code: from typing import TYPE_CHECKING
  
  from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available
  
  
  a     :			Optional[Any]					    =						{
      """configuration_utils""": ["""GenerationConfig"""],
      """streamers""": ["""TextIteratorStreamer""", """TextStreamer"""],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """Constraint""",
       """ConstraintListState""",
       """DisjunctiveConstraint""",
       """PhrasalConstraint""",
   ]
   a     :			Optional[int]					    =						[
       """BeamHypotheses""",
       """BeamScorer""",
       """BeamSearchScorer""",
       """ConstrainedBeamSearchScorer""",
   ]
   a     :			Any					    =						[
       """EpsilonLogitsWarper""",
       """EtaLogitsWarper""",
       """ForcedBOSTokenLogitsProcessor""",
       """ForcedEOSTokenLogitsProcessor""",
       """HammingDiversityLogitsProcessor""",
       """InfNanRemoveLogitsProcessor""",
       """LogitsProcessor""",
       """LogitsProcessorList""",
       """LogitsWarper""",
       """MinLengthLogitsProcessor""",
       """MinNewTokensLengthLogitsProcessor""",
       """NoBadWordsLogitsProcessor""",
       """NoRepeatNGramLogitsProcessor""",
       """PrefixConstrainedLogitsProcessor""",
       """RepetitionPenaltyLogitsProcessor""",
       """SequenceBiasLogitsProcessor""",
       """EncoderRepetitionPenaltyLogitsProcessor""",
       """TemperatureLogitsWarper""",
       """TopKLogitsWarper""",
       """TopPLogitsWarper""",
       """TypicalLogitsWarper""",
       """EncoderNoRepeatNGramLogitsProcessor""",
       """ExponentialDecayLengthPenalty""",
       """LogitNormalization""",
   ]
   a     :			int					    =						[
       """MaxNewTokensCriteria""",
       """MaxLengthCriteria""",
       """MaxTimeCriteria""",
       """StoppingCriteria""",
       """StoppingCriteriaList""",
       """validate_stopping_criteria""",
   ]
   a     :			Dict					    =						[
       """GenerationMixin""",
       """top_k_top_p_filtering""",
       """GreedySearchEncoderDecoderOutput""",
       """GreedySearchDecoderOnlyOutput""",
       """SampleEncoderDecoderOutput""",
       """SampleDecoderOnlyOutput""",
       """BeamSearchEncoderDecoderOutput""",
       """BeamSearchDecoderOnlyOutput""",
       """BeamSampleEncoderDecoderOutput""",
       """BeamSampleDecoderOnlyOutput""",
       """ContrastiveSearchEncoderDecoderOutput""",
       """ContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Optional[Any]					    =						[
       """TFForcedBOSTokenLogitsProcessor""",
       """TFForcedEOSTokenLogitsProcessor""",
       """TFLogitsProcessor""",
       """TFLogitsProcessorList""",
       """TFLogitsWarper""",
       """TFMinLengthLogitsProcessor""",
       """TFNoBadWordsLogitsProcessor""",
       """TFNoRepeatNGramLogitsProcessor""",
       """TFRepetitionPenaltyLogitsProcessor""",
       """TFTemperatureLogitsWarper""",
       """TFTopKLogitsWarper""",
       """TFTopPLogitsWarper""",
       """TFForceTokensLogitsProcessor""",
       """TFSuppressTokensAtBeginLogitsProcessor""",
       """TFSuppressTokensLogitsProcessor""",
   ]
   a     :			Any					    =						[
       """TFGenerationMixin""",
       """tf_top_k_top_p_filtering""",
       """TFGreedySearchDecoderOnlyOutput""",
       """TFGreedySearchEncoderDecoderOutput""",
       """TFSampleEncoderDecoderOutput""",
       """TFSampleDecoderOnlyOutput""",
       """TFBeamSearchEncoderDecoderOutput""",
       """TFBeamSearchDecoderOnlyOutput""",
       """TFBeamSampleEncoderDecoderOutput""",
       """TFBeamSampleDecoderOnlyOutput""",
       """TFContrastiveSearchEncoderDecoderOutput""",
       """TFContrastiveSearchDecoderOnlyOutput""",
   ]
  
