






import math
from typing import Optional

import numpy as np

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


lowerCamelCase	:							Any				=logging.get_logger(__name__)

lowerCamelCase	:							List[str]				={
    '''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__       ):
 _lowerCAmelCase       :       Tuple        = '''encodec'''







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[int]=[1.5, 3.0, 6.0, 1_2.0, 2_4.0]							,   SCREAMING_SNAKE_CASE					:   List[str]=2_40_00							,   SCREAMING_SNAKE_CASE					:   int=1							,   SCREAMING_SNAKE_CASE					:   int=False							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   List[str]=None							,   SCREAMING_SNAKE_CASE					:   Dict=1_28							,   SCREAMING_SNAKE_CASE					:   Tuple=32							,   SCREAMING_SNAKE_CASE					:   int=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=[8, 5, 4, 2]							,   SCREAMING_SNAKE_CASE					:   Tuple="weight_norm"							,   SCREAMING_SNAKE_CASE					:   int=7							,   SCREAMING_SNAKE_CASE					:   Optional[int]=7							,   SCREAMING_SNAKE_CASE					:   Optional[int]=3							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=2							,   SCREAMING_SNAKE_CASE					:   Any=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="reflect"							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   Dict=10_24							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   **SCREAMING_SNAKE_CASE					:   Optional[int]							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  target_bandwidths
      UpperCamelCase__			:   str       				=  sampling_rate
      UpperCamelCase__			:   List[Any]       				=  audio_channels
      UpperCamelCase__			:   Union[str, Any]       				=  normalize
      UpperCamelCase__			:   str       				=  chunk_length_s
      UpperCamelCase__			:   Any       				=  overlap
      UpperCamelCase__			:   Any       				=  hidden_size
      UpperCamelCase__			:   int       				=  num_filters
      UpperCamelCase__			:   Dict       				=  num_residual_layers
      UpperCamelCase__			:   Any       				=  upsampling_ratios
      UpperCamelCase__			:   Optional[int]       				=  norm_type
      UpperCamelCase__			:   Any       				=  kernel_size
      UpperCamelCase__			:   Optional[int]       				=  last_kernel_size
      UpperCamelCase__			:   Union[str, Any]       				=  residual_kernel_size
      UpperCamelCase__			:   List[Any]       				=  dilation_growth_rate
      UpperCamelCase__			:   Union[str, Any]       				=  use_causal_conv
      UpperCamelCase__			:   List[str]       				=  pad_mode
      UpperCamelCase__			:   int       				=  compress
      UpperCamelCase__			:   Tuple       				=  num_lstm_layers
      UpperCamelCase__			:   List[str]       				=  trim_right_ratio
      UpperCamelCase__			:   Dict       				=  codebook_size
      UpperCamelCase__			:   Dict       				=  codebook_dim if codebook_dim is not None else hidden_size
      UpperCamelCase__			:   Any       				=  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__(**SCREAMING_SNAKE_CASE						)







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      if self.chunk_length_s is None:
           return None
      else:
           return int(self.chunk_length_s * self.sampling_rate						)







 @property
 def 					__lowercase					(		self					:   Dict						):


      '''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 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  np.prod(self.upsampling_ratios						)
      return math.ceil(self.sampling_rate / hop_length						)







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10)						)







import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
 import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class 			__a  (    unittest.TestCase       ,   A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  load_tool("text-to-speech"						)
      self.tool.setup()







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   Union[str, Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   Any       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  self.tool("hey"						)
      UpperCamelCase__			:   int       				=  result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]							,   torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)							,   )						)