

from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


UpperCAmelCase__ :     List[str]				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : List[str]			       =["""audio_values""", """audio_mask"""]


  def __init__(			self    : str				,				UpperCAmelCase__    : Optional[Any]=2_0_4_8				,				UpperCAmelCase__    : Optional[Any]=1				,				UpperCAmelCase__    : List[str]=[1_6, 1_6]				,				UpperCAmelCase__    : Optional[int]=1_2_8				,				UpperCAmelCase__    : List[Any]=4_4_1_0_0				,				UpperCAmelCase__    : Union[str, Any]=8_6				,				UpperCAmelCase__    : int=2_0_4_8				,				UpperCAmelCase__    : Optional[int]=0.0				,				**UpperCAmelCase__    : List[str]				,				)					->Union[str, Any]:
        """simple docstring"""







        super().__init__(
            feature_size=UpperCAmelCase__				,				sampling_rate=UpperCAmelCase__				,				padding_value=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

        SCREAMING_SNAKE_CASE :      Tuple       =   spectrogram_length
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   num_channels
        SCREAMING_SNAKE_CASE :      Any       =   patch_size
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   feature_size // self.patch_size[1]
        SCREAMING_SNAKE_CASE :      int       =   n_fft
        SCREAMING_SNAKE_CASE :      Dict       =   sampling_rate // hop_length_to_sampling_rate
        SCREAMING_SNAKE_CASE :      Dict       =   sampling_rate
        SCREAMING_SNAKE_CASE :      Tuple       =   padding_value
        SCREAMING_SNAKE_CASE :      Dict       =   mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2				,				num_mel_filters=UpperCAmelCase__				,				min_frequency=0.0				,				max_frequency=2_20_50.0				,				sampling_rate=UpperCAmelCase__				,				norm="""slaney"""				,				mel_scale="""slaney"""				,				).T


  def  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : np.array		)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   spectrogram(
            UpperCAmelCase__				,				window_function(self.n_fft				,				"""hann"""		)				,				frame_length=self.n_fft				,				hop_length=self.hop_length				,				power=2.0				,				mel_filters=self.mel_filters.T				,				log_mel="""dB"""				,				db_range=80.0				,				)
        SCREAMING_SNAKE_CASE :      Optional[int]       =   log_spec[:, :-1]
        SCREAMING_SNAKE_CASE :      Tuple       =   log_spec - 20.0
        SCREAMING_SNAKE_CASE :      Tuple       =   np.clip(log_spec / 40.0				,				-2.0				,				0.0		) + 1.0
        return log_spec




  def __call__(			self    : Dict				,				UpperCAmelCase__    : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : Optional[bool] = True				,				UpperCAmelCase__    : Optional[int] = None				,				UpperCAmelCase__    : bool = False				,				UpperCAmelCase__    : bool = False				,				**UpperCAmelCase__    : Any				,				)					->BatchFeature:
        """simple docstring"""







        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        """This feature extractor is set to support sampling rate"""
                        f" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                        f" with {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."""		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   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}"		)
        SCREAMING_SNAKE_CASE :      int       =   is_batched_numpy or (
            isinstance(UpperCAmelCase__				,				(list, tuple)		) and (isinstance(raw_speech[0]				,				(np.ndarray, tuple, list)		))
        )
        if is_batched:
              SCREAMING_SNAKE_CASE :      List[Any]       =   [np.asarray([speech]				,				dtype=np.floataa		).T for speech in raw_speech]
        elif not is_batched and not isinstance(UpperCAmelCase__				,				np.ndarray		):
              SCREAMING_SNAKE_CASE :      Tuple       =   np.asarray(UpperCAmelCase__				,				dtype=np.floataa		)
        elif isinstance(UpperCAmelCase__				,				np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
              SCREAMING_SNAKE_CASE :      int       =   raw_speech.astype(np.floataa		)
        # always return batch
        if not is_batched:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   [np.asarray([raw_speech]		).T]

        # Convert audio signals to log mel spectrograms, truncate by time axis
        SCREAMING_SNAKE_CASE :      Any       =   [
            self._np_extract_fbank_features(waveform.squeeze()		).T[: self.spectrogram_length] for waveform in raw_speech
        ]
        if isinstance(audio_features[0]				,				UpperCAmelCase__		):
              SCREAMING_SNAKE_CASE :      Optional[int]       =   [np.asarray(UpperCAmelCase__				,				dtype=np.floataa		) for feature in audio_features]

