"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__:Tuple			=	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[Any]			=	[
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE__:Dict			=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

"""simple docstring"""



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   import tensorflow as tf

   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
   from ..tf_utils import stable_softmax

if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE__:Optional[Any]			=	logging.get_logger(__name__)






@add_end_docstrings(snake_case_   )
class 				snake_case__     (   snake_case_   ):


 def __init__(				self  ,						*lowerCamelCase  ,						**lowerCamelCase		):
        super().__init__(*lowerCamelCase  ,						**lowerCamelCase		)
        requires_backends(self  ,						"vision"		)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)


 def 		a__ (				self  ,						lowerCamelCase=None		):
        __a     				=  {}
        if top_k is not None:
               __a     				=  top_k
        return {}, {}, postprocess_params


 def __call__(				self  ,						lowerCamelCase  ,						**lowerCamelCase		):
        return super().__call__(lowerCamelCase  ,						**lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  load_image(lowerCamelCase		)
        __a     				=  self.image_processor(images=lowerCamelCase  ,						return_tensors=self.framework		)
        return model_inputs


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  self.model(**lowerCamelCase		)
        return model_outputs






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase=5		):
        if top_k > self.model.config.num_labels:
               __a     				=  self.model.config.num_labels

        if self.framework == "pt":
               __a     				=  model_outputs.logits.softmax(-1		)[0]
               __a				, __a     				=  probs.topk(lowerCamelCase		)
        elif self.framework == "tf":
               __a     				=  stable_softmax(model_outputs.logits  ,						axis=-1		)[0]
               __a     				=  tf.math.top_k(lowerCamelCase  ,						k=lowerCamelCase		)
               __a				, __a     				=  topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(F"Unsupported framework: {self.framework}"		)

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