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		)





import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




