



'''simple docstring'''

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       a__				( lowercase__							,						unittest.TestCase      ):







				"""simple docstring"""



				__UpperCamelCase    :		Dict	=	DiTPipeline
				__UpperCamelCase    :		Tuple	=	CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
				__UpperCamelCase    :		Tuple	=	PipelineTesterMixin.required_optional_params - {
				    """latents""",
				    """num_images_per_prompt""",
				    """callback""",
				    """callback_steps""",
				}
				__UpperCamelCase    :		List[str]	=	CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
				__UpperCamelCase    :		int	=	False




				def 					_snake_case  (self		):
										torch.manual_seed(0		)
										__lowerCAmelCase							       =				TransformeraDModel(
										    sample_size=16       ,			num_layers=2       ,			patch_size=4       ,			attention_head_dim=8       ,			num_attention_heads=2       ,			in_channels=4       ,			out_channels=8       ,			attention_bias=__lowercase       ,			activation_fn='''gelu-approximate'''       ,			num_embeds_ada_norm=10_00       ,			norm_type='''ada_norm_zero'''       ,			norm_elementwise_affine=__lowercase       ,			)
										__lowerCAmelCase							       =				AutoencoderKL()
										__lowerCAmelCase							       =				DDIMScheduler()
										__lowerCAmelCase							       =				{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
										return components




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase=0		):
										if str(__lowercase		).startswith('''mps'''		):
																__lowerCAmelCase							       =				torch.manual_seed(__lowercase		)
										else:
																__lowerCAmelCase							       =				torch.Generator(device=__lowercase		).manual_seed(__lowercase		)
										__lowerCAmelCase							       =				{
										    '''class_labels''': [1],
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				'''cpu'''

										__lowerCAmelCase							       =				self.get_dummy_components()
										__lowerCAmelCase							       =				self.pipeline_class(**__lowercase		)
										pipe.to(__lowercase		)
										pipe.set_progress_bar_config(disable=__lowercase		)

										__lowerCAmelCase							       =				self.get_dummy_inputs(__lowercase		)
										__lowerCAmelCase							       =				pipe(**__lowercase		).images
										__lowerCAmelCase							       =				image[0, -3:, -3:, -1]

										self.assertEqual(image.shape       ,			(1, 16, 16, 3)		)
										__lowerCAmelCase							       =				np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7]		)
										__lowerCAmelCase							       =				np.abs(image_slice.flatten() - expected_slice		).max()
										self.assertLessEqual(__lowercase       ,			1e-3		)




				def 					_snake_case  (self		):
										self._test_inference_batch_single_identical(relax_max_difference=__lowercase       ,			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 					_snake_case  (self		):
										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)


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







				"""simple docstring"""



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




				def 					_snake_case  (self		):
										__lowerCAmelCase							       =				torch.manual_seed(0		)

										__lowerCAmelCase							       =				DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''		)
										pipe.to('''cuda'''		)

										__lowerCAmelCase							       =				['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
										__lowerCAmelCase							       =				pipe.get_label_ids(__lowercase		)

										__lowerCAmelCase							       =				pipe(__lowercase       ,			generator=__lowercase       ,			num_inference_steps=40       ,			output_type='''np'''		).images

										for word, image in zip(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				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 					_snake_case  (self		):
										__lowerCAmelCase							       =				DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''		)
										__lowerCAmelCase							       =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config		)
										pipe.to('''cuda'''		)

										__lowerCAmelCase							       =				['''vase''', '''umbrella''']
										__lowerCAmelCase							       =				pipe.get_label_ids(__lowercase		)

										__lowerCAmelCase							       =				torch.manual_seed(0		)
										__lowerCAmelCase							       =				pipe(__lowercase       ,			generator=__lowercase       ,			num_inference_steps=25       ,			output_type='''np'''		).images

										for word, image in zip(__lowercase       ,			__lowercase		):
																__lowerCAmelCase							       =				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 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				BeautifulSoup(requests.get(lowerCamelCase,				params=lowerCamelCase).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				: Union[str, Any]							    =				{
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))

