




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,
				)


lowercase_		=							logging.get_logger(__name__)


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





@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class 		A__				(   __SCREAMING_SNAKE_CASE ):






	def __init__(	self				,     *lowerCamelCase				,     **lowerCamelCase						)      ->   Tuple:




						"""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  lowercase      (	self				,     **lowerCamelCase						)      ->   Dict:




						"""simple docstring"""






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

						return preprocess_kwargs, {}, postprocess_kwargs






	def __call__(	self				,     lowerCamelCase				,     **lowerCamelCase						)      ->   Union[Predictions, List[Prediction]]:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None						)      ->   Optional[Any]:




						"""simple docstring"""






						__magic_name__					:							List[str]		   =							load_image(lowerCamelCase						)
						__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__					:							List[str]		   =							{'''task_inputs''': [subtask]}
											__magic_name__					:							int		   =							self.image_processor(images=[image]				,     return_tensors='''pt'''				,     **lowerCamelCase						)
											__magic_name__					:							Dict		   =							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__					:							List[str]		   =							target_size
						return inputs






	def  lowercase      (	self				,     lowerCamelCase						)      ->   str:




						"""simple docstring"""






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






	def  lowercase      (	self				,     lowerCamelCase				,     lowerCamelCase=None				,     lowerCamelCase=0.9				,     lowerCamelCase=0.5				,     lowerCamelCase=0.5						)      ->   Dict:




						"""simple docstring"""






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

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

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

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

											__magic_name__					:							Union[str, Any]		   =							[]
											__magic_name__					:							Dict		   =							outputs.numpy()
											__magic_name__					:							Any		   =							np.unique(lowerCamelCase						)

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









def  lowerCAmelCase  (	UpperCAmelCase       )  ->int:






					"""simple docstring"""






					return n * (2 * n - 1)







def  lowerCAmelCase  (	UpperCAmelCase       )  ->bool:






					"""simple docstring"""






					__magic_name__					:							Any		   =							(1 + 24 * n) ** 0.5
					return ((1 + root) / 6) % 1 == 0







def  lowerCAmelCase  (	UpperCAmelCase = 144       )  ->int:






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							start
					__magic_name__					:							int		   =							hexagonal_num(UpperCAmelCase       )
					while not is_pentagonal(UpperCAmelCase       ):
										n += 1
										__magic_name__					:							Optional[int]		   =							hexagonal_num(UpperCAmelCase       )
					return num


if __name__ == "__main__":
				print(f"{solution()} = ")



