

def        __lowerCAmelCase    (       _UpperCamelCase		:  int = 2_00_00_00	)		->							int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE								=			[0 for i in range(n + 1	)]
 SCREAMING_SNAKE_CASE								=			1
 SCREAMING_SNAKE_CASE								=			1

 for i in range(2		,							int(n**0.5	) + 1	):
  if primality_list[i] == 0:
   for j in range(i * i		,							n + 1		,							_UpperCamelCase	):
    SCREAMING_SNAKE_CASE								=			1
 SCREAMING_SNAKE_CASE								=			0
 for i in range(_UpperCamelCase	):
  if primality_list[i] == 0:
   sum_of_primes += i
 return sum_of_primes


if __name__ == "__main__":
      print(F"""{solution() = }""")









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      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
    __UpperCamelCase         =AudioLDMPipeline
    __UpperCamelCase         =TEXT_TO_AUDIO_PARAMS
    __UpperCamelCase         =TEXT_TO_AUDIO_BATCH_PARAMS
    __UpperCamelCase         =frozenset(
        [
            "num_inference_steps",
            "num_waveforms_per_prompt",
            "generator",
            "latents",
            "output_type",
            "return_dict",
            "callback",
            "callback_steps",
        ]   )

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			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')							,    cross_attention_dim=(3_2, 6_4)							,    class_embed_type='simple_projection'							,    projection_class_embeddings_input_dim=3_2							,    class_embeddings_concat=snake_case__							,    )
     SCREAMING_SNAKE_CASE								=			DDIMScheduler(
         beta_start=0.00_085							,    beta_end=0.012							,    beta_schedule='scaled_linear'							,    clip_sample=snake_case__							,    set_alpha_to_one=snake_case__							,    )
     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			AutoencoderKL(
         block_out_channels=[3_2, 6_4]							,    in_channels=1							,    out_channels=1							,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,    latent_channels=4							,    )
     torch.manual_seed(0    )
     SCREAMING_SNAKE_CASE								=			ClapTextConfig(
         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							,    projection_dim=3_2							,    )
     SCREAMING_SNAKE_CASE								=			ClapTextModelWithProjection(snake_case__    )
     SCREAMING_SNAKE_CASE								=			RobertaTokenizer.from_pretrained('hf-internal-testing/tiny-random-roberta'							,    model_max_length=7_7    )

     SCREAMING_SNAKE_CASE								=			SpeechTaHifiGanConfig(
         model_in_dim=8							,    sampling_rate=1_6_0_0_0							,    upsample_initial_channel=1_6							,    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=snake_case__							,    )

     SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(snake_case__    )

     SCREAMING_SNAKE_CASE								=			{
         'unet': unet,
         'scheduler': scheduler,
         'vae': vae,
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'vocoder': vocoder,
     }
     return components

    def UpperCamelCase			(			self :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      int=0    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			'cpu'  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 2_5_6

     SCREAMING_SNAKE_CASE								=			audio[:1_0]
     SCREAMING_SNAKE_CASE								=			np.array(
         [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]    )

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

    def UpperCamelCase			(			self :      Dict    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * [inputs['prompt']]

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * [inputs.pop('prompt'    )]

     SCREAMING_SNAKE_CASE								=			audioldm_pipe.tokenizer(
         snake_case__							,    padding='max_length'							,    max_length=audioldm_pipe.tokenizer.model_max_length							,    truncation=snake_case__							,    return_tensors='pt'							,    )
     SCREAMING_SNAKE_CASE								=			text_inputs['input_ids'].to(snake_case__    )

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

     SCREAMING_SNAKE_CASE								=			prompt_embeds

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

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

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * ['this is a negative prompt']
     SCREAMING_SNAKE_CASE								=			negative_prompt
     SCREAMING_SNAKE_CASE								=			3 * [inputs['prompt']]

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			3 * [inputs.pop('prompt'    )]

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

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

      embeds.append(snake_case__    )

     SCREAMING_SNAKE_CASE						,	SCREAMING_SNAKE_CASE								=			embeds

     # forward
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

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

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			self.get_dummy_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			'egg cracking'
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__							,    negative_prompt=snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 2_5_6

