





'''simple docstring'''

import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
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


enable_full_determinism()







class __a (   unittest.TestCase			):





   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

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





   @property
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       1
      __SCREAMING_SNAKE_CASE      							=       3
      __SCREAMING_SNAKE_CASE      							=       (32, 32)

      __SCREAMING_SNAKE_CASE      							=       floats_tensor((batch_size, num_channels) + sizes				,rng=random.Random(0    )    ).to(lowerCamelCase    )
      return image





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       UNetaDConditionModel(
          block_out_channels=(32, 32, 64)				,layers_per_block=2				,sample_size=32				,in_channels=7				,out_channels=4				,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""", """CrossAttnDownBlock2D""")				,up_block_types=("""CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """UpBlock2D""")				,cross_attention_dim=32				,attention_head_dim=8				,use_linear_projection=lowerCamelCase				,only_cross_attention=(True, True, False)				,num_class_embeds=100				,)
      return model





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       AutoencoderKL(
          block_out_channels=[32, 32, 64]				,in_channels=3				,out_channels=3				,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,latent_channels=4				,)
      return model





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       CLIPTextConfig(
          bos_token_id=0				,eos_token_id=2				,hidden_size=32				,intermediate_size=37				,layer_norm_eps=1E-0_5				,num_attention_heads=4				,num_hidden_layers=5				,pad_token_id=1				,vocab_size=1000				,hidden_act="""gelu"""				,projection_dim=512				,)
      return CLIPTextModel(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE      							=       self.dummy_cond_unet_upscale
      __SCREAMING_SNAKE_CASE      							=       DDPMScheduler()
      __SCREAMING_SNAKE_CASE      							=       DDIMScheduler(prediction_type="""v_prediction"""    )
      __SCREAMING_SNAKE_CASE      							=       self.dummy_vae
      __SCREAMING_SNAKE_CASE      							=       self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE      							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_image.cpu().permute(0				,2				,3				,1    )[0]
      __SCREAMING_SNAKE_CASE      							=       Image.fromarray(np.uinta(lowerCamelCase    )    ).convert("""RGB"""    ).resize((64, 64)    )

      # make sure here that pndm scheduler skips prk
      __SCREAMING_SNAKE_CASE      							=       StableDiffusionUpscalePipeline(
          unet=lowerCamelCase				,low_res_scheduler=lowerCamelCase				,scheduler=lowerCamelCase				,vae=lowerCamelCase				,text_encoder=lowerCamelCase				,tokenizer=lowerCamelCase				,max_noise_level=350				,)
      __SCREAMING_SNAKE_CASE      							=       sd_pipe.to(lowerCamelCase    )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """A painting of a squirrel eating a burger"""
      __SCREAMING_SNAKE_CASE      							=       torch.Generator(device=lowerCamelCase    ).manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe(
          [prompt]				,image=lowerCamelCase				,generator=lowerCamelCase				,guidance_scale=6.0				,noise_level=20				,num_inference_steps=2				,output_type="""np"""				,)

      __SCREAMING_SNAKE_CASE      							=       output.images

      __SCREAMING_SNAKE_CASE      							=       torch.Generator(device=lowerCamelCase    ).manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe(
          [prompt]				,image=lowerCamelCase				,generator=lowerCamelCase				,guidance_scale=6.0				,noise_level=20				,num_inference_steps=2				,output_type="""np"""				,return_dict=lowerCamelCase				,)[0]

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

      __SCREAMING_SNAKE_CASE      							=       low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)
      __SCREAMING_SNAKE_CASE      							=       np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]    )

      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      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      __SCREAMING_SNAKE_CASE      							=       self.dummy_cond_unet_upscale
      __SCREAMING_SNAKE_CASE      							=       DDPMScheduler()
      __SCREAMING_SNAKE_CASE      							=       DDIMScheduler(prediction_type="""v_prediction"""    )
      __SCREAMING_SNAKE_CASE      							=       self.dummy_vae
      __SCREAMING_SNAKE_CASE      							=       self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE      							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_image.cpu().permute(0				,2				,3				,1    )[0]
      __SCREAMING_SNAKE_CASE      							=       Image.fromarray(np.uinta(lowerCamelCase    )    ).convert("""RGB"""    ).resize((64, 64)    )

      # make sure here that pndm scheduler skips prk
      __SCREAMING_SNAKE_CASE      							=       StableDiffusionUpscalePipeline(
          unet=lowerCamelCase				,low_res_scheduler=lowerCamelCase				,scheduler=lowerCamelCase				,vae=lowerCamelCase				,text_encoder=lowerCamelCase				,tokenizer=lowerCamelCase				,max_noise_level=350				,)
      __SCREAMING_SNAKE_CASE      							=       sd_pipe.to(lowerCamelCase    )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """A painting of a squirrel eating a burger"""
      __SCREAMING_SNAKE_CASE      							=       sd_pipe(
          2 * [prompt]				,image=2 * [low_res_image]				,guidance_scale=6.0				,noise_level=20				,num_inference_steps=2				,output_type="""np"""				,)
      __SCREAMING_SNAKE_CASE      							=       output.images
      assert image.shape[0] == 2

      __SCREAMING_SNAKE_CASE      							=       torch.Generator(device=lowerCamelCase    ).manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe(
          [prompt]				,image=lowerCamelCase				,generator=lowerCamelCase				,num_images_per_prompt=2				,guidance_scale=6.0				,noise_level=20				,num_inference_steps=2				,output_type="""np"""				,)
      __SCREAMING_SNAKE_CASE      							=       output.images
      assert image.shape[0] == 2





