


"""simple docstring"""


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Any				      =(DEISMultistepScheduler,)
							__UpperCAmelCase						:    Optional[int]				      =(("""num_inference_steps""", 2_5),)

							def 			snake_case				(     self    ,   **__a	):
										__lowerCAmelCase				    =			{
										    "num_train_timesteps": 10_00,
										    "beta_start": 0.0_0_0_1,
										    "beta_end": 0.0_2,
										    "beta_schedule": "linear",
										    "solver_order": 2,
										}

										config.update(**__a	)
										return config

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
													__lowerCAmelCase				    =			scheduler_class(**__a	)
													scheduler.set_timesteps(__a	)
													# copy over dummy past residuals
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a	)
																__lowerCAmelCase				    =			scheduler_class.from_pretrained(__a	)
																new_scheduler.set_timesteps(__a	)
																# copy over dummy past residuals
																__lowerCAmelCase				    =			dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase    ,	__lowerCAmelCase				    =			sample, sample
													for t in range(__a    ,   time_step + scheduler.config.solver_order + 1	):
																__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
																__lowerCAmelCase				    =			new_scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

																assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

							def 			snake_case				(     self	):
										pass

							def 			snake_case				(     self    ,   __a=0    ,   **__a	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)
										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)
										__lowerCAmelCase				    =			self.dummy_sample
										__lowerCAmelCase				    =			0.1 * sample
										__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**__a	)
													scheduler.set_timesteps(__a	)

													# copy over dummy past residuals (must be after setting timesteps)
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__a	)
																__lowerCAmelCase				    =			scheduler_class.from_pretrained(__a	)
																# copy over dummy past residuals
																new_scheduler.set_timesteps(__a	)

																# copy over dummy past residual (must be after setting timesteps)
																__lowerCAmelCase				    =			dummy_past_residuals[: new_scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
													__lowerCAmelCase				    =			new_scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

													assert torch.sum(torch.abs(output - new_output	)	) < 1e-5, "Scheduler outputs are not identical"

							def 			snake_case				(     self    ,   __a=None    ,   **__a	):
										if scheduler is None:
													__lowerCAmelCase				    =			self.scheduler_classes[0]
													__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
													__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(**__a	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter
										scheduler.set_timesteps(__a	)

										for i, t in enumerate(scheduler.timesteps	):
													__lowerCAmelCase				    =			model(__a    ,   __a	)
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a	).prev_sample

										return sample

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			dict(self.forward_default_kwargs	)

										__lowerCAmelCase				    =			kwargs.pop("num_inference_steps"    ,   __a	)

										for scheduler_class in self.scheduler_classes:
													__lowerCAmelCase				    =			self.get_scheduler_config()
													__lowerCAmelCase				    =			scheduler_class(**__a	)

													__lowerCAmelCase				    =			self.dummy_sample
													__lowerCAmelCase				    =			0.1 * sample

													if num_inference_steps is not None and hasattr(__a    ,   "set_timesteps"	):
																scheduler.set_timesteps(__a	)
													elif num_inference_steps is not None and not hasattr(__a    ,   "set_timesteps"	):
																__lowerCAmelCase				    =			num_inference_steps

													# copy over dummy past residuals (must be done after set_timesteps)
													__lowerCAmelCase				    =			[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
													__lowerCAmelCase				    =			dummy_past_residuals[: scheduler.config.solver_order]

													__lowerCAmelCase				    =			scheduler.timesteps[5]
													__lowerCAmelCase				    =			scheduler.timesteps[6]

													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a    ,   **__a	).prev_sample

													self.assertEqual(output_a.shape    ,   sample.shape	)
													self.assertEqual(output_a.shape    ,   output_a.shape	)

							def 			snake_case				(     self	):
										# make sure that iterating over schedulers with same config names gives same results
										# for defaults
										__lowerCAmelCase				    =			DEISMultistepScheduler(**self.get_scheduler_config()	)
										__lowerCAmelCase				    =			self.full_loop(scheduler=__a	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

										__lowerCAmelCase				    =			DPMSolverSinglestepScheduler.from_config(scheduler.config	)
										__lowerCAmelCase				    =			DPMSolverMultistepScheduler.from_config(scheduler.config	)
										__lowerCAmelCase				    =			UniPCMultistepScheduler.from_config(scheduler.config	)
										__lowerCAmelCase				    =			DEISMultistepScheduler.from_config(scheduler.config	)

										__lowerCAmelCase				    =			self.full_loop(scheduler=__a	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

							def 			snake_case				(     self	):
										for timesteps in [25, 50, 1_00, 9_99, 10_00]:
													self.check_over_configs(num_train_timesteps=__a	)

							def 			snake_case				(     self	):
										self.check_over_configs(thresholding=__a	)
										for order in [1, 2, 3]:
													for solver_type in ["logrho"]:
																for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    thresholding=__a    ,   prediction_type=__a    ,   sample_max_value=__a    ,   algorithm_type="deis"    ,   solver_order=__a    ,   solver_type=__a    ,   )

							def 			snake_case				(     self	):
										for prediction_type in ["epsilon", "v_prediction"]:
													self.check_over_configs(prediction_type=__a	)

							def 			snake_case				(     self	):
										for algorithm_type in ["deis"]:
													for solver_type in ["logrho"]:
																for order in [1, 2, 3]:
																			for prediction_type in ["epsilon", "sample"]:
																						self.check_over_configs(
																						    solver_order=__a    ,   solver_type=__a    ,   prediction_type=__a    ,   algorithm_type=__a    ,   )
																						__lowerCAmelCase				    =			self.full_loop(
																						    solver_order=__a    ,   solver_type=__a    ,   prediction_type=__a    ,   algorithm_type=__a    ,   )
																						assert not torch.isnan(__a	).any(), "Samples have nan numbers"

							def 			snake_case				(     self	):
										self.check_over_configs(lower_order_final=__a	)
										self.check_over_configs(lower_order_final=__a	)

							def 			snake_case				(     self	):
										for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
													self.check_over_forward(num_inference_steps=__a    ,   time_step=0	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop()
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.2_3_9_1_6	) < 1e-3

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.full_loop(prediction_type="v_prediction"	)
										__lowerCAmelCase				    =			torch.mean(torch.abs(__a	)	)

										assert abs(result_mean.item() - 0.0_9_1	) < 1e-3



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.scheduler_classes[0]
										__lowerCAmelCase				    =			self.get_scheduler_config(thresholding=__a    ,   dynamic_thresholding_ratio=0	)
										__lowerCAmelCase				    =			scheduler_class(**__a	)

										__lowerCAmelCase				    =			10
										__lowerCAmelCase				    =			self.dummy_model()
										__lowerCAmelCase				    =			self.dummy_sample_deter.half()
										scheduler.set_timesteps(__a	)

										for i, t in enumerate(scheduler.timesteps	):
													__lowerCAmelCase				    =			model(__a    ,   __a	)
													__lowerCAmelCase				    =			scheduler.step(__a    ,   __a    ,   __a	).prev_sample

										assert sample.dtype == torch.floataa










import requests
from bsa import BeautifulSoup







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       str:



       """simple docstring"""

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


if __name__ == "__main__":
  _lowerCamelCase	:		Optional[Any]							       =   {
      """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))


