

"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class        _lowercase					(     __UpperCAmelCase     ):







     def __init__(      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      super().__init__()

      # make sure scheduler can always be converted to DDIM
      __magic_name__     =      DDIMScheduler.from_config(scheduler.config       )

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







     @torch.no_grad()
     def __call__(      self ,						UpperCamelCase_ = 1 ,						UpperCamelCase_ = None ,						UpperCamelCase_ = 0.0 ,						UpperCamelCase_ = 50 ,						UpperCamelCase_ = None ,						UpperCamelCase_ = "pil" ,						UpperCamelCase_ = True ,						):

      # Sample gaussian noise to begin loop
      if isinstance(self.unet.config.sample_size ,						UpperCamelCase_       ):
       __magic_name__     =      (
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
      else:
       __magic_name__     =      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

      if isinstance(UpperCamelCase_ ,						UpperCamelCase_       ) and len(UpperCamelCase_       ) != batch_size:
       raise ValueError(
           f'''You have passed a list of generators of length {len(UpperCamelCase_       )}, but requested an effective batch'''
           f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''       )

      __magic_name__     =      randn_tensor(UpperCamelCase_ ,						generator=UpperCamelCase_ ,						device=self.device ,						dtype=self.unet.dtype       )

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

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

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       __magic_name__     =      self.scheduler.step(
           UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						eta=UpperCamelCase_ ,						use_clipped_model_output=UpperCamelCase_ ,						generator=UpperCamelCase_       ).prev_sample

      __magic_name__     =      (image / 2 + 0.5).clamp(0 ,						1       )
      __magic_name__     =      image.cpu().permute(0 ,						2 ,						3 ,						1       ).numpy()
      if output_type == "pil":
       __magic_name__     =      self.numpy_to_pil(UpperCamelCase_       )

      if not return_dict:
       return (image,)

      return ImagePipelineOutput(images=UpperCamelCase_       )








"""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


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):







     def __init__(      self ,						**UpperCamelCase_       ):
      requires_backends(self ,						['''bs4''']       )
      super().__init__(**UpperCamelCase_       )







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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_       ):
      __magic_name__     =      BeautifulSoup(UpperCamelCase_ ,						'''html.parser'''       )

      __magic_name__     =      []
      __magic_name__     =      []
      __magic_name__     =      []

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

        __magic_name__     =      html.unescape(UpperCamelCase_       ).strip()
        if not text_in_this_tag:
         continue

        all_doc_strings.append(UpperCamelCase_       )

        __magic_name__   ,						__magic_name__     =      self.xpath_soup(UpperCamelCase_       )
        stringaxtag_seq.append(UpperCamelCase_       )
        stringaxsubs_seq.append(UpperCamelCase_       )

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

      return all_doc_strings, stringaxtag_seq, stringaxsubs_seq







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      ''''''
      for tagname, subs in zip(UpperCamelCase_ ,						UpperCamelCase_       ):
       xpath += f'''/{tagname}'''
       if subs != 0:
        xpath += f'''[{subs}]'''
      return xpath







     def __call__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      False

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

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

      __magic_name__     =      bool(isinstance(UpperCamelCase_ ,						(list, tuple)       ) and (isinstance(html_strings[0] ,						UpperCamelCase_       ))       )

      if not is_batched:
       __magic_name__     =      [html_strings]

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

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

      return encoded_inputs

