code: 
  
  
  
  
  
  
  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     __a						(					A__ ):
  
  
  
  
  
  
  
   def __init__(	self      :     Optional[int]							,     SCREAMING_SNAKE_CASE      :     AutoencoderKL							,     SCREAMING_SNAKE_CASE      :     CLIPTextModel							,     SCREAMING_SNAKE_CASE      :     CLIPTokenizer							,     SCREAMING_SNAKE_CASE      :     UNetaDConditionModel							,     SCREAMING_SNAKE_CASE      :     Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().__init__()
  
        self.register_modules(vae=SCREAMING_SNAKE_CASE							,     text_encoder=SCREAMING_SNAKE_CASE							,     tokenizer=SCREAMING_SNAKE_CASE							,     unet=SCREAMING_SNAKE_CASE							,     scheduler=SCREAMING_SNAKE_CASE		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Dict							,     SCREAMING_SNAKE_CASE      :     Dict		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        with torch.no_grad():
             UpperCamelCase__    :     Any        =						self.vae.encode(tfms.ToTensor()(SCREAMING_SNAKE_CASE		).unsqueeze(0		).to(self.device		) * 2 - 1		)
             UpperCamelCase__    :     Tuple        =						0.1_8_2_1_5 * latent.latent_dist.sample()
        return latent
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any							,     SCREAMING_SNAKE_CASE      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						(1 / 0.1_8_2_1_5) * latent
        with torch.no_grad():
             UpperCamelCase__    :     Tuple        =						self.vae.decode(SCREAMING_SNAKE_CASE		).sample
        UpperCamelCase__    :     List[Any]        =						(img / 2 + 0.5).clamp(0							,     1		)
        UpperCamelCase__    :     Optional[Any]        =						img.detach().cpu().permute(0							,     2							,     3							,     1		).numpy()
        UpperCamelCase__    :     Tuple        =						(img * 2_55).round().astype("uint8"		)
        return Image.fromarray(img[0]		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[str]							,     SCREAMING_SNAKE_CASE      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[Any]        =						self.tokenizer(
            SCREAMING_SNAKE_CASE							,     padding="max_length"							,     max_length=self.tokenizer.model_max_length							,     truncation=SCREAMING_SNAKE_CASE							,     return_tensors="pt"							,     )
  
        UpperCamelCase__    :     List[Any]        =						self.text_encoder(text_input.input_ids.to(self.device		)		)[0]
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer(
            ""							,     padding="max_length"							,     max_length=self.tokenizer.model_max_length							,     truncation=SCREAMING_SNAKE_CASE							,     return_tensors="pt"							,     )
  
        UpperCamelCase__    :     Dict        =						self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]
  
        return torch.cat([uncond_embedding, text_embedding]		)
  
  
  
  
  
  
  
   def __call__(	self      :     List[Any]							,     SCREAMING_SNAKE_CASE      :     Image.Image							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     float = 0.3							,     SCREAMING_SNAKE_CASE      :     float = 0.6							,     SCREAMING_SNAKE_CASE      :     float = 0.5							,     SCREAMING_SNAKE_CASE      :     int = 42							,     SCREAMING_SNAKE_CASE      :     int = 50							,     SCREAMING_SNAKE_CASE      :     float = 7.5							,     ):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Dict        =						steps - int(kmin * steps		)
        UpperCamelCase__    :     Optional[int]        =						steps - int(kmax * steps		)
  
        UpperCamelCase__    :     List[str]        =						self.prep_text(SCREAMING_SNAKE_CASE		)
  
        self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__       ,  UpperCamelCase__    :     Optional[int]        =						img.size
        UpperCamelCase__    :     str        =						self.encode(SCREAMING_SNAKE_CASE		)
  
        torch.manual_seed(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)							,     ).to(self.device		)
  
        UpperCamelCase__    :     List[Any]        =						self.scheduler.add_noise(
            SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     timesteps=self.scheduler.timesteps[tmax]							,     )
  
        UpperCamelCase__    :     Tuple        =						torch.cat([latents] * 2		)
  
