
"""simple docstring"""


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
A_						       =				False
A_						       =				Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
A_						       =				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    __lowerCamelCase     :




				def __init__(							self       ,	UpperCAmelCase=sys.argv[1:]      ):
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     None
							lowerCamelCase_ 		=     1
							lowerCamelCase_		,   lowerCamelCase_ 		=     getopt.getopt(UpperCAmelCase       ,	'''i:o:b:s'''       ,	['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']      )
							for opt, arg in opts:
										if opt in ("-i", "--inputdir"):
													lowerCamelCase_ 		=     arg
										elif opt in ("-o", "--outfile"):
													lowerCamelCase_ 		=     arg
										elif opt in ("-b", "--batch_size"):
													lowerCamelCase_ 		=     int(UpperCAmelCase      )
										elif opt in ("-s", "--subset_list"):
													lowerCamelCase_ 		=     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_ 		=     {self._vqa_file_split()[0] for x in tryload(UpperCAmelCase      )}
							else:
										lowerCamelCase_ 		=     None

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




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     int(file.split('''.'''      )[0].split('''_'''      )[-1]      )
							lowerCamelCase_ 		=     os.path.join(self.inputdir       ,	UpperCAmelCase      )
							return (img_id, filepath)




				@property
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     []
							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_ 		=     batch
													lowerCamelCase_ 		=     []
													yield list(map(UpperCAmelCase       ,	zip(*UpperCAmelCase      )      )      )

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





				def __call__(							self      ):
							# make writer
							if not TEST:
										lowerCamelCase_ 		=     datasets.ArrowWriter(features=self.schema       ,	path=self.outputfile      )
							# do file generator
							for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
										lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     self.preprocess(UpperCAmelCase      )
										lowerCamelCase_ 		=     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_ 		=     output_dict.pop('''normalized_boxes'''      )
										if not TEST:
													lowerCamelCase_ 		=     np.array(UpperCAmelCase      )
													lowerCamelCase_ 		=     self.schema.encode_batch(UpperCAmelCase      )
													writer.write_batch(UpperCAmelCase      )
										if TEST:
													break
										# finalizer the writer
							if not TEST:
										lowerCamelCase_		,   lowerCamelCase_ 		=     writer.finalize()
										print(f"Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"      )






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


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





"""simple docstring"""


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    __lowerCamelCase     (   unittest.TestCase ):
				a__:			Union[str, Any]									=						'hf-internal-testing/tiny-stable-diffusion-pipe'
				a__:			Tuple									=						1
				a__:			int									=						77
				a__:			List[str]									=						32
				a__:			List[Any]									=						4
				a__:			str									=						64
				a__:			Optional[Any]									=						torch.manual_seed(0 )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase=0.9_9_9_9      ):
							lowerCamelCase_ 		=     UNetaDConditionModel.from_pretrained(self.model_id       ,	subfolder='''unet'''      )
							lowerCamelCase_ 		=     unet.to(UpperCAmelCase      )
							lowerCamelCase_ 		=     EMAModel(unet.parameters()       ,	decay=UpperCAmelCase       ,	model_cls=UpperCAmelCase       ,	model_config=unet.config      )
							return unet, ema_unet




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     torch.randn(
							    self.batch_size       ,	self.num_in_channels       ,	self.latent_height       ,	self.latent_width       ,	generator=self.generator      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     torch.randint(0       ,	1000       ,	size=(self.batch_size,)       ,	generator=self.generator      ).to(UpperCAmelCase      )
							lowerCamelCase_ 		=     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 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     {}
							for k, param in unet.state_dict().items():
										lowerCamelCase_ 		=     torch.randn_like(UpperCAmelCase      ) + (param * torch.randn_like(UpperCAmelCase      ))
										updated_state_dict.update({k: updated_param}      )
							unet.load_state_dict(UpperCAmelCase      )
							return unet




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     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_ 		=     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 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     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_ 		=     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_ 		=     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 					UpperCAmelCase__	(							self      ):
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()

							# First backprop + EMA
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_one.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							# Second backprop + EMA
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_two.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

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




				def 					UpperCAmelCase__	(							self      ):
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models(decay=0.0      )
							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_one.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

							lowerCamelCase_ 		=     self.simulate_backprop(UpperCAmelCase      )
							ema_unet.step(unet_step_two.parameters()      )
							lowerCamelCase_ 		=     ema_unet.shadow_params

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





				@skip_mps
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_models()
							lowerCamelCase_		,   lowerCamelCase_		,   lowerCamelCase_ 		=     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(UpperCAmelCase      )
										lowerCamelCase_ 		=     UNetaDConditionModel.from_pretrained(UpperCAmelCase       ,	model_cls=UpperCAmelCase      )
										lowerCamelCase_ 		=     loaded_unet.to(unet.device      )

							# Since no EMA step has been performed the outputs should match.
							lowerCamelCase_ 		=     unet(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ).sample
							lowerCamelCase_ 		=     loaded_unet(UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ).sample

							assert torch.allclose(UpperCAmelCase       ,	UpperCAmelCase       ,	atol=1e-4      )



