




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 __snake_case			(			a		):
   UpperCAmelCase__  :    torch.FloatTensor







class __snake_case			(			a ,  a		):







   @register_to_config
   def __init__(      self		:						List[str]					,						_snake_case		:						int = 65536					,						_snake_case		:						Optional[int] = None					,						_snake_case		:						int = 2					,						_snake_case		:						int = 2					,						_snake_case		:						int = 0					,						_snake_case		:						str = "fourier"					,						_snake_case		:						bool = True					,						_snake_case		:						bool = False					,						_snake_case		:						float = 0.0					,						_snake_case		:						Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")					,						_snake_case		:						Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")					,						_snake_case		:						Tuple[str] = "UNetMidBlock1D"					,						_snake_case		:						str = None					,						_snake_case		:						Tuple[int] = (32, 32, 64)					,						_snake_case		:						str = None					,						_snake_case		:						int = 8					,						_snake_case		:						int = 1					,						_snake_case		:						bool = False					,						):

        """simple docstring"""


        super().__init__()
        UpperCAmelCase_          =							sample_size

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

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

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

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

             if i == 0:
                  input_channel += extra_in_channels

             UpperCAmelCase_          =							i == len(_snake_case) - 1

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

        # mid
        UpperCAmelCase_          =							get_mid_block(
            _snake_case					,						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=_snake_case					,						add_downsample=_snake_case					,						)

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

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

             UpperCAmelCase_          =							i == len(_snake_case) - 1

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

        # out
        UpperCAmelCase_          =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,						32)
        UpperCAmelCase_          =							get_out_block(
            out_block_type=_snake_case					,						num_groups_out=_snake_case					,						embed_dim=block_out_channels[0]					,						out_channels=_snake_case					,						act_fn=_snake_case					,						fc_dim=block_out_channels[-1] // 4					,						)







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						torch.FloatTensor					,						_snake_case		:						Union[torch.Tensor, float, int]					,						_snake_case		:						bool = True					,						):

        """simple docstring"""


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

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

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

        # 3. mid
        if self.mid_block:
             UpperCAmelCase_          =							self.mid_block(_snake_case					,						_snake_case)

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

        # 5. post-process
        if self.out_block:
             UpperCAmelCase_          =							self.out_block(_snake_case					,						_snake_case)

        if not return_dict:
             return (sample,)

        return UNetaDOutput(sample=_snake_case)










'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


a_  :				int						= logging.get_logger(__name__)





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  ["input_features", "attention_mask"]






  def __init__(    self			,  UpperCamelCase=80			,  UpperCamelCase=1_6000			,  UpperCamelCase=80			,  UpperCamelCase=0.0			,  UpperCamelCase=True			,  UpperCamelCase=True			,  UpperCamelCase=True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   super().__init__(feature_size=UpperCamelCase			,  sampling_rate=UpperCamelCase			,  padding_value=UpperCamelCase			,  **UpperCamelCase     )
   lowerCamelCase_      =  num_mel_bins
   lowerCamelCase_      =  do_ceptral_normalize
   lowerCamelCase_      =  normalize_means
   lowerCamelCase_      =  normalize_vars
   lowerCamelCase_      =  True






  def   snake_case		(    self			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
   lowerCamelCase_      =  torch.from_numpy(UpperCamelCase     ).unsqueeze(0     )
   lowerCamelCase_      =  ta_kaldi.fbank(UpperCamelCase			,  num_mel_bins=self.num_mel_bins			,  sample_frequency=self.sampling_rate     )
   return features.numpy()






  @staticmethod
  def   snake_case		(    UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase = True			,  UpperCamelCase = True			,  UpperCamelCase = 0.0			,  ):







   """simple docstring"""
   # make sure we normalize float32 arrays
   if normalize_means:
    lowerCamelCase_      =  x[:input_length].mean(axis=0     )
    lowerCamelCase_      =  np.subtract(UpperCamelCase			,  UpperCamelCase     )
   if normalize_vars:
    lowerCamelCase_      =  x[:input_length].std(axis=0     )
    lowerCamelCase_      =  np.divide(UpperCamelCase			,  UpperCamelCase     )

   if input_length < x.shape[0]:
    lowerCamelCase_      =  padding_value

   # make sure array is in float32
   lowerCamelCase_      =  x.astype(np.floataa     )

   return x






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase = None     ):







