



def      __lowercase(						UpperCAmelCase__ = 10 , UpperCAmelCase__ = 22			):


      """simple docstring"""



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


if __name__ == "__main__":
     print(f"""{solution(1_0, 2_2) = }""")




import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      AudioLDMPipeline
     _A      =      TEXT_TO_AUDIO_PARAMS
     _A      =      TEXT_TO_AUDIO_BATCH_PARAMS
     _A      =      frozenset(
         [
             'num_inference_steps',
             'num_waveforms_per_prompt',
             'generator',
             'latents',
             'output_type',
             'return_dict',
             'callback',
             'callback_steps',
         ])







     def     _a   (self     ):






           '''simple docstring'''



           torch.manual_seed(0     )
           lowerCamelCase             =		UNetaDConditionModel(
               block_out_channels=(32, 64)					,      layers_per_block=2					,      sample_size=32					,      in_channels=4					,      out_channels=4					,      down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")					,      up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")					,      cross_attention_dim=(32, 64)					,      class_embed_type="simple_projection"					,      projection_class_embeddings_input_dim=32					,      class_embeddings_concat=__a					,      )
           lowerCamelCase             =		DDIMScheduler(
               beta_start=0.0_0085					,      beta_end=0.012					,      beta_schedule="scaled_linear"					,      clip_sample=__a					,      set_alpha_to_one=__a					,      )
           torch.manual_seed(0     )
           lowerCamelCase             =		AutoencoderKL(
               block_out_channels=[32, 64]					,      in_channels=1					,      out_channels=1					,      down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]					,      up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]					,      latent_channels=4					,      )
           torch.manual_seed(0     )
           lowerCamelCase             =		ClapTextConfig(
               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=10_00					,      projection_dim=32					,      )
           lowerCamelCase             =		ClapTextModelWithProjection(__a     )
           lowerCamelCase             =		RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"					,      model_max_length=77     )

           lowerCamelCase             =		SpeechTaHifiGanConfig(
               model_in_dim=8					,      sampling_rate=1_60_00					,      upsample_initial_channel=16					,      upsample_rates=[2, 2]					,      upsample_kernel_sizes=[4, 4]					,      resblock_kernel_sizes=[3, 7]					,      resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]					,      normalize_before=__a					,      )

           lowerCamelCase             =		SpeechTaHifiGan(__a     )

           lowerCamelCase             =		{
               "unet": unet,
               "scheduler": scheduler,
               "vae": vae,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "vocoder": vocoder,
           }
           return components







     def     _a   (self					,      __a					,      __a=0     ):






           '''simple docstring'''



           if str(__a     ).startswith("mps"     ):
                 lowerCamelCase             =		torch.manual_seed(__a     )
           else:
                 lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(__a     )
           lowerCamelCase             =		{
               "prompt": "A hammer hitting a wooden surface",
               "generator": generator,
               "num_inference_steps": 2,
               "guidance_scale": 6.0,
           }
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator

           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 2_56

           lowerCamelCase             =		audio[:10]
           lowerCamelCase             =		np.array(
               [-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]     )

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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs["prompt"]]

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs.pop("prompt"     )]

           lowerCamelCase             =		audioldm_pipe.tokenizer(
               __a					,      padding="max_length"					,      max_length=audioldm_pipe.tokenizer.model_max_length					,      truncation=__a					,      return_tensors="pt"					,      )
           lowerCamelCase             =		text_inputs["input_ids"].to(__a     )

           lowerCamelCase             =		audioldm_pipe.text_encoder(
               __a					,      )
           lowerCamelCase             =		prompt_embeds.text_embeds
           # additional L_2 normalization over each hidden-state
           lowerCamelCase             =		F.normalize(__a					,      dim=-1     )

           lowerCamelCase             =		prompt_embeds

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert np.abs(audio_a - audio_a     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * ["this is a negative prompt"]
           lowerCamelCase             =		negative_prompt
           lowerCamelCase             =		3 * [inputs["prompt"]]

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		3 * [inputs.pop("prompt"     )]

           lowerCamelCase             =		[]
           for p in [prompt, negative_prompt]:
                 lowerCamelCase             =		audioldm_pipe.tokenizer(
                     __a					,      padding="max_length"					,      max_length=audioldm_pipe.tokenizer.model_max_length					,      truncation=__a					,      return_tensors="pt"					,      )
                 lowerCamelCase             =		text_inputs["input_ids"].to(__a     )

                 lowerCamelCase             =		audioldm_pipe.text_encoder(
                     __a					,      )
                 lowerCamelCase             =		text_embeds.text_embeds
                 # additional L_2 normalization over each hidden-state
                 lowerCamelCase             =		F.normalize(__a					,      dim=-1     )

                 embeds.append(__a     )

           lowerCamelCase      ,				lowerCamelCase             =		embeds

           # forward
           lowerCamelCase             =		audioldm_pipe(**__a     )
           lowerCamelCase             =		output.audios[0]

           assert np.abs(audio_a - audio_a     ).max() < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		PNDMScheduler(skip_prk_steps=__a     )
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		"egg cracking"
           lowerCamelCase             =		audioldm_pipe(**__a					,      negative_prompt=__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 2_56

           lowerCamelCase             =		audio[:10]
           lowerCamelCase             =		np.array(
               [-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]     )

