
import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast





@dataclass
class 							UpperCAmelCase				(  datasets.BuilderConfig  ):




	'''simple docstring'''





	snake_case_    =					None





class 							UpperCAmelCase				(  datasets.ArrowBasedBuilder  ):




	'''simple docstring'''





	snake_case_    =					PandasConfig






	def     UpperCamelCase_  (	self :			Optional[int]			):
					return datasets.DatasetInfo(features=self.config.features			)






	def     UpperCamelCase_  (	self :			str							,A :			List[Any]			):
					if not self.config.data_files:
									raise ValueError(f'''At least one data file must be specified, but got data_files={self.config.data_files}'''			)
					__A             =			dl_manager.download_and_extract(self.config.data_files			)
					if isinstance(A							,(str, list, tuple)			):
									__A             =			data_files
									if isinstance(A							,A			):
													__A             =			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__A             =			[dl_manager.iter_files(A			) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN							,gen_kwargs={"files": files}			)]
					__A             =			[]
					for split_name, files in data_files.items():
									if isinstance(A							,A			):
													__A             =			[files]
									# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
									__A             =			[dl_manager.iter_files(A			) for file in files]
									splits.append(datasets.SplitGenerator(name=A							,gen_kwargs={"files": files}			)			)
					return splits






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			pa.Table			):
					if self.config.features is not None:
									# more expensive cast to support nested features with keys in a different order
									# allows str <-> int/float or str to Audio for example
									__A             =			table_cast(A							,self.config.features.arrow_schema			)
					return pa_table







	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					for i, file in enumerate(itertools.chain.from_iterable(A			)			):
									with open(A							,"rb"			) as f:
													__A             =			pa.Table.from_pandas(pd.read_pickle(A			)			)
													yield i, self._cast_table(A			)


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()
