'''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, KandinskyInpaintPipeline, 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 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						KandinskyInpaintPipeline
     SCREAMING_SNAKE_CASE__		   =						['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
     SCREAMING_SNAKE_CASE__		   =						[
         '''prompt''',
         '''negative_prompt''',
         '''image_embeds''',
         '''negative_image_embeds''',
         '''image''',
         '''mask_image''',
     ]
     SCREAMING_SNAKE_CASE__		   =						[
         '''generator''',
         '''height''',
         '''width''',
         '''latents''',
         '''guidance_scale''',
         '''negative_prompt''',
         '''num_inference_steps''',
         '''return_dict''',
         '''guidance_scale''',
         '''num_images_per_prompt''',
         '''output_type''',
         '''return_dict''',
     ]
     SCREAMING_SNAKE_CASE__		   =						False







     @property
     def lowerCAmelCase     (		self    ):
         return 3_2







     @property
     def lowerCAmelCase     (		self    ):
         return 3_2







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







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







     @property
     def lowerCAmelCase     (		self    ):
         return 1_0_0







     @property
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
         return tokenizer







     @property
     def lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	Dict		    =  MCLIPConfig(
             numDims=self.cross_attention_dim     ,			transformerDimensions=self.text_embedder_hidden_size     ,			hidden_size=self.text_embedder_hidden_size     ,			intermediate_size=3_7     ,			num_attention_heads=4     ,			num_hidden_layers=5     ,			vocab_size=1_0_0_5     ,			)

         __UpperCamelCase	:	int		    =  MultilingualCLIP(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  text_encoder.eval()

         return text_encoder







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

         __UpperCamelCase	:	str		    =  {
             'in_channels': 9,
             # 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	:	Tuple		    =  UNetaDConditionModel(**_lowerCamelCase    )
         return model







     @property
     def lowerCAmelCase     (		self    ):
         return {
             "block_out_channels": [3_2, 6_4],
             "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": 1_2,
             "out_channels": 3,
             "up_block_types": [
                 "AttnUpDecoderBlock2D",
                 "UpDecoderBlock2D",
             ],
             "vq_embed_dim": 4,
         }







     @property
     def lowerCAmelCase     (		self    ):
         torch.manual_seed(0    )
         __UpperCamelCase	:	int		    =  VQModel(**self.dummy_movq_kwargs    )
         return model







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  self.dummy_text_encoder
         __UpperCamelCase	:	List[str]		    =  self.dummy_tokenizer
         __UpperCamelCase	:	List[Any]		    =  self.dummy_unet
         __UpperCamelCase	:	List[Any]		    =  self.dummy_movq

         __UpperCamelCase	:	List[str]		    =  DDIMScheduler(
             num_train_timesteps=1_0_0_0     ,			beta_schedule='linear'     ,			beta_start=0.0_0_0_8_5     ,			beta_end=0.0_1_2     ,			clip_sample=_lowerCamelCase     ,			set_alpha_to_one=_lowerCamelCase     ,			steps_offset=1     ,			prediction_type='epsilon'     ,			thresholding=_lowerCamelCase     ,			)

         __UpperCamelCase	:	Optional[int]		    =  {
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=0    ):
         __UpperCamelCase	:	Dict		    =  floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1    )    ).to(_lowerCamelCase    )
         # create init_image
         __UpperCamelCase	:	List[Any]		    =  floats_tensor((1, 3, 6_4, 6_4)     ,			rng=random.Random(_lowerCamelCase    )    ).to(_lowerCamelCase    )
         __UpperCamelCase	:	Optional[Any]		    =  image.cpu().permute(0     ,			2     ,			3     ,			1    )[0]
         __UpperCamelCase	:	Optional[int]		    =  Image.fromarray(np.uinta(_lowerCamelCase    )    ).convert('RGB'    ).resize((2_5_6, 2_5_6)    )
         # create mask
         __UpperCamelCase	:	Optional[int]		    =  np.ones((6_4, 6_4)     ,			dtype=np.floataa    )
         __UpperCamelCase	:	str		    =  0

         if str(_lowerCamelCase    ).startswith('mps'    ):
             __UpperCamelCase	:	int		    =  torch.manual_seed(_lowerCamelCase    )
         else:
             __UpperCamelCase	:	str		    =  torch.Generator(device=_lowerCamelCase    ).manual_seed(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  {
             'prompt': 'horse',
             'image': init_image,
             'mask_image': mask,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 6_4,
             'width': 6_4,
             'num_inference_steps': 2,
             'guidance_scale': 4.0,
             'output_type': 'np',
         }
         return inputs







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  'cpu'

         __UpperCamelCase	:	Union[str, Any]		    =  self.get_dummy_components()

         __UpperCamelCase	:	Optional[int]		    =  self.pipeline_class(**_lowerCamelCase    )
         __UpperCamelCase	:	Optional[int]		    =  pipe.to(_lowerCamelCase    )

         pipe.set_progress_bar_config(disable=_lowerCamelCase    )

         __UpperCamelCase	:	Optional[Any]		    =  pipe(**self.get_dummy_inputs(_lowerCamelCase    )    )
         __UpperCamelCase	:	List[Any]		    =  output.images

         __UpperCamelCase	:	Dict		    =  pipe(
             **self.get_dummy_inputs(_lowerCamelCase    )     ,			return_dict=_lowerCamelCase     ,			)[0]

         __UpperCamelCase	:	Dict		    =  image[0, -3:, -3:, -1]
         __UpperCamelCase	:	Optional[int]		    =  image_from_tuple[0, -3:, -3:, -1]

         print(f"""image.shape {image.shape}"""    )

         assert image.shape == (1, 6_4, 6_4, 3)

         __UpperCamelCase	:	List[Any]		    =  np.array(
             [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]    )

         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()}"""







     def lowerCAmelCase     (		self    ):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






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







     """simple docstring"""






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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'    )

         __UpperCamelCase	:	Any		    =  load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'    )
         __UpperCamelCase	:	Union[str, Any]		    =  np.ones((7_6_8, 7_6_8)     ,			dtype=np.floataa    )
         __UpperCamelCase	:	Tuple		    =  0

         __UpperCamelCase	:	int		    =  'a hat'

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

         __UpperCamelCase	:	int		    =  KandinskyInpaintPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1-inpaint'     ,			torch_dtype=torch.floataa    )
         __UpperCamelCase	:	Dict		    =  pipeline.to(_lowerCamelCase    )
         pipeline.set_progress_bar_config(disable=_lowerCamelCase    )

         __UpperCamelCase	:	int		    =  torch.Generator(device='cpu'    ).manual_seed(0    )
         __UpperCamelCase	:	int		    =  pipe_prior(
             _lowerCamelCase     ,			generator=_lowerCamelCase     ,			num_inference_steps=5     ,			negative_prompt=''     ,			).to_tuple()

         __UpperCamelCase	:	List[Any]		    =  pipeline(
             _lowerCamelCase     ,			image=_lowerCamelCase     ,			mask_image=_lowerCamelCase     ,			image_embeds=_lowerCamelCase     ,			negative_image_embeds=_lowerCamelCase     ,			generator=_lowerCamelCase     ,			num_inference_steps=1_0_0     ,			height=7_6_8     ,			width=7_6_8     ,			output_type='np'     ,			)

         __UpperCamelCase	:	List[str]		    =  output.images[0]

         assert image.shape == (7_6_8, 7_6_8, 3)

         assert_mean_pixel_difference(_lowerCamelCase     ,			_lowerCamelCase    )
'''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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     def __init__(		self     ,			**_lowerCamelCase    ):
         requires_backends(self     ,			['bs4']    )
         super().__init__(**_lowerCamelCase    )







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







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  BeautifulSoup(_lowerCamelCase     ,			'html.parser'    )

         __UpperCamelCase	:	int		    =  []
         __UpperCamelCase	:	List[Any]		    =  []
         __UpperCamelCase	:	int		    =  []

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

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

                 all_doc_strings.append(_lowerCamelCase    )

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







     def __call__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[Any]		    =  False

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

         __UpperCamelCase	:	Dict		    =  bool(isinstance(_lowerCamelCase     ,			(list, tuple)    ) and (isinstance(html_strings[0]     ,			_lowerCamelCase    ))    )

         if not is_batched:
             __UpperCamelCase	:	Tuple		    =  [html_strings]

         # Get nodes + xpaths
         __UpperCamelCase	:	Tuple		    =  []
         __UpperCamelCase	:	Tuple		    =  []
         for html_string in html_strings:
             __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self.get_three_from_single(_lowerCamelCase    )
             nodes.append(_lowerCamelCase    )
             __UpperCamelCase	:	Union[str, Any]		    =  []
             for node, tag_list, sub_list in zip(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
                 __UpperCamelCase	:	Optional[Any]		    =  self.construct_xpath(_lowerCamelCase     ,			_lowerCamelCase    )
                 xpath_strings.append(_lowerCamelCase    )
             xpaths.append(_lowerCamelCase    )

         # return as Dict
         __UpperCamelCase	:	Optional[int]		    =  {'nodes': nodes, 'xpaths': xpaths}
         __UpperCamelCase	:	Union[str, Any]		    =  BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )

         return encoded_inputs



