






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      UpperCamelCase_							(				UpperCamelCase       ):




						def __init__(    self		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	lowercase		,	)			->						Optional[Any]:
													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      snake_case__(    self		,	lowercase       )			->						int:
													with torch.no_grad():
																				_a  :    List[Any]			=						self.vae.encode(tfms.ToTensor()(__SCREAMING_SNAKE_CASE       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
																				_a  :    List[str]			=						0.18215 * latent.latent_dist.sample()
													return latent




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




						def      snake_case__(    self		,	lowercase       )			->						Optional[int]:
													_a  :    Dict			=						self.tokenizer(
													    __SCREAMING_SNAKE_CASE		,	padding='''max_length'''		,	max_length=self.tokenizer.model_max_length		,	truncation=__SCREAMING_SNAKE_CASE		,	return_tensors='''pt'''		,	)

													_a  :    Dict			=						self.text_encoder(text_input.input_ids.to(self.device       )       )[0]

													_a  :    Dict			=						self.tokenizer(
													    ''''''		,	padding='''max_length'''		,	max_length=self.tokenizer.model_max_length		,	truncation=__SCREAMING_SNAKE_CASE		,	return_tensors='''pt'''		,	)

													_a  :    Dict			=						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		,	)			->						List[str]:
													_a  :    Any			=						steps - int(kmin * steps       )
													_a  :    Tuple			=						steps - int(kmax * steps       )

													_a  :    List[Any]			=						self.prep_text(__SCREAMING_SNAKE_CASE       )

													self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       )

													_a  :    Optional[int]			=						img.size
													_a  :    List[str]			=						self.encode(__SCREAMING_SNAKE_CASE       )

													torch.manual_seed(__SCREAMING_SNAKE_CASE       )
													_a  :    int			=						torch.randn(
													    (1, self.unet.config.in_channels, height // 8, width // 8)		,	).to(self.device       )

													_a  :    Any			=						self.scheduler.add_noise(
													    __SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE		,	timesteps=self.scheduler.timesteps[tmax]		,	)

													_a  :    int			=						torch.cat([latents] * 2       )

													_a  :    List[Any]			=						self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,	self.scheduler.timesteps[tmax]       )

													with torch.no_grad():
																				_a  :    Any			=						self.unet(
																				    __SCREAMING_SNAKE_CASE		,	self.scheduler.timesteps[tmax]		,	encoder_hidden_states=__SCREAMING_SNAKE_CASE		,	).sample

													_a  :    int			=						pred.chunk(2       )
													_a  :    List[Any]			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

													_a  :    Dict			=						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
																																		_a  :    List[str]			=						self.scheduler.add_noise(
																																		    __SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE		,	timesteps=__SCREAMING_SNAKE_CASE		,	)

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

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

																											_a  :    Any			=						self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE       )

																											with torch.no_grad():
																																		_a  :    Optional[Any]			=						self.unet(
																																		    __SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE		,	encoder_hidden_states=__SCREAMING_SNAKE_CASE		,	).sample

																											_a  :    str			=						pred.chunk(2       )
																											_a  :    Tuple			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

																											_a  :    Optional[Any]			=						self.scheduler.step(__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE		,	__SCREAMING_SNAKE_CASE       ).prev_sample

													return self.decode(__SCREAMING_SNAKE_CASE       )

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase												=   {'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase												=   [
       'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWDForCTC',
       'SEWDForSequenceClassification',
       'SEWDModel',
       'SEWDPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase												=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


