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#
# 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]







from math import loga




def 							_UpperCAmelCase  (UpperCamelCase_ : int       ):
				'''simple docstring'''
				if a < 0:
								raise ValueError("""Input value must be a positive integer"""       )
				elif isinstance(UpperCamelCase_      ,    UpperCamelCase_       ):
								raise TypeError("""Input value must be a 'int' type"""       )
				return 0 if (a == 0) else int(loga(a & -a       )       )


if __name__ == "__main__":
	import doctest

	doctest.testmod()

