

'''simple docstring'''

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase				   =  {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}

def 				__UpperCamelCase							(	lowercase__						:  List[Any] ):






     '''simple docstring'''





     __lowercase					=F'''pretrained_models/{model_name}'''
     if not os.path.isfile(lowercase__ ):
          os.makedirs('pretrained_models',					exist_ok=lowercase__ )
          __lowercase					=F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
          download_url(lowercase__,					'pretrained_models' )
     __lowercase					=torch.load(lowercase__,					map_location=lambda lowercase__,					lowercase__ : storage )
     return model

def 				__UpperCamelCase							(	lowercase__						:  int ):






     '''simple docstring'''





     __lowercase					=download_model(pretrained_models[args.image_size] )

     __lowercase					=state_dict['x_embedder.proj.weight']
     __lowercase					=state_dict['x_embedder.proj.bias']
     state_dict.pop('x_embedder.proj.weight' )
     state_dict.pop('x_embedder.proj.bias' )

     for depth in range(28 ):
          __lowercase					=state_dict[
              't_embedder.mlp.0.weight'
          ]
          __lowercase					=state_dict[
              't_embedder.mlp.0.bias'
          ]
          __lowercase					=state_dict[
              't_embedder.mlp.2.weight'
          ]
          __lowercase					=state_dict[
              't_embedder.mlp.2.bias'
          ]
          __lowercase					=state_dict[
              'y_embedder.embedding_table.weight'
          ]

          __lowercase					=state_dict[
              F'''blocks.{depth}.adaLN_modulation.1.weight'''
          ]
          __lowercase					=state_dict[
              F'''blocks.{depth}.adaLN_modulation.1.bias'''
          ]

          __lowercase		,     __lowercase		,     __lowercase					=torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight'''],					3,					dim=0 )
          __lowercase		,     __lowercase		,     __lowercase					=torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias'''],					3,					dim=0 )

          __lowercase					=q
          __lowercase					=q_bias
          __lowercase					=k
          __lowercase					=k_bias
          __lowercase					=v
          __lowercase					=v_bias

          __lowercase					=state_dict[
              F'''blocks.{depth}.attn.proj.weight'''
          ]
          __lowercase					=state_dict[F'''blocks.{depth}.attn.proj.bias''']

          __lowercase					=state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
          __lowercase					=state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
          __lowercase					=state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
          __lowercase					=state_dict[F'''blocks.{depth}.mlp.fc2.bias''']

          state_dict.pop(F'''blocks.{depth}.attn.qkv.weight''' )
          state_dict.pop(F'''blocks.{depth}.attn.qkv.bias''' )
          state_dict.pop(F'''blocks.{depth}.attn.proj.weight''' )
          state_dict.pop(F'''blocks.{depth}.attn.proj.bias''' )
          state_dict.pop(F'''blocks.{depth}.mlp.fc1.weight''' )
          state_dict.pop(F'''blocks.{depth}.mlp.fc1.bias''' )
          state_dict.pop(F'''blocks.{depth}.mlp.fc2.weight''' )
          state_dict.pop(F'''blocks.{depth}.mlp.fc2.bias''' )
          state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.weight''' )
          state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.bias''' )

     state_dict.pop('t_embedder.mlp.0.weight' )
     state_dict.pop('t_embedder.mlp.0.bias' )
     state_dict.pop('t_embedder.mlp.2.weight' )
     state_dict.pop('t_embedder.mlp.2.bias' )
     state_dict.pop('y_embedder.embedding_table.weight' )

     __lowercase					=state_dict['final_layer.adaLN_modulation.1.weight']
     __lowercase					=state_dict['final_layer.adaLN_modulation.1.bias']
     __lowercase					=state_dict['final_layer.linear.weight']
     __lowercase					=state_dict['final_layer.linear.bias']

     state_dict.pop('final_layer.linear.weight' )
     state_dict.pop('final_layer.linear.bias' )
     state_dict.pop('final_layer.adaLN_modulation.1.weight' )
     state_dict.pop('final_layer.adaLN_modulation.1.bias' )

     # DiT XL/2
     __lowercase					=TransformeraDModel(
         sample_size=args.image_size // 8,					num_layers=28,					attention_head_dim=72,					in_channels=4,					out_channels=8,					patch_size=2,					attention_bias=lowercase__,					num_attention_heads=16,					activation_fn='gelu-approximate',					num_embeds_ada_norm=10_00,					norm_type='ada_norm_zero',					norm_elementwise_affine=lowercase__,					)
     transformer.load_state_dict(lowercase__,					strict=lowercase__ )

     __lowercase					=DDIMScheduler(
         num_train_timesteps=10_00,					beta_schedule='linear',					prediction_type='epsilon',					clip_sample=lowercase__,					)

     __lowercase					=AutoencoderKL.from_pretrained(args.vae_model )

     __lowercase					=DiTPipeline(transformer=lowercase__,					vae=lowercase__,					scheduler=lowercase__ )

     if args.save:
          pipeline.save_pretrained(args.checkpoint_path )


if __name__ == "__main__":
       UpperCAmelCase				   =  argparse.ArgumentParser()

       parser.add_argument(
           '''--image_size''',
           default=256,
           type=int,
           required=False,
           help='''Image size of pretrained model, either 256 or 512.''',
       )
       parser.add_argument(
           '''--vae_model''',
           default='''stabilityai/sd-vae-ft-ema''',
           type=str,
           required=False,
           help='''Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.''',
       )
       parser.add_argument(
           '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted pipeline or not.'''
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the output pipeline.'''
       )

       UpperCAmelCase				   =  parser.parse_args()
       main(args)





'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

UpperCAmelCase				   =  {'''UserAgent''': UserAgent().random}
UpperCAmelCase				   =  '''https://www.mywaifulist.moe/random'''

def 				__UpperCamelCase							(	lowercase__						:  str,					lowercase__						:  str ):






     '''simple docstring'''





     __lowercase					=requests.get(lowercase__,					headers=lowercase__ )
     with open(lowercase__,					'wb' ) as file:
          file.write(image.content )

def 				__UpperCamelCase							(	):






     '''simple docstring'''





     __lowercase					=BeautifulSoup(requests.get(lowercase__,					headers=lowercase__ ).text,					'html.parser' )
     __lowercase					=soup.find('meta',					attrs={'property': 'og:title'} ).attrs['content']
     __lowercase					=soup.find('meta',					attrs={'property': 'og:image'} ).attrs['content']
     __lowercase					=soup.find('p',					id='description' ).get_text()
     __lowercase		,     __lowercase					=os.path.splitext(os.path.basename(lowercase__ ) )
     __lowercase					=title.strip().replace(' ',					'_' )
     __lowercase					=F'''{image_title}{image_extension}'''
     save_image(lowercase__,					lowercase__ )
     return (title, description, image_title)


if __name__ == "__main__":
       UpperCAmelCase   ,			UpperCAmelCase   ,			UpperCAmelCase				   =  random_anime_character()
       print(F'''{title}\n\n{desc}\n\nImage saved : {image_title}''')