  try:
   if not is_flax_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   a     :			Union[str, Any]					    =						[
       """FlaxForcedBOSTokenLogitsProcessor""",
       """FlaxForcedEOSTokenLogitsProcessor""",
       """FlaxLogitsProcessor""",
       """FlaxLogitsProcessorList""",
       """FlaxLogitsWarper""",
       """FlaxMinLengthLogitsProcessor""",
       """FlaxTemperatureLogitsWarper""",
       """FlaxTopKLogitsWarper""",
       """FlaxTopPLogitsWarper""",
   ]
   a     :			Dict					    =						[
       """FlaxGenerationMixin""",
       """FlaxGreedySearchOutput""",
       """FlaxSampleOutput""",
       """FlaxBeamSearchOutput""",
   ]
  
  if TYPE_CHECKING:
   from .configuration_utils import GenerationConfig
   from .streamers import TextIteratorStreamer, TextStreamer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
    from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
    from .logits_process import (
        EncoderNoRepeatNGramLogitsProcessor,
        EncoderRepetitionPenaltyLogitsProcessor,
        EpsilonLogitsWarper,
        EtaLogitsWarper,
        ExponentialDecayLengthPenalty,
        ForcedBOSTokenLogitsProcessor,
        ForcedEOSTokenLogitsProcessor,
        HammingDiversityLogitsProcessor,
        InfNanRemoveLogitsProcessor,
        LogitNormalization,
        LogitsProcessor,
        LogitsProcessorList,
        LogitsWarper,
        MinLengthLogitsProcessor,
        MinNewTokensLengthLogitsProcessor,
        NoBadWordsLogitsProcessor,
        NoRepeatNGramLogitsProcessor,
        PrefixConstrainedLogitsProcessor,
        RepetitionPenaltyLogitsProcessor,
        SequenceBiasLogitsProcessor,
        TemperatureLogitsWarper,
        TopKLogitsWarper,
        TopPLogitsWarper,
        TypicalLogitsWarper,
    )
    from .stopping_criteria import (
        MaxLengthCriteria,
        MaxNewTokensCriteria,
        MaxTimeCriteria,
        StoppingCriteria,
        StoppingCriteriaList,
        validate_stopping_criteria,
    )
    from .utils import (
        BeamSampleDecoderOnlyOutput,
        BeamSampleEncoderDecoderOutput,
        BeamSearchDecoderOnlyOutput,
        BeamSearchEncoderDecoderOutput,
        ContrastiveSearchDecoderOnlyOutput,
        ContrastiveSearchEncoderDecoderOutput,
        GenerationMixin,
        GreedySearchDecoderOnlyOutput,
        GreedySearchEncoderDecoderOutput,
        SampleDecoderOnlyOutput,
        SampleEncoderDecoderOutput,
        top_k_top_p_filtering,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tf_logits_process import (
        TFForcedBOSTokenLogitsProcessor,
        TFForcedEOSTokenLogitsProcessor,
        TFForceTokensLogitsProcessor,
        TFLogitsProcessor,
        TFLogitsProcessorList,
        TFLogitsWarper,
        TFMinLengthLogitsProcessor,
        TFNoBadWordsLogitsProcessor,
        TFNoRepeatNGramLogitsProcessor,
        TFRepetitionPenaltyLogitsProcessor,
        TFSuppressTokensAtBeginLogitsProcessor,
        TFSuppressTokensLogitsProcessor,
        TFTemperatureLogitsWarper,
        TFTopKLogitsWarper,
        TFTopPLogitsWarper,
    )
    from .tf_utils import (
        TFBeamSampleDecoderOnlyOutput,
        TFBeamSampleEncoderDecoderOutput,
        TFBeamSearchDecoderOnlyOutput,
        TFBeamSearchEncoderDecoderOutput,
        TFContrastiveSearchDecoderOnlyOutput,
        TFContrastiveSearchEncoderDecoderOutput,
        TFGenerationMixin,
        TFGreedySearchDecoderOnlyOutput,
        TFGreedySearchEncoderDecoderOutput,
        TFSampleDecoderOnlyOutput,
        TFSampleEncoderDecoderOutput,
        tf_top_k_top_p_filtering,
    )
  
   try:
    if not is_flax_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .flax_logits_process import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxLogitsWarper,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )
    from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
  else:
   import sys
  
