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#
# 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						(     UpperCamelCase_						):
							_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    UpperCAmelCase__			(       self     :       Tuple):
									super().setup()
									lowerCAmelCase_			:     Tuple									= self.model.config
									lowerCAmelCase_			:     Dict									= -1
									for idx, label in config.idalabel.items():
											if label.lower().startswith('''entail'''):
													lowerCAmelCase_			:     Tuple									= 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     :       Any				,				A_     :       Union[str, Any]				,				A_     :       int):
									lowerCAmelCase_			:     List[str]									= 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     :       Optional[int]				,				A_     :       List[Any]):
									lowerCAmelCase_			:     List[str]									= outputs.logits
									lowerCAmelCase_			:     List[Any]									= torch.argmax(logits[:, 2]).item()
									return self._labels[label_id]










A__  :  Any								=					'''Tobias Carryer'''

from time import time



class 				__snake_case						:




							def __init__(       self     :       Any				,				A_     :       Tuple				,				A_     :       Dict				,				A_     :       Tuple				,				A_     :       str=int(time())):  # noqa: B008
									lowerCAmelCase_			:     int									= multiplier
									lowerCAmelCase_			:     int									= increment
									lowerCAmelCase_			:     str									= modulo
									lowerCAmelCase_			:     str									= seed







							def    UpperCAmelCase__			(       self     :       int):
									lowerCAmelCase_			:     Optional[int]									= (self.multiplier * self.seed + self.increment) % self.modulo
									return self.seed


if __name__ == "__main__":
						# Show the LCG in action.
						A__  :  Union[str, Any]								=					LinearCongruentialGenerator(166_4525, 10_1390_4223, 2 << 31)
						while True:
												print(lcg.next_number())





