






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


SCREAMING_SNAKE_CASE__											=     logging.get_logger(__name__)






class        a_		(    lowercase_       ):
  lowercase  	=   ['''input_features''', '''is_longer''']

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE=64    ,	_SCREAMING_SNAKE_CASE=48000    ,	_SCREAMING_SNAKE_CASE=480    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=0.0    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE = 0    ,	_SCREAMING_SNAKE_CASE = 14000    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = "fusion"    ,	_SCREAMING_SNAKE_CASE = "repeatpad"    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   Any:



        """simple docstring"""






        super().__init__(
            feature_size=_SCREAMING_SNAKE_CASE    ,	sampling_rate=_SCREAMING_SNAKE_CASE    ,	padding_value=_SCREAMING_SNAKE_CASE    ,	return_attention_mask=_SCREAMING_SNAKE_CASE    ,	**_SCREAMING_SNAKE_CASE    ,	)
        UpperCamelCase       						=      top_db
        UpperCamelCase       						=      truncation
        UpperCamelCase       						=      padding
        UpperCamelCase       						=      fft_window_size
        UpperCamelCase       						=      (fft_window_size >> 1) + 1
        UpperCamelCase       						=      hop_length
        UpperCamelCase       						=      max_length_s
        UpperCamelCase       						=      max_length_s * sampling_rate
        UpperCamelCase       						=      sampling_rate
        UpperCamelCase       						=      frequency_min
        UpperCamelCase       						=      frequency_max
        UpperCamelCase       						=      mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins    ,	num_mel_filters=_SCREAMING_SNAKE_CASE    ,	min_frequency=_SCREAMING_SNAKE_CASE    ,	max_frequency=_SCREAMING_SNAKE_CASE    ,	sampling_rate=_SCREAMING_SNAKE_CASE    ,	norm=_SCREAMING_SNAKE_CASE    ,	mel_scale="""htk"""    ,	)
        UpperCamelCase       						=      mel_filter_bank(
            num_frequency_bins=self.nb_frequency_bins    ,	num_mel_filters=_SCREAMING_SNAKE_CASE    ,	min_frequency=_SCREAMING_SNAKE_CASE    ,	max_frequency=_SCREAMING_SNAKE_CASE    ,	sampling_rate=_SCREAMING_SNAKE_CASE    ,	norm="""slaney"""    ,	mel_scale="""slaney"""    ,	)

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      copy.deepcopy(self.__dict__   )
        UpperCamelCase       						=      self.__class__.__name__
        if "mel_filters" in output:
              del output["mel_filters"]
        if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
        return output

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      spectrogram(
            _SCREAMING_SNAKE_CASE    ,	window_function(self.fft_window_size    ,	"""hann"""   )    ,	frame_length=self.fft_window_size    ,	hop_length=self.hop_length    ,	power=2.0    ,	mel_filters=_SCREAMING_SNAKE_CASE    ,	log_mel="""dB"""    ,	)
        return log_mel_spectrogram.T

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      np.array_split(list(range(0    ,	total_frames - chunk_frames + 1   )   )    ,	3   )
        if len(ranges[1]   ) == 0:
              # if the audio is too short, we just use the first chunk
              UpperCamelCase       						=      [0]
        if len(ranges[2]   ) == 0:
              # if the audio is too short, we just use the first chunk
              UpperCamelCase       						=      [0]
        # randomly choose index for each part
        UpperCamelCase       						=      np.random.choice(ranges[0]   )
        UpperCamelCase       						=      np.random.choice(ranges[1]   )
        UpperCamelCase       						=      np.random.choice(ranges[2]   )

        UpperCamelCase       						=      mel[idx_front : idx_front + chunk_frames, :]
        UpperCamelCase       						=      mel[idx_middle : idx_middle + chunk_frames, :]
        UpperCamelCase       						=      mel[idx_back : idx_back + chunk_frames, :]

        UpperCamelCase       						=      torch.tensor(mel[None, None, :]   )
        UpperCamelCase       						=      torch.nn.functional.interpolate(
            _SCREAMING_SNAKE_CASE    ,	size=[chunk_frames, 64]    ,	mode="""bilinear"""    ,	align_corners=_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      mel_shrink[0][0].numpy()
        UpperCamelCase       						=      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,	axis=0   )
        return mel_fusion

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   str:



