
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
SCREAMING_SNAKE_CASE : str          =    False
SCREAMING_SNAKE_CASE : int          =    Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
SCREAMING_SNAKE_CASE : Any          =    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, 20_48), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)





class 					snake_case__  :





	def __init__(  self				,    UpperCamelCase_=sys.argv[1:]							)		->			Dict:
								"""simple docstring"""






								a_       :   Optional[int]			=	None
								a_       :   Dict			=	None
								a_       :   Dict			=	None
								a_       :   List[Any]			=	1
								a_       :   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"):
																						a_       :   Tuple			=	arg
															elif opt in ("-o", "--outfile"):
																						a_       :   int			=	arg
															elif opt in ("-b", "--batch_size"):
																						a_       :   Optional[int]			=	int(UpperCamelCase_							)
															elif opt in ("-s", "--subset_list"):
																						a_       :   List[Any]			=	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:
																						a_       :   int			=	{self._vqa_file_split()[0] for x in tryload(UpperCamelCase_							)}
								else:
															a_       :   Tuple			=	None

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





	def     A				(  self				,    UpperCamelCase_							)		->			List[Any]:
								"""simple docstring"""






								a_       :   Any			=	int(file.split("""."""							)[0].split("""_"""							)[-1]							)
								a_       :   Optional[int]			=	os.path.join(self.inputdir				,    UpperCamelCase_							)
								return (img_id, filepath)





	@property
	def     A				(  self							)		->			Dict:
								"""simple docstring"""






								a_       :   List[str]			=	[]
								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:
																						a_       :   str			=	batch
																						a_       :   Any			=	[]
																						yield list(map(UpperCamelCase_				,    zip(*UpperCamelCase_							)							)							)

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





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






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





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]      ):






							"""simple docstring"""



							try:
														a_       :   Optional[Any]			=	json.load(SCREAMING_SNAKE_CASE_      )
														try:
																					a_       :   Any			=	list(data.keys()      )
														except Exception:
																					a_       :   List[str]			=	[d["""img_id"""] for d in data]
							except Exception:
														try:
																					a_       :   Optional[int]			=	eval(stream.read()      )
														except Exception:
																					a_       :   Tuple			=	stream.read().split("""\n"""      )
							return data


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






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : str          =    {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							List[str]         = """mobilevit"""





	def __init__(  self				,    UpperCamelCase_=3				,    UpperCamelCase_=256				,    UpperCamelCase_=2				,    UpperCamelCase_=[144, 192, 240]				,    UpperCamelCase_=[16, 32, 64, 96, 128, 160, 640]				,    UpperCamelCase_=4				,    UpperCamelCase_=2.0				,    UpperCamelCase_=4.0				,    UpperCamelCase_="silu"				,    UpperCamelCase_=3				,    UpperCamelCase_=32				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.0				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-5				,    UpperCamelCase_=True				,    UpperCamelCase_=256				,    UpperCamelCase_=[6, 12, 18]				,    UpperCamelCase_=0.1				,    UpperCamelCase_=255				,    **UpperCamelCase_				,    )		->			str:
								"""simple docstring"""






								super().__init__(**UpperCamelCase_							)

								a_       :   Optional[int]			=	num_channels
								a_       :   Optional[Any]			=	image_size
								a_       :   int			=	patch_size
								a_       :   List[str]			=	hidden_sizes
								a_       :   Optional[int]			=	neck_hidden_sizes
								a_       :   List[Any]			=	num_attention_heads
								a_       :   Union[str, Any]			=	mlp_ratio
								a_       :   List[str]			=	expand_ratio
								a_       :   str			=	hidden_act
								a_       :   Union[str, Any]			=	conv_kernel_size
								a_       :   Optional[Any]			=	output_stride
								a_       :   Union[str, Any]			=	hidden_dropout_prob
								a_       :   List[Any]			=	attention_probs_dropout_prob
								a_       :   List[Any]			=	classifier_dropout_prob
								a_       :   Union[str, Any]			=	initializer_range
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   str			=	qkv_bias

								# decode head attributes for semantic segmentation
								a_       :   Optional[Any]			=	aspp_out_channels
								a_       :   Any			=	atrous_rates
								a_       :   Union[str, Any]			=	aspp_dropout_prob
								a_       :   Any			=	semantic_loss_ignore_index





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]							)





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								if self.task == "image-classification":
															return OrderedDict([("""logits""", {0: """batch"""})]							)
								else:
															return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]							)





	@property
	def     A				(  self							)		->			float:
								"""simple docstring"""






								return 1e-4


