


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

_snake_case     :		Union[str, Any]						=				logging.get_logger(__name__)

@add_end_docstrings(__lowercase      )
class 				a		(__lowercase      ):




							"""simple docstring"""


							def __init__(							self       :  Tuple    ,	*lowerCamelCase       :  int    ,	**lowerCamelCase       :  Any				)	->    int:
														super().__init__(*lowerCamelCase    ,	**lowerCamelCase				)
														self.check_model_type(lowerCamelCase				)

							def     __snake_case					(							self       :  Union[str, Any]    ,	lowerCamelCase       :  Dict=None    ,	lowerCamelCase       :  Dict=None    ,	lowerCamelCase       :  Union[str, Any]=None    ,	**lowerCamelCase       :  List[Any]				)	->    List[Any]:
														__snake_case		:			List[str]							 =   {}, {}
														if padding is not None:
																					__snake_case		:			Tuple							 =   padding
														if truncation is not None:
																					__snake_case		:			str							 =   truncation
														if top_k is not None:
																					__snake_case		:			int							 =   top_k
														return preprocess_params, {}, postprocess_params

							def __call__(							self       :  int    ,	lowerCamelCase       :  Union["Image.Image", str]    ,	lowerCamelCase       :  str = None    ,	**lowerCamelCase       :  Any				)	->    int:
														if isinstance(lowerCamelCase    ,	(Image.Image, str)				) and isinstance(lowerCamelCase    ,	lowerCamelCase				):
																					__snake_case		:			Tuple							 =   {"image": image, "question": question}
														else:
																					__snake_case		:			Dict							 =   image
														__snake_case		:			Union[str, Any]							 =   super().__call__(lowerCamelCase    ,	**lowerCamelCase				)
														return results

							def     __snake_case					(							self       :  Optional[Any]    ,	lowerCamelCase       :  Optional[int]    ,	lowerCamelCase       :  Dict=False    ,	lowerCamelCase       :  Optional[int]=False				)	->    int:
														__snake_case		:			Optional[Any]							 =   load_image(inputs["image"]				)
														__snake_case		:			Dict							 =   self.tokenizer(
														    inputs["question"]    ,	return_tensors=self.framework    ,	padding=lowerCamelCase    ,	truncation=lowerCamelCase				)
														__snake_case		:			List[Any]							 =   self.image_processor(images=lowerCamelCase    ,	return_tensors=self.framework				)
														model_inputs.update(lowerCamelCase				)
														return model_inputs

							def     __snake_case					(							self       :  List[str]    ,	lowerCamelCase       :  List[Any]				)	->    Optional[Any]:
														__snake_case		:			List[str]							 =   self.model(**lowerCamelCase				)
														return model_outputs






							def     __snake_case					(							self       :  List[Any]    ,	lowerCamelCase       :  List[str]    ,	lowerCamelCase       :  str=5				)	->    Any:
														if top_k > self.model.config.num_labels:
																					__snake_case		:			Any							 =   self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:			Tuple							 =   model_outputs.logits.sigmoid()[0]
																					__snake_case		:			List[Any]							 =   probs.topk(lowerCamelCase				)
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'				)

														__snake_case		:			Optional[Any]							 =   scores.tolist()
														__snake_case		:			Any							 =   ids.tolist()
														return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase    ,	lowerCamelCase				)]



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase  :					List[str]													=       {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase  :					Optional[Any]													=       [
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		lowercase  :					Optional[Any]													=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)