        """simple docstring"""






        if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                    UpperCamelCase       						=      True
                    # random crop to max_length (for compatibility) -> this should be handled by self.pad
                    UpperCamelCase       						=      len(_SCREAMING_SNAKE_CASE   ) - max_length
                    UpperCamelCase       						=      np.random.randint(0    ,	overflow + 1   )
                    UpperCamelCase       						=      waveform[idx : idx + max_length]
                    UpperCamelCase       						=      self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,	self.mel_filters_slaney   )[None, :]
              elif truncation == "fusion":
                    UpperCamelCase       						=      self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,	self.mel_filters   )
                    UpperCamelCase       						=      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                    UpperCamelCase       						=      mel.shape[0]
                    if chunk_frames == total_frames:
                          # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                          # In this case, we just use the whole audio.
                          UpperCamelCase       						=      np.stack([mel, mel, mel, mel]    ,	axis=0   )
                          UpperCamelCase       						=      False
                    else:
                          UpperCamelCase       						=      self._random_mel_fusion(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                          UpperCamelCase       						=      True
              else:
                    raise NotImplementedError(F"data_truncating {truncation} not implemented"   )

        else:
              UpperCamelCase       						=      False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                    if padding == "repeat":
                          UpperCamelCase       						=      int(max_length / len(_SCREAMING_SNAKE_CASE   )   )
                          UpperCamelCase       						=      np.stack(np.tile(_SCREAMING_SNAKE_CASE    ,	n_repeat + 1   )   )[:max_length]
                    if padding == "repeatpad":
                          UpperCamelCase       						=      int(max_length / len(_SCREAMING_SNAKE_CASE   )   )
                          UpperCamelCase       						=      np.stack(np.tile(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )   )
                    UpperCamelCase       						=      np.pad(_SCREAMING_SNAKE_CASE    ,	(0, max_length - waveform.shape[0])    ,	mode="""constant"""    ,	constant_values=0   )

              if truncation == "fusion":
                    UpperCamelCase       						=      self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,	self.mel_filters   )
                    UpperCamelCase       						=      np.stack([input_mel, input_mel, input_mel, input_mel]    ,	axis=0   )
              else:
                    UpperCamelCase       						=      self._np_extract_fbank_features(_SCREAMING_SNAKE_CASE    ,	self.mel_filters_slaney   )[None, :]

        return input_mel, longer


  def __call__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE = None    ,	**_SCREAMING_SNAKE_CASE    ,	)						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      truncation if truncation is not None else self.truncation
        UpperCamelCase       						=      padding if padding else self.padding

        if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                    raise ValueError(
                        F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                        F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                        F" was sampled 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."""   )

        UpperCamelCase       						=      isinstance(_SCREAMING_SNAKE_CASE    ,	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}"   )
        UpperCamelCase       						=      is_batched_numpy or (
            isinstance(_SCREAMING_SNAKE_CASE    ,	(list, tuple)   ) and (isinstance(raw_speech[0]    ,	(np.ndarray, tuple, list)   ))
        )

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

        # always return batch
        if not is_batched:
              UpperCamelCase       						=      [np.asarray(_SCREAMING_SNAKE_CASE   )]

        # convert to mel spectrogram, truncate and pad if needed.
        UpperCamelCase       						=      [
            self._get_input_mel(_SCREAMING_SNAKE_CASE    ,	max_length if max_length else self.nb_max_samples    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
            for waveform in raw_speech
        ]

        UpperCamelCase       						=      []
        UpperCamelCase       						=      []
        for mel, longer in padded_inputs:
              input_mel.append(_SCREAMING_SNAKE_CASE   )
              is_longer.append(_SCREAMING_SNAKE_CASE   )

        if truncation == "fusion" and sum(_SCREAMING_SNAKE_CASE   ) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              UpperCamelCase       						=      np.random.randint(0    ,	len(_SCREAMING_SNAKE_CASE   )   )
              UpperCamelCase       						=      True

        if isinstance(input_mel[0]    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      [np.asarray(_SCREAMING_SNAKE_CASE    ,	dtype=np.floataa   ) for feature in input_mel]

        # is_longer is a list of bool
        UpperCamelCase       						=      [[longer] for longer in is_longer]

        UpperCamelCase       						=      {"""input_features""": input_mel, """is_longer""": is_longer}
        UpperCamelCase       						=      BatchFeature(_SCREAMING_SNAKE_CASE   )

        if return_tensors is not None:
              UpperCamelCase       						=      input_features.convert_to_tensors(_SCREAMING_SNAKE_CASE   )

        return input_features








'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=1					)->				Tuple:
      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("""."""					)[n_shave_prefix_segments:]					)
      else:
            return ".".join(path.split("""."""					)[:n_shave_prefix_segments]					)







