
'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Dict										=  KandinskyVaaControlnetImgaImgPipeline
						_lowercase						: int										=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						_lowercase						: Tuple										=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
						_lowercase						: Dict										=  [
						    '''generator''',
						    '''height''',
						    '''width''',
						    '''strength''',
						    '''guidance_scale''',
						    '''num_inference_steps''',
						    '''return_dict''',
						    '''guidance_scale''',
						    '''num_images_per_prompt''',
						    '''output_type''',
						    '''return_dict''',
						]
						_lowercase						: str										=  False




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 32




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.time_input_dim * 4




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return 100




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )

												_lowerCAmelCase    		=					{
												    """in_channels""": 8,
												    # Out channels is double in channels because predicts mean and variance
												    """out_channels""": 8,
												    """addition_embed_type""": """image_hint""",
												    """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""": """image_proj""",
												    """cross_attention_dim""": self.cross_attention_dim,
												    """attention_head_dim""": 4,
												    """resnet_time_scale_shift""": """scale_shift""",
												    """class_embed_type""": None,
												}

												_lowerCAmelCase    		=					UNetaDConditionModel(**_lowercase )
												return model




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return {
												    "block_out_channels": [32, 32, 64, 64],
												    "down_block_types": [
												        "DownEncoderBlock2D",
												        "DownEncoderBlock2D",
												        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
												    "vq_embed_dim": 4,
												}




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												torch.manual_seed(0 )
												_lowerCAmelCase    		=					VQModel(**self.dummy_movq_kwargs )
												return model




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.dummy_unet
												_lowerCAmelCase    		=					self.dummy_movq

												_lowerCAmelCase    		=					{
												    """num_train_timesteps""": 1_000,
												    """beta_schedule""": """linear""",
												    """beta_start""": 0.0_0085,
												    """beta_end""": 0.012,
												    """clip_sample""": False,
												    """set_alpha_to_one""": False,
												    """steps_offset""": 0,
												    """prediction_type""": """epsilon""",
												    """thresholding""": False,
												}

												_lowerCAmelCase    		=					DDIMScheduler(**_lowercase )

												_lowerCAmelCase    		=					{
												    """unet""": unet,
												    """scheduler""": scheduler,
												    """movq""": movq,
												}

												return components




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=0 ):

												"""simple docstring"""



												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					floats_tensor((1, self.text_embedder_hidden_size)       ,  rng=random.Random(seed + 1 ) ).to(
												    _lowercase )
												# create init_image
												_lowerCAmelCase    		=					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )
												_lowerCAmelCase    		=					image.cpu().permute(0       ,  2       ,  3       ,  1 )[0]
												_lowerCAmelCase    		=					Image.fromarray(np.uinta(_lowercase ) ).convert("""RGB""" ).resize((256, 256) )
												# create hint
												_lowerCAmelCase    		=					floats_tensor((1, 3, 64, 64)       ,  rng=random.Random(_lowercase ) ).to(_lowercase )

												if str(_lowercase ).startswith("""mps""" ):
																		_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												else:
																		_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(_lowercase )
												_lowerCAmelCase    		=					{
												    """image""": init_image,
												    """image_embeds""": image_embeds,
												    """negative_image_embeds""": negative_image_embeds,
												    """hint""": hint,
												    """generator""": generator,
												    """height""": 64,
												    """width""": 64,
												    """num_inference_steps""": 10,
												    """guidance_scale""": 7.0,
												    """strength""": 0.2,
												    """output_type""": """np""",
												}
												return inputs




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""

												_lowerCAmelCase    		=					self.get_dummy_components()

												_lowerCAmelCase    		=					self.pipeline_class(**_lowercase )
												_lowerCAmelCase    		=					pipe.to(_lowercase )

												pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					pipe(**self.get_dummy_inputs(_lowercase ) )
												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					pipe(
												    **self.get_dummy_inputs(_lowercase )       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

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

												_lowerCAmelCase    		=					np.array(
												    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736] )
												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     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					load_numpy(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""" )

												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""" )
												_lowerCAmelCase    		=					init_image.resize((512, 512) )

												_lowerCAmelCase    		=					load_image(
												    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
												    """/kandinskyv22/hint_image_cat.png""" )
												_lowerCAmelCase    		=					torch.from_numpy(np.array(_lowercase ) ).float() / 255.0
												_lowerCAmelCase    		=					hint.permute(2       ,  0       ,  1 ).unsqueeze(0 )

												_lowerCAmelCase    		=					"""A robot, 4k photo"""

												_lowerCAmelCase    		=					KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-prior"""       ,  torch_dtype=torch.floataa )
												pipe_prior.to(_lowercase )

												_lowerCAmelCase    		=					KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
												    """kandinsky-community/kandinsky-2-2-controlnet-depth"""       ,  torch_dtype=torch.floataa )
												_lowerCAmelCase    		=					pipeline.to(_lowercase )

												pipeline.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					torch.Generator(device="""cpu""" ).manual_seed(0 )

												_lowerCAmelCase		,       _lowerCAmelCase    		=					pipe_prior(
												    _lowercase       ,  image=_lowercase       ,  strength=0.85       ,  generator=_lowercase       ,  negative_prompt=""""""       ,  ).to_tuple()

												_lowerCAmelCase    		=					pipeline(
												    image=_lowercase       ,  image_embeds=_lowercase       ,  negative_image_embeds=_lowercase       ,  hint=_lowercase       ,  generator=_lowercase       ,  num_inference_steps=100       ,  height=512       ,  width=512       ,  strength=0.5       ,  output_type="""np"""       ,  )

												_lowerCAmelCase    		=					output.images[0]

												assert image.shape == (512, 512, 3)

												assert_mean_pixel_difference(_lowercase       ,  _lowercase )


'''simple docstring'''



import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
						print("""Googling.....""")
						_lowercase          =						"""https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
						_lowercase          =						requests.get(url, headers={"""UserAgent""": UserAgent().random})
						# res.raise_for_status()
						with open("""project1a.html""", """wb""") as out_file:  # only for knowing the class
												for data in res.iter_content(10000):
																		out_file.write(data)
						_lowercase          =						BeautifulSoup(res.text, """html.parser""")
						_lowercase          =						list(soup.select(""".eZt8xd"""))[:5]

						print(len(links))
						for link in links:
												if link.text == "Maps":
																		webbrowser.open(link.get("""href"""))
												else:
																		webbrowser.open(F"""https://google.com{link.get('href')}""")
