
'''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__		: Tuple  							=							logging.get_logger(__name__)


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


@add_end_docstrings(lowerCAmelCase__)
class 			UpperCamelCase__	(     lowerCAmelCase__):






 def __init__(      self	:Optional[int]						,					*_A	:int						,					**_A	:Dict      )					->      Tuple:







       '''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	:Dict						,					**_A	:Any      )					->      List[Any]:







       '''simple docstring'''



       __A													=      {}
       __A													=      {}
       if "subtask" in kwargs:
             __A													=      kwargs['subtask']
             __A													=      kwargs['subtask']
       if "threshold" in kwargs:
             __A													=      kwargs['threshold']
       if "mask_threshold" in kwargs:
             __A													=      kwargs['mask_threshold']
       if "overlap_mask_area_threshold" in kwargs:
             __A													=      kwargs['overlap_mask_area_threshold']

       return preprocess_kwargs, {}, postprocess_kwargs






 def __call__(      self	:Optional[Any]						,					_A	:Union[str, Any]						,					**_A	:Optional[Any]      )					->      Optional[int]:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Tuple						,					_A	:List[str]						,					_A	:str=None      )					->      str:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:int						,					_A	:Dict      )					->      Dict:







       '''simple docstring'''



       __A													=      model_inputs.pop('target_size'      )
       __A													=      self.model(**_A      )
       __A													=      target_size
       return model_outputs






 def 						lowercase_    (      self	:List[str]						,					_A	:Optional[Any]						,					_A	:Optional[int]=None						,					_A	:List[Any]=0.9						,					_A	:List[str]=0.5						,					_A	:Union[str, Any]=0.5      )					->      Any:







       '''simple docstring'''



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

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

             __A													=      []
             __A													=      outputs['segmentation']

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

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

             __A													=      []
             __A													=      outputs.numpy()
             __A													=      np.unique(_A      )

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







import matplotlib.pyplot as plt
import numpy as np







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:str										=      len(SCREAMING_SNAKE_CASE     )  # Number of training samples
		SCREAMING_SNAKE_CASE_						:List[Any]										=      np.eye(SCREAMING_SNAKE_CASE     )  # Initialize weights as identity matrix
		for j in range(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Tuple										=      point - x_train[j]
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.exp(diff @ diff.T / (-2.0 * tau**2)     )

		return weights







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      weight_matrix(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:str										=      np.linalg.inv(x_train.T @ weight_mat @ x_train     ) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      np.zeros(len(SCREAMING_SNAKE_CASE     )     )  # Initialize array of predictions
		for i, item in enumerate(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      item @ local_weight(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )

		return y_pred







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		import seaborn as sns

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      sns.load_dataset(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.array(data[x_name]     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.array(data[y_name]     )

		SCREAMING_SNAKE_CASE_						:str										=      np.ones(len(SCREAMING_SNAKE_CASE     )     )

		# pairing elements of one and x_data
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      np.column_stack((one, x_data)     )

		return x_train, x_data, y_data







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		):
		SCREAMING_SNAKE_CASE_						:Tuple										=      np.sort(SCREAMING_SNAKE_CASE ,		axis=0     )
		plt.scatter(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		color='blue'     )
		plt.plot(
		    x_train_sorted[:, 1] ,		preds[x_train[:, 1].argsort(0     )] ,		color='yellow' ,		linewidth=5 ,		)
		plt.title('Local Weighted Regression'     )
		plt.xlabel(SCREAMING_SNAKE_CASE     )
		plt.ylabel(SCREAMING_SNAKE_CASE     )
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__	,							SCREAMING_SNAKE_CASE__     : str				=   load_data("""tips""", """total_bill""", """tip""")
							SCREAMING_SNAKE_CASE__     : str				=   local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, """total_bill""", """tip""")
