

"""simple docstring"""

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)






class  __a				(	SCREAMING_SNAKE_CASE__					):





       """simple docstring"""


       def __init__(      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case = 0.93 ,				snake_case = True ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							mixing_coeff
        lowerCAmelCase__      :							List[Any]    =							leapfrog_steps

        super().__init__()
        self.register_modules(
            vae=snake_case ,				text_encoder=snake_case ,				tokenizer=snake_case ,				unet=snake_case ,				scheduler=snake_case ,				)

        lowerCAmelCase__      :							Dict    =							2 ** (len(self.vae.config.block_out_channels      ) - 1)
        lowerCAmelCase__      :							List[Any]    =							VaeImageProcessor(vae_scale_factor=self.vae_scale_factor      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case = None ,				snake_case = False      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							self.tokenizer(
            snake_case ,				padding="max_length" ,				max_length=self.tokenizer.model_max_length ,				truncation=snake_case ,				return_tensors="pt" ,				)

        lowerCAmelCase__      :							Any    =							self.text_encoder(text_inputs.input_ids.to(self.device      )      ).last_hidden_state

        lowerCAmelCase__      :							Any    =							prompt_embeds.to(dtype=self.text_encoder.dtype ,				device=self.device      )

        if do_classifier_free_guidance:
         lowerCAmelCase__      :							List[Any]    =							"" if negative_prompt is None else negative_prompt

         lowerCAmelCase__      :							Optional[int]    =							self.tokenizer(
             snake_case ,				padding="max_length" ,				max_length=self.tokenizer.model_max_length ,				truncation=snake_case ,				return_tensors="pt" ,				)

         lowerCAmelCase__      :							Union[str, Any]    =							self.text_encoder(uncond_input.input_ids.to(self.device      )      ).last_hidden_state

         lowerCAmelCase__      :							Dict    =							torch.cat([negative_prompt_embeds, prompt_embeds]      )

        return prompt_embeds






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							self.mixing_coeff * x + (1 - self.mixing_coeff) * y
        lowerCAmelCase__      :							Union[str, Any]    =							self.mixing_coeff * y + (1 - self.mixing_coeff) * x

        return [x, y]






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
        lowerCAmelCase__      :							List[Any]    =							(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

        return [x, y]






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							int    =							int(snake_case      )

        lowerCAmelCase__      :							List[Any]    =							self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

        return alpha_prod, 1 - alpha_prod






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self._get_alpha_and_beta(snake_case      )
        lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							self._get_alpha_and_beta(snake_case      )

        lowerCAmelCase__      :							str    =							(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        lowerCAmelCase__      :							Dict    =							-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

        lowerCAmelCase__      :							Optional[int]    =							(base - b_t * model_output) / a_t

        return model_input, next_model_input.to(base.dtype      )






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Union[str, Any]    =							timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

        lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							self._get_alpha_and_beta(snake_case      )
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							self._get_alpha_and_beta(snake_case      )

        lowerCAmelCase__      :							Optional[Any]    =							(alpha_prod_t_prev / alpha_prod_t) ** 0.5
        lowerCAmelCase__      :							Dict    =							-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
        lowerCAmelCase__      :							Dict    =							a_t * base + b_t * model_output

        return model_input, next_model_input.to(base.dtype      )






       @torch.no_grad()
       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							Optional[Any]    =							1 / self.vae.config.scaling_factor * latents
        lowerCAmelCase__      :							str    =							self.vae.decode(snake_case      ).sample
        lowerCAmelCase__      :							List[str]    =							(image / 2 + 0.5).clamp(0 ,				1      )
        return image






       @torch.no_grad()
       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case = None ,				):





        """simple docstring"""

        lowerCAmelCase__      :							Dict    =							guidance_scale > 1.0

        lowerCAmelCase__      :							Tuple    =							image.to(device=self.device ,				dtype=text_embeds.dtype      )
        lowerCAmelCase__      :							Optional[int]    =							self.vae.encode(snake_case      ).latent_dist.sample(snake_case      )

        lowerCAmelCase__      :							Optional[int]    =							self.vae.config.scaling_factor * latent

        lowerCAmelCase__      :							Optional[Any]    =							[latent.clone(), latent.clone()]

        for i, t in tqdm(enumerate(snake_case      ) ,				total=len(snake_case      )      ):
         lowerCAmelCase__      :							int    =							self.noise_mixing_layer(x=coupled_latents[0] ,				y=coupled_latents[1]      )