   @unittest.skipIf(torch_device != """cuda"""				,"""This test requires a GPU"""    )
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.dummy_cond_unet_upscale
      __SCREAMING_SNAKE_CASE      							=       DDPMScheduler()
      __SCREAMING_SNAKE_CASE      							=       DDIMScheduler(prediction_type="""v_prediction"""    )
      __SCREAMING_SNAKE_CASE      							=       self.dummy_vae
      __SCREAMING_SNAKE_CASE      							=       self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE      							=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

      __SCREAMING_SNAKE_CASE      							=       self.dummy_image.cpu().permute(0				,2				,3				,1    )[0]
      __SCREAMING_SNAKE_CASE      							=       Image.fromarray(np.uinta(lowerCamelCase    )    ).convert("""RGB"""    ).resize((64, 64)    )

      # put models in fp16, except vae as it overflows in fp16
      __SCREAMING_SNAKE_CASE      							=       unet.half()
      __SCREAMING_SNAKE_CASE      							=       text_encoder.half()

      # make sure here that pndm scheduler skips prk
      __SCREAMING_SNAKE_CASE      							=       StableDiffusionUpscalePipeline(
          unet=lowerCamelCase				,low_res_scheduler=lowerCamelCase				,scheduler=lowerCamelCase				,vae=lowerCamelCase				,text_encoder=lowerCamelCase				,tokenizer=lowerCamelCase				,max_noise_level=350				,)
      __SCREAMING_SNAKE_CASE      							=       sd_pipe.to(lowerCamelCase    )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       """A painting of a squirrel eating a burger"""
      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       sd_pipe(
          [prompt]				,image=lowerCamelCase				,generator=lowerCamelCase				,num_inference_steps=2				,output_type="""np"""				,).images

      __SCREAMING_SNAKE_CASE      							=       low_res_image.size[0] * 4
      assert image.shape == (1, expected_height_width, expected_height_width, 3)







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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-upscale/low_res_cat.png"""    )
      __SCREAMING_SNAKE_CASE      							=       load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
          """/upsampled_cat.npy"""    )

      __SCREAMING_SNAKE_CASE      							=       """stabilityai/stable-diffusion-x4-upscaler"""
      __SCREAMING_SNAKE_CASE      							=       StableDiffusionUpscalePipeline.from_pretrained(lowerCamelCase    )
      pipe.to(lowerCamelCase    )
      pipe.set_progress_bar_config(disable=lowerCamelCase    )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE      							=       """a cat sitting on a park bench"""

      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       pipe(
          prompt=lowerCamelCase				,image=lowerCamelCase				,generator=lowerCamelCase				,output_type="""np"""				,)
      __SCREAMING_SNAKE_CASE      							=       output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image    ).max() < 1E-3





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-upscale/low_res_cat.png"""    )
      __SCREAMING_SNAKE_CASE      							=       load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale"""
          """/upsampled_cat_fp16.npy"""    )

      __SCREAMING_SNAKE_CASE      							=       """stabilityai/stable-diffusion-x4-upscaler"""
      __SCREAMING_SNAKE_CASE      							=       StableDiffusionUpscalePipeline.from_pretrained(
          lowerCamelCase				,torch_dtype=torch.floataa				,)
      pipe.to(lowerCamelCase    )
      pipe.set_progress_bar_config(disable=lowerCamelCase    )
      pipe.enable_attention_slicing()

      __SCREAMING_SNAKE_CASE      							=       """a cat sitting on a park bench"""

      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       pipe(
          prompt=lowerCamelCase				,image=lowerCamelCase				,generator=lowerCamelCase				,output_type="""np"""				,)
      __SCREAMING_SNAKE_CASE      							=       output.images[0]

      assert image.shape == (512, 512, 3)
      assert np.abs(expected_image - image    ).max() < 5E-1





   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __SCREAMING_SNAKE_CASE      							=       load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/sd2-upscale/low_res_cat.png"""    )

      __SCREAMING_SNAKE_CASE      							=       """stabilityai/stable-diffusion-x4-upscaler"""
      __SCREAMING_SNAKE_CASE      							=       StableDiffusionUpscalePipeline.from_pretrained(
          lowerCamelCase				,torch_dtype=torch.floataa				,)
      pipe.to(lowerCamelCase    )
      pipe.set_progress_bar_config(disable=lowerCamelCase    )
      pipe.enable_attention_slicing(1    )
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE      							=       """a cat sitting on a park bench"""

      __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       pipe(
          prompt=lowerCamelCase				,image=lowerCamelCase				,generator=lowerCamelCase				,num_inference_steps=5				,output_type="""np"""				,)

      __SCREAMING_SNAKE_CASE      							=       torch.cuda.max_memory_allocated()
      # make sure that less than 2.9 GB is allocated
      assert mem_bytes < 2.9 * 10**9












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase							,	params=__UpperCAmelCase      ).content							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       soup.find("""div"""							,	attrs={"""class""": """gs_ri"""}      )
   __SCREAMING_SNAKE_CASE      							=       div.find("""div"""							,	attrs={"""class""": """gs_fl"""}      ).find_all("""a"""      )
   return anchors[2].get_text()


if __name__ == "__main__":
    a        =						{
        "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))





