


"""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(__UpperCAmelCase     )
class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    Optional[Any]  ,     *snake_case__  :    Tuple  ,     **snake_case__  :    Any				):

              '''simple docstring'''






              super().__init__(*snake_case__  ,     **snake_case__				)
              self.check_model_type(snake_case__				)






       def  __snake_case						(   self  :    List[str]  ,     snake_case__  :    List[Any]=None  ,     snake_case__  :    Optional[int]=None  ,     snake_case__  :    Dict=None  ,     **snake_case__  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				{}, {}
              if padding is not None:
                     lowercase		:Optional[Any]       							=				padding
              if truncation is not None:
                     lowercase		:int       							=				truncation
              if top_k is not None:
                     lowercase		:Optional[int]       							=				top_k
              return preprocess_params, {}, postprocess_params






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

              '''simple docstring'''






              if isinstance(snake_case__  ,     (Image.Image, str)				) and isinstance(snake_case__  ,     snake_case__				):
                     lowercase		:Dict       							=				{'''image''': image, '''question''': question}
              else:
                     lowercase		:List[str]       							=				image
              lowercase		:Any       							=				super().__call__(snake_case__  ,     **snake_case__				)
              return results






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    Optional[int]=False  ,     snake_case__  :    Tuple=False				):

              '''simple docstring'''






              lowercase		:Tuple       							=				load_image(inputs['''image''']				)
              lowercase		:int       							=				self.tokenizer(
                  inputs['''question''']  ,     return_tensors=self.framework  ,     padding=snake_case__  ,     truncation=snake_case__				)
              lowercase		:Any       							=				self.image_processor(images=snake_case__  ,     return_tensors=self.framework				)
              model_inputs.update(snake_case__				)
              return model_inputs






       def  __snake_case						(   self  :    str  ,     snake_case__  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:str       							=				self.model(**snake_case__				)
              return model_outputs







       def  __snake_case						(   self  :    Optional[Any]  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]=5				):

              '''simple docstring'''






              if top_k > self.model.config.num_labels:
                     lowercase		:List[Any]       							=				self.model.config.num_labels

              if self.framework == "pt":
                     lowercase		:Optional[Any]       							=				model_outputs.logits.sigmoid()[0]
                     lowercase							,  lowercase		:Union[str, Any]       							=				probs.topk(snake_case__				)
              else:
                     raise ValueError(f"""Unsupported framework: {self.framework}"""				)

              lowercase		:Tuple       							=				scores.tolist()
              lowercase		:List[Any]       							=				ids.tolist()
              return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__  ,     snake_case__				)]





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase      		=			{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3TokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LayoutLMv3ForQuestionAnswering''',
     '''LayoutLMv3ForSequenceClassification''',
     '''LayoutLMv3ForTokenClassification''',
     '''LayoutLMv3Model''',
     '''LayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFLayoutLMv3ForQuestionAnswering''',
     '''TFLayoutLMv3ForSequenceClassification''',
     '''TFLayoutLMv3ForTokenClassification''',
     '''TFLayoutLMv3Model''',
     '''TFLayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3FeatureExtractor''']
 UpperCAmelCase      		=			['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

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

