



"""simple docstring"""


def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any = 10					,     _SCREAMING_SNAKE_CASE   :						Tuple = 22				):






				'''simple docstring'''



				_UpperCAmelCase					=    range(1					,     __SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    range(1					,     __SCREAMING_SNAKE_CASE				)
				return sum(
				    1 for power in powers for base in bases if len(str(base**power				)				) == power				)


if __name__ == "__main__":
					print(f'''{solution(10, 22) = }''')










"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 			_a (					lowerCAmelCase			,							lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	StableDiffusionXLImgaImgPipeline
			UpperCamelCase__			      =	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
			UpperCamelCase__			      =	PipelineTesterMixin.required_optional_params - {"""latents"""}
			UpperCamelCase__			      =	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			UpperCamelCase__			      =	IMAGE_TO_IMAGE_IMAGE_PARAMS
			UpperCamelCase__			      =	IMAGE_TO_IMAGE_IMAGE_PARAMS



			def  lowercase__						(		self     :    Tuple   )->int:
							torch.manual_seed(0   )
							_UpperCAmelCase					=    UNetaDConditionModel(
							    block_out_channels=(3_2, 6_4)				,   layers_per_block=2				,   sample_size=3_2				,   in_channels=4				,   out_channels=4				,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,   attention_head_dim=(2, 4)				,   use_linear_projection=__UpperCamelCase				,   addition_embed_type='''text_time'''				,   addition_time_embed_dim=8				,   transformer_layers_per_block=(1, 2)				,   projection_class_embeddings_input_dim=8_0				,   cross_attention_dim=6_4				,   )
							_UpperCAmelCase					=    EulerDiscreteScheduler(
							    beta_start=0.0_0_0_8_5				,   beta_end=0.0_1_2				,   steps_offset=1				,   beta_schedule='''scaled_linear'''				,   timestep_spacing='''leading'''				,   )
							torch.manual_seed(0   )
							_UpperCAmelCase					=    AutoencoderKL(
							    block_out_channels=[3_2, 6_4]				,   in_channels=3				,   out_channels=3				,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,   latent_channels=4				,   sample_size=1_2_8				,   )
							torch.manual_seed(0   )
							_UpperCAmelCase					=    CLIPTextConfig(
							    bos_token_id=0				,   eos_token_id=2				,   hidden_size=3_2				,   intermediate_size=3_7				,   layer_norm_eps=1e-05				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=1_0_0_0				,   hidden_act='''gelu'''				,   projection_dim=3_2				,   )
							_UpperCAmelCase					=    CLIPTextModel(__UpperCamelCase   )
							_UpperCAmelCase					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,   local_files_only=__UpperCamelCase   )

							_UpperCAmelCase					=    CLIPTextModelWithProjection(__UpperCamelCase   )
							_UpperCAmelCase					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				,   local_files_only=__UpperCamelCase   )

							_UpperCAmelCase					=    {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''text_encoder_2''': text_encoder_a,
							    '''tokenizer_2''': tokenizer_a,
							    # "safety_checker": None,
							    # "feature_extractor": None,
							}
							return components



			def  lowercase__						(		self     :    int				,   __UpperCamelCase     :    Optional[int]				,   __UpperCamelCase     :    int=0   )->Any:
							_UpperCAmelCase					=    floats_tensor((1, 3, 3_2, 3_2)				,   rng=random.Random(__UpperCamelCase   )   ).to(__UpperCamelCase   )
							_UpperCAmelCase					=    image / 2 + 0.5
							if str(__UpperCamelCase   ).startswith('''mps'''   ):
											_UpperCAmelCase					=    torch.manual_seed(__UpperCamelCase   )
							else:
											_UpperCAmelCase					=    torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
							_UpperCAmelCase					=    {
							    '''prompt''': '''A painting of a squirrel eating a burger''',
							    '''image''': image,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''guidance_scale''': 5.0,
							    '''output_type''': '''numpy''',
							    '''strength''': 0.7_5,
							}
							return inputs



			def  lowercase__						(		self     :    Any   )->int:
							_UpperCAmelCase					=    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
							_UpperCAmelCase					=    self.get_dummy_components()
							_UpperCAmelCase					=    StableDiffusionXLImgaImgPipeline(**__UpperCamelCase   )
							_UpperCAmelCase					=    sd_pipe.to(__UpperCamelCase   )
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

