

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 	lowercase    (				SCREAMING_SNAKE_CASE__							):


		"""simple docstring"""




		a__					       =  ["image_processor", "tokenizer"]
		a__					       =  "BridgeTowerImageProcessor"
		a__					       =  ("RobertaTokenizer", "RobertaTokenizerFast")


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(snake_case__	,       snake_case__)


		def __call__(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = True	,       __snake_case = False	,       __snake_case = None	,       __snake_case = None	,       __snake_case = 0	,       __snake_case = None	,       __snake_case = None	,       __snake_case = None	,       __snake_case = False	,       __snake_case = False	,       __snake_case = False	,       __snake_case = False	,       __snake_case = True	,       __snake_case = None	,       **__snake_case	,       ):
			_UpperCamelCase   :		Optional[int]			=  self.tokenizer(
			    text=snake_case__	,       add_special_tokens=snake_case__	,       padding=snake_case__	,       truncation=snake_case__	,       max_length=snake_case__	,       stride=snake_case__	,       pad_to_multiple_of=snake_case__	,       return_token_type_ids=snake_case__	,       return_attention_mask=snake_case__	,       return_overflowing_tokens=snake_case__	,       return_special_tokens_mask=snake_case__	,       return_offsets_mapping=snake_case__	,       return_length=snake_case__	,       verbose=snake_case__	,       return_tensors=snake_case__	,       **snake_case__	,       )
			# add pixel_values + pixel_mask
			_UpperCamelCase   :		Optional[int]			=  self.image_processor(
			    snake_case__	,       return_tensors=snake_case__	,       do_normalize=snake_case__	,       do_center_crop=snake_case__	,       **snake_case__)
			encoding.update(snake_case__)

			return encoding


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


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






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=  self.tokenizer.model_input_names
			_UpperCamelCase   :		List[Any]			=  self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







# 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 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "facebook/bart-large-mnli"
		a__					       =  (
		    "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__					       =  "text_classifier"
		a__					       =  AutoTokenizer
		a__					       =  AutoModelForSequenceClassification

		a__					       =  ["text", ["text"]]
		a__					       =  ["text"]


		def 		A__					(				self):
			super().setup()
			_UpperCamelCase   :		List[Any]			=						self.model.config
			_UpperCamelCase   :		Optional[int]			=						-1
			for idx, label in config.idalabel.items():
				if label.lower().startswith('entail'):
					_UpperCamelCase   :		Tuple			=						int(__snake_case)
			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	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




