






import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Any							       = DiTPipeline
				_UpperCamelCase					:       int							       = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				_UpperCamelCase					:       Dict							       = PipelineTesterMixin.required_optional_params - {
				    "latents",
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				_UpperCamelCase					:       Union[str, Any]							       = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				_UpperCamelCase					:       Union[str, Any]							       = False


				def 						__a					(					self      ):
											torch.manual_seed(0      )
											_lowercase							:   str 							=   TransformeraDModel(
											    sample_size=1_6 ,						num_layers=2 ,						patch_size=4 ,						attention_head_dim=8 ,						num_attention_heads=2 ,						in_channels=4 ,						out_channels=8 ,						attention_bias=_lowerCAmelCase ,						activation_fn='gelu-approximate' ,						num_embeds_ada_norm=1_0_0_0 ,						norm_type='ada_norm_zero' ,						norm_elementwise_affine=_lowerCAmelCase ,						)
											_lowercase							:   int 							=   AutoencoderKL()
											_lowercase							:   List[str] 							=   DDIMScheduler()
											_lowercase							:   Optional[int] 							=   {'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
											return components


				def 						__a					(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=0      ):
											if str(_lowerCAmelCase      ).startswith('mps'      ):
																		_lowercase							:   List[str] 							=   torch.manual_seed(_lowerCAmelCase      )
											else:
																		_lowercase							:   Any 							=   torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
											_lowercase							:   Union[str, Any] 							=   {
											    'class_labels': [1],
											    'generator': generator,
											    'num_inference_steps': 2,
											    'output_type': 'numpy',
											}
											return inputs


				def 						__a					(					self      ):
											_lowercase							:   Dict 							=   'cpu'

											_lowercase							:   List[Any] 							=   self.get_dummy_components()
											_lowercase							:   List[str] 							=   self.pipeline_class(**_lowerCAmelCase      )
											pipe.to(_lowerCAmelCase      )
											pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   self.get_dummy_inputs(_lowerCAmelCase      )
											_lowercase							:   Dict 							=   pipe(**_lowerCAmelCase      ).images
											_lowercase							:   List[Any] 							=   image[0, -3:, -3:, -1]

											self.assertEqual(image.shape ,						(1, 1_6, 1_6, 3)      )
											_lowercase							:   Optional[int] 							=   np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]      )
											_lowercase							:   Union[str, Any] 							=   np.abs(image_slice.flatten() - expected_slice      ).max()
											self.assertLessEqual(_lowerCAmelCase ,						1E-3      )


				def 						__a					(					self      ):
											self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase ,						expected_max_diff=1E-3      )


				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available() ,						reason='XFormers attention is only available with CUDA and `xformers` installed' ,						)
				def 						__a					(					self      ):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )




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


				def 						__a					(					self      ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   torch.manual_seed(0      )

											_lowercase							:   Optional[Any] 							=   DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'      )
											pipe.to('cuda'      )

											_lowercase							:   List[Any] 							=   ['vase', 'umbrella', 'white shark', 'white wolf']
											_lowercase							:   List[str] 							=   pipe.get_label_ids(_lowerCAmelCase      )

											_lowercase							:   Tuple 							=   pipe(_lowerCAmelCase ,						generator=_lowerCAmelCase ,						num_inference_steps=4_0 ,						output_type='np'      ).images

											for word, image in zip(_lowerCAmelCase ,						_lowerCAmelCase      ):
																		_lowercase							:   Optional[Any] 							=   load_numpy(
																		    F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy"""      )
																		assert np.abs((expected_image - image).max()      ) < 1E-2


				def 						__a					(					self      ):
											_lowercase							:   str 							=   DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'      )
											_lowercase							:   int 							=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
											pipe.to('cuda'      )

											_lowercase							:   Optional[Any] 							=   ['vase', 'umbrella']
											_lowercase							:   List[Any] 							=   pipe.get_label_ids(_lowerCAmelCase      )

											_lowercase							:   List[str] 							=   torch.manual_seed(0      )
											_lowercase							:   List[Any] 							=   pipe(_lowerCAmelCase ,						generator=_lowerCAmelCase ,						num_inference_steps=2_5 ,						output_type='np'      ).images

											for word, image in zip(_lowerCAmelCase ,						_lowerCAmelCase      ):
																		_lowercase							:   Union[str, Any] 							=   load_numpy(
																		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
																		    F"""/dit/{word}_512.npy"""      )

																		assert np.abs((expected_image - image).max()      ) < 1E-1










'''simple docstring'''






import requests
from bsa import BeautifulSoup






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   str:
      snake_case__       :   List[Any]	      =    BeautifulSoup(requests.get(__SCREAMING_SNAKE_CASE				,      params=__SCREAMING_SNAKE_CASE		).content				,      'html.parser'		)
      snake_case__       :   Optional[int]	      =    soup.find('div'				,      attrs={'class': 'gs_ri'}		)
      snake_case__       :   Dict	      =    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))
