



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 		__SCREAMING_SNAKE_CASE      (				lowercase__					,			unittest.TestCase      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  PaintByExamplePipeline
				SCREAMING_SNAKE_CASE_ 				=  IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
				SCREAMING_SNAKE_CASE_ 				=  IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_ 				=  frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					torch.manual_seed(0      )
					_snake_case					:							Optional[Any]					     =   UNetaDConditionModel(
					    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=9						,	out_channels=4						,	down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,	up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,	cross_attention_dim=32						,	)
					_snake_case					:							str					     =   PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE__      )
					torch.manual_seed(0      )
					_snake_case					:							str					     =   AutoencoderKL(
					    block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=4						,	)
					torch.manual_seed(0      )
					_snake_case					:							List[Any]					     =   CLIPVisionConfig(
					    hidden_size=32						,	projection_dim=32						,	intermediate_size=37						,	layer_norm_eps=1e-05						,	num_attention_heads=4						,	num_hidden_layers=5						,	image_size=32						,	patch_size=4						,	)
					_snake_case					:							List[str]					     =   PaintByExampleImageEncoder(SCREAMING_SNAKE_CASE__						,	proj_size=32      )
					_snake_case					:							Dict					     =   CLIPImageProcessor(crop_size=32						,	size=32      )

					_snake_case					:							Union[str, Any]					     =   {
					    """unet""": unet,
					    """scheduler""": scheduler,
					    """vae""": vae,
					    """image_encoder""": image_encoder,
					    """safety_checker""": None,
					    """feature_extractor""": feature_extractor,
					}
					return components







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   np.array(image.convert("""RGB"""      )      )
					_snake_case					:							int					     =   image[None].transpose(0						,	3						,	1						,	2      )
					_snake_case					:							int					     =   torch.from_numpy(SCREAMING_SNAKE_CASE__      ).to(dtype=torch.floataa      ) / 127.5 - 1.0
					return image







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__="cpu"						,	SCREAMING_SNAKE_CASE__=0      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   floats_tensor((1, 3, 32, 32)						,	rng=random.Random(SCREAMING_SNAKE_CASE__      )      ).to(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   image.cpu().permute(0						,	2						,	3						,	1      )[0]
					_snake_case					:							List[str]					     =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__      )      ).convert("""RGB"""      ).resize((64, 64)      )
					_snake_case					:							List[str]					     =   Image.fromarray(np.uinta(image + 4      )      ).convert("""RGB"""      ).resize((64, 64)      )
					_snake_case					:							List[Any]					     =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__      )      ).convert("""RGB"""      ).resize((32, 32)      )

					if str(SCREAMING_SNAKE_CASE__      ).startswith("""mps"""      ):
						_snake_case					:							Optional[int]					     =   torch.manual_seed(SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Optional[int]					     =   torch.Generator(device=SCREAMING_SNAKE_CASE__      ).manual_seed(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[Any]					     =   {
					    """example_image""": example_image,
					    """image""": init_image,
					    """mask_image""": mask_image,
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 6.0,
					    """output_type""": """numpy""",
					}
					return inputs







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							List[Any]					     =   self.get_dummy_components()

					# make sure here that pndm scheduler skips prk
					_snake_case					:							Any					     =   PaintByExamplePipeline(**SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   pipe.to("""cpu"""      )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Dict					     =   self.get_dummy_inputs()
					_snake_case					:							Optional[int]					     =   pipe(**SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[Any]					     =   output.images

					_snake_case					:							List[str]					     =   image[0, -3:, -3:, -1]

					assert image.shape == (1, 64, 64, 3)
					_snake_case					:							List[Any]					     =   np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]      )

					assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   """cpu"""
					_snake_case					:							List[Any]					     =   self.get_dummy_inputs()
					inputs.pop("""mask_image"""      )
					_snake_case					:							List[str]					     =   self.convert_to_pt(inputs.pop("""image"""      )      )
					_snake_case					:							List[Any]					     =   image.clamp(0						,	1      ) / 2

					# make sure here that pndm scheduler skips prk
					_snake_case					:							List[Any]					     =   PaintByExamplePipeline(**self.get_dummy_components()      )
					_snake_case					:							List[str]					     =   pipe.to(SCREAMING_SNAKE_CASE__      )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Tuple					     =   pipe(image=SCREAMING_SNAKE_CASE__						,	mask_image=mask_image[:, 0]						,	**SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Any					     =   output.images

