




import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput






class 			A			(		nn.Module		):
     """simple docstring"""

     def __init__(    self  :   Optional[Any]	,  A__  :   List[Any]	,  A__  :   str=1.0			)				->       List[Any]:
           super().__init__()

           lowerCamelCase__        =				cut_size
           lowerCamelCase__        =				cut_power




     def  __snake_case   (    self  :   List[str]	,  A__  :   str	,  A__  :   str			)				->       Any:
           lowerCamelCase__  ,		lowerCamelCase__        =				pixel_values.shape[2:4]
           lowerCamelCase__        =				min(A__	,  A__			)
           lowerCamelCase__        =				min(A__	,  A__	,  self.cut_size			)
           lowerCamelCase__        =				[]
           for _ in range(A__			):
                 lowerCamelCase__        =				int(torch.rand([]			) ** self.cut_power * (max_size - min_size) + min_size			)
                 lowerCamelCase__        =				torch.randint(0	,  sideX - size + 1	,  ()			)
                 lowerCamelCase__        =				torch.randint(0	,  sideY - size + 1	,  ()			)
                 lowerCamelCase__        =				pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
                 cutouts.append(F.adaptive_avg_poolad(A__	,  self.cut_size			)			)
           return torch.cat(A__			)

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       Dict						)					->			Optional[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				F.normalize(__snake_case     ,				dim=-1						)
      lowerCamelCase__        =				F.normalize(__snake_case     ,				dim=-1						)
      return (x - y).norm(dim=-1						).div(2						).arcsin().pow(2						).mul(2						)

def 			lowerCAmelCase_			(  lowerCamelCase:       int     ,				lowerCamelCase:       Optional[Any]						)					->			Union[str, Any]:


      '''simple docstring'''




      for param in model.parameters():
            lowerCamelCase__        =				value






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(    self  :   int	,  A__  :   Dict	,  A__  :   Dict	,  A__  :   Any	,  A__  :   List[Any]	,  A__  :   Optional[Any]	,  A__  :   List[Any]	,  A__  :   int	,  )				->       Tuple:
           super().__init__()
           self.register_modules(
               vae=A__	,  text_encoder=A__	,  clip_model=A__	,  tokenizer=A__	,  unet=A__	,  scheduler=A__	,  feature_extractor=A__	,  )

           lowerCamelCase__        =				transforms.Normalize(mean=feature_extractor.image_mean	,  std=feature_extractor.image_std			)
           lowerCamelCase__        =				(
               feature_extractor.size
               if isinstance(feature_extractor.size	,  A__			)
               else feature_extractor.size['shortest_edge']
           )
           lowerCamelCase__        =				MakeCutouts(self.cut_out_size			)

           set_requires_grad(self.text_encoder	,  A__			)
           set_requires_grad(self.clip_model	,  A__			)




     def  __snake_case   (    self  :   Any	,  A__  :   Dict = "auto"			)				->       Union[str, Any]:







           if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 lowerCamelCase__        =				self.unet.config.attention_head_dim // 2
           self.unet.set_attention_slice(A__			)




     def  __snake_case   (    self  :   Union[str, Any]			)				->       Optional[int]:
           self.enable_attention_slicing(A__			)




     def  __snake_case   (    self  :   str			)				->       Optional[Any]:
           set_requires_grad(self.vae	,  A__			)




     def  __snake_case   (    self  :   Dict			)				->       Optional[Any]:
           set_requires_grad(self.vae	,  A__			)




     def  __snake_case   (    self  :   str			)				->       Tuple:
           set_requires_grad(self.unet	,  A__			)




     def  __snake_case   (    self  :   Tuple			)				->       int:
           set_requires_grad(self.unet	,  A__			)




     @torch.enable_grad()
     def  __snake_case   (    self  :   Dict	,  A__  :   Optional[int]	,  A__  :   Any	,  A__  :   int	,  A__  :   Union[str, Any]	,  A__  :   Dict	,  A__  :   str	,  A__  :   Dict	,  A__  :   Dict	,  A__  :   Any=True	,  )				->       int:
           lowerCamelCase__        =				latents.detach().requires_grad_()

           lowerCamelCase__        =				self.scheduler.scale_model_input(A__	,  A__			)

           # predict the noise residual
           lowerCamelCase__        =				self.unet(A__	,  A__	,  encoder_hidden_states=A__			).sample

           if isinstance(self.scheduler	,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)			):
                 lowerCamelCase__        =				self.scheduler.alphas_cumprod[timestep]
                 lowerCamelCase__        =				1 - alpha_prod_t
                 # compute predicted original sample from predicted noise also called
                 # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
                 lowerCamelCase__        =				(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

                 lowerCamelCase__        =				torch.sqrt(A__			)
                 lowerCamelCase__        =				pred_original_sample * (fac) + latents * (1 - fac)
           elif isinstance(self.scheduler	,  A__			):
                 lowerCamelCase__        =				self.scheduler.sigmas[index]
                 lowerCamelCase__        =				latents - sigma * noise_pred
           else:
                 raise ValueError(F"""scheduler type {type(self.scheduler			)} not supported"""			)

