


'''simple docstring'''


import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCAmelCase_		:       Tuple	       =		logging.getLogger(__name__)







def       snake_case_       (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				argparse.ArgumentParser(description="""Train a unigram tokenizer on the wikitext dataset."""					)
				parser.add_argument(
				    """--dataset_name"""							,				type=SCREAMING_SNAKE_CASE__							,				default="""wikitext"""							,				help="""Name of the training. Explore datasets at: hf.co/datasets."""							,				)
				parser.add_argument(
				    """--dataset_config"""							,				type=SCREAMING_SNAKE_CASE__							,				default="""wikitext-103-raw-v1"""							,				help="""Configuration name of the dataset."""					)
				parser.add_argument(
				    """--batch_size"""							,				type=SCREAMING_SNAKE_CASE__							,				default=1000							,				help="""Batch size during training."""							,				)
				parser.add_argument(
				    """--vocab_size"""							,				type=SCREAMING_SNAKE_CASE__							,				default=1_0048							,				help="""Size of the desired vocabulary."""							,				)
				parser.add_argument(
				    """--limit"""							,				default=SCREAMING_SNAKE_CASE__							,				type=SCREAMING_SNAKE_CASE__							,				help="""Limit the number of shards (used for debugging)."""							,				)
				parser.add_argument(
				    """--export_to_hub"""							,				action="""store_true"""							,				)

				_SCREAMING_SNAKE_CASE			:	Any							=				parser.parse_args()
				return args







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[str]							=				datasets.load_dataset(args.dataset_name							,				args.dataset_config							,				split="""train"""					)

				if args.limit is not None:
								_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				min(len(SCREAMING_SNAKE_CASE__					)							,				args.limit					)
								_SCREAMING_SNAKE_CASE			:	Optional[int]							=				dataset.select(range(SCREAMING_SNAKE_CASE__					)					)
								logger.info(f"""Limiting the dataset to {args.limit} entries."""					)

				def batch_iterator():
								for i in range(0							,				len(SCREAMING_SNAKE_CASE__					)							,				args.batch_size					):
												yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
				_SCREAMING_SNAKE_CASE			:	int							=				Tokenizer(Unigram()					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				normalizers.Sequence([normalizers.Replace("""``"""							,				"""\""""					), normalizers.Replace("""''"""							,				"""\""""					)]					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				pre_tokenizers.Metaspace()

				# Prepare the trainer.
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				UnigramTrainer(
				    unk_token="""<unk>"""							,				special_tokens=["""[CLS]""", """[SEP]""", """<unk>""", """<pad>""", """[MASK]"""]							,				vocab_size=args.vocab_size							,				)

				logger.info("""Training the tokenizer."""					)
				tokenizer.train_from_iterator(batch_iterator()							,				trainer=SCREAMING_SNAKE_CASE__					)
				logger.info("""Tokenizer training complete!"""					)

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				tokenizer.token_to_id("""[CLS]"""					)
				_SCREAMING_SNAKE_CASE			:	int							=				tokenizer.token_to_id("""[SEP]"""					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				processors.TemplateProcessing(
				    single="""[CLS]:0 $A:0 [SEP]:0"""							,				pair="""[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"""							,				special_tokens=[
				        ("""[CLS]""", cls_token_id),
				        ("""[SEP]""", sep_token_id),
				    ]							,				)
				_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				decoders.Metaspace()

				if args.export_to_hub:
								logger.info("""Exporting the trained tokenzier to Hub."""					)
								_SCREAMING_SNAKE_CASE			:	Tuple							=				AlbertTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE__					)
								new_tokenizer.push_to_hub("""unigram-tokenizer-dataset"""					)


if __name__ == "__main__":
				UpperCAmelCase_		:       Union[str, Any]	       =		parse_args()
				main(args)








'''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




