



"""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"""







def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[Any]     )	-> Dict:






	"""simple docstring"""





	A__											=					[]
	A__											=					set({"(", "[", "{"}     )
	A__											=					set({")", "]", "}"}     )
	A__											=					{"{": "}", "[": "]", "(": ")"}

	for i in range(len(UpperCAmelCase_     )     ):
		if s[i] in open_brackets:
			stack.append(s[i]     )

		elif s[i] in closed_brackets and (
		    len(UpperCAmelCase_     ) == 0 or (len(UpperCAmelCase_     ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(UpperCAmelCase_     ) == 0

def       _lowerCamelCase				(							)	-> str:






	"""simple docstring"""





	A__											=					input("Enter sequence of brackets: "     )
	if is_balanced(UpperCAmelCase_     ):
		print(UpperCAmelCase_,	"is balanced"     )
	else:
		print(UpperCAmelCase_,	"is not balanced"     )


if __name__ == "__main__":
			main()




