import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 			lowerCamelCase_  (     UpperCAmelCase_		,			UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Optional[Any]      			=   IFInpaintingPipeline
    a__			:		Tuple      			=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
    a__			:		List[str]      			=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    a__			:		Dict      			=   PipelineTesterMixin.required_optional_params - {"""latents"""}







    def     UpperCamelCase__     (			self) ->			Optional[int]:
        return self._get_dummy_components()







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0) ->			Tuple:
        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :Any    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :Any    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)

        __UpperCamelCase  :Any    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)
        __UpperCamelCase  :int    						=	floats_tensor((1, 3, 32, 32)				,				rng=random.Random(__lowercase)).to(__lowercase)

        __UpperCamelCase  :str    						=	{
            '''prompt''': '''A painting of a squirrel eating a burger''',
            '''image''': image,
            '''mask_image''': mask_image,
            '''generator''': generator,
            '''num_inference_steps''': 2,
            '''output_type''': '''numpy''',
        }

        return inputs







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







    def     UpperCamelCase__     (			self) ->			int:
        self._test_save_load_optional_components()







    @unittest.skipIf(torch_device != '''cuda'''				,				reason='''float16 requires CUDA''')
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
        super().test_save_load_floataa(expected_max_diff=1E-1)







    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)







    def     UpperCamelCase__     (			self) ->			str:
        self._test_save_load_local()







    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2				,				)






import requests
from bsa import BeautifulSoup



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	BeautifulSoup(requests.get(SCREAMING_SNAKE_CASE						,    params=SCREAMING_SNAKE_CASE			).content						,    '''html.parser'''			)
    __UpperCamelCase  :int    						=	soup.find('''div'''						,    attrs={'''class''': '''gs_ri'''}			)
    __UpperCamelCase  :Union[str, Any]    						=	div.find('''div'''						,    attrs={'''class''': '''gs_fl'''}			).find_all('''a'''			)
    return anchors[2].get_text()


if __name__ == "__main__":
    __lowercase											=      {
        '''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))





