




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


__lowerCAmelCase		:				Tuple       			=		logging.get_logger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					["""audio_values""", """audio_mask"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     Optional[int]=20_48					,      __lowerCamelCase				:     List[str]=1					,      __lowerCamelCase				:     Union[str, Any]=[16, 16]					,      __lowerCamelCase				:     Union[str, Any]=1_28					,      __lowerCamelCase				:     Any=4_41_00					,      __lowerCamelCase				:     str=86					,      __lowerCamelCase				:     str=20_48					,      __lowerCamelCase				:     Tuple=0.0					,      **__lowerCamelCase				:     List[str]					,      )					->     List[Any]:
							super().__init__(
							    feature_size=__lowerCamelCase					,      sampling_rate=__lowerCamelCase					,      padding_value=__lowerCamelCase					,      **__lowerCamelCase					,      )

							a    =			spectrogram_length
							a    =			num_channels
							a    =			patch_size
							a    =			feature_size // self.patch_size[1]
							a    =			n_fft
							a    =			sampling_rate // hop_length_to_sampling_rate
							a    =			sampling_rate
							a    =			padding_value
							a    =			mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2					,      num_mel_filters=__lowerCamelCase					,      min_frequency=0.0					,      max_frequency=22_050.0					,      sampling_rate=__lowerCamelCase					,      norm="slaney"					,      mel_scale="slaney"					,      ).T





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     np.array							)					->     np.ndarray:
							a    =			spectrogram(
							    __lowerCamelCase					,      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					,      )
							a    =			log_spec[:, :-1]
							a    =			log_spec - 20.0
							a    =			np.clip(log_spec / 40.0					,      -2.0					,      0.0							) + 1.0
							return log_spec





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

							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."							)

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

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

							# Create audio attention mask
							a    =			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:
										a    =			[
										    (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
										]
										a    =			np.array(__lowerCamelCase							).astype(np.floataa							)

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

							# return as BatchFeature
							if return_attention_mask:
										a    =			{"audio_values": padded_audio_features, "audio_mask": audio_mask}
							else:
										a    =			{"audio_values": padded_audio_features}

							a    =			BatchFeature(data=__lowerCamelCase					,      tensor_type=__lowerCamelCase							)
							return encoded_inputs











"""simple docstring"""
import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		str     ,       UpperCamelCase__		:		Optional[Any]							):
    _UpperCAmelCase							:  int											=    OmegaConf.load(UpperCamelCase__							)
    _UpperCAmelCase							:  str											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)['''model''']
    _UpperCAmelCase							:  Optional[Any]											=    list(state_dict.keys()							)

    # extract state_dict for VQVAE
    _UpperCAmelCase							:  Any											=    {}
    _UpperCAmelCase							:  Any											=    '''first_stage_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    # extract state_dict for UNetLDM
    _UpperCAmelCase							:  Tuple											=    {}
    _UpperCAmelCase							:  int											=    '''model.diffusion_model.'''
    for key in keys:
        if key.startswith(UpperCamelCase__							):
            _UpperCAmelCase							:  Dict											=    state_dict[key]

    _UpperCAmelCase							:  List[str]											=    config.model.params.first_stage_config.params
    _UpperCAmelCase							:  Union[str, Any]											=    config.model.params.unet_config.params

    _UpperCAmelCase							:  Any											=    VQModel(**UpperCamelCase__							).eval()
    vqvae.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Union[str, Any]											=    UNetLDMModel(**UpperCamelCase__							).eval()
    unet.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    DDIMScheduler(
        timesteps=config.model.params.timesteps     ,       beta_schedule='''scaled_linear'''     ,       beta_start=config.model.params.linear_start     ,       beta_end=config.model.params.linear_end     ,       clip_sample=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[Any]											=    LDMPipeline(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
    pipeline.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  _lowerCAmelCase    :Union[str, Any]     =					argparse.ArgumentParser()
  parser.add_argument('--checkpoint_path', type=str, required=True)
  parser.add_argument('--config_path', type=str, required=True)
  parser.add_argument('--output_path', type=str, required=True)
  _lowerCAmelCase    :List[Any]     =					parser.parse_args()

  convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


