




'''simple docstring'''







import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch


@require_torch
class     _a						:
       @property
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           return self.get_dummy_input()
       @property
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           if self.block_type == "down":
               return (4, 32, 16, 16)
           elif self.block_type == "mid":
               return (4, 32, 32, 32)
           elif self.block_type == "up":
               return (4, 32, 64, 64)

           raise ValueError(f"'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."       )
       def 					A   (			self		:			Union[str, Any]     ,       lowercase		:			Tuple=True     ,       lowercase		:			str=False     ,       lowercase		:			List[str]=False     ,       lowercase		:			Union[str, Any]=False     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	4
           UpperCAmelCase     =	32
           UpperCAmelCase     =	(32, 32)

           UpperCAmelCase     =	torch.manual_seed(0       )
           UpperCAmelCase     =	torch.device(lowercase       )
           UpperCAmelCase     =	(batch_size, num_channels) + sizes
           UpperCAmelCase     =	randn_tensor(lowercase     ,       generator=lowercase     ,       device=lowercase       )
           UpperCAmelCase     =	{'''hidden_states''': hidden_states}

           if include_temb:
               UpperCAmelCase     =	128
               UpperCAmelCase     =	randn_tensor((batch_size, temb_channels)     ,       generator=lowercase     ,       device=lowercase       )

           if include_res_hidden_states_tuple:
               UpperCAmelCase     =	torch.manual_seed(1       )
               UpperCAmelCase     =	(randn_tensor(lowercase     ,       generator=lowercase     ,       device=lowercase       ),)

           if include_encoder_hidden_states:
               UpperCAmelCase     =	floats_tensor((batch_size, 32, 32)       ).to(lowercase       )

           if include_skip_sample:
               UpperCAmelCase     =	randn_tensor(((batch_size, 3) + sizes)     ,       generator=lowercase     ,       device=lowercase       )

           return dummy_input
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	{
               '''in_channels''': 32,
               '''out_channels''': 32,
               '''temb_channels''': 128,
           }
           if self.block_type == "up":
               UpperCAmelCase     =	32

           if self.block_type == "mid":
               init_dict.pop('''out_channels'''       )

           UpperCAmelCase     =	self.dummy_input
           return init_dict, inputs_dict
       def 					A   (			self		:			Dict     ,       lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.prepare_init_args_and_inputs_for_common()
           UpperCAmelCase     =	self.block_class(**lowercase       )
           unet_block.to(lowercase       )
           unet_block.eval()

           with torch.no_grad():
               UpperCAmelCase     =	unet_block(**lowercase       )

           if isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	output[0]

           self.assertEqual(output.shape     ,       self.output_shape       )

           UpperCAmelCase     =	output[0, -1, -3:, -3:]
           UpperCAmelCase     =	torch.tensor(lowercase       ).to(lowercase       )
           assert torch_all_close(output_slice.flatten()     ,       lowercase     ,       atol=5E-3       )







       @unittest.skipIf(torch_device == '''mps'''     ,       '''Training is not supported in mps'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase ,  UpperCAmelCase     =	self.prepare_init_args_and_inputs_for_common()
           UpperCAmelCase     =	self.block_class(**lowercase       )
           model.to(lowercase       )
           model.train()
           UpperCAmelCase     =	model(**lowercase       )

           if isinstance(lowercase     ,       lowercase       ):
               UpperCAmelCase     =	output[0]

           UpperCAmelCase     =	torch.device(lowercase       )
           UpperCAmelCase     =	randn_tensor(output.shape     ,       device=lowercase       )
           UpperCAmelCase     =	torch.nn.functional.mse_loss(lowercase     ,       lowercase       )
           loss.backward()










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



           '''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		:			List[str]       ):



           '''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		:			Optional[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		:			Union[str, 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)       )




