import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		Any      			=   ShapEImgaImgPipeline
    a__			:		str      			=   ["""image"""]
    a__			:		Dict      			=   ["""image"""]
    a__			:		Dict      			=   [
        """num_images_per_prompt""",
        """num_inference_steps""",
        """generator""",
        """latents""",
        """guidance_scale""",
        """frame_size""",
        """output_type""",
        """return_dict""",
    ]
    a__			:		Tuple      			=   False







    @property
    def     UpperCamelCase__     (			self) ->			Tuple:
        return 32







    @property
    def     UpperCamelCase__     (			self) ->			str:
        return 32







    @property
    def     UpperCamelCase__     (			self) ->			Dict:
        return self.time_input_dim * 4







    @property
    def     UpperCamelCase__     (			self) ->			str:
        return 8







    @property
    def     UpperCamelCase__     (			self) ->			List[Any]:
        torch.manual_seed(0)
        __UpperCamelCase  :int    						=	CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size				,				image_size=64				,				projection_dim=self.text_embedder_hidden_size				,				intermediate_size=37				,				num_attention_heads=4				,				num_channels=3				,				num_hidden_layers=5				,				patch_size=1				,				)

        __UpperCamelCase  :Union[str, Any]    						=	CLIPVisionModel(__lowercase)
        return model







    @property
    def     UpperCamelCase__     (			self) ->			Optional[Any]:
        __UpperCamelCase  :Optional[int]    						=	CLIPImageProcessor(
            crop_size=224				,				do_center_crop=__lowercase				,				do_normalize=__lowercase				,				do_resize=__lowercase				,				image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,				image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,				resample=3				,				size=224				,				)

        return image_processor







    @property
    def     UpperCamelCase__     (			self) ->			Optional[int]:
        torch.manual_seed(0)

        __UpperCamelCase  :Any    						=	{
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 16,
            '''embedding_dim''': self.time_input_dim,
            '''num_embeddings''': 32,
            '''embedding_proj_dim''': self.text_embedder_hidden_size,
            '''time_embed_dim''': self.time_embed_dim,
            '''num_layers''': 1,
            '''clip_embed_dim''': self.time_input_dim * 2,
            '''additional_embeddings''': 0,
            '''time_embed_act_fn''': '''gelu''',
            '''norm_in_type''': '''layer''',
            '''embedding_proj_norm_type''': '''layer''',
            '''encoder_hid_proj_type''': None,
            '''added_emb_type''': None,
        }

        __UpperCamelCase  :List[str]    						=	PriorTransformer(**__lowercase)
        return model







    @property
    def     UpperCamelCase__     (			self) ->			Union[str, Any]:
        torch.manual_seed(0)

        __UpperCamelCase  :Dict    						=	{
            '''param_shapes''': (
                (self.renderer_dim, 93),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
                (self.renderer_dim, 8),
            ),
            '''d_latent''': self.time_input_dim,
            '''d_hidden''': self.renderer_dim,
            '''n_output''': 12,
            '''background''': (
                0.1,
                0.1,
                0.1,
            ),
        }
        __UpperCamelCase  :str    						=	ShapERenderer(**__lowercase)
        return model







    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Optional[int]    						=	self.dummy_prior
        __UpperCamelCase  :Optional[Any]    						=	self.dummy_image_encoder
        __UpperCamelCase  :str    						=	self.dummy_image_processor
        __UpperCamelCase  :List[str]    						=	self.dummy_renderer

