




import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_lowerCamelCase					:     Any					  =  False
_lowerCamelCase					:     Dict					  =  Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase					:     Tuple					  =  datasets.Features(
    OrderedDict(
        {
            "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
            "img_id": datasets.Value("int32"),
            "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
            "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class 				__snake_case     :



				def __init__(					self					:						List[Any]						,		_UpperCAmelCase					:						int=sys.argv[1:]					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					None
								_lowerCAmelCase			:			int        =					None
								_lowerCAmelCase			:			List[Any]        =					None
								_lowerCAmelCase			:			Dict        =					1
								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					getopt.getopt(_UpperCAmelCase						,		"""i:o:b:s"""						,		["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]					)
								for opt, arg in opts:
												if opt in ("-i", "--inputdir"):
																_lowerCAmelCase			:			Dict        =					arg
												elif opt in ("-o", "--outfile"):
																_lowerCAmelCase			:			Tuple        =					arg
												elif opt in ("-b", "--batch_size"):
																_lowerCAmelCase			:			Any        =					int(_UpperCAmelCase					)
												elif opt in ("-s", "--subset_list"):
																_lowerCAmelCase			:			List[str]        =					arg

								assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
								assert outputfile is not None and not os.path.isfile(_UpperCAmelCase					), f"{outputfile}"
								if subset_list is not None:
												with open(os.path.realpath(_UpperCAmelCase					)					) as f:
																_lowerCAmelCase			:			str        =					{self._vqa_file_split()[0] for x in tryload(_UpperCAmelCase					)}
								else:
												_lowerCAmelCase			:			Tuple        =					None

								_lowerCAmelCase			:			Optional[int]        =					CONFIG
								if torch.cuda.is_available():
												_lowerCAmelCase			:			List[str]        =					"""cuda"""
								_lowerCAmelCase			:			Any        =					os.path.realpath(_UpperCAmelCase					)
								_lowerCAmelCase			:			Union[str, Any]        =					os.path.realpath(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					Preprocess(self.config					)
								_lowerCAmelCase			:			str        =					GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""						,		config=self.config					)
								_lowerCAmelCase			:			Tuple        =					batch_size if batch_size != 0 else 1
								_lowerCAmelCase			:			str        =					DEFAULT_SCHEMA



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Tuple					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					int(file.split("""."""					)[0].split("""_"""					)[-1]					)
								_lowerCAmelCase			:			List[str]        =					os.path.join(self.inputdir						,		_UpperCAmelCase					)
								return (img_id, filepath)



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			int:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					[]
								for i, file in enumerate(os.listdir(self.inputdir					)					):
												if self.subset_list is not None and i not in self.subset_list:
																continue
												batch.append(self._vqa_file_split(_UpperCAmelCase					)					)
												if len(_UpperCAmelCase					) == self.batch:
																_lowerCAmelCase			:			Any        =					batch
																_lowerCAmelCase			:			str        =					[]
																yield list(map(_UpperCAmelCase						,		zip(*_UpperCAmelCase					)					)					)

								for i in range(1					):
												yield list(map(_UpperCAmelCase						,		zip(*_UpperCAmelCase					)					)					)



				def __call__(					self					:						Optional[int]					)     ->			int:





								'''simple docstring'''




								if not TEST:
												_lowerCAmelCase			:			List[Any]        =					datasets.ArrowWriter(features=self.schema						,		path=self.outputfile					)
								# do file generator
								for i, (img_ids, filepaths) in enumerate(self.file_generator					):
												_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			int        =					self.preprocess(_UpperCAmelCase					)
												_lowerCAmelCase			:			List[str]        =					self.model(
												    _UpperCAmelCase						,		_UpperCAmelCase						,		scales_yx=_UpperCAmelCase						,		padding="""max_detections"""						,		max_detections=self.config.MAX_DETECTIONS						,		pad_value=0						,		return_tensors="""np"""						,		location="""cpu"""						,		)
												_lowerCAmelCase			:			int        =					output_dict.pop("""normalized_boxes"""					)
												if not TEST:
																_lowerCAmelCase			:			List[Any]        =					np.array(_UpperCAmelCase					)
																_lowerCAmelCase			:			Union[str, Any]        =					self.schema.encode_batch(_UpperCAmelCase					)
																writer.write_batch(_UpperCAmelCase					)
												if TEST:
																break
												# finalizer the writer
								if not TEST:
												_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					writer.finalize()
												print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"					)






def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				try:
								_lowerCAmelCase			:			List[Any]        =					json.load(UpperCamelCase_				)
								try:
												_lowerCAmelCase			:			List[str]        =					list(data.keys()				)
								except Exception:
												_lowerCAmelCase			:			str        =					[d["""img_id"""] for d in data]
				except Exception:
								try:
												_lowerCAmelCase			:			Union[str, Any]        =					eval(stream.read()				)
								except Exception:
												_lowerCAmelCase			:			Optional[int]        =					stream.read().split("""\n"""				)
				return data


if __name__ == "__main__":
	_lowerCamelCase					:     Optional[int]					  =  Extract(sys.argv[1:])
	extract()
	if not TEST:
		_lowerCamelCase					:     Any					  =  datasets.Dataset.from_file(extract.outputfile)
		# wala!
		# print(np.array(dataset[0:2]["roi_features"]).shape)



"""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,
	)


snake_case    =		logging.get_logger(__name__)


snake_case    =		Dict[str, Any]
snake_case    =		List[Prediction]




@add_end_docstrings(__magic_name__      )
class      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				def __init__(				self		,	*lowercase__		,	**lowercase__			)    ->    Dict:
									"""simple docstring"""







									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 							A					(				self		,	**lowercase__			)    ->    Optional[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		,	lowercase__		,	**lowercase__			)    ->    Union[Predictions, List[Prediction]]:
									"""simple docstring"""







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


				def 							A					(				self		,	lowercase__		,	lowercase__=None			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	load_image(lowercase__			)
									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'		,	**lowercase__			)
														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 							A					(				self		,	lowercase__			)    ->    str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	model_inputs.pop('target_size'			)
									SCREAMING_SNAKE_CASE				=	self.model(**lowercase__			)
									SCREAMING_SNAKE_CASE				=	target_size
									return model_outputs




				def 							A					(				self		,	lowercase__		,	lowercase__=None		,	lowercase__=0.9		,	lowercase__=0.5		,	lowercase__=0.5			)    ->    Union[str, Any]:
									"""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(
														    lowercase__		,	threshold=lowercase__		,	mask_threshold=lowercase__		,	overlap_mask_area_threshold=lowercase__		,	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(
														    lowercase__		,	target_sizes=model_outputs['target_size']			)[0]

														SCREAMING_SNAKE_CASE				=	[]
														SCREAMING_SNAKE_CASE				=	outputs.numpy()
														SCREAMING_SNAKE_CASE				=	np.unique(lowercase__			)

														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
