
import unittest

import numpy as np


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_ = None		,       )		->     np.ndarray:







				"""simple docstring"""



				__A             =			np.shape(a_						)
				__A             =			np.shape(a_						)
				__A             =			np.shape(a_						)

				if shape_a[0] != shape_b[0]:
								__A             =			(
								    "Expected the same number of rows for A and B. "
								    F'''Instead found A of size {shape_a} and B of size {shape_b}'''
								)
								raise ValueError(a_						)

				if shape_b[1] != shape_c[1]:
								__A             =			(
								    "Expected the same number of columns for B and C. "
								    F'''Instead found B of size {shape_b} and C of size {shape_c}'''
								)
								raise ValueError(a_						)

				__A             =			pseudo_inv
				if a_inv is None:
								try:
												__A             =			np.linalg.inv(a_						)
								except np.linalg.LinAlgError:
												raise ValueError(
												    "Input matrix A is not invertible. Cannot compute Schur complement."						)

				return mat_c - mat_b.T @ a_inv @ mat_b





class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
					__A             =			np.array([[0, 3], [3, 0], [2, 3]]			)
					__A             =			np.array([[2, 1], [6, 3]]			)

					__A             =			schur_complement(A							,A							,A			)

					__A             =			np.block([[a, b], [b.T, c]]			)

					__A             =			np.linalg.det(A			)
					__A             =			np.linalg.det(A			)
					__A             =			np.linalg.det(A			)

					self.assertAlmostEqual(A							,det_a * det_s			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
					__A             =			np.array([[0, 3], [3, 0], [2, 3]]			)
					__A             =			np.array([[2, 1], [6, 3]]			)

					with self.assertRaises(A			):
									schur_complement(A							,A							,A			)







	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]			)
					__A             =			np.array([[0, 3], [3, 0], [2, 3]]			)
					__A             =			np.array([[2, 1, 3], [6, 3, 5]]			)

					with self.assertRaises(A			):
									schur_complement(A							,A							,A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					unittest.main()


# 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 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "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."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






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







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
