"""simple docstring"""





from __future__ import annotations

lowerCAmelCase_     =	1.6021E-19  # units = C



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	)->    tuple[str, float]:
							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"""





# Copyright 2023 The HuggingFace Inc. 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.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"facebook/bart-large-mnli"
				a			=						(
				    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
				    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
				    "It returns the most likely label in the list of provided `labels` for the input text."
				)
				a			=						"text_classifier"
				a			=						AutoTokenizer
				a			=						AutoModelForSequenceClassification

				a			=						["text", ["text"]]
				a			=						["text"]





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											super().setup()
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model.config
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1
											for idx, label in config.idalabel.items():
																		if label.lower().startswith("""entail"""):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    int(_A)
											if self.entailment_id == -1:
																		raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init.""")





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    labels
											return self.pre_processor(
											    [text] * len(_A)      ,    [f"""This example is {label}""" for label in labels]      ,    return_tensors="""pt"""      ,    padding="""max_length"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.argmax(logits[:, 2]).item()
											return self._labels[label_id]


