





from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, 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_torch_available():
       from ..models.auto.modeling_auto import (
           MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
           MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
           MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
           MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
       )


lowercase     :			List[str]     						=	logging.get_logger(__name__)


lowercase     :			Optional[int]     						=	Dict[str, Any]
lowercase     :			List[Any]     						=	List[Prediction]

@add_end_docstrings(__UpperCAmelCase			)
class 			__A(    __UpperCAmelCase			):

     def __init__(   self,							*A,							**A   ):





            """simple docstring"""

            super().__init__(*A,							**A   )

            if self.framework == "tf":
                   raise ValueError(F'''The {self.__class__} is only available in PyTorch.'''   )

            requires_backends(self,							'''vision'''   )
            self.check_model_type(
                dict(
                    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                    + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                    + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                    + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()   )   )

     def 						_UpperCamelCase  (   self,							**A   ):





            """simple docstring"""

            _UpperCamelCase														=    {}
            _UpperCamelCase														=    {}
            if "subtask" in kwargs:
                   _UpperCamelCase														=    kwargs['''subtask''']
                   _UpperCamelCase														=    kwargs['''subtask''']
            if "threshold" in kwargs:
                   _UpperCamelCase														=    kwargs['''threshold''']
            if "mask_threshold" in kwargs:
                   _UpperCamelCase														=    kwargs['''mask_threshold''']
            if "overlap_mask_area_threshold" in kwargs:
                   _UpperCamelCase														=    kwargs['''overlap_mask_area_threshold''']

            return preprocess_kwargs, {}, postprocess_kwargs

     def __call__(   self,							A,							**A   ):





            """simple docstring"""

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

     def 						_UpperCamelCase  (   self,							A,							A=None   ):





            """simple docstring"""

            _UpperCamelCase														=    load_image(A   )
            _UpperCamelCase														=    [(image.height, image.width)]
            if self.model.config.__class__.__name__ == "OneFormerConfig":
                   if subtask is None:
                          _UpperCamelCase														=    {}
                   else:
                          _UpperCamelCase														=    {'''task_inputs''': [subtask]}
                   _UpperCamelCase														=    self.image_processor(images=[image],							return_tensors='''pt''',							**A   )
                   _UpperCamelCase														=    self.tokenizer(
                       inputs['''task_inputs'''],							padding='''max_length''',							max_length=self.model.config.task_seq_len,							return_tensors=self.framework,							)['''input_ids''']
            else:
                   _UpperCamelCase														=    self.image_processor(images=[image],							return_tensors='''pt'''   )
            _UpperCamelCase														=    target_size
            return inputs

     def 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    model_inputs.pop('''target_size'''   )
            _UpperCamelCase														=    self.model(**A   )
            _UpperCamelCase														=    target_size
            return model_outputs



     def 						_UpperCamelCase  (   self,							A,							A=None,							A=0.9,							A=0.5,							A=0.5   ):





            """simple docstring"""

            _UpperCamelCase														=    None
            if subtask in {"panoptic", None} and hasattr(self.image_processor,							'''post_process_panoptic_segmentation'''   ):
                   _UpperCamelCase														=    self.image_processor.post_process_panoptic_segmentation
            elif subtask in {"instance", None} and hasattr(self.image_processor,							'''post_process_instance_segmentation'''   ):
                   _UpperCamelCase														=    self.image_processor.post_process_instance_segmentation

            if fn is not None:
                   _UpperCamelCase														=    fn(
                       A,							threshold=A,							mask_threshold=A,							overlap_mask_area_threshold=A,							target_sizes=model_outputs['''target_size'''],							)[0]

                   _UpperCamelCase														=    []
                   _UpperCamelCase														=    outputs['''segmentation''']

                   for segment in outputs["segments_info"]:
                          _UpperCamelCase														=    (segmentation == segment['''id''']) * 255
                          _UpperCamelCase														=    Image.fromarray(mask.numpy().astype(np.uinta   ),							mode='''L'''   )
                          _UpperCamelCase														=    self.model.config.idalabel[segment['''label_id''']]
                          _UpperCamelCase														=    segment['''score''']
                          annotation.append({'''score''': score, '''label''': label, '''mask''': mask}   )

            elif subtask in {"semantic", None} and hasattr(self.image_processor,							'''post_process_semantic_segmentation'''   ):
                   _UpperCamelCase														=    self.image_processor.post_process_semantic_segmentation(
                       A,							target_sizes=model_outputs['''target_size''']   )[0]

                   _UpperCamelCase														=    []
                   _UpperCamelCase														=    outputs.numpy()
                   _UpperCamelCase														=    np.unique(A   )

                   for label in labels:
                          _UpperCamelCase														=    (segmentation == label) * 255
                          _UpperCamelCase														=    Image.fromarray(mask.astype(np.uinta   ),							mode='''L'''   )
                          _UpperCamelCase														=    self.model.config.idalabel[label]
                          annotation.append({'''score''': None, '''label''': label, '''mask''': mask}   )
            else:
                   raise ValueError(F'''Subtask {subtask} is not supported for model {type(self.model   )}'''   )
            return annotation













def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       ):
       _UpperCamelCase														=    index
       _UpperCamelCase														=    2 * index + 1
       _UpperCamelCase														=    2 * index + 2
       if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
              _UpperCamelCase														=    left_index

       if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
              _UpperCamelCase														=    right_index

       if largest != index:
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[index], unsorted[largest]
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )


def      SCREAMING_SNAKE_CASE      (   lowerCAmelCase       ):
       _UpperCamelCase														=    len(lowerCAmelCase       )
       for i in range(n // 2 - 1     ,							-1     ,							-1       ):
              heapify(lowerCAmelCase     ,							lowerCAmelCase     ,							lowerCAmelCase       )
       for i in range(n - 1     ,							0     ,							-1       ):
              _UpperCamelCase							,		_UpperCamelCase														=    unsorted[i], unsorted[0]
              heapify(lowerCAmelCase     ,							0     ,							lowerCAmelCase       )
       return unsorted


if __name__ == "__main__":
       lowercase     :			Union[str, Any]     						=	input("""Enter numbers separated by a comma:\n""").strip()
       lowercase     :			Dict     						=	[int(item) for item in user_input.split(""",""")]
       print(heap_sort(unsorted))






