





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							:Tuple							=	logging.get_logger(__name__)


__lowercase							:List[str]							=	Dict[str, Any]
__lowercase							:Union[str, Any]							=	List[Prediction]



@add_end_docstrings(lowercase__				)
class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  int				,						*a					:  Dict				,						**a					:  Optional[Any]     )						->Optional[Any]:
													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   A_	(   self					:  int				,						**a					:  Union[str, Any]     )						->Optional[Any]:
													SCREAMING_SNAKE_CASE__  :					int =       {}
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       {}
													if "subtask" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					List[str] =       kwargs["subtask"]
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs["subtask"]
													if "threshold" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					int =       kwargs["threshold"]
													if "mask_threshold" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       kwargs["mask_threshold"]
													if "overlap_mask_area_threshold" in kwargs:
																			SCREAMING_SNAKE_CASE__  :					Any =       kwargs["overlap_mask_area_threshold"]

													return preprocess_kwargs, {}, postprocess_kwargs

							def __call__(   self					:  Optional[Any]				,						a					:  str				,						**a					:  List[str]     )						->Union[str, Any]:
													return super().__call__(_lowerCAmelCase				,						**_lowerCAmelCase     )

							def   A_	(   self					:  List[Any]				,						a					:  Optional[Any]				,						a					:  Union[str, Any]=None     )						->str:
													SCREAMING_SNAKE_CASE__  :					Any =       load_image(_lowerCAmelCase     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       [(image.height, image.width)]
													if self.model.config.__class__.__name__ == "OneFormerConfig":
																			if subtask is None:
																									SCREAMING_SNAKE_CASE__  :					int =       {}
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       {"task_inputs": [subtask]}
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.image_processor(images=[image]				,						return_tensors="pt"				,						**_lowerCAmelCase     )
																			SCREAMING_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:
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.image_processor(images=[image]				,						return_tensors="pt"     )
													SCREAMING_SNAKE_CASE__  :					Dict =       target_size
													return inputs

							def   A_	(   self					:  int				,						a					:  int     )						->Optional[Any]:
													SCREAMING_SNAKE_CASE__  :					Tuple =       model_inputs.pop("target_size"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.model(**_lowerCAmelCase     )
													SCREAMING_SNAKE_CASE__  :					List[Any] =       target_size
													return model_outputs






							def   A_	(   self					:  Union[str, Any]				,						a					:  Any				,						a					:  Optional[int]=None				,						a					:  Optional[Any]=0.9				,						a					:  int=0.5				,						a					:  Tuple=0.5     )						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					Tuple =       None
													if subtask in {"panoptic", None} and hasattr(self.image_processor				,						"post_process_panoptic_segmentation"     ):
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.image_processor.post_process_panoptic_segmentation
													elif subtask in {"instance", None} and hasattr(self.image_processor				,						"post_process_instance_segmentation"     ):
																			SCREAMING_SNAKE_CASE__  :					List[str] =       self.image_processor.post_process_instance_segmentation

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

																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       []
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       outputs["segmentation"]

																			for segment in outputs["segments_info"]:
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       (segmentation == segment["id"]) * 2_55
																									SCREAMING_SNAKE_CASE__  :					List[str] =       Image.fromarray(mask.numpy().astype(np.uinta     )				,						mode="L"     )
																									SCREAMING_SNAKE_CASE__  :					str =       self.model.config.idalabel[segment["label_id"]]
																									SCREAMING_SNAKE_CASE__  :					List[str] =       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__  :					Tuple =       self.image_processor.post_process_semantic_segmentation(
																			    _lowerCAmelCase				,						target_sizes=model_outputs["target_size"]     )[0]

																			SCREAMING_SNAKE_CASE__  :					str =       []
																			SCREAMING_SNAKE_CASE__  :					str =       outputs.numpy()
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       np.unique(_lowerCAmelCase     )

																			for label in labels:
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       (segmentation == label) * 2_55
																									SCREAMING_SNAKE_CASE__  :					List[str] =       Image.fromarray(mask.astype(np.uinta     )				,						mode="L"     )
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       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 unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class  __lowerCAmelCase							(				unittest.TestCase   ,			lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase     :Any   						=    load_tool("image-segmentation"					)
         self.tool.setup()
         lowercase     :int   						=    load_tool("image-segmentation"							,       remote=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Any   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Dict   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[Any]   						=    self.remote_tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase     :Optional[Any]   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         lowercase     :int   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[str]   						=    self.remote_tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)



