
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
lowercase_											=	False
lowercase_											=	Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowercase_											=	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   __UpperCamelCase				:






     """simple docstring"""







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







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						None
       __SCREAMING_SNAKE_CASE     :					Optional[int]			=						1
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						getopt.getopt(_A      ,		'''i:o:b:s'''      ,		['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']				)
       for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						arg
         elif opt in ("-o", "--outfile"):
           __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						arg
         elif opt in ("-b", "--batch_size"):
           __SCREAMING_SNAKE_CASE     :					str			=						int(_A				)
         elif opt in ("-s", "--subset_list"):
           __SCREAMING_SNAKE_CASE     :					int			=						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:
           __SCREAMING_SNAKE_CASE     :					Dict			=						{self._vqa_file_split()[0] for x in tryload(_A				)}
       else:
         __SCREAMING_SNAKE_CASE     :					Tuple			=						None

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



     def 				UpperCAmelCase__    (							self    :    int      ,		_A    :    Optional[int]				):







       """simple docstring"""




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



     @property
     def 				UpperCAmelCase__    (							self    :    str				):







       """simple docstring"""




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

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




     def __call__(							self    :    Optional[Any]				):







       """simple docstring"""




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






def     a__			(    snake_case    ):





  """simple docstring"""
  try:
    __SCREAMING_SNAKE_CASE     :					Any			=						json.load(snake_case    )
    try:
      __SCREAMING_SNAKE_CASE     :					int			=						list(data.keys()    )
    except Exception:
      __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						[d['''img_id'''] for d in data]
  except Exception:
    try:
      __SCREAMING_SNAKE_CASE     :					Optional[int]			=						eval(stream.read()    )
    except Exception:
      __SCREAMING_SNAKE_CASE     :					Any			=						stream.read().split('''\n'''    )
  return data


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


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   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    AutoencoderKL      ,		_A    :    CLIPTextModel      ,		_A    :    CLIPTokenizer      ,		_A    :    UNetaDConditionModel      ,		_A    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		):







       """simple docstring"""




       super().__init__()

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



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Dict				):







       """simple docstring"""




       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.vae.encode(tfms.ToTensor()(_A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						0.1_82_15 * latent.latent_dist.sample()
       return latent



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						self.vae.decode(_A				).sample
       __SCREAMING_SNAKE_CASE     :					Any			=						(img / 2 + 0.5).clamp(0      ,		1				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						img.detach().cpu().permute(0      ,		2      ,		3      ,		1				).numpy()
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(img * 255).round().astype('''uint8'''				)
       return Image.fromarray(img[0]				)



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tokenizer(
           _A      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.tokenizer(
           ''''''      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					str			=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

       return torch.cat([uncond_embedding, text_embedding]				)




     def __call__(							self    :    Union[str, 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      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						steps - int(kmin * steps				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						steps - int(kmax * steps				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.prep_text(_A				)

       self.scheduler.set_timesteps(_A				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						img.size
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.encode(_A				)

       torch.manual_seed(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)      ,		).to(self.device				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.add_noise(
           _A      ,		_A      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([latents] * 2				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.scheduler.scale_model_input(_A      ,		self.scheduler.timesteps[tmax]				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.unet(
             _A      ,		self.scheduler.timesteps[tmax]      ,		encoder_hidden_states=_A      ,		).sample

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					List[str]			=						pred.chunk(2				)
       __SCREAMING_SNAKE_CASE     :					str			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

       __SCREAMING_SNAKE_CASE     :					Any			=						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
             __SCREAMING_SNAKE_CASE     :					List[str]			=						self.scheduler.add_noise(
                 _A      ,		_A      ,		timesteps=_A      ,		)

             __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						(mix_factor * latents) + (
                 1 - mix_factor
             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
             __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						torch.cat([input] * 2				)

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

           __SCREAMING_SNAKE_CASE     :					str			=						self.scheduler.scale_model_input(_A      ,		_A				)

           with torch.no_grad():
             __SCREAMING_SNAKE_CASE     :					int			=						self.unet(
                 _A      ,		_A      ,		encoder_hidden_states=_A      ,		).sample

           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred.chunk(2				)
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

           __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		_A      ,		_A				).prev_sample

       return self.decode(_A				)
