

def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					_enforce_args(UpperCAmelCase__		,					UpperCAmelCase__      )
					if n == 0:
										return 0
					__lowerCAmelCase					=							float('-inf'      )
					for i in range(1		,					n + 1      ):
										__lowerCAmelCase					=							max(
										    UpperCAmelCase__		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					UpperCAmelCase__      )      )

					return max_revue


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					_enforce_args(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							[float('-inf'      ) for _ in range(n + 1      )]
					return _top_down_cut_rod_recursive(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if max_rev[n] >= 0:
										return max_rev[n]
					elif n == 0:
										return 0
					else:
										__lowerCAmelCase					=							float('-inf'      )
										for i in range(1		,					n + 1      ):
															__lowerCAmelCase					=							max(
															    UpperCAmelCase__		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					UpperCAmelCase__		,					UpperCAmelCase__      )		,					)

										__lowerCAmelCase					=							max_revenue

					return max_rev[n]


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					_enforce_args(UpperCAmelCase__		,					UpperCAmelCase__      )

					# length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
					# length 0.
					__lowerCAmelCase					=							[float('-inf'      ) for _ in range(n + 1      )]
					__lowerCAmelCase					=							0

					for i in range(1		,					n + 1      ):
										__lowerCAmelCase					=							max_rev[i]
										for j in range(1		,					i + 1      ):
															__lowerCAmelCase					=							max(UpperCAmelCase__		,					prices[j - 1] + max_rev[i - j]      )

										__lowerCAmelCase					=							max_revenue_i

					return max_rev[n]


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					if n < 0:
										__lowerCAmelCase					=							F"""n must be greater than or equal to 0. Got n = {n}"""
										raise ValueError(UpperCAmelCase__      )

					if n > len(UpperCAmelCase__      ):
										__lowerCAmelCase					=							(
										    'Each integral piece of rod must have a corresponding price. '
										    F"""Got n = {n} but length of prices = {len(UpperCAmelCase__      )}"""
										)
										raise ValueError(UpperCAmelCase__      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							[6, 10, 12, 15, 20, 23]
					__lowerCAmelCase					=							len(UpperCAmelCase__      )

					# the best revenue comes from cutting the rod into 6 pieces, each
					# of length 1 resulting in a revenue of 6 * 6 = 36.
					__lowerCAmelCase					=							36

					__lowerCAmelCase					=							top_down_cut_rod(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							bottom_up_cut_rod(UpperCAmelCase__		,					UpperCAmelCase__      )
					__lowerCAmelCase					=							naive_cut_rod_recursive(UpperCAmelCase__		,					UpperCAmelCase__      )

					assert expected_max_revenue == max_rev_top_down
					assert max_rev_top_down == max_rev_bottom_up
					assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
		main()









# 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     snake_case_			(				_a							):


							"""simple docstring"""





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

							__UpperCAmelCase       =["""text""", ["""text"""]]
							__UpperCAmelCase       =["""text"""]




							def        A__  ( self							):
												super().setup()
												__lowerCAmelCase					=							self.model.config
												__lowerCAmelCase					=							-1
												for idx, label in config.idalabel.items():
																	if label.lower().startswith('entail'							):
																						__lowerCAmelCase					=							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        A__  ( self					,						_A					,						_A							):
												__lowerCAmelCase					=							labels
												return self.pre_processor(
												    [text] * len(_A							)					,						[F"""This example is {label}""" for label in labels]					,						return_tensors='pt'					,						padding='max_length'					,						)




							def        A__  ( self					,						_A							):
												__lowerCAmelCase					=							outputs.logits
												__lowerCAmelCase					=							torch.argmax(logits[:, 2]							).item()
												return self._labels[label_id]