           lowerCamelCase__        =				1 / self.vae.config.scaling_factor * sample
           lowerCamelCase__        =				self.vae.decode(A__			).sample
           lowerCamelCase__        =				(image / 2 + 0.5).clamp(0	,  1			)

           if use_cutouts:
                 lowerCamelCase__        =				self.make_cutouts(A__	,  A__			)
           else:
                 lowerCamelCase__        =				transforms.Resize(self.cut_out_size			)(A__			)
           lowerCamelCase__        =				self.normalize(A__			).to(latents.dtype			)

           lowerCamelCase__        =				self.clip_model.get_image_features(A__			)
           lowerCamelCase__        =				image_embeddings_clip / image_embeddings_clip.norm(p=2	,  dim=-1	,  keepdim=A__			)

           if use_cutouts:
                 lowerCamelCase__        =				spherical_dist_loss(A__	,  A__			)
                 lowerCamelCase__        =				dists.view([num_cutouts, sample.shape[0], -1]			)
                 lowerCamelCase__        =				dists.sum(2			).mean(0			).sum() * clip_guidance_scale
           else:
                 lowerCamelCase__        =				spherical_dist_loss(A__	,  A__			).mean() * clip_guidance_scale

           lowerCamelCase__        =				-torch.autograd.grad(A__	,  A__			)[0]

           if isinstance(self.scheduler	,  A__			):
                 lowerCamelCase__        =				latents.detach() + grads * (sigma**2)
                 lowerCamelCase__        =				noise_pred_original
           else:
                 lowerCamelCase__        =				noise_pred_original - torch.sqrt(A__			) * grads
           return noise_pred, latents




     @torch.no_grad()
     def __call__(    self  :   Any	,  A__  :   Tuple	,  A__  :   int = 5_1_2	,  A__  :   List[str] = 5_1_2	,  A__  :   Any = 5_0	,  A__  :   List[Any] = 7.5	,  A__  :   Optional[Any] = 1	,  A__  :   Optional[Any] = 0.0	,  A__  :   List[str] = 1_0_0	,  A__  :   Optional[Any] = None	,  A__  :   Union[str, Any] = 4	,  A__  :   str = True	,  A__  :   Union[str, Any] = None	,  A__  :   Dict = None	,  A__  :   List[str] = "pil"	,  A__  :   List[str] = True	,  )				->       Optional[Any]:







           if isinstance(A__	,  A__			):
                 lowerCamelCase__        =				1
           elif isinstance(A__	,  A__			):
                 lowerCamelCase__        =				len(A__			)
           else:
                 raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(A__			)}"""			)

           if height % 8 != 0 or width % 8 != 0:
                 raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""			)

           # get prompt text embeddings
           lowerCamelCase__        =				self.tokenizer(
               A__	,  padding='max_length'	,  max_length=self.tokenizer.model_max_length	,  truncation=A__	,  return_tensors='pt'	,  )
           lowerCamelCase__        =				self.text_encoder(text_input.input_ids.to(self.device			)			)[0]
           # duplicate text embeddings for each generation per prompt
           lowerCamelCase__        =				text_embeddings.repeat_interleave(A__	,  dim=0			)

           if clip_guidance_scale > 0:
                 if clip_prompt is not None:
                       lowerCamelCase__        =				self.tokenizer(
                           A__	,  padding='max_length'	,  max_length=self.tokenizer.model_max_length	,  truncation=A__	,  return_tensors='pt'	,  ).input_ids.to(self.device			)
                 else:
                       lowerCamelCase__        =				text_input.input_ids.to(self.device			)
                 lowerCamelCase__        =				self.clip_model.get_text_features(A__			)
                 lowerCamelCase__        =				text_embeddings_clip / text_embeddings_clip.norm(p=2	,  dim=-1	,  keepdim=A__			)
                 # duplicate text embeddings clip for each generation per prompt
                 lowerCamelCase__        =				text_embeddings_clip.repeat_interleave(A__	,  dim=0			)

           # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
           # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
           # corresponds to doing no classifier free guidance.
           lowerCamelCase__        =				guidance_scale > 1.0
           # get unconditional embeddings for classifier free guidance
           if do_classifier_free_guidance:
                 lowerCamelCase__        =				text_input.input_ids.shape[-1]
                 lowerCamelCase__        =				self.tokenizer(['']	,  padding='max_length'	,  max_length=A__	,  return_tensors='pt'			)
                 lowerCamelCase__        =				self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
                 # duplicate unconditional embeddings for each generation per prompt
                 lowerCamelCase__        =				uncond_embeddings.repeat_interleave(A__	,  dim=0			)

                 # For classifier free guidance, we need to do two forward passes.
                 # Here we concatenate the unconditional and text embeddings into a single batch
                 # to avoid doing two forward passes
                 lowerCamelCase__        =				torch.cat([uncond_embeddings, text_embeddings]			)