        __UpperCamelCase  :Tuple    						=	HeunDiscreteScheduler(
            beta_schedule='''exp'''				,				num_train_timesteps=1_024				,				prediction_type='''sample'''				,				use_karras_sigmas=__lowercase				,				clip_sample=__lowercase				,				clip_sample_range=1.0				,				)
        __UpperCamelCase  :int    						=	{
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''image_processor''': image_processor,
            '''renderer''': renderer,
            '''scheduler''': scheduler,
        }

        return components







    def     UpperCamelCase__     (			self				,				__lowercase				,				__lowercase=0) ->			Optional[Any]:
        __UpperCamelCase  :Any    						=	floats_tensor((1, 3, 64, 64)				,				rng=random.Random(__lowercase)).to(__lowercase)

        if str(__lowercase).startswith('''mps'''):
            __UpperCamelCase  :str    						=	torch.manual_seed(__lowercase)
        else:
            __UpperCamelCase  :Optional[int]    						=	torch.Generator(device=__lowercase).manual_seed(__lowercase)
        __UpperCamelCase  :Any    						=	{
            '''image''': input_image,
            '''generator''': generator,
            '''num_inference_steps''': 1,
            '''frame_size''': 32,
            '''output_type''': '''np''',
        }
        return inputs







    def     UpperCamelCase__     (			self) ->			Dict:
        __UpperCamelCase  :Optional[Any]    						=	'''cpu'''

        __UpperCamelCase  :Union[str, Any]    						=	self.get_dummy_components()

        __UpperCamelCase  :Dict    						=	self.pipeline_class(**__lowercase)
        __UpperCamelCase  :Any    						=	pipe.to(__lowercase)

        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Dict    						=	pipe(**self.get_dummy_inputs(__lowercase))
        __UpperCamelCase  :Tuple    						=	output.images[0]
        __UpperCamelCase  :Optional[int]    						=	image[0, -3:, -3:, -1]

        assert image.shape == (20, 32, 32, 3)

        __UpperCamelCase  :List[Any]    						=	np.array(
            [
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
                0.00_03_92_16,
            ])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2







    def     UpperCamelCase__     (			self) ->			Dict:
        # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
        self._test_inference_batch_consistent(batch_sizes=[1, 2])







    def     UpperCamelCase__     (			self) ->			List[Any]:
        __UpperCamelCase  :Dict    						=	torch_device == '''cpu'''
        __UpperCamelCase  :Union[str, Any]    						=	True
        self._test_inference_batch_single_identical(
            batch_size=2				,				test_max_difference=__lowercase				,				relax_max_difference=__lowercase				,				)







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :int    						=	self.get_dummy_components()
        __UpperCamelCase  :Tuple    						=	self.pipeline_class(**__lowercase)
        __UpperCamelCase  :Union[str, Any]    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Dict    						=	1
        __UpperCamelCase  :Dict    						=	2

        __UpperCamelCase  :Optional[Any]    						=	self.get_dummy_inputs(__lowercase)

        for key in inputs.keys():
            if key in self.batch_params:
                __UpperCamelCase  :Any    						=	batch_size * [inputs[key]]

        __UpperCamelCase  :Optional[int]    						=	pipe(**__lowercase				,				num_images_per_prompt=__lowercase)[0]

        assert images.shape[0] == batch_size * num_images_per_prompt


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






    '''simple docstring'''






    def     UpperCamelCase__     (			self) ->			Tuple:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def     UpperCamelCase__     (			self) ->			int:
        __UpperCamelCase  :Union[str, Any]    						=	load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''')
        __UpperCamelCase  :List[Any]    						=	load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/shap_e/test_shap_e_img2img_out.npy''')
        __UpperCamelCase  :List[Any]    						=	ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''')
        __UpperCamelCase  :Tuple    						=	pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)

        __UpperCamelCase  :Union[str, Any]    						=	torch.Generator(device=__lowercase).manual_seed(0)

        __UpperCamelCase  :Union[str, Any]    						=	pipe(
            __lowercase				,				generator=__lowercase				,				guidance_scale=3.0				,				num_inference_steps=64				,				frame_size=64				,				output_type='''np'''				,				).images[0]

        assert images.shape == (20, 64, 64, 3)

        assert_mean_pixel_difference(__lowercase				,				__lowercase)






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))





