




"""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, 2_048), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)
class 			lowerCamelCase__    :


			def __init__(     self				, SCREAMING_SNAKE_CASE=sys.argv[1:]							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				None
					snake_case		:						List[Any]								=				None
					snake_case		:						Any								=				None
					snake_case		:						int								=				1
					snake_case		:						Optional[Any]								=				getopt.getopt(lowerCamelCase__				, "i:o:b:s"				, ["inputdir=", "outfile=", "batch_size=", "subset_list="]							)
					for opt, arg in opts:
							if opt in ("-i", "--inputdir"):
									snake_case		:						Union[str, Any]								=				arg
							elif opt in ("-o", "--outfile"):
									snake_case		:						Tuple								=				arg
							elif opt in ("-b", "--batch_size"):
									snake_case		:						Any								=				int(lowerCamelCase__							)
							elif opt in ("-s", "--subset_list"):
									snake_case		:						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(lowerCamelCase__							), F'''{outputfile}'''
					if subset_list is not None:
							with open(os.path.realpath(lowerCamelCase__							)							) as f:
									snake_case		:						Optional[int]								=				{self._vqa_file_split()[0] for x in tryload(lowerCamelCase__							)}
					else:
							snake_case		:						Optional[Any]								=				None

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


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				[]
					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(lowerCamelCase__							)							)
							if len(lowerCamelCase__							) == self.batch:
									snake_case		:						str								=				batch
									snake_case		:						Tuple								=				[]
									yield list(map(lowerCamelCase__				, zip(*lowerCamelCase__							)							)							)

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



			def __call__(     self							):



					"""simple docstring"""







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







def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
		try:
				snake_case		:						Dict								=				json.load(lowercase__   )
				try:
						snake_case		:						List[Any]								=				list(data.keys()   )
				except Exception:
						snake_case		:						List[Any]								=				[d["img_id"] for d in data]
		except Exception:
				try:
						snake_case		:						Optional[Any]								=				eval(stream.read()   )
				except Exception:
						snake_case		:						Any								=				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 gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 			lowerCamelCase__    (  lowerCamelCase_				,  unittest.TestCase			):
			a__ :      Optional[Any]								=     RePaintPipeline
			a__ :      List[Any]								=     IMAGE_INPAINTING_PARAMS - {"""width""", """height""", """guidance_scale"""}
			a__ :      List[Any]								=     PipelineTesterMixin.required_optional_params - {
			    """latents""",
			    """num_images_per_prompt""",
			    """callback""",
			    """callback_steps""",
			}
			a__ :      List[Any]								=     IMAGE_INPAINTING_BATCH_PARAMS
			a__ :      List[str]								=     False


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					torch.manual_seed(0							)
					torch.manual_seed(0							)
					snake_case		:						Union[str, Any]								=				UNetaDModel(
					    block_out_channels=(32, 64)				, layers_per_block=2				, sample_size=32				, in_channels=3				, out_channels=3				, down_block_types=("DownBlock2D", "AttnDownBlock2D")				, up_block_types=("AttnUpBlock2D", "UpBlock2D")				, )
					snake_case		:						Union[str, Any]								=				RePaintScheduler()
					snake_case		:						Optional[Any]								=				{"unet": unet, "scheduler": scheduler}
					return components


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=0							):



					"""simple docstring"""







					if str(SCREAMING_SNAKE_CASE							).startswith("mps"							):
							snake_case		:						str								=				torch.manual_seed(SCREAMING_SNAKE_CASE							)
					else:
							snake_case		:						List[Any]								=				torch.Generator(device=SCREAMING_SNAKE_CASE							).manual_seed(SCREAMING_SNAKE_CASE							)
					snake_case		:						Any								=				np.random.RandomState(SCREAMING_SNAKE_CASE							).standard_normal((1, 3, 32, 32)							)
					snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
					snake_case		:						List[str]								=				(image > 0).to(device=SCREAMING_SNAKE_CASE				, dtype=torch.floataa							)
					snake_case		:						int								=				{
					    "image": image,
					    "mask_image": mask,
					    "generator": generator,
					    "num_inference_steps": 5,
					    "eta": 0.0,
					    "jump_length": 2,
					    "jump_n_sample": 2,
					    "output_type": "numpy",
					}
					return inputs


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						List[Any]								=				"cpu"  # ensure determinism for the device-dependent torch.Generator
					snake_case		:						Any								=				self.get_dummy_components()
					snake_case		:						Optional[int]								=				RePaintPipeline(**SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[Any]								=				sd_pipe.to(SCREAMING_SNAKE_CASE							)
					sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				self.get_dummy_inputs(SCREAMING_SNAKE_CASE							)
					snake_case		:						Dict								=				sd_pipe(**SCREAMING_SNAKE_CASE							).images
					snake_case		:						Optional[int]								=				image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					snake_case		:						List[str]								=				np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]							)

					assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3


			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_save_load_local()


			@unittest.skip("non-deterministic pipeline"							)
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_inference_batch_single_identical()


			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_dict_tuple_outputs_equivalent()


			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_save_load_optional_components()



			@skip_mps
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return super().test_attention_slicing_forward_pass()







@nightly
@require_torch_gpu
class 			lowerCamelCase__    (  unittest.TestCase			):


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()



			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
					    "repaint/celeba_hq_256.png"							)
					snake_case		:						Any								=				load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"							)
					snake_case		:						List[Any]								=				load_numpy(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/"
					    "repaint/celeba_hq_256_result.npy"							)

					snake_case		:						str								=				"google/ddpm-ema-celebahq-256"
					snake_case		:						Optional[Any]								=				UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE							)

					snake_case		:						str								=				RePaintPipeline(unet=SCREAMING_SNAKE_CASE				, scheduler=SCREAMING_SNAKE_CASE							).to(SCREAMING_SNAKE_CASE							)
					repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE							)
					repaint.enable_attention_slicing()

					snake_case		:						Union[str, Any]								=				torch.manual_seed(0							)
					snake_case		:						int								=				repaint(
					    SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, num_inference_steps=250				, eta=0.0				, jump_length=10				, jump_n_sample=10				, generator=SCREAMING_SNAKE_CASE				, output_type="np"				, )
					snake_case		:						List[Any]								=				output.images[0]

					assert image.shape == (256, 256, 3)
					assert np.abs(expected_image - image							).mean() < 1E-2

