


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,
  )


__UpperCAmelCase			:			int			      =					logging.get_logger(__name__)


__UpperCAmelCase			:			Union[str, Any]			      =					Dict[str, Any]
__UpperCAmelCase			:			Dict			      =					List[Prediction]



@add_end_docstrings(_UpperCAmelCase)
class  UpperCAmelCase_						(				_UpperCAmelCase):
       '''simple docstring'''






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




              """simple docstring"""




              super().__init__(*UpperCAmelCase_			,       **UpperCAmelCase_    )

              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 			_lowercase (  self			,       **__SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						int 						=		{}
              UpperCamelCase     :						Any 						=		{}
              if "subtask" in kwargs:
                     UpperCamelCase     :						List[str] 						=		kwargs['''subtask''']
                     UpperCamelCase     :						str 						=		kwargs['''subtask''']
              if "threshold" in kwargs:
                     UpperCamelCase     :						Optional[Any] 						=		kwargs['''threshold''']
              if "mask_threshold" in kwargs:
                     UpperCamelCase     :						Optional[int] 						=		kwargs['''mask_threshold''']
              if "overlap_mask_area_threshold" in kwargs:
                     UpperCamelCase     :						Dict 						=		kwargs['''overlap_mask_area_threshold''']

              return preprocess_kwargs, {}, postprocess_kwargs






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




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None    ):




              """simple docstring"""




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






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE    ):




              """simple docstring"""




              UpperCamelCase     :						Optional[Any] 						=		model_inputs.pop('''target_size'''    )
              UpperCamelCase     :						str 						=		self.model(**UpperCAmelCase_    )
              UpperCamelCase     :						Any 						=		target_size
              return model_outputs






       def 			_lowercase (  self			,       __SCREAMING_SNAKE_CASE			,       __SCREAMING_SNAKE_CASE=None			,       __SCREAMING_SNAKE_CASE=0.9			,       __SCREAMING_SNAKE_CASE=0.5			,       __SCREAMING_SNAKE_CASE=0.5    ):




              """simple docstring"""




              UpperCamelCase     :						List[str] 						=		None
              if subtask in {"panoptic", None} and hasattr(self.image_processor			,       '''post_process_panoptic_segmentation'''    ):
                     UpperCamelCase     :						Dict 						=		self.image_processor.post_process_panoptic_segmentation
              elif subtask in {"instance", None} and hasattr(self.image_processor			,       '''post_process_instance_segmentation'''    ):
                     UpperCamelCase     :						Optional[Any] 						=		self.image_processor.post_process_instance_segmentation

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

                     UpperCamelCase     :						Optional[Any] 						=		[]
                     UpperCamelCase     :						Union[str, Any] 						=		outputs['''segmentation''']

                     for segment in outputs["segments_info"]:
                            UpperCamelCase     :						Optional[int] 						=		(segmentation == segment['''id''']) * 255
                            UpperCamelCase     :						Dict 						=		Image.fromarray(mask.numpy().astype(np.uinta    )			,       mode='''L'''    )
                            UpperCamelCase     :						List[Any] 						=		self.model.config.idalabel[segment['''label_id''']]
                            UpperCamelCase     :						Any 						=		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     :						Union[str, Any] 						=		self.image_processor.post_process_semantic_segmentation(
                         UpperCAmelCase_			,       target_sizes=model_outputs['''target_size''']    )[0]

                     UpperCamelCase     :						Optional[Any] 						=		[]
                     UpperCamelCase     :						Optional[Any] 						=		outputs.numpy()
                     UpperCamelCase     :						Dict 						=		np.unique(UpperCAmelCase_    )

                     for label in labels:
                            UpperCamelCase     :						Optional[int] 						=		(segmentation == label) * 255
                            UpperCamelCase     :						str 						=		Image.fromarray(mask.astype(np.uinta    )			,       mode='''L'''    )
                            UpperCamelCase     :						int 						=		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









from __future__ import annotations

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    len(snake_case__						) - 1
						while left <= right:
												_SCREAMING_SNAKE_CASE       =    (left + right) // 2
												if left == right:
																		if sorted_list[middle] < item:
																								_SCREAMING_SNAKE_CASE       =    middle + 1
																		break
												elif sorted_list[middle] < item:
																		_SCREAMING_SNAKE_CASE       =    middle + 1
												else:
																		_SCREAMING_SNAKE_CASE       =    middle - 1
						sorted_list.insert(snake_case__			,snake_case__						)
						return sorted_list

def 			__lowerCamelCase     (						snake_case__			,snake_case__						)				->    Optional[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    []
						while left and right:
												if left[0][0] < right[0][0]:
																		result.append(left.pop(0						)						)
												else:
																		result.append(right.pop(0						)						)
						return result + left + right

def 			__lowerCamelCase     (						snake_case__						)				->    List[str]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    len(snake_case__						)
						if length <= 1:
												return list_ad
						_SCREAMING_SNAKE_CASE       =    length // 2
						return merge(sortlist_ad(list_ad[:middle]						)			,sortlist_ad(list_ad[middle:]						)						)

def 			__lowerCamelCase     (						snake_case__						)				->    list[int]:






						"""simple docstring"""





						if len(snake_case__						) <= 1:
												return collection
						_SCREAMING_SNAKE_CASE       =    []
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(0			,len(snake_case__						)			,2						):
												if i == len(snake_case__						) - 1:
																		_SCREAMING_SNAKE_CASE       =    True
												else:
																		if collection[i] < collection[i + 1]:
																								two_paired_list.append([collection[i], collection[i + 1]]						)
																		else:
																								two_paired_list.append([collection[i + 1], collection[i]]						)
						_SCREAMING_SNAKE_CASE       =    sortlist_ad(snake_case__						)
						_SCREAMING_SNAKE_CASE       =    [i[0] for i in sorted_list_ad]
						result.append(sorted_list_ad[-1][1]						)
						if has_last_odd_item:
												_SCREAMING_SNAKE_CASE       =    collection[-1]
												_SCREAMING_SNAKE_CASE       =    binary_search_insertion(snake_case__			,snake_case__						)
						_SCREAMING_SNAKE_CASE       =    False
						for i in range(len(snake_case__						) - 1						):
												if result[i] == collection[-1] and has_last_odd_item:
																		_SCREAMING_SNAKE_CASE       =    True
												_SCREAMING_SNAKE_CASE       =    sorted_list_ad[i][1]
												# If last_odd_item is inserted before the item's index,
												# you should forward index one more.
												if is_last_odd_item_inserted_before_this_index:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 2] + binary_search_insertion(result[i + 2 :]			,snake_case__						)
												else:
																		_SCREAMING_SNAKE_CASE       =    result[: i + 1] + binary_search_insertion(result[i + 1 :]			,snake_case__						)

						return result


if __name__ == "__main__":
		UpperCamelCase												=      input('''Enter numbers separated by a comma:\n''').strip()
		UpperCamelCase												=      [int(item) for item in user_input.split(''',''')]
		print(merge_insertion_sort(unsorted))

