

'''simple docstring'''
import math
from typing import Optional

import numpy as np

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


_UpperCAmelCase  :		int  =		logging.get_logger(__name__)

_UpperCAmelCase  :		Optional[Any]  =		{
    '''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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):
 UpperCamelCase__     =       'encodec'







 def __init__(       self  , snake_case_=[1.5, 3.0, 6.0, 12.0, 24.0]  , snake_case_=2_40_00  , snake_case_=1  , snake_case_=False  , snake_case_=None  , snake_case_=None  , snake_case_=1_28  , snake_case_=32  , snake_case_=1  , snake_case_=[8, 5, 4, 2]  , snake_case_="weight_norm"  , snake_case_=7  , snake_case_=7  , snake_case_=3  , snake_case_=2  , snake_case_=True  , snake_case_="reflect"  , snake_case_=2  , snake_case_=2  , snake_case_=1.0  , snake_case_=10_24  , snake_case_=None  , snake_case_=True  , **snake_case_  , ):
   lowercase									=target_bandwidths
   lowercase									=sampling_rate
   lowercase									=audio_channels
   lowercase									=normalize
   lowercase									=chunk_length_s
   lowercase									=overlap
   lowercase									=hidden_size
   lowercase									=num_filters
   lowercase									=num_residual_layers
   lowercase									=upsampling_ratios
   lowercase									=norm_type
   lowercase									=kernel_size
   lowercase									=last_kernel_size
   lowercase									=residual_kernel_size
   lowercase									=dilation_growth_rate
   lowercase									=use_causal_conv
   lowercase									=pad_mode
   lowercase									=compress
   lowercase									=num_lstm_layers
   lowercase									=trim_right_ratio
   lowercase									=codebook_size
   lowercase									=codebook_dim if codebook_dim is not None else hidden_size
   lowercase									=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__(**snake_case_   )







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







 @property
 def 						_A(       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 						_A(       self   ):
   lowercase									=np.prod(self.upsampling_ratios   )
   return math.ceil(self.sampling_rate / hop_length   )







 @property
 def 						_A(       self   ):
   return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )






import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


UpperCamelCase__ :   str						=							logging.get_logger(__name__)







class 				lowerCAmelCase_       (					lowerCamelCase_							):






				def __init__(			self      ,*snake_case__      ,**snake_case__ ):
							warnings.warn(
							    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use PoolFormerImageProcessor instead.'      ,snake_case__      ,)
							super().__init__(*snake_case__      ,**snake_case__ )



