

'''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   _SCREAMING_SNAKE_CASE    (    a_	):



 def __init__(    self		:    Any ,      *a__		:    int ,      **a__		:    Optional[int]			):
      super().__init__(*lowercase_ ,      **lowercase_			)

      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__		:    Union[str, Any]			):
      __magic_name__						   =				{}
      __magic_name__						   =				{}
      if "subtask" in kwargs:
           __magic_name__						   =				kwargs['''subtask''']
           __magic_name__						   =				kwargs['''subtask''']
      if "threshold" in kwargs:
           __magic_name__						   =				kwargs['''threshold''']
      if "mask_threshold" in kwargs:
           __magic_name__						   =				kwargs['''mask_threshold''']
      if "overlap_mask_area_threshold" in kwargs:
           __magic_name__						   =				kwargs['''overlap_mask_area_threshold''']

      return preprocess_kwargs, {}, postprocess_kwargs



 def __call__(    self		:    Dict ,      a__		:    Dict ,      **a__		:    Any			):
      return super().__call__(lowercase_ ,      **lowercase_			)



 def  snake_case__  (    self		:    Optional[int] ,      a__		:    Union[str, Any] ,      a__		:    Optional[int]=None			):
      __magic_name__						   =				load_image(lowercase_			)
      __magic_name__						   =				[(image.height, image.width)]
      if self.model.config.__class__.__name__ == "OneFormerConfig":
           if subtask is None:
                __magic_name__						   =				{}
           else:
                __magic_name__						   =				{'''task_inputs''': [subtask]}
           __magic_name__						   =				self.image_processor(images=[image] ,      return_tensors='''pt''' ,      **lowercase_			)
           __magic_name__						   =				self.tokenizer(
               inputs['''task_inputs'''] ,      padding='''max_length''' ,      max_length=self.model.config.task_seq_len ,      return_tensors=self.framework ,      )['''input_ids''']
      else:
           __magic_name__						   =				self.image_processor(images=[image] ,      return_tensors='''pt'''			)
      __magic_name__						   =				target_size
      return inputs



 def  snake_case__  (    self		:    str ,      a__		:    Optional[Any]			):
      __magic_name__						   =				model_inputs.pop('''target_size'''			)
      __magic_name__						   =				self.model(**lowercase_			)
      __magic_name__						   =				target_size
      return model_outputs



 def  snake_case__  (    self		:    Dict ,      a__		:    int ,      a__		:    List[str]=None ,      a__		:    Any=0.9 ,      a__		:    Tuple=0.5 ,      a__		:    Optional[int]=0.5			):
      __magic_name__						   =				None
      if subtask in {"panoptic", None} and hasattr(self.image_processor ,      '''post_process_panoptic_segmentation'''			):
           __magic_name__						   =				self.image_processor.post_process_panoptic_segmentation
      elif subtask in {"instance", None} and hasattr(self.image_processor ,      '''post_process_instance_segmentation'''			):
           __magic_name__						   =				self.image_processor.post_process_instance_segmentation

      if fn is not None:
           __magic_name__						   =				fn(
               lowercase_ ,      threshold=lowercase_ ,      mask_threshold=lowercase_ ,      overlap_mask_area_threshold=lowercase_ ,      target_sizes=model_outputs['''target_size'''] ,      )[0]

           __magic_name__						   =				[]
           __magic_name__						   =				outputs['''segmentation''']

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

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

           __magic_name__						   =				[]
           __magic_name__						   =				outputs.numpy()
           __magic_name__						   =				np.unique(lowercase_			)

           for label in labels:
                __magic_name__						   =				(segmentation == label) * 255
                __magic_name__						   =				Image.fromarray(mask.astype(np.uinta			) ,      mode='''L'''			)
                __magic_name__						   =				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














import math
def      lowerCamelCase					(						a_	)	->			Dict:
							lowerCAmelCase_										=     0
							if n % 2 == 0:
														count += 1
														while n % 2 == 0:
																					lowerCAmelCase_										=     int(n / 2	)
    # the n input value must be odd so that
    # we can skip one element (ie i += 2)

							lowerCAmelCase_										=     3

							while i <= int(math.sqrt(a_	)	):
														if n % i == 0:
																					count += 1
																					while n % i == 0:
																												lowerCAmelCase_										=     int(n / i	)
														lowerCAmelCase_										=     i + 2

							# this condition checks the prime
							# number n is greater than 2

							if n > 2:
														count += 1
							return count


if __name__ == "__main__":
		lowerCamelCase_						     =  5_1_2_4_2_1_8_3
		print(f'''The number of distinct prime factors is/are {exact_prime_factor_count(n)}''')
		print(f'''The value of log(log(n)) is {math.log(math.log(n)):.4f}''')