     SCREAMING_SNAKE_CASE								=			audio[:1_0]
     SCREAMING_SNAKE_CASE								=			np.array(
         [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]    )

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

    def UpperCamelCase			(			self :      str    ):

     """simple docstring"""






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

     SCREAMING_SNAKE_CASE								=			'A hammer hitting a wooden surface'

     # test num_waveforms_per_prompt=1 (default)
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=2    ).audios

     assert audios.shape == (1, 2_5_6)

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

     assert audios.shape == (batch_size, 2_5_6)

     # test num_waveforms_per_prompt for single prompt
     SCREAMING_SNAKE_CASE								=			2
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=2							,    num_waveforms_per_prompt=snake_case__    ).audios

     assert audios.shape == (num_waveforms_per_prompt, 2_5_6)

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

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 2_5_6)

    def UpperCamelCase			(			self :      Union[str, Any]    ):

     """simple docstring"""






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

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

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

     SCREAMING_SNAKE_CASE								=			audioldm_pipe(audio_length_in_s=0.032							,    **snake_case__    )
     SCREAMING_SNAKE_CASE								=			output.audios[0]

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

    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			self.get_dummy_components()
     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline(**snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			['hey']

     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=1    )
     SCREAMING_SNAKE_CASE								=			output.audios.shape
     assert audio_shape == (1, 2_5_6)

     SCREAMING_SNAKE_CASE								=			audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     SCREAMING_SNAKE_CASE								=			SpeechTaHifiGan(snake_case__    ).to(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(snake_case__							,    num_inference_steps=1    )
     SCREAMING_SNAKE_CASE								=			output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 2_5_6)

    def UpperCamelCase			(			self :      Tuple    ):

     """simple docstring"""






     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=snake_case__    )

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     self._test_inference_batch_single_identical(test_mean_pixel_difference=snake_case__    )




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

     """simple docstring"""






     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=snake_case__    )




@slow
class      UpperCamelCase (     unittest.TestCase   ):

    def UpperCamelCase			(			self :      Any    ):

     """simple docstring"""






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

    def UpperCamelCase			(			self :      int							,    snake_case__ :      int							,    snake_case__ :      Tuple="cpu"							,    snake_case__ :      List[str]=torch.floataa							,    snake_case__ :      Optional[Any]=0    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )
     SCREAMING_SNAKE_CASE								=			np.random.RandomState(snake_case__    ).standard_normal((1, 8, 1_2_8, 1_6)    )
     SCREAMING_SNAKE_CASE								=			torch.from_numpy(snake_case__    ).to(device=snake_case__							,    dtype=snake_case__    )
     SCREAMING_SNAKE_CASE								=			{
         'prompt': 'A hammer hitting a wooden surface',
         'latents': latents,
         'generator': generator,
         'num_inference_steps': 3,
         'guidance_scale': 2.5,
     }
     return inputs

    def UpperCamelCase			(			self :      int    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			2_5
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    ).audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 8_1_9_2_0

     SCREAMING_SNAKE_CASE								=			audio[7_7_2_3_0:7_7_2_4_0]
     SCREAMING_SNAKE_CASE								=			np.array(
         [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]    )
     SCREAMING_SNAKE_CASE								=			np.abs(expected_slice - audio_slice    ).max()
     assert max_diff < 1E-2




    def UpperCamelCase			(			self :      Optional[Any]    ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE								=			AudioLDMPipeline.from_pretrained('cvssp/audioldm'    )
     SCREAMING_SNAKE_CASE								=			LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe.to(snake_case__    )
     audioldm_pipe.set_progress_bar_config(disable=snake_case__    )

     SCREAMING_SNAKE_CASE								=			self.get_inputs(snake_case__    )
     SCREAMING_SNAKE_CASE								=			audioldm_pipe(**snake_case__    ).audios[0]

     assert audio.ndim == 1
     assert len(snake_case__    ) == 8_1_9_2_0

     SCREAMING_SNAKE_CASE								=			audio[2_7_7_8_0:2_7_7_9_0]
     SCREAMING_SNAKE_CASE								=			np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]    )
     SCREAMING_SNAKE_CASE								=			np.abs(expected_slice - audio_slice    ).max()
     assert max_diff < 3E-2

