




"""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


a      =							logging.get_logger(__name__)






class       SCREAMING_SNAKE_CASE__					(					_a					):


						def __init__(							self				:			Optional[int]						,						**lowerCAmelCase				:			List[str]					):
										requires_backends(self						,						["""bs4"""]					)
										super().__init__(**lowerCAmelCase					)


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				element if element.name else element.parent
										for parent in child.parents:  # type: bs4.element.Tag
														lowerCAmelCase  =				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					)					)
														lowerCAmelCase  =				parent
										xpath_tags.reverse()
										xpath_subscripts.reverse()
										return xpath_tags, xpath_subscripts


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Optional[int]					):
										lowerCAmelCase  =				BeautifulSoup(lowerCAmelCase						,						"""html.parser"""					)

										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]
										lowerCAmelCase  =				[]

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

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

																		all_doc_strings.append(lowerCAmelCase					)

																		lowerCAmelCase ,						lowerCAmelCase  =				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   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			List[str]					):
										lowerCAmelCase  =				""""""
										for tagname, subs in zip(lowerCAmelCase						,						lowerCAmelCase					):
														xpath += f'''/{tagname}'''
														if subs != 0:
																		xpath += f'''[{subs}]'''
										return xpath




						def __call__(							self				:			Dict						,						lowerCAmelCase				:			Optional[Any]					):
										lowerCAmelCase  =				False

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

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

										if not is_batched:
														lowerCAmelCase  =				[html_strings]

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

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

										return encoded_inputs











"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@property
						def   __lowercase (							self				:			Any					):
										torch.manual_seed(0					)
										lowerCAmelCase  =				UNetaDModel(
										    block_out_channels=(32, 64)						,						layers_per_block=2						,						sample_size=32						,						in_channels=3						,						out_channels=3						,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,						)
										return model




						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.dummy_uncond_unet
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=2						,						generator=lowerCAmelCase						,						output_type="""numpy"""						,						return_dict=lowerCAmelCase					)[0]

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										lowerCAmelCase  =				image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCAmelCase  =				np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2






@slow
@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				"""google/ncsnpp-celebahq-256"""
										lowerCAmelCase  =				UNetaDModel.from_pretrained(lowerCAmelCase					)
										lowerCAmelCase  =				KarrasVeScheduler()

										lowerCAmelCase  =				KarrasVePipeline(unet=lowerCAmelCase						,						scheduler=lowerCAmelCase					)
										pipe.to(lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=lowerCAmelCase					)

										lowerCAmelCase  =				torch.manual_seed(0					)
										lowerCAmelCase  =				pipe(num_inference_steps=20						,						generator=lowerCAmelCase						,						output_type="""numpy"""					).images

										lowerCAmelCase  =				image[0, -3:, -3:, -1]
										assert image.shape == (1, 256, 256, 3)
										lowerCAmelCase  =				np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2





