


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


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




@add_end_docstrings(_SCREAMING_SNAKE_CASE							)
class 			a  (  _SCREAMING_SNAKE_CASE							):
 def __init__(			self   ,					*__magic_name__   ,					**__magic_name__    )       ->				Dict:
      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__    )       ->				List[str]:
      _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   ,					__magic_name__   ,					**__magic_name__    )       ->				Union[Predictions, List[Prediction]]:
      return super().__call__(__magic_name__   ,					**__magic_name__    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__=None    )       ->				Union[str, Any]:
      _a         =				load_image(__magic_name__    )
      _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'   ,					**__magic_name__    )
           _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   ,					__magic_name__    )       ->				List[str]:
      _a         =				model_inputs.pop('target_size'    )
      _a         =				self.model(**__magic_name__    )
      _a         =				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    )       ->				Optional[int]:
      _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(
               __magic_name__   ,					threshold=__magic_name__   ,					mask_threshold=__magic_name__   ,					overlap_mask_area_threshold=__magic_name__   ,					target_sizes=model_outputs['target_size']   ,					)[0]

           _a         =				[]
           _a         =				outputs['segmentation']

           for segment in outputs["segments_info"]:
                _a         =				(segmentation == segment['id']) * 2_55
                _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(
               __magic_name__   ,					target_sizes=model_outputs['target_size']    )[0]

           _a         =				[]
           _a         =				outputs.numpy()
           _a         =				np.unique(__magic_name__    )

           for label in labels:
                _a         =				(segmentation == label) * 2_55
                _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





'''simple docstring'''






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .image_encoder import PaintByExampleImageEncoder
 from .pipeline_paint_by_example import PaintByExamplePipeline

