


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCamelCase		=	{512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Optional[int]					  =      f'pretrained_models/{model_name}'
		if not os.path.isfile(lowercase__ ):
				os.makedirs('''pretrained_models'''			,     exist_ok=lowercase__ )
				lowerCAmelCase__     :    Tuple					  =      f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
				download_url(lowercase__			,     '''pretrained_models''' )
		lowerCAmelCase__     :    Tuple					  =      torch.load(lowercase__			,     map_location=lambda _a			,     _a : storage )
		return model






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    List[Any]					  =      download_model(pretrained_models[args.image_size] )

		lowerCAmelCase__     :    Any					  =      state_dict['''x_embedder.proj.weight''']
		lowerCAmelCase__     :    List[str]					  =      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__     :    Dict					  =      state_dict[
				    '''t_embedder.mlp.0.weight'''
				]
				lowerCAmelCase__     :    Dict					  =      state_dict[
				    '''t_embedder.mlp.0.bias'''
				]
				lowerCAmelCase__     :    Optional[Any]					  =      state_dict[
				    '''t_embedder.mlp.2.weight'''
				]
				lowerCAmelCase__     :    Dict					  =      state_dict[
				    '''t_embedder.mlp.2.bias'''
				]
				lowerCAmelCase__     :    Dict					  =      state_dict[
				    '''y_embedder.embedding_table.weight'''
				]

				lowerCAmelCase__     :    Optional[int]					  =      state_dict[
				    f'blocks.{depth}.adaLN_modulation.1.weight'
				]
				lowerCAmelCase__     :    Any					  =      state_dict[
				    f'blocks.{depth}.adaLN_modulation.1.bias'
				]

				lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     :    str					  =      torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']			,     3			,     dim=0 )
				lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     :    List[str]					  =      torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']			,     3			,     dim=0 )

				lowerCAmelCase__     :    List[str]					  =      q
				lowerCAmelCase__     :    Union[str, Any]					  =      q_bias
				lowerCAmelCase__     :    Tuple					  =      k
				lowerCAmelCase__     :    str					  =      k_bias
				lowerCAmelCase__     :    List[str]					  =      v
				lowerCAmelCase__     :    Dict					  =      v_bias

				lowerCAmelCase__     :    Union[str, Any]					  =      state_dict[
				    f'blocks.{depth}.attn.proj.weight'
				]
				lowerCAmelCase__     :    List[Any]					  =      state_dict[f'blocks.{depth}.attn.proj.bias']

				lowerCAmelCase__     :    Dict					  =      state_dict[f'blocks.{depth}.mlp.fc1.weight']
				lowerCAmelCase__     :    Any					  =      state_dict[f'blocks.{depth}.mlp.fc1.bias']
				lowerCAmelCase__     :    int					  =      state_dict[f'blocks.{depth}.mlp.fc2.weight']
				lowerCAmelCase__     :    Optional[int]					  =      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__     :    Optional[Any]					  =      state_dict['''final_layer.adaLN_modulation.1.weight''']
		lowerCAmelCase__     :    Tuple					  =      state_dict['''final_layer.adaLN_modulation.1.bias''']
		lowerCAmelCase__     :    Union[str, Any]					  =      state_dict['''final_layer.linear.weight''']
		lowerCAmelCase__     :    List[str]					  =      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__     :    Union[str, Any]					  =      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=1_000			,     norm_type='''ada_norm_zero'''			,     norm_elementwise_affine=lowercase__			,     )
		transformer.load_state_dict(lowercase__			,     strict=lowercase__ )

		lowerCAmelCase__     :    Tuple					  =      DDIMScheduler(
		    num_train_timesteps=1_000			,     beta_schedule='''linear'''			,     prediction_type='''epsilon'''			,     clip_sample=lowercase__			,     )

		lowerCAmelCase__     :    Tuple					  =      AutoencoderKL.from_pretrained(args.vae_model )

		lowerCAmelCase__     :    Dict					  =      DiTPipeline(transformer=lowercase__			,     vae=lowercase__			,     scheduler=lowercase__ )

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


if __name__ == "__main__":
					lowerCamelCase		=	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.'''
					)

					lowerCamelCase		=	parser.parse_args()
					main(args)








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




	def __init__(      self		:	str	,      _SCREAMING_SNAKE_CASE		:	Dict=sys.argv[1:]	)-> str:
			lowerCAmelCase__     :    int					  =      None
			lowerCAmelCase__     :    Optional[int]					  =      None
			lowerCAmelCase__     :    Union[str, Any]					  =      None
			lowerCAmelCase__     :    Union[str, Any]					  =      1
			lowerCAmelCase__  ,			lowerCAmelCase__     :    List[str]					  =      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"):
							lowerCAmelCase__     :    List[Any]					  =      arg
					elif opt in ("-o", "--outfile"):
							lowerCAmelCase__     :    Optional[Any]					  =      arg
					elif opt in ("-b", "--batch_size"):
							lowerCAmelCase__     :    Tuple					  =      int(_SCREAMING_SNAKE_CASE	)
					elif opt in ("-s", "--subset_list"):
							lowerCAmelCase__     :    str					  =      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:
							lowerCAmelCase__     :    Union[str, Any]					  =      {self._vqa_file_split()[0] for x in tryload(_SCREAMING_SNAKE_CASE	)}
			else:
					lowerCAmelCase__     :    int					  =      None

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




	def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	)-> Optional[Any]:
			lowerCAmelCase__     :    Optional[Any]					  =      int(file.split('''.'''	)[0].split('''_'''	)[-1]	)
			lowerCAmelCase__     :    List[Any]					  =      os.path.join(self.inputdir	,      _SCREAMING_SNAKE_CASE	)
			return (img_id, filepath)




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> Tuple:
			lowerCAmelCase__     :    Any					  =      []
			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:
							lowerCAmelCase__     :    Optional[int]					  =      batch
							lowerCAmelCase__     :    Union[str, 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		:	int	)-> Dict:
			# make writer
			if not TEST:
					lowerCAmelCase__     :    Union[str, Any]					  =      datasets.ArrowWriter(features=self.schema	,      path=self.outputfile	)
			# do file generator
			for i, (img_ids, filepaths) in enumerate(self.file_generator	):
					lowerCAmelCase__  ,			lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[Any]					  =      self.preprocess(_SCREAMING_SNAKE_CASE	)
					lowerCAmelCase__     :    List[Any]					  =      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'''	,      )
					lowerCAmelCase__     :    Any					  =      output_dict.pop('''normalized_boxes'''	)
					if not TEST:
							lowerCAmelCase__     :    Optional[int]					  =      np.array(_SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__     :    str					  =      self.schema.encode_batch(_SCREAMING_SNAKE_CASE	)
							writer.write_batch(_SCREAMING_SNAKE_CASE	)
					if TEST:
							break
					# finalizer the writer
			if not TEST:
					lowerCAmelCase__  ,			lowerCAmelCase__     :    Optional[int]					  =      writer.finalize()
					print(F'Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'	)






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		try:
				lowerCAmelCase__     :    Any					  =      json.load(_a )
				try:
						lowerCAmelCase__     :    Tuple					  =      list(data.keys() )
				except Exception:
						lowerCAmelCase__     :    List[str]					  =      [d['''img_id'''] for d in data]
		except Exception:
				try:
						lowerCAmelCase__     :    int					  =      eval(stream.read() )
				except Exception:
						lowerCAmelCase__     :    Tuple					  =      stream.read().split('''\n''' )
		return data


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




