


'''simple docstring'''


from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


UpperCAmelCase_		:       Optional[int]							       =		'\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

UpperCAmelCase_		:       List[Any]							       =		'\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


UpperCAmelCase_		:       Union[str, Any]							       =		'\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Dict							=				[rgram for rgrams in rgramslist for rgram in rgrams]
				_SCREAMING_SNAKE_CASE			:	Dict							=				Counter(SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				Counter(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				Counter()
				for sgram, scount in sgramcounter.items():
								_SCREAMING_SNAKE_CASE			:	Dict							=				scount * numref

				_SCREAMING_SNAKE_CASE			:	int							=				Counter(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				Counter()
				for cgram, ccount in cgramcounter.items():
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				ccount * numref

				# KEEP
				_SCREAMING_SNAKE_CASE			:	Tuple							=				sgramcounter_rep & cgramcounter_rep
				_SCREAMING_SNAKE_CASE			:	Tuple							=				keepgramcounter_rep & rgramcounter
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				sgramcounter_rep & rgramcounter

				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				0
				_SCREAMING_SNAKE_CASE			:	Tuple							=				0
				for keepgram in keepgramcountergood_rep:
								keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
								# Fix an alleged bug [2] in the keep score computation.
								# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
								keeptmpscorea += keepgramcountergood_rep[keepgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				#      a target exactly.
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				1
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				1
				if len(SCREAMING_SNAKE_CASE__					) > 0:
								_SCREAMING_SNAKE_CASE			:	Dict							=				keeptmpscorea / len(SCREAMING_SNAKE_CASE__					)
				if len(SCREAMING_SNAKE_CASE__					) > 0:
								# Fix an alleged bug [2] in the keep score computation.
								# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				keeptmpscorea / sum(keepgramcounterall_rep.values()					)
				_SCREAMING_SNAKE_CASE			:	Any							=				0
				if keepscore_precision > 0 or keepscore_recall > 0:
								_SCREAMING_SNAKE_CASE			:	int							=				2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

				# DELETION
				_SCREAMING_SNAKE_CASE			:	str							=				sgramcounter_rep - cgramcounter_rep
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				delgramcounter_rep - rgramcounter
				_SCREAMING_SNAKE_CASE			:	List[str]							=				sgramcounter_rep - rgramcounter
				_SCREAMING_SNAKE_CASE			:	Any							=				0
				_SCREAMING_SNAKE_CASE			:	Dict							=				0
				for delgram in delgramcountergood_rep:
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
								deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				1
				if len(SCREAMING_SNAKE_CASE__					) > 0:
								_SCREAMING_SNAKE_CASE			:	List[str]							=				deltmpscorea / len(SCREAMING_SNAKE_CASE__					)

				# ADDITION
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				set(SCREAMING_SNAKE_CASE__					) - set(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				set(SCREAMING_SNAKE_CASE__					) & set(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				set(SCREAMING_SNAKE_CASE__					) - set(SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				0
				for addgram in addgramcountergood:
								addtmpscore += 1

				# Define 0/0=1 instead of 0 to give higher scores for predictions that match
				# a target exactly.
				_SCREAMING_SNAKE_CASE			:	int							=				1
				_SCREAMING_SNAKE_CASE			:	Dict							=				1
				if len(SCREAMING_SNAKE_CASE__					) > 0:
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				addtmpscore / len(SCREAMING_SNAKE_CASE__					)
				if len(SCREAMING_SNAKE_CASE__					) > 0:
								_SCREAMING_SNAKE_CASE			:	Dict							=				addtmpscore / len(SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				0
				if addscore_precision > 0 or addscore_recall > 0:
								_SCREAMING_SNAKE_CASE			:	Any							=				2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

				return (keepscore, delscore_precision, addscore)







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				len(SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	Dict							=				ssent.split(""" """					)
				_SCREAMING_SNAKE_CASE			:	Dict							=				csent.split(""" """					)
				_SCREAMING_SNAKE_CASE			:	str							=				[]
				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				[]
				_SCREAMING_SNAKE_CASE			:	List[Any]							=				[]
				_SCREAMING_SNAKE_CASE			:	Any							=				[]
				_SCREAMING_SNAKE_CASE			:	Any							=				[]
				_SCREAMING_SNAKE_CASE			:	List[str]							=				[]

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				[]
				_SCREAMING_SNAKE_CASE			:	List[str]							=				[]
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				[]
				_SCREAMING_SNAKE_CASE			:	List[str]							=				[]
				for rsent in rsents:
								_SCREAMING_SNAKE_CASE			:	Dict							=				rsent.split(""" """					)
								_SCREAMING_SNAKE_CASE			:	Any							=				[]
								_SCREAMING_SNAKE_CASE			:	Any							=				[]
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				[]
								ragramslist.append(SCREAMING_SNAKE_CASE__					)
								for i in range(0							,				len(SCREAMING_SNAKE_CASE__					) - 1					):
												if i < len(SCREAMING_SNAKE_CASE__					) - 1:
																_SCREAMING_SNAKE_CASE			:	List[Any]							=				ragrams[i] + """ """ + ragrams[i + 1]
																ragrams.append(SCREAMING_SNAKE_CASE__					)
												if i < len(SCREAMING_SNAKE_CASE__					) - 2:
																_SCREAMING_SNAKE_CASE			:	Optional[int]							=				ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
																ragrams.append(SCREAMING_SNAKE_CASE__					)
												if i < len(SCREAMING_SNAKE_CASE__					) - 3:
																_SCREAMING_SNAKE_CASE			:	str							=				ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
																ragrams.append(SCREAMING_SNAKE_CASE__					)
								ragramslist.append(SCREAMING_SNAKE_CASE__					)
								ragramslist.append(SCREAMING_SNAKE_CASE__					)
								ragramslist.append(SCREAMING_SNAKE_CASE__					)

