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,
				)


__SCREAMING_SNAKE_CASE						:    int     = logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE						:    Optional[Any]     = Dict[str, Any]
__SCREAMING_SNAKE_CASE						:    List[str]     = List[Prediction]



@add_end_docstrings(__snake_case		)
class    lowercase_    (     __snake_case		):



							def __init__(						self      ,				*lowercase_      ,				**lowercase_				):
													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        UpperCamelCase       (						self      ,				**lowercase_				):
													_snake_case						:     Any				=    {}
													_snake_case						:     List[Any]				=    {}
													if "subtask" in kwargs:
																			_snake_case						:     Any				=    kwargs["subtask"]
																			_snake_case						:     int				=    kwargs["subtask"]
													if "threshold" in kwargs:
																			_snake_case						:     List[str]				=    kwargs["threshold"]
													if "mask_threshold" in kwargs:
																			_snake_case						:     List[str]				=    kwargs["mask_threshold"]
													if "overlap_mask_area_threshold" in kwargs:
																			_snake_case						:     int				=    kwargs["overlap_mask_area_threshold"]

													return preprocess_kwargs, {}, postprocess_kwargs



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



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



							def        UpperCamelCase       (						self      ,				lowercase_				):
													_snake_case						:     int				=    model_inputs.pop("target_size"				)
													_snake_case						:     int				=    self.model(**lowercase_				)
													_snake_case						:     Any				=    target_size
													return model_outputs



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_=None      ,				lowercase_=0.9      ,				lowercase_=0.5      ,				lowercase_=0.5				):
													_snake_case						:     List[Any]				=    None
													if subtask in {"panoptic", None} and hasattr(self.image_processor      ,				"post_process_panoptic_segmentation"				):
																			_snake_case						:     Dict				=    self.image_processor.post_process_panoptic_segmentation
													elif subtask in {"instance", None} and hasattr(self.image_processor      ,				"post_process_instance_segmentation"				):
																			_snake_case						:     Dict				=    self.image_processor.post_process_instance_segmentation

													if fn is not None:
																			_snake_case						:     List[Any]				=    fn(
																			    lowercase_      ,				threshold=lowercase_      ,				mask_threshold=lowercase_      ,				overlap_mask_area_threshold=lowercase_      ,				target_sizes=model_outputs["target_size"]      ,				)[0]

																			_snake_case						:     Optional[Any]				=    []
																			_snake_case						:     int				=    outputs["segmentation"]

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

																			_snake_case						:     Tuple				=    []
																			_snake_case						:     int				=    outputs.numpy()
																			_snake_case						:     Tuple				=    np.unique(lowercase_				)

																			for label in labels:
																									_snake_case						:     Optional[int]				=    (segmentation == label) * 255
																									_snake_case						:     Dict				=    Image.fromarray(mask.astype(np.uinta				)      ,				mode="L"				)
																									_snake_case						:     int				=    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()} = ")



