
import math
from typing import Optional

import numpy as np

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


lowercase  :					Optional[Any]						    =							logging.get_logger(__name__)

lowercase  :					List[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 __snake_case		(      lowerCAmelCase     ):
    _a		:						int=				"encodec"
    def __init__(  self		,snake_case=[1.5, 3.0, 6.0, 12.0, 24.0]		,snake_case=24000		,snake_case=1		,snake_case=False		,snake_case=None		,snake_case=None		,snake_case=128		,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=1024		,snake_case=None		,snake_case=True		,**snake_case		,):



           '''simple docstring'''







           lowercase							:     Tuple       	=      target_bandwidths
           lowercase							:     int       	=      sampling_rate
           lowercase							:     List[str]       	=      audio_channels
           lowercase							:     Tuple       	=      normalize
           lowercase							:     Optional[Any]       	=      chunk_length_s
           lowercase							:     List[str]       	=      overlap
           lowercase							:     List[Any]       	=      hidden_size
           lowercase							:     Tuple       	=      num_filters
           lowercase							:     Dict       	=      num_residual_layers
           lowercase							:     str       	=      upsampling_ratios
           lowercase							:     str       	=      norm_type
           lowercase							:     List[Any]       	=      kernel_size
           lowercase							:     Tuple       	=      last_kernel_size
           lowercase							:     Any       	=      residual_kernel_size
           lowercase							:     Union[str, Any]       	=      dilation_growth_rate
           lowercase							:     Union[str, Any]       	=      use_causal_conv
           lowercase							:     int       	=      pad_mode
           lowercase							:     List[str]       	=      compress
           lowercase							:     List[str]       	=      num_lstm_layers
           lowercase							:     Union[str, Any]       	=      trim_right_ratio
           lowercase							:     List[Any]       	=      codebook_size
           lowercase							:     List[Any]       	=      codebook_dim if codebook_dim is not None else hidden_size
           lowercase							:     Optional[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__(**snake_case      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''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      ):



           '''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      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      np.prod(self.upsampling_ratios      )
           return math.ceil(self.sampling_rate / hop_length      )
    @property
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)      )






"""simple docstring"""




from math import ceil



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					list(range(0       , snake_case__				)				)

			lowerCamelCase			      =					[item for sublist in list(device_map.values()				) for item in sublist]

			# Duplicate check
			lowerCamelCase			      =					[]
			for i in device_map_blocks:
						if device_map_blocks.count(snake_case__				) > 1 and i not in duplicate_blocks:
									duplicate_blocks.append(snake_case__				)
    # Missing blocks
			lowerCamelCase			      =					[i for i in blocks if i not in device_map_blocks]
			lowerCamelCase			      =					[i for i in device_map_blocks if i not in blocks]

			if len(snake_case__				) != 0:
						raise ValueError(
						    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
						    """ These attention blocks were specified more than once: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
						    """blocks to a device on the device_map: """ + str(snake_case__				)				)
			if len(snake_case__				) != 0:
						raise ValueError(
						    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
						    + str(snake_case__				)				)



def     a__ (				snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					list(range(snake_case__				)				)
			lowerCamelCase			      =					int(ceil(n_layers / len(snake_case__				)				)				)
			lowerCamelCase			      =					[layers[i : i + n_blocks] for i in range(0       , snake_case__       , snake_case__				)]

			return dict(zip(snake_case__       , snake_case__				)				)



