



'''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

_lowerCAmelCase										=     logging.get_logger(__name__)






@add_end_docstrings(_a					)
class 	lowerCAmelCase_(    _a					):
  '''simple docstring'''




  def __init__(   self  ,*__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		Optional[int]:
      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 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=None				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  List[Any]											= {}
      if top_k is not None:
          lowerCAmelCase__			:  Optional[int]											= top_k
      return {}, {}, postprocess_params




  def __call__(   self  ,__UpperCAmelCase  ,**__UpperCAmelCase				)   ->		List[Any]:
      return super().__call__(__lowerCAmelCase  ,**__lowerCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Tuple:
      lowerCAmelCase__			:  List[Any]											= load_image(__lowerCAmelCase				)
      lowerCAmelCase__			:  Any											= self.image_processor(images=__lowerCAmelCase  ,return_tensors=self.framework				)
      return model_inputs




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase				)   ->		Dict:
      lowerCAmelCase__			:  Optional[int]											= self.model(**__lowerCAmelCase				)
      return model_outputs






  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=5				)   ->		Optional[int]:
      if top_k > self.model.config.num_labels:
          lowerCAmelCase__			:  Optional[int]											= self.model.config.num_labels

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

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







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


