






"""simple docstring"""
def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__					):
	return not any(
	    neighbour == 1 and colored_vertices[i] == color
	    for i, neighbour in enumerate(snake_case__					)					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					):

	# Base Case
	if index == len(snake_case__					):
		return True

	# Recursive Step
	for i in range(snake_case__					):
		if valid_coloring(graph[index]   ,   snake_case__   ,   snake_case__					):
			# Color current vertex
			A_		:						Dict	    =   i
			# Validate coloring
			if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   index + 1					):
				return True
			# Backtrack
			A_		:						Union[str, Any]	    =   -1
	return False






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	A_		:						int	    =   [-1] * len(snake_case__					)

	if util_color(snake_case__   ,   snake_case__   ,   snake_case__   ,   0					):
		return colored_vertices

	return []









"""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      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				"""facebook/bart-large-mnli"""
	_A       :				str    =				(
	    """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       :				List[str]    =				"""text_classifier"""
	_A       :				Optional[int]    =				AutoTokenizer
	_A       :				Optional[Any]    =				AutoModelForSequenceClassification

	_A       :				List[str]    =				["""text""", ["""text"""]]
	_A       :				Dict    =				["""text"""]





	def    lowerCamelCase(self							):
		super().setup()
		A_		:						int	    =   self.model.config
		A_		:						List[str]	    =   -1
		for idx, label in config.idalabel.items():
			if label.lower().startswith("""entail"""							):
				A_		:						List[Any]	    =   int(lowerCAmelCase_							)
		if self.entailment_id == -1:
			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						List[Any]	    =   labels
		return self.pre_processor(
		    [text] * len(lowerCAmelCase_							)     ,     [f"""This example is {label}""" for label in labels]     ,     return_tensors="""pt"""     ,     padding="""max_length"""     ,     )





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						str	    =   outputs.logits
		A_		:						Optional[int]	    =   torch.argmax(logits[:, 2]							).item()
		return self._labels[label_id]

