"""simple docstring"""
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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     lowercase       ,     )      ->     Tuple:





        '''simple docstring'''





        super().__init__()

        self.register_modules(vae=lowercase       ,     text_encoder=lowercase       ,     tokenizer=lowercase       ,     unet=lowercase       ,     scheduler=lowercase)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        with torch.no_grad():
          a__:	List[str]     				=      self.vae.encode(tfms.ToTensor()(lowercase).unsqueeze(0).to(self.device) * 2 - 1)
          a__:	Optional[Any]     				=      0.18215 * latent.latent_dist.sample()
        return latent



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      (1 / 0.18215) * latent
        with torch.no_grad():
          a__:	Optional[int]     				=      self.vae.decode(lowercase).sample
        a__:	Dict     				=      (img / 2 + 0.5).clamp(0       ,     1)
        a__:	List[Any]     				=      img.detach().cpu().permute(0       ,     2       ,     3       ,     1).numpy()
        a__:	Tuple     				=      (img * 2_55).round().astype('uint8')
        return Image.fromarray(img[0])



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        a__:	Any     				=      self.tokenizer(
            lowercase       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[Any]     				=      self.text_encoder(text_input.input_ids.to(self.device))[0]

        a__:	str     				=      self.tokenizer(
            ''       ,     padding='max_length'       ,     max_length=self.tokenizer.model_max_length       ,     truncation=lowercase       ,     return_tensors='pt'       ,     )

        a__:	Optional[int]     				=      self.text_encoder(uncond_input.input_ids.to(self.device))[0]

        return torch.cat([uncond_embedding, text_embedding])



      def __call__(		self       ,     lowercase       ,     lowercase       ,     lowercase = 0.3       ,     lowercase = 0.6       ,     lowercase = 0.5       ,     lowercase = 42       ,     lowercase = 50       ,     lowercase = 7.5       ,     )      ->     Image.Image:





        '''simple docstring'''





        a__:	Optional[Any]     				=      steps - int(kmin * steps)
        a__:	Optional[int]     				=      steps - int(kmax * steps)

        a__:	Optional[int]     				=      self.prep_text(lowercase)

        self.scheduler.set_timesteps(lowercase)

        a__				,       a__:	Union[str, Any]     				=      img.size
        a__:	str     				=      self.encode(lowercase)

        torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      torch.randn(
            (1, self.unet.config.in_channels, height // 8, width // 8)       ,     ).to(self.device)

        a__:	Tuple     				=      self.scheduler.add_noise(
            lowercase       ,     lowercase       ,     timesteps=self.scheduler.timesteps[tmax]       ,     )

        a__:	List[Any]     				=      torch.cat([latents] * 2)

        a__:	List[str]     				=      self.scheduler.scale_model_input(lowercase       ,     self.scheduler.timesteps[tmax])

        with torch.no_grad():
          a__:	Optional[Any]     				=      self.unet(
              lowercase       ,     self.scheduler.timesteps[tmax]       ,     encoder_hidden_states=lowercase       ,     ).sample

        a__				,       a__:	List[str]     				=      pred.chunk(2)
        a__:	Union[str, Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

        a__:	int     				=      self.scheduler.step(lowercase       ,     self.scheduler.timesteps[tmax]       ,     lowercase).prev_sample

        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
          if i > tmax:
            if i < tmin:  # layout generation phase
              a__:	List[Any]     				=      self.scheduler.add_noise(
                  lowercase       ,     lowercase       ,     timesteps=lowercase       ,     )

              a__:	Dict     				=      (mix_factor * latents) + (
                  1 - mix_factor
              ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
              a__:	List[Any]     				=      torch.cat([input] * 2)

            else:  # content generation phase
              a__:	Dict     				=      torch.cat([latents] * 2)

            a__:	Union[str, Any]     				=      self.scheduler.scale_model_input(lowercase       ,     lowercase)

            with torch.no_grad():
              a__:	Union[str, Any]     				=      self.unet(
                  lowercase       ,     lowercase       ,     encoder_hidden_states=lowercase       ,     ).sample

            a__				,       a__:	Union[str, Any]     				=      pred.chunk(2)
            a__:	Optional[Any]     				=      pred_uncond + guidance_scale * (pred_text - pred_uncond)

            a__:	List[str]     				=      self.scheduler.step(lowercase       ,     lowercase       ,     lowercase).prev_sample

        return self.decode(lowercase)







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {}







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						"fsmt_decoder"


