



"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block


@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @register_to_config
    def __init__(				self			:				Optional[int] ,  lowerCAmelCase_			:				int = 6_5_5_3_6 ,  lowerCAmelCase_			:				Optional[int] = None ,  lowerCAmelCase_			:				int = 2 ,  lowerCAmelCase_			:				int = 2 ,  lowerCAmelCase_			:				int = 0 ,  lowerCAmelCase_			:				str = "fourier" ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				bool = False ,  lowerCAmelCase_			:				float = 0.0 ,  lowerCAmelCase_			:				Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,  lowerCAmelCase_			:				Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,  lowerCAmelCase_			:				Tuple[str] = "UNetMidBlock1D" ,  lowerCAmelCase_			:				str = None ,  lowerCAmelCase_			:				Tuple[int] = (3_2, 3_2, 6_4) ,  lowerCAmelCase_			:				str = None ,  lowerCAmelCase_			:				int = 8 ,  lowerCAmelCase_			:				int = 1 ,  lowerCAmelCase_			:				bool = False ,  ):







      """simple docstring"""







      super().__init__()
      lowercase_								= sample_size

      # time
      if time_embedding_type == "fourier":
        lowercase_								= GaussianFourierProjection(
            embedding_size=8 ,  set_W_to_weight=lowerCAmelCase_ ,  log=lowerCAmelCase_ ,  flip_sin_to_cos=lowerCAmelCase_)
        lowercase_								= 2 * block_out_channels[0]
      elif time_embedding_type == "positional":
        lowercase_								= Timesteps(
            block_out_channels[0] ,  flip_sin_to_cos=lowerCAmelCase_ ,  downscale_freq_shift=lowerCAmelCase_)
        lowercase_								= block_out_channels[0]

      if use_timestep_embedding:
        lowercase_								= block_out_channels[0] * 4
        lowercase_								= TimestepEmbedding(
            in_channels=lowerCAmelCase_ ,  time_embed_dim=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  out_dim=block_out_channels[0] ,  )

      lowercase_								= nn.ModuleList([])
      lowercase_								= None
      lowercase_								= nn.ModuleList([])
      lowercase_								= None

      # down
      lowercase_								= in_channels
      for i, down_block_type in enumerate(lowerCAmelCase_):
        lowercase_								= output_channel
        lowercase_								= block_out_channels[i]

        if i == 0:
          input_channel += extra_in_channels

        lowercase_								= i == len(lowerCAmelCase_) - 1

        lowercase_								= get_down_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_downsample=not is_final_block or downsample_each_block ,  )
        self.down_blocks.append(lowerCAmelCase_)

      # mid
      lowercase_								= get_mid_block(
          lowerCAmelCase_ ,  in_channels=block_out_channels[-1] ,  mid_channels=block_out_channels[-1] ,  out_channels=block_out_channels[-1] ,  embed_dim=block_out_channels[0] ,  num_layers=lowerCAmelCase_ ,  add_downsample=lowerCAmelCase_ ,  )

      # up
      lowercase_								= list(reversed(lowerCAmelCase_))
      lowercase_								= reversed_block_out_channels[0]
      if out_block_type is None:
        lowercase_								= out_channels
      else:
        lowercase_								= block_out_channels[0]

      for i, up_block_type in enumerate(lowerCAmelCase_):
        lowercase_								= output_channel
        lowercase_								= (
            reversed_block_out_channels[i + 1] if i < len(lowerCAmelCase_) - 1 else final_upsample_channels
        )

        lowercase_								= i == len(lowerCAmelCase_) - 1

        lowercase_								= get_up_block(
            lowerCAmelCase_ ,  num_layers=lowerCAmelCase_ ,  in_channels=lowerCAmelCase_ ,  out_channels=lowerCAmelCase_ ,  temb_channels=block_out_channels[0] ,  add_upsample=not is_final_block ,  )
        self.up_blocks.append(lowerCAmelCase_)
        lowercase_								= output_channel

      # out
      lowercase_								= norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,  3_2)
      lowercase_								= get_out_block(
          out_block_type=lowerCAmelCase_ ,  num_groups_out=lowerCAmelCase_ ,  embed_dim=block_out_channels[0] ,  out_channels=lowerCAmelCase_ ,  act_fn=lowerCAmelCase_ ,  fc_dim=block_out_channels[-1] // 4 ,  )




    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				torch.FloatTensor ,  lowerCAmelCase_			:				Union[torch.Tensor, float, int] ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= timestep
      if not torch.is_tensor(lowerCAmelCase_):
        lowercase_								= torch.tensor([timesteps] ,  dtype=torch.long ,  device=sample.device)
      elif torch.is_tensor(lowerCAmelCase_) and len(timesteps.shape) == 0:
        lowercase_								= timesteps[None].to(sample.device)

      lowercase_								= self.time_proj(lowerCAmelCase_)
      if self.config.use_timestep_embedding:
        lowercase_								= self.time_mlp(lowerCAmelCase_)
      else:
        lowercase_								= timestep_embed[..., None]
        lowercase_								= timestep_embed.repeat([1, 1, sample.shape[2]]).to(sample.dtype)
        lowercase_								= timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]))

      # 2. down
      lowercase_								= ()
      for downsample_block in self.down_blocks:
        lowercase_					,    lowercase_								= downsample_block(hidden_states=lowerCAmelCase_ ,  temb=lowerCAmelCase_)
        down_block_res_samples += res_samples

      # 3. mid
      if self.mid_block:
        lowercase_								= self.mid_block(lowerCAmelCase_ ,  lowerCAmelCase_)

      # 4. up
      for i, upsample_block in enumerate(self.up_blocks):
        lowercase_								= down_block_res_samples[-1:]
        lowercase_								= down_block_res_samples[:-1]
        lowercase_								= upsample_block(lowerCAmelCase_ ,  res_hidden_states_tuple=lowerCAmelCase_ ,  temb=lowerCAmelCase_)

      # 5. post-process
      if self.out_block:
        lowercase_								= self.out_block(lowerCAmelCase_ ,  lowerCAmelCase_)

      if not return_dict:
        return (sample,)

      return UNetaDOutput(sample=lowerCAmelCase_)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase						:		Optional[Any]			= {"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase						:		str			= [
           "WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
           "WavLMForAudioFrameClassification",
           "WavLMForCTC",
           "WavLMForSequenceClassification",
           "WavLMForXVector",
           "WavLMModel",
           "WavLMPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavlm import (
                  WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavLMForAudioFrameClassification,
                  WavLMForCTC,
                  WavLMForSequenceClassification,
                  WavLMForXVector,
                  WavLMModel,
                  WavLMPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase						:		Optional[Any]			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

