'''simple docstring'''




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase				:		int       			=		logging.get_logger(__name__)

_UpperCamelCase				:		List[Any]       			=		{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}

class   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   __lowerCamelCase    =  '''oneformer'''
   __lowerCamelCase    =  {'''hidden_size''': '''hidden_dim'''}





   def __init__(							self:    List[str]			,a:    Optional[Dict] = None			,a:    int = 255			,a:    int = 150			,a:    int = 0.1			,a:    float = 2.0			,a:    float = 5.0			,a:    float = 5.0			,a:    float = 0.5			,a:    float = 0.07			,a:    int = 12544			,a:    float = 3.0			,a:    float = 0.75			,a:    float = 0.02			,a:    float = 1.0			,a:    float = 1e-05			,a:    bool = False			,a:    bool = True			,a:    bool = True			,a:    Optional[list] = [4, 8, 16, 32]			,a:    int = 77			,a:    int = 256			,a:    int = 77			,a:    int = 6			,a:    int = 49408			,a:    int = 2			,a:    int = 16			,a:    int = 256			,a:    int = 256			,a:    int = 256			,a:    int = 1024			,a:    str = "GN"			,a:    int = 6			,a:    int = 10			,a:    bool = True			,a:    int = 8			,a:    float = 0.1			,a:    int = 2048			,a:    bool = False			,a:    bool = False			,a:    int = 2			,a:    int = 4			,**a:    Any			,):
    if backbone_config is None:
     logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'	)
     __UpperCAmelCase         =					CONFIG_MAPPING['swin'](
         image_size=224			,in_channels=3			,patch_size=4			,embed_dim=96			,depths=[2, 2, 6, 2]			,num_heads=[3, 6, 12, 24]			,window_size=7			,drop_path_rate=0.3			,use_absolute_embeddings=a			,out_features=['stage1', 'stage2', 'stage3', 'stage4']			,)
    elif isinstance(a			,a	):
     __UpperCAmelCase         =					backbone_config.get('model_type'	)
     __UpperCAmelCase         =					CONFIG_MAPPING[backbone_model_type]
     __UpperCAmelCase         =					config_class.from_dict(a	)

    __UpperCAmelCase         =					backbone_config

    __UpperCAmelCase         =					ignore_value
    __UpperCAmelCase         =					num_queries
    __UpperCAmelCase         =					no_object_weight
    __UpperCAmelCase         =					class_weight
    __UpperCAmelCase         =					mask_weight
    __UpperCAmelCase         =					dice_weight
    __UpperCAmelCase         =					contrastive_weight
    __UpperCAmelCase         =					contrastive_temperature
    __UpperCAmelCase         =					train_num_points
    __UpperCAmelCase         =					oversample_ratio
    __UpperCAmelCase         =					importance_sample_ratio
    __UpperCAmelCase         =					init_std
    __UpperCAmelCase         =					init_xavier_std
    __UpperCAmelCase         =					layer_norm_eps
    __UpperCAmelCase         =					is_training
    __UpperCAmelCase         =					use_auxiliary_loss
    __UpperCAmelCase         =					output_auxiliary_logits
    __UpperCAmelCase         =					strides
    __UpperCAmelCase         =					task_seq_len
    __UpperCAmelCase         =					text_encoder_width
    __UpperCAmelCase         =					text_encoder_context_length
    __UpperCAmelCase         =					text_encoder_num_layers
    __UpperCAmelCase         =					text_encoder_vocab_size
    __UpperCAmelCase         =					text_encoder_proj_layers
    __UpperCAmelCase         =					text_encoder_n_ctx
    __UpperCAmelCase         =					conv_dim
    __UpperCAmelCase         =					mask_dim
    __UpperCAmelCase         =					hidden_dim
    __UpperCAmelCase         =					encoder_feedforward_dim
    __UpperCAmelCase         =					norm
    __UpperCAmelCase         =					encoder_layers
    __UpperCAmelCase         =					decoder_layers
    __UpperCAmelCase         =					use_task_norm
    __UpperCAmelCase         =					num_attention_heads
    __UpperCAmelCase         =					dropout
    __UpperCAmelCase         =					dim_feedforward
    __UpperCAmelCase         =					pre_norm
    __UpperCAmelCase         =					enforce_input_proj
    __UpperCAmelCase         =					query_dec_layers
    __UpperCAmelCase         =					common_stride
    __UpperCAmelCase         =					decoder_layers

    super().__init__(**a	)





   def 			snake_case	(							self:    Dict	):
    __UpperCAmelCase         =					copy.deepcopy(self.__dict__	)
    __UpperCAmelCase         =					self.backbone_config.to_dict()
    __UpperCAmelCase         =					self.__class__.model_type
    return output