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				Dict:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item.replace("""in_layers.0"""	,		"""norm1"""					)
            UpperCamelCase       						=      new_item.replace("""in_layers.2"""	,		"""conv1"""					)

            UpperCamelCase       						=      new_item.replace("""out_layers.0"""	,		"""norm2"""					)
            UpperCamelCase       						=      new_item.replace("""out_layers.3"""	,		"""conv2"""					)

            UpperCamelCase       						=      new_item.replace("""emb_layers.1"""	,		"""time_emb_proj"""					)
            UpperCamelCase       						=      new_item.replace("""skip_connection"""	,		"""conv_shortcut"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase=0					)->				List[str]:
      UpperCamelCase       						=      []
      for old_item in old_list:
            UpperCamelCase       						=      old_item

            UpperCamelCase       						=      new_item.replace("""norm.weight"""	,		"""group_norm.weight"""					)
            UpperCamelCase       						=      new_item.replace("""norm.bias"""	,		"""group_norm.bias"""					)

            UpperCamelCase       						=      new_item.replace("""proj_out.weight"""	,		"""proj_attn.weight"""					)
            UpperCamelCase       						=      new_item.replace("""proj_out.bias"""	,		"""proj_attn.bias"""					)

            UpperCamelCase       						=      shave_segments(__UpperCamelCase	,		n_shave_prefix_segments=__UpperCamelCase					)

            mapping.append({"""old""": old_item, """new""": new_item}					)

      return mapping







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		__UpperCamelCase=None	,		__UpperCamelCase=None					)->				str:
      assert isinstance(__UpperCamelCase	,		__UpperCamelCase					), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  UpperCamelCase       						=      old_checkpoint[path]
                  UpperCamelCase       						=      old_tensor.shape[0] // 3

                  UpperCamelCase       						=      (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                  UpperCamelCase       						=      old_tensor.shape[0] // config["""num_head_channels"""] // 3

                  UpperCamelCase       						=      old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
                  UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      old_tensor.split(channels // num_heads	,		dim=1					)

                  UpperCamelCase       						=      query.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      key.reshape(__UpperCamelCase					)
                  UpperCamelCase       						=      value.reshape(__UpperCamelCase					)

      for path in paths:
            UpperCamelCase       						=      path["""new"""]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            UpperCamelCase       						=      new_path.replace("""middle_block.0"""	,		"""mid_block.resnets.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.1"""	,		"""mid_block.attentions.0"""					)
            UpperCamelCase       						=      new_path.replace("""middle_block.2"""	,		"""mid_block.resnets.1"""					)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        UpperCamelCase       						=      new_path.replace(replacement["""old"""]	,		replacement["""new"""]					)

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]][:, :, 0]
            else:
                  UpperCamelCase       						=      old_checkpoint[path["""old"""]]







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase					)->				Optional[int]:
      UpperCamelCase       						=      {}

      UpperCamelCase       						=      checkpoint["""time_embed.0.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.0.bias"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.weight"""]
      UpperCamelCase       						=      checkpoint["""time_embed.2.bias"""]

      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.weight"""]
      UpperCamelCase       						=      checkpoint["""input_blocks.0.0.bias"""]

      UpperCamelCase       						=      checkpoint["""out.0.weight"""]
      UpperCamelCase       						=      checkpoint["""out.0.bias"""]
      UpperCamelCase       						=      checkpoint["""out.2.weight"""]
      UpperCamelCase       						=      checkpoint["""out.2.bias"""]

      # Retrieves the keys for the input blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """input_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the middle blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """middle_block""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      # Retrieves the keys for the output blocks only
      UpperCamelCase       						=      len({""".""".join(layer.split("""."""					)[:2]					) for layer in checkpoint if """output_blocks""" in layer}					)
      UpperCamelCase       						=      {
          layer_id: [key for key in checkpoint if F"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase					)
      }

      for i in range(1	,		__UpperCamelCase					):
            UpperCamelCase       						=      (i - 1) // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      (i - 1) % (config["""num_res_blocks"""] + 1)

