


'''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     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      Any      	=  """facebook/bart-large-mnli"""
							A_							:      List[Any]      	=  (
							    """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_							:      Union[str, Any]      	=  """text_classifier"""
							A_							:      Union[str, Any]      	=  AutoTokenizer
							A_							:      List[Any]      	=  AutoModelForSequenceClassification

							A_							:      Tuple      	=  ["""text""", ["""text"""]]
							A_							:      List[str]      	=  ["""text"""]






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








'''simple docstring'''


def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				return int((input_a, input_a).count(0     ) != 0     )







def 			snake_case_   (  ):







				"""simple docstring"""





				assert nand_gate(0							,      0     ) == 1
				assert nand_gate(0							,      1     ) == 1
				assert nand_gate(1							,      0     ) == 1
				assert nand_gate(1							,      1     ) == 0


if __name__ == "__main__":
				print(nand_gate(0, 0))
				print(nand_gate(0, 1))
				print(nand_gate(1, 0))
				print(nand_gate(1, 1))