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







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		PNDMScheduler(skip_prk_steps=__a     )
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		"A hammer hitting a wooden surface"

           # test num_waveforms_per_prompt=1 (default)
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=2     ).audios

           assert audios.shape == (1, 2_56)

           # test num_waveforms_per_prompt=1 (default) for batch of prompts
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe([prompt] * batch_size					,      num_inference_steps=2     ).audios

           assert audios.shape == (batch_size, 2_56)

           # test num_waveforms_per_prompt for single prompt
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=2					,      num_waveforms_per_prompt=__a     ).audios

           assert audios.shape == (num_waveforms_per_prompt, 2_56)

           # test num_waveforms_per_prompt for batch of prompts
           lowerCamelCase             =		2
           lowerCamelCase             =		audioldm_pipe(
               [prompt] * batch_size					,      num_inference_steps=2					,      num_waveforms_per_prompt=__a     ).audios

           assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_56)







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"cpu"  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )
           lowerCamelCase             =		audioldm_pipe.vocoder.config.sampling_rate

           lowerCamelCase             =		self.get_dummy_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(audio_length_in_s=0.016					,      **__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) / vocoder_sampling_rate == 0.016

           lowerCamelCase             =		audioldm_pipe(audio_length_in_s=0.032					,      **__a     )
           lowerCamelCase             =		output.audios[0]

           assert audio.ndim == 1
           assert len(__a     ) / vocoder_sampling_rate == 0.032







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.get_dummy_components()
           lowerCamelCase             =		AudioLDMPipeline(**__a     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		["hey"]

           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=1     )
           lowerCamelCase             =		output.audios.shape
           assert audio_shape == (1, 2_56)

           lowerCamelCase             =		audioldm_pipe.vocoder.config
           config.model_in_dim *= 2
           lowerCamelCase             =		SpeechTaHifiGan(__a     ).to(__a     )
           lowerCamelCase             =		audioldm_pipe(__a					,      num_inference_steps=1     )
           lowerCamelCase             =		output.audios.shape
           # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
           assert audio_shape == (1, 2_56)







     def     _a   (self     ):






           '''simple docstring'''



           self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__a     )







     def     _a   (self     ):






           '''simple docstring'''



           self._test_inference_batch_single_identical(test_mean_pixel_difference=__a     )







     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()					,      reason="XFormers attention is only available with CUDA and `xformers` installed"					,      )
     def     _a   (self     ):






           '''simple docstring'''



           self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__a     )





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



     def     _a   (self     ):






           '''simple docstring'''



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







     def     _a   (self					,      __a					,      __a="cpu"					,      __a=torch.floataa					,      __a=0     ):






           '''simple docstring'''



           lowerCamelCase             =		torch.Generator(device=__a     ).manual_seed(__a     )
           lowerCamelCase             =		np.random.RandomState(__a     ).standard_normal((1, 8, 1_28, 16)     )
           lowerCamelCase             =		torch.from_numpy(__a     ).to(device=__a					,      dtype=__a     )
           lowerCamelCase             =		{
               "prompt": "A hammer hitting a wooden surface",
               "latents": latents,
               "generator": generator,
               "num_inference_steps": 3,
               "guidance_scale": 2.5,
           }
           return inputs







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_inputs(__a     )
           lowerCamelCase             =		25
           lowerCamelCase             =		audioldm_pipe(**__a     ).audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 8_19_20

           lowerCamelCase             =		audio[7_72_30:7_72_40]
           lowerCamelCase             =		np.array(
               [-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]     )
           lowerCamelCase             =		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 1E-2







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		AudioLDMPipeline.from_pretrained("cvssp/audioldm"     )
           lowerCamelCase             =		LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config     )
           lowerCamelCase             =		audioldm_pipe.to(__a     )
           audioldm_pipe.set_progress_bar_config(disable=__a     )

           lowerCamelCase             =		self.get_inputs(__a     )
           lowerCamelCase             =		audioldm_pipe(**__a     ).audios[0]

           assert audio.ndim == 1
           assert len(__a     ) == 8_19_20

           lowerCamelCase             =		audio[2_77_80:2_77_90]
           lowerCamelCase             =		np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212]     )
           lowerCamelCase             =		np.abs(expected_slice - audio_slice     ).max()
           assert max_diff < 3E-2