





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

a__:      int									=		logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__      )
class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 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 				UpperCamelCase			(							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 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						load_image(__lowerCamelCase )
        A__											=						self.image_processor(images=__lowerCamelCase,return_tensors=self.framework )
        return model_inputs



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.model(**__lowerCamelCase )
        return model_outputs




 def 				UpperCamelCase			(							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 )]












def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       a__:      Dict									=		input('Enter numbers separated by a comma:\n').strip()
       a__:      Any									=		[int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))





