





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


__lowercase  :   Any						      =logging.get_logger(__name__)

class      A       (	__UpperCAmelCase						):



      def __init__(	self:						Optional[Any]		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Dict:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						List[Any]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=BeautifulSoup(_lowerCamelCase		,  "html.parser"       )

         UpperCAmelCase_											=[]
         UpperCAmelCase_											=[]
         UpperCAmelCase_											=[]

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

               UpperCAmelCase_											=html.unescape(_lowerCamelCase       ).strip()
               if not text_in_this_tag:
                  continue

               all_doc_strings.append(_lowerCamelCase       )

               UpperCAmelCase_						,       UpperCAmelCase_											=self.xpath_soup(_lowerCamelCase       )
               stringaxtag_seq.append(_lowerCamelCase       )
               stringaxsubs_seq.append(_lowerCamelCase       )

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

         return all_doc_strings, stringaxtag_seq, stringaxsubs_seq



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Optional[int]		,  _lowerCAmelCase:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=""
         for tagname, subs in zip(_lowerCamelCase		,  _lowerCamelCase       ):
            xpath += F'/{tagname}'
            if subs != 0:
               xpath += F'[{subs}]'
         return xpath






      def __call__(	self:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Dict:
         '''simple docstring'''

         UpperCAmelCase_											=False

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

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

         UpperCAmelCase_											=bool(isinstance(_lowerCamelCase		,  (list, tuple)       ) and (isinstance(html_strings[0]		,  _lowerCamelCase       ))       )

         if not is_batched:
            UpperCAmelCase_											=[html_strings]

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

         # return as Dict
         UpperCAmelCase_											={"nodes": nodes, "xpaths": xpaths}
         UpperCAmelCase_											=BatchFeature(data=_lowerCamelCase		,  tensor_type=_lowerCamelCase       )

         return encoded_inputs











import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDIMParallelScheduler,)
      _snake_case							     =(('''eta''', 0.0), ('''num_inference_steps''', 50))



      def 	lowerCAmelCase__    (	self:						str		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						int		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(**_lowerCAmelCase       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter

         scheduler.set_timesteps(_lowerCAmelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       ).prev_sample

         return sample



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=_lowerCAmelCase       )

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(steps_offset=1       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps		,  torch.LongTensor([801, 601, 401, 201, 1]       )       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''simple docstring'''

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,  [0.0_02, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

         self.check_over_configs(thresholding=_lowerCAmelCase       )
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(
                   thresholding=_lowerCAmelCase		,  prediction_type=_lowerCAmelCase		,  sample_max_value=_lowerCAmelCase		,  )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

         for t in [1, 10, 49]:
            self.check_over_forward(time_step=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         for t, num_inference_steps in zip([1, 10, 50]		,  [10, 50, 500]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  num_inference_steps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Tuple:
         '''simple docstring'''

         for t, eta in zip([1, 10, 49]		,  [0.0, 0.5, 1.0]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  eta=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420		,  400       ) - 0.1_47_71       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980		,  960       ) - 0.3_24_60       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487		,  486       ) - 0.0_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		,  998       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0
         scheduler.set_timesteps(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=self.dummy_sample_deter + 0.1
         UpperCAmelCase_											=self.dummy_sample_deter - 0.1

         UpperCAmelCase_											=samplea.shape[0]
         UpperCAmelCase_											=torch.stack([samplea, samplea, samplea]		,  dim=0       )
         UpperCAmelCase_											=torch.arange(_lowerCAmelCase       )[0:3, None].repeat(1		,  _lowerCAmelCase       )

         UpperCAmelCase_											=model(samples.flatten(0		,  1       )		,  timesteps.flatten(0		,  1       )       )
         UpperCAmelCase_											=scheduler.batch_step_no_noise(_lowerCAmelCase		,  timesteps.flatten(0		,  1       )		,  samples.flatten(0		,  1       )		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 11_47.79_04       ) < 1e-2
         assert abs(result_mean.item() - 0.49_82       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop()

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_72.00_67       ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(prediction_type="v_prediction"       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 52.53_02       ) < 1e-2
         assert abs(result_mean.item() - 0.06_84       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.82_95       ) < 1e-2
         assert abs(result_mean.item() - 0.19_51       ) < 1e-3






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.07_84       ) < 1e-2
         assert abs(result_mean.item() - 0.19_41       ) < 1e-3




