




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      :						Optional[int]							=  logging.get_logger(__name__)


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    def __init__(     self :      Union[str, Any]   ,					**UpperCAmelCase__ :      Optional[int])		->Optional[int]:





           '''simple docstring'''

           requires_backends(self   ,					['''bs4'''])
           super().__init__(**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Dict)		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       []
           A__ 					=       []
           A__ 					=       element if element.name else element.parent
           for parent in child.parents:  # type: bs4.element.Tag
                  A__ 					=       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))
                  A__ 					=       parent
           xpath_tags.reverse()
           xpath_subscripts.reverse()
           return xpath_tags, xpath_subscripts


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Dict)		->Optional[int]:





           '''simple docstring'''

           A__ 					=       BeautifulSoup(UpperCAmelCase__   ,					'''html.parser''')

           A__ 					=       []
           A__ 					=       []
           A__ 					=       []

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

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

                         all_doc_strings.append(UpperCAmelCase__)

                         A__					,				A__ 					=       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 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Optional[int])		->Optional[Any]:





           '''simple docstring'''

           A__ 					=       ''''''
           for tagname, subs in zip(UpperCAmelCase__   ,					UpperCAmelCase__):
                  xpath += f"""/{tagname}"""
                  if subs != 0:
                         xpath += f"""[{subs}]"""
           return xpath





    def __call__(     self :      Optional[Any]   ,					UpperCAmelCase__ :      Tuple)		->BatchFeature:





           '''simple docstring'''

           A__ 					=       False

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

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

           if not is_batched:
                  A__ 					=       [html_strings]

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

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

           return encoded_inputs




"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Any           =     KandinskyImgaImgPipeline
      UpperCamelCase							:   int           =     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
      UpperCamelCase							:   Optional[Any]           =     [
          """prompt""",
          """negative_prompt""",
          """image_embeds""",
          """negative_image_embeds""",
          """image""",
      ]
      UpperCamelCase							:   Union[str, Any]           =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """negative_prompt""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      UpperCamelCase							:   List[str]           =     False



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return 32



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim



      @property
      def  __snake_case							(   self      ):
          return self.time_input_dim * 4



      @property
      def  __snake_case							(   self      ):
          return 100



      @property
      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'      )
          return tokenizer



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[int]     							=					MCLIPConfig(
              numDims=self.cross_attention_dim     ,	transformerDimensions=self.text_embedder_hidden_size     ,	hidden_size=self.text_embedder_hidden_size     ,	intermediate_size=37     ,	num_attention_heads=4     ,	num_hidden_layers=5     ,	vocab_size=1_005     ,	)

          UpperCAmelCase__	:      Tuple     							=					MultilingualCLIP(UpperCamelCase_      )
          UpperCAmelCase__	:      List[Any]     							=					text_encoder.eval()

          return text_encoder



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )

          UpperCAmelCase__	:      Optional[Any]     							=					{
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'encoder_hid_dim': self.text_embedder_hidden_size,
              'encoder_hid_dim_type': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          UpperCAmelCase__	:      Optional[Any]     							=					UNetaDConditionModel(**UpperCamelCase_      )
          return model



      @property
      def  __snake_case							(   self      ):
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }



      @property
      def  __snake_case							(   self      ):
          torch.manual_seed(0      )
          UpperCAmelCase__	:      Optional[Any]     							=					VQModel(**self.dummy_movq_kwargs      )
          return model



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					self.dummy_text_encoder
          UpperCAmelCase__	:      int     							=					self.dummy_tokenizer
          UpperCAmelCase__	:      int     							=					self.dummy_unet
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_movq

          UpperCAmelCase__	:      Any     							=					{
              'num_train_timesteps': 1_000,
              'beta_schedule': 'linear',
              'beta_start': 0.00085,
              'beta_end': 0.012,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCAmelCase__	:      List[Any]     							=					DDIMScheduler(**UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          UpperCAmelCase__	:      int     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					floats_tensor((1, self.cross_attention_dim)     ,	rng=random.Random(seed + 1      )      ).to(UpperCamelCase_      )
          # create init_image
          UpperCAmelCase__	:      Optional[int]     							=					floats_tensor((1, 3, 64, 64)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      )[0]
          UpperCAmelCase__	:      Union[str, Any]     							=					Image.fromarray(np.uinta(UpperCamelCase_      )      ).convert('RGB'      ).resize((256, 256)      )

          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Dict     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      Optional[int]     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'cpu'

          UpperCAmelCase__	:      Any     							=					self.get_dummy_components()

          UpperCAmelCase__	:      Any     							=					self.pipeline_class(**UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
          UpperCAmelCase__	:      Optional[int]     							=					output.images

          UpperCAmelCase__	:      Dict     							=					pipe(
              **self.get_dummy_inputs(UpperCamelCase_      )     ,	return_dict=UpperCamelCase_     ,	)[0]

          UpperCAmelCase__	:      Dict     							=					image[0, -3:, -3:, -1]
          UpperCAmelCase__	:      Any     							=					image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCAmelCase__	:      Any     							=					np.array(
              [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]      )
          assert (
              np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2
          ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1E-2
          ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





@slow
@require_torch_gpu
class 				a			(					unittest.TestCase      ):



      def  __snake_case							(   self      ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'      )

          UpperCAmelCase__	:      List[Any]     							=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'      )
          UpperCAmelCase__	:      Any     							=					'A red cartoon frog, 4k'

          UpperCAmelCase__	:      Union[str, Any]     							=					KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior'     ,	torch_dtype=torch.floataa      )
          pipe_prior.to(UpperCamelCase_      )

          UpperCAmelCase__	:      Any     							=					KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1'     ,	torch_dtype=torch.floataa      )
          UpperCAmelCase__	:      Optional[Any]     							=					pipeline.to(UpperCamelCase_      )

          pipeline.set_progress_bar_config(disable=UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					torch.Generator(device='cpu'      ).manual_seed(0      )
          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					pipe_prior(
              UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=5     ,	negative_prompt=''     ,	).to_tuple()

          UpperCAmelCase__	:      Optional[Any]     							=					pipeline(
              UpperCamelCase_     ,	image=UpperCamelCase_     ,	image_embeds=UpperCamelCase_     ,	negative_image_embeds=UpperCamelCase_     ,	generator=UpperCamelCase_     ,	num_inference_steps=100     ,	height=768     ,	width=768     ,	strength=0.2     ,	output_type='np'     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(UpperCamelCase_     ,	UpperCamelCase_      )

