






import math
from typing import Optional

import numpy as np

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


__snake_case												=  logging.get_logger(__name__)

__snake_case												=  {
    """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_						(					snake_case__ ):



      """simple docstring"""
      UpperCamelCase_				:					str           ="encodec"
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 12.0, 24.0]    ,				SCREAMING_SNAKE_CASE_=2_4000    ,				SCREAMING_SNAKE_CASE_=1    ,				SCREAMING_SNAKE_CASE_=False    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=128    ,				SCREAMING_SNAKE_CASE_=32    ,				SCREAMING_SNAKE_CASE_=1    ,				SCREAMING_SNAKE_CASE_=[8, 5, 4, 2]    ,				SCREAMING_SNAKE_CASE_="weight_norm"    ,				SCREAMING_SNAKE_CASE_=7    ,				SCREAMING_SNAKE_CASE_=7    ,				SCREAMING_SNAKE_CASE_=3    ,				SCREAMING_SNAKE_CASE_=2    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_="reflect"    ,				SCREAMING_SNAKE_CASE_=2    ,				SCREAMING_SNAKE_CASE_=2    ,				SCREAMING_SNAKE_CASE_=1.0    ,				SCREAMING_SNAKE_CASE_=1024    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=True    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Tuple:
          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__(**SCREAMING_SNAKE_CASE_ )
      @property
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          if self.chunk_length_s is None:
              return None
          else:
              return int(self.chunk_length_s * self.sampling_rate )
      @property
      def     UpperCAmelCase						(				self )				->      Dict:
          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 )				->      Optional[int]:
          UpperCamelCase       :Dict								=   np.prod(self.upsampling_ratios )
          return math.ceil(self.sampling_rate / hop_length )




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






'''simple docstring'''




import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

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

      def __init__(       self						:				Optional[int]						,					A						:				Union[List[ControlNetModel], Tuple[ControlNetModel]]       ):
             super().__init__()
             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList(A       )




      def      _A	(       self						:				Dict						,					A						:				torch.FloatTensor						,					A						:				Union[torch.Tensor, float, int]						,					A						:				torch.Tensor						,					A						:				List[torch.tensor]						,					A						:				List[float]						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[Dict[str, Any]] = None						,					A						:				bool = False						,					A						:				bool = True						,					):
             for i, (image, scale, controlnet) in enumerate(zip(A						,					A						,					self.nets       )       ):
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    controlnet(
                        A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					)

                    # merge samples
                    if i == 0:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    down_samples, mid_sample
                    else:
                           _UpperCAmelCase     :  Optional[int]							      =    [
                               samples_prev + samples_curr
                               for samples_prev, samples_curr in zip(A						,					A       )
                           ]
                           mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample




      def      _A	(       self						:				List[str]						,					A						:				Union[str, os.PathLike]						,					A						:				bool = True						,					A						:				Callable = None						,					A						:				bool = False						,					A						:				Optional[str] = None						,					):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  str							      =    save_directory
             for controlnet in self.nets:
                    controlnet.save_pretrained(
                        A						,					is_main_process=A						,					save_function=A						,					safe_serialization=A						,					variant=A						,					)

                    idx += 1
                    _UpperCAmelCase     :  Tuple							      =    model_path_to_save + F"""_{idx}"""





      @classmethod
      def      _A	(       cls						:				int						,					A						:				Optional[Union[str, os.PathLike]]						,					**A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  int							      =    []

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             _UpperCAmelCase     :  int							      =    pretrained_model_path
             while os.path.isdir(A       ):
                    _UpperCAmelCase     :  List[str]							      =    ControlNetModel.from_pretrained(A						,					**A       )
                    controlnets.append(A       )

                    idx += 1
                    _UpperCAmelCase     :  Dict							      =    pretrained_model_path + F"""_{idx}"""

             logger.info(F"""{len(A       )} controlnets loaded from {pretrained_model_path}."""       )

             if len(A       ) == 0:
                    raise ValueError(
                        F"""No ControlNets found under {os.path.dirname(A       )}. Expected at least {pretrained_model_path + '_0'}."""       )

             return cls(A       )

