"""simple docstring"""


from functools import lru_cache







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							2
     lowercase__				 =							set()
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.add(A      )
     if n > 1:
          factors.add(A      )
     return factors







@lru_cache
def 					__a   (		A      ):






     '''simple docstring'''



     return len(unique_prime_factors(A      )      )







def 					__a   (		A      ):






     '''simple docstring'''



     return len(set(A      )      ) in (0, 1)







def 					__a   (		A      ):






     '''simple docstring'''



     lowercase__				 =							2

     while True:
          # Increment each value of a generated range
          lowercase__				 =							[base + i for i in range(A      )]

          # Run elements through out unique_prime_factors function
          # Append our target number to the end.
          lowercase__				 =							[upf_len(A      ) for x in group]
          checker.append(A      )

          # If all numbers in the list are equal, return the group variable.
          if equality(A      ):
               return group

          # Increment our base variable by 1
          base += 1







def 					__a   (		A = 4      ):






     '''simple docstring'''



     lowercase__				 =							run(A      )
     return results[0] if len(A      ) else None


if __name__ == "__main__":
 print(solution())





"""simple docstring"""


from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .notes_encoder import SpectrogramNotesEncoder
 from .continous_encoder import SpectrogramContEncoder
 from .pipeline_spectrogram_diffusion import (
     SpectrogramContEncoder,
     SpectrogramDiffusionPipeline,
     TaFilmDecoder,
 )

try:
 if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
 from .midi_utils import MidiProcessor




