


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,
							)


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


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

@add_end_docstrings(UpperCamelCase__					)
class   A__							(						UpperCamelCase__					):




						def __init__(    self   : Optional[Any]			,      *_a   : Any			,      **_a   : List[Any]					)							->       Union[str, Any]:


										"""simple docstring"""





										super().__init__(*_a			,      **_a					)

										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   : Dict			,      **_a   : Dict					)							->       int:


										"""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   : Tuple			,      _a   : str			,      **_a   : str					)							->       Union[Predictions, List[Prediction]]:


										"""simple docstring"""





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




						def 							__UpperCamelCase			(    self   : Union[str, Any]			,      _a   : str			,      _a   : List[Any]=None					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=load_image(_a					)
										_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'''			,      **_a					)
														_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[Any]			,      _a   : List[str]					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=model_inputs.pop('''target_size'''					)
										_SCREAMING_SNAKE_CASE     					=self.model(**_a					)
										_SCREAMING_SNAKE_CASE     					=target_size
										return model_outputs




						def 							__UpperCamelCase			(    self   : int			,      _a   : List[Any]			,      _a   : str=None			,      _a   : Optional[int]=0.9			,      _a   : List[Any]=0.5			,      _a   : Union[str, Any]=0.5					)							->       List[str]:


										"""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(
														    _a			,      threshold=_a			,      mask_threshold=_a			,      overlap_mask_area_threshold=_a			,      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(
														    _a			,      target_sizes=model_outputs['''target_size''']					)[0]

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

														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



from ..utils import DummyObject, requires_backends

class   A__							(						metaclass=UpperCamelCase__					):
						UpperCAmelCase     =  ["torch", "librosa"]




						def __init__(    self   : Dict			,      *_a   : Union[str, Any]			,      **_a   : List[str]					)							->       Optional[int]:


										"""simple docstring"""





										requires_backends(self			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : Dict			,      *_a   : Tuple			,      **_a   : Tuple					)							->       Optional[int]:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : List[Any]			,      *_a   : Any			,      **_a   : List[str]					)							->       Dict:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)

class   A__							(						metaclass=UpperCamelCase__					):
						UpperCAmelCase     =  ["torch", "librosa"]




						def __init__(    self   : List[Any]			,      *_a   : int			,      **_a   : int					)							->       Optional[Any]:


										"""simple docstring"""





										requires_backends(self			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : str			,      *_a   : Dict			,      **_a   : Optional[Any]					)							->       Union[str, Any]:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)




						@classmethod
						def 							__UpperCamelCase			(    cls   : List[str]			,      *_a   : Optional[int]			,      **_a   : Union[str, Any]					)							->       Optional[Any]:


										"""simple docstring"""





										requires_backends(cls			,      ['''torch''', '''librosa''']					)