

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 					__lowerCamelCase							(      nn.Module      ):







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase=1.0  )		->			Tuple:
        super().__init__()

        snake_case_					= cut_size
        snake_case_					= cut_power







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



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Optional[int]:





  '''simple docstring'''





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



def   UpperCamelCase(   lowercase_     ,   lowercase_				)      ->							Optional[int]:





  '''simple docstring'''





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







class 					__lowerCamelCase							(      a_      ):







      def __init__(	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    )		->			List[str]:
        super().__init__()
        self.register_modules(
            vae=lowerCamelCase				,    text_encoder=lowerCamelCase				,    clip_model=lowerCamelCase				,    tokenizer=lowerCamelCase				,    unet=lowerCamelCase				,    scheduler=lowerCamelCase				,    feature_extractor=lowerCamelCase				,    )

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

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







      def       lowerCAmelCase_       (	self				,    lowerCamelCase = "auto"  )		->			Dict:
        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          snake_case_					= self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Union[str, Any]:
        self.enable_attention_slicing(lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			str:
        set_requires_grad(self.vae				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Dict:
        set_requires_grad(self.vae				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Dict:
        set_requires_grad(self.unet				,    lowerCamelCase  )







      def       lowerCAmelCase_       (	self  )		->			Optional[Any]:
        set_requires_grad(self.unet				,    lowerCamelCase  )







      @torch.enable_grad()
      def       lowerCAmelCase_       (	self				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase				,    lowerCamelCase=True				,    )		->			List[Any]:
        snake_case_					= latents.detach().requires_grad_()

        snake_case_					= self.scheduler.scale_model_input(lowerCamelCase				,    lowerCamelCase  )

        # predict the noise residual
        snake_case_					= self.unet(lowerCamelCase				,    lowerCamelCase				,    encoder_hidden_states=lowerCamelCase  ).sample

        if isinstance(self.scheduler				,    (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)  ):
          snake_case_					= self.scheduler.alphas_cumprod[timestep]
          snake_case_					= 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
          snake_case_					= (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

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

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

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

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

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

        snake_case_					= -torch.autograd.grad(lowerCamelCase				,    lowerCamelCase  )[0]

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







      @torch.no_grad()
      def __call__(	self				,    lowerCamelCase				,    lowerCamelCase = 512				,    lowerCamelCase = 512				,    lowerCamelCase = 50				,    lowerCamelCase = 7.5				,    lowerCamelCase = 1				,    lowerCamelCase = 0.0				,    lowerCamelCase = 100				,    lowerCamelCase = None				,    lowerCamelCase = 4				,    lowerCamelCase = True				,    lowerCamelCase = None				,    lowerCamelCase = None				,    lowerCamelCase = "pil"				,    lowerCamelCase = True				,    )		->			Any:
        if isinstance(lowerCamelCase				,    lowerCamelCase  ):
          snake_case_					= 1
        elif isinstance(lowerCamelCase				,    lowerCamelCase  ):
          snake_case_					= len(lowerCamelCase  )
        else:
          raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase  )}'''  )

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

        if clip_guidance_scale > 0:
          if clip_prompt is not None:
            snake_case_					= self.tokenizer(
                lowerCamelCase				,    padding="""max_length"""				,    max_length=self.tokenizer.model_max_length				,    truncation=lowerCamelCase				,    return_tensors="""pt"""				,    ).input_ids.to(self.device  )
          else:
            snake_case_					= text_input.input_ids.to(self.device  )
          snake_case_					= self.clip_model.get_text_features(lowerCamelCase  )
          snake_case_					= text_embeddings_clip / text_embeddings_clip.norm(p=2				,    dim=-1				,    keepdim=lowerCamelCase  )
          # duplicate text embeddings clip for each generation per prompt
          snake_case_					= text_embeddings_clip.repeat_interleave(lowerCamelCase				,    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.
        snake_case_					= guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
          snake_case_					= text_input.input_ids.shape[-1]
          snake_case_					= self.tokenizer([""""""]				,    padding="""max_length"""				,    max_length=lowerCamelCase				,    return_tensors="""pt"""  )
          snake_case_					= self.text_encoder(uncond_input.input_ids.to(self.device  )  )[0]
          # duplicate unconditional embeddings for each generation per prompt
          snake_case_					= uncond_embeddings.repeat_interleave(lowerCamelCase				,    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
          snake_case_					= 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`.
        snake_case_					= (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
        snake_case_					= text_embeddings.dtype
        if latents is None:
          if self.device.type == "mps":
            # randn does not work reproducibly on mps
            snake_case_					= torch.randn(lowerCamelCase				,    generator=lowerCamelCase				,    device="""cpu"""				,    dtype=lowerCamelCase  ).to(
                self.device  )
          else:
            snake_case_					= torch.randn(lowerCamelCase				,    generator=lowerCamelCase				,    device=self.device				,    dtype=lowerCamelCase  )
        else:
          if latents.shape != latents_shape:
            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''  )
          snake_case_					= latents.to(self.device  )