				for i in range(0							,				len(SCREAMING_SNAKE_CASE__					) - 1					):
								if i < len(SCREAMING_SNAKE_CASE__					) - 1:
												_SCREAMING_SNAKE_CASE			:	Any							=				sagrams[i] + """ """ + sagrams[i + 1]
												sagrams.append(SCREAMING_SNAKE_CASE__					)
								if i < len(SCREAMING_SNAKE_CASE__					) - 2:
												_SCREAMING_SNAKE_CASE			:	str							=				sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
												sagrams.append(SCREAMING_SNAKE_CASE__					)
								if i < len(SCREAMING_SNAKE_CASE__					) - 3:
												_SCREAMING_SNAKE_CASE			:	Tuple							=				sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
												sagrams.append(SCREAMING_SNAKE_CASE__					)

				for i in range(0							,				len(SCREAMING_SNAKE_CASE__					) - 1					):
								if i < len(SCREAMING_SNAKE_CASE__					) - 1:
												_SCREAMING_SNAKE_CASE			:	List[str]							=				cagrams[i] + """ """ + cagrams[i + 1]
												cagrams.append(SCREAMING_SNAKE_CASE__					)
								if i < len(SCREAMING_SNAKE_CASE__					) - 2:
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
												cagrams.append(SCREAMING_SNAKE_CASE__					)
								if i < len(SCREAMING_SNAKE_CASE__					) - 3:
												_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
												cagrams.append(SCREAMING_SNAKE_CASE__					)

				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))			:	Tuple							=				SARIngram(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))			:	int							=				SARIngram(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))			:	Optional[int]							=				SARIngram(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				((_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE)		,	(_SCREAMING_SNAKE_CASE))			:	Tuple							=				SARIngram(SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				sum([keepascore, keepascore, keepascore, keepascore]					) / 4
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				sum([delascore, delascore, delascore, delascore]					) / 4
				_SCREAMING_SNAKE_CASE			:	List[str]							=				sum([addascore, addascore, addascore, addascore]					) / 4
				_SCREAMING_SNAKE_CASE			:	str							=				(avgkeepscore + avgdelscore + avgaddscore) / 3
				return finalscore







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__ = True							,				SCREAMING_SNAKE_CASE__ = "13a"							,				SCREAMING_SNAKE_CASE__ = True					):







				"""simple docstring"""





				if lowercase:
								_SCREAMING_SNAKE_CASE			:	int							=				sentence.lower()

				if tokenizer in ["13a", "intl"]:
								if version.parse(sacrebleu.__version__					).major >= 2:
												_SCREAMING_SNAKE_CASE			:	Tuple							=				sacrebleu.metrics.bleu._get_tokenizer(SCREAMING_SNAKE_CASE__					)()(SCREAMING_SNAKE_CASE__					)
								else:
												_SCREAMING_SNAKE_CASE			:	Optional[int]							=				sacrebleu.TOKENIZERS[tokenizer]()(SCREAMING_SNAKE_CASE__					)
				elif tokenizer == "moses":
								_SCREAMING_SNAKE_CASE			:	List[str]							=				sacremoses.MosesTokenizer().tokenize(SCREAMING_SNAKE_CASE__							,				return_str=SCREAMING_SNAKE_CASE__							,				escape=SCREAMING_SNAKE_CASE__					)
				elif tokenizer == "penn":
								_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				sacremoses.MosesTokenizer().penn_tokenize(SCREAMING_SNAKE_CASE__							,				return_str=SCREAMING_SNAKE_CASE__					)
				else:
								_SCREAMING_SNAKE_CASE			:	str							=				sentence

				if not return_str:
								_SCREAMING_SNAKE_CASE			:	List[Any]							=				normalized_sent.split()

				return normalized_sent







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,					_KWARGS_DESCRIPTION )
class    lowercase__  (						datasets.Metric ):





							'''simple docstring'''


							def UpperCAmelCase_     (   self  ):
											return datasets.MetricInfo(
											    description=_DESCRIPTION   , citation=_CITATION   , inputs_description=_KWARGS_DESCRIPTION   , features=datasets.Features(
											        {
											            """sources""": datasets.Value("""string"""   , id="""sequence"""  ),
											            """predictions""": datasets.Value("""string"""   , id="""sequence"""  ),
											            """references""": datasets.Sequence(datasets.Value("""string"""   , id="""sequence"""  )   , id="""references"""  ),
											        }  )   , codebase_urls=[
											        """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
											        """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
											    ]   , reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]   , )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case  ):
											if not (len(__snake_case  ) == len(__snake_case  ) == len(__snake_case  )):
															raise ValueError("""Sources length must match predictions and references lengths."""  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				0
											for src, pred, refs in zip(__snake_case   , __snake_case   , __snake_case  ):
															sari_score += SARIsent(normalize(__snake_case  )   , normalize(__snake_case  )   , [normalize(__snake_case  ) for sent in refs]  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				sari_score / len(__snake_case  )
											return {"sari": 100 * sari_score}








'''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




