

from collections.abc import Callable

import numpy as np






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     str							,       UpperCAmelCase_							:     Tuple	)						->				np.array:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[int]			=  int(np.ceil((x_end - xa) / step_size	)	)
	_UpperCamelCase   :		int			=  np.zeros((n + 1,)	)
	_UpperCamelCase   :		Any			=  ya
	_UpperCamelCase   :		List[Any]			=  xa

	for k in range(_A	):
		_UpperCamelCase   :		List[str]			=  y[k] + step_size * ode_func(_A							,       y[k]	)
		_UpperCamelCase   :		Dict			=  y[k] + (
		    (step_size / 2) * (ode_func(_A							,       y[k]	) + ode_func(x + step_size							,       _A	))
		)
		x += step_size

	return y


if __name__ == "__main__":
							import doctest

							doctest.testmod()







# 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 	lowercase    (				_lowercase							):


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


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




