





import random

from .binary_exp_mod import bin_exp_mod

def   a_   (			lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Union[str, Any]=1000     ):
   if n < 2:
      return False

   if n % 2 == 0:
      return n == 2

   # this means n is odd
   __lowerCAmelCase							     =     n - 1
   __lowerCAmelCase							     =     0
   while d % 2 == 0:
      d /= 2
      exp += 1

   # n - 1=d*(2**exp)
   __lowerCAmelCase							     =     0
   while count < prec:
      __lowerCAmelCase							     =     random.randint(2,     n - 1     )
      __lowerCAmelCase							     =     bin_exp_mod(lowerCAmelCase_,     lowerCAmelCase_,     lowerCAmelCase_     )
      if b != 1:
         __lowerCAmelCase							     =     True
         for _ in range(lowerCAmelCase_     ):
            if b == n - 1:
               __lowerCAmelCase							     =     False
               break
            __lowerCAmelCase							     =     b * b
            b %= n
         if flag:
            return False
         count += 1
   return True


if __name__ == "__main__":
     _snake_case  :   List[Any]						   =  abs(int(input('Enter bound : ').strip()))
     print('Here\'s the list of primes:')
     print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))








import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class     _UpperCAmelCase   (   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


  a_      =			DanceDiffusionPipeline
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  a_      =			PipelineTesterMixin.required_optional_params - {
      """callback""",
      """latents""",
      """callback_steps""",
      """output_type""",
      """num_images_per_prompt""",
  }
  a_      =			UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  a_      =			False
  a_      =			False






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         block_out_channels=(3_2, 3_2, 6_4) ,  extra_in_channels=1_6 ,  sample_size=5_1_2 ,  sample_rate=1_6_0_0_0 ,  in_channels=2 ,  out_channels=2 ,  flip_sin_to_cos=lowerCAmelCase_ ,  use_timestep_embedding=lowerCAmelCase_ ,  time_embedding_type='fourier' ,  mid_block_type='UNetMidBlock1D' ,  down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') ,  up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') ,  )
     __lowerCAmelCase							     =     IPNDMScheduler()

     __lowerCAmelCase							     =     {
         'unet': unet,
         'scheduler': scheduler,
     }
     return components






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[str]=0					)							->						Any:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 4,
     }
     return inputs






  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     self.get_dummy_components()
     __lowerCAmelCase							     =     DanceDiffusionPipeline(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.get_dummy_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, components["unet"].sample_size)
     __lowerCAmelCase							     =     np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]					)
     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  @skip_mps
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return super().test_save_load_local()






  @skip_mps
  def     lowercase		(    self			:		List[str]					)							->						Dict:
     return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






  @skip_mps
  def     lowercase		(    self			:		str					)							->						List[str]:
     return super().test_save_load_optional_components()






  @skip_mps
  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     return super().test_attention_slicing_forward_pass()






  def     lowercase		(    self			:		str					)							->						int:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





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

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		List[str]					)							->						List[str]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]					)

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






  def     lowercase		(    self			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' ,  torch_dtype=torch.floataa					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

     __lowerCAmelCase							     =     audio[0, -3:, -3:]

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]					)

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

