




'''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,
						)


_lowerCamelCase				:		Optional[Any]				      =    logging.get_logger(__name__)


_lowerCamelCase				:		Dict				      =    Dict[str, Any]
_lowerCamelCase				:		str				      =    List[Prediction]





@add_end_docstrings(snake_case_				)
class 	lowerCamelCase_				(			snake_case_				):




					'''simple docstring'''






					def __init__(       self,    *__UpperCamelCase,    **__UpperCamelCase					):
							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 							__SCREAMING_SNAKE_CASE      (       self,    **__UpperCamelCase					):
							_snake_case      			=			{}
							_snake_case      			=			{}
							if "subtask" in kwargs:
									_snake_case      			=			kwargs["subtask"]
									_snake_case      			=			kwargs["subtask"]
							if "threshold" in kwargs:
									_snake_case      			=			kwargs["threshold"]
							if "mask_threshold" in kwargs:
									_snake_case      			=			kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
									_snake_case      			=			kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs






					def __call__(       self,    __UpperCamelCase,    **__UpperCamelCase					):
							return super().__call__(_lowerCAmelCase,    **_lowerCAmelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None					):
							_snake_case      			=			load_image(_lowerCAmelCase					)
							_snake_case      			=			[(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
									if subtask is None:
											_snake_case      			=			{}
									else:
											_snake_case      			=			{"task_inputs": [subtask]}
									_snake_case      			=			self.image_processor(images=[image],    return_tensors="pt",    **_lowerCAmelCase					)
									_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:
									_snake_case      			=			self.image_processor(images=[image],    return_tensors="pt"					)
							_snake_case      			=			target_size
							return inputs






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase					):
							_snake_case      			=			model_inputs.pop("target_size"					)
							_snake_case      			=			self.model(**_lowerCAmelCase					)
							_snake_case      			=			target_size
							return model_outputs






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase=None,    __UpperCamelCase=0.9,    __UpperCamelCase=0.5,    __UpperCamelCase=0.5					):
							_snake_case      			=			None
							if subtask in {"panoptic", None} and hasattr(self.image_processor,    "post_process_panoptic_segmentation"					):
									_snake_case      			=			self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor,    "post_process_instance_segmentation"					):
									_snake_case      			=			self.image_processor.post_process_instance_segmentation

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

									_snake_case      			=			[]
									_snake_case      			=			outputs["segmentation"]

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

									_snake_case      			=			[]
									_snake_case      			=			outputs.numpy()
									_snake_case      			=			np.unique(_lowerCAmelCase					)

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










def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->str:
      return " ".join(sorted(set(sentence.split()					)					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()