




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     : int      =logging.get_logger(__name__)


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






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

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




     def 			__snake_case (     self	: Optional[Any]						,	A__	: List[str]						,	A__	: Any=None      )					->							int:
           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	: str						,	A__	: Any      )					->							List[Any]:
           lowerCamelCase__   	=					model_inputs.pop('target_size'      )
           lowerCamelCase__   	=					self.model(**A__      )
           lowerCamelCase__   	=					target_size
           return model_outputs




     def 			__snake_case (     self	: Dict						,	A__	: Optional[int]						,	A__	: List[str]=None						,	A__	: Any=0.9						,	A__	: List[str]=0.5						,	A__	: Dict=0.5      )					->							Any:
           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





def       lowerCAmelCase_      (	lowerCamelCase:							str    )       ->      int:


      '''simple docstring'''




      lowerCamelCase__   	=					str(lowerCamelCase    ).strip()
      if not bin_string:
            raise ValueError('Empty string was passed to the function'    )
      lowerCamelCase__   	=					bin_string[0] == '-'
      if is_negative:
            lowerCamelCase__   	=					bin_string[1:]
      if not all(char in '01' for char in bin_string    ):
            raise ValueError('Non-binary value was passed to the function'    )
      lowerCamelCase__   	=					0
      for char in bin_string:
            lowerCamelCase__   	=					2 * decimal_number + int(lowerCamelCase    )
      return -decimal_number if is_negative else decimal_number


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

      testmod()