

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  def __init__(							self						:		List[Any]     ,       __lowerCamelCase						:		TransformeraDModel     ,       __lowerCamelCase						:		AutoencoderKL     ,       __lowerCamelCase						:		KarrasDiffusionSchedulers     ,       __lowerCamelCase						:		Optional[Dict[int, str]] = None     ,       )		->							str:
       super().__init__()
       self.register_modules(transformer=__lowerCamelCase     ,       vae=__lowerCamelCase     ,       scheduler=__lowerCamelCase )

       # create a imagenet -> id dictionary for easier use
       SCREAMING_SNAKE_CASE__   =   {}
       if idalabel is not None:
            for key, value in idalabel.items():
                 for label in value.split(''',''' ):
                      SCREAMING_SNAKE_CASE__   =   int(__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   dict(sorted(self.labels.items() ) )






  def 				lowercase_		(							self						:		Tuple     ,       __lowerCamelCase						:		Union[str, List[str]] )		->							List[int]:

       if not isinstance(__lowerCamelCase     ,       __lowerCamelCase ):
            SCREAMING_SNAKE_CASE__   =   list(__lowerCamelCase )

       for l in label:
            if l not in self.labels:
                 raise ValueError(
                     f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )

       return [self.labels[l] for l in label]







  @torch.no_grad()
  def __call__(							self						:		Optional[int]     ,       __lowerCamelCase						:		List[int]     ,       __lowerCamelCase						:		float = 4.0     ,       __lowerCamelCase						:		Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,       __lowerCamelCase						:		int = 50     ,       __lowerCamelCase						:		Optional[str] = "pil"     ,       __lowerCamelCase						:		bool = True     ,       )		->							Union[ImagePipelineOutput, Tuple]:
       SCREAMING_SNAKE_CASE__   =   len(__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   self.transformer.config.sample_size
       SCREAMING_SNAKE_CASE__   =   self.transformer.config.in_channels

       SCREAMING_SNAKE_CASE__   =   randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)     ,       generator=__lowerCamelCase     ,       device=self.device     ,       dtype=self.transformer.dtype     ,       )
       SCREAMING_SNAKE_CASE__   =   torch.cat([latents] * 2 ) if guidance_scale > 1 else latents

       SCREAMING_SNAKE_CASE__   =   torch.tensor(__lowerCamelCase     ,       device=self.device ).reshape(-1 )
       SCREAMING_SNAKE_CASE__   =   torch.tensor([1000] * batch_size     ,       device=self.device )
       SCREAMING_SNAKE_CASE__   =   torch.cat([class_labels, class_null]     ,       0 ) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(__lowerCamelCase )

       for t in self.progress_bar(self.scheduler.timesteps ):
            if guidance_scale > 1:
                 SCREAMING_SNAKE_CASE__   =   latent_model_input[: len(__lowerCamelCase ) // 2]
                 SCREAMING_SNAKE_CASE__   =   torch.cat([half, half]     ,       dim=0 )
            SCREAMING_SNAKE_CASE__   =   self.scheduler.scale_model_input(__lowerCamelCase     ,       __lowerCamelCase )

            SCREAMING_SNAKE_CASE__   =   t
            if not torch.is_tensor(__lowerCamelCase ):
                 # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                 # This would be a good case for the `match` statement (Python 3.10+)
                 SCREAMING_SNAKE_CASE__   =   latent_model_input.device.type == '''mps'''
                 if isinstance(__lowerCamelCase     ,       __lowerCamelCase ):
                      SCREAMING_SNAKE_CASE__   =   torch.floataa if is_mps else torch.floataa
                 else:
                      SCREAMING_SNAKE_CASE__   =   torch.intaa if is_mps else torch.intaa
                 SCREAMING_SNAKE_CASE__   =   torch.tensor([timesteps]     ,       dtype=__lowerCamelCase     ,       device=latent_model_input.device )
            elif len(timesteps.shape ) == 0:
                 SCREAMING_SNAKE_CASE__   =   timesteps[None].to(latent_model_input.device )
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            SCREAMING_SNAKE_CASE__   =   timesteps.expand(latent_model_input.shape[0] )
            # predict noise model_output
            SCREAMING_SNAKE_CASE__   =   self.transformer(
                __lowerCamelCase     ,       timestep=__lowerCamelCase     ,       class_labels=__lowerCamelCase ).sample

            # perform guidance
            if guidance_scale > 1:
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   torch.split(__lowerCamelCase     ,       len(__lowerCamelCase ) // 2     ,       dim=0 )

                 SCREAMING_SNAKE_CASE__   =   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                 SCREAMING_SNAKE_CASE__   =   torch.cat([half_eps, half_eps]     ,       dim=0 )

                 SCREAMING_SNAKE_CASE__   =   torch.cat([eps, rest]     ,       dim=1 )

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                 SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   torch.split(__lowerCamelCase     ,       __lowerCamelCase     ,       dim=1 )
            else:
                 SCREAMING_SNAKE_CASE__   =   noise_pred

            # compute previous image: x_t -> x_t-1
            SCREAMING_SNAKE_CASE__   =   self.scheduler.step(__lowerCamelCase     ,       __lowerCamelCase     ,       __lowerCamelCase ).prev_sample

       if guidance_scale > 1:
            SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   latent_model_input.chunk(2     ,       dim=0 )
       else:
            SCREAMING_SNAKE_CASE__   =   latent_model_input

       SCREAMING_SNAKE_CASE__   =   1 / self.vae.config.scaling_factor * latents
       SCREAMING_SNAKE_CASE__   =   self.vae.decode(__lowerCamelCase ).sample

       SCREAMING_SNAKE_CASE__   =   (samples / 2 + 0.5).clamp(0     ,       1 )

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       SCREAMING_SNAKE_CASE__   =   samples.cpu().permute(0     ,       2     ,       3     ,       1 ).float().numpy()

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__   =   self.numpy_to_pil(__lowerCamelCase )

       if not return_dict:
            return (samples,)

       return ImagePipelineOutput(images=__lowerCamelCase )





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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "vivit"






  def __init__(							self						:		str     ,       __lowerCamelCase						:		List[Any]=224     ,       __lowerCamelCase						:		Optional[int]=32     ,       __lowerCamelCase						:		Tuple=[2, 16, 16]     ,       __lowerCamelCase						:		Union[str, Any]=3     ,       __lowerCamelCase						:		Optional[Any]=768     ,       __lowerCamelCase						:		Any=12     ,       __lowerCamelCase						:		Optional[Any]=12     ,       __lowerCamelCase						:		List[str]=3072     ,       __lowerCamelCase						:		Any="gelu_fast"     ,       __lowerCamelCase						:		Union[str, Any]=0.0     ,       __lowerCamelCase						:		int=0.0     ,       __lowerCamelCase						:		str=0.02     ,       __lowerCamelCase						:		Any=1e-06     ,       __lowerCamelCase						:		Dict=True     ,       **__lowerCamelCase						:		Any     ,       )		->							List[str]:
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps

       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   num_frames
       SCREAMING_SNAKE_CASE__   =   tubelet_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   qkv_bias

       super().__init__(**__lowerCamelCase )


