"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase_     =	{
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TapasForMaskedLM''',
			    '''TapasForQuestionAnswering''',
			    '''TapasForSequenceClassification''',
			    '''TapasModel''',
			    '''TapasPreTrainedModel''',
			    '''load_tf_weights_in_tapas''',
			]
try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFTapasForMaskedLM''',
			    '''TFTapasForQuestionAnswering''',
			    '''TFTapasForSequenceClassification''',
			    '''TFTapasModel''',
			    '''TFTapasPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
			from .tokenization_tapas import TapasTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tapas import (
						    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TapasForMaskedLM,
						    TapasForQuestionAnswering,
						    TapasForSequenceClassification,
						    TapasModel,
						    TapasPreTrainedModel,
						    load_tf_weights_in_tapas,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_tapas import (
						    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFTapasForMaskedLM,
						    TFTapasForQuestionAnswering,
						    TFTapasForSequenceClassification,
						    TFTapasModel,
						    TFTapasPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



"""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  _snake_case		(    __snake_case ):







				"""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 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											super().setup()
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model.config
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1
											for idx, label in config.idalabel.items():
																		if label.lower().startswith("""entail"""):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    labels
											return self.pre_processor(
											    [text] * len(_A)      ,    [f"""This example is {label}""" for label in labels]      ,    return_tensors="""pt"""      ,    padding="""max_length"""      ,    )





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.argmax(logits[:, 2]).item()
											return self._labels[label_id]


