

"""simple docstring"""





import warnings
from typing import List, Optional, Union

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




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Dict   		=	["image_processor", "tokenizer"]
 __UpperCAmelCase	:					List[Any]   		=	"ViltImageProcessor"
 __UpperCAmelCase	:					Optional[int]   		=	("BertTokenizer", "BertTokenizerFast")


 def __init__(			self							:			Any,					UpperCAmelCase__							:			List[Any]=None,					UpperCAmelCase__							:			Optional[int]=None,					**UpperCAmelCase__							:			str	):
    __lowercase								=						None
    if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead.",					UpperCAmelCase__,					)
       __lowercase								=						kwargs.pop("feature_extractor"	)

    __lowercase								=						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__(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						self.image_processor


 def __call__(			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Union[bool, str, PaddingStrategy] = False,					UpperCAmelCase__							:			Union[bool, str, TruncationStrategy] = None,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			int = 0,					UpperCAmelCase__							:			Optional[int] = None,					UpperCAmelCase__							:			Optional[bool] = None,					UpperCAmelCase__							:			Optional[bool] = None,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = False,					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Union[str, TensorType]] = None,					**UpperCAmelCase__							:			Tuple,					):
    __lowercase								=						self.tokenizer(
        text=UpperCAmelCase__,					add_special_tokens=UpperCAmelCase__,					padding=UpperCAmelCase__,					truncation=UpperCAmelCase__,					max_length=UpperCAmelCase__,					stride=UpperCAmelCase__,					pad_to_multiple_of=UpperCAmelCase__,					return_token_type_ids=UpperCAmelCase__,					return_attention_mask=UpperCAmelCase__,					return_overflowing_tokens=UpperCAmelCase__,					return_special_tokens_mask=UpperCAmelCase__,					return_offsets_mapping=UpperCAmelCase__,					return_length=UpperCAmelCase__,					verbose=UpperCAmelCase__,					return_tensors=UpperCAmelCase__,					**UpperCAmelCase__,					)
    # add pixel_values + pixel_mask
    __lowercase								=						self.image_processor(UpperCAmelCase__,					return_tensors=UpperCAmelCase__	)
    encoding.update(UpperCAmelCase__	)

    return encoding


 def 					_lowercase (			self							:			List[str],					*UpperCAmelCase__							:			int,					**UpperCAmelCase__							:			Any	):
    return self.tokenizer.batch_decode(*UpperCAmelCase__,					**UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any],					*UpperCAmelCase__							:			Optional[int],					**UpperCAmelCase__							:			List[Any]	):
    return self.tokenizer.decode(*UpperCAmelCase__,					**UpperCAmelCase__	)


 @property
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.tokenizer.model_input_names
    __lowercase								=						self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)


 @property
 def 					_lowercase (			self							:			Optional[Any]	):
    warnings.warn(
        "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.",					UpperCAmelCase__,					)
    return self.image_processor_class



 @property
 def 					_lowercase (			self							:			Dict	):
    warnings.warn(
        "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.",					UpperCAmelCase__,					)
    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 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      """facebook/bart-large-mnli"""
    a__	:						int						=      (
        """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."""
    )
    a__	:						Optional[Any]						=      """text_classifier"""
    a__	:						Any						=      AutoTokenizer
    a__	:						str						=      AutoModelForSequenceClassification

    a__	:						str						=      ["""text""", ["""text"""]]
    a__	:						Optional[int]						=      ["""text"""]







    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        super().setup()
        __UpperCamelCase     :int			  =		self.model.config
        __UpperCamelCase     :Optional[Any]			  =		-1
        for idx, label in config.idalabel.items():
            if label.lower().startswith('''entail'''):
                __UpperCamelCase     :List[Any]			  =		int(__lowercase)
        if self.entailment_id == -1:
            raise ValueError('''Could not determine the entailment ID from the model config, please pass it at init.''')







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		labels
        return self.pre_processor(
            [text] * len(__lowercase) , [f"""This example is {label}""" for label in labels] , return_tensors='''pt''' , padding='''max_length''' , )







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :List[Any]			  =		outputs.logits
        __UpperCamelCase     :Any			  =		torch.argmax(logits[:, 2]).item()
        return self._labels[label_id]