         # j - model_input index, k - base index
         for j in range(2      ):
          lowerCAmelCase__      :							str    =							j ^ 1

          if self.leapfrog_steps:
           if i % 2 == 0:
            lowerCAmelCase__ ,	lowerCAmelCase__      :							List[str]    =							j, k

          lowerCAmelCase__      :							Tuple    =							coupled_latents[j]
          lowerCAmelCase__      :							Tuple    =							coupled_latents[k]

          lowerCAmelCase__      :							List[Any]    =							torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

          lowerCAmelCase__      :							int    =							self.unet(snake_case ,				snake_case ,				encoder_hidden_states=snake_case      ).sample

          if do_classifier_free_guidance:
           lowerCAmelCase__ ,	lowerCAmelCase__      :							Dict    =							noise_pred.chunk(2      )
           lowerCAmelCase__      :							Tuple    =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          lowerCAmelCase__ ,	lowerCAmelCase__      :							List[str]    =							self.noise_step(
              base=snake_case ,				model_input=snake_case ,				model_output=snake_case ,				timestep=snake_case ,				)

          lowerCAmelCase__      :							List[Any]    =							model_input

        return coupled_latents






       @torch.no_grad()
       def __call__(      self ,				snake_case ,				snake_case ,				snake_case ,				snake_case = 3.0 ,				snake_case = 50 ,				snake_case = 0.8 ,				snake_case = None ,				snake_case = None ,				snake_case = "pil" ,				):





        """simple docstring"""

        lowerCAmelCase__      :							List[Any]    =							guidance_scale > 1.0

        lowerCAmelCase__      :							Dict    =							self.image_processor.preprocess(snake_case      )

        lowerCAmelCase__      :							str    =							self._encode_prompt(snake_case ,				snake_case ,				snake_case      )
        lowerCAmelCase__      :							List[str]    =							self._encode_prompt(snake_case ,				snake_case ,				snake_case      )

        self.scheduler.set_timesteps(snake_case ,				self.device      )

        lowerCAmelCase__      :							List[str]    =							num_inference_steps - int(num_inference_steps * strength      )
        lowerCAmelCase__      :							Tuple    =							self.scheduler.timesteps[t_limit:]
        lowerCAmelCase__      :							List[str]    =							fwd_timesteps.flip(0      )

        lowerCAmelCase__      :							List[Any]    =							self.prepare_latents(snake_case ,				snake_case ,				snake_case ,				snake_case ,				snake_case      )

        for i, t in tqdm(enumerate(snake_case      ) ,				total=len(snake_case      )      ):
         # j - model_input index, k - base index
         for k in range(2      ):
          lowerCAmelCase__      :							List[Any]    =							k ^ 1

          if self.leapfrog_steps:
           if i % 2 == 1:
            lowerCAmelCase__ ,	lowerCAmelCase__      :							Optional[int]    =							j, k

          lowerCAmelCase__      :							List[Any]    =							coupled_latents[j]
          lowerCAmelCase__      :							Any    =							coupled_latents[k]

          lowerCAmelCase__      :							Optional[Any]    =							torch.cat([model_input] * 2      ) if do_classifier_free_guidance else model_input

          lowerCAmelCase__      :							Optional[int]    =							self.unet(snake_case ,				snake_case ,				encoder_hidden_states=snake_case      ).sample

          if do_classifier_free_guidance:
           lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							noise_pred.chunk(2      )
           lowerCAmelCase__      :							List[str]    =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							self.denoise_step(
              base=snake_case ,				model_input=snake_case ,				model_output=snake_case ,				timestep=snake_case ,				)

          lowerCAmelCase__      :							List[str]    =							model_input

         lowerCAmelCase__      :							Optional[Any]    =							self.denoise_mixing_layer(x=coupled_latents[0] ,				y=coupled_latents[1]      )

