
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy







class 	A__				(	__magic_name__      ):
				lowercase            =    'MarkupLMFeatureExtractor'
				lowercase            =    ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
				lowercase            =    True






				def __call__(		self   : Tuple       ,	a   : Union[str, Any]=None       ,	a   : List[str]=None       ,	a   : Dict=None       ,	a   : Dict=None       ,	a   : List[str]=None       ,	a   : bool = True       ,	a   : Union[bool, str, PaddingStrategy] = False       ,	a   : Union[bool, str, TruncationStrategy] = None       ,	a   : Optional[int] = None       ,	a   : int = 0       ,	a   : Optional[int] = None       ,	a   : Optional[bool] = None       ,	a   : Optional[bool] = None       ,	a   : bool = False       ,	a   : bool = False       ,	a   : bool = False       ,	a   : bool = False       ,	a   : bool = True       ,	a   : Optional[Union[str, TensorType]] = None       ,	**a   : List[Any]       ,	):






							'''simple docstring'''







							if self.parse_html:
										if html_strings is None:
													raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'  )

										if nodes is not None or xpaths is not None or node_labels is not None:
													raise ValueError(
													    'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'  )

										lowerCAmelCase__ : int   =						self.feature_extractor(a  )
										lowerCAmelCase__ : Any   =						features['nodes']
										lowerCAmelCase__ : List[Any]   =						features['xpaths']
							else:
										if html_strings is not None:
													raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'  )
										if nodes is None or xpaths is None:
													raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'  )

        # # second, apply the tokenizer
							if questions is not None and self.parse_html:
										if isinstance(a       ,	a  ):
													lowerCAmelCase__ : Optional[Any]   =						[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

							lowerCAmelCase__ : Dict   =						self.tokenizer(
							    text=questions if questions is not None else nodes       ,	text_pair=nodes if questions is not None else None       ,	xpaths=a       ,	node_labels=a       ,	add_special_tokens=a       ,	padding=a       ,	truncation=a       ,	max_length=a       ,	stride=a       ,	pad_to_multiple_of=a       ,	return_token_type_ids=a       ,	return_attention_mask=a       ,	return_overflowing_tokens=a       ,	return_special_tokens_mask=a       ,	return_offsets_mapping=a       ,	return_length=a       ,	verbose=a       ,	return_tensors=a       ,	**a       ,	)

							return encoded_inputs






				def 				_lowerCamelCase						(		self   : Any       ,	*a   : Optional[Any]       ,	**a   : Optional[Any]  ):






							'''simple docstring'''







							return self.tokenizer.batch_decode(*a       ,	**a  )






				def 				_lowerCamelCase						(		self   : Optional[int]       ,	*a   : Any       ,	**a   : Tuple  ):






							'''simple docstring'''







							return self.tokenizer.decode(*a       ,	**a  )






				@property
				def 				_lowerCamelCase						(		self   : str  ):






							'''simple docstring'''







							lowerCAmelCase__ : Tuple   =						self.tokenizer.model_input_names
							return tokenizer_input_names

from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 	A__				(	__magic_name__      ):






				def __init__(		self   : Any       ,	a   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







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






				@torch.no_grad()
				def __call__(		self   : Optional[Any]       ,	a   : int = 1       ,	a   : Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,	a   : int = 1_000       ,	a   : Optional[str] = "pil"       ,	a   : bool = True       ,	):






							'''simple docstring'''







							if isinstance(self.unet.config.sample_size       ,	a  ):
										lowerCAmelCase__ : Union[str, Any]   =						(
										    batch_size,
										    self.unet.config.in_channels,
										    self.unet.config.sample_size,
										    self.unet.config.sample_size,
										)
							else:
										lowerCAmelCase__ : str   =						(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if self.device.type == "mps":
										# randn does not work reproducibly on mps
										lowerCAmelCase__ : Optional[int]   =						randn_tensor(a       ,	generator=a  )
										lowerCAmelCase__ : Optional[int]   =						image.to(self.device  )
							else:
										lowerCAmelCase__ : Dict   =						randn_tensor(a       ,	generator=a       ,	device=self.device  )

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

							for t in self.progress_bar(self.scheduler.timesteps  ):
										# 1. predict noise model_output
										lowerCAmelCase__ : Tuple   =						self.unet(a       ,	a  ).sample

										# 2. compute previous image: x_t -> x_t-1
										lowerCAmelCase__ : Optional[Any]   =						self.scheduler.step(a       ,	a       ,	a       ,	generator=a  ).prev_sample

							lowerCAmelCase__ : str   =						(image / 2 + 0.5).clamp(0       ,	1  )
							lowerCAmelCase__ : Optional[int]   =						image.cpu().permute(0       ,	2       ,	3       ,	1  ).numpy()
							if output_type == "pil":
										lowerCAmelCase__ : int   =						self.numpy_to_pil(a  )

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=a  )