

"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase =				{
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCAmelCase =				[
						    'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TableTransformerForObjectDetection',
						    'TableTransformerModel',
						    'TableTransformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_table_transformer import (
						    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TableTransformerConfig,
						    TableTransformerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_table_transformer import (
												    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TableTransformerForObjectDetection,
												    TableTransformerModel,
												    TableTransformerPreTrainedModel,
												)

else:
						import sys

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








"""simple docstring"""




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

__UpperCAmelCase =				logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase					)
class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			List[str]					,*A		:			Optional[int]					,**A		:			List[str]    ):






   '''simple docstring'''
   super().__init__(*A					,**A    )
   self.check_model_type(A    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Dict=None					,A		:			Optional[int]=None					,A		:			Tuple=None					,**A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					{}, {}
   if padding is not None:
    UpperCAmelCase__					:  Union[str, Any]					   =					padding
   if truncation is not None:
    UpperCAmelCase__					:  List[str]					   =					truncation
   if top_k is not None:
    UpperCAmelCase__					:  List[Any]					   =					top_k
   return preprocess_params, {}, postprocess_params

  def __call__(  self		:			List[Any]					,A		:			Union["Image.Image", str]					,A		:			str = None					,**A		:			Union[str, Any]    ):






   '''simple docstring'''
   if isinstance(A					,(Image.Image, str)    ) and isinstance(A					,A    ):
    UpperCAmelCase__					:  Any					   =					{"""image""": image, """question""": question}
   else:
    UpperCAmelCase__					:  Dict					   =					image
   UpperCAmelCase__					:  Dict					   =					super().__call__(A					,**A    )
   return results

  def    __lowercase       (  self		:			Optional[int]					,A		:			Optional[Any]					,A		:			Optional[Any]=False					,A		:			List[Any]=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					load_image(inputs["""image"""]    )
   UpperCAmelCase__					:  Optional[Any]					   =					self.tokenizer(
       inputs["""question"""]					,return_tensors=self.framework					,padding=A					,truncation=A    )
   UpperCAmelCase__					:  int					   =					self.image_processor(images=A					,return_tensors=self.framework    )
   model_inputs.update(A    )
   return model_inputs

  def    __lowercase       (  self		:			Dict					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model(**A    )
   return model_outputs




  def    __lowercase       (  self		:			int					,A		:			List[Any]					,A		:			List[str]=5    ):






   '''simple docstring'''
   if top_k > self.model.config.num_labels:
    UpperCAmelCase__					:  Any					   =					self.model.config.num_labels

   if self.framework == "pt":
    UpperCAmelCase__					:  Optional[int]					   =					model_outputs.logits.sigmoid()[0]
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					probs.topk(A    )
   else:
    raise ValueError(f"Unsupported framework: {self.framework}"    )

   UpperCAmelCase__					:  List[str]					   =					scores.tolist()
   UpperCAmelCase__					:  Optional[int]					   =					ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(A					,A    )]





