






"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	_validate_point(snake_case__					)
	_validate_point(snake_case__					)
	if len(snake_case__					) != len(snake_case__					):
		raise ValueError("""Both points must be in the same n-dimensional space"""					)

	return float(sum(abs(a - b					) for a, b in zip(snake_case__   ,   snake_case__					)					)					)






def 			__UpperCamelCase	(		snake_case__					):
	if point:
		if isinstance(snake_case__   ,   snake_case__					):
			for item in point:
				if not isinstance(snake_case__   ,   (int, float)					):
					A_		:						Optional[int]	    =   (
					    """Expected a list of numbers as input, found """
					    F"""{type(snake_case__					).__name__}"""
					)
					raise TypeError(snake_case__					)
		else:
			A_		:						Any	    =   F"""Expected a list of numbers as input, found {type(snake_case__					).__name__}"""
			raise TypeError(snake_case__					)
	else:
		raise ValueError("""Missing an input"""					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	_validate_point(snake_case__					)
	_validate_point(snake_case__					)
	if len(snake_case__					) != len(snake_case__					):
		raise ValueError("""Both points must be in the same n-dimensional space"""					)

	return float(sum(abs(x - y					) for x, y in zip(snake_case__   ,   snake_case__					)					)					)


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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""facebook/bart-large-mnli"""
	_A       :				str    =				(
	    """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       :				List[str]    =				"""text_classifier"""
	_A       :				Optional[int]    =				AutoTokenizer
	_A       :				Optional[Any]    =				AutoModelForSequenceClassification

	_A       :				List[str]    =				["""text""", ["""text"""]]
	_A       :				Dict    =				["""text"""]





	def    lowerCamelCase(self							):
		super().setup()
		A_		:						int	    =   self.model.config
		A_		:						List[str]	    =   -1
		for idx, label in config.idalabel.items():
			if label.lower().startswith("""entail"""							):
				A_		:						List[Any]	    =   int(lowerCAmelCase_							)
		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     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   labels
		return self.pre_processor(
		    [text] * len(lowerCAmelCase_							)     ,     [f"""This example is {label}""" for label in labels]     ,     return_tensors="""pt"""     ,     padding="""max_length"""     ,     )





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   outputs.logits
		A_		:						Optional[int]	    =   torch.argmax(logits[:, 2]							).item()
		return self._labels[label_id]

