






"""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
_UpperCamelCase						:    Optional[int]		=						False
_UpperCamelCase						:    List[str]		=						Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCamelCase						:    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 			a	:







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

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







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







    @property
    def      UpperCamelCase_ (     self			):
      lowercase				       =			[]
      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:
          lowercase				       =			batch
          lowercase				       =			[]
          yield list(map(_lowerCamelCase				,						zip(*_lowerCamelCase			)			)			)

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







    def __call__(     self			):
      # make writer
      if not TEST:
        lowercase				       =			datasets.ArrowWriter(features=self.schema				,						path=self.outputfile			)
      # do file generator
      for i, (img_ids, filepaths) in enumerate(self.file_generator			):
        lowercase       ,	lowercase       ,	lowercase				       =			self.preprocess(_lowerCamelCase			)
        lowercase				       =			self.model(
            _lowerCamelCase				,						_lowerCamelCase				,						scales_yx=_lowerCamelCase				,						padding='max_detections'				,						max_detections=self.config.MAX_DETECTIONS				,						pad_value=0				,						return_tensors='np'				,						location='cpu'				,						)
        lowercase				       =			output_dict.pop('normalized_boxes'			)
        if not TEST:
          lowercase				       =			np.array(_lowerCamelCase			)
          lowercase				       =			self.schema.encode_batch(_lowerCamelCase			)
          writer.write_batch(_lowerCamelCase			)
        if TEST:
          break
        # finalizer the writer
      if not TEST:
        lowercase       ,	lowercase				       =			writer.finalize()
        print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'			)


def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str       ):





  '''simple docstring'''





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


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









import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					F'''pretrained_models/{model_name}'''
  if not os.path.isfile(__lowercase):
    os.makedirs('''pretrained_models'''  ,				exist_ok=__lowercase)
    __lowerCAmelCase								=					F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
    download_url(__lowercase  ,				'''pretrained_models''')
  __lowerCAmelCase								=					torch.load(__lowercase  ,				map_location=lambda __lowercase  ,				__lowercase: storage)
  return model


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

  __lowerCAmelCase								=					state_dict['''x_embedder.proj.weight''']
  __lowerCAmelCase								=					state_dict['''x_embedder.proj.bias''']
  state_dict.pop('''x_embedder.proj.weight''')
  state_dict.pop('''x_embedder.proj.bias''')

  for depth in range(28):
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']  ,				3  ,				dim=0)
    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']  ,				3  ,				dim=0)

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.bias''']

    state_dict.pop(F'''blocks.{depth}.attn.qkv.weight''')
    state_dict.pop(F'''blocks.{depth}.attn.qkv.bias''')
    state_dict.pop(F'''blocks.{depth}.attn.proj.weight''')
    state_dict.pop(F'''blocks.{depth}.attn.proj.bias''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc1.weight''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc1.bias''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc2.weight''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc2.bias''')
    state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.weight''')
    state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.bias''')

  state_dict.pop('''t_embedder.mlp.0.weight''')
  state_dict.pop('''t_embedder.mlp.0.bias''')
  state_dict.pop('''t_embedder.mlp.2.weight''')
  state_dict.pop('''t_embedder.mlp.2.bias''')
  state_dict.pop('''y_embedder.embedding_table.weight''')

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.bias''']

  state_dict.pop('''final_layer.linear.weight''')
  state_dict.pop('''final_layer.linear.bias''')
  state_dict.pop('''final_layer.adaLN_modulation.1.weight''')
  state_dict.pop('''final_layer.adaLN_modulation.1.bias''')

  # DiT XL/2
  __lowerCAmelCase								=					TransformeraDModel(
      sample_size=args.image_size // 8  ,				num_layers=28  ,				attention_head_dim=72  ,				in_channels=4  ,				out_channels=8  ,				patch_size=2  ,				attention_bias=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

  if args.save:
    pipeline.save_pretrained(args.checkpoint_path)


if __name__ == "__main__":
      __lowercase				    =      argparse.ArgumentParser()

      parser.add_argument(
          """--image_size""",
          default=256,
          type=int,
          required=False,
          help="""Image size of pretrained model, either 256 or 512.""",
      )
      parser.add_argument(
          """--vae_model""",
          default="""stabilityai/sd-vae-ft-ema""",
          type=str,
          required=False,
          help="""Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.""",
      )
      parser.add_argument(
          """--save""", default=True, type=bool, required=False, help="""Whether to save the converted pipeline or not."""
      )
      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output pipeline."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
