






# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a		:       int			     = {
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       List[str]			     = [
           "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "CpmAntForCausalLM",
           "CpmAntModel",
           "CpmAntPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
       from .tokenization_cpmant import CpmAntTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_cpmant import (
                  CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CpmAntForCausalLM,
                  CpmAntModel,
                  CpmAntPreTrainedModel,
              )


else:
       import sys

       a		:       Optional[Any]			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










from math import log

from scipy.constants import Boltzmann, physical_constants

a		:       Any			     = 300  # TEMPERATURE (unit = K)






def 				lowerCamelCase__      (    __lowerCamelCase			:     float  ,		__lowerCamelCase			:     float  ,		__lowerCamelCase			:     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()


