
"""simple docstring"""


import math
from typing import Optional

import numpy as np

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


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)

SCREAMING_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   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					List[str]   =			'''encodec'''






       def __init__(  self :							int       ,      UpperCAmelCase_ :							List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0]       ,      UpperCAmelCase_ :							int=24_000       ,      UpperCAmelCase_ :							Union[str, Any]=1       ,      UpperCAmelCase_ :							Tuple=False       ,      UpperCAmelCase_ :							str=None       ,      UpperCAmelCase_ :							Optional[Any]=None       ,      UpperCAmelCase_ :							Union[str, Any]=128       ,      UpperCAmelCase_ :							Tuple=32       ,      UpperCAmelCase_ :							Any=1       ,      UpperCAmelCase_ :							Union[str, Any]=[8, 5, 4, 2]       ,      UpperCAmelCase_ :							List[Any]="weight_norm"       ,      UpperCAmelCase_ :							str=7       ,      UpperCAmelCase_ :							List[Any]=7       ,      UpperCAmelCase_ :							Any=3       ,      UpperCAmelCase_ :							str=2       ,      UpperCAmelCase_ :							Dict=True       ,      UpperCAmelCase_ :							Tuple="reflect"       ,      UpperCAmelCase_ :							List[Any]=2       ,      UpperCAmelCase_ :							List[Any]=2       ,      UpperCAmelCase_ :							int=1.0       ,      UpperCAmelCase_ :							Tuple=1_024       ,      UpperCAmelCase_ :							Optional[int]=None       ,      UpperCAmelCase_ :							str=True       ,      **UpperCAmelCase_ :							Union[str, Any]       ,      )->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		target_bandwidths
              UpperCamelCase					 =		sampling_rate
              UpperCamelCase					 =		audio_channels
              UpperCamelCase					 =		normalize
              UpperCamelCase					 =		chunk_length_s
              UpperCamelCase					 =		overlap
              UpperCamelCase					 =		hidden_size
              UpperCamelCase					 =		num_filters
              UpperCamelCase					 =		num_residual_layers
              UpperCamelCase					 =		upsampling_ratios
              UpperCamelCase					 =		norm_type
              UpperCamelCase					 =		kernel_size
              UpperCamelCase					 =		last_kernel_size
              UpperCamelCase					 =		residual_kernel_size
              UpperCamelCase					 =		dilation_growth_rate
              UpperCamelCase					 =		use_causal_conv
              UpperCamelCase					 =		pad_mode
              UpperCamelCase					 =		compress
              UpperCamelCase					 =		num_lstm_layers
              UpperCamelCase					 =		trim_right_ratio
              UpperCamelCase					 =		codebook_size
              UpperCamelCase					 =		codebook_dim if codebook_dim is not None else hidden_size
              UpperCamelCase					 =		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__(**UpperCAmelCase_			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              if self.chunk_length_s is None:
                     return None
              else:
                     return int(self.chunk_length_s * self.sampling_rate			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Optional[int]:







              """simple docstring"""
              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 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		np.prod(self.upsampling_ratios			)
              return math.ceil(self.sampling_rate / hop_length			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







              """simple docstring"""
              return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)			)







"""simple docstring"""


def 	lowerCamelCase__			(							UpperCAmelCase_ = 4_00_00_00				)->							int:







       """simple docstring"""
       UpperCamelCase					 =		[0, 1]
       UpperCamelCase					 =		0
       while fib[i] <= n:
              fib.append(fib[i] + fib[i + 1]				)
              if fib[i + 2] > n:
                     break
              i += 1
       UpperCamelCase					 =		0
       for j in range(len(UpperCAmelCase_				) - 1				):
              if fib[j] % 2 == 0:
                     total += fib[j]

       return total


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





