




import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard
@slow
@require_flax
class     a__					(      unittest.TestCase   ):






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											super().tearDown()
											gc.collect()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase     ,							__lowerCAmelCase            = FlaxStableDiffusionPipeline.from_pretrained(
											    "stabilityai/stable-diffusion-2"	,    revision="bf16"	,    dtype=jnp.bfloataa	,    )

											__lowerCAmelCase            = "A painting of a squirrel eating a burger"
											__lowerCAmelCase            = jax.device_count()
											__lowerCAmelCase            = num_samples * [prompt]
											__lowerCAmelCase            = sd_pipe.prepare_inputs(_lowerCAmelCase    )

											__lowerCAmelCase            = replicate(_lowerCAmelCase    )
											__lowerCAmelCase            = shard(_lowerCAmelCase    )

											__lowerCAmelCase            = jax.random.PRNGKey(0    )
											__lowerCAmelCase            = jax.random.split(_lowerCAmelCase	,    jax.device_count()    )

											__lowerCAmelCase            = sd_pipe(_lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    num_inference_steps=2_5	,    jit=_lowerCAmelCase    )[0]
											assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

											__lowerCAmelCase            = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
											__lowerCAmelCase            = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

											__lowerCAmelCase            = jnp.asarray(jax.device_get(image_slice.flatten()    )    )
											__lowerCAmelCase            = jnp.array([0.42_38, 0.44_14, 0.43_95, 0.44_53, 0.46_29, 0.45_90, 0.45_31, 0.4_55_08, 0.45_12]    )
											print(f"""output_slice: {output_slice}"""    )
											assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = "stabilityai/stable-diffusion-2"
											__lowerCAmelCase     ,							__lowerCAmelCase            = FlaxDPMSolverMultistepScheduler.from_pretrained(_lowerCAmelCase	,    subfolder="scheduler"    )
											__lowerCAmelCase     ,							__lowerCAmelCase            = FlaxStableDiffusionPipeline.from_pretrained(
											    _lowerCAmelCase	,    scheduler=_lowerCAmelCase	,    revision="bf16"	,    dtype=jnp.bfloataa	,    )
											__lowerCAmelCase            = scheduler_params

											__lowerCAmelCase            = "A painting of a squirrel eating a burger"
											__lowerCAmelCase            = jax.device_count()
											__lowerCAmelCase            = num_samples * [prompt]
											__lowerCAmelCase            = sd_pipe.prepare_inputs(_lowerCAmelCase    )

											__lowerCAmelCase            = replicate(_lowerCAmelCase    )
											__lowerCAmelCase            = shard(_lowerCAmelCase    )

											__lowerCAmelCase            = jax.random.PRNGKey(0    )
											__lowerCAmelCase            = jax.random.split(_lowerCAmelCase	,    jax.device_count()    )

											__lowerCAmelCase            = sd_pipe(_lowerCAmelCase	,    _lowerCAmelCase	,    _lowerCAmelCase	,    num_inference_steps=2_5	,    jit=_lowerCAmelCase    )[0]
											assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

											__lowerCAmelCase            = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
											__lowerCAmelCase            = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

											__lowerCAmelCase            = jnp.asarray(jax.device_get(image_slice.flatten()    )    )
											__lowerCAmelCase            = jnp.array([0.43_36, 0.4_29_69, 0.44_53, 0.41_99, 0.42_97, 0.45_31, 0.44_34, 0.44_34, 0.42_97]    )
											print(f"""output_slice: {output_slice}"""    )
											assert jnp.abs(output_slice - expected_slice    ).max() < 1E-2










import requests
from bsa import BeautifulSoup







def 			_a   (		SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       dict  ):
					__lowerCAmelCase            = BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE_   ,   params=SCREAMING_SNAKE_CASE_  ).content   ,   "html.parser"  )
					__lowerCAmelCase            = soup.find("div"   ,   attrs={"class": "gs_ri"}  )
					__lowerCAmelCase            = div.find("div"   ,   attrs={"class": "gs_fl"}  ).find_all("a"  )
					return anchors[2].get_text()


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




