





import math


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a = 0      ,       __a = 0      ):
	snake_case_						:	List[Any]      =		end or len(snake_case__      )
	for i in range(snake_case__      ,       snake_case__      ):
		snake_case_						:	Union[str, Any]      =		i
		snake_case_						:	Optional[int]      =		array[i]
		while temp_index != start and temp_index_value < array[temp_index - 1]:
			snake_case_						:	Optional[Any]      =		array[temp_index - 1]
			temp_index -= 1
		snake_case_						:	str      =		temp_index_value
	return array


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ):  # Max Heap
	snake_case_						:	Optional[int]      =		index
	snake_case_						:	List[str]      =		2 * index + 1  # Left Node
	snake_case_						:	Optional[int]      =		2 * index + 2  # Right Node

	if left_index < heap_size and array[largest] < array[left_index]:
		snake_case_						:	List[Any]      =		left_index

	if right_index < heap_size and array[largest] < array[right_index]:
		snake_case_						:	Dict      =		right_index

	if largest != index:
		snake_case_						:	List[str]      =		array[largest], array[index]
		heapify(snake_case__      ,       snake_case__      ,       snake_case__      )


def 							SCREAMING_SNAKE_CASE__  (			__a      ):
	snake_case_						:	int      =		len(snake_case__      )

	for i in range(n // 2      ,       -1      ,       -1      ):
		heapify(snake_case__      ,       snake_case__      ,       snake_case__      )

	for i in range(n - 1      ,       0      ,       -1      ):
		snake_case_						:	Tuple      =		array[0], array[i]
		heapify(snake_case__      ,       0      ,       snake_case__      )

	return array


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ):



	if (array[first_index] > array[middle_index]) != (
	    array[first_index] > array[last_index]
	):
		return array[first_index]
	elif (array[middle_index] > array[first_index]) != (
	    array[middle_index] > array[last_index]
	):
		return array[middle_index]
	else:
		return array[last_index]


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ):
	snake_case_						:	Optional[Any]      =		low
	snake_case_						:	str      =		high
	while True:
		while array[i] < pivot:
			i += 1
		j -= 1
		while pivot < array[j]:
			j -= 1
		if i >= j:
			return i
		snake_case_						:	int      =		array[j], array[i]
		i += 1


def 							SCREAMING_SNAKE_CASE__  (			__a      ):



	if len(snake_case__      ) == 0:
		return array
	snake_case_						:	Any      =		2 * math.ceil(math.loga(len(snake_case__      )      )      )
	snake_case_						:	Tuple      =		16
	return intro_sort(snake_case__      ,       0      ,       len(snake_case__      )      ,       snake_case__      ,       snake_case__      )


def 							SCREAMING_SNAKE_CASE__  (			__a      ,       __a      ,       __a      ,       __a      ,       __a      ):



	while end - start > size_threshold:
		if max_depth == 0:
			return heap_sort(snake_case__      )
		max_depth -= 1
		snake_case_						:	Dict      =		median_of_a(snake_case__      ,       snake_case__      ,       start + ((end - start) // 2) + 1      ,       end - 1      )
		snake_case_						:	Optional[Any]      =		partition(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      )
		intro_sort(snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      )
		snake_case_						:	Tuple      =		p
	return insertion_sort(snake_case__      ,       snake_case__      ,       snake_case__      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_SCREAMING_SNAKE_CASE							=						input("""Enter numbers separated by a comma : """).strip()
						_SCREAMING_SNAKE_CASE							=						[float(item) for item in user_input.split(""",""")]
						print(sort(unsorted))



'''simple docstring'''




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

lowercase			:				str    		=			logging.get_logger(__name__)





@add_end_docstrings(__snake_case						)
class  A				(							__snake_case						):





     def __init__(							self						,			*SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	) ->				List[Any]:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






         A,  A       :   Dict					=						{}, {}
         if padding is not None:
             A       :   List[str]					=						padding
         if truncation is not None:
             A       :   Dict					=						truncation
         if top_k is not None:
             A       :   Optional[Any]					=						top_k
         return preprocess_params, {}, postprocess_params





     def __call__(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None						,			**SCREAMING_SNAKE_CASE	) ->				Dict:






         """simple docstring"""






         if isinstance(SCREAMING_SNAKE_CASE						,			(Image.Image, str)	) and isinstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	):
             A       :   int					=						{'''image''': image, '''question''': question}
         else:
             A       :   Any					=						image
         A       :   Any					=						super().__call__(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         return results





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False	) ->				Any:






         """simple docstring"""






         A       :   Union[str, Any]					=						load_image(inputs['''image''']	)
         A       :   Optional[Any]					=						self.tokenizer(
             inputs['''question''']						,			return_tensors=self.framework						,			padding=SCREAMING_SNAKE_CASE						,			truncation=SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						self.image_processor(images=SCREAMING_SNAKE_CASE						,			return_tensors=self.framework	)
         model_inputs.update(SCREAMING_SNAKE_CASE	)
         return model_inputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[str]:






         """simple docstring"""






         A       :   List[Any]					=						self.model(**SCREAMING_SNAKE_CASE	)
         return model_outputs





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=5	) ->				int:






         """simple docstring"""






         if top_k > self.model.config.num_labels:
             A       :   Dict					=						self.model.config.num_labels

         if self.framework == "pt":
             A       :   Optional[int]					=						model_outputs.logits.sigmoid()[0]
             A,  A       :   int					=						probs.topk(SCREAMING_SNAKE_CASE	)
         else:
             raise ValueError(F'Unsupported framework: {self.framework}'	)

         A       :   int					=						scores.tolist()
         A       :   List[str]					=						ids.tolist()
         return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)]




