


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


snake_case__				:	Union[str, Any]					       =					logging.get_logger(__name__)





class   SCREAMING_SNAKE_CASE_			(a__						):




       '''simple docstring'''



       _a   							=							["audio_values", "audio_mask"]



       def __init__(						self				:				Any		,				__a				:				List[Any]=2_048		,				__a				:				int=1		,				__a				:				int=[16, 16]		,				__a				:				List[str]=128		,				__a				:				Tuple=44_100		,				__a				:				Tuple=86		,				__a				:				Dict=2_048		,				__a				:				List[str]=0.0		,				**__a				:				List[str]		,				)					->List[Any]:
          super().__init__(
              feature_size=__a		,				sampling_rate=__a		,				padding_value=__a		,				**__a		,				)

          lowerCamelCase_						:   List[str]		= spectrogram_length
          lowerCamelCase_						:   Union[str, Any]		= num_channels
          lowerCamelCase_						:   Optional[Any]		= patch_size
          lowerCamelCase_						:   str		= feature_size // self.patch_size[1]
          lowerCamelCase_						:   Optional[Any]		= n_fft
          lowerCamelCase_						:   Tuple		= sampling_rate // hop_length_to_sampling_rate
          lowerCamelCase_						:   Any		= sampling_rate
          lowerCamelCase_						:   List[str]		= padding_value
          lowerCamelCase_						:   List[str]		= mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2		,				num_mel_filters=__a		,				min_frequency=0.0		,				max_frequency=22_050.0		,				sampling_rate=__a		,				norm="""slaney"""		,				mel_scale="""slaney"""		,				).T



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]		,				__a				:				np.array							)					->np.ndarray:
          lowerCamelCase_						:   Optional[Any]		= spectrogram(
              __a		,				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		,				)
          lowerCamelCase_						:   Optional[int]		= log_spec[:, :-1]
          lowerCamelCase_						:   str		= log_spec - 20.0
          lowerCamelCase_						:   Any		= np.clip(log_spec / 40.0		,				-2.0		,				0.0							) + 1.0
          return log_spec




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

          lowerCamelCase_						:   Dict		= isinstance(__a		,				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_						:   Union[str, Any]		= is_batched_numpy or (
              isinstance(__a		,				(list, tuple)							) and (isinstance(raw_speech[0]		,				(np.ndarray, tuple, list)							))
          )
          if is_batched:
             lowerCamelCase_						:   Optional[Any]		= [np.asarray([speech]		,				dtype=np.floataa							).T for speech in raw_speech]
          elif not is_batched and not isinstance(__a		,				np.ndarray							):
             lowerCamelCase_						:   Optional[Any]		= np.asarray(__a		,				dtype=np.floataa							)
          elif isinstance(__a		,				np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
             lowerCamelCase_						:   List[Any]		= raw_speech.astype(np.floataa							)
          # always return batch
          if not is_batched:
             lowerCamelCase_						:   Optional[Any]		= [np.asarray([raw_speech]							).T]

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

          # Create audio attention mask
          lowerCamelCase_						:   Optional[int]		= 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:
             lowerCamelCase_						:   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
             ]
             lowerCamelCase_						:   Optional[int]		= np.array(__a							).astype(np.floataa							)

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

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

          lowerCamelCase_						:   str		= BatchFeature(data=__a		,				tensor_type=__a							)
          return encoded_inputs









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

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

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





