from typing import List, Optional, Tuple, Union

import torch

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





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : UNetaDModel
   __UpperCAmelCase : ScoreSdeVeScheduler


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						UNetaDModel       ,  __UpperCAmelCase     :						ScoreSdeVeScheduler		)						->				List[Any]:
         """simple docstring"""



         super().__init__()
         self.register_modules(unet=__UpperCAmelCase       ,  scheduler=__UpperCAmelCase		)





   @torch.no_grad()
   def __call__(self     :						List[Any]       ,  __UpperCAmelCase     :						int = 1       ,  __UpperCAmelCase     :						int = 2_0_0_0       ,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,  __UpperCAmelCase     :						Optional[str] = "pil"       ,  __UpperCAmelCase     :						bool = True       ,  **__UpperCAmelCase     :						str       ,  )						->				Union[ImagePipelineOutput, Tuple]:
         """simple docstring"""



         UpperCAmelCase__								=  self.unet.config.sample_size
         UpperCAmelCase__								=  (batch_size, 3, img_size, img_size)

         UpperCAmelCase__								=  self.unet

         UpperCAmelCase__								=  randn_tensor(__UpperCAmelCase       ,  generator=__UpperCAmelCase		) * self.scheduler.init_noise_sigma
         UpperCAmelCase__								=  sample.to(self.device		)

         self.scheduler.set_timesteps(__UpperCAmelCase		)
         self.scheduler.set_sigmas(__UpperCAmelCase		)

         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
               UpperCAmelCase__								=  self.scheduler.sigmas[i] * torch.ones(shape[0]       ,  device=self.device		)

               # correction step
               for _ in range(self.scheduler.config.correct_steps		):
                     UpperCAmelCase__								=  self.unet(__UpperCAmelCase       ,  __UpperCAmelCase		).sample
                     UpperCAmelCase__								=  self.scheduler.step_correct(__UpperCAmelCase       ,  __UpperCAmelCase       ,  generator=__UpperCAmelCase		).prev_sample

               # prediction step
               UpperCAmelCase__								=  model(__UpperCAmelCase       ,  __UpperCAmelCase		).sample
               UpperCAmelCase__								=  self.scheduler.step_pred(__UpperCAmelCase       ,  __UpperCAmelCase       ,  __UpperCAmelCase       ,  generator=__UpperCAmelCase		)

               UpperCAmelCase__		,      UpperCAmelCase__								=  output.prev_sample, output.prev_sample_mean

         UpperCAmelCase__								=  sample_mean.clamp(0       ,  1		)
         UpperCAmelCase__								=  sample.cpu().permute(0       ,  2       ,  3       ,  1		).numpy()
         if output_type == "pil":
               UpperCAmelCase__								=  self.numpy_to_pil(__UpperCAmelCase		)

         if not return_dict:
               return (sample,)

         return ImagePipelineOutput(images=__UpperCAmelCase		)




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[torch.FloatTensor]      =			None
   __UpperCAmelCase : torch.FloatTensor      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None
   __UpperCAmelCase : Optional[Tuple[torch.FloatTensor]]      =			None





class      A							(				UpperCAmelCase_     ):


   def __init__(self     :						Union[str, Any]       ,  __UpperCAmelCase     :						Tuple=1       ,  __UpperCAmelCase     :						str=0       ,  __UpperCAmelCase     :						str=2       ,  __UpperCAmelCase     :						Union[str, Any]=5_1_2       ,  __UpperCAmelCase     :						List[str]="cls"       ,  __UpperCAmelCase     :						Optional[int]=False       ,  __UpperCAmelCase     :						str=True       ,  **__UpperCAmelCase     :						str       ,  )						->				int:
         """simple docstring"""



         super().__init__(pad_token_id=__UpperCAmelCase       ,  bos_token_id=__UpperCAmelCase       ,  eos_token_id=__UpperCAmelCase       ,  **__UpperCAmelCase		)
         UpperCAmelCase__								=  project_dim
         UpperCAmelCase__								=  pooler_fn
         UpperCAmelCase__								=  learn_encoder
         UpperCAmelCase__								=  use_attention_mask





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Tuple      =			[r'pooler', r'logit_scale']
   __UpperCAmelCase : int      =			[r'position_ids', r'predictions.decoder.bias']
   __UpperCAmelCase : Any      =			'roberta'
   __UpperCAmelCase : List[str]      =			RobertaSeriesConfig


   def __init__(self     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]		)						->				int:
         """simple docstring"""



         super().__init__(__UpperCAmelCase		)
         UpperCAmelCase__								=  XLMRobertaModel(__UpperCAmelCase		)
         UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
         UpperCAmelCase__								=  getattr(__UpperCAmelCase       ,  "has_pre_transformation"       ,  __UpperCAmelCase		)
         if self.has_pre_transformation:
               UpperCAmelCase__								=  nn.Linear(config.hidden_size       ,  config.project_dim		)
               UpperCAmelCase__								=  nn.LayerNorm(config.hidden_size       ,  eps=config.layer_norm_eps		)
         self.post_init()





   def 			lowercase_      (self     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[torch.Tensor] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  __UpperCAmelCase     :						Optional[bool] = None       ,  )						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  return_dict if return_dict is not None else self.config.use_return_dict

         UpperCAmelCase__								=  self.base_model(
             input_ids=__UpperCAmelCase       ,  attention_mask=__UpperCAmelCase       ,  token_type_ids=__UpperCAmelCase       ,  position_ids=__UpperCAmelCase       ,  head_mask=__UpperCAmelCase       ,  inputs_embeds=__UpperCAmelCase       ,  encoder_hidden_states=__UpperCAmelCase       ,  encoder_attention_mask=__UpperCAmelCase       ,  output_attentions=__UpperCAmelCase       ,  output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,  return_dict=__UpperCAmelCase       ,  )

         if self.has_pre_transformation:
               UpperCAmelCase__								=  outputs["hidden_states"][-2]
               UpperCAmelCase__								=  self.pre_LN(__UpperCAmelCase		)
               UpperCAmelCase__								=  self.transformation_pre(__UpperCAmelCase		)

               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )
         else:
               UpperCAmelCase__								=  self.transformation(outputs.last_hidden_state		)
               return TransformationModelOutput(
                   projection_state=__UpperCAmelCase       ,  last_hidden_state=outputs.last_hidden_state       ,  hidden_states=outputs.hidden_states       ,  attentions=outputs.attentions       ,  )



