





"""simple docstring"""
from __future__ import annotations

# Define the Gravitational Constant G and the function
__UpperCamelCase    :	str						=     6.6_743e-11  # unit of G : m^3 * kg^-1 * s^-2

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						float     ,    _UpperCAmelCase				:						float     ,    _UpperCAmelCase				:						float     ,    _UpperCAmelCase				:						float ):
 lowerCAmelCase													=   mass_a * mass_a

 if (force, mass_a, mass_a, distance).count(0 ) != 1:
  raise ValueError('One and only one argument must be 0' )
 if force < 0:
  raise ValueError('Gravitational force can not be negative' )
 if distance < 0:
  raise ValueError('Distance can not be negative' )
 if mass_a < 0 or mass_a < 0:
  raise ValueError('Mass can not be negative' )
 if force == 0:
  lowerCAmelCase													=   GRAVITATIONAL_CONSTANT * product_of_mass / (distance**2)
  return {"force": force}
 elif mass_a == 0:
  lowerCAmelCase													=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
  return {"mass_1": mass_a}
 elif mass_a == 0:
  lowerCAmelCase													=   (force) * (distance**2) / (GRAVITATIONAL_CONSTANT * mass_a)
  return {"mass_2": mass_a}
 elif distance == 0:
  lowerCAmelCase													=   (GRAVITATIONAL_CONSTANT * product_of_mass / (force)) ** 0.5
  return {"distance": distance}
 raise ValueError('One and only one argument must be 0' )


# Run doctest
if __name__ == "__main__":
       import doctest

       doctest.testmod()












"""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    :	str						=     logging.get_logger(__name__)


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







@add_end_docstrings(a__     )
class 			a       (      a__     ):



 def __init__(    self							,					*_snake_case							,					**_snake_case    ):







  """simple docstring"""






  super().__init__(*_snake_case							,					**_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       UpperCamelCase__     (    self							,					**_snake_case    ):







  """simple docstring"""






  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							,					_snake_case							,					**_snake_case    ):







  """simple docstring"""






  return super().__call__(_snake_case							,					**_snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






  lowerCAmelCase													=   load_image(_snake_case    )
  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'							,					**_snake_case    )
   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       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   model_inputs.pop('target_size'    )
  lowerCAmelCase													=   self.model(**_snake_case    )
  lowerCAmelCase													=   target_size
  return model_outputs



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None							,					_snake_case=0.9							,					_snake_case=0.5							,					_snake_case=0.5    ):







  """simple docstring"""






  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(
       _snake_case							,					threshold=_snake_case							,					mask_threshold=_snake_case							,					overlap_mask_area_threshold=_snake_case							,					target_sizes=model_outputs['target_size']							,					)[0]

   lowerCAmelCase													=   []
   lowerCAmelCase													=   outputs['segmentation']

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

   lowerCAmelCase													=   []
   lowerCAmelCase													=   outputs.numpy()
   lowerCAmelCase													=   np.unique(_snake_case    )

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





