





'''simple docstring'''




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


a__					:			List[str]          =     False






class 				__snake_case     (					unittest.TestCase							):





	def   _snake_case   (		self		)      ->		List[Any]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	@property
	def   _snake_case   (		self		)      ->		Tuple:
		return 12





	@property
	def   _snake_case   (		self		)      ->		Any:
		return 12





	@property
	def   _snake_case   (		self		)      ->		int:
		return 32





	@property
	def   _snake_case   (		self		)      ->		Tuple:
		torch.manual_seed(0		)
		snake_case__	       =   VQModel(
		    block_out_channels=[32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=3      ,       num_vq_embeddings=self.num_embed      ,       vq_embed_dim=3      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)
		return tokenizer





	@property
	def   _snake_case   (		self		)      ->		Any:
		torch.manual_seed(0		)
		snake_case__	       =   CLIPTextConfig(
		    bos_token_id=0      ,       eos_token_id=2      ,       hidden_size=self.text_embedder_hidden_size      ,       intermediate_size=37      ,       layer_norm_eps=1E-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	@property
	def   _snake_case   (		self		)      ->		int:
		torch.manual_seed(0		)

		snake_case__	       =   12
		snake_case__	       =   12

		snake_case__	       =   {
		    'attention_bias': True,
		    'cross_attention_dim': 32,
		    'attention_head_dim': height * width,
		    'num_attention_heads': 1,
		    'num_vector_embeds': self.num_embed,
		    'num_embeds_ada_norm': self.num_embeds_ada_norm,
		    'norm_num_groups': 32,
		    'sample_size': width,
		    'activation_fn': 'geglu-approximate',
		}

		snake_case__	       =   TransformeraDModel(**UpperCamelCase_		)
		return model





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'

		snake_case__	       =   self.dummy_vqvae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   self.dummy_tokenizer
		snake_case__	       =   self.dummy_transformer
		snake_case__	       =   VQDiffusionScheduler(self.num_embed		)
		snake_case__	       =   LearnedClassifierFreeSamplingEmbeddings(learnable=UpperCamelCase_		)

		snake_case__	       =   VQDiffusionPipeline(
		    vqvae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       transformer=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       learned_classifier_free_sampling_embeddings=UpperCamelCase_      ,       )
		snake_case__	       =   pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'teddy bear playing in the pool'

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   pipe([prompt]      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'		)
		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   pipe(
		    [prompt]      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       num_inference_steps=2		)[0]

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

		assert image.shape == (1, 24, 24, 3)

		snake_case__	       =   np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   'cpu'

		snake_case__	       =   self.dummy_vqvae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   self.dummy_tokenizer
		snake_case__	       =   self.dummy_transformer
		snake_case__	       =   VQDiffusionScheduler(self.num_embed		)
		snake_case__	       =   LearnedClassifierFreeSamplingEmbeddings(
		    learnable=UpperCamelCase_      ,       hidden_size=self.text_embedder_hidden_size      ,       length=tokenizer.model_max_length		)

		snake_case__	       =   VQDiffusionPipeline(
		    vqvae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       transformer=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       learned_classifier_free_sampling_embeddings=UpperCamelCase_      ,       )
		snake_case__	       =   pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'teddy bear playing in the pool'

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   pipe([prompt]      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'		)
		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   pipe(
		    [prompt]      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       num_inference_steps=2		)[0]

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

		assert image.shape == (1, 24, 24, 3)

		snake_case__	       =   np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]		)

		assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
		assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2






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





	def   _snake_case   (		self		)      ->		List[Any]:
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'		)

		snake_case__	       =   VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'		)
		snake_case__	       =   pipeline.to(UpperCamelCase_		)
		pipeline.set_progress_bar_config(disable=UpperCamelCase_		)

		# requires GPU generator for gumbel softmax
		# don't use GPU generator in tests though
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   pipeline(
		    'teddy bear playing in the pool'      ,       num_images_per_prompt=1      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )

		snake_case__	       =   output.images[0]

		assert image.shape == (256, 256, 3)
		assert np.abs(expected_image - image		).max() < 2.0










'''simple docstring'''




import requests
from bsa import BeautifulSoup




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->str:
	snake_case__	       =   BeautifulSoup(requests.get(UpperCAmelCase_					,				params=UpperCAmelCase_							).content					,				'html.parser'							)
	snake_case__	       =   soup.find('div'					,				attrs={'class': 'gs_ri'}							)
	snake_case__	       =   div.find('div'					,				attrs={'class': 'gs_fl'}							).find_all('a'							)
	return anchors[2].get_text()


if __name__ == "__main__":
			a__					:			int          =     {
			    '''title''': (
			        '''Precisely geometry controlled microsupercapacitors for ultrahigh areal '''
			        '''capacitance, volumetric capacitance, and energy density'''
			    ),
			    '''journal''': '''Chem. Mater.''',
			    '''volume''': 30,
			    '''pages''': '''3979-3990''',
			    '''year''': 2018,
			    '''hl''': '''en''',
			}
			print(get_citation('''https://scholar.google.com/scholar_lookup''', params=params))



