


"""simple docstring"""






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(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							42
		A    =							42
		def __init__( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



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





		@torch.no_grad()
		def __call__( self					,				SCREAMING_SNAKE_CASE__ = 1					,				SCREAMING_SNAKE_CASE__ = 20_00					,				SCREAMING_SNAKE_CASE__ = None					,				SCREAMING_SNAKE_CASE__ = "pil"					,				SCREAMING_SNAKE_CASE__ = True					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Union[ImagePipelineOutput, Tuple]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	self.unet.config.sample_size
					_UpperCamelCase					:str					     =	(batch_size, 3, img_size, img_size)

					_UpperCamelCase					:Tuple					     =	self.unet

					_UpperCamelCase					:Optional[int]					     =	randn_tensor(SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ) * self.scheduler.init_noise_sigma
					_UpperCamelCase					:Union[str, Any]					     =	sample.to(self.device   )

					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE__   )
					self.scheduler.set_sigmas(SCREAMING_SNAKE_CASE__   )

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

								# correction step
								for _ in range(self.scheduler.config.correct_steps   ):
											_UpperCamelCase					:int					     =	self.unet(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample
											_UpperCamelCase					:List[Any]					     =	self.scheduler.step_correct(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   ).prev_sample

								# prediction step
								_UpperCamelCase					:Union[str, Any]					     =	model(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ).sample
								_UpperCamelCase					:int					     =	self.scheduler.step_pred(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				generator=SCREAMING_SNAKE_CASE__   )

								_UpperCamelCase     ,      _UpperCamelCase					:Optional[Any]					     =	output.prev_sample, output.prev_sample_mean

					_UpperCamelCase					:List[Any]					     =	sample_mean.clamp(0					,				1   )
					_UpperCamelCase					:Any					     =	sample.cpu().permute(0					,				2					,				3					,				1   ).numpy()
					if output_type == "pil":
								_UpperCamelCase					:Optional[Any]					     =	self.numpy_to_pil(SCREAMING_SNAKE_CASE__   )

					if not return_dict:
								return (sample,)

					return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE__   )





"""simple docstring"""






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


UpperCamelCase__				:str			   =	logging.get_logger(__name__)

UpperCamelCase__				:Optional[int]			   =	{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"markuplm"
		def __init__( self					,				SCREAMING_SNAKE_CASE__=3_05_22					,				SCREAMING_SNAKE_CASE__=7_68					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=30_72					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=5_12					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=1E-12					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2_56					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=2_16					,				SCREAMING_SNAKE_CASE__=10_01					,				SCREAMING_SNAKE_CASE__=32					,				SCREAMING_SNAKE_CASE__=50					,				SCREAMING_SNAKE_CASE__="absolute"					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



					super().__init__(
					    pad_token_id=SCREAMING_SNAKE_CASE__					,				bos_token_id=SCREAMING_SNAKE_CASE__					,				eos_token_id=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__					,				)
					_UpperCamelCase					:Any					     =	vocab_size
					_UpperCamelCase					:Union[str, Any]					     =	hidden_size
					_UpperCamelCase					:List[Any]					     =	num_hidden_layers
					_UpperCamelCase					:int					     =	num_attention_heads
					_UpperCamelCase					:Tuple					     =	hidden_act
					_UpperCamelCase					:str					     =	intermediate_size
					_UpperCamelCase					:Optional[int]					     =	hidden_dropout_prob
					_UpperCamelCase					:Any					     =	attention_probs_dropout_prob
					_UpperCamelCase					:Union[str, Any]					     =	max_position_embeddings
					_UpperCamelCase					:Dict					     =	type_vocab_size
					_UpperCamelCase					:int					     =	initializer_range
					_UpperCamelCase					:Optional[int]					     =	layer_norm_eps
					_UpperCamelCase					:Any					     =	position_embedding_type
					_UpperCamelCase					:Any					     =	use_cache
					_UpperCamelCase					:List[str]					     =	classifier_dropout
					# additional properties
					_UpperCamelCase					:Union[str, Any]					     =	max_depth
					_UpperCamelCase					:Union[str, Any]					     =	max_xpath_tag_unit_embeddings
					_UpperCamelCase					:Optional[Any]					     =	max_xpath_subs_unit_embeddings
					_UpperCamelCase					:int					     =	tag_pad_id
					_UpperCamelCase					:str					     =	subs_pad_id
					_UpperCamelCase					:List[str]					     =	xpath_unit_hidden_size

