

"""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,
 )


A									=						logging.get_logger(__name__)


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

@add_end_docstrings(lowerCamelCase__	)
class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  def __init__( self					: Union[str, Any]       ,       *snake_case					: List[Any]       ,       **snake_case					: List[Any]      )	->			int:


         '''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					: List[Any]       ,       **snake_case					: List[str]      )	->			List[Any]:


         '''simple docstring'''





         __magic_name__			:		Dict        =     {}
         __magic_name__			:		List[Any]        =     {}
         if "subtask" in kwargs:
                __magic_name__			:		Optional[int]        =     kwargs['''subtask''']
                __magic_name__			:		Optional[Any]        =     kwargs['''subtask''']
         if "threshold" in kwargs:
                __magic_name__			:		Optional[Any]        =     kwargs['''threshold''']
         if "mask_threshold" in kwargs:
                __magic_name__			:		Dict        =     kwargs['''mask_threshold''']
         if "overlap_mask_area_threshold" in kwargs:
                __magic_name__			:		List[str]        =     kwargs['''overlap_mask_area_threshold''']

         return preprocess_kwargs, {}, postprocess_kwargs



  def __call__( self					: Optional[int]       ,       snake_case					: Dict       ,       **snake_case					: Union[str, Any]      )	->			Union[Predictions, List[Prediction]]:


         '''simple docstring'''





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



  def 							_UpperCAmelCase      ( self					: List[Any]       ,       snake_case					: str       ,       snake_case					: Dict=None      )	->			List[str]:


         '''simple docstring'''





         __magic_name__			:		int        =     load_image(snake_case      )
         __magic_name__			:		Union[str, Any]        =     [(image.height, image.width)]
         if self.model.config.__class__.__name__ == "OneFormerConfig":
                if subtask is None:
                       __magic_name__			:		Optional[Any]        =     {}
                else:
                       __magic_name__			:		Dict        =     {'''task_inputs''': [subtask]}
                __magic_name__			:		int        =     self.image_processor(images=[image]       ,       return_tensors='''pt'''       ,       **snake_case      )
                __magic_name__			:		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:
                __magic_name__			:		int        =     self.image_processor(images=[image]       ,       return_tensors='''pt'''      )
         __magic_name__			:		Optional[Any]        =     target_size
         return inputs



  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: Any      )	->			int:


         '''simple docstring'''





         __magic_name__			:		str        =     model_inputs.pop('''target_size'''      )
         __magic_name__			:		Any        =     self.model(**snake_case      )
         __magic_name__			:		Optional[Any]        =     target_size
         return model_outputs




  def 							_UpperCAmelCase      ( self					: Optional[Any]       ,       snake_case					: Optional[int]       ,       snake_case					: Union[str, Any]=None       ,       snake_case					: Any=0.9       ,       snake_case					: Union[str, Any]=0.5       ,       snake_case					: str=0.5      )	->			Tuple:


         '''simple docstring'''





         __magic_name__			:		Dict        =     None
         if subtask in {"panoptic", None} and hasattr(self.image_processor       ,       '''post_process_panoptic_segmentation'''      ):
                __magic_name__			:		Optional[Any]        =     self.image_processor.post_process_panoptic_segmentation
         elif subtask in {"instance", None} and hasattr(self.image_processor       ,       '''post_process_instance_segmentation'''      ):
                __magic_name__			:		Optional[Any]        =     self.image_processor.post_process_instance_segmentation

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

                __magic_name__			:		Dict        =     []
                __magic_name__			:		Optional[Any]        =     outputs['''segmentation''']

                for segment in outputs["segments_info"]:
                       __magic_name__			:		Any        =     (segmentation == segment['''id''']) * 255
                       __magic_name__			:		Optional[Any]        =     Image.fromarray(mask.numpy().astype(np.uinta      )       ,       mode='''L'''      )
                       __magic_name__			:		int        =     self.model.config.idalabel[segment['''label_id''']]
                       __magic_name__			:		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'''      ):
                __magic_name__			:		List[Any]        =     self.image_processor.post_process_semantic_segmentation(
                    snake_case       ,       target_sizes=model_outputs['''target_size''']      )[0]

                __magic_name__			:		Union[str, Any]        =     []
                __magic_name__			:		Tuple        =     outputs.numpy()
                __magic_name__			:		Optional[int]        =     np.unique(snake_case      )

                for label in labels:
                       __magic_name__			:		int        =     (segmentation == label) * 255
                       __magic_name__			:		Optional[int]        =     Image.fromarray(mask.astype(np.uinta      )       ,       mode='''L'''      )
                       __magic_name__			:		Optional[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"""
from collections import defaultdict







def       UpperCamelCase_					(     lowerCamelCase							:					str   )						->     dict:



       """simple docstring"""







       __magic_name__			:		defaultdict[str, int]        =     defaultdict(lowerCamelCase   )
       # Creating a dictionary containing count of each word
       for word in sentence.split():
              occurrence[word] += 1
       return occurrence


if __name__ == "__main__":
 for word, count in word_occurrence("""INPUT STRING""").items():
  print(F"""{word}: {count}""")