							_UpperCAmelCase					=    self.get_dummy_inputs(__UpperCamelCase   )
							_UpperCAmelCase					=    sd_pipe(**__UpperCamelCase   ).images
							_UpperCAmelCase					=    image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)

							_UpperCAmelCase					=    np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]   )

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



			def  lowercase__						(		self     :    Optional[Any]   )->Optional[int]:
							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )



			def  lowercase__						(		self     :    Optional[Any]   )->List[Any]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3   )



			def  lowercase__						(		self     :    List[str]   )->str:
							pass



			def  lowercase__						(		self     :    Dict   )->List[str]:
							_UpperCAmelCase					=    self.get_dummy_components()
							_UpperCAmelCase					=    StableDiffusionXLImgaImgPipeline(**__UpperCamelCase   )
							_UpperCAmelCase					=    sd_pipe.to(__UpperCamelCase   )
							_UpperCAmelCase					=    sd_pipe.to(__UpperCamelCase   )
							sd_pipe.set_progress_bar_config(disable=__UpperCamelCase   )

							# forward without prompt embeds
							_UpperCAmelCase					=    self.get_dummy_inputs(__UpperCamelCase   )
							_UpperCAmelCase					=    3 * ['''this is a negative prompt''']
							_UpperCAmelCase					=    negative_prompt
							_UpperCAmelCase					=    3 * [inputs['''prompt''']]

							_UpperCAmelCase					=    sd_pipe(**__UpperCamelCase   )
							_UpperCAmelCase					=    output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							_UpperCAmelCase					=    self.get_dummy_inputs(__UpperCamelCase   )
							_UpperCAmelCase					=    3 * ['''this is a negative prompt''']
							_UpperCAmelCase					=    3 * [inputs.pop('''prompt'''   )]

							(
							    (
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						(
							    _UpperCAmelCase
							)		,						
							)					=    sd_pipe.encode_prompt(__UpperCamelCase				,   negative_prompt=__UpperCamelCase   )

							_UpperCAmelCase					=    sd_pipe(
							    **__UpperCamelCase				,   prompt_embeds=__UpperCamelCase				,   negative_prompt_embeds=__UpperCamelCase				,   pooled_prompt_embeds=__UpperCamelCase				,   negative_pooled_prompt_embeds=__UpperCamelCase				,   )
							_UpperCAmelCase					=    output.images[0, -3:, -3:, -1]

							# make sure that it's equal
							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()   ).max() < 1e-4



@slow
@require_torch_gpu
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def  lowercase__						(		self     :    Union[str, Any]   )->Any:
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Union[str, Any]				,   __UpperCamelCase     :    Dict="cpu"				,   __UpperCamelCase     :    int=torch.floataa				,   __UpperCamelCase     :    Optional[Any]=0   )->Union[str, Any]:
							_UpperCAmelCase					=    torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
							_UpperCAmelCase					=    np.random.RandomState(__UpperCamelCase   ).standard_normal((1, 4, 6_4, 6_4)   )
							_UpperCAmelCase					=    torch.from_numpy(__UpperCamelCase   ).to(device=__UpperCamelCase				,   dtype=__UpperCamelCase   )
							_UpperCAmelCase					=    {
							    '''prompt''': '''a photograph of an astronaut riding a horse''',
							    '''latents''': latents,
							    '''generator''': generator,
							    '''num_inference_steps''': 3,
							    '''guidance_scale''': 7.5,
							    '''output_type''': '''numpy''',
							}
							return inputs



			def  lowercase__						(		self     :    Optional[Any]   )->Optional[Any]:
							_UpperCAmelCase					=    DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''   )
							pipe.to(__UpperCamelCase   )
							pipe.set_progress_bar_config(disable=__UpperCamelCase   )

							_UpperCAmelCase					=    self.get_inputs(__UpperCamelCase   )
							_UpperCAmelCase					=    pipe(**__UpperCamelCase   ).images
							_UpperCAmelCase					=    image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							_UpperCAmelCase					=    np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]   )
							assert np.abs(image_slice - expected_slice   ).max() < 7e-3



