


"""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,
   )


__UpperCAmelCase    				=      logging.get_logger(__name__)


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

@add_end_docstrings(a_						)
class  lowercase_  ( a_						):







      def __init__(      self :							Tuple		,  *_lowercase :							List[Any]		,  **_lowercase :							str    ):
        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 				_lowerCAmelCase       (      self :							int		,  **_lowercase :							Union[str, Any]    ):
        lowerCAmelCase__    :    Tuple								=					{}
        lowerCAmelCase__    :    List[str]								=					{}
        if "subtask" in kwargs:
          lowerCAmelCase__    :    int								=					kwargs["subtask"]
          lowerCAmelCase__    :    Tuple								=					kwargs["subtask"]
        if "threshold" in kwargs:
          lowerCAmelCase__    :    Dict								=					kwargs["threshold"]
        if "mask_threshold" in kwargs:
          lowerCAmelCase__    :    Union[str, Any]								=					kwargs["mask_threshold"]
        if "overlap_mask_area_threshold" in kwargs:
          lowerCAmelCase__    :    str								=					kwargs["overlap_mask_area_threshold"]

        return preprocess_kwargs, {}, postprocess_kwargs







      def __call__(      self :							Any		,  _lowercase :							Dict		,  **_lowercase :							List[Any]    ):
        return super().__call__(_lowercase		,  **_lowercase    )







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







      def 				_lowerCAmelCase       (      self :							Any		,  _lowercase :							Dict    ):
        lowerCAmelCase__    :    Optional[int]								=					model_inputs.pop("target_size"    )
        lowerCAmelCase__    :    Any								=					self.model(**_lowercase    )
        lowerCAmelCase__    :    Any								=					target_size
        return model_outputs







      def 				_lowerCAmelCase       (      self :							Dict		,  _lowercase :							str		,  _lowercase :							Optional[int]=None		,  _lowercase :							Union[str, Any]=0.9		,  _lowercase :							List[Any]=0.5		,  _lowercase :							List[Any]=0.5    ):
        lowerCAmelCase__    :    List[Any]								=					None
        if subtask in {"panoptic", None} and hasattr(self.image_processor		,  "post_process_panoptic_segmentation"    ):
          lowerCAmelCase__    :    Optional[int]								=					self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor		,  "post_process_instance_segmentation"    ):
          lowerCAmelCase__    :    Any								=					self.image_processor.post_process_instance_segmentation

        if fn is not None:
          lowerCAmelCase__    :    Union[str, Any]								=					fn(
              _lowercase		,  threshold=_lowercase		,  mask_threshold=_lowercase		,  overlap_mask_area_threshold=_lowercase		,  target_sizes=model_outputs["target_size"]		,  )[0]

          lowerCAmelCase__    :    Optional[Any]								=					[]
          lowerCAmelCase__    :    Union[str, Any]								=					outputs["segmentation"]

          for segment in outputs["segments_info"]:
            lowerCAmelCase__    :    Optional[int]								=					(segmentation == segment["id"]) * 2_5_5
            lowerCAmelCase__    :    Optional[int]								=					Image.fromarray(mask.numpy().astype(np.uinta    )		,  mode="L"    )
            lowerCAmelCase__    :    str								=					self.model.config.idalabel[segment["label_id"]]
            lowerCAmelCase__    :    Optional[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"    ):
          lowerCAmelCase__    :    Union[str, Any]								=					self.image_processor.post_process_semantic_segmentation(
              _lowercase		,  target_sizes=model_outputs["target_size"]    )[0]

          lowerCAmelCase__    :    Dict								=					[]
          lowerCAmelCase__    :    Tuple								=					outputs.numpy()
          lowerCAmelCase__    :    List[Any]								=					np.unique(_lowercase    )

          for label in labels:
            lowerCAmelCase__    :    str								=					(segmentation == label) * 2_5_5
            lowerCAmelCase__    :    Tuple								=					Image.fromarray(mask.astype(np.uinta    )		,  mode="L"    )
            lowerCAmelCase__    :    Optional[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




"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase    				=      {"configuration_sew_d": ["SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWDConfig"]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase    				=      [
       "SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST",
       "SEWDForCTC",
       "SEWDForSequenceClassification",
       "SEWDModel",
       "SEWDPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase    				=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
