






"""simple docstring"""




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			(	snake_case_      ):


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

   # create a imagenet -> id dictionary for easier use
   _a  :       str             =	{}
   if idalabel is not None:
     for key, value in idalabel.items():
       for label in value.split(""","""							):
         _a  :       List[Any]             =	int(UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	dict(sorted(self.labels.items()							)							)


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						Union[str, List[str]]							)					->       List[int]:

   if not isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
     _a  :       Any             =	list(UpperCAmelCase__							)

   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		:						Tuple							,						UpperCAmelCase__		:						List[int]							,						UpperCAmelCase__		:						float = 4.0							,						UpperCAmelCase__		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None							,						UpperCAmelCase__		:						int = 50							,						UpperCAmelCase__		:						Optional[str] = "pil"							,						UpperCAmelCase__		:						bool = True							,						)					->       Union[ImagePipelineOutput, Tuple]:
   _a  :       int             =	len(UpperCAmelCase__							)
   _a  :       Dict             =	self.transformer.config.sample_size
   _a  :       Optional[int]             =	self.transformer.config.in_channels

   _a  :       Dict             =	randn_tensor(
       shape=(batch_size, latent_channels, latent_size, latent_size)							,						generator=UpperCAmelCase__							,						device=self.device							,						dtype=self.transformer.dtype							,						)
   _a  :       Any             =	torch.cat([latents] * 2							) if guidance_scale > 1 else latents

   _a  :       Any             =	torch.tensor(UpperCAmelCase__							,						device=self.device							).reshape(-1							)
   _a  :       Optional[int]             =	torch.tensor([1000] * batch_size							,						device=self.device							)
   _a  :       List[Any]             =	torch.cat([class_labels, class_null]							,						0							) if guidance_scale > 1 else class_labels

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

   for t in self.progress_bar(self.scheduler.timesteps							):
     if guidance_scale > 1:
       _a  :       Optional[Any]             =	latent_model_input[: len(UpperCAmelCase__							) // 2]
       _a  :       List[str]             =	torch.cat([half, half]							,						dim=0							)
     _a  :       Any             =	self.scheduler.scale_model_input(UpperCAmelCase__							,						UpperCAmelCase__							)

     _a  :       List[str]             =	t
     if not torch.is_tensor(UpperCAmelCase__							):
       # 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+)
       _a  :       Any             =	latent_model_input.device.type == """mps"""
       if isinstance(UpperCAmelCase__							,						UpperCAmelCase__							):
         _a  :       Tuple             =	torch.floataa if is_mps else torch.floataa
       else:
         _a  :       List[Any]             =	torch.intaa if is_mps else torch.intaa
       _a  :       Optional[int]             =	torch.tensor([timesteps]							,						dtype=UpperCAmelCase__							,						device=latent_model_input.device							)
     elif len(timesteps.shape							) == 0:
       _a  :       Dict             =	timesteps[None].to(latent_model_input.device							)
     # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
     _a  :       Union[str, Any]             =	timesteps.expand(latent_model_input.shape[0]							)
     # predict noise model_output
     _a  :       Union[str, Any]             =	self.transformer(
         UpperCAmelCase__							,						timestep=UpperCAmelCase__							,						class_labels=UpperCAmelCase__							).sample

     # perform guidance
     if guidance_scale > 1:
       _a      ,  _a  :       Union[str, Any]             =	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
       _a      ,  _a  :       List[Any]             =	torch.split(UpperCAmelCase__							,						len(UpperCAmelCase__							) // 2							,						dim=0							)

       _a  :       Optional[int]             =	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
       _a  :       str             =	torch.cat([half_eps, half_eps]							,						dim=0							)

       _a  :       Dict             =	torch.cat([eps, rest]							,						dim=1							)

     # learned sigma
     if self.transformer.config.out_channels // 2 == latent_channels:
       _a      ,  _a  :       List[str]             =	torch.split(UpperCAmelCase__							,						UpperCAmelCase__							,						dim=1							)
     else:
       _a  :       List[str]             =	noise_pred

     # compute previous image: x_t -> x_t-1
     _a  :       Any             =	self.scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   if guidance_scale > 1:
     _a      ,  _a  :       Optional[Any]             =	latent_model_input.chunk(2							,						dim=0							)
   else:
     _a  :       Any             =	latent_model_input

   _a  :       int             =	1 / self.vae.config.scaling_factor * latents
   _a  :       Union[str, Any]             =	self.vae.decode(UpperCAmelCase__							).sample

   _a  :       str             =	(samples / 2 + 0.5).clamp(0							,						1							)

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

   if output_type == "pil":
     _a  :       List[Any]             =	self.numpy_to_pil(UpperCAmelCase__							)

   if not return_dict:
     return (samples,)

   return ImagePipelineOutput(images=UpperCAmelCase__							)









"""simple docstring"""




import warnings

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


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

