





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 		_lowerCAmelCase(		__lowercase):

  """simple docstring"""







  def __init__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							)->   str:
     super().__init__()

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

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     with torch.no_grad():
        __A    =  self.vae.encode(tfms.ToTensor()(A_      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
        __A    =  0.18215 * latent.latent_dist.sample()
     return latent

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   Optional[Any]:
     __A    =  (1 / 0.18215) * latent
     with torch.no_grad():
        __A    =  self.vae.decode(A_      ).sample
     __A    =  (img / 2 + 0.5).clamp(0    ,							1      )
     __A    =  img.detach().cpu().permute(0    ,							2    ,							3    ,							1      ).numpy()
     __A    =  (img * 2_55).round().astype('''uint8'''      )
     return Image.fromarray(img[0]      )

  def 						SCREAMING_SNAKE_CASE__			(   self    ,							UpperCAmelCase      )->   List[Any]:
     __A    =  self.tokenizer(
         A_    ,							padding='''max_length'''    ,							max_length=self.tokenizer.model_max_length    ,							truncation=A_    ,							return_tensors='''pt'''    ,							)

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

     __A    =  self.tokenizer(
         ''''''    ,							padding='''max_length'''    ,							max_length=self.tokenizer.model_max_length    ,							truncation=A_    ,							return_tensors='''pt'''    ,							)

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

     return torch.cat([uncond_embedding, text_embedding]      )





  def __call__(   self    ,							UpperCAmelCase    ,							UpperCAmelCase    ,							UpperCAmelCase = 0.3    ,							UpperCAmelCase = 0.6    ,							UpperCAmelCase = 0.5    ,							UpperCAmelCase = 42    ,							UpperCAmelCase = 50    ,							UpperCAmelCase = 7.5    ,							)->   Image.Image:
     __A    =  steps - int(kmin * steps      )
     __A    =  steps - int(kmax * steps      )

     __A    =  self.prep_text(A_      )

     self.scheduler.set_timesteps(A_      )

     __A   ,		__A    =  img.size
     __A    =  self.encode(A_      )

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

     __A    =  self.scheduler.add_noise(
         A_    ,							A_    ,							timesteps=self.scheduler.timesteps[tmax]    ,							)

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

     __A    =  self.scheduler.scale_model_input(A_    ,							self.scheduler.timesteps[tmax]      )

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

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

     __A    =  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
              __A    =  self.scheduler.add_noise(
                  A_    ,							A_    ,							timesteps=A_    ,							)

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

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

           __A    =  self.scheduler.scale_model_input(A_    ,							A_      )

           with torch.no_grad():
              __A    =  self.unet(
                  A_    ,							A_    ,							encoder_hidden_states=A_    ,							).sample

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

           __A    =  self.scheduler.step(A_    ,							A_    ,							A_      ).prev_sample

     return self.decode(A_      )


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__						      =			{
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPRContextEncoderTokenizerFast',
         'DPRQuestionEncoderTokenizerFast',
         'DPRReaderTokenizerFast',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPRContextEncoder',
         'DPRPretrainedContextEncoder',
         'DPRPreTrainedModel',
         'DPRPretrainedQuestionEncoder',
         'DPRPretrainedReader',
         'DPRQuestionEncoder',
         'DPRReader',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFDPRContextEncoder',
         'TFDPRPretrainedContextEncoder',
         'TFDPRPretrainedQuestionEncoder',
         'TFDPRPretrainedReader',
         'TFDPRQuestionEncoder',
         'TFDPRReader',
     ]


if TYPE_CHECKING:
     from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
     from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
     )

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_dpr_fast import (
              DPRContextEncoderTokenizerFast,
              DPRQuestionEncoderTokenizerFast,
              DPRReaderTokenizerFast,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpr import (
              DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPRContextEncoder,
              DPRPretrainedContextEncoder,
              DPRPreTrainedModel,
              DPRPretrainedQuestionEncoder,
              DPRPretrainedReader,
              DPRQuestionEncoder,
              DPRReader,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_dpr import (
              TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDPRContextEncoder,
              TFDPRPretrainedContextEncoder,
              TFDPRPretrainedQuestionEncoder,
              TFDPRPretrainedReader,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )

else:
     import sys

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