

"""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	=			logging.get_logger(__name__)


__UpperCAmelCase	=			Dict[str, Any]
__UpperCAmelCase	=			List[Prediction]







@add_end_docstrings(__lowerCamelCase   )
class        __lowercase     (      __lowerCamelCase   ):

  def __init__(					self						:     Union[str, Any]   ,*A						:     Any   ,**A						:     str      ):






   '''simple docstring'''
   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 				__lowercase    (					self						:     List[str]   ,**A						:     Optional[Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					List[str]   				=   {}
   UpperCAmelCase__   :					Optional[Any]   				=   {}
   if "subtask" in kwargs:
    UpperCAmelCase__   :					Optional[int]   				=   kwargs["""subtask"""]
    UpperCAmelCase__   :					List[str]   				=   kwargs["""subtask"""]
   if "threshold" in kwargs:
    UpperCAmelCase__   :					str   				=   kwargs["""threshold"""]
   if "mask_threshold" in kwargs:
    UpperCAmelCase__   :					Union[str, Any]   				=   kwargs["""mask_threshold"""]
   if "overlap_mask_area_threshold" in kwargs:
    UpperCAmelCase__   :					List[Any]   				=   kwargs["""overlap_mask_area_threshold"""]

   return preprocess_kwargs, {}, postprocess_kwargs

  def __call__(					self						:     Any   ,A						:     Tuple   ,**A						:     int      ):






   '''simple docstring'''
   return super().__call__(A   ,**A      )

  def 				__lowercase    (					self						:     int   ,A						:     Tuple   ,A						:     int=None      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Dict   				=   load_image(A      )
   UpperCAmelCase__   :					Optional[Any]   				=   [(image.height, image.width)]
   if self.model.config.__class__.__name__ == "OneFormerConfig":
    if subtask is None:
     UpperCAmelCase__   :					List[Any]   				=   {}
    else:
     UpperCAmelCase__   :					Optional[int]   				=   {"""task_inputs""": [subtask]}
    UpperCAmelCase__   :					List[str]   				=   self.image_processor(images=[image]   ,return_tensors="""pt"""   ,**A      )
    UpperCAmelCase__   :					Union[str, Any]   				=   self.tokenizer(
        inputs["""task_inputs"""]   ,padding="""max_length"""   ,max_length=self.model.config.task_seq_len   ,return_tensors=self.framework   ,)["""input_ids"""]
   else:
    UpperCAmelCase__   :					Tuple   				=   self.image_processor(images=[image]   ,return_tensors="""pt"""      )
   UpperCAmelCase__   :					List[Any]   				=   target_size
   return inputs

  def 				__lowercase    (					self						:     List[Any]   ,A						:     Union[str, Any]      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[Any]   				=   model_inputs.pop("""target_size"""      )
   UpperCAmelCase__   :					Any   				=   self.model(**A      )
   UpperCAmelCase__   :					List[str]   				=   target_size
   return model_outputs




  def 				__lowercase    (					self						:     Optional[int]   ,A						:     List[str]   ,A						:     Dict=None   ,A						:     Optional[int]=0.9   ,A						:     str=0.5   ,A						:     int=0.5      ):






   '''simple docstring'''
   UpperCAmelCase__   :					Optional[int]   				=   None
   if subtask in {"panoptic", None} and hasattr(self.image_processor   ,"""post_process_panoptic_segmentation"""      ):
    UpperCAmelCase__   :					List[str]   				=   self.image_processor.post_process_panoptic_segmentation
   elif subtask in {"instance", None} and hasattr(self.image_processor   ,"""post_process_instance_segmentation"""      ):
    UpperCAmelCase__   :					List[Any]   				=   self.image_processor.post_process_instance_segmentation

   if fn is not None:
    UpperCAmelCase__   :					int   				=   fn(
        A   ,threshold=A   ,mask_threshold=A   ,overlap_mask_area_threshold=A   ,target_sizes=model_outputs["""target_size"""]   ,)[0]

    UpperCAmelCase__   :					List[Any]   				=   []
    UpperCAmelCase__   :					str   				=   outputs["""segmentation"""]

    for segment in outputs["segments_info"]:
     UpperCAmelCase__   :					Dict   				=   (segmentation == segment["""id"""]) * 255
     UpperCAmelCase__   :					Union[str, Any]   				=   Image.fromarray(mask.numpy().astype(np.uinta      )   ,mode="""L"""      )
     UpperCAmelCase__   :					List[Any]   				=   self.model.config.idalabel[segment["""label_id"""]]
     UpperCAmelCase__   :					Optional[Any]   				=   segment["""score"""]
     annotation.append({"""score""": score, """label""": label, """mask""": mask}      )

   elif subtask in {"semantic", None} and hasattr(self.image_processor   ,"""post_process_semantic_segmentation"""      ):
    UpperCAmelCase__   :					List[Any]   				=   self.image_processor.post_process_semantic_segmentation(
        A   ,target_sizes=model_outputs["""target_size"""]      )[0]

    UpperCAmelCase__   :					List[str]   				=   []
    UpperCAmelCase__   :					List[Any]   				=   outputs.numpy()
    UpperCAmelCase__   :					str   				=   np.unique(A      )

    for label in labels:
     UpperCAmelCase__   :					Union[str, Any]   				=   (segmentation == label) * 255
     UpperCAmelCase__   :					Dict   				=   Image.fromarray(mask.astype(np.uinta      )   ,mode="""L"""      )
     UpperCAmelCase__   :					List[Any]   				=   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"""




def 					lowerCAmelCase  (   __UpperCamelCase ):
 '''simple docstring'''





 UpperCAmelCase__   :					Union[str, Any]   				=   1
 UpperCAmelCase__   :					Union[str, Any]   				=   0
 UpperCAmelCase__   :					List[str]   				=   1
 UpperCAmelCase__   :					Any   				=   1
 UpperCAmelCase__   :					Optional[int]   				=   3
 UpperCAmelCase__   :					Any   				=   3
 UpperCAmelCase__   :					Optional[Any]   				=   limit
 UpperCAmelCase__   :					Dict   				=   0

 UpperCAmelCase__   :					int   				=   """"""
 while counter != decimal + 1:
  if 4 * q + r - t < n * t:
   result += str(__UpperCamelCase )
   if counter == 0:
    result += "."

   if decimal == counter:
    break

   counter += 1
   UpperCAmelCase__   :					Union[str, Any]   				=   10 * (r - n * t)
   UpperCAmelCase__   :					int   				=   ((10 * (3 * q + r)) // t) - 10 * n
   q *= 10
   UpperCAmelCase__   :					str   				=   nr
  else:
   UpperCAmelCase__   :					List[str]   				=   (2 * q + r) * l
   UpperCAmelCase__   :					Union[str, Any]   				=   (q * (7 * k) + 2 + (r * l)) // (t * l)
   q *= k
   t *= l
   l += 2
   k += 1
   UpperCAmelCase__   :					Optional[Any]   				=   nn
   UpperCAmelCase__   :					str   				=   nr
 return result
def 					lowerCAmelCase  (   ):
 '''simple docstring'''





 print(F"{calculate_pi(50 ) = }" )
 import doctest

 doctest.testmod()


if __name__ == "__main__":
      main()