   a     :			str					    =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
style_context: import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  def 		snake_case__  (		lowercase       ,							lowercase=1				):
     if n_shave_prefix_segments >= 0:
        return ".".join(path.split("."				)[n_shave_prefix_segments:]				)
     else:
        return ".".join(path.split("."				)[:n_shave_prefix_segments]				)
  def 		snake_case__  (		lowercase       ,							lowercase=0				):
     lowerCAmelCase_:       List[Any]         =  []
     for old_item in old_list:
        lowerCAmelCase_:       Any         =  old_item
        lowerCAmelCase_:       Dict         =  new_item.replace("block."       ,							"resnets."				)
        lowerCAmelCase_:       Tuple         =  new_item.replace("conv_shorcut"       ,							"conv1"				)
        lowerCAmelCase_:       Optional[int]         =  new_item.replace("in_shortcut"       ,							"conv_shortcut"				)
        lowerCAmelCase_:       Any         =  new_item.replace("temb_proj"       ,							"time_emb_proj"				)
  
        lowerCAmelCase_:       Tuple         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
  
        mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 		snake_case__  (		lowercase       ,							lowercase=0       ,							lowercase=False				):
     lowerCAmelCase_:       Any         =  []
     for old_item in old_list:
        lowerCAmelCase_:       List[Any]         =  old_item
  
        # In `model.mid`, the layer is called `attn`.
        if not in_mid:
           lowerCAmelCase_:       str         =  new_item.replace("attn"       ,							"attentions"				)
        lowerCAmelCase_:       str         =  new_item.replace(".k."       ,							".key."				)
        lowerCAmelCase_:       Tuple         =  new_item.replace(".v."       ,							".value."				)
        lowerCAmelCase_:       Any         =  new_item.replace(".q."       ,							".query."				)
  
        lowerCAmelCase_:       List[Any]         =  new_item.replace("proj_out"       ,							"proj_attn"				)
        lowerCAmelCase_:       List[str]         =  new_item.replace("norm"       ,							"group_norm"				)
  
        lowerCAmelCase_:       Dict         =  shave_segments(lowercase       ,							n_shave_prefix_segments=lowercase				)
        mapping.append({"old": old_item, "new": new_item}				)
  
     return mapping
  def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase       ,							lowercase=None       ,							lowercase=None       ,							lowercase=None				):
     assert isinstance(lowercase       ,							lowercase				), "Paths should be a list of dicts containing 'old' and 'new' keys."
  
     if attention_paths_to_split is not None:
        if config is None:
           raise ValueError("Please specify the config if setting 'attention_paths_to_split' to 'True'."				)
  
        for path, path_map in attention_paths_to_split.items():
           lowerCAmelCase_:       List[Any]         =  old_checkpoint[path]
           lowerCAmelCase_:       Optional[Any]         =  old_tensor.shape[0] // 3
  
           lowerCAmelCase_:       Any         =  (-1, channels) if len(old_tensor.shape				) == 3 else (-1)
  
           lowerCAmelCase_:       Optional[int]         =  old_tensor.shape[0] // config.get("num_head_channels"       ,							1				) // 3
  
           lowerCAmelCase_:       Optional[int]         =  old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
           lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  old_tensor.split(channels // num_heads       ,							dim=1				)
  
           lowerCAmelCase_:       List[str]         =  query.reshape(lowercase				).squeeze()
           lowerCAmelCase_:       str         =  key.reshape(lowercase				).squeeze()
           lowerCAmelCase_:       Tuple         =  value.reshape(lowercase				).squeeze()
  
     for path in paths:
        lowerCAmelCase_:       str         =  path["new"]
  
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
           continue
  
        lowerCAmelCase_:       str         =  new_path.replace("down."       ,							"down_blocks."				)
        lowerCAmelCase_:       Tuple         =  new_path.replace("up."       ,							"up_blocks."				)
  
        if additional_replacements is not None:
           for replacement in additional_replacements:
              lowerCAmelCase_:       Union[str, Any]         =  new_path.replace(replacement["old"]       ,							replacement["new"]				)
  
        if "attentions" in new_path:
           lowerCAmelCase_:       List[Any]         =  old_checkpoint[path["old"]].squeeze()
        else:
           lowerCAmelCase_:       Union[str, Any]         =  old_checkpoint[path["old"]]
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Any         =  {}
  
     lowerCAmelCase_:       List[str]         =  checkpoint["temb.dense.0.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["temb.dense.0.bias"]
     lowerCAmelCase_:       Any         =  checkpoint["temb.dense.1.weight"]
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["temb.dense.1.bias"]
  
