

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

lowerCAmelCase_       	=	logging.get_logger(__name__)





@add_end_docstrings(__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				Any							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				Optional[Any]							)      ->Union[str, Any]:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)
									self.check_model_type(_UpperCamelCase							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Dict=None							,   _UpperCamelCase :				Dict=None							,   _UpperCamelCase :				Optional[int]=None							,   **_UpperCamelCase :				List[str]							)      ->str:
									snake_case_,    snake_case_   =	{}, {}
									if padding is not None:
															snake_case_   =	padding
									if truncation is not None:
															snake_case_   =	truncation
									if top_k is not None:
															snake_case_   =	top_k
									return preprocess_params, {}, postprocess_params







			def __call__(     self :				Optional[Any]							,   _UpperCamelCase :				Union["Image.Image", str]							,   _UpperCamelCase :				str = None							,   **_UpperCamelCase :				Any							)      ->Any:
									if isinstance(_UpperCamelCase							,   (Image.Image, str)							) and isinstance(_UpperCamelCase							,   _UpperCamelCase							):
															snake_case_   =	{'''image''': image, '''question''': question}
									else:
															snake_case_   =	image
									snake_case_   =	super().__call__(_UpperCamelCase							,   **_UpperCamelCase							)
									return results







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Tuple=False							,   _UpperCamelCase :				List[Any]=False							)      ->Optional[Any]:
									snake_case_   =	load_image(inputs['''image''']							)
									snake_case_   =	self.tokenizer(
									    inputs['''question''']							,   return_tensors=self.framework							,   padding=_UpperCamelCase							,   truncation=_UpperCamelCase							)
									snake_case_   =	self.image_processor(images=_UpperCamelCase							,   return_tensors=self.framework							)
									model_inputs.update(_UpperCamelCase							)
									return model_inputs







			def 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				List[Any]							)      ->List[Any]:
									snake_case_   =	self.model(**_UpperCamelCase							)
									return model_outputs







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Optional[int]=5							)      ->Any:
									if top_k > self.model.config.num_labels:
															snake_case_   =	self.model.config.num_labels

									if self.framework == "pt":
															snake_case_   =	model_outputs.logits.sigmoid()[0]
															snake_case_,    snake_case_   =	probs.topk(_UpperCamelCase							)
									else:
															raise ValueError(f'''Unsupported framework: {self.framework}'''							)

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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase    :int     =					{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :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

  _lowerCAmelCase    :int     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


