import os
import sys

A  :	Tuple         =							os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


A  :	Dict         =							[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]


@add_start_docstrings(AutoConfig.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoConfig.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)


@add_start_docstrings(AutoTokenizer.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoTokenizer.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)


@add_start_docstrings(AutoModel.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoModel.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)


@add_start_docstrings(AutoModelForCausalLM.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoModelForCausalLM.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)


@add_start_docstrings(AutoModelForMaskedLM.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoModelForMaskedLM.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoModelForSequenceClassification.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__					)
def   a__			(		*__UpperCamelCase  ,   **__UpperCamelCase					):
      return AutoModelForQuestionAnswering.from_pretrained(*__UpperCamelCase  ,   **__UpperCamelCase					)






from math import log

from scipy.constants import Boltzmann, physical_constants

A			:						Any        =  3_0_0  # TEMPERATURE (unit = K)






def 					UpperCamelCase   (      __magic_name__	:				float       ,       __magic_name__	:				float       ,       __magic_name__	:				float       ,       )  ->    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()

