


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase			:  int     ={
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Optional[int]     =["""LxmertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  int     =[
						    """LxmertEncoder""",
						    """LxmertForPreTraining""",
						    """LxmertForQuestionAnswering""",
						    """LxmertModel""",
						    """LxmertPreTrainedModel""",
						    """LxmertVisualFeatureEncoder""",
						    """LxmertXLayer""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCAmelCase			:  Tuple     =[
						    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFLxmertForPreTraining""",
						    """TFLxmertMainLayer""",
						    """TFLxmertModel""",
						    """TFLxmertPreTrainedModel""",
						    """TFLxmertVisualFeatureEncoder""",
						]


if TYPE_CHECKING:
						from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
						from .tokenization_lxmert import LxmertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_lxmert_fast import LxmertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_lxmert import (
												    LxmertEncoder,
												    LxmertForPreTraining,
												    LxmertForQuestionAnswering,
												    LxmertModel,
												    LxmertPreTrainedModel,
												    LxmertVisualFeatureEncoder,
												    LxmertXLayer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_lxmert import (
												    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFLxmertForPreTraining,
												    TFLxmertMainLayer,
												    TFLxmertModel,
												    TFLxmertPreTrainedModel,
												    TFLxmertVisualFeatureEncoder,
												)

else:
						import sys

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



from __future__ import annotations



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   )-> tuple:
				if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
								raise ValueError('''You cannot supply more or less than 2 values''' )
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative in a semiconductor''' )
				elif hole_conc < 0:
								raise ValueError('''Hole concentration cannot be negative in a semiconductor''' )
				elif intrinsic_conc < 0:
								raise ValueError(
								    '''Intrinsic concentration cannot be negative in a semiconductor''' )
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    intrinsic_conc**2 / hole_conc,
								)
				elif hole_conc == 0:
								return (
								    "hole_conc",
								    intrinsic_conc**2 / electron_conc,
								)
				elif intrinsic_conc == 0:
								return (
								    "intrinsic_conc",
								    (electron_conc * hole_conc) ** 0.5,
								)
				else:
								return (-1, -1)


if __name__ == "__main__":
						import doctest

						doctest.testmod()