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     Tuple=0  ,lowerCamelCase_:     List[str]=0							)	->      Any:
       super().__init__()
       UpperCAmelCase_     :       Optional[Any]  		=					vocab_size
       UpperCAmelCase_     :       Union[str, Any]  		=					bos_token_id







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Dict       =						"fsmt"
   A__      : int       =						{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}


   def __init__(    self:     int  ,lowerCamelCase_:     Optional[Any]=["en", "de"]  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     Any=42024  ,lowerCamelCase_:     List[str]="relu"  ,lowerCamelCase_:     Any=1024  ,lowerCamelCase_:     Optional[int]=200  ,lowerCamelCase_:     str=1024  ,lowerCamelCase_:     Any=4096  ,lowerCamelCase_:     Dict=12  ,lowerCamelCase_:     Tuple=16  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     str=4096  ,lowerCamelCase_:     Union[str, Any]=12  ,lowerCamelCase_:     Any=16  ,lowerCamelCase_:     List[str]=0.0  ,lowerCamelCase_:     str=0.0  ,lowerCamelCase_:     Optional[int]=0.1  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     Tuple=0.0_2  ,lowerCamelCase_:     str=2  ,lowerCamelCase_:     List[str]=True  ,lowerCamelCase_:     Dict=True  ,lowerCamelCase_:     Any=False  ,lowerCamelCase_:     Any=5  ,lowerCamelCase_:     Optional[int]=1.0  ,lowerCamelCase_:     List[str]=False  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     List[str]=1  ,lowerCamelCase_:     int=0  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Any=2  ,**lowerCamelCase_:     Optional[Any]  ,)	->      int:
       UpperCAmelCase_     :       Dict  		=					langs
       UpperCAmelCase_     :       Tuple  		=					src_vocab_size
       UpperCAmelCase_     :       Optional[int]  		=					tgt_vocab_size
       UpperCAmelCase_     :       Optional[Any]  		=					d_model  # encoder_embed_dim and decoder_embed_dim

       UpperCAmelCase_     :       List[str]  		=					encoder_ffn_dim
       UpperCAmelCase_     :       List[Any]  		=					encoder_layers
       UpperCAmelCase_     :       List[Any]  		=					encoder_attention_heads
       UpperCAmelCase_     :       Union[str, Any]  		=					encoder_layerdrop
       UpperCAmelCase_     :       List[Any]  		=					decoder_layerdrop
       UpperCAmelCase_     :       str  		=					decoder_ffn_dim
       UpperCAmelCase_     :       Optional[int]  		=					decoder_layers
       UpperCAmelCase_     :       int  		=					decoder_attention_heads
       UpperCAmelCase_     :       List[Any]  		=					max_position_embeddings
       UpperCAmelCase_     :       Dict  		=					init_std  # Normal(0, this parameter)
       UpperCAmelCase_     :       Dict  		=					activation_function

       UpperCAmelCase_     :       List[str]  		=					DecoderConfig(vocab_size=lowerCamelCase_  ,bos_token_id=lowerCamelCase_							)
       if "decoder" in common_kwargs:
           del common_kwargs["decoder"]

       UpperCAmelCase_     :       Any  		=					scale_embedding  # scale factor will be sqrt(d_model) if True

       # 3 Types of Dropout
       UpperCAmelCase_     :       int  		=					attention_dropout
       UpperCAmelCase_     :       int  		=					activation_dropout
       UpperCAmelCase_     :       Any  		=					dropout

       UpperCAmelCase_     :       Any  		=					use_cache
       super().__init__(
           pad_token_id=lowerCamelCase_  ,bos_token_id=lowerCamelCase_  ,eos_token_id=lowerCamelCase_  ,decoder_start_token_id=lowerCamelCase_  ,is_encoder_decoder=lowerCamelCase_  ,tie_word_embeddings=lowerCamelCase_  ,forced_eos_token_id=lowerCamelCase_  ,max_length=lowerCamelCase_  ,num_beams=lowerCamelCase_  ,length_penalty=lowerCamelCase_  ,early_stopping=lowerCamelCase_  ,**lowerCamelCase_  ,)






   def       A__    (    self:     List[str]							)	->      Union[str, Any]:
       UpperCAmelCase_     :       str  		=					copy.deepcopy(self.__dict__							)
       UpperCAmelCase_     :       int  		=					self.decoder.to_dict()
       UpperCAmelCase_     :       Dict  		=					self.__class__.model_type
       return output
