


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      :	List[str]  =logging.get_logger(__name__)


_UpperCAmelCase      :	Optional[Any]  =Dict[str, Any]
_UpperCAmelCase      :	Tuple  =List[Prediction]







@add_end_docstrings(UpperCAmelCase__				)
class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						def __init__(					self ,    *__lowercase ,    **__lowercase				)       ->     List[Any]:
										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     lowercase_			(					self ,    **__lowercase				)       ->     str:
										lowerCAmelCase_			:   Optional[Any]     					=					{}
										lowerCAmelCase_			:   Any     					=					{}
										if "subtask" in kwargs:
														lowerCAmelCase_			:   Optional[int]     					=					kwargs['''subtask''']
														lowerCAmelCase_			:   Tuple     					=					kwargs['''subtask''']
										if "threshold" in kwargs:
														lowerCAmelCase_			:   Any     					=					kwargs['''threshold''']
										if "mask_threshold" in kwargs:
														lowerCAmelCase_			:   int     					=					kwargs['''mask_threshold''']
										if "overlap_mask_area_threshold" in kwargs:
														lowerCAmelCase_			:   Tuple     					=					kwargs['''overlap_mask_area_threshold''']

										return preprocess_kwargs, {}, postprocess_kwargs




						def __call__(					self ,    __lowercase ,    **__lowercase				)       ->     Union[Predictions, List[Prediction]]:
										return super().__call__(__lowercase ,    **__lowercase				)




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




						def     lowercase_			(					self ,    __lowercase				)       ->     Tuple:
										lowerCAmelCase_			:   Optional[int]     					=					model_inputs.pop('''target_size'''				)
										lowerCAmelCase_			:   int     					=					self.model(**__lowercase				)
										lowerCAmelCase_			:   Tuple     					=					target_size
										return model_outputs




						def     lowercase_			(					self ,    __lowercase ,    __lowercase=None ,    __lowercase=0.9 ,    __lowercase=0.5 ,    __lowercase=0.5				)       ->     Optional[Any]:
										lowerCAmelCase_			:   str     					=					None
										if subtask in {"panoptic", None} and hasattr(self.image_processor ,    '''post_process_panoptic_segmentation'''				):
														lowerCAmelCase_			:   Optional[int]     					=					self.image_processor.post_process_panoptic_segmentation
										elif subtask in {"instance", None} and hasattr(self.image_processor ,    '''post_process_instance_segmentation'''				):
														lowerCAmelCase_			:   List[str]     					=					self.image_processor.post_process_instance_segmentation

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

														lowerCAmelCase_			:   Tuple     					=					[]
														lowerCAmelCase_			:   str     					=					outputs['''segmentation''']

														for segment in outputs["segments_info"]:
																		lowerCAmelCase_			:   Any     					=					(segmentation == segment['''id''']) * 2_5_5
																		lowerCAmelCase_			:   List[str]     					=					Image.fromarray(mask.numpy().astype(np.uinta				) ,    mode='''L'''				)
																		lowerCAmelCase_			:   Optional[Any]     					=					self.model.config.idalabel[segment['''label_id''']]
																		lowerCAmelCase_			:   Dict     					=					segment['''score''']
																		annotation.append({'''score''': score, '''label''': label, '''mask''': mask}				)

										elif subtask in {"semantic", None} and hasattr(self.image_processor ,    '''post_process_semantic_segmentation'''				):
														lowerCAmelCase_			:   Optional[Any]     					=					self.image_processor.post_process_semantic_segmentation(
														    __lowercase ,    target_sizes=model_outputs['''target_size''']				)[0]

														lowerCAmelCase_			:   Tuple     					=					[]
														lowerCAmelCase_			:   Optional[int]     					=					outputs.numpy()
														lowerCAmelCase_			:   Union[str, Any]     					=					np.unique(__lowercase				)

														for label in labels:
																		lowerCAmelCase_			:   Union[str, Any]     					=					(segmentation == label) * 2_5_5
																		lowerCAmelCase_			:   Optional[Any]     					=					Image.fromarray(mask.astype(np.uinta				) ,    mode='''L'''				)
																		lowerCAmelCase_			:   List[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



from transformers import PretrainedConfig







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     Dict:
										lowerCAmelCase_			:   List[Any]     					=					attribute
										super().__init__(**__lowercase				)







class 					snake_case__(				UpperCAmelCase__				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	int       =				"""custom"""




						def __init__(					self ,    __lowercase=1 ,    **__lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					attribute