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

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

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

        # scale the initial noise by the standard deviation required by the scheduler
        snake_case_					= 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]
        snake_case_					= 'eta' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )
        snake_case_					= {}
        if accepts_eta:
          snake_case_					= eta

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

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

          # predict the noise residual
          snake_case_					= self.unet(lowerCamelCase				,    lowerCamelCase				,    encoder_hidden_states=lowerCamelCase  ).sample

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

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

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

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

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

        if output_type == "pil":
          snake_case_					= self.numpy_to_pil(lowerCamelCase  )

        if not return_dict:
          return (image, None)

        return StableDiffusionPipelineOutput(images=lowerCamelCase				,    nsfw_content_detected=lowerCamelCase  )
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, 20_48), dtype='''float32'''),
            '''sizes''': datasets.Sequence(length=2, feature=datasets.Value('''float32''')),
            '''preds_per_image''': datasets.Value(dtype='''int32'''),
        }
    )
)




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		Optional[Any]							,      lowerCamelCase							:		int=sys.argv[1:]	)			->				int:
        _UpperCamelCase  :							Tuple	    =	None
        _UpperCamelCase  :							Dict	    =	None
        _UpperCamelCase  :							List[Any]	    =	None
        _UpperCamelCase  :							Dict	    =	1
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	getopt.getopt(lowerCamelCase							,      'i:o:b:s'							,      ['inputdir=', 'outfile=', 'batch_size=', 'subset_list=']	)
        for opt, arg in opts:
               if opt in ("-i", "--inputdir"):
                      _UpperCamelCase  :							Dict	    =	arg
               elif opt in ("-o", "--outfile"):
                      _UpperCamelCase  :							int	    =	arg
               elif opt in ("-b", "--batch_size"):
                      _UpperCamelCase  :							List[str]	    =	int(lowerCamelCase	)
               elif opt in ("-s", "--subset_list"):
                      _UpperCamelCase  :							Dict	    =	arg

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

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

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		Any	)			->				int:
        _UpperCamelCase  :							Tuple	    =	int(file.split('.'	)[0].split('_'	)[-1]	)
        _UpperCamelCase  :							List[Any]	    =	os.path.join(self.inputdir							,      lowerCamelCase	)
        return (img_id, filepath)

 @property
 def        __snake_case       (      self							:		Optional[Any]	)			->				Union[str, Any]:
        _UpperCamelCase  :							str	    =	[]
        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(lowerCamelCase	)	)
               if len(lowerCamelCase	) == self.batch:
                      _UpperCamelCase  :							Dict	    =	batch
                      _UpperCamelCase  :							List[Any]	    =	[]
                      yield list(map(lowerCamelCase							,      zip(*lowerCamelCase	)	)	)

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

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







def SCREAMING_SNAKE_CASE					(  lowercase_:						str					):
       try:
              _UpperCamelCase  :							Optional[Any]	    =	json.load(lowercase_					)
              try:
                     _UpperCamelCase  :							Union[str, Any]	    =	list(data.keys()					)
              except Exception:
                     _UpperCamelCase  :							Optional[Any]	    =	[d['img_id'] for d in data]
       except Exception:
              try:
                     _UpperCamelCase  :							Any	    =	eval(stream.read()					)
              except Exception:
                     _UpperCamelCase  :							Optional[int]	    =	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)