					_snake_case					:							Optional[Any]					     =   image.cpu().permute(0						,	2						,	3						,	1      )[0]
					_snake_case					:							Optional[int]					     =   mask_image.cpu().permute(0						,	2						,	3						,	1      )[0]

					_snake_case					:							int					     =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__      )      ).convert("""RGB"""      )
					_snake_case					:							Union[str, Any]					     =   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE__      )      ).convert("""RGB"""      )

					_snake_case					:							Any					     =   pipe(**self.get_dummy_inputs()      )
					_snake_case					:							Tuple					     =   output.images

					assert out_a.shape == (1, 64, 64, 3)
					assert np.abs(out_a.flatten() - out_a.flatten()      ).max() < 5e-2







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





@slow
@require_torch_gpu
class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/paint_by_example/dog_in_bucket.png"""      )
					_snake_case					:							Union[str, Any]					     =   load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/paint_by_example/mask.png"""      )
					_snake_case					:							Optional[int]					     =   load_image(
					    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
					    """/paint_by_example/panda.jpg"""      )

					_snake_case					:							Dict					     =   PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""      )
					_snake_case					:							Union[str, Any]					     =   pipe.to(SCREAMING_SNAKE_CASE__      )
					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							int					     =   torch.manual_seed(3_21      )
					_snake_case					:							Any					     =   pipe(
					    image=SCREAMING_SNAKE_CASE__						,	mask_image=SCREAMING_SNAKE_CASE__						,	example_image=SCREAMING_SNAKE_CASE__						,	generator=SCREAMING_SNAKE_CASE__						,	guidance_scale=5.0						,	num_inference_steps=50						,	output_type="""np"""						,	)

					_snake_case					:							int					     =   output.images

					_snake_case					:							Optional[int]					     =   image[0, -3:, -3:, -1]

					assert image.shape == (1, 5_12, 5_12, 3)
					_snake_case					:							str					     =   np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]      )

					assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2










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
UpperCAmelCase_			      =  False
UpperCAmelCase_			      =  Config.from_pretrained('''unc-nlp/frcnn-vg-finetuned''')
UpperCAmelCase_			      =  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 		__SCREAMING_SNAKE_CASE      :






				"""simple docstring"""

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






					"""simple docstring"""

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

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							List[str]					     =   int(file.split("""."""      )[0].split("""_"""      )[-1]      )
					_snake_case					:							Optional[int]					     =   os.path.join(self.inputdir						,	SCREAMING_SNAKE_CASE__      )
					return (img_id, filepath)







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							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(SCREAMING_SNAKE_CASE__      )      )
						if len(SCREAMING_SNAKE_CASE__      ) == self.batch:
							_snake_case					:							Union[str, Any]					     =   batch
							_snake_case					:							Optional[Any]					     =   []
							yield list(map(SCREAMING_SNAKE_CASE__						,	zip(*SCREAMING_SNAKE_CASE__      )      )      )

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







				def __call__(				self      ):






					"""simple docstring"""

					if not TEST:
						_snake_case					:							Optional[Any]					     =   datasets.ArrowWriter(features=self.schema						,	path=self.outputfile      )
					# do file generator
					for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
						_snake_case     , _snake_case     , _snake_case					:							List[Any]					     =   self.preprocess(SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Tuple					     =   self.model(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	scales_yx=SCREAMING_SNAKE_CASE__						,	padding="""max_detections"""						,	max_detections=self.config.MAX_DETECTIONS						,	pad_value=0						,	return_tensors="""np"""						,	location="""cpu"""						,	)
						_snake_case					:							Dict					     =   output_dict.pop("""normalized_boxes"""      )
						if not TEST:
							_snake_case					:							List[str]					     =   np.array(SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   self.schema.encode_batch(SCREAMING_SNAKE_CASE__      )
							writer.write_batch(SCREAMING_SNAKE_CASE__      )
						if TEST:
							break
						# finalizer the writer
					if not TEST:
						_snake_case     , _snake_case					:							Optional[int]					     =   writer.finalize()
						print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''      )
def   UpperCAmelCase							(		A__     )							->					List[str]:
	try:
		_snake_case					:							Optional[int]					     =   json.load(A__     )
		try:
			_snake_case					:							Union[str, Any]					     =   list(data.keys()     )
		except Exception:
			_snake_case					:							Optional[int]					     =   [d["""img_id"""] for d in data]
	except Exception:
		try:
			_snake_case					:							List[str]					     =   eval(stream.read()     )
		except Exception:
			_snake_case					:							Any					     =   stream.read().split("""\n"""     )
	return data


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





