'''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				:		Union[str, Any]       			=		False
_UpperCamelCase				:		List[Any]       			=		Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_UpperCamelCase				:		Union[str, 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, 20_48), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)

class   _lowercase:





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

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





   def 			snake_case	(							self:    Union[str, Any]			,a:    Optional[int]	):
    __UpperCAmelCase         =					int(file.split('.'	)[0].split('_'	)[-1]	)
    __UpperCAmelCase         =					os.path.join(self.inputdir			,__a	)
    return (img_id, filepath)





   @property
   def 			snake_case	(							self:    List[Any]	):
    __UpperCAmelCase         =					[]
    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:
      __UpperCAmelCase         =					batch
      __UpperCAmelCase         =					[]
      yield list(map(__a			,zip(*__a	)	)	)

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





   def __call__(							self:    Dict	):





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




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


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




'''simple docstring'''




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    AutoencoderKL			,a:    CLIPTextModel			,a:    CLIPTokenizer			,a:    UNetaDConditionModel			,a:    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,):
    super().__init__()

    self.register_modules(vae=a			,text_encoder=a			,tokenizer=a			,unet=a			,scheduler=a	)





   def 			snake_case	(							self:    Union[str, Any]			,a:    Any	):
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.encode(tfms.ToTensor()(a	).unsqueeze(0	).to(self.device	) * 2 - 1	)
     __UpperCAmelCase         =					0.18215 * latent.latent_dist.sample()
    return latent





   def 			snake_case	(							self:    Any			,a:    List[str]	):
    __UpperCAmelCase         =					(1 / 0.18215) * latent
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.decode(a	).sample
    __UpperCAmelCase         =					(img / 2 + 0.5).clamp(0			,1	)
    __UpperCAmelCase         =					img.detach().cpu().permute(0			,2			,3			,1	).numpy()
    __UpperCAmelCase         =					(img * 255).round().astype('uint8'	)
    return Image.fromarray(img[0]	)





   def 			snake_case	(							self:    Any			,a:    str	):
    __UpperCAmelCase         =					self.tokenizer(
        a			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

    __UpperCAmelCase         =					self.tokenizer(
        ''			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

    return torch.cat([uncond_embedding, text_embedding]	)





   def __call__(							self:    Optional[Any]			,a:    Image.Image			,a:    str			,a:    float = 0.3			,a:    float = 0.6			,a:    float = 0.5			,a:    int = 42			,a:    int = 50			,a:    float = 7.5			,):
    __UpperCAmelCase         =					steps - int(kmin * steps	)
    __UpperCAmelCase         =					steps - int(kmax * steps	)

    __UpperCAmelCase         =					self.prep_text(a	)

    self.scheduler.set_timesteps(a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					img.size
    __UpperCAmelCase         =					self.encode(a	)

    torch.manual_seed(a	)
    __UpperCAmelCase         =					torch.randn(
        (1, self.unet.config.in_channels, height // 8, width // 8)			,).to(self.device	)

    __UpperCAmelCase         =					self.scheduler.add_noise(
        a			,a			,timesteps=self.scheduler.timesteps[tmax]			,)

    __UpperCAmelCase         =					torch.cat([latents] * 2	)

    __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,self.scheduler.timesteps[tmax]	)

    with torch.no_grad():
     __UpperCAmelCase         =					self.unet(
         a			,self.scheduler.timesteps[tmax]			,encoder_hidden_states=a			,).sample

    __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
    __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

    __UpperCAmelCase         =					self.scheduler.step(a			,self.scheduler.timesteps[tmax]			,a	).prev_sample

    for i, t in enumerate(tqdm(self.scheduler.timesteps	)	):
     if i > tmax:
      if i < tmin:  # layout generation phase
       __UpperCAmelCase         =					self.scheduler.add_noise(
           a			,a			,timesteps=a			,)

       __UpperCAmelCase         =					(mix_factor * latents) + (
           1 - mix_factor
       ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
       __UpperCAmelCase         =					torch.cat([input] * 2	)

      else:  # content generation phase
       __UpperCAmelCase         =					torch.cat([latents] * 2	)

      __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,a	)

      with torch.no_grad():
       __UpperCAmelCase         =					self.unet(
           a			,a			,encoder_hidden_states=a			,).sample

      __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
      __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __UpperCAmelCase         =					self.scheduler.step(a			,a			,a	).prev_sample

    return self.decode(a	)



