




'''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,
 )


lowerCamelCase_	 =		logging.get_logger(__name__)


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







@add_end_docstrings(A     )
class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			int						,   *__lowerCamelCase      :			Any						,   **__lowerCamelCase      :			Optional[int]					):

      """simple docstring"""
      super().__init__(*__lowerCamelCase						,   **__lowerCamelCase					)

      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      :			int						,   **__lowerCamelCase      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   {}
      _SCREAMING_SNAKE_CASE =   {}
      if "subtask" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs["subtask"]
         _SCREAMING_SNAKE_CASE =   kwargs["subtask"]
      if "threshold" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs["threshold"]
      if "mask_threshold" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs["mask_threshold"]
      if "overlap_mask_area_threshold" in kwargs:
         _SCREAMING_SNAKE_CASE =   kwargs["overlap_mask_area_threshold"]

      return preprocess_kwargs, {}, postprocess_kwargs



   def __call__(					self      :			Dict						,   __lowerCamelCase      :			Tuple						,   **__lowerCamelCase      :			Optional[int]					):

      """simple docstring"""
      return super().__call__(__lowerCamelCase						,   **__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			Dict						,   __lowerCamelCase      :			Optional[Any]=None					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   load_image(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   [(image.height, image.width)]
      if self.model.config.__class__.__name__ == "OneFormerConfig":
         if subtask is None:
            _SCREAMING_SNAKE_CASE =   {}
         else:
            _SCREAMING_SNAKE_CASE =   {"task_inputs": [subtask]}
         _SCREAMING_SNAKE_CASE =   self.image_processor(images=[image]						,   return_tensors="pt"						,   **__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   self.tokenizer(
             inputs["task_inputs"]						,   padding="max_length"						,   max_length=self.model.config.task_seq_len						,   return_tensors=self.framework						,   )["input_ids"]
      else:
         _SCREAMING_SNAKE_CASE =   self.image_processor(images=[image]						,   return_tensors="pt"					)
      _SCREAMING_SNAKE_CASE =   target_size
      return inputs



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   model_inputs.pop("target_size"					)
      _SCREAMING_SNAKE_CASE =   self.model(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   target_size
      return model_outputs



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			List[Any]						,   __lowerCamelCase      :			Any=None						,   __lowerCamelCase      :			Optional[Any]=0.9						,   __lowerCamelCase      :			str=0.5						,   __lowerCamelCase      :			Optional[Any]=0.5					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   None
      if subtask in {"panoptic", None} and hasattr(self.image_processor						,   "post_process_panoptic_segmentation"					):
         _SCREAMING_SNAKE_CASE =   self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor						,   "post_process_instance_segmentation"					):
         _SCREAMING_SNAKE_CASE =   self.image_processor.post_process_instance_segmentation

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

         _SCREAMING_SNAKE_CASE =   []
         _SCREAMING_SNAKE_CASE =   outputs["segmentation"]

         for segment in outputs["segments_info"]:
            _SCREAMING_SNAKE_CASE =   (segmentation == segment["id"]) * 2_5_5
            _SCREAMING_SNAKE_CASE =   Image.fromarray(mask.numpy().astype(np.uinta					)						,   mode="L"					)
            _SCREAMING_SNAKE_CASE =   self.model.config.idalabel[segment["label_id"]]
            _SCREAMING_SNAKE_CASE =   segment["score"]
            annotation.append({"score": score, "label": label, "mask": mask}					)

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

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

         for label in labels:
            _SCREAMING_SNAKE_CASE =   (segmentation == label) * 2_5_5
            _SCREAMING_SNAKE_CASE =   Image.fromarray(mask.astype(np.uinta					)						,   mode="L"					)
            _SCREAMING_SNAKE_CASE =   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'''



lowerCamelCase_	 =		(1, 1_53, 3_70, 3_71, 16_34, 24_67_80_51, 1_15_13_22_19_01_87_63_99_25_65_09_55_97_97_39_71_52_24_01)
lowerCamelCase_	 =		(-1_53, -1, 0, 1.2, 2_00, "A", [], {}, None)
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Initialization of sum and number of digits.
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   # Calculation of digits of the number
   while temp > 0:
      number_of_digits += 1
      temp //= 10
   # Dividing number into separate digits and find Armstrong number
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE =   temp % 10
      total += rem**number_of_digits
      temp //= 10
   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False

   # Init a "histogram" of the digits
   _SCREAMING_SNAKE_CASE =   [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   0
   _SCREAMING_SNAKE_CASE =   n
   while temp > 0:
      _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   divmod(__A ,  10						)
      digit_histogram[rem] += 1
      digit_total += 1

   for cnt, i in zip(__A ,  range(len(__A						)						)						):
      total += cnt * i**digit_total

   return n == total
def 				SCREAMING_SNAKE_CASE_				(							__A       :				int						)				->				bool:
   if not isinstance(__A ,  __A						) or n < 1:
      return False
   _SCREAMING_SNAKE_CASE =   len(str(__A						)						)  # the power that all digits will be raised to
   # check if sum of each digit multiplied expo times is equal to number
   return n == sum(int(__A						) ** expo for i in str(__A						)						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   int(input("Enter an integer to see if it is an Armstrong number: "						).strip()						)
   print(f"""{num} is {"" if armstrong_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if narcissistic_number(__A						) else "not "}an Armstrong number."""						)
   print(f"""{num} is {"" if pluperfect_number(__A						) else "not "}an Armstrong number."""						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

