






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,
		)


UpperCamelCase												=      logging.get_logger(__name__)


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



@add_end_docstrings(_UpperCAmelCase       )
class  __UpperCAmelCase						(_UpperCAmelCase       ):







			def __init__(      self:    Optional[int] ,      *UpperCAmelCase_:    Optional[Any] ,      **UpperCAmelCase_:    Optional[int]	):







									'''simple docstring'''




									super().__init__(*UpperCAmelCase_ ,      **UpperCAmelCase_	)

									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[str] ,      **UpperCAmelCase_:    List[Any]	):







									'''simple docstring'''




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

									return preprocess_kwargs, {}, postprocess_kwargs







			def __call__(      self:    Union[str, Any] ,      UpperCAmelCase_:    List[Any] ,      **UpperCAmelCase_:    Union[str, Any]	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    Dict ,      UpperCAmelCase_:    Union[str, Any] ,      UpperCAmelCase_:    int=None	):







									'''simple docstring'''




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







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    Optional[int]	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE       =    model_inputs.pop("""target_size"""	)
									_SCREAMING_SNAKE_CASE       =    self.model(**UpperCAmelCase_	)
									_SCREAMING_SNAKE_CASE       =    target_size
									return model_outputs







			def 				UpperCamelCase (      self:    List[str] ,      UpperCAmelCase_:    str ,      UpperCAmelCase_:    int=None ,      UpperCAmelCase_:    Any=0.9 ,      UpperCAmelCase_:    Union[str, Any]=0.5 ,      UpperCAmelCase_:    Any=0.5	):







									'''simple docstring'''




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

									if fn is not None:
															_SCREAMING_SNAKE_CASE       =    fn(
															    UpperCAmelCase_ ,      threshold=UpperCAmelCase_ ,      mask_threshold=UpperCAmelCase_ ,      overlap_mask_area_threshold=UpperCAmelCase_ ,      target_sizes=model_outputs["""target_size"""] ,      )[0]

															_SCREAMING_SNAKE_CASE       =    []
															_SCREAMING_SNAKE_CASE       =    outputs["""segmentation"""]

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

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

															_SCREAMING_SNAKE_CASE       =    []
															_SCREAMING_SNAKE_CASE       =    outputs.numpy()
															_SCREAMING_SNAKE_CASE       =    np.unique(UpperCAmelCase_	)

															for label in labels:
																					_SCREAMING_SNAKE_CASE       =    (segmentation == label) * 255
																					_SCREAMING_SNAKE_CASE       =    Image.fromarray(mask.astype(np.uinta	) ,      mode="""L"""	)
																					_SCREAMING_SNAKE_CASE       =    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
from timeit import timeit

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE       =    0
						_SCREAMING_SNAKE_CASE       =    abs(snake_case__						)
						while True:
												_SCREAMING_SNAKE_CASE       =    n // 10
												digits += 1
												if n == 0:
																		break
						return digits

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						return 1 if n == 0 else math.floor(math.log(abs(snake_case__						)			,10						) + 1						)

def 			__lowerCamelCase     (						snake_case__						)				->    int:






						"""simple docstring"""





						return len(str(abs(snake_case__						)						)						)

def 			__lowerCamelCase     (						)				->    None:






						"""simple docstring"""





						from collections.abc import Callable

						def benchmark_a_function(snake_case__			,snake_case__						) -> None:
												_SCREAMING_SNAKE_CASE       =    F'{func.__name__}({value})'
												_SCREAMING_SNAKE_CASE       =    timeit(F'__main__.{call}'			,setup="""import __main__"""						)
												print(F'{call}: {func(snake_case__						)} -- {timing} seconds'						)

						for value in (26_21_44, 11_25_89_99_06_84_26_24, 1_26_76_50_60_02_28_22_94_01_49_67_03_20_53_76):
												for func in (num_digits, num_digits_fast, num_digits_faster):
																		benchmark_a_function(snake_case__			,snake_case__						)
												print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()

