






import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
	import bsa
	from bsa import BeautifulSoup


lowerCamelCase : Any						   =       logging.get_logger(__name__)

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     **__a				:	List[str]							)			->  List[Any]:


												"""simple docstring"""

												requires_backends(self		,     ["""bs4"""]							)
												super().__init__(**__a							)



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	List[str]							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							str				=     []
												__lowercase      :							int				=     []
												__lowercase      :							Optional[int]				=     element if element.name else element.parent
												for parent in child.parents:  # type: bs4.element.Tag
																			__lowercase      :							Optional[Any]				=     parent.find_all(child.name		,     recursive=__a							)
																			xpath_tags.append(child.name							)
																			xpath_subscripts.append(
																			    0 if 1 == len(__a							) else next(i for i, s in enumerate(__a		,     1							) if s is child							)							)
																			__lowercase      :							List[str]				=     parent
												xpath_tags.reverse()
												xpath_subscripts.reverse()
												return xpath_tags, xpath_subscripts



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Dict							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							str				=     BeautifulSoup(__a		,     """html.parser"""							)

												__lowercase      :							Optional[int]				=     []
												__lowercase      :							Optional[int]				=     []
												__lowercase      :							str				=     []

												for element in html_code.descendants:
																			if type(__a							) == bsa.element.NavigableString:
																										if type(element.parent							) != bsa.element.Tag:
																																	continue

																										__lowercase      :							str				=     html.unescape(__a							).strip()
																										if not text_in_this_tag:
																																	continue

																										all_doc_strings.append(__a							)

																										__lowercase			,			__lowercase      :							str				=     self.xpath_soup(__a							)
																										stringaxtag_seq.append(__a							)
																										stringaxsubs_seq.append(__a							)

												if len(__a							) != len(__a							):
																			raise ValueError("""Number of doc strings and xtags does not correspond"""							)
												if len(__a							) != len(__a							):
																			raise ValueError("""Number of doc strings and xsubs does not correspond"""							)

												return all_doc_strings, stringaxtag_seq, stringaxsubs_seq



					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Tuple		,     __a				:	Any							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     """"""
												for tagname, subs in zip(__a		,     __a							):
																			xpath += F"/{tagname}"
																			if subs != 0:
																										xpath += F"[{subs}]"
												return xpath



					def __call__(  self				:	Optional[Any]		,     __a				:	List[str]							)			->  BatchFeature:


												"""simple docstring"""

												__lowercase      :							List[str]				=     False

												# Check that strings has a valid type
												if isinstance(__a		,     __a							):
																			__lowercase      :							List[str]				=     True
												elif isinstance(__a		,     (list, tuple)							):
																			if len(__a							) == 0 or isinstance(html_strings[0]		,     __a							):
																										__lowercase      :							Union[str, Any]				=     True

												if not valid_strings:
																			raise ValueError(
																			    """HTML strings must of type `str`, `List[str]` (batch of examples), """
																			    F"but is of type {type(__a							)}."							)

												__lowercase      :							Union[str, Any]				=     bool(isinstance(__a		,     (list, tuple)							) and (isinstance(html_strings[0]		,     __a							))							)

												if not is_batched:
																			__lowercase      :							int				=     [html_strings]

												# Get nodes + xpaths
												__lowercase      :							Tuple				=     []
												__lowercase      :							Any				=     []
												for html_string in html_strings:
																			__lowercase			,			__lowercase			,			__lowercase      :							Dict				=     self.get_three_from_single(__a							)
																			nodes.append(__a							)
																			__lowercase      :							List[str]				=     []
																			for node, tag_list, sub_list in zip(__a		,     __a		,     __a							):
																										__lowercase      :							List[Any]				=     self.construct_xpath(__a		,     __a							)
																										xpath_strings.append(__a							)
																			xpaths.append(__a							)

												# return as Dict
												__lowercase      :							str				=     {"""nodes""": nodes, """xpaths""": xpaths}
												__lowercase      :							Optional[Any]				=     BatchFeature(data=__a		,     tensor_type=__a							)

												return encoded_inputs







# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin

class 		lowerCAmelCase     ( __a      ,       __a			):

					'''simple docstring'''

					_A						:			str									=      1



					@register_to_config
					def __init__(  self				:	Optional[int]		,     __a				:	Tuple=2000		,     __a				:	List[str]=0.1		,     __a				:	str=20		,     __a				:	Optional[int]=1E-3							)			->  int:


												"""simple docstring"""

												__lowercase      :							Tuple				=     None
												__lowercase      :							Union[str, Any]				=     None
												__lowercase      :							int				=     None



					def       lowerCAmelCase				(  self				:	List[Any]		,     __a				:	Any		,     __a				:	Union[str, torch.device] = None							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     torch.linspace(1		,     self.config.sampling_eps		,     __a		,     device=__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     __a				:	List[Any]		,     __a				:	Tuple		,     __a				:	int		,     __a				:	Optional[int]=None							)			->  str:


												"""simple docstring"""

												if self.timesteps is None:
																			raise ValueError(
																			    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""							)

												# TODO(Patrick) better comments + non-PyTorch
												# postprocess model score
												__lowercase      :							Dict				=     (
												    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
												)
												__lowercase      :							int				=     torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
												__lowercase      :							Union[str, Any]				=     std.flatten()
												while len(std.shape							) < len(score.shape							):
																			__lowercase      :							Optional[Any]				=     std.unsqueeze(-1							)
												__lowercase      :							List[Any]				=     -score / std

												# compute
												__lowercase      :							Dict				=     -1.0 / len(self.timesteps							)

												__lowercase      :							int				=     self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
												__lowercase      :							List[Any]				=     beta_t.flatten()
												while len(beta_t.shape							) < len(x.shape							):
																			__lowercase      :							Union[str, Any]				=     beta_t.unsqueeze(-1							)
												__lowercase      :							List[str]				=     -0.5 * beta_t * x

												__lowercase      :							int				=     torch.sqrt(__a							)
												__lowercase      :							Union[str, Any]				=     drift - diffusion**2 * score
												__lowercase      :							Optional[Any]				=     x + drift * dt

												# add noise
												__lowercase      :							List[str]				=     randn_tensor(x.shape		,     layout=x.layout		,     generator=__a		,     device=x.device		,     dtype=x.dtype							)
												__lowercase      :							str				=     x_mean + diffusion * math.sqrt(-dt							) * noise

												return x, x_mean



					def __len__(  self				:	Tuple							)			->  Optional[int]:


												"""simple docstring"""

												return self.config.num_train_timesteps