            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.0" in key]
            UpperCamelCase       						=      [key for key in input_blocks[i] if F"input_blocks.{i}.1" in key]

            if F"input_blocks.{i}.0.op.weight" in checkpoint:
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.weight"
                  ]
                  UpperCamelCase       						=      checkpoint[
                      F"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
            UpperCamelCase       						=      {"""old""": F"input_blocks.{i}.0", """new""": F"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            UpperCamelCase       						=      {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
            assign_to_checkpoint(
                __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path, resnet_op]	,		config=__UpperCamelCase					)

            if len(__UpperCamelCase					):
                  UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      {
                      """old""": F"input_blocks.{i}.1",
                      """new""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  UpperCamelCase       						=      {
                      F"input_blocks.{i}.1.qkv.bias": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                      },
                      F"input_blocks.{i}.1.qkv.weight": {
                          """key""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                          """query""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                          """value""": F"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                      },
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase	,		)

      UpperCamelCase       						=      middle_blocks[0]
      UpperCamelCase       						=      middle_blocks[1]
      UpperCamelCase       						=      middle_blocks[2]

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
      assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		config=__UpperCamelCase					)

      UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
      UpperCamelCase       						=      {
          """middle_block.1.qkv.bias""": {
              """key""": """mid_block.attentions.0.key.bias""",
              """query""": """mid_block.attentions.0.query.bias""",
              """value""": """mid_block.attentions.0.value.bias""",
          },
          """middle_block.1.qkv.weight""": {
              """key""": """mid_block.attentions.0.key.weight""",
              """query""": """mid_block.attentions.0.query.weight""",
              """value""": """mid_block.attentions.0.value.weight""",
          },
      }
      assign_to_checkpoint(
          __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		attention_paths_to_split=__UpperCamelCase	,		config=__UpperCamelCase					)

      for i in range(__UpperCamelCase					):
            UpperCamelCase       						=      i // (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      i % (config["""num_res_blocks"""] + 1)
            UpperCamelCase       						=      [shave_segments(__UpperCamelCase	,		2					) for name in output_blocks[i]]
            UpperCamelCase       						=      {}

            for layer in output_block_layers:
                  UpperCamelCase						,UpperCamelCase       						=      layer.split("""."""					)[0], shave_segments(__UpperCamelCase	,		1					)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase					)
                  else:
                        UpperCamelCase       						=      [layer_name]

            if len(__UpperCamelCase					) > 1:
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.0" in key]
                  UpperCamelCase       						=      [key for key in output_blocks[i] if F"output_blocks.{i}.1" in key]

                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase					)

                  UpperCamelCase       						=      {"""old""": F"output_blocks.{i}.0", """new""": F"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		config=__UpperCamelCase					)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        UpperCamelCase       						=      list(output_block_list.values()					).index(["""conv.weight""", """conv.bias"""]					)
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.weight"
                        ]
                        UpperCamelCase       						=      checkpoint[
                            F"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase					) == 2:
                              UpperCamelCase       						=      []

                  if len(__UpperCamelCase					):
                        UpperCamelCase       						=      renew_attention_paths(__UpperCamelCase					)
                        UpperCamelCase       						=      {
                            """old""": F"output_blocks.{i}.1",
                            """new""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        UpperCamelCase       						=      {
                            F"output_blocks.{i}.1.qkv.bias": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                            },
                            F"output_blocks.{i}.1.qkv.weight": {
                                """key""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                                """query""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                                """value""": F"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                            },
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		additional_replacements=[meta_path]	,		attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions					) else None	,		config=__UpperCamelCase	,		)
            else:
                  UpperCamelCase       						=      renew_resnet_paths(__UpperCamelCase	,		n_shave_prefix_segments=1					)
                  for path in resnet_0_paths:
                        UpperCamelCase       						=      """.""".join(["""output_blocks""", str(__UpperCamelCase					), path["""old"""]]					)
                        UpperCamelCase       						=      """.""".join(["""up_blocks""", str(__UpperCamelCase					), """resnets""", str(__UpperCamelCase					), path["""new"""]]					)

                        UpperCamelCase       						=      checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__											=     argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
     )

     parser.add_argument(
         '--config_file',
         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.')

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     torch.load(args.checkpoint_path)

     with open(args.config_file) as f:
          SCREAMING_SNAKE_CASE__											=     json.loads(f.read())

     SCREAMING_SNAKE_CASE__											=     convert_ldm_checkpoint(checkpoint, config)

     if "ldm" in config:
          del config["ldm"]

     SCREAMING_SNAKE_CASE__											=     UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

     try:
          SCREAMING_SNAKE_CASE__											=     DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
          SCREAMING_SNAKE_CASE__											=     VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

          SCREAMING_SNAKE_CASE__											=     LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)
