






'''simple docstring'''







import importlib.metadata
import subprocess
import sys


def 						_UpperCamelCase							(     snake_case__ = False							):
      __A		   =      False
      if "IPython" in sys.modules:
            __A		   =      "google.colab" in str(sys.modules["IPython"].get_ipython()							)

      if in_colab:
            if upgrade:
                  __A		   =      ["pip", "install", "-U", "torch"]
                  subprocess.run(snake_case__							,			check=snake_case__							)
            # get the current version of torch
            __A		   =      importlib.metadata.version("torch"							)
            __A		   =      torch_version[: torch_version.rindex("."							)]
            __A		   =      f"https://storage.googleapis.com/tpu-pytorch/wheels/colab/torch_xla-{torch_version_trunc}-cp37-cp37m-linux_x86_64.whl"
            __A		   =      ["pip", "install", xla_wheel]
            subprocess.run(snake_case__							,			check=snake_case__							)
      else:
            raise RuntimeError("`install_xla` utility works only on google colab."							)










'''simple docstring'''







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,
       )


__A      :       int      =	logging.get_logger(__name__)


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






@add_end_docstrings(lowerCamelCase						)
class 					a__    (			lowerCamelCase						):
     '''simple docstring'''







     def __init__(self    :	Optional[Any]				,				*lowercase__    :	Dict				,				**lowercase__    :	Union[str, Any]					)  ->       Tuple:
           super().__init__(*lowercase__				,				**lowercase__					)

           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    :	List[Any]				,				**lowercase__    :	Dict					)  ->       List[Any]:
           __A		   =      {}
           __A		   =      {}
           if "subtask" in kwargs:
                 __A		   =      kwargs["subtask"]
                 __A		   =      kwargs["subtask"]
           if "threshold" in kwargs:
                 __A		   =      kwargs["threshold"]
           if "mask_threshold" in kwargs:
                 __A		   =      kwargs["mask_threshold"]
           if "overlap_mask_area_threshold" in kwargs:
                 __A		   =      kwargs["overlap_mask_area_threshold"]

           return preprocess_kwargs, {}, postprocess_kwargs






     def __call__(self    :	int				,				lowercase__    :	Any				,				**lowercase__    :	Dict					)  ->       Union[Predictions, List[Prediction]]:
           return super().__call__(lowercase__				,				**lowercase__					)






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






     def     _UpperCamelCase   (self    :	Any				,				lowercase__    :	Optional[int]					)  ->       Dict:
           __A		   =      model_inputs.pop("target_size"					)
           __A		   =      self.model(**lowercase__					)
           __A		   =      target_size
           return model_outputs






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	Any				,				lowercase__    :	List[Any]=None				,				lowercase__    :	Tuple=0.9				,				lowercase__    :	str=0.5				,				lowercase__    :	Tuple=0.5					)  ->       Tuple:
           __A		   =      None
           if subtask in {"panoptic", None} and hasattr(self.image_processor				,				"post_process_panoptic_segmentation"					):
                 __A		   =      self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor				,				"post_process_instance_segmentation"					):
                 __A		   =      self.image_processor.post_process_instance_segmentation

           if fn is not None:
                 __A		   =      fn(
                     lowercase__				,				threshold=lowercase__				,				mask_threshold=lowercase__				,				overlap_mask_area_threshold=lowercase__				,				target_sizes=model_outputs["target_size"]				,				)[0]

                 __A		   =      []
                 __A		   =      outputs["segmentation"]

                 for segment in outputs["segments_info"]:
                       __A		   =      (segmentation == segment["id"]) * 255
                       __A		   =      Image.fromarray(mask.numpy().astype(np.uinta					)				,				mode="L"					)
                       __A		   =      self.model.config.idalabel[segment["label_id"]]
                       __A		   =      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		   =      self.image_processor.post_process_semantic_segmentation(
                     lowercase__				,				target_sizes=model_outputs["target_size"]					)[0]

                 __A		   =      []
                 __A		   =      outputs.numpy()
                 __A		   =      np.unique(lowercase__					)

                 for label in labels:
                       __A		   =      (segmentation == label) * 255
                       __A		   =      Image.fromarray(mask.astype(np.uinta					)				,				mode="L"					)
                       __A		   =      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


