



'''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_							(					snake_case_			):
						_lowerCAmelCase     :   Tuple	     =				'facebook/bart-large-mnli'
						_lowerCAmelCase     :   Tuple	     =				(
						    '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     :   Union[str, Any]	     =				'text_classifier'
						_lowerCAmelCase     :   str	     =				AutoTokenizer
						_lowerCAmelCase     :   str	     =				AutoModelForSequenceClassification

						_lowerCAmelCase     :   Optional[int]	     =				['text', ['text']]
						_lowerCAmelCase     :   Optional[int]	     =				['text']







						def 					__lowercase    (  self   :      Tuple				):

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







						def 					__lowercase    (  self   :      str				,    lowerCAmelCase__   :      Optional[Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			labels
													return self.pre_processor(
													    [text] * len(lowerCAmelCase__				)				,    [F"""This example is {label}""" for label in labels]				,    return_tensors='''pt'''				,    padding='''max_length'''				,    )







						def 					__lowercase    (  self   :      Optional[Any]				,    lowerCAmelCase__   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			outputs.logits
													SCREAMING_SNAKE_CASE					:      int     					=			torch.argmax(logits[:, 2]				).item()
													return self._labels[label_id]









'''simple docstring'''



from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id'''					,		['''canonical_dataset_name''', '''org-name/dataset-name''']				)
@pytest.mark.parametrize('''path'''					,		['''filename.csv''', '''filename with blanks.csv''']				)
@pytest.mark.parametrize('''revision'''					,		[None, '''v2''']				)
def 					UpperCAmelCase   (						A  :				int					,		A  :				Tuple					,		A  :				Any				):
							SCREAMING_SNAKE_CASE					:      int     					=			hf_hub_url(repo_id=A					,		path=A					,		revision=A				)
							assert url == F"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or "main"}/{quote(A				)}"""




