
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   :		Optional[Any]					 =       Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
SCREAMING_SNAKE_CASE   :		int					 =       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, 2048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)






class 					__lowercase		:


					def __init__(	self       ,       a__=sys.argv[1:]						)		->							List[str]:





									'''simple docstring'''
									A_       	=  None
									A_       	=  None
									A_       	=  None
									A_       	=  1
									A_	,					A_       	=  getopt.getopt(a__       ,       '''i:o:b:s'''       ,       ['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']						)
									for opt, arg in opts:
													if opt in ("-i", "--inputdir"):
																	A_       	=  arg
													elif opt in ("-o", "--outfile"):
																	A_       	=  arg
													elif opt in ("-b", "--batch_size"):
																	A_       	=  int(a__						)
													elif opt in ("-s", "--subset_list"):
																	A_       	=  arg

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

									A_       	=  CONFIG
									if torch.cuda.is_available():
													A_       	=  '''cuda'''
									A_       	=  os.path.realpath(a__						)
									A_       	=  os.path.realpath(a__						)
									A_       	=  Preprocess(self.config						)
									A_       	=  GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned'''       ,       config=self.config						)
									A_       	=  batch_size if batch_size != 0 else 1
									A_       	=  DEFAULT_SCHEMA


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[Any]:





									'''simple docstring'''
									A_       	=  int(file.split('''.'''						)[0].split('''_'''						)[-1]						)
									A_       	=  os.path.join(self.inputdir       ,       a__						)
									return (img_id, filepath)


					@property
					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									A_       	=  []
									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(a__						)						)
													if len(a__						) == self.batch:
																	A_       	=  batch
																	A_       	=  []
																	yield list(map(a__       ,       zip(*a__						)						)						)

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


					def __call__(	self						)		->							Union[str, Any]:





									'''simple docstring'''
									# make writer
									if not TEST:
													A_       	=  datasets.ArrowWriter(features=self.schema       ,       path=self.outputfile						)
									# do file generator
									for i, (img_ids, filepaths) in enumerate(self.file_generator						):
													A_	,					A_	,					A_       	=  self.preprocess(a__						)
													A_       	=  self.model(
													    a__       ,       a__       ,       scales_yx=a__       ,       padding='''max_detections'''       ,       max_detections=self.config.MAX_DETECTIONS       ,       pad_value=0       ,       return_tensors='''np'''       ,       location='''cpu'''       ,       )
													A_       	=  output_dict.pop('''normalized_boxes'''						)
													if not TEST:
																	A_       	=  np.array(a__						)
																	A_       	=  self.schema.encode_batch(a__						)
																	writer.write_batch(a__						)
													if TEST:
																	break
													# finalizer the writer
									if not TEST:
													A_	,					A_       	=  writer.finalize()
													print(F"Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb"						)



def        lowerCamelCase_  (   __UpperCamelCase     ):
				try:
								A_       	=  json.load(__UpperCamelCase     )
								try:
												A_       	=  list(data.keys()     )
								except Exception:
												A_       	=  [d['''img_id'''] for d in data]
				except Exception:
								try:
												A_       	=  eval(stream.read()     )
								except Exception:
												A_       	=  stream.read().split('''\n'''     )
				return data


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

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	{
					    '''sld_warmup_steps''': 15,
					    '''sld_guidance_scale''': 20,
					    '''sld_threshold''': 0.0,
					    '''sld_momentum_scale''': 0.0,
					    '''sld_mom_beta''': 0.0,
					}
					__magic_name__						:							Optional[int]     	=	{
					    '''sld_warmup_steps''': 10,
					    '''sld_guidance_scale''': 10_00,
					    '''sld_threshold''': 0.01,
					    '''sld_momentum_scale''': 0.3,
					    '''sld_mom_beta''': 0.4,
					}
					__magic_name__						:							str     	=	{
					    '''sld_warmup_steps''': 7,
					    '''sld_guidance_scale''': 20_00,
					    '''sld_threshold''': 0.0_25,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}
					__magic_name__						:							List[Any]     	=	{
					    '''sld_warmup_steps''': 0,
					    '''sld_guidance_scale''': 50_00,
					    '''sld_threshold''': 1.0,
					    '''sld_momentum_scale''': 0.5,
					    '''sld_mom_beta''': 0.7,
					}






@dataclass
class 					__lowercase		(					A       ):
					__magic_name__						:							Union[List[PIL.Image.Image], np.ndarray]
					__magic_name__						:							Optional[List[bool]]
					__magic_name__						:							Optional[Union[List[PIL.Image.Image], np.ndarray]]
					__magic_name__						:							Optional[str]


if is_transformers_available() and is_torch_available():
						from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
						from .safety_checker import SafeStableDiffusionSafetyChecker