


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


snake_case__			 =						logging.get_logger(__name__)


class 					lowerCAmelCase_ (       _a):




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






									"""simple docstring"""






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




						def  _snake_case					(	self						:   Optional[Any]   ,     __A						:   str						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    []
									a__ :str						  =    []
									a__ :Tuple						  =    element if element.name else element.parent
									for parent in child.parents:  # type: bs4.element.Tag
												a__ :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						)						)
												a__ :Optional[Any]						  =    parent
									xpath_tags.reverse()
									xpath_subscripts.reverse()
									return xpath_tags, xpath_subscripts




						def  _snake_case					(	self						:   int   ,     __A						:   Tuple						)      ->Tuple:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    BeautifulSoup(__A   ,     "html.parser"						)

									a__ :List[str]						  =    []
									a__ :Dict						  =    []
									a__ :Optional[int]						  =    []

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

															a__ :Tuple						  =    html.unescape(__A						).strip()
															if not text_in_this_tag:
																		continue

															all_doc_strings.append(__A						)

															a__  ,  a__ :Dict						  =    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  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   Optional[int]   ,     __A						:   Optional[int]						)      ->Dict:






									"""simple docstring"""






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






						def __call__(	self						:   Union[str, Any]   ,     __A						:   int						)      ->BatchFeature:






									"""simple docstring"""






									a__ :List[str]						  =    False

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

									a__ :str						  =    bool(isinstance(__A   ,     (list, tuple)						) and (isinstance(html_strings[0]   ,     __A						))						)

									if not is_batched:
												a__ :str						  =    [html_strings]

									# Get nodes + xpaths
									a__ :int						  =    []
									a__ :Optional[int]						  =    []
									for html_string in html_strings:
												a__  ,  a__  ,  a__ :Union[str, Any]						  =    self.get_three_from_single(__A						)
												nodes.append(__A						)
												a__ :List[Any]						  =    []
												for node, tag_list, sub_list in zip(__A   ,     __A   ,     __A						):
															a__ :Dict						  =    self.construct_xpath(__A   ,     __A						)
															xpath_strings.append(__A						)
												xpaths.append(__A						)

									# return as Dict
									a__ :List[Any]						  =    {"nodes": nodes, "xpaths": xpaths}
									a__ :Optional[Any]						  =    BatchFeature(data=__A   ,     tensor_type=__A						)

									return encoded_inputs






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






@nightly
@require_torch_gpu
class 					lowerCAmelCase_ (       unittest.TestCase):




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






									"""simple docstring"""






									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									assert np.abs(image - new_image						).sum() < 1E-5, "Models don't have the same forward pass"






						def  _snake_case					(	self						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

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


