

"""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       lowerCamelCase  (UpperCAmelCase__	):
			lowerCamelCase__      :						Tuple    						=							'facebook/bart-large-mnli'
			lowerCamelCase__      :						List[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.'
			)
			lowerCamelCase__      :						str    						=							'text_classifier'
			lowerCamelCase__      :						int    						=							AutoTokenizer
			lowerCamelCase__      :						Union[str, Any]    						=							AutoModelForSequenceClassification

			lowerCamelCase__      :						Any    						=							['text', ['text']]
			lowerCamelCase__      :						Optional[int]    						=							['text']

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

			def     SCREAMING_SNAKE_CASE (    self :  List[Any]			, __UpperCAmelCase :  Dict			, __UpperCAmelCase :  List[str]				)      ->			Optional[Any]:
						SCREAMING_SNAKE_CASE__									=					labels
						return self.pre_processor(
						    [text] * len(UpperCAmelCase__				)			, [F"""This example is {label}""" for label in labels]			, return_tensors="""pt"""			, padding="""max_length"""			, )





			def     SCREAMING_SNAKE_CASE (    self :  List[str]			, __UpperCAmelCase :  Tuple				)      ->			Optional[int]:
						SCREAMING_SNAKE_CASE__									=					outputs.logits
						SCREAMING_SNAKE_CASE__									=					torch.argmax(logits[:, 2]				).item()
						return self._labels[label_id]








def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Any:



       """simple docstring"""

       A__       =    [0] * len(lowercase_						)
       A__       =    []
       A__       =    [1] * len(lowercase_						)

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(lowercase_						)						):
              if indegree[i] == 0:
                     queue.append(lowercase_						)

       while queue:
              A__       =    queue.pop(0						)
              for x in graph[vertex]:
                     indegree[x] -= 1

                     if long_dist[vertex] + 1 > long_dist[x]:
                            A__       =    long_dist[vertex] + 1

                     if indegree[x] == 0:
                            queue.append(lowercase_						)

       print(max(lowercase_						)						)


# Adjacency list of Graph
_lowerCamelCase	:		Optional[int]							       =   {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)


