






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(lowercase  )
class 						lowercase_   (      lowercase  ):






      '''simple docstring'''




      def __init__( self :		Any					,   *__UpperCAmelCase :		int					,   **__UpperCAmelCase :		str    )       ->List[Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Optional[int]					,   __UpperCAmelCase :		Union[str, Any]=None					,   __UpperCAmelCase :		int=None					,   __UpperCAmelCase :		List[str]=None					,   **__UpperCAmelCase :		Optional[int]    )       ->List[str]:




       """simple docstring"""


       a    ,	a						    = {}, {}
       if padding is not None:
        a						    = padding
       if truncation is not None:
        a						    = truncation
       if top_k is not None:
        a						    = top_k
       return preprocess_params, {}, postprocess_params







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




       """simple docstring"""


       if isinstance(__UpperCAmelCase					,   (Image.Image, str)    ) and isinstance(__UpperCAmelCase					,   __UpperCAmelCase    ):
        a						    = {'''image''': image, '''question''': question}
       else:
        a						    = image
       a						    = super().__call__(__UpperCAmelCase					,   **__UpperCAmelCase    )
       return results







      def  __lowerCAmelCase   ( self :		Any					,   __UpperCAmelCase :		Optional[Any]					,   __UpperCAmelCase :		Dict=False					,   __UpperCAmelCase :		Optional[int]=False    )       ->Tuple:




       """simple docstring"""


       a						    = load_image(inputs['''image''']    )
       a						    = self.tokenizer(
           inputs['''question''']					,   return_tensors=self.framework					,   padding=__UpperCAmelCase					,   truncation=__UpperCAmelCase    )
       a						    = self.image_processor(images=__UpperCAmelCase					,   return_tensors=self.framework    )
       model_inputs.update(__UpperCAmelCase    )
       return model_inputs







      def  __lowerCAmelCase   ( self :		str					,   __UpperCAmelCase :		Tuple    )       ->Dict:




       """simple docstring"""


       a						    = self.model(**__UpperCAmelCase    )
       return model_outputs







      def  __lowerCAmelCase   ( self :		Union[str, Any]					,   __UpperCAmelCase :		List[Any]					,   __UpperCAmelCase :		List[str]=5    )       ->Any:




       """simple docstring"""


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

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

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










from typing import TYPE_CHECKING

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


UpperCAmelCase__							  =						{
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "RagModel",
         "RagPreTrainedModel",
         "RagSequenceForGeneration",
         "RagTokenForGeneration",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "TFRagModel",
         "TFRagPreTrainedModel",
         "TFRagSequenceForGeneration",
         "TFRagTokenForGeneration",
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     UpperCAmelCase__							  =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


