def 						A__  (					__A	):







    '''simple docstring'''

    def merge(__A   , __A	) -> list:

        def _merge():
            while left and right:
                yield (left if left[0] <= right[0] else right).pop(0	)
            yield from left
            yield from right

        return list(_merge()	)

    if len(__A	) <= 1:
        return collection
    _lowerCamelCase     :		Optional[int] 						=						len(__A	) // 2
    return merge(merge_sort(collection[:mid]	)   , merge_sort(collection[mid:]	)	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    lowerCAmelCase			:							Dict     =input("Enter numbers separated by a comma:\n").strip()
    lowerCAmelCase			:							Dict     =[int(item) for item in user_input.split(",")]
    print(*merge_sort(unsorted), sep=",")





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




@add_end_docstrings(__lowerCAmelCase		)
class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				str    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				List[str])				->Tuple:


          """simple docstring"""


          super().__init__(*_UpperCamelCase    ,  **_UpperCamelCase)
          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 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				List[str]=None)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{}
          if top_k is not None:
              _lowerCamelCase     :		str 						=						top_k
          return {}, {}, postprocess_params



      def __call__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,  **_UpperCamelCase     :				Optional[int])				->Dict:


          """simple docstring"""


          return super().__call__(_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						load_image(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						self.image_processor(images=_UpperCamelCase    ,  return_tensors=self.framework)
          return model_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model(**_UpperCamelCase)
          return model_outputs




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[str]=5)				->str:


          """simple docstring"""


          if top_k > self.model.config.num_labels:
              _lowerCamelCase     :		Union[str, Any] 						=						self.model.config.num_labels

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

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




