
from __future__ import annotations

__a  			=					1.6021E-19  # units = C




def 				a						(							snake_case__:					float						,   snake_case__:					float						,   snake_case__:					float						,   ):


				'''simple docstring'''
				if (conductivity, electron_conc, mobility).count(0					) != 1:
								raise ValueError('''You cannot supply more or less than 2 values'''					)
				elif conductivity < 0:
								raise ValueError('''Conductivity cannot be negative'''					)
				elif electron_conc < 0:
								raise ValueError('''Electron concentration cannot be negative'''					)
				elif mobility < 0:
								raise ValueError('''mobility cannot be negative'''					)
				elif conductivity == 0:
								return (
								    "conductivity",
								    mobility * electron_conc * ELECTRON_CHARGE,
								)
				elif electron_conc == 0:
								return (
								    "electron_conc",
								    conductivity / (mobility * ELECTRON_CHARGE),
								)
				else:
								return (
								    "mobility",
								    conductivity / (electron_conc * ELECTRON_CHARGE),
								)


if __name__ == "__main__":
						import doctest

						doctest.testmod()










'''simple docstring'''

from typing import TYPE_CHECKING

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


a  :				Tuple									=  {
    'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  ['LlamaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				str									=  ['LlamaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :				Optional[Any]									=  [
							    'LlamaForCausalLM',
							    'LlamaModel',
							    'LlamaPreTrainedModel',
							    'LlamaForSequenceClassification',
							]


if TYPE_CHECKING:
							from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama import LlamaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_llama_fast import LlamaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
							import sys

							a  :				str									=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

