class     __UpperCAmelCase:






 """simple docstring"""




 def __init__(    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				data
        A_							:       Dict	      =				None
        A_							:       Dict	      =				None
 def __str__(    self      ):

        """simple docstring"""

        return f"""{self.data}"""






class     __UpperCAmelCase:






 """simple docstring"""




 def __init__(    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				None
        A_							:       Dict	      =				None
 def __iter__(    self      ):

        """simple docstring"""

        A_							:       List[Any]	      =				self.head
        while node:
               yield node.data
               A_							:       Optional[Any]	      =				node.next
 def __str__(    self      ):

        """simple docstring"""

        return "->".join([str(__magic_name__      ) for item in self]      )
 def __len__(    self      ):

        """simple docstring"""

        return sum(1 for _ in self      )
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        self.insert_at_nth(0 ,       __magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        self.insert_at_nth(len(self      ) ,       __magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__      ):

        """simple docstring"""

        A_							:       List[Any]	      =				len(self      )

        if not 0 <= index <= length:
               raise IndexError('''list index out of range'''      )
        A_							:       Dict	      =				Node(__magic_name__      )
        if self.head is None:
               A_							:       Dict	      =				new_node
        elif index == 0:
               A_							:       Tuple	      =				new_node
               A_							:       str	      =				self.head
               A_							:       Tuple	      =				new_node
        elif index == length:
               A_							:       Union[str, Any]	      =				new_node
               A_							:       Union[str, Any]	      =				self.tail
               A_							:       Tuple	      =				new_node
        else:
               A_							:       Optional[int]	      =				self.head
               for _ in range(0 ,       __magic_name__      ):
                      A_							:       Tuple	      =				temp.next
               A_							:       Union[str, Any]	      =				new_node
               A_							:       Optional[Any]	      =				temp.previous
               A_							:       str	      =				temp
               A_							:       Optional[Any]	      =				new_node
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return self.delete_at_nth(0      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return self.delete_at_nth(len(self      ) - 1      )
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				len(self      )

        if not 0 <= index <= length - 1:
               raise IndexError('''list index out of range'''      )
        A_							:       Optional[Any]	      =				self.head  # default first node
        if length == 1:
               A_							:       Union[str, Any]	      =				None
        elif index == 0:
               A_							:       List[Any]	      =				self.head.next
               A_							:       int	      =				None
        elif index == length - 1:
               A_							:       Any	      =				self.tail
               A_							:       Union[str, Any]	      =				self.tail.previous
               A_							:       Any	      =				None
        else:
               A_							:       Optional[Any]	      =				self.head
               for _ in range(0 ,       __magic_name__      ):
                      A_							:       Tuple	      =				temp.next
               A_							:       int	      =				temp
               A_							:       List[str]	      =				temp.previous
               A_							:       Optional[Any]	      =				temp.next
        return delete_node.data
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				self.head

        while current.data != data:  # Find the position to delete
               if current.next:
                      A_							:       Optional[Any]	      =				current.next
               else:  # We have reached the end an no value matches
                      raise ValueError('''No data matching given value'''      )

        if current == self.head:
               self.delete_head()

        elif current == self.tail:
               self.delete_tail()

        else:  # Before: 1 <--> 2(current) <--> 3
               A_							:       Optional[int]	      =				current.next  # 1 --> 3
               A_							:       Any	      =				current.previous  # 1 <--> 3
        return data
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        return len(self      ) == 0


def       a__      (					)		->	None:
       A_							:       Tuple	      =				DoublyLinkedList()
       assert linked_list.is_empty() is True
       assert str(a							) == ""

       try:
              linked_list.delete_head()
              raise AssertionError  # This should not happen.
       except IndexError:
              assert True  # This should happen.

       try:
              linked_list.delete_tail()
              raise AssertionError  # This should not happen.
       except IndexError:
              assert True  # This should happen.

       for i in range(1_0							):
              assert len(a							) == i
              linked_list.insert_at_nth(a  ,   i + 1							)
       assert str(a							) == "->".join(str(a							) for i in range(1  ,   1_1							)							)

       linked_list.insert_at_head(0							)
       linked_list.insert_at_tail(1_1							)
       assert str(a							) == "->".join(str(a							) for i in range(0  ,   1_2							)							)

       assert linked_list.delete_head() == 0
       assert linked_list.delete_at_nth(9							) == 1_0
       assert linked_list.delete_tail() == 1_1
       assert len(a							) == 9
       assert str(a							) == "->".join(str(a							) for i in range(1  ,   1_0							)							)


if __name__ == "__main__":
    from doctest import testmod

    testmod()



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,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


_lowerCAmelCase        =       Dict[str, Any]
_lowerCAmelCase        =       List[Prediction]






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




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

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        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 ,       **__magic_name__      ):

        """simple docstring"""

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

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

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

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

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

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

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

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

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

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

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