        UpperCamelCase__    :     Optional[int]        =						self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]		)
  
        with torch.no_grad():
             UpperCamelCase__    :     str        =						self.unet(
                 SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]							,     encoder_hidden_states=SCREAMING_SNAKE_CASE							,     ).sample
  
        UpperCamelCase__       ,  UpperCamelCase__    :     int        =						pred.chunk(2		)
        UpperCamelCase__    :     Optional[Any]        =						pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
        UpperCamelCase__    :     List[str]        =						self.scheduler.step(SCREAMING_SNAKE_CASE							,     self.scheduler.timesteps[tmax]							,     SCREAMING_SNAKE_CASE		).prev_sample
  
        for i, t in enumerate(tqdm(self.scheduler.timesteps		)		):
             if i > tmax:
                  if i < tmin:  # layout generation phase
                       UpperCamelCase__    :     Any        =						self.scheduler.add_noise(
                           SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     timesteps=SCREAMING_SNAKE_CASE							,     )
  
                       UpperCamelCase__    :     List[Any]        =						(mix_factor * latents) + (
                           1 - mix_factor
                       ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                       UpperCamelCase__    :     Optional[int]        =						torch.cat([input] * 2		)
  
                  else:  # content generation phase
                       UpperCamelCase__    :     Dict        =						torch.cat([latents] * 2		)
  
                  UpperCamelCase__    :     Union[str, Any]        =						self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
                  with torch.no_grad():
                       UpperCamelCase__    :     Optional[int]        =						self.unet(
                           SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     encoder_hidden_states=SCREAMING_SNAKE_CASE							,     ).sample
  
                  UpperCamelCase__       ,  UpperCamelCase__    :     Optional[int]        =						pred.chunk(2		)
                  UpperCamelCase__    :     Optional[Any]        =						pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                  UpperCamelCase__    :     List[str]        =						self.scheduler.step(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		).prev_sample
  
        return self.decode(SCREAMING_SNAKE_CASE		)
style_context: 
  
  
  
  
  
  
  import os
  import unittest
  
  from transformers import MPNetTokenizerFast
  from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  
  
  
  @require_tokenizers
  class     __a						(					A__ ,     unittest.TestCase ):
   _lowerCAmelCase : str 				=				MPNetTokenizer
   _lowerCAmelCase : List[str] 				=				MPNetTokenizerFast
   _lowerCAmelCase : List[Any] 				=				True
   _lowerCAmelCase : Union[str, Any] 				=				True
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     List[Any]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        super().setUp()
  
        UpperCamelCase__    :     List[Any]        =						[
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        UpperCamelCase__    :     int        =						os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES["vocab_file"]		)
        with open(self.vocab_file							,     "w"							,     encoding="utf-8"		) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     List[str]		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     List[str]        =						"UNwant\u00E9d,running"
        UpperCamelCase__    :     List[str]        =						"unwanted, running"
        return input_text, output_text
  
  
  
  
  
  
  
   def       __lowercase      (	self      :     Any		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     str        =						self.tokenizer_class(self.vocab_file		)
  
        UpperCamelCase__    :     Any        =						tokenizer.tokenize("UNwant\u00E9d,running"		)
        self.assertListEqual(SCREAMING_SNAKE_CASE							,     ["un", "##want", "##ed", ",", "runn", "##ing"]		)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE		)							,     [9, 6, 7, 12, 10, 11]		)
  
  
  
  
  
  
  
   @slow
   def       __lowercase      (	self      :     str		):
  
  
        '''simple docstring'''
  
  
  
  
  
  
        UpperCamelCase__    :     Union[str, Any]        =						self.tokenizer_class.from_pretrained("microsoft/mpnet-base"		)
  
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("sequence builders"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.encode("multi-sequence build"							,     add_special_tokens=SCREAMING_SNAKE_CASE		)
  
        UpperCamelCase__    :     Tuple        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE		)
        UpperCamelCase__    :     Optional[int]        =						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE		)
  
        assert encoded_sentence == [0] + text + [2]
        assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]