

from __future__ import annotations

import math






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				bool:




	'''simple docstring'''




	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5							,       int(math.sqrt(UpperCAmelCase_	) + 1	)							,       6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				list[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						str(UpperCAmelCase_	)
	_UpperCamelCase   :		str			=						[n]
	for i in range(1							,       len(UpperCAmelCase_	)	):
		list_nums.append(int(str_num[i:]	)	)
		list_nums.append(int(str_num[:-i]	)	)
	return list_nums






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				bool:




	'''simple docstring'''




	if len(str(UpperCAmelCase_	)	) > 3:
		if not is_prime(int(str(UpperCAmelCase_	)[-3:]	)	) or not is_prime(int(str(UpperCAmelCase_	)[:3]	)	):
			return False
	return True






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int = 1_1	)						->				list[int]:




	'''simple docstring'''




	_UpperCamelCase   :		list[int]			=						[]
	_UpperCamelCase   :		Any			=						1_3
	while len(UpperCAmelCase_	) != count:
		if validate(UpperCAmelCase_	):
			_UpperCamelCase   :		List[str]			=						list_truncated_nums(UpperCAmelCase_	)
			if all(is_prime(UpperCAmelCase_	) for i in list_nums	):
				list_truncated_primes.append(UpperCAmelCase_	)
		num += 2
	return list_truncated_primes






def 					lowerCamelCase_     (							)						->				int:




	'''simple docstring'''




	return sum(compute_truncated_primes(1_1	)	)


if __name__ == "__main__":
							print(f'{sum(compute_truncated_primes(1_1)) = }')







# 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]




