


import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def       lowercase_		(		_A			:    Any						):

      """simple docstring"""




      return (torch.arange(state.num_processes						) + 1.0 + (state.num_processes * state.process_index)).to(state.device						)

def       lowercase_		(		_A			:    List[Any]						):

      """simple docstring"""




      lowerCamelCase__		:				int    =  create_tensor(_a						)
      lowerCamelCase__		:				Union[str, Any]    =  gather(_a						)
      assert gathered_tensor.tolist() == list(range(1    ,					state.num_processes**2 + 1						)						)

def       lowercase_		(		_A			:    str						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  [state.process_index]
      lowerCamelCase__		:				str    =  gather_object(_a						)
      assert len(_a						) == state.num_processes, F"{gathered_obj}, {len(_a						)} != {state.num_processes}"
      assert gathered_obj == list(range(state.num_processes						)						), F"{gathered_obj} != {list(range(state.num_processes						)						)}"

def       lowercase_		(		_A			:    Optional[int]						):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  create_tensor(_a						)
      lowerCamelCase__		:				Tuple    =  broadcast(_a						)
      assert broadcasted_tensor.shape == torch.Size([state.num_processes]						)
      assert broadcasted_tensor.tolist() == list(range(1    ,					state.num_processes + 1						)						)

def       lowercase_		(		_A			:    Optional[Any]						):

      """simple docstring"""




      if state.is_main_process:
            lowerCamelCase__		:				Optional[Any]    =  torch.arange(state.num_processes + 1						).to(state.device						)
      else:
            lowerCamelCase__		:				List[Any]    =  torch.arange(state.num_processes						).to(state.device						)
      lowerCamelCase__		:				int    =  pad_across_processes(_a						)
      assert padded_tensor.shape == torch.Size([state.num_processes + 1]						)
      if not state.is_main_process:
            assert padded_tensor.tolist() == list(range(0    ,					state.num_processes						)						) + [0]

def       lowercase_		(		_A			:    Tuple						):

      """simple docstring"""




      if state.num_processes != 2:
            return
      lowerCamelCase__		:				Any    =  create_tensor(_a						)
      lowerCamelCase__		:				int    =  reduce(_a    ,					"sum"						)
      lowerCamelCase__		:				Optional[int]    =  torch.tensor([4.0, 6]						).to(state.device						)
      assert torch.allclose(_a    ,					_a						), F"{reduced_tensor} != {truth_tensor}"

def       lowercase_		(		_A			:    Optional[Any]						):

      """simple docstring"""




      if state.num_processes != 2:
            return
      lowerCamelCase__		:				Any    =  create_tensor(_a						)
      lowerCamelCase__		:				List[str]    =  reduce(_a    ,					"mean"						)
      lowerCamelCase__		:				Optional[int]    =  torch.tensor([2.0, 3]						).to(state.device						)
      assert torch.allclose(_a    ,					_a						), F"{reduced_tensor} != {truth_tensor}"

def       lowercase_		(		_A			:    Any						):

      """simple docstring"""




      main()

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[int]    =  PartialState()
      state.print(F"State: {state}"						)
      state.print("testing gather"						)
      test_gather(_a						)
      state.print("testing gather_object"						)
      test_gather_object(_a						)
      state.print("testing broadcast"						)
      test_broadcast(_a						)
      state.print("testing pad_across_processes"						)
      test_pad_across_processes(_a						)
      state.print("testing reduce_sum"						)
      test_reduce_sum(_a						)
      state.print("testing reduce_mean"						)
      test_reduce_mean(_a						)


if __name__ == "__main__":
    main()








'''simple docstring'''







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 			_a  (	__a ):
       __a				:     Union[str, Any]   				=					"""encodec"""
       def __init__(	self						:	Tuple   ,				lowercase						:	List[str]=[1.5, 3.0, 6.0, 12.0, 24.0]   ,				lowercase						:	Any=24_000   ,				lowercase						:	str=1   ,				lowercase						:	Optional[int]=False   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	str=None   ,				lowercase						:	Tuple=128   ,				lowercase						:	Union[str, Any]=32   ,				lowercase						:	Union[str, Any]=1   ,				lowercase						:	Optional[Any]=[8, 5, 4, 2]   ,				lowercase						:	Any="weight_norm"   ,				lowercase						:	Tuple=7   ,				lowercase						:	int=7   ,				lowercase						:	Dict=3   ,				lowercase						:	List[Any]=2   ,				lowercase						:	str=True   ,				lowercase						:	List[str]="reflect"   ,				lowercase						:	List[Any]=2   ,				lowercase						:	Optional[Any]=2   ,				lowercase						:	int=1.0   ,				lowercase						:	Dict=1_024   ,				lowercase						:	str=None   ,				lowercase						:	Union[str, Any]=True   ,				**lowercase						:	Optional[int]   ,				):



           '''simple docstring'''

           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						:	Dict				):



           '''simple docstring'''

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



           '''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     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		np.prod(self.upsampling_ratios				)
           return math.ceil(self.sampling_rate / hop_length				)







       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)				)




