import math
from typing import Optional

import numpy as np

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


_lowercase:	Tuple       			=		logging.get_logger(__name__)

_lowercase:	str       			=		{
    '''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        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							="encodec"





			def __init__(       self	:	Dict  ,  lowercase__	:	List[str]=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]  ,  lowercase__	:	List[Any]=2_40_00  ,  lowercase__	:	Tuple=1  ,  lowercase__	:	List[str]=False  ,  lowercase__	:	List[Any]=None  ,  lowercase__	:	List[Any]=None  ,  lowercase__	:	List[Any]=1_28  ,  lowercase__	:	List[str]=32  ,  lowercase__	:	List[Any]=1  ,  lowercase__	:	Union[str, Any]=[8, 5, 4, 2]  ,  lowercase__	:	List[Any]="weight_norm"  ,  lowercase__	:	List[Any]=7  ,  lowercase__	:	Optional[Any]=7  ,  lowercase__	:	Any=3  ,  lowercase__	:	Any=2  ,  lowercase__	:	Dict=True  ,  lowercase__	:	Union[str, Any]="reflect"  ,  lowercase__	:	Dict=2  ,  lowercase__	:	Union[str, Any]=2  ,  lowercase__	:	Union[str, Any]=1.0  ,  lowercase__	:	Any=10_24  ,  lowercase__	:	Union[str, Any]=None  ,  lowercase__	:	List[str]=True  ,  **lowercase__	:	int  ,  ):
										_lowerCAmelCase							=							target_bandwidths
										_lowerCAmelCase							=							sampling_rate
										_lowerCAmelCase							=							audio_channels
										_lowerCAmelCase							=							normalize
										_lowerCAmelCase							=							chunk_length_s
										_lowerCAmelCase							=							overlap
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_filters
										_lowerCAmelCase							=							num_residual_layers
										_lowerCAmelCase							=							upsampling_ratios
										_lowerCAmelCase							=							norm_type
										_lowerCAmelCase							=							kernel_size
										_lowerCAmelCase							=							last_kernel_size
										_lowerCAmelCase							=							residual_kernel_size
										_lowerCAmelCase							=							dilation_growth_rate
										_lowerCAmelCase							=							use_causal_conv
										_lowerCAmelCase							=							pad_mode
										_lowerCAmelCase							=							compress
										_lowerCAmelCase							=							num_lstm_layers
										_lowerCAmelCase							=							trim_right_ratio
										_lowerCAmelCase							=							codebook_size
										_lowerCAmelCase							=							codebook_dim if codebook_dim is not None else hidden_size
										_lowerCAmelCase							=							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__(**lowercase__						)





			@property
			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										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	:	Dict						):
										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						):
										_lowerCAmelCase							=							np.prod(self.upsampling_ratios						)
										return math.ceil(self.sampling_rate / hop_length						)





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







def _lowerCamelCase  (		snake_case  ):
							assert (
							    isinstance(snake_case     , snake_case  ) and number_of_steps > 0
							), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
							if number_of_steps == 1:
														return 1
							_lowerCAmelCase			,		_lowerCAmelCase							=							1, 1
							for _ in range(number_of_steps - 1  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							current + previous, current
							return current


if __name__ == "__main__":
				import doctest

				doctest.testmod()