   """simple docstring"""
   lowerCamelCase_      =  attention_mask.sum(-1     ) if attention_mask is not None else [x.shape[0] for x in input_features]
   return [
       self.utterance_cmvn(UpperCamelCase			,  UpperCamelCase			,  self.normalize_means			,  self.normalize_vars			,  self.padding_value     )
       for x, n in zip(UpperCamelCase			,  UpperCamelCase     )
   ]







  def __call__(    self			,  UpperCamelCase			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = False			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = None			,  **UpperCamelCase			,  ):







   """simple docstring"""

   if sampling_rate is not None:
    if sampling_rate != self.sampling_rate:
     raise ValueError(
         f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
         f''' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'''
         f''' {self.sampling_rate} and not {sampling_rate}.'''     )
   else:
    logger.warning(
        "It is strongly recommended to pass the `sampling_rate` argument to this function. "
        "Failing to do so can result in silent errors that might be hard to debug."     )

   lowerCamelCase_      =  isinstance(UpperCamelCase			,  np.ndarray     ) and len(raw_speech.shape     ) > 1
   if is_batched_numpy and len(raw_speech.shape     ) > 2:
    raise ValueError(f'''Only mono-channel audio is supported for input to {self}'''     )
   lowerCamelCase_      =  is_batched_numpy or (
       isinstance(UpperCamelCase			,  (list, tuple)     ) and (isinstance(raw_speech[0]			,  (np.ndarray, tuple, list)     ))
   )

   if is_batched:
    lowerCamelCase_      =  [np.asarray(UpperCamelCase			,  dtype=np.floataa     ) for speech in raw_speech]
   elif not is_batched and not isinstance(UpperCamelCase			,  np.ndarray     ):
    lowerCamelCase_      =  np.asarray(UpperCamelCase			,  dtype=np.floataa     )
   elif isinstance(UpperCamelCase			,  np.ndarray     ) and raw_speech.dtype is np.dtype(np.floataa     ):
    lowerCamelCase_      =  raw_speech.astype(np.floataa     )

   # always return batch
   if not is_batched:
    lowerCamelCase_      =  [raw_speech]

   # extract fbank features
   lowerCamelCase_      =  [self._extract_fbank_features(UpperCamelCase     ) for waveform in raw_speech]

   # convert into correct format for padding
   lowerCamelCase_      =  BatchFeature({"input_features": features}     )

   lowerCamelCase_      =  self.pad(
       UpperCamelCase			,  padding=UpperCamelCase			,  max_length=UpperCamelCase			,  truncation=UpperCamelCase			,  pad_to_multiple_of=UpperCamelCase			,  return_attention_mask=UpperCamelCase			,  **UpperCamelCase			,  )

   # make sure list is in array format
   lowerCamelCase_      =  padded_inputs.get("input_features"     )
   if isinstance(input_features[0]			,  UpperCamelCase     ):
    lowerCamelCase_      =  [np.asarray(UpperCamelCase			,  dtype=np.floataa     ) for feature in input_features]

   lowerCamelCase_      =  padded_inputs.get("attention_mask"     )
   if attention_mask is not None:
    lowerCamelCase_      =  [np.asarray(UpperCamelCase			,  dtype=np.intaa     ) for array in attention_mask]

   # Utterance-level cepstral mean and variance normalization
   if self.do_ceptral_normalize:
    lowerCamelCase_      =  (
        np.array(UpperCamelCase			,  dtype=np.intaa     )
        if self._get_padding_strategies(UpperCamelCase			,  max_length=UpperCamelCase     ) is not PaddingStrategy.DO_NOT_PAD
        else None
    )
    lowerCamelCase_      =  self.normalize(
        padded_inputs["input_features"]			,  attention_mask=UpperCamelCase     )

   if return_tensors is not None:
    lowerCamelCase_      =  padded_inputs.convert_to_tensors(UpperCamelCase     )

   return padded_inputs