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["norm_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["norm_out.bias"]
  
     lowerCAmelCase_:       int         =  checkpoint["conv_in.weight"]
     lowerCAmelCase_:       int         =  checkpoint["conv_in.bias"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["conv_out.weight"]
     lowerCAmelCase_:       Dict         =  checkpoint["conv_out.bias"]
  
     lowerCAmelCase_:       Dict         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "down" in layer}				)
     lowerCAmelCase_:       Tuple         =  {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
     }
  
     lowerCAmelCase_:       str         =  len({".".join(layer.split("."				)[:2]				) for layer in checkpoint if "up" in layer}				)
     lowerCAmelCase_:       Optional[int]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}
  
     for i in range(lowercase				):
        lowerCAmelCase_:       List[str]         =  (i - 1) // (config["layers_per_block"] + 1)
  
        if any("downsample" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Any         =  checkpoint[
               F'''down.{i}.downsample.op.weight'''
           ]
           lowerCAmelCase_:       List[Any]         =  checkpoint[F'''down.{i}.downsample.op.bias''']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
        #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']
  
        if any("block" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       str         =  {
               layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)
  
        if any("attn" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       List[Any]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in down_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)
  
     lowerCAmelCase_:       List[str]         =  [key for key in checkpoint if "mid.block_1" in key]
     lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]
  
     # Mid new 2
     lowerCAmelCase_:       Optional[int]         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)
  
     lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)
  
     lowerCAmelCase_:       int         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)
  
     for i in range(lowercase				):
        lowerCAmelCase_:       Any         =  num_up_blocks - 1 - i
  
        if any("upsample" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       str         =  checkpoint[
               F'''up.{i}.upsample.conv.weight'''
           ]
           lowerCAmelCase_:       Union[str, Any]         =  checkpoint[F'''up.{i}.upsample.conv.bias''']
  
        if any("block" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       List[str]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       List[Any]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       List[Any]         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
        if any("attn" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Optional[int]         =  len(
               {".".join(shave_segments(lowercase       ,							2				).split("."				)[:2]				) for layer in up_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       Optional[int]         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       Dict         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
     lowerCAmelCase_:       Tuple         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
     return new_checkpoint
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Optional[int]         =  {}
  
     lowerCAmelCase_:       Any         =  checkpoint["encoder.norm_out.weight"]
     lowerCAmelCase_:       Tuple         =  checkpoint["encoder.norm_out.bias"]
  
     lowerCAmelCase_:       Optional[int]         =  checkpoint["encoder.conv_in.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_in.bias"]
     lowerCAmelCase_:       List[str]         =  checkpoint["encoder.conv_out.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["encoder.conv_out.bias"]
  
     lowerCAmelCase_:       Dict         =  checkpoint["decoder.norm_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["decoder.norm_out.bias"]
  
     lowerCAmelCase_:       Dict         =  checkpoint["decoder.conv_in.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_in.bias"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["decoder.conv_out.weight"]
     lowerCAmelCase_:       List[str]         =  checkpoint["decoder.conv_out.bias"]
  
     lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "down" in layer}				)
     lowerCAmelCase_:       Any         =  {
         layer_id: [key for key in checkpoint if F'''down.{layer_id}''' in key] for layer_id in range(lowercase				)
     }
  
     lowerCAmelCase_:       List[str]         =  len({".".join(layer.split("."				)[:3]				) for layer in checkpoint if "up" in layer}				)
     lowerCAmelCase_:       Union[str, Any]         =  {layer_id: [key for key in checkpoint if F'''up.{layer_id}''' in key] for layer_id in range(lowercase				)}
  
     for i in range(lowercase				):
        lowerCAmelCase_:       Any         =  (i - 1) // (config["layers_per_block"] + 1)
  
        if any("downsample" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  checkpoint[
               F'''encoder.down.{i}.downsample.conv.weight'''
           ]
           lowerCAmelCase_:       Any         =  checkpoint[
               F'''encoder.down.{i}.downsample.conv.bias'''
           ]
  
        if any("block" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Union[str, Any]         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               layer_id: [key for key in down_blocks[i] if F'''block.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       Dict         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase				)
  
        if any("attn" in layer for layer in down_blocks[i]				):
           lowerCAmelCase_:       Dict         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in down_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       List[str]         =  {
               layer_id: [key for key in down_blocks[i] if F'''attn.{layer_id}''' in key]
               for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"]				):
                 lowerCAmelCase_:       List[str]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							config=lowercase				)
  
