




'''simple docstring'''







from typing import TYPE_CHECKING

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


A									={
    'configuration_ernie': ['ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ErnieConfig', 'ErnieOnnxConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ErnieForCausalLM',
       'ErnieForMaskedLM',
       'ErnieForMultipleChoice',
       'ErnieForNextSentencePrediction',
       'ErnieForPreTraining',
       'ErnieForQuestionAnswering',
       'ErnieForSequenceClassification',
       'ErnieForTokenClassification',
       'ErnieModel',
       'ErniePreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ernie import (
          ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
          ErnieForCausalLM,
          ErnieForMaskedLM,
          ErnieForMultipleChoice,
          ErnieForNextSentencePrediction,
          ErnieForPreTraining,
          ErnieForQuestionAnswering,
          ErnieForSequenceClassification,
          ErnieForTokenClassification,
          ErnieModel,
          ErniePreTrainedModel,
      )

else:
   import sys

   A									=_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

A									=logging.get_logger(__name__)


@add_end_docstrings(__a				)
class     _a						(			__a				):
       def __init__(			self		:			Optional[int]     ,       *lowercase		:			Any     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           super().__init__(*lowercase     ,       **lowercase       )
           self.check_model_type(lowercase       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			str=None     ,       lowercase		:			List[str]=None     ,       lowercase		:			List[str]=None     ,       **lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	{}, {}
           if padding is not None:
               UpperCAmelCase     =	padding
           if truncation is not None:
               UpperCAmelCase     =	truncation
           if top_k is not None:
               UpperCAmelCase     =	top_k
           return preprocess_params, {}, postprocess_params
       def __call__(			self		:			Optional[Any]     ,       lowercase		:			Union["Image.Image", str]     ,       lowercase		:			str = None     ,       **lowercase		:			Optional[int]       ):



           '''simple docstring'''

           if isinstance(lowercase     ,       (Image.Image, str)       ) and isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	{'''image''': image, '''question''': question}
           else:
               UpperCAmelCase     =	image
           UpperCAmelCase     =	super().__call__(lowercase     ,       **lowercase       )
           return results
       def 					A   (			self		:			List[Any]     ,       lowercase		:			List[str]     ,       lowercase		:			Any=False     ,       lowercase		:			Any=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	load_image(inputs['''image''']       )
           UpperCAmelCase     =	self.tokenizer(
               inputs['''question''']     ,       return_tensors=self.framework     ,       padding=lowercase     ,       truncation=lowercase       )
           UpperCAmelCase     =	self.image_processor(images=lowercase     ,       return_tensors=self.framework       )
           model_inputs.update(lowercase       )
           return model_inputs
       def 					A   (			self		:			Dict     ,       lowercase		:			Optional[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model(**lowercase       )
           return model_outputs







       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			int     ,       lowercase		:			Optional[Any]=5       ):



           '''simple docstring'''

           if top_k > self.model.config.num_labels:
               UpperCAmelCase     =	self.model.config.num_labels

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

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




