'''simple docstring'''




import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				str						,					A						:				VQModel						,					A						:				UNetaDModel						,					A						:				DDIMScheduler       ):
             super().__init__()
             self.register_modules(vqvae=A						,					unet=A						,					scheduler=A       )





      @torch.no_grad()
      def __call__(       self						:				int						,					A						:				int = 1						,					A						:				Optional[Union[torch.Generator, List[torch.Generator]]] = None						,					A						:				float = 0.0						,					A						:				int = 50						,					A						:				Optional[str] = "pil"						,					A						:				bool = True						,					**A						:				Union[str, Any]						,					):
             _UpperCAmelCase     :  Optional[int]							      =    randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,					generator=A						,					)
             _UpperCAmelCase     :  Optional[int]							      =    latents.to(self.device       )

             # scale the initial noise by the standard deviation required by the scheduler
             _UpperCAmelCase     :  str							      =    latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(A       )

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _UpperCAmelCase     :  List[Any]							      =    "eta" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

             _UpperCAmelCase     :  Any							      =    {}
             if accepts_eta:
                    _UpperCAmelCase     :  List[str]							      =    eta

             for t in self.progress_bar(self.scheduler.timesteps       ):
                    _UpperCAmelCase     :  Any							      =    self.scheduler.scale_model_input(A						,					A       )
                    # predict the noise residual
                    _UpperCAmelCase     :  Dict							      =    self.unet(A						,					A       ).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _UpperCAmelCase     :  Dict							      =    self.scheduler.step(A						,					A						,					A						,					**A       ).prev_sample

             # decode the image latents with the VAE
             _UpperCAmelCase     :  Tuple							      =    self.vqvae.decode(A       ).sample

             _UpperCAmelCase     :  Dict							      =    (image / 2 + 0.5).clamp(0						,					1       )
             _UpperCAmelCase     :  List[Any]							      =    image.cpu().permute(0						,					2						,					3						,					1       ).numpy()
             if output_type == "pil":
                    _UpperCAmelCase     :  Optional[Any]							      =    self.numpy_to_pil(A       )

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=A       )


'''simple docstring'''




import math
from typing import Optional

import numpy as np

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


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     List[Any]     =  {
    """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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						"encodec"




      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0]						,					A						:				List[Any]=24000						,					A						:				Union[str, Any]=1						,					A						:				List[Any]=False						,					A						:				Optional[int]=None						,					A						:				int=None						,					A						:				str=128						,					A						:				List[Any]=32						,					A						:				List[Any]=1						,					A						:				int=[8, 5, 4, 2]						,					A						:				Optional[int]="weight_norm"						,					A						:				List[Any]=7						,					A						:				Any=7						,					A						:				Dict=3						,					A						:				Optional[int]=2						,					A						:				Dict=True						,					A						:				Dict="reflect"						,					A						:				Any=2						,					A						:				Dict=2						,					A						:				str=1.0						,					A						:				Optional[int]=1024						,					A						:				Any=None						,					A						:				Any=True						,					**A						:				str						,					):
             _UpperCAmelCase     :  Optional[int]							      =    target_bandwidths
             _UpperCAmelCase     :  List[str]							      =    sampling_rate
             _UpperCAmelCase     :  Optional[int]							      =    audio_channels
             _UpperCAmelCase     :  str							      =    normalize
             _UpperCAmelCase     :  int							      =    chunk_length_s
             _UpperCAmelCase     :  str							      =    overlap
             _UpperCAmelCase     :  Optional[Any]							      =    hidden_size
             _UpperCAmelCase     :  int							      =    num_filters
             _UpperCAmelCase     :  Optional[Any]							      =    num_residual_layers
             _UpperCAmelCase     :  Optional[int]							      =    upsampling_ratios
             _UpperCAmelCase     :  int							      =    norm_type
             _UpperCAmelCase     :  List[Any]							      =    kernel_size
             _UpperCAmelCase     :  List[Any]							      =    last_kernel_size
             _UpperCAmelCase     :  List[Any]							      =    residual_kernel_size
             _UpperCAmelCase     :  List[str]							      =    dilation_growth_rate
             _UpperCAmelCase     :  Dict							      =    use_causal_conv
             _UpperCAmelCase     :  Tuple							      =    pad_mode
             _UpperCAmelCase     :  Tuple							      =    compress
             _UpperCAmelCase     :  List[str]							      =    num_lstm_layers
             _UpperCAmelCase     :  List[Any]							      =    trim_right_ratio
             _UpperCAmelCase     :  int							      =    codebook_size
             _UpperCAmelCase     :  Optional[Any]							      =    codebook_dim if codebook_dim is not None else hidden_size
             _UpperCAmelCase     :  Optional[int]							      =    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__(**A       )




      @property
      def      _A	(       self						:				Any       ):
             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]       ):
             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]       ):
             _UpperCAmelCase     :  Dict							      =    np.prod(self.upsampling_ratios       )
             return math.ceil(self.sampling_rate / hop_length       )





      @property
      def      _A	(       self						:				str       ):
             return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10)       )