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.block_1" in key]
     lowerCAmelCase_:       List[Any]         =  [key for key in checkpoint if "mid.block_2" in key]
     lowerCAmelCase_:       Any         =  [key for key in checkpoint if "mid.attn_1" in key]
  
     # Mid new 2
     lowerCAmelCase_:       Dict         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,							)
  
     lowerCAmelCase_:       List[str]         =  renew_resnet_paths(lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,							)
  
     lowerCAmelCase_:       List[Any]         =  renew_attention_paths(lowercase       ,							in_mid=lowercase				)
     assign_to_checkpoint(
         lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,							)
  
     for i in range(lowercase				):
        lowerCAmelCase_:       str         =  num_up_blocks - 1 - i
  
        if any("upsample" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Optional[Any]         =  checkpoint[
               F'''decoder.up.{i}.upsample.conv.weight'''
           ]
           lowerCAmelCase_:       str         =  checkpoint[
               F'''decoder.up.{i}.upsample.conv.bias'''
           ]
  
        if any("block" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       str         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "block" in layer}				)
           lowerCAmelCase_:       Optional[Any]         =  {
               layer_id: [key for key in up_blocks[i] if F'''block.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_blocks > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       Dict         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       str         =  renew_resnet_paths(blocks[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
        if any("attn" in layer for layer in up_blocks[i]				):
           lowerCAmelCase_:       Dict         =  len(
               {".".join(shave_segments(lowercase       ,							3				).split("."				)[:3]				) for layer in up_blocks[i] if "attn" in layer}				)
           lowerCAmelCase_:       Optional[int]         =  {
               layer_id: [key for key in up_blocks[i] if F'''attn.{layer_id}''' in key] for layer_id in range(lowercase				)
           }
  
           if num_attn > 0:
              for j in range(config["layers_per_block"] + 1				):
                 lowerCAmelCase_:       Any         =  {"old": F'''up_blocks.{i}''', "new": F'''up_blocks.{block_id}'''}
                 lowerCAmelCase_:       List[Any]         =  renew_attention_paths(attns[j]				)
                 assign_to_checkpoint(lowercase       ,							lowercase       ,							lowercase       ,							additional_replacements=[replace_indices]				)
  
     lowerCAmelCase_:       Optional[Any]         =  {k.replace("mid_new_2"       ,							"mid_block"				): v for k, v in new_checkpoint.items()}
     lowerCAmelCase_:       Optional[int]         =  checkpoint["quant_conv.weight"]
     lowerCAmelCase_:       Optional[Any]         =  checkpoint["quant_conv.bias"]
     if "quantize.embedding.weight" in checkpoint:
        lowerCAmelCase_:       List[Any]         =  checkpoint["quantize.embedding.weight"]
     lowerCAmelCase_:       List[Any]         =  checkpoint["post_quant_conv.weight"]
     lowerCAmelCase_:       Any         =  checkpoint["post_quant_conv.bias"]
  
     return new_checkpoint
  
  
  if __name__ == "__main__":
   a     :			Any					    =						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.""")
  
   a     :			Union[str, Any]					    =						parser.parse_args()
   a     :			List[Any]					    =						torch.load(args.checkpoint_path)
  
   with open(args.config_file) as f:
    a     :			Any					    =						json.loads(f.read())
  
   # unet case
   a     :			List[str]					    =						{key.split(""".""")[0] for key in checkpoint.keys()}
   if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
    a     :			Optional[Any]					    =						convert_vq_autoenc_checkpoint(checkpoint, config)
   else:
    a     :			Optional[Any]					    =						convert_ddpm_checkpoint(checkpoint, config)
  
   if "ddpm" in config:
    del config["ddpm"]
  
   if config["_class_name"] == "VQModel":
    a     :			Any					    =						VQModel(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   elif config["_class_name"] == "AutoencoderKL":
    a     :			Tuple					    =						AutoencoderKL(**config)
    model.load_state_dict(converted_checkpoint)
    model.save_pretrained(args.dump_path)
   else:
    a     :			Optional[Any]					    =						UNetaDModel(**config)
    model.load_state_dict(converted_checkpoint)
  
    a     :			Dict					    =						DDPMScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
    a     :			int					    =						DDPMPipeline(unet=model, scheduler=scheduler)
    pipe.save_pretrained(args.dump_path)