




'''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,
      )


__SCREAMING_SNAKE_CASE    :   Tuple       =logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE    :   Optional[Any]       =Dict[str, Any]
__SCREAMING_SNAKE_CASE    :   str       =List[Prediction]






@add_end_docstrings(_lowerCAmelCase		)
class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(    self  :   Optional[Any]	,  *A__  :   int	,  **A__  :   Optional[Any]			)				->       int:
           super().__init__(*A__	,  **A__			)

           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  __snake_case   (    self  :   str	,  **A__  :   Dict			)				->       Tuple:
           lowerCamelCase__        =				{}
           lowerCamelCase__        =				{}
           if "subtask" in kwargs:
                 lowerCamelCase__        =				kwargs['subtask']
                 lowerCamelCase__        =				kwargs['subtask']
           if "threshold" in kwargs:
                 lowerCamelCase__        =				kwargs['threshold']
           if "mask_threshold" in kwargs:
                 lowerCamelCase__        =				kwargs['mask_threshold']
           if "overlap_mask_area_threshold" in kwargs:
                 lowerCamelCase__        =				kwargs['overlap_mask_area_threshold']

           return preprocess_kwargs, {}, postprocess_kwargs




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




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




     def  __snake_case   (    self  :   Tuple	,  A__  :   Any			)				->       Tuple:
           lowerCamelCase__        =				model_inputs.pop('target_size'			)
           lowerCamelCase__        =				self.model(**A__			)
           lowerCamelCase__        =				target_size
           return model_outputs




     def  __snake_case   (    self  :   Any	,  A__  :   List[str]	,  A__  :   List[str]=None	,  A__  :   str=0.9	,  A__  :   Tuple=0.5	,  A__  :   Optional[Any]=0.5			)				->       List[str]:
           lowerCamelCase__        =				None
           if subtask in {"panoptic", None} and hasattr(self.image_processor	,  'post_process_panoptic_segmentation'			):
                 lowerCamelCase__        =				self.image_processor.post_process_panoptic_segmentation
           elif subtask in {"instance", None} and hasattr(self.image_processor	,  'post_process_instance_segmentation'			):
                 lowerCamelCase__        =				self.image_processor.post_process_instance_segmentation

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

                 lowerCamelCase__        =				[]
                 lowerCamelCase__        =				outputs['segmentation']

                 for segment in outputs["segments_info"]:
                       lowerCamelCase__        =				(segmentation == segment['id']) * 2_5_5
                       lowerCamelCase__        =				Image.fromarray(mask.numpy().astype(np.uinta			)	,  mode='L'			)
                       lowerCamelCase__        =				self.model.config.idalabel[segment['label_id']]
                       lowerCamelCase__        =				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__        =				self.image_processor.post_process_semantic_segmentation(
                     A__	,  target_sizes=model_outputs['target_size']			)[0]

                 lowerCamelCase__        =				[]
                 lowerCamelCase__        =				outputs.numpy()
                 lowerCamelCase__        =				np.unique(A__			)

                 for label in labels:
                       lowerCamelCase__        =				(segmentation == label) * 2_5_5
                       lowerCamelCase__        =				Image.fromarray(mask.astype(np.uinta			)	,  mode='L'			)
                       lowerCamelCase__        =				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 ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)