
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		List[Any]:
			lowerCAmelCase__      :      str         =		0
			while b > 0:
						if b & 1:
									res += a

						a += a
						b >>= 1

			return res
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		str:
			lowerCAmelCase__      :      Dict         =		0
			while b > 0:
						if b & 1:
									lowerCAmelCase__      :      Tuple         =		((res % c) + (a % c)) % c

						a += a
						b >>= 1

			return res

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


lowerCamelCase__          =		False







@skip_mps
class      A__							(     __magic_name__						,   __magic_name__						,   __magic_name__						,   unittest.TestCase	):
				lowercase			    =  StableDiffusionAttendAndExcitePipeline
				lowercase			    =  False
				lowercase			    =  TEXT_TO_IMAGE_PARAMS
				lowercase			    =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}	)
				lowercase			    =  TEXT_TO_IMAGE_IMAGE_PARAMS
				lowercase			    =  TEXT_TO_IMAGE_IMAGE_PARAMS






				@classmethod
				def 							_lowerCamelCase		(						cls					:      Tuple   ):






							'''simple docstring'''







							super().setUpClass()
							torch.use_deterministic_algorithms(a   )






				@classmethod
				def 							_lowerCamelCase		(						cls					:      Any   ):






							'''simple docstring'''







							super().tearDownClass()
							torch.use_deterministic_algorithms(a   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							torch.manual_seed(0   )
							lowerCAmelCase__      :      Any         =		UNetaDConditionModel(
							    block_out_channels=(32, 64)    ,     layers_per_block=1    ,     sample_size=32    ,     in_channels=4    ,     out_channels=4    ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,     cross_attention_dim=32    ,     attention_head_dim=(2, 4)    ,     use_linear_projection=a    ,     )
							lowerCAmelCase__      :      Any         =		DDIMScheduler(
							    beta_start=0.0_0_0_8_5    ,     beta_end=0.0_1_2    ,     beta_schedule='scaled_linear'    ,     clip_sample=a    ,     set_alpha_to_one=a    ,     )
							torch.manual_seed(0   )
							lowerCAmelCase__      :      Union[str, Any]         =		AutoencoderKL(
							    block_out_channels=[32, 64]    ,     in_channels=3    ,     out_channels=3    ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,     latent_channels=4    ,     sample_size=128    ,     )
							torch.manual_seed(0   )
							lowerCAmelCase__      :      Tuple         =		CLIPTextConfig(
							    bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=32    ,     intermediate_size=37    ,     layer_norm_eps=1E-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=1_000    ,     hidden_act='gelu'    ,     projection_dim=512    ,     )
							lowerCAmelCase__      :      str         =		CLIPTextModel(a   )
							lowerCAmelCase__      :      Optional[Any]         =		CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

							lowerCAmelCase__      :      Union[str, Any]         =		{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components






				def 							_lowerCamelCase		(						self					:      Union[str, Any]    ,     a					:      Tuple    ,     a					:      Union[str, Any]=0   ):






							'''simple docstring'''







							if str(a   ).startswith('mps'   ):
										lowerCAmelCase__      :      List[str]         =		torch.manual_seed(a   )
							else:
										lowerCAmelCase__      :      Any         =		torch.Generator(device=a   ).manual_seed(a   )
							lowerCAmelCase__      :      Optional[int]         =		{
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Tuple         =		'cpu'

							lowerCAmelCase__      :      Optional[int]         =		self.get_dummy_components()
							lowerCAmelCase__      :      Dict         =		self.pipeline_class(**a   )
							pipe.to(a   )
							pipe.set_progress_bar_config(disable=a   )

							lowerCAmelCase__      :      Union[str, Any]         =		self.get_dummy_inputs(a   )
							lowerCAmelCase__      :      Union[str, Any]         =		pipe(**a   ).images
							lowerCAmelCase__      :      Optional[int]         =		image[0, -3:, -3:, -1]

							self.assertEqual(image.shape    ,     (1, 64, 64, 3)   )
							lowerCAmelCase__      :      Dict         =		np.array(
							    [0.6_3_9_0_5_3_6_4, 0.6_2_8_9_7_3_0_7, 0.4_8_5_9_9_0_1_7, 0.5_1_3_3_6_2_4, 0.5_5_5_0_0_4_8, 0.4_5_7_6_9_5_1_6, 0.5_0_3_2_6_9_7_3, 0.5_0_2_3_1_3_9, 0.4_5_3_8_4_4_9_6]   )
							lowerCAmelCase__      :      Optional[int]         =		np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(a    ,     1E-3   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							self._test_inference_batch_consistent(batch_sizes=[1, 2]   )






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							self._test_inference_batch_single_identical(batch_size=2    ,     expected_max_diff=7E-4   )






				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3   )






				def 							_lowerCamelCase		(						self					:      List[str]   ):






							'''simple docstring'''







							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							super().test_save_load_local(expected_max_difference=5E-4   )






				def 							_lowerCamelCase		(						self					:      Dict   ):






							'''simple docstring'''







							super().test_save_load_optional_components(expected_max_difference=4E-4   )







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






				@classmethod
				def 							_lowerCamelCase		(						cls					:      List[str]   ):






							'''simple docstring'''







							super().setUpClass()
							torch.use_deterministic_algorithms(a   )






				@classmethod
				def 							_lowerCamelCase		(						cls					:      List[str]   ):






							'''simple docstring'''







							super().tearDownClass()
							torch.use_deterministic_algorithms(a   )






				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		torch.manual_seed(51   )

							lowerCAmelCase__      :      Any         =		StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'    ,     safety_checker=a    ,     torch_dtype=torch.floataa   )
							pipe.to('cuda'   )

							lowerCAmelCase__      :      Optional[int]         =		'a painting of an elephant with glasses'
							lowerCAmelCase__      :      Any         =		[5, 7]

							lowerCAmelCase__      :      Optional[Any]         =		pipe(
							    prompt=a    ,     token_indices=a    ,     guidance_scale=7.5    ,     generator=a    ,     num_inference_steps=5    ,     max_iter_to_alter=5    ,     output_type='numpy'    ,     ).images[0]

							lowerCAmelCase__      :      str         =		load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'   )
							assert np.abs((expected_image - image).max()   ) < 5E-1