
'''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__		: Tuple  							=							False
a__		: Dict  							=							Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
a__		: Any  							=							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 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]						,					_A	:List[Any]=sys.argv[1:]      )					->      Optional[int]:







       '''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 						lowercase_    (      self	:Union[str, Any]						,					_A	:int      )					->      Tuple:







       '''simple docstring'''



       __A													=      int(file.split('.'      )[0].split('_'      )[-1]      )
       __A													=      os.path.join(self.inputdir						,					_A      )
       return (img_id, filepath)






 @property
 def 						lowercase_    (      self	:List[Any]      )					->      Optional[Any]:







       '''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	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       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 >> 20} mb'      )





def 							snake_case				(		UpperCAmelCase						)->      List[Any]:







      """simple docstring"""
      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__":
 a__		: Any  							=							Extract(sys.argv[1:])
 extract()
 if not TEST:
  a__		: List[str]  							=							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 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  unittest.TestCase):
 UpperCAmelCase__     :     Union[str, Any]    					=       RePaintPipeline
 UpperCAmelCase__     :     Any    					=       IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 UpperCAmelCase__     :     Optional[int]    					=       PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 UpperCAmelCase__     :     Optional[int]    					=       IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase__     :     Any    					=       False






 def 						lowercase_    (      self	:Any      )					->      int:







       '''simple docstring'''



       torch.manual_seed(0      )
       torch.manual_seed(0      )
       __A													=      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')						,					)
       __A													=      RePaintScheduler()
       __A													=      {'unet': unet, 'scheduler': scheduler}
       return components






 def 						lowercase_    (      self	:Any						,					_A	:List[Any]						,					_A	:Tuple=0      )					->      List[str]:







       '''simple docstring'''



       if str(_A      ).startswith('mps'      ):
             __A													=      torch.manual_seed(_A      )
       else:
             __A													=      torch.Generator(device=_A      ).manual_seed(_A      )
       __A													=      np.random.RandomState(_A      ).standard_normal((1, 3, 32, 32)      )
       __A													=      torch.from_numpy(_A      ).to(device=_A						,					dtype=torch.floataa      )
       __A													=      (image > 0).to(device=_A						,					dtype=torch.floataa      )
       __A													=      {
           '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 						lowercase_    (      self	:Union[str, Any]      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      'cpu'  # ensure determinism for the device-dependent torch.Generator
       __A													=      self.get_dummy_components()
       __A													=      RePaintPipeline(**_A      )
       __A													=      sd_pipe.to(_A      )
       sd_pipe.set_progress_bar_config(disable=_A      )

       __A													=      self.get_dummy_inputs(_A      )
       __A													=      sd_pipe(**_A      ).images
       __A													=      image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       __A													=      np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274]      )

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






 @skip_mps
 def 						lowercase_    (      self	:Dict      )					->      Optional[int]:







       '''simple docstring'''



       return super().test_save_load_local()






 @unittest.skip('non-deterministic pipeline'      )
 def 						lowercase_    (      self	:Union[str, Any]      )					->      int:







       '''simple docstring'''



       return super().test_inference_batch_single_identical()






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







       '''simple docstring'''



       return super().test_dict_tuple_outputs_equivalent()






 @skip_mps
 def 						lowercase_    (      self	:List[str]      )					->      int:







       '''simple docstring'''



       return super().test_save_load_optional_components()






 @skip_mps
 def 						lowercase_    (      self	:Optional[int]      )					->      Optional[Any]:







       '''simple docstring'''



       return super().test_attention_slicing_forward_pass()


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






 def 						lowercase_    (      self	:Optional[int]      )					->      int:







       '''simple docstring'''



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






 def 						lowercase_    (      self	:Dict      )					->      Optional[Any]:







       '''simple docstring'''



       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256.png'      )
       __A													=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'      )
       __A													=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
           'repaint/celeba_hq_256_result.npy'      )

       __A													=      'google/ddpm-ema-celebahq-256'
       __A													=      UNetaDModel.from_pretrained(_A      )
       __A													=      RePaintScheduler.from_pretrained(_A      )

       __A													=      RePaintPipeline(unet=_A						,					scheduler=_A      ).to(_A      )
       repaint.set_progress_bar_config(disable=_A      )
       repaint.enable_attention_slicing()

       __A													=      torch.manual_seed(0      )
       __A													=      repaint(
           _A						,					_A						,					num_inference_steps=250						,					eta=0.0						,					jump_length=10						,					jump_n_sample=10						,					generator=_A						,					output_type='np'						,					)
       __A													=      output.images[0]

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






