

import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class     snake_case_			(				_a							):


							"""simple docstring"""





							__UpperCAmelCase       =["""image_processor""", """tokenizer"""]
							__UpperCAmelCase       ="""FlavaImageProcessor"""
							__UpperCAmelCase       =("""BertTokenizer""", """BertTokenizerFast""")




							def __init__( self					,						_A=None					,						_A=None					,						**_A							):
												__lowerCAmelCase					=							None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'					,						_A					,						)
																	__lowerCAmelCase					=							kwargs.pop('feature_extractor'							)

												__lowerCAmelCase					=							image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('You need to specify an `image_processor`.'							)
												if tokenizer is None:
																	raise ValueError('You need to specify a `tokenizer`.'							)

												super().__init__(_A					,						_A							)
												__lowerCAmelCase					=							self.image_processor




							def __call__( self					,						_A = None					,						_A = None					,						_A = True					,						_A = False					,						_A = False					,						_A = None					,						_A = 0					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = None					,						_A = False					,						_A = False					,						_A = False					,						_A = False					,						_A = True					,						_A = None					,						**_A					,						):

												if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'							)

												if text is not None:
																	__lowerCAmelCase					=							self.tokenizer(
																	    text=_A					,						add_special_tokens=_A					,						padding=_A					,						truncation=_A					,						max_length=_A					,						stride=_A					,						pad_to_multiple_of=_A					,						return_token_type_ids=_A					,						return_attention_mask=_A					,						return_overflowing_tokens=_A					,						return_special_tokens_mask=_A					,						return_offsets_mapping=_A					,						return_length=_A					,						verbose=_A					,						return_tensors=_A					,						**_A					,						)
												if images is not None:
																	__lowerCAmelCase					=							self.image_processor(
																	    _A					,						return_image_mask=_A					,						return_codebook_pixels=_A					,						return_tensors=_A					,						**_A					,						)

												if text is not None and images is not None:
																	encoding.update(_A							)
																	return encoding
												elif text is not None:
																	return encoding
												else:
																	return BatchEncoding(data=dict(**_A							)					,						tensor_type=_A							)




							def        A__  ( self					,						*_A					,						**_A							):
												return self.tokenizer.batch_decode(*_A					,						**_A							)




							def        A__  ( self					,						*_A					,						**_A							):
												return self.tokenizer.decode(*_A					,						**_A							)




							@property
							def        A__  ( self							):
												__lowerCAmelCase					=							self.tokenizer.model_input_names
												__lowerCAmelCase					=							self.image_processor.model_input_names
												return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




							@property
							def        A__  ( self							):
												warnings.warn(
												    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'					,						_A					,						)
												return self.image_processor_class




							@property
							def        A__  ( self							):
												warnings.warn(
												    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'					,						_A					,						)
												return self.image_processor









# 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]






