

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      :   Union[str, Any]          =	logging.get_logger(__name__)






class       __A (							UpperCamelCase__       ):






    def __init__(		self	:			List[str]			, **UpperCAmelCase_	:			Dict			):
        requires_backends(self			, ['bs4']			)
        super().__init__(**lowerCAmelCase__			)






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






    def 		lowercase__       (		self	:			int			, UpperCAmelCase_	:			str			):
        lowerCAmelCase :	Tuple				=					BeautifulSoup(lowerCAmelCase__			, 'html.parser'			)

        lowerCAmelCase :	Optional[Any]				=					[]
        lowerCAmelCase :	Optional[int]				=					[]
        lowerCAmelCase :	Tuple				=					[]

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

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

                all_doc_strings.append(lowerCAmelCase__			)

                lowerCAmelCase :	Any				=					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	:			int			, UpperCAmelCase_	:			Optional[int]			, UpperCAmelCase_	:			str			):
        lowerCAmelCase :	List[str]				=					""
        for tagname, subs in zip(lowerCAmelCase__			, lowerCAmelCase__			):
            xpath += f"/{tagname}"
            if subs != 0:
                xpath += f"[{subs}]"
        return xpath






    def __call__(		self	:			Dict			, UpperCAmelCase_	:			Tuple			):
        lowerCAmelCase :	List[Any]				=					False

        # Check that strings has a valid type
        if isinstance(lowerCAmelCase__			, lowerCAmelCase__			):
            lowerCAmelCase :	Dict				=					True
        elif isinstance(lowerCAmelCase__			, (list, tuple)			):
            if len(lowerCAmelCase__			) == 0 or isinstance(html_strings[0]			, lowerCAmelCase__			):
                lowerCAmelCase :	Optional[Any]				=					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 :	Optional[Any]				=					bool(isinstance(lowerCAmelCase__			, (list, tuple)			) and (isinstance(html_strings[0]			, lowerCAmelCase__			))			)

        if not is_batched:
            lowerCAmelCase :	Tuple				=					[html_strings]

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

        # return as Dict
        lowerCAmelCase :	Optional[Any]				=					{"nodes": nodes, "xpaths": xpaths}
        lowerCAmelCase :	int				=					BatchFeature(data=lowerCAmelCase__			, tensor_type=lowerCAmelCase__			)

        return encoded_inputs












import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCAmelCase	:	int		     =       False







class 				__lowerCAmelCase       (    unittest.TestCase):
      pass







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







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




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







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)

        a__	:					Optional[Any]										=torch.manual_seed(0							)
        a__	:					Optional[Any]										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=2		,      output_type="numpy"		,      ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(lowerCAmelCase__							)
          a__	:					str										=VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase__		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										=generator.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt="first prompt"		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      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    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					str										=VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"		,      torch_dtype=torch.floataa							)
        pipe.to(lowerCAmelCase__							)
        pipe.set_progress_bar_config(disable=lowerCAmelCase__							)

        a__	:					Optional[Any]										="cyberpunk 2077"
        a__	:					int										=load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
        a__	:					Union[str, Any]										=torch.manual_seed(0							)
        a__	:					Tuple										=pipe.dual_guided(
            prompt=lowerCAmelCase__		,      image=lowerCAmelCase__		,      text_to_image_strength=0.75		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"		,      ).images

        a__	:					int										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]							)

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

        a__	:					str										="A painting of a squirrel eating a burger "
        a__	:					Optional[int]										=torch.manual_seed(0							)
        a__	:					str										=pipe.text_to_image(
            prompt=lowerCAmelCase__		,      generator=lowerCAmelCase__		,      guidance_scale=7.5		,      num_inference_steps=5_0		,      output_type="numpy"							).images

        a__	:					Any										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Optional[int]										=np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]							)

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

        a__	:					Optional[Any]										=pipe.image_variation(lowerCAmelCase__		,      generator=lowerCAmelCase__		,      output_type="numpy"							).images

        a__	:					Union[str, Any]										=image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        a__	:					Any										=np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]							)

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






