





import math
from typing import Optional

import numpy as np

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


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

_snake_case  :   Dict						   =  {
    '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"""


  a_      =			"""encodec"""






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any]=[1.5, 3.0, 6.0, 12.0, 24.0] ,  lowerCAmelCase_			:		Optional[Any]=2_4_0_0_0 ,  lowerCAmelCase_			:		int=1 ,  lowerCAmelCase_			:		Union[str, Any]=False ,  lowerCAmelCase_			:		Optional[Any]=None ,  lowerCAmelCase_			:		Union[str, Any]=None ,  lowerCAmelCase_			:		Optional[Any]=1_2_8 ,  lowerCAmelCase_			:		List[Any]=3_2 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		Optional[int]=[8, 5, 4, 2] ,  lowerCAmelCase_			:		Optional[Any]="weight_norm" ,  lowerCAmelCase_			:		Tuple=7 ,  lowerCAmelCase_			:		Optional[int]=7 ,  lowerCAmelCase_			:		Dict=3 ,  lowerCAmelCase_			:		Optional[Any]=2 ,  lowerCAmelCase_			:		List[Any]=True ,  lowerCAmelCase_			:		Dict="reflect" ,  lowerCAmelCase_			:		List[Any]=2 ,  lowerCAmelCase_			:		int=2 ,  lowerCAmelCase_			:		Dict=1.0 ,  lowerCAmelCase_			:		str=1_0_2_4 ,  lowerCAmelCase_			:		Dict=None ,  lowerCAmelCase_			:		List[Any]=True ,  **lowerCAmelCase_			:		Optional[Any] ,  )							->						Union[str, Any]:
     __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__(**lowerCAmelCase_					)






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






  @property
  def     lowercase		(    self			:		Optional[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     lowercase		(    self			:		Any					)							->						int:
     __lowerCAmelCase							     =     np.prod(self.upsampling_ratios					)
     return math.ceil(self.sampling_rate / hop_length					)






  @property
  def     lowercase		(    self			:		str					)							->						int:
     return int(1_0_0_0 * self.target_bandwidths[-1] // (self.frame_rate * 1_0)					)








import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad



class     _UpperCAmelCase   (   unittest.TestCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Optional[int]:
     __lowerCAmelCase							     =     1_0






  def     lowercase		(    self			:		int					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     [1, 2, 3, 4]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Optional[Any]					)							->						List[str]:
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Any					)							->						Optional[Any]:
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
     __lowerCAmelCase							     =     [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
     self.assertEqual(truncate_or_pad(lowerCAmelCase_ ,  self.block_size ,  0					) ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		List[str]					)							->						Any:
     __lowerCAmelCase							     =     'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)
     self.assertEqual(lowerCAmelCase_ ,  []					)






  def     lowercase		(    self			:		Any					)							->						str:
     __lowerCAmelCase							     =     ''
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)
     self.assertEqual(lowerCAmelCase_ ,  []					)
     self.assertEqual(lowerCAmelCase_ ,  []					)






  def     lowercase		(    self			:		int					)							->						int:
     __lowerCAmelCase							     =     (
         'It was the year of Our Lord one thousand seven hundred and '
         'seventy-five\n\nSpiritual revelations were conceded to England '
         'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
     )
     __lowerCAmelCase     ,     __lowerCAmelCase							     =     process_story(lowerCAmelCase_					)

     __lowerCAmelCase							     =     [
         'It was the year of Our Lord one thousand seven hundred and seventy-five.',
         'Spiritual revelations were conceded to England at that favoured period, as at this.',
     ]
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     ['It was the best of times.']
     self.assertEqual(lowerCAmelCase_ ,  lowerCAmelCase_					)






  def     lowercase		(    self			:		Dict					)							->						Any:
     __lowerCAmelCase							     =     torch.tensor([1, 2, 3, 4]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  0					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		List[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  2_3					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		str					)							->						List[Any]:
     __lowerCAmelCase							     =     torch.tensor([8, 2, 3, 4, 1, 1, 1]					)
     __lowerCAmelCase							     =     torch.tensor([1, 1, 1, 1, 0, 0, 0]					)
     np.testing.assert_array_equal(build_mask(lowerCAmelCase_ ,  1					).numpy() ,  expected.numpy()					)






  def     lowercase		(    self			:		Optional[Any]					)							->						Optional[int]:
     __lowerCAmelCase							     =     1_0_1
     __lowerCAmelCase							     =     torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]]					)
     __lowerCAmelCase							     =     torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]					)

     __lowerCAmelCase							     =     compute_token_type_ids(lowerCAmelCase_ ,  lowerCAmelCase_					)
     np.testing.assert_array_equal(lowerCAmelCase_ ,  lowerCAmelCase_					)

