




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)







import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()
class 				__snake_case     (unittest.TestCase						):
				lowerCAmelCase__			=   "hf-internal-testing/tiny-stable-diffusion-pipe"
				lowerCAmelCase__			=   1
				lowerCAmelCase__			=   7_7
				lowerCAmelCase__			=   3_2
				lowerCAmelCase__			=   4
				lowerCAmelCase__			=   lowerCAmelCase__			=   6_4
				lowerCAmelCase__			=   torch.manual_seed(0						)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						Optional[Any]=0.9999					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					UNetaDConditionModel.from_pretrained(self.model_id						,		subfolder="""unet"""					)
								_lowerCAmelCase			:			Optional[Any]        =					unet.to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					EMAModel(unet.parameters()						,		decay=_UpperCAmelCase						,		model_cls=_UpperCAmelCase						,		model_config=unet.config					)
								return unet, ema_unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.num_in_channels						,		self.latent_height						,		self.latent_width						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					torch.randint(0						,		1000						,		size=(self.batch_size,)						,		generator=self.generator					).to(_UpperCAmelCase					)
								_lowerCAmelCase			:			Any        =					torch.randn(
								    self.batch_size						,		self.prompt_length						,		self.text_encoder_hidden_dim						,		generator=self.generator					).to(_UpperCAmelCase					)
								return noisy_latents, timesteps, encoder_hidden_states



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						Dict					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					{}
								for k, param in unet.state_dict().items():
												_lowerCAmelCase			:			List[Any]        =					torch.randn_like(_UpperCAmelCase					) + (param * torch.randn_like(_UpperCAmelCase					))
												updated_state_dict.update({k: updated_param}					)
								unet.load_state_dict(_UpperCAmelCase					)
								return unet



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					self.get_models()
								# Take the first (hypothetical) EMA step.
								ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 1

								# Take two more.
								for _ in range(2					):
												ema_unet.step(unet.parameters()					)
								assert ema_unet.optimization_step == 3



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					self.get_models()
								# Since the `unet` is not being updated (i.e., backprop'd)
								# there won't be any difference between the `params` of `unet`
								# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
								ema_unet.step(unet.parameters()					)
								_lowerCAmelCase			:			Optional[int]        =					list(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# The above holds true even if we call `ema.step()` multiple times since
								# `unet` params are still not being updated.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



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





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.get_models()
								# Here we simulate the parameter updates for `unet`. Since there might
								# be some parameters which are initialized to zero we take extra care to
								# initialize their values to something non-zero before the multiplication.
								_lowerCAmelCase			:			str        =					self.simulate_backprop(_UpperCAmelCase					)

								# Take the EMA step.
								ema_unet.step(unet_pseudo_updated_step_one.parameters()					)

								# Now the EMA'd parameters won't be equal to the original model parameters.
								_lowerCAmelCase			:			Tuple        =					list(unet_pseudo_updated_step_one.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)

								# Ensure this is the case when we take multiple EMA steps.
								for _ in range(4					):
												ema_unet.step(unet.parameters()					)
								for s_param, param in zip(ema_unet.shadow_params						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			str        =					self.get_models()

								# First backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			List[str]        =					ema_unet.shadow_params

								# Second backprop + EMA
								_lowerCAmelCase			:			Optional[int]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			Tuple        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert ~torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



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





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			int        =					self.get_models(decay=0.0					)
								_lowerCAmelCase			:			Dict        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_one.parameters()					)
								_lowerCAmelCase			:			Any        =					ema_unet.shadow_params

								_lowerCAmelCase			:			List[Any]        =					self.simulate_backprop(_UpperCAmelCase					)
								ema_unet.step(unet_step_two.parameters()					)
								_lowerCAmelCase			:			int        =					ema_unet.shadow_params

								for step_one, step_two in zip(_UpperCAmelCase						,		_UpperCAmelCase					):
												assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase					)



				@skip_mps
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_models()
								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Tuple        =					self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdir:
												ema_unet.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			Union[str, Any]        =					UNetaDConditionModel.from_pretrained(_UpperCAmelCase						,		model_cls=_UpperCAmelCase					)
												_lowerCAmelCase			:			Any        =					loaded_unet.to(unet.device					)

								# Since no EMA step has been performed the outputs should match.
								_lowerCAmelCase			:			str        =					unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample
								_lowerCAmelCase			:			Union[str, Any]        =					loaded_unet(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					).sample

								assert torch.allclose(_UpperCAmelCase						,		_UpperCAmelCase						,		atol=1E-4					)

