





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,
       )


snake_case__   :				int						=	logging.get_logger(__name__)


snake_case__   :				List[str]						=	Dict[str, Any]
snake_case__   :				Any						=	List[Prediction]





@add_end_docstrings(_a  )
class A_   (     _a  ):




   def __init__(self      :str       ,  *_UpperCamelCase      :int       ,  **_UpperCamelCase      :Any					)->   Any:
         super().__init__(*_SCREAMING_SNAKE_CASE       ,  **_SCREAMING_SNAKE_CASE					)

         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      :Any       ,  **_UpperCamelCase      :Dict					)->   Optional[int]:
         __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      :Optional[Any]       ,  _UpperCamelCase      :Any       ,  **_UpperCamelCase      :str					)->   Union[Predictions, List[Prediction]]:
         return super().__call__(_SCREAMING_SNAKE_CASE       ,  **_SCREAMING_SNAKE_CASE					)




   def _lowerCAmelCase (self      :List[Any]       ,  _UpperCamelCase      :Tuple       ,  _UpperCamelCase      :str=None					)->   Dict:
         __A			  =							load_image(_SCREAMING_SNAKE_CASE					)
         __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'''       ,  **_SCREAMING_SNAKE_CASE					)
               __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 _lowerCAmelCase (self      :List[str]       ,  _UpperCamelCase      :List[str]					)->   Union[str, Any]:
         __A			  =							model_inputs.pop('''target_size'''					)
         __A			  =							self.model(**_SCREAMING_SNAKE_CASE					)
         __A			  =							target_size
         return model_outputs






   def _lowerCAmelCase (self      :str       ,  _UpperCamelCase      :List[Any]       ,  _UpperCamelCase      :str=None       ,  _UpperCamelCase      :Union[str, Any]=0.9       ,  _UpperCamelCase      :str=0.5       ,  _UpperCamelCase      :Optional[int]=0.5					)->   str:
         __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(
                   _SCREAMING_SNAKE_CASE       ,  threshold=_SCREAMING_SNAKE_CASE       ,  mask_threshold=_SCREAMING_SNAKE_CASE       ,  overlap_mask_area_threshold=_SCREAMING_SNAKE_CASE       ,  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(
                   _SCREAMING_SNAKE_CASE       ,  target_sizes=model_outputs['''target_size''']					)[0]

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

               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







'''simple docstring'''


__SCREAMING_SNAKE_CASE						=      {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'
def 					__magic_name__(       UpperCAmelCase__ :       str	):
						UpperCamelCase_     :	str					     =     hex_string.strip().lower()
						if not hex_string:
												raise ValueError("""Empty string was passed to the function"""	)
						UpperCamelCase_     :	Dict					     =     hex_string[0] == """-"""
						if is_negative:
												UpperCamelCase_     :	List[str]					     =     hex_string[1:]
						if not all(char in hex_table for char in hex_string	):
												raise ValueError("""Non-hexadecimal value was passed to the function"""	)
						UpperCamelCase_     :	int					     =     0
						for char in hex_string:
												UpperCamelCase_     :	Any					     =     16 * decimal_number + hex_table[char]
						return -decimal_number if is_negative else decimal_number


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

	testmod()




