



"""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       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[Any]							  =     "facebook/bart-large-mnli"
			A__	:					int							  =     (
			    "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__	:					int							  =     AutoTokenizer
			A__	:					str							  =     AutoModelForSequenceClassification

			A__	:					Union[str, Any]							  =     ["text", ["text"]]
			A__	:					int							  =     ["text"]


			def   snake_case__	(      self		)      ->	Tuple:
				super().setup()
				A__											=					self.model.config
				A__											=					-1
				for idx, label in config.idalabel.items():
					if label.lower().startswith("entail"		):
						A__											=					int(SCREAMING_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   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					labels
				return self.pre_processor(
				    [text] * len(SCREAMING_SNAKE_CASE__		)						,	[f"""This example is {label}""" for label in labels]						,	return_tensors="pt"						,	padding="max_length"						,	)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	Dict:
				A__											=					outputs.logits
				A__											=					torch.argmax(logits[:, 2]		).item()
				return self._labels[label_id]









'''simple docstring'''





__a  			=						"Alexander Joslin"

import operator as op

from .stack import Stack





def 		__snake_case(							_lowerCAmelCase						)						->					int:
      snake_case__    :       str       		= {"""*""": op.mul, """/""": op.truediv, """+""": op.add, """-""": op.sub}

      snake_case__    :       Stack[int]       		= Stack()
      snake_case__    :       Stack[str]       		= Stack()

      for i in equation:
            if i.isdigit():
                  # RULE 1
                  operand_stack.push(int(_lowerCAmelCase						)						)
            elif i in operators:
                  # RULE 2
                  operator_stack.push(_lowerCAmelCase						)
            elif i == ")":
                  # RULE 4
                  snake_case__    :       Union[str, Any]       		= operator_stack.peek()
                  operator_stack.pop()
                  snake_case__    :       Any       		= operand_stack.peek()
                  operand_stack.pop()
                  snake_case__    :       str       		= operand_stack.peek()
                  operand_stack.pop()

                  snake_case__    :       Optional[Any]       		= operators[opr](_lowerCAmelCase	,  _lowerCAmelCase						)
                  operand_stack.push(_lowerCAmelCase						)

    # RULE 5
      return operand_stack.peek()


if __name__ == "__main__":
       __a  			=						"(5 + ((4 * 2) * (2 + 3)))"
       # answer = 45
       print(F"{equation} = {dijkstras_two_stack_algorithm(equation)}")
