
from __future__ import annotations


def 						UpperCAmelCase      (     a_						)		->     bool:







				"""simple docstring"""



				__A             =			len(a_						)
				# We need to create solution object to save path.
				__A             =			[[0 for _ in range(a_						)] for _ in range(a_						)]
				__A             =			run_maze(a_		,       0		,       0		,       a_						)
				if solved:
								print("\n".join(str(a_						) for row in solutions						)						)
				else:
								print("No solution exists!"						)
				return solved


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_						)		->     bool:







				"""simple docstring"""



				__A             =			len(a_						)
				# Final check point.
				if i == j == (size - 1):
								__A             =			1
								return True

				__A             =			(not i < 0) and (not j < 0)  # Check lower bounds
				__A             =			(i < size) and (j < size)  # Check upper bounds

				if lower_flag and upper_flag:
								# check for already visited and block points.
								__A             =			(not solutions[i][j]) and (not maze[i][j])
								if block_flag:
												# check visited
												__A             =			1

												# check for directions
												if (
												    run_maze(a_		,       i + 1		,       a_		,       a_						)
												    or run_maze(a_		,       a_		,       j + 1		,       a_						)
												    or run_maze(a_		,       i - 1		,       a_		,       a_						)
												    or run_maze(a_		,       a_		,       j - 1		,       a_						)
												):
																return True

												__A             =			0
												return False
				return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()


# 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				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "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."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






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