        # Create audio attention mask
        SCREAMING_SNAKE_CASE :      str       =   max(
            [ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len for feature in audio_features]		)  # The maximum number of audio patches in a batch
        if return_attention_mask:
              SCREAMING_SNAKE_CASE :      Dict       =   [
                  (ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [1]
                  + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]		) * self.freq_len) * [0]
                  for feature in audio_features
              ]
              SCREAMING_SNAKE_CASE :      str       =   np.array(UpperCAmelCase__		).astype(np.floataa		)

        # convert into correct format for padding
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
        SCREAMING_SNAKE_CASE :      int       =   np.ones([len(UpperCAmelCase__		), 1, max_time_len, self.feature_size]		).astype(np.floataa		)
        SCREAMING_SNAKE_CASE :      Tuple       =   padded_audio_features * self.padding_value
        for i in range(len(UpperCAmelCase__		)		):
              SCREAMING_SNAKE_CASE :      List[str]       =   audio_features[i]
              SCREAMING_SNAKE_CASE :      int       =   feature

        # return as BatchFeature
        if return_attention_mask:
              SCREAMING_SNAKE_CASE :      str       =   {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
        else:
              SCREAMING_SNAKE_CASE :      List[Any]       =   {"""audio_values""": padded_audio_features}

        SCREAMING_SNAKE_CASE :      Any       =   BatchFeature(data=UpperCAmelCase__				,				tensor_type=UpperCAmelCase__		)
        return encoded_inputs







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCAmelCase__ :     Optional[Any]				    =    False
UpperCAmelCase__ :     Union[str, Any]				    =    True
UpperCAmelCase__ :     int				    =    False


if __name__ == "__main__":
  UpperCAmelCase__ :     List[Any]				    =    argparse.ArgumentParser()

  parser.add_argument(
      """--repo_path""",
      default=None,
      type=str,
      required=True,
      help="""The config json file corresponding to the architecture.""",
  )

  parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

  UpperCAmelCase__ :     List[str]				    =    parser.parse_args()

  UpperCAmelCase__ :     Dict				    =    {
      """image_size""": """sample_size""",
      """num_res_blocks""": """layers_per_block""",
      """block_channels""": """block_out_channels""",
      """down_blocks""": """down_block_types""",
      """up_blocks""": """up_block_types""",
      """downscale_freq_shift""": """freq_shift""",
      """resnet_num_groups""": """norm_num_groups""",
      """resnet_act_fn""": """act_fn""",
      """resnet_eps""": """norm_eps""",
      """num_head_channels""": """attention_head_dim""",
  }

  UpperCAmelCase__ :     str				    =    {
      """time_steps""": """time_proj""",
      """mid""": """mid_block""",
      """downsample_blocks""": """down_blocks""",
      """upsample_blocks""": """up_blocks""",
  }

  UpperCAmelCase__ :     Union[str, Any]				    =    """""" if has_file(args.repo_path, """config.json""") else """unet"""

  with open(os.path.join(args.repo_path, subfolder, """config.json"""), """r""", encoding="""utf-8""") as reader:
    UpperCAmelCase__ :     Union[str, Any]				    =    reader.read()
    UpperCAmelCase__ :     Any				    =    json.loads(text)

  if do_only_config:
    for key in config_parameters_to_change.keys():
      config.pop(key, None)

  if has_file(args.repo_path, """config.json"""):
    UpperCAmelCase__ :     int				    =    UNetaDModel(**config)
  else:
    UpperCAmelCase__ :     Optional[Any]				    =    UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
    UpperCAmelCase__ :     int				    =    class_name(**config)

  if do_only_config:
    model.save_config(os.path.join(args.repo_path, subfolder))

  UpperCAmelCase__ :     Optional[Any]				    =    dict(model.config)

  if do_only_renaming:
    for key, value in config_parameters_to_change.items():
      if key in config:
        UpperCAmelCase__ :     str				    =    config[key]
        del config[key]

    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
    UpperCAmelCase__ :     Optional[int]				    =    [k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

  if do_only_weights:
    UpperCAmelCase__ :     Dict				    =    torch.load(os.path.join(args.repo_path, subfolder, """diffusion_pytorch_model.bin"""))

    UpperCAmelCase__ :     Optional[Any]				    =    {}
    for param_key, param_value in state_dict.items():
      if param_key.endswith(""".op.bias""") or param_key.endswith(""".op.weight"""):
        continue
      UpperCAmelCase__ :     Union[str, Any]				    =    False
      for key, new_key in key_parameters_to_change.items():
        if not has_changed and param_key.split(""".""")[0] == key:
          UpperCAmelCase__ :     List[Any]				    =    param_value
          UpperCAmelCase__ :     List[str]				    =    True
      if not has_changed:
        UpperCAmelCase__ :     Any				    =    param_value

    model.load_state_dict(new_state_dict)
    model.save_pretrained(os.path.join(args.repo_path, subfolder))




