




# 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		):
				lowerCAmelCase__				=     "facebook/bart-large-mnli"
				lowerCAmelCase__				=     (
				    "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__				=     "text_classifier"
				lowerCAmelCase__				=     AutoTokenizer
				lowerCAmelCase__				=     AutoModelForSequenceClassification

				lowerCAmelCase__				=     ["text", ["text"]]
				lowerCAmelCase__				=     ["text"]



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								super().setup()
								_lowerCAmelCase    :    List[str]										=      self.model.config
								_lowerCAmelCase    :    Any										=      -1
								for idx, label in config.idalabel.items():
												if label.lower().startswith("""entail"""      ):
																_lowerCAmelCase    :    Tuple										=      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      :		int		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		Optional[Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      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      :		Dict		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      outputs.logits
								_lowerCAmelCase    :    str										=      torch.argmax(logits[:, 2]      ).item()
								return self._labels[label_id]







import sys

_lowerCamelCase      :  Optional[Any]      						=						(
    "73167176531330624919225119674426574742355349194934"
    "96983520312774506326239578318016984801869478851843"
    "85861560789112949495459501737958331952853208805511"
    "12540698747158523863050715693290963295227443043557"
    "66896648950445244523161731856403098711121722383113"
    "62229893423380308135336276614282806444486645238749"
    "30358907296290491560440772390713810515859307960866"
    "70172427121883998797908792274921901699720888093776"
    "65727333001053367881220235421809751254540594752243"
    "52584907711670556013604839586446706324415722155397"
    "53697817977846174064955149290862569321978468622482"
    "83972241375657056057490261407972968652414535100474"
    "82166370484403199890008895243450658541227588666881"
    "16427171479924442928230863465674813919123162824586"
    "17866458359124566529476545682848912883142607690042"
    "24219022671055626321111109370544217506941658960408"
    "07198403850962455444362981230987879927244284909188"
    "84580156166097919133875499200524063689912560717606"
    "05886116467109405077541002256983155200055935729725"
    "71636269561882670428252483600823257530420752963450"
)




def 							_UpperCAmelCase  (UpperCamelCase_ : str = N       ):
				'''simple docstring'''
				_lowerCAmelCase    :    List[str]										=      -sys.maxsize - 1
				for i in range(len(UpperCamelCase_       ) - 12       ):
								_lowerCAmelCase    :    List[str]										=      1
								for j in range(13       ):
												product *= int(n[i + j]       )
								if product > largest_product:
												_lowerCAmelCase    :    Any										=      product
				return largest_product


if __name__ == "__main__":
	print(F'''{solution() = }''')

