


"""simple docstring"""




import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       0
							__a					 =       False
							__a					 =       3.0




class 			__snake_case   (unittest.TestCase  ):




							def   __a   (     self:    int   ):
										# If no defaults are changed, `to_kwargs` returns an empty dict.
										self.assertDictEqual(MockClass().to_kwargs()     ,							{}   )
										self.assertDictEqual(MockClass(a=2   ).to_kwargs()     ,							{"""a""": 2}   )
										self.assertDictEqual(MockClass(a=2     ,							b=A_   ).to_kwargs()     ,							{"""a""": 2, """b""": True}   )
										self.assertDictEqual(MockClass(a=2     ,							c=2.25   ).to_kwargs()     ,							{"""a""": 2, """c""": 2.25}   )




							@require_cuda
							def   __a   (     self:    int   ):
										# If no defaults are changed, `to_kwargs` returns an empty dict.
										__lowerCamelCase          =      GradScalerKwargs(init_scale=10_24     ,							growth_factor=2   )
										AcceleratorState._reset_state()
										__lowerCamelCase          =      Accelerator(mixed_precision="""fp16"""     ,							kwargs_handlers=[scaler_handler]   )
										print(accelerator.use_fpaa   )
										__lowerCamelCase          =      accelerator.scaler

										# Check the kwargs have been applied
										self.assertEqual(scaler._init_scale     ,							1_024.0   )
										self.assertEqual(scaler._growth_factor     ,							2.0   )

										# Check the other values are at the default
										self.assertEqual(scaler._backoff_factor     ,							0.5   )
										self.assertEqual(scaler._growth_interval     ,							20_00   )
										self.assertEqual(scaler._enabled     ,							A_   )




							@require_multi_gpu
							def   __a   (     self:    List[Any]   ):
										__lowerCamelCase          =      ["""torchrun""", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
										execute_subprocess_async(A_     ,							env=os.environ.copy()   )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				DistributedDataParallelKwargs(bucket_cap_mb=1_5, find_unused_parameters=True)
					__magic_name__						: List[str]    =				Accelerator(kwargs_handlers=[ddp_scaler])
					__magic_name__						: Union[str, Any]    =				torch.nn.Linear(1_0_0, 2_0_0)
					__magic_name__						: Optional[Any]    =				accelerator.prepare(model)

					# Check the values changed in kwargs
					__magic_name__						: Any    =				''
					__magic_name__						: Optional[Any]    =				model.bucket_bytes_cap // (1_0_2_4 * 1_0_2_4)
					if observed_bucket_cap_map != 1_5:
										error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
					if model.find_unused_parameters is not True:
										error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

					# Check the values of the defaults
					if model.dim != 0:
										error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
					if model.broadcast_buffers is not True:
										error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
					if model.gradient_as_bucket_view is not False:
										error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)








"""simple docstring"""




import math
from typing import Optional

import numpy as np

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


__magic_name__						: List[str]    =				logging.get_logger(__name__)

__magic_name__						: Tuple    =				{
    '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					 =       '''encodec'''




							def __init__(     self:    Union[str, Any]     ,							A_:    Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]     ,							A_:    Any=2_40_00     ,							A_:    List[Any]=1     ,							A_:    Optional[Any]=False     ,							A_:    Optional[int]=None     ,							A_:    int=None     ,							A_:    List[str]=1_28     ,							A_:    Union[str, Any]=32     ,							A_:    List[str]=1     ,							A_:    Dict=[8, 5, 4, 2]     ,							A_:    List[Any]="weight_norm"     ,							A_:    Any=7     ,							A_:    List[Any]=7     ,							A_:    Tuple=3     ,							A_:    List[str]=2     ,							A_:    Optional[Any]=True     ,							A_:    Optional[int]="reflect"     ,							A_:    Dict=2     ,							A_:    Union[str, Any]=2     ,							A_:    Union[str, Any]=1.0     ,							A_:    List[str]=10_24     ,							A_:    str=None     ,							A_:    List[str]=True     ,							**A_:    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__(**A_   )




							@property
							def   __a   (     self:    Union[str, Any]   ):
										if self.chunk_length_s is None:
													return None
										else:
													return int(self.chunk_length_s * self.sampling_rate   )




							@property
							def   __a   (     self:    List[Any]   ):
										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:    List[Any]   ):
										__lowerCamelCase          =      np.prod(self.upsampling_ratios   )
										return math.ceil(self.sampling_rate / hop_length   )




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




