


'''simple docstring'''
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool






class 		UpperCamelCase_		(       UpperCAmelCase__ ):





						"""simple docstring"""




						UpperCAmelCase__						:							Union[str, Any]       =  '''facebook/bart-large-mnli'''
						UpperCAmelCase__						:							str       =  (
						    '''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__						:							Union[str, Any]       =  '''text_classifier'''
						UpperCAmelCase__						:							Optional[int]       =  AutoTokenizer
						UpperCAmelCase__						:							Optional[Any]       =  AutoModelForSequenceClassification

						UpperCAmelCase__						:							Tuple       =  ['''text''', ['''text''']]
						UpperCAmelCase__						:							Optional[int]       =  ['''text''']
						def        __A       (			self					:       Union[str, Any]	)				->       str:
										super().setup()
										__magic_name__							=			self.model.config
										__magic_name__							=			-1
										for idx, label in config.idalabel.items():
														if label.lower().startswith("entail"	):
																		__magic_name__							=			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        __A       (			self					:       Optional[Any]						,					_lowerCamelCase					:       Tuple						,					_lowerCamelCase					:       List[Any]	)				->       Dict:
										__magic_name__							=			labels
										return self.pre_processor(
										    [text] * len(__lowerCAmelCase	)						,					[f'This example is {label}' for label in labels]						,					return_tensors="pt"						,					padding="max_length"						,					)

						def        __A       (			self					:       Any						,					_lowerCamelCase					:       Optional[int]	)				->       Optional[Any]:
										__magic_name__							=			outputs.logits
										__magic_name__							=			torch.argmax(logits[:, 2]	).item()
										return self._labels[label_id]









'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

