

"""simple docstring"""



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


SCREAMING_SNAKE_CASE_    :		List[str]							     =  logging.get_logger(__name__)



class     a  (				UpperCAmelCase__ ):



    """simple docstring"""



    def __init__(   self:       Any			,  **UpperCamelCase:       Dict				):






         """simple docstring"""


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

    def  UpperCamelCase		(   self:       Any			,  UpperCamelCase:       Dict				):






         """simple docstring"""


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

    def  UpperCamelCase		(   self:       Union[str, Any]			,  UpperCamelCase:       Any				):






         """simple docstring"""


         A__								=     BeautifulSoup(snake_case_			,  """html.parser"""				)

         A__								=     []
         A__								=     []
         A__								=     []

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

                   A__								=     html.unescape(snake_case_				).strip()
                   if not text_in_this_tag:
                        continue

                   all_doc_strings.append(snake_case_				)

                   A__								=     self.xpath_soup(snake_case_				)
                   stringaxtag_seq.append(snake_case_				)
                   stringaxsubs_seq.append(snake_case_				)

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

         return all_doc_strings, stringaxtag_seq, stringaxsubs_seq

    def  UpperCamelCase		(   self:       Optional[int]			,  UpperCamelCase:       int			,  UpperCamelCase:       Optional[Any]				):






         """simple docstring"""


         A__								=     ''
         for tagname, subs in zip(snake_case_			,  snake_case_				):
              xpath += f"""/{tagname}"""
              if subs != 0:
                   xpath += f"""[{subs}]"""
         return xpath





    def __call__(   self:       Tuple			,  UpperCamelCase:       List[str]				):






         """simple docstring"""


         A__								=     False

         # Check that strings has a valid type
         if isinstance(snake_case_			,  snake_case_				):
              A__								=     True
         elif isinstance(snake_case_			,  (list, tuple)				):
              if len(snake_case_				) == 0 or isinstance(html_strings[0]			,  snake_case_				):
                   A__								=     True

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

         A__								=     bool(isinstance(snake_case_			,  (list, tuple)				) and (isinstance(html_strings[0]			,  snake_case_				))				)

         if not is_batched:
              A__								=     [html_strings]

         # Get nodes + xpaths
         A__								=     []
         A__								=     []
         for html_string in html_strings:
              A__								=     self.get_three_from_single(snake_case_				)
              nodes.append(snake_case_				)
              A__								=     []
              for node, tag_list, sub_list in zip(snake_case_			,  snake_case_			,  snake_case_				):
                   A__								=     self.construct_xpath(snake_case_			,  snake_case_				)
                   xpath_strings.append(snake_case_				)
              xpaths.append(snake_case_				)

         # return as Dict
         A__								=     {'nodes': nodes, 'xpaths': xpaths}
         A__								=     BatchFeature(data=snake_case_			,  tensor_type=snake_case_				)

         return encoded_inputs













"""simple docstring"""




import torch

from diffusers import DiffusionPipeline

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        super().__init__()

        self.register_modules(unet=snake_case_					,      scheduler=snake_case_  )

      def __call__(     self  ):

        """simple docstring"""

        A_  :       Optional[Any]										=  torch.randn(
            (1, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)					,      )
        A_  :       List[str]										=  1

        A_  :       List[str]										=  self.unet(snake_case_					,      snake_case_  ).sample
        A_  :       Optional[int]										=  self.scheduler.step(snake_case_					,      snake_case_					,      snake_case_  ).prev_sample

        A_  :       List[Any]										=  scheduler_output - scheduler_output + torch.ones_like(snake_case_  )

        return result