
											__A             =			'''gelu_new'''

							super().__init__(
							    pad_token_id=_lowerCamelCase							,eos_token_id=_lowerCamelCase							,is_encoder_decoder=_lowerCamelCase							,**_lowerCamelCase							,)





class 							UpperCAmelCase				(  a__  ):




			'''simple docstring'''





			@property
			def     UpperCamelCase_  (	self :			str			):
							__A             =			{
							    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
							    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
							}
							if self.use_past:
											__A             =			'''past_encoder_sequence + sequence'''
											__A             =			{0: '''batch'''}
											__A             =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
							else:
											__A             =			{0: '''batch''', 1: '''decoder_sequence'''}
											__A             =			{0: '''batch''', 1: '''decoder_sequence'''}

							if self.use_past:
											self.fill_with_past_key_values_(_lowerCamelCase							,direction="inputs"			)

							return common_inputs







			@property
			def     UpperCamelCase_  (	self :			Optional[Any]			):
							return 13


from math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE							:Dict										=    300  # TEMPERATURE (unit = K)


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







				"""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()
