


"""simple docstring"""




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


__SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					=						{
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}





class __snake_case		(					A__      ):







   """simple docstring"""




   lowerCAmelCase_				:   Any     	=			'videomae'




   def __init__(		self							:Dict				,  UpperCamelCase__							:Union[str, Any]=224				,  UpperCamelCase__							:Any=16				,  UpperCamelCase__							:Tuple=3				,  UpperCamelCase__							:Dict=16				,  UpperCamelCase__							:int=2				,  UpperCamelCase__							:Tuple=768				,  UpperCamelCase__							:List[Any]=12				,  UpperCamelCase__							:Optional[int]=12				,  UpperCamelCase__							:Optional[Any]=3_072				,  UpperCamelCase__							:str="gelu"				,  UpperCamelCase__							:Any=0.0				,  UpperCamelCase__							:Dict=0.0				,  UpperCamelCase__							:int=0.02				,  UpperCamelCase__							:Optional[Any]=1E-12				,  UpperCamelCase__							:Optional[Any]=True				,  UpperCamelCase__							:Optional[Any]=True				,  UpperCamelCase__							:Tuple=6				,  UpperCamelCase__							:List[str]=384				,  UpperCamelCase__							:int=4				,  UpperCamelCase__							:Optional[int]=1_536				,  UpperCamelCase__							:Union[str, Any]=True				,  **UpperCamelCase__							:Tuple				,  ):
          super().__init__(**_lowerCAmelCase       )

          _a          =				image_size
          _a          =				patch_size
          _a          =				num_channels
          _a          =				num_frames
          _a          =				tubelet_size

          _a          =				hidden_size
          _a          =				num_hidden_layers
          _a          =				num_attention_heads
          _a          =				intermediate_size
          _a          =				hidden_act
          _a          =				hidden_dropout_prob
          _a          =				attention_probs_dropout_prob
          _a          =				initializer_range
          _a          =				layer_norm_eps
          _a          =				qkv_bias
          _a          =				use_mean_pooling

          _a          =				decoder_num_attention_heads
          _a          =				decoder_hidden_size
          _a          =				decoder_num_hidden_layers
          _a          =				decoder_intermediate_size
          _a          =				norm_pix_loss




"""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		(					_SCREAMING_SNAKE_CASE      ):







   """simple docstring"""




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

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




   def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
          with torch.no_grad():
                 _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                 _a          =				0.18215 * latent.latent_dist.sample()
          return latent




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




   def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
          _a          =				self.tokenizer(
              UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  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=UpperCamelCase__				,  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							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
          _a          =				steps - int(kmin * steps       )
          _a          =				steps - int(kmax * steps       )

          _a          =				self.prep_text(UpperCamelCase__       )

          self.scheduler.set_timesteps(UpperCamelCase__       )

          _a   , _a          =				img.size
          _a          =				self.encode(UpperCamelCase__       )

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

          _a          =				self.scheduler.add_noise(
              UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )

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

          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )

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

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

          _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).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(
                                   UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )

                               _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(UpperCamelCase__				,  UpperCamelCase__       )

                        with torch.no_grad():
                               _a          =				self.unet(
                                   UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample

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

                        _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample

          return self.decode(UpperCamelCase__       )
