

import math
from typing import Optional

import numpy as np

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
    '''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}


class 				SCREAMING_SNAKE_CASE__ (__UpperCamelCase					):
    __lowerCamelCase						:	List[Any]														=					"encodec"





    def __init__(				self						,       a=[1.5, 3.0, 6.0, 12.0, 24.0]						,       a=2_4000						,       a=1						,       a=False						,       a=None						,       a=None						,       a=128						,       a=32						,       a=1						,       a=[8, 5, 4, 2]						,       a="weight_norm"						,       a=7						,       a=7						,       a=3						,       a=2						,       a=True						,       a="reflect"						,       a=2						,       a=2						,       a=1.0						,       a=1024						,       a=None						,       a=True						,       **a						,       ):
         lowercase__   :       Optional[int]				=	target_bandwidths
         lowercase__   :       int				=	sampling_rate
         lowercase__   :       List[Any]				=	audio_channels
         lowercase__   :       str				=	normalize
         lowercase__   :       Union[str, Any]				=	chunk_length_s
         lowercase__   :       Union[str, Any]				=	overlap
         lowercase__   :       Union[str, Any]				=	hidden_size
         lowercase__   :       Union[str, Any]				=	num_filters
         lowercase__   :       Optional[Any]				=	num_residual_layers
         lowercase__   :       List[Any]				=	upsampling_ratios
         lowercase__   :       List[str]				=	norm_type
         lowercase__   :       Union[str, Any]				=	kernel_size
         lowercase__   :       Optional[int]				=	last_kernel_size
         lowercase__   :       Optional[Any]				=	residual_kernel_size
         lowercase__   :       Dict				=	dilation_growth_rate
         lowercase__   :       int				=	use_causal_conv
         lowercase__   :       Tuple				=	pad_mode
         lowercase__   :       str				=	compress
         lowercase__   :       Optional[Any]				=	num_lstm_layers
         lowercase__   :       List[Any]				=	trim_right_ratio
         lowercase__   :       Any				=	codebook_size
         lowercase__   :       int				=	codebook_dim if codebook_dim is not None else hidden_size
         lowercase__   :       int				=	use_conv_shortcut

         if self.norm_type not in ["weight_norm", "time_group_norm"]:
              raise ValueError(
                  f"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""")

         super().__init__(**_lowerCAmelCase)





    @property
    def 				snake_case_    (				self):
         if self.chunk_length_s is None:
              return None
         else:
              return int(self.chunk_length_s * self.sampling_rate)





    @property
    def 				snake_case_    (				self):
         if self.chunk_length_s is None or self.overlap is None:
              return None
         else:
              return max(1						,       int((1.0 - self.overlap) * self.chunk_length))





    @property
    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	np.prod(self.upsampling_ratios)
         return math.ceil(self.sampling_rate / hop_length)





    @property
    def 				snake_case_    (				self):
         return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10))




def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int = 50_000_000      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	set()
     lowercase__   :       Any				=	int((limit - 24) ** (1 / 2)      )

     lowercase__   :       Any				=	set(range(3     ,					prime_square_limit + 1     ,					2      )      )
     primes.add(2      )
     for p in range(3     ,					prime_square_limit + 1     ,					2      ):
          if p not in primes:
               continue
          primes.difference_update(set(range(p * p     ,					prime_square_limit + 1     ,					SCREAMING_SNAKE_CASE_      )      )      )

     for primea in primes:
          lowercase__   :       Any				=	primea * primea
          for primea in primes:
               lowercase__   :       Optional[int]				=	primea * primea * primea
               if square + cube >= limit - 16:
                    break
               for primea in primes:
                    lowercase__   :       Dict				=	primea * primea * primea * primea
                    lowercase__   :       List[Any]				=	square + cube + tetr
                    if total >= limit:
                         break
                    ret.add(SCREAMING_SNAKE_CASE_      )

     return len(SCREAMING_SNAKE_CASE_      )


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

