


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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class       __magic_name__	(     __lowerCAmelCase):


     def __init__(      self							:     List[Any]							,	lowerCamelCase__							:     VQModel							,	lowerCamelCase__							:     UNetaDModel							,	lowerCamelCase__							:     DDIMScheduler			)  ->      str:





      '''simple docstring'''






      super().__init__()
      self.register_modules(vqvae=lowerCamelCase__							,	unet=lowerCamelCase__							,	scheduler=lowerCamelCase__			)


     @torch.no_grad()
     def __call__(      self							:     str							,	lowerCamelCase__							:     int = 1							,	lowerCamelCase__							:     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,	lowerCamelCase__							:     float = 0.0							,	lowerCamelCase__							:     int = 50							,	lowerCamelCase__							:     Optional[str] = "pil"							,	lowerCamelCase__							:     bool = True							,	**lowerCamelCase__							:     Tuple							,	)  ->      Union[Tuple, ImagePipelineOutput]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,	generator=lowerCamelCase__							,	)
      UpperCamelCase__					:    Tuple          =   latents.to(self.device			)

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCamelCase__					:    int          =   latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(lowerCamelCase__			)

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      UpperCamelCase__					:    Any          =   '''eta''' in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

      UpperCamelCase__					:    str          =   {}
      if accepts_eta:
       UpperCamelCase__					:    Tuple          =   eta

      for t in self.progress_bar(self.scheduler.timesteps			):
       UpperCamelCase__					:    Tuple          =   self.scheduler.scale_model_input(lowerCamelCase__							,	lowerCamelCase__			)
       # predict the noise residual
       UpperCamelCase__					:    Optional[Any]          =   self.unet(lowerCamelCase__							,	lowerCamelCase__			).sample
       # compute the previous noisy sample x_t -> x_t-1
       UpperCamelCase__					:    Union[str, Any]          =   self.scheduler.step(lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__							,	**lowerCamelCase__			).prev_sample

      # decode the image latents with the VAE
      UpperCamelCase__					:    Optional[Any]          =   self.vqvae.decode(lowerCamelCase__			).sample

      UpperCamelCase__					:    Tuple          =   (image / 2 + 0.5).clamp(0							,	1			)
      UpperCamelCase__					:    Tuple          =   image.cpu().permute(0							,	2							,	3							,	1			).numpy()
      if output_type == "pil":
       UpperCamelCase__					:    str          =   self.numpy_to_pil(lowerCamelCase__			)

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=lowerCamelCase__			)






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__UpperCamelCase       :       Any						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}

class       __magic_name__	(     __lowerCAmelCase):
     A:		List[Any]			=      "roberta-prelayernorm"


     def __init__(      self							:     Tuple							,	lowerCamelCase__							:     List[Any]=50265							,	lowerCamelCase__							:     Optional[Any]=768							,	lowerCamelCase__							:     str=12							,	lowerCamelCase__							:     Union[str, Any]=12							,	lowerCamelCase__							:     Dict=3072							,	lowerCamelCase__							:     int="gelu"							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     Tuple=0.1							,	lowerCamelCase__							:     List[str]=512							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Tuple=0.02							,	lowerCamelCase__							:     List[Any]=1E-1_2							,	lowerCamelCase__							:     str=1							,	lowerCamelCase__							:     int=0							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Union[str, Any]="absolute"							,	lowerCamelCase__							:     Optional[Any]=True							,	lowerCamelCase__							:     Dict=None							,	**lowerCamelCase__							:     Any							,	)  ->      Optional[int]:





      '''simple docstring'''






      super().__init__(pad_token_id=lowerCamelCase__							,	bos_token_id=lowerCamelCase__							,	eos_token_id=lowerCamelCase__							,	**lowerCamelCase__			)

      UpperCamelCase__					:    Optional[int]          =   vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_size
      UpperCamelCase__					:    List[str]          =   num_hidden_layers
      UpperCamelCase__					:    Optional[int]          =   num_attention_heads
      UpperCamelCase__					:    List[str]          =   hidden_act
      UpperCamelCase__					:    Optional[int]          =   intermediate_size
      UpperCamelCase__					:    Optional[int]          =   hidden_dropout_prob
      UpperCamelCase__					:    List[str]          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Optional[int]          =   max_position_embeddings
      UpperCamelCase__					:    Optional[Any]          =   type_vocab_size
      UpperCamelCase__					:    Union[str, Any]          =   initializer_range
      UpperCamelCase__					:    Dict          =   layer_norm_eps
      UpperCamelCase__					:    Union[str, Any]          =   position_embedding_type
      UpperCamelCase__					:    Optional[int]          =   use_cache
      UpperCamelCase__					:    int          =   classifier_dropout



class       __magic_name__	(     __lowerCAmelCase):


     @property
     def      UpperCAmelCase__	(      self							:     int			)  ->      Mapping[str, Mapping[int, str]]:





      '''simple docstring'''






      if self.task == "multiple-choice":
       UpperCamelCase__					:    Dict          =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
       UpperCamelCase__					:    Any          =   {0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]			)