        # either one is fine
        lowerCAmelCase__      :							List[str]    =							coupled_latents[0]

        if output_type not in ["latent", "pt", "np", "pil"]:
         lowerCAmelCase__      :							Optional[int]    =							(
             F"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
             "`pil`, `np`, `pt`, `latent`"
         )
         deprecate("Unsupported output_type" ,				"1.0.0" ,				snake_case ,				standard_warn=snake_case      )
         lowerCAmelCase__      :							Tuple    =							"np"

        if output_type == "latent":
         lowerCAmelCase__      :							Optional[Any]    =							final_latent
        else:
         lowerCAmelCase__      :							Any    =							self.decode_latents(snake_case      )
         lowerCAmelCase__      :							str    =							self.image_processor.postprocess(snake_case ,				output_type=snake_case      )

        return image









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





       """simple docstring"""


       def __init__(      self ,				snake_case=sys.argv[1:]      ):





        """simple docstring"""

        lowerCAmelCase__      :							Tuple    =							None
        lowerCAmelCase__      :							str    =							None
        lowerCAmelCase__      :							int    =							None
        lowerCAmelCase__      :							Any    =							1
        lowerCAmelCase__ ,	lowerCAmelCase__      :							List[Any]    =							getopt.getopt(snake_case ,				"i:o:b:s" ,				["inputdir=", "outfile=", "batch_size=", "subset_list="]      )
        for opt, arg in opts:
         if opt in ("-i", "--inputdir"):
          lowerCAmelCase__      :							List[str]    =							arg
         elif opt in ("-o", "--outfile"):
          lowerCAmelCase__      :							Optional[int]    =							arg
         elif opt in ("-b", "--batch_size"):
          lowerCAmelCase__      :							Optional[int]    =							int(snake_case      )
         elif opt in ("-s", "--subset_list"):
          lowerCAmelCase__      :							Optional[Any]    =							arg

        assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
        assert outputfile is not None and not os.path.isfile(snake_case      ), F"""{outputfile}"""
        if subset_list is not None:
         with open(os.path.realpath(snake_case      )      ) as f:
          lowerCAmelCase__      :							Union[str, Any]    =							{self._vqa_file_split()[0] for x in tryload(snake_case      )}
        else:
         lowerCAmelCase__      :							Optional[int]    =							None

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






       def  SCREAMING_SNAKE_CASE_  (      self ,				snake_case      ):





        """simple docstring"""

        lowerCAmelCase__      :							List[str]    =							int(file.split("."      )[0].split("_"      )[-1]      )
        lowerCAmelCase__      :							str    =							os.path.join(self.inputdir ,				snake_case      )
        return (img_id, filepath)






       @property
       def  SCREAMING_SNAKE_CASE_  (      self      ):





        """simple docstring"""

        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(snake_case      )      )
         if len(snake_case      ) == self.batch:
          lowerCAmelCase__      :							str    =							batch
          lowerCAmelCase__      :							str    =							[]
          yield list(map(snake_case ,				zip(*snake_case      )      )      )

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






       def __call__(      self      ):





        """simple docstring"""

        if not TEST:
         lowerCAmelCase__      :							Tuple    =							datasets.ArrowWriter(features=self.schema ,				path=self.outputfile      )
        # do file generator
        for i, (img_ids, filepaths) in enumerate(self.file_generator      ):
         lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__      :							Union[str, Any]    =							self.preprocess(snake_case      )
         lowerCAmelCase__      :							List[Any]    =							self.model(
             snake_case ,				snake_case ,				scales_yx=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(snake_case      )
          lowerCAmelCase__      :							Optional[int]    =							self.schema.encode_batch(snake_case      )
          writer.write_batch(snake_case      )
         if TEST:
          break
         # finalizer the writer
        if not TEST:
         lowerCAmelCase__ ,	lowerCAmelCase__      :							Any    =							writer.finalize()
         print(F"""Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb"""      )




def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	int:
 try:
  lowerCAmelCase__      :							Optional[int]    =							json.load(lowercase__      )
  try:
   lowerCAmelCase__      :							str    =							list(data.keys()      )
  except Exception:
   lowerCAmelCase__      :							str    =							[d["img_id"] for d in data]
 except Exception:
  try:
   lowerCAmelCase__      :							int    =							eval(stream.read()      )
  except Exception:
   lowerCAmelCase__      :							List[str]    =							stream.read().split("\n"      )
 return data


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






