


'''simple docstring'''


from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_		:       List[Any]							       =		logging.get_logger(__name__)

UpperCAmelCase_		:       Any							       =		{
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							A_							:				List[Any]				     =  """led"""
							A_							:				str				     =  {
							    """num_attention_heads""": """encoder_attention_heads""",
							    """hidden_size""": """d_model""",
							    """attention_probs_dropout_prob""": """attention_dropout""",
							    """initializer_range""": """init_std""",
							}






							def __init__(   self   , __snake_case=5_0265   , __snake_case=1_6384   , __snake_case=1024   , __snake_case=12   , __snake_case=4096   , __snake_case=16   , __snake_case=12   , __snake_case=4096   , __snake_case=16   , __snake_case=0.0   , __snake_case=0.0   , __snake_case=True   , __snake_case=True   , __snake_case="gelu"   , __snake_case=1024   , __snake_case=0.1   , __snake_case=0.0   , __snake_case=0.0   , __snake_case=0.02   , __snake_case=2   , __snake_case=0.0   , __snake_case=1   , __snake_case=0   , __snake_case=2   , __snake_case = 512   , **__snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				vocab_size
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_encoder_position_embeddings
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				max_decoder_position_embeddings
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				d_model
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				encoder_ffn_dim
											_SCREAMING_SNAKE_CASE			:	str							=				encoder_layers
											_SCREAMING_SNAKE_CASE			:	Dict							=				encoder_attention_heads
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				decoder_ffn_dim
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				decoder_layers
											_SCREAMING_SNAKE_CASE			:	Tuple							=				decoder_attention_heads
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				dropout
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				attention_dropout
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				activation_dropout
											_SCREAMING_SNAKE_CASE			:	Any							=				activation_function
											_SCREAMING_SNAKE_CASE			:	Dict							=				init_std
											_SCREAMING_SNAKE_CASE			:	Any							=				encoder_layerdrop
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				decoder_layerdrop
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				classifier_dropout
											_SCREAMING_SNAKE_CASE			:	Tuple							=				use_cache
											_SCREAMING_SNAKE_CASE			:	Tuple							=				encoder_layers
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				attention_window

											super().__init__(
											    pad_token_id=__snake_case   , bos_token_id=__snake_case   , eos_token_id=__snake_case   , is_encoder_decoder=__snake_case   , decoder_start_token_id=__snake_case   , **__snake_case   , )








'''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,
				)


UpperCAmelCase_		:       Optional[int]							       =		logging.get_logger(__name__)


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





@add_end_docstrings(_snake_case )
class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def __init__(   self   , *__snake_case   , **__snake_case  ):
											super().__init__(*__snake_case   , **__snake_case  )

											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   , **__snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{}
											_SCREAMING_SNAKE_CASE			:	Any							=				{}
											if "subtask" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Any							=				kwargs["""subtask"""]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				kwargs["""subtask"""]
											if "threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				kwargs["""threshold"""]
											if "mask_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Dict							=				kwargs["""mask_threshold"""]
											if "overlap_mask_area_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				kwargs["""overlap_mask_area_threshold"""]

											return preprocess_kwargs, {}, postprocess_kwargs






							def __call__(   self   , __snake_case   , **__snake_case  ):
											return super().__call__(__snake_case   , **__snake_case  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_image(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				[(image.height, image.width)]
											if self.model.config.__class__.__name__ == "OneFormerConfig":
															if subtask is None:
																			_SCREAMING_SNAKE_CASE			:	int							=				{}
															else:
																			_SCREAMING_SNAKE_CASE			:	str							=				{"""task_inputs""": [subtask]}
															_SCREAMING_SNAKE_CASE			:	Any							=				self.image_processor(images=[image]   , return_tensors="""pt"""   , **__snake_case  )
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				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			:	int							=				self.image_processor(images=[image]   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				target_size
											return inputs






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				model_inputs.pop("""target_size"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				self.model(**__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				target_size
											return model_outputs






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None   , __snake_case=0.9   , __snake_case=0.5   , __snake_case=0.5  ):
											_SCREAMING_SNAKE_CASE			:	str							=				None
											if subtask in {"panoptic", None} and hasattr(self.image_processor   , """post_process_panoptic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor.post_process_panoptic_segmentation
											elif subtask in {"instance", None} and hasattr(self.image_processor   , """post_process_instance_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	str							=				self.image_processor.post_process_instance_segmentation

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

															_SCREAMING_SNAKE_CASE			:	Dict							=				[]
															_SCREAMING_SNAKE_CASE			:	int							=				outputs["""segmentation"""]

															for segment in outputs["segments_info"]:
																			_SCREAMING_SNAKE_CASE			:	str							=				(segmentation == segment["""id"""]) * 255
																			_SCREAMING_SNAKE_CASE			:	int							=				Image.fromarray(mask.numpy().astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.model.config.idalabel[segment["""label_id"""]]
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				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			:	Union[str, Any]							=				self.image_processor.post_process_semantic_segmentation(
															    __snake_case   , target_sizes=model_outputs["""target_size"""]  )[0]

															_SCREAMING_SNAKE_CASE			:	str							=				[]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				outputs.numpy()
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.unique(__snake_case  )

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