           # get the initial random noise unless the user supplied it

           # Unlike in other pipelines, latents need to be generated in the target device
           # for 1-to-1 results reproducibility with the CompVis implementation.
           # However this currently doesn't work in `mps`.
           lowerCamelCase__        =				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
           lowerCamelCase__        =				text_embeddings.dtype
           if latents is None:
                 if self.device.type == "mps":
                       # randn does not work reproducibly on mps
                       lowerCamelCase__        =				torch.randn(A__	,  generator=A__	,  device='cpu'	,  dtype=A__			).to(
                           self.device			)
                 else:
                       lowerCamelCase__        =				torch.randn(A__	,  generator=A__	,  device=self.device	,  dtype=A__			)
           else:
                 if latents.shape != latents_shape:
                       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""			)
                 lowerCamelCase__        =				latents.to(self.device			)

           # set timesteps
           lowerCamelCase__        =				'offset' in set(inspect.signature(self.scheduler.set_timesteps			).parameters.keys()			)
           lowerCamelCase__        =				{}
           if accepts_offset:
                 lowerCamelCase__        =				1

           self.scheduler.set_timesteps(A__	,  **A__			)

           # Some schedulers like PNDM have timesteps as arrays
           # It's more optimized to move all timesteps to correct device beforehand
           lowerCamelCase__        =				self.scheduler.timesteps.to(self.device			)

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCamelCase__        =				latents * self.scheduler.init_noise_sigma

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
           # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
           # and should be between [0, 1]
           lowerCamelCase__        =				'eta' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
           lowerCamelCase__        =				{}
           if accepts_eta:
                 lowerCamelCase__        =				eta

           # check if the scheduler accepts generator
           lowerCamelCase__        =				'generator' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)
           if accepts_generator:
                 lowerCamelCase__        =				generator

           for i, t in enumerate(self.progress_bar(A__			)			):
                 # expand the latents if we are doing classifier free guidance
                 lowerCamelCase__        =				torch.cat([latents] * 2			) if do_classifier_free_guidance else latents
                 lowerCamelCase__        =				self.scheduler.scale_model_input(A__	,  A__			)

                 # predict the noise residual
                 lowerCamelCase__        =				self.unet(A__	,  A__	,  encoder_hidden_states=A__			).sample

                 # perform classifier free guidance
                 if do_classifier_free_guidance:
                       lowerCamelCase__  ,		lowerCamelCase__        =				noise_pred.chunk(2			)
                       lowerCamelCase__        =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                 # perform clip guidance
                 if clip_guidance_scale > 0:
                       lowerCamelCase__        =				(
                           text_embeddings.chunk(2			)[1] if do_classifier_free_guidance else text_embeddings
                       )
                       lowerCamelCase__  ,		lowerCamelCase__        =				self.cond_fn(
                           A__	,  A__	,  A__	,  A__	,  A__	,  A__	,  A__	,  A__	,  A__	,  )

                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCamelCase__        =				self.scheduler.step(A__	,  A__	,  A__	,  **A__			).prev_sample

           # scale and decode the image latents with vae
           lowerCamelCase__        =				1 / self.vae.config.scaling_factor * latents
           lowerCamelCase__        =				self.vae.decode(A__			).sample

           lowerCamelCase__        =				(image / 2 + 0.5).clamp(0	,  1			)
           lowerCamelCase__        =				image.cpu().permute(0	,  2	,  3	,  1			).numpy()

           if output_type == "pil":
                 lowerCamelCase__        =				self.numpy_to_pil(A__			)

           if not return_dict:
                 return (image, None)

           return StableDiffusionPipelineOutput(images=A__	,  nsfw_content_detected=A__			)





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
__SCREAMING_SNAKE_CASE    :   Optional[Any]       =False
__SCREAMING_SNAKE_CASE    :   int       =Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
__SCREAMING_SNAKE_CASE    :   Optional[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_048), 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  :   List[str]	,  A__  :   int=sys.argv[1:]			)				->       Optional[Any]:
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				None
           lowerCamelCase__        =				1
           lowerCamelCase__  ,		lowerCamelCase__        =				getopt.getopt(A__	,  'i:o:b:s'	,  ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']			)
           for opt, arg in opts:
                 if opt in ("-i", "--inputdir"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-o", "--outfile"):
                       lowerCamelCase__        =				arg
                 elif opt in ("-b", "--batch_size"):
                       lowerCamelCase__        =				int(A__			)
                 elif opt in ("-s", "--subset_list"):
                       lowerCamelCase__        =				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:
                       lowerCamelCase__        =				{self._vqa_file_split()[0] for x in tryload(A__			)}
           else:
                 lowerCamelCase__        =				None

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




     def  __snake_case   (    self  :   Dict	,  A__  :   Any			)				->       Union[str, Any]:
           lowerCamelCase__        =				int(file.split('.'			)[0].split('_'			)[-1]			)
           lowerCamelCase__        =				os.path.join(self.inputdir	,  A__			)
           return (img_id, filepath)




     @property
     def  __snake_case   (    self  :   int			)				->       Optional[Any]:
           lowerCamelCase__        =				[]
           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:
                       lowerCamelCase__        =				batch
                       lowerCamelCase__        =				[]
                       yield list(map(A__	,  zip(*A__			)			)			)

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




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

def 			lowerCAmelCase_			(  lowerCamelCase:       Tuple						)					->			Optional[Any]:


      '''simple docstring'''




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


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