'''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   _lowercase(     _lowerCamelCase				):



   """simple docstring"""





   def __init__(							self:    List[str]			,a:    AutoencoderKL			,a:    CLIPTextModel			,a:    CLIPTokenizer			,a:    UNetaDConditionModel			,a:    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]			,):
    super().__init__()

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





   def 			snake_case	(							self:    Union[str, Any]			,a:    Any	):
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.encode(tfms.ToTensor()(a	).unsqueeze(0	).to(self.device	) * 2 - 1	)
     __UpperCAmelCase         =					0.18215 * latent.latent_dist.sample()
    return latent





   def 			snake_case	(							self:    Any			,a:    List[str]	):
    __UpperCAmelCase         =					(1 / 0.18215) * latent
    with torch.no_grad():
     __UpperCAmelCase         =					self.vae.decode(a	).sample
    __UpperCAmelCase         =					(img / 2 + 0.5).clamp(0			,1	)
    __UpperCAmelCase         =					img.detach().cpu().permute(0			,2			,3			,1	).numpy()
    __UpperCAmelCase         =					(img * 255).round().astype('uint8'	)
    return Image.fromarray(img[0]	)





   def 			snake_case	(							self:    Any			,a:    str	):
    __UpperCAmelCase         =					self.tokenizer(
        a			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

    __UpperCAmelCase         =					self.tokenizer(
        ''			,padding='max_length'			,max_length=self.tokenizer.model_max_length			,truncation=a			,return_tensors='pt'			,)

    __UpperCAmelCase         =					self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

    return torch.cat([uncond_embedding, text_embedding]	)





   def __call__(							self:    Optional[Any]			,a:    Image.Image			,a:    str			,a:    float = 0.3			,a:    float = 0.6			,a:    float = 0.5			,a:    int = 42			,a:    int = 50			,a:    float = 7.5			,):
    __UpperCAmelCase         =					steps - int(kmin * steps	)
    __UpperCAmelCase         =					steps - int(kmax * steps	)

    __UpperCAmelCase         =					self.prep_text(a	)

    self.scheduler.set_timesteps(a	)

    __UpperCAmelCase  ,			__UpperCAmelCase         =					img.size
    __UpperCAmelCase         =					self.encode(a	)

    torch.manual_seed(a	)
    __UpperCAmelCase         =					torch.randn(
        (1, self.unet.config.in_channels, height // 8, width // 8)			,).to(self.device	)

    __UpperCAmelCase         =					self.scheduler.add_noise(
        a			,a			,timesteps=self.scheduler.timesteps[tmax]			,)

    __UpperCAmelCase         =					torch.cat([latents] * 2	)

    __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,self.scheduler.timesteps[tmax]	)

    with torch.no_grad():
     __UpperCAmelCase         =					self.unet(
         a			,self.scheduler.timesteps[tmax]			,encoder_hidden_states=a			,).sample

    __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
    __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

    __UpperCAmelCase         =					self.scheduler.step(a			,self.scheduler.timesteps[tmax]			,a	).prev_sample

    for i, t in enumerate(tqdm(self.scheduler.timesteps	)	):
     if i > tmax:
      if i < tmin:  # layout generation phase
       __UpperCAmelCase         =					self.scheduler.add_noise(
           a			,a			,timesteps=a			,)

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

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

      __UpperCAmelCase         =					self.scheduler.scale_model_input(a			,a	)

      with torch.no_grad():
       __UpperCAmelCase         =					self.unet(
           a			,a			,encoder_hidden_states=a			,).sample

      __UpperCAmelCase  ,			__UpperCAmelCase         =					pred.chunk(2	)
      __UpperCAmelCase         =					pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __UpperCAmelCase         =					self.scheduler.step(a			,a			,a	).prev_sample

    return self.decode(a	)



