



"""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_						(						a_				):
							__lowerCAmelCase    					=					"facebook/bart-large-mnli"
							__lowerCAmelCase    					=					(
							    "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."
							)
							__lowerCAmelCase    					=					"text_classifier"
							__lowerCAmelCase    					=					AutoTokenizer
							__lowerCAmelCase    					=					AutoModelForSequenceClassification

							__lowerCAmelCase    					=					["text", ["text"]]
							__lowerCAmelCase    					=					["text"]






							def snake_case_  (   self      ):
									super().setup()
									a_     :      List[Any] 						=     self.model.config
									a_     :      Any 						=     -1
									for idx, label in config.idalabel.items():
											if label.lower().startswith("entail"      ):
													a_     :      Union[str, 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 snake_case_  (   self			,     a_			,     a_      ):
									a_     :      List[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 snake_case_  (   self			,     a_      ):
									a_     :      Optional[Any] 						=     outputs.logits
									a_     :      Dict 						=     torch.argmax(logits[:, 2]      ).item()
									return self._labels[label_id]




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_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(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      None:
		if point:
				if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	):
						for item in point:
								if not isinstance(SCREAMING_SNAKE_CASE__, (int, float)	):
										a_     :      int 						=     (
										    "Expected a list of numbers as input, found "
										    F"""{type(SCREAMING_SNAKE_CASE__	).__name__}"""
										)
										raise TypeError(SCREAMING_SNAKE_CASE__	)
				else:
						a_     :      Dict 						=     F"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE__	).__name__}"""
						raise TypeError(SCREAMING_SNAKE_CASE__	)
		else:
				raise ValueError("Missing an input"	)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)			->      float:
		_validate_point(SCREAMING_SNAKE_CASE__	)
		_validate_point(SCREAMING_SNAKE_CASE__	)
		if len(SCREAMING_SNAKE_CASE__	) != len(SCREAMING_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(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)	)	)


if __name__ == "__main__":
					import doctest

					doctest.testmod()