

"""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 				SCREAMING_SNAKE_CASE					(		a_    ,							unittest.TestCase       ):






					"""simple docstring"""



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







					@property
					def        __lowerCAmelCase (       self			:				int		):
						return 3_2







					@property
					def        __lowerCAmelCase (       self			:				str		):
						return 3_2







					@property
					def        __lowerCAmelCase (       self			:				str		):
						return self.time_input_dim







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







					@property
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						return 1_0_0







					@property
					def        __lowerCAmelCase (       self			:				Tuple		):
						lowerCAmelCase__	:						Optional[int]  				=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
						return tokenizer







					@property
					def        __lowerCAmelCase (       self			:				Dict		):
						torch.manual_seed(0		)
						lowerCAmelCase__	:						List[str]  				=	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   ,)

						lowerCAmelCase__	:						Any  				=	MultilingualCLIP(lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	text_encoder.eval()

						return text_encoder







					@property
					def        __lowerCAmelCase (       self			:				List[Any]		):
						torch.manual_seed(0		)

						lowerCAmelCase__	:						Optional[Any]  				=	{
						    '''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,
						}

						lowerCAmelCase__	:						str  				=	UNetaDConditionModel(**lowercase_		)
						return model







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







					def        __lowerCAmelCase (       self			:				Any		):
						lowerCAmelCase__	:						Union[str, Any]  				=	self.dummy_text_encoder
						lowerCAmelCase__	:						List[str]  				=	self.dummy_tokenizer
						lowerCAmelCase__	:						List[str]  				=	self.dummy_unet
						lowerCAmelCase__	:						int  				=	self.dummy_movq

						lowerCAmelCase__	:						Tuple  				=	DDIMScheduler(
						    num_train_timesteps=1_0_0_0   ,beta_schedule='''linear'''   ,beta_start=0.0_0085   ,beta_end=0.012   ,clip_sample=lowercase_   ,set_alpha_to_one=lowercase_   ,steps_offset=1   ,prediction_type='''epsilon'''   ,thresholding=lowercase_   ,)

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

						return components







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

						if str(lowercase_		).startswith('''mps'''		):
							lowerCAmelCase__	:						Union[str, Any]  				=	torch.manual_seed(lowercase_		)
						else:
							lowerCAmelCase__	:						Dict  				=	torch.Generator(device=lowercase_		).manual_seed(lowercase_		)
						lowerCAmelCase__	:						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			:				Any		):
						lowerCAmelCase__	:						List[Any]  				=	'''cpu'''

						lowerCAmelCase__	:						Optional[Any]  				=	self.get_dummy_components()

						lowerCAmelCase__	:						List[Any]  				=	self.pipeline_class(**lowercase_		)
						lowerCAmelCase__	:						Optional[Any]  				=	pipe.to(lowercase_		)

						pipe.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	pipe(**self.get_dummy_inputs(lowercase_		)		)
						lowerCAmelCase__	:						Optional[int]  				=	output.images

						lowerCAmelCase__	:						str  				=	pipe(
						    **self.get_dummy_inputs(lowercase_		)   ,return_dict=lowercase_   ,)[0]

						lowerCAmelCase__	:						List[Any]  				=	image[0, -3:, -3:, -1]
						lowerCAmelCase__	:						str  				=	image_from_tuple[0, -3:, -3:, -1]

						print(F'image.shape {image.shape}'		)

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

						lowerCAmelCase__	:						List[Any]  				=	np.array(
						    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]		)

						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			:				Any		):
						super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







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






					"""simple docstring"""



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







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''		)

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

						lowerCAmelCase__	:						List[str]  				=	'''a hat'''

						lowerCAmelCase__	:						List[str]  				=	KandinskyPriorPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-1-prior'''   ,torch_dtype=torch.floataa		)
						pipe_prior.to(lowercase_		)

						lowerCAmelCase__	:						int  				=	KandinskyInpaintPipeline.from_pretrained(
						    '''kandinsky-community/kandinsky-2-1-inpaint'''   ,torch_dtype=torch.floataa		)
						lowerCAmelCase__	:						List[Any]  				=	pipeline.to(lowercase_		)
						pipeline.set_progress_bar_config(disable=lowercase_		)

						lowerCAmelCase__	:						str  				=	torch.Generator(device='''cpu'''		).manual_seed(0		)
						lowerCAmelCase__				,lowerCAmelCase__	:						Optional[Any]  				=	pipe_prior(
						    lowercase_   ,generator=lowercase_   ,num_inference_steps=5   ,negative_prompt=''''''   ,).to_tuple()

						lowerCAmelCase__	:						Union[str, Any]  				=	pipeline(
						    lowercase_   ,image=lowercase_   ,mask_image=lowercase_   ,image_embeds=lowercase_   ,negative_image_embeds=lowercase_   ,generator=lowercase_   ,num_inference_steps=1_0_0   ,height=7_6_8   ,width=7_6_8   ,output_type='''np'''   ,)

						lowerCAmelCase__	:						Any  				=	output.images[0]

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

						assert_mean_pixel_difference(lowercase_   ,lowercase_		)







"""simple docstring"""





import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							__UpperCamelCase  :      Tuple							      =		'''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

							print('''Googling.....''')

							__UpperCamelCase  :      Optional[int]							      =		F'''https://www.google.com/search?q={query}&num=100'''

							__UpperCamelCase  :      Optional[Any]							      =		requests.get(
							    url,
							    headers={'''User-Agent''': str(UserAgent().random)},
							)

							try:
														__UpperCamelCase  :      Union[str, Any]							      =		(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''yuRUbf'''})
														    .find('''a''')
														    .get('''href''')
														)

							except AttributeError:
														__UpperCamelCase  :      str							      =		parse_qs(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''kCrYT'''})
														    .find('''a''')
														    .get('''href''')
														)['''url'''][0]

							webbrowser.open(link)




