

'''simple docstring'''

import math
from typing import Optional

import numpy as np

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


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "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_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  """encodec"""


     def __init__(		self			:					List[Any]					,      lowercase_			:					List[str]=[1.5, 3.0, 6.0, 12.0, 24.0]					,      lowercase_			:					int=24_000					,      lowercase_			:					str=1					,      lowercase_			:					str=False					,      lowercase_			:					Tuple=None					,      lowercase_			:					List[str]=None					,      lowercase_			:					Optional[int]=128					,      lowercase_			:					Any=32					,      lowercase_			:					Dict=1					,      lowercase_			:					Tuple=[8, 5, 4, 2]					,      lowercase_			:					List[str]="weight_norm"					,      lowercase_			:					Optional[int]=7					,      lowercase_			:					Optional[Any]=7					,      lowercase_			:					Optional[Any]=3					,      lowercase_			:					Any=2					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					Union[str, Any]="reflect"					,      lowercase_			:					Optional[Any]=2					,      lowercase_			:					Any=2					,      lowercase_			:					Union[str, Any]=1.0					,      lowercase_			:					Optional[int]=1_024					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					Optional[int]=True					,      **lowercase_			:					str					,      )						->     Optional[Any]:
      UpperCAmelCase	:     Any						 =			target_bandwidths
      UpperCAmelCase	:     str						 =			sampling_rate
      UpperCAmelCase	:     List[str]						 =			audio_channels
      UpperCAmelCase	:     List[str]						 =			normalize
      UpperCAmelCase	:     Union[str, Any]						 =			chunk_length_s
      UpperCAmelCase	:     List[Any]						 =			overlap
      UpperCAmelCase	:     int						 =			hidden_size
      UpperCAmelCase	:     List[str]						 =			num_filters
      UpperCAmelCase	:     Tuple						 =			num_residual_layers
      UpperCAmelCase	:     List[Any]						 =			upsampling_ratios
      UpperCAmelCase	:     Union[str, Any]						 =			norm_type
      UpperCAmelCase	:     int						 =			kernel_size
      UpperCAmelCase	:     Union[str, Any]						 =			last_kernel_size
      UpperCAmelCase	:     Dict						 =			residual_kernel_size
      UpperCAmelCase	:     Union[str, Any]						 =			dilation_growth_rate
      UpperCAmelCase	:     Tuple						 =			use_causal_conv
      UpperCAmelCase	:     List[str]						 =			pad_mode
      UpperCAmelCase	:     List[str]						 =			compress
      UpperCAmelCase	:     Dict						 =			num_lstm_layers
      UpperCAmelCase	:     Dict						 =			trim_right_ratio
      UpperCAmelCase	:     List[str]						 =			codebook_size
      UpperCAmelCase	:     str						 =			codebook_dim if codebook_dim is not None else hidden_size
      UpperCAmelCase	:     Dict						 =			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        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[int]:
      if self.chunk_length_s is None:
       return None
      else:
       return int(self.chunk_length_s * self.sampling_rate	)


     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Optional[int]:
      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        UpperCAmelCase_	(		self			:					Any	)						->     int:
      UpperCAmelCase	:     Union[str, Any]						 =			np.prod(self.upsampling_ratios	)
      return math.ceil(self.sampling_rate / hop_length	)




     @property
     def        UpperCAmelCase_	(		self			:					Any	)						->     int:
      return int(1_000 * self.target_bandwidths[-1] // (self.frame_rate * 10)	)







'''simple docstring'''

import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def        UpperCAmelCase_	(		self			:					str	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			inspect.getfile(accelerate.test_utils	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
      UpperCAmelCase	:     Dict						 =			os.path.sep.join(
          mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
      UpperCAmelCase	:     List[Any]						 =			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Optional[int]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     List[Any]:
      print(f"""Found {torch.cuda.device_count()} devices."""	)
      UpperCAmelCase	:     int						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.operation_file_path]
      print(f"""Command: {cmd}"""	)
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__	)]
      with patch_environment(omp_num_threads=1	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)




     @require_multi_gpu
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      print(f"""Found {torch.cuda.device_count()} devices, using 2 devices only"""	)
      UpperCAmelCase	:     Dict						 =			['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", self.data_loop_file_path]
      with patch_environment(omp_num_threads=1					,      cuda_visible_devices='0,1'	):
       execute_subprocess_async(lowercase_					,      env=os.environ.copy()	)


if __name__ == "__main__":
 lowercase__         =      Accelerator()
 lowercase__         =      (accelerator.state.process_index + 2, 10)
 lowercase__         =      torch.randint(0, 10, shape).to(accelerator.device)

 lowercase__         =      ""

 lowercase__         =      accelerator.pad_across_processes(tensor)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
  error_msg += "Padding was not done with the right value (0)."

 lowercase__         =      accelerator.pad_across_processes(tensor, pad_first=True)
 if tensora.shape[0] != accelerator.state.num_processes + 1:
  error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
 lowercase__         =      accelerator.state.num_processes - accelerator.state.process_index - 1
 if not torch.equal(tensora[index:], tensor):
  error_msg += "Tensors have different values."
 if not torch.all(tensora[:index] == 0):
  error_msg += "Padding was not done with the right value (0)."

 # 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)




