


import math
from typing import Optional

import numpy as np

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


_A			=						logging.get_logger(__name__)

_A			=						{
    "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    UpperCAmelCase__	( _UpperCAmelCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Any              =  "encodec"






    def __init__(      self ,       A_=[1.5, 3.0, 6.0, 12.0, 24.0] ,       A_=24000 ,       A_=1 ,       A_=False ,       A_=None ,       A_=None ,       A_=128 ,       A_=32 ,       A_=1 ,       A_=[8, 5, 4, 2] ,       A_="weight_norm" ,       A_=7 ,       A_=7 ,       A_=3 ,       A_=2 ,       A_=True ,       A_="reflect" ,       A_=2 ,       A_=2 ,       A_=1.0 ,       A_=1024 ,       A_=None ,       A_=True ,       **A_ ,       )  ->	Any:
         __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__(**lowercase_   )






    @property
    def _a (      self   )  ->	int:
         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   )  ->	Any:
         __UpperCamelCase   							=np.prod(self.upsampling_ratios   )
         return math.ceil(self.sampling_rate / hop_length   )






    @property
    def _a (      self   )  ->	Union[str, Any]:
         return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)   )









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    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		int              =  0
    UpperCAmelCase__						:		bool              =  False
    UpperCAmelCase__						:		float              =  3.0






class    UpperCAmelCase__	( unittest.TestCase		):



    """simple docstring"""






    def _a (      self   )  ->	Any:
         # 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   )  ->	Union[str, Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __UpperCamelCase   							=GradScalerKwargs(init_scale=1024 ,       growth_factor=2   )
         AcceleratorState._reset_state()
         __UpperCamelCase   							=Accelerator(mixed_precision='fp16' ,       kwargs_handlers=[scaler_handler]   )
         print(accelerator.use_fpaa   )
         __UpperCamelCase   							=accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale ,       1024.0   )
         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 ,       2000   )
         self.assertEqual(scaler._enabled ,       A_   )






    @require_multi_gpu
    def _a (      self   )  ->	Optional[int]:
         __UpperCamelCase   							=['torchrun', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__   )]
         execute_subprocess_async(A_ ,       env=os.environ.copy()   )


if __name__ == "__main__":
   _A			=						DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A			=						Accelerator(kwargs_handlers=[ddp_scaler])
   _A			=						torch.nn.Linear(100, 200)
   _A			=						accelerator.prepare(model)

   # Check the values changed in kwargs
   _A			=						''
   _A			=						model.bucket_bytes_cap // (1024 * 1024)
   if observed_bucket_cap_map != 15